From 182ea3a1615141fcd90de3a5c8da40f472062142 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Sat, 25 Jul 2026 15:44:38 +0800
Subject: [PATCH 01/34] Add LingBot-Video (Dense-1.3B) support with
 attention-only LoRA SFT

Model code:
- LingBotVideoDiT (diffsynth/models/lingbot_video_dit.py) video denoiser
- Qwen3-VL text-encoder wrapper (lingbot_video_text_encoder.py)
- T2V/V2V pipeline (diffsynth/pipelines/lingbot_video.py)
- LingBotVideoUniPCScheduler (FlowMatchScheduler subclass): UniPC multistep
  for inference, flow-matching schedule for training
- Reuse QwenImageVAE via an additive, backward-compatible 5D-video
  encode/decode path in qwen_image_vae.py
- Register models + state-dict converters in model_configs.py

Training (issue's core deliverable):
- LingBotVideoTrainingModule (examples/lingbot_video/model_training/train.py)
  with the flow-matching SFT objective
- Attention-only LoRA launch script (to_q,to_k,to_v,to_out; MoE/FFN/router
  left frozen)

Examples + docs:
- T2V/V2V inference and low-VRAM examples
- examples/lingbot_video/README.md

Part of the SFT training support for modelscope/DiffSynth-Studio#1530.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 diffsynth/configs/model_configs.py            |  28 +-
 diffsynth/diffusion/flow_match.py             | 291 ++++++++
 diffsynth/models/lingbot_video_dit.py         | 693 ++++++++++++++++++
 .../models/lingbot_video_text_encoder.py      | 104 +++
 diffsynth/models/qwen_image_vae.py            |  43 +-
 diffsynth/pipelines/lingbot_video.py          | 339 +++++++++
 .../lingbot_video_dit.py                      |  16 +
 .../lingbot_video_text_encoder.py             |  16 +
 examples/lingbot_video/README.md              | 106 +++
 .../lingbot-video-dense-1.3b.py               |  36 +
 .../lingbot-video-dense-1.3b_low_vram.py      |  27 +
 .../lora/lingbot-video-dense-1.3b.sh          |  32 +
 .../lingbot_video/model_training/train.py     | 171 +++++
 13 files changed, 1900 insertions(+), 2 deletions(-)
 create mode 100644 diffsynth/models/lingbot_video_dit.py
 create mode 100644 diffsynth/models/lingbot_video_text_encoder.py
 create mode 100644 diffsynth/pipelines/lingbot_video.py
 create mode 100644 diffsynth/utils/state_dict_converters/lingbot_video_dit.py
 create mode 100644 diffsynth/utils/state_dict_converters/lingbot_video_text_encoder.py
 create mode 100644 examples/lingbot_video/README.md
 create mode 100644 examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
 create mode 100644 examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_low_vram.py
 create mode 100644 examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
 create mode 100644 examples/lingbot_video/model_training/train.py

diff --git a/diffsynth/configs/model_configs.py b/diffsynth/configs/model_configs.py
index 7f5b95ce0..19f2c3af9 100644
--- a/diffsynth/configs/model_configs.py
+++ b/diffsynth/configs/model_configs.py
@@ -1322,8 +1322,34 @@
     },
 ]
 
+lingbot_video_series = [
+    {
+        # Example: ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors")
+        # Dense 1.3B DiT (num_experts=0 -> pure SwiGLU MLP FFN). Model __init__
+        # defaults already match transformer/config.json, so no extra_kwargs.
+        "model_hash": "2bcf511fe5e0000519394d242b4d8abd",
+        "model_name": "lingbot_video_dit",
+        "model_class": "diffsynth.models.lingbot_video_dit.LingBotVideoDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.lingbot_video_dit.LingBotVideoDiTStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/*.safetensors")
+        # Qwen3-VL text encoder (2 shards). Hash is over the merged key set of both
+        # shards. The checkpoint stores only the `model.*` submodule (713 tensors,
+        # no tied lm_head), matching a bare Qwen3VLModel exactly -> identity converter.
+        "model_hash": "b8750c24f732c87797f551196c4cef78",
+        "model_name": "lingbot_video_text_encoder",
+        "model_class": "diffsynth.models.lingbot_video_text_encoder.LingBotVideoTextEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.lingbot_video_text_encoder.LingBotVideoTextEncoderStateDictConverter",
+    },
+    # VAE: the LingBot-Video VAE (vae/*.safetensors) is byte-structurally identical
+    # to QwenImageVAE (hash ed4ea5824d55ec3107b09815e318123a, registered in
+    # qwen_image_series). It is loaded via that existing entry as model_name
+    # "qwen_image_vae"; the pipeline fetches it and uses the 5D-video code path.
+]
+
 MODEL_CONFIGS = (
     stable_diffusion_xl_series + stable_diffusion_series + qwen_image_series + wan_series + flux_series + flux2_series + ernie_image_series
     + z_image_series + ltx2_series + anima_series + mova_series + joyai_image_series + boogu_image_series + ace_step_series + hidream_o1_image_series
-    + image_metrics_series + ideogram4_series + krea2_series
+    + image_metrics_series + ideogram4_series + krea2_series + lingbot_video_series
 )
diff --git a/diffsynth/diffusion/flow_match.py b/diffsynth/diffusion/flow_match.py
index b9104e869..d20f6f966 100644
--- a/diffsynth/diffusion/flow_match.py
+++ b/diffsynth/diffusion/flow_match.py
@@ -400,3 +400,294 @@ def step(self, model_output, timestep, sample):
         noise = self.clip_noise(torch.randn(denoised.shape, device=denoised.device, dtype=denoised.dtype))
         sample = sigma_ * noise * self.noise_scale_schedule[timestep_id] + (1.0 - sigma_) * denoised
         return sample
+
+
+class LingBotVideoUniPCScheduler(FlowMatchScheduler):
+    """
+    UniPC multistep predictor-corrector scheduler for flow-matching, ported from
+    LingBot-Video's ``FlowUniPCMultistepScheduler`` (a vendored diffusers UniPC
+    variant, ``prediction_type="flow_prediction"``).
+
+    This scheduler is used for **inference** sampling only. Its ``step`` implements
+    the stateful multistep UniP predictor + UniC corrector. Training reuses the
+    flow-matching interpolation (``add_noise``) and velocity target
+    (``training_target`` / ``training_weight``) inherited from ``FlowMatchScheduler``
+    — UniPC is a sampler, not a training objective.
+
+    Base-sigma grid matches the original exactly: ``sigma = 1 - linspace(1, 1/N, N)[::-1]``
+    (i.e. ``sigma in [0, 1-1/N]``), which is offset by one position from the native
+    diffusers UniPC grid and is required for numerical parity with LingBot-Video.
+    """
+    order = 1
+
+    def __init__(
+        self,
+        shift=1.0,
+        num_train_timesteps=1000,
+        solver_order=2,
+        predict_x0=True,
+        solver_type="bh2",
+        lower_order_final=True,
+        disable_corrector=(),
+        final_sigmas_type="zero",
+    ):
+        if solver_type not in ("bh1", "bh2"):
+            raise NotImplementedError(f"solver_type={solver_type} is not implemented")
+        if final_sigmas_type not in ("zero", "sigma_min"):
+            raise ValueError("final_sigmas_type must be one of 'zero' or 'sigma_min'")
+        self.num_train_timesteps = num_train_timesteps
+        self.shift = shift
+        self.solver_order = solver_order
+        self.predict_x0 = predict_x0
+        self.solver_type = solver_type
+        self.lower_order_final = lower_order_final
+        self.disable_corrector = list(disable_corrector)
+        self.final_sigmas_type = final_sigmas_type
+        self.prediction_type = "flow_prediction"
+        self.num_inference_steps = None
+        self.training = False
+        # base sigma grid (drives sigma_min / sigma_max and the training schedule)
+        self.sigmas, self.timesteps = self._flow_sigmas(num_train_timesteps, shift)
+        self.sigma_min = self.sigmas[-1].item()
+        self.sigma_max = self.sigmas[0].item()
+        self._reset_multistep_state()
+
+    def _flow_sigmas(self, n, shift):
+        # sigma = 1 - alpha, alpha = linspace(1, 1/n, n)[::-1]  ->  sigma in [0, 1-1/n]
+        alphas = np.linspace(1, 1 / n, n)[::-1].copy()
+        sigmas = torch.from_numpy(1.0 - alphas).to(dtype=torch.float32)
+        if shift != 1.0:
+            sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+        timesteps = sigmas * n
+        return sigmas, timesteps
+
+    def _reset_multistep_state(self):
+        self.model_outputs = [None] * self.solver_order
+        self.timestep_list = [None] * self.solver_order
+        self.lower_order_nums = 0
+        self.last_sample = None
+        self.this_order = None
+        self._step_index = None
+        self._begin_index = None
+
+    @property
+    def step_index(self):
+        return self._step_index
+
+    @property
+    def begin_index(self):
+        return self._begin_index
+
+    def set_begin_index(self, begin_index=0):
+        self._begin_index = begin_index
+
+    def set_timesteps(self, num_inference_steps=50, denoising_strength=1.0, shift=None, training=False, **kwargs):
+        if shift is None:
+            shift = self.shift
+        if training:
+            # Flow-matching training schedule (UniPC is inference-only): full-resolution
+            # sigma grid + per-timestep loss weights from the base class.
+            self.sigmas, self.timesteps = self._flow_sigmas(self.num_train_timesteps, shift)
+            self.set_training_weight()
+            self.training = True
+            return
+        self.training = False
+        # Inference grid. denoising_strength<1 starts partway down the chain
+        # (DiffSynth convention), matching the original at strength=1.0.
+        sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min) * denoising_strength
+        sigmas = np.linspace(sigma_start, self.sigma_min, num_inference_steps + 1)[:-1].copy()
+        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+        sigma_last = 0.0 if self.final_sigmas_type == "zero" else self.sigma_min
+        timesteps = sigmas * self.num_train_timesteps
+        sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
+        self.sigmas = torch.from_numpy(sigmas)
+        # int64 timesteps match the values the original model was conditioned on
+        self.timesteps = torch.from_numpy(timesteps).to(dtype=torch.int64)
+        self.num_inference_steps = len(timesteps)
+        self._reset_multistep_state()
+
+    def _sigma_to_alpha_sigma_t(self, sigma):
+        return 1 - sigma, sigma
+
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+        indices = (schedule_timesteps == timestep).nonzero()
+        pos = 1 if len(indices) > 1 else 0
+        return indices[pos].item()
+
+    def _init_step_index(self, timestep):
+        if self.begin_index is None:
+            if isinstance(timestep, torch.Tensor):
+                timestep = timestep.to(self.timesteps.device)
+            self._step_index = self.index_for_timestep(timestep)
+        else:
+            self._step_index = self._begin_index
+
+    def convert_model_output(self, model_output, sample):
+        # prediction_type == "flow_prediction"
+        sigma_t = self.sigmas[self.step_index]
+        if self.predict_x0:
+            return sample - sigma_t * model_output
+        return sample - (1 - sigma_t) * model_output
+
+    def multistep_uni_p_bh_update(self, model_output, sample, order):
+        model_output_list = self.model_outputs
+        m0 = model_output_list[-1]
+        x = sample
+
+        sigma_t, sigma_s0 = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]
+        alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
+        alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
+
+        lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
+        lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
+        h = lambda_t - lambda_s0
+        device = sample.device
+
+        rks, D1s = [], []
+        for i in range(1, order):
+            si = self.step_index - i
+            mi = model_output_list[-(i + 1)]
+            alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
+            lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
+            rk = (lambda_si - lambda_s0) / h
+            rks.append(rk)
+            D1s.append((mi - m0) / rk)
+        rks.append(1.0)
+        rks = torch.tensor(rks, device=device)
+
+        R, b = [], []
+        hh = -h if self.predict_x0 else h
+        h_phi_1 = torch.expm1(hh)
+        h_phi_k = h_phi_1 / hh - 1
+        factorial_i = 1
+        B_h = hh if self.solver_type == "bh1" else torch.expm1(hh)
+        for i in range(1, order + 1):
+            R.append(torch.pow(rks, i - 1))
+            b.append(h_phi_k * factorial_i / B_h)
+            factorial_i *= i + 1
+            h_phi_k = h_phi_k / hh - 1 / factorial_i
+        R = torch.stack(R)
+        b = torch.tensor(b, device=device)
+
+        if len(D1s) > 0:
+            D1s = torch.stack(D1s, dim=1)
+            if order == 2:
+                rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device)
+            else:
+                rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]).to(device).to(x.dtype)
+        else:
+            D1s = None
+
+        if self.predict_x0:
+            x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
+            pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s) if D1s is not None else 0
+            x_t = x_t_ - alpha_t * B_h * pred_res
+        else:
+            x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
+            pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s) if D1s is not None else 0
+            x_t = x_t_ - sigma_t * B_h * pred_res
+        return x_t.to(x.dtype)
+
+    def multistep_uni_c_bh_update(self, this_model_output, last_sample, this_sample, order):
+        model_output_list = self.model_outputs
+        m0 = model_output_list[-1]
+        x = last_sample
+        model_t = this_model_output
+
+        sigma_t, sigma_s0 = self.sigmas[self.step_index], self.sigmas[self.step_index - 1]
+        alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
+        alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
+
+        lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
+        lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
+        h = lambda_t - lambda_s0
+        device = this_sample.device
+
+        rks, D1s = [], []
+        for i in range(1, order):
+            si = self.step_index - (i + 1)
+            mi = model_output_list[-(i + 1)]
+            alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
+            lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
+            rk = (lambda_si - lambda_s0) / h
+            rks.append(rk)
+            D1s.append((mi - m0) / rk)
+        rks.append(1.0)
+        rks = torch.tensor(rks, device=device)
+
+        R, b = [], []
+        hh = -h if self.predict_x0 else h
+        h_phi_1 = torch.expm1(hh)
+        h_phi_k = h_phi_1 / hh - 1
+        factorial_i = 1
+        B_h = hh if self.solver_type == "bh1" else torch.expm1(hh)
+        for i in range(1, order + 1):
+            R.append(torch.pow(rks, i - 1))
+            b.append(h_phi_k * factorial_i / B_h)
+            factorial_i *= i + 1
+            h_phi_k = h_phi_k / hh - 1 / factorial_i
+        R = torch.stack(R)
+        b = torch.tensor(b, device=device)
+
+        D1s = torch.stack(D1s, dim=1) if len(D1s) > 0 else None
+        if order == 1:
+            rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device)
+        else:
+            rhos_c = torch.linalg.solve(R, b).to(device).to(x.dtype)
+
+        if self.predict_x0:
+            x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
+            corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s) if D1s is not None else 0
+            D1_t = model_t - m0
+            x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t)
+        else:
+            x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
+            corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s) if D1s is not None else 0
+            D1_t = model_t - m0
+            x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t)
+        return x_t.to(x.dtype)
+
+    def step(self, model_output, timestep, sample, **kwargs):
+        if self.num_inference_steps is None:
+            raise ValueError("Run set_timesteps() before step().")
+        if self.step_index is None:
+            self._init_step_index(timestep)
+
+        use_corrector = (
+            self.step_index > 0
+            and self.step_index - 1 not in self.disable_corrector
+            and self.last_sample is not None
+        )
+
+        model_output_convert = self.convert_model_output(model_output, sample)
+        if use_corrector:
+            sample = self.multistep_uni_c_bh_update(
+                this_model_output=model_output_convert,
+                last_sample=self.last_sample,
+                this_sample=sample,
+                order=self.this_order,
+            )
+
+        for i in range(self.solver_order - 1):
+            self.model_outputs[i] = self.model_outputs[i + 1]
+            self.timestep_list[i] = self.timestep_list[i + 1]
+        self.model_outputs[-1] = model_output_convert
+        self.timestep_list[-1] = timestep
+
+        if self.lower_order_final:
+            this_order = min(self.solver_order, len(self.timesteps) - self.step_index)
+        else:
+            this_order = self.solver_order
+        self.this_order = min(this_order, self.lower_order_nums + 1)
+        assert self.this_order > 0
+
+        self.last_sample = sample
+        prev_sample = self.multistep_uni_p_bh_update(
+            model_output=model_output_convert, sample=sample, order=self.this_order,
+        )
+        if self.lower_order_nums < self.solver_order:
+            self.lower_order_nums += 1
+        self._step_index += 1
+        return prev_sample
diff --git a/diffsynth/models/lingbot_video_dit.py b/diffsynth/models/lingbot_video_dit.py
new file mode 100644
index 000000000..59fe8fd1d
--- /dev/null
+++ b/diffsynth/models/lingbot_video_dit.py
@@ -0,0 +1,693 @@
+import math
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..core.attention import attention_forward
+from ..core.gradient import gradient_checkpoint_forward
+from ..core.device.npu_compatible_device import get_device_type
+
+
+# Modules kept in fp32 regardless of the model's bulk compute dtype. The custom
+# `to()` below honours this list so the sensitive AdaLN / norm / router paths stay
+# in full precision, matching the original lingbot-video precision policy.
+LINGBOT_VIDEO_FP32_MODULES = (
+    "time_embedder",
+    "time_modulation",
+    "scale_shift_table",
+    "norm",
+    "norm1",
+    "norm2",
+    "norm_q",
+    "norm_k",
+    "norm_post_attn",
+    "norm_post_ffn",
+    "norm_out",
+    "norm_out_modulation",
+    "router",
+)
+
+
+def should_keep_in_fp32(name: str) -> bool:
+    return any(module_name in name.split(".") for module_name in LINGBOT_VIDEO_FP32_MODULES)
+
+
+def get_timestep_embedding(
+    timesteps: torch.Tensor,
+    embedding_dim: int,
+    flip_sin_to_cos: bool = True,
+    downscale_freq_shift: float = 0.0,
+    scale: float = 1.0,
+    max_period: int = 10000,
+) -> torch.Tensor:
+    """Sinusoidal timestep embedding (matches diffusers.get_timestep_embedding)."""
+    assert timesteps.ndim == 1
+    half_dim = embedding_dim // 2
+    exponent = -math.log(max_period) * torch.arange(
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
+    )
+    exponent = exponent / (half_dim - downscale_freq_shift)
+    emb = torch.exp(exponent)
+    emb = timesteps[:, None].float() * emb[None, :]
+    emb = scale * emb
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+    if embedding_dim % 2 == 1:
+        emb = F.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+class Timesteps(nn.Module):
+    """Parameter-free sinusoidal timestep projection (matches diffusers.Timesteps)."""
+
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool = True, downscale_freq_shift: float = 0.0, scale: int = 1):
+        super().__init__()
+        self.num_channels = num_channels
+        self.flip_sin_to_cos = flip_sin_to_cos
+        self.downscale_freq_shift = downscale_freq_shift
+        self.scale = scale
+
+    def forward(self, timesteps: torch.Tensor) -> torch.Tensor:
+        return get_timestep_embedding(
+            timesteps,
+            self.num_channels,
+            flip_sin_to_cos=self.flip_sin_to_cos,
+            downscale_freq_shift=self.downscale_freq_shift,
+            scale=self.scale,
+        )
+
+
+class TimestepEmbedding(nn.Module):
+    """Two-layer timestep MLP (matches diffusers.TimestepEmbedding, act_fn='silu').
+
+    Submodule names `linear_1` / `linear_2` are kept identical so the original
+    checkpoint keys (`time_embedder.linear_1.*`, `time_embedder.linear_2.*`) load
+    without renaming.
+    """
+
+    def __init__(self, in_channels: int, time_embed_dim: int, sample_proj_bias: bool = True):
+        super().__init__()
+        self.linear_1 = nn.Linear(in_channels, time_embed_dim, bias=sample_proj_bias)
+        self.act = nn.SiLU()
+        self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim, bias=sample_proj_bias)
+
+    def forward(self, sample: torch.Tensor) -> torch.Tensor:
+        return self.linear_2(self.act(self.linear_1(sample)))
+
+
+class LingBotVideoRMSNorm(nn.Module):
+    """RMSNorm with fp32 accumulation (ported verbatim from lingbot-video)."""
+
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return (self.weight * hidden_states).to(input_dtype)
+
+
+def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
+    """Apply complex RoPE to `(B, S, H, D)` attention tensors."""
+    with torch.amp.autocast(get_device_type(), enabled=False):
+        x_c = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
+        out = torch.view_as_real(x_c * freqs_cis.unsqueeze(2)).flatten(3)
+        return out.type_as(x)
+
+
+class LingBotVideoRotaryEmbedding(nn.Module):
+    """Complex64 RoPE table indexed by 3D position ids. Holds no persistent state."""
+
+    def __init__(self, axes_dims: Tuple[int, ...], axes_lens: Tuple[int, ...], theta: float):
+        super().__init__()
+        self.axes_dims = tuple(axes_dims)
+        self.axes_lens = list(axes_lens)
+        self.theta = theta
+        self.freqs_cis = None
+
+    @staticmethod
+    def precompute_freqs_cis(dim: Tuple[int, ...], end: Tuple[int, ...], theta: float):
+        freqs_cis = []
+        for d, e in zip(dim, end):
+            freqs = 1.0 / (theta ** (torch.arange(0, d, 2, dtype=torch.float64, device="cpu") / d))
+            timestep = torch.arange(e, device=freqs.device, dtype=torch.float64)
+            freqs = torch.outer(timestep, freqs).float()
+            freqs_cis.append(torch.polar(torch.ones_like(freqs), freqs).to(torch.complex64))
+        return freqs_cis
+
+    def forward(self, position_ids: torch.Tensor) -> torch.Tensor:
+        # position_ids: (S, 3) int -> (S, head_dim/2) complex64
+        device = position_ids.device
+        max_vals = position_ids.max(dim=0).values.tolist()
+        needs_rebuild = self.freqs_cis is None or any(m >= l for m, l in zip(max_vals, self.axes_lens))
+        if needs_rebuild:
+            for i in range(len(self.axes_lens)):
+                if max_vals[i] >= self.axes_lens[i]:
+                    self.axes_lens[i] = int(max_vals[i] * 1.5) + 1
+            self.freqs_cis = self.precompute_freqs_cis(self.axes_dims, tuple(self.axes_lens), theta=self.theta)
+            self.freqs_cis = [freqs_cis.to(device) for freqs_cis in self.freqs_cis]
+        elif self.freqs_cis[0].device != device:
+            self.freqs_cis = [freqs_cis.to(device) for freqs_cis in self.freqs_cis]
+        return torch.cat([self.freqs_cis[i][position_ids[:, i]] for i in range(len(self.axes_dims))], dim=-1)
+
+
+def make_joint_position_ids(text_len: int, grid_t: int, grid_h: int, grid_w: int, device: torch.device) -> torch.Tensor:
+    """3D positions in [video; text] order. Text t-axis is 1..text_len; video t-axis starts at text_len+1."""
+    tt = torch.arange(grid_t, device=device, dtype=torch.int32) + (text_len + 1)
+    hh = torch.arange(grid_h, device=device, dtype=torch.int32)
+    ww = torch.arange(grid_w, device=device, dtype=torch.int32)
+    grid = torch.stack(torch.meshgrid(tt, hh, ww, indexing="ij"), dim=-1).flatten(0, 2)
+    text_t = torch.arange(text_len, device=device, dtype=torch.int32) + 1
+    text_pos = torch.stack([text_t, torch.zeros_like(text_t), torch.zeros_like(text_t)], dim=-1)
+    return torch.cat([grid, text_pos], dim=0)  # (Nx + L, 3)
+
+
+def _cat_interleave(a: torch.Tensor, len_a: list, b: torch.Tensor, len_b: list) -> torch.Tensor:
+    a_split = torch.split(a, len_a, dim=1)
+    b_split = torch.split(b, len_b, dim=1)
+    blocks = []
+    for x_part, text_part in zip(a_split, b_split):
+        blocks.extend([x_part, text_part])
+    return torch.cat(blocks, dim=1)
+
+
+class LingBotVideoTextEmbedder(nn.Module):
+    """CondProjection: RMSNorm(text_dim) -> Linear -> SiLU -> Linear."""
+
+    def __init__(self, text_dim: int, hidden_size: int):
+        super().__init__()
+        self.norm = LingBotVideoRMSNorm(text_dim, eps=1e-6)
+        self.linear_1 = nn.Linear(text_dim, hidden_size, bias=True)
+        self.linear_2 = nn.Linear(hidden_size, hidden_size, bias=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.norm(x)
+        return self.linear_2(F.silu(self.linear_1(x)))
+
+
+class LingBotVideoAttention(nn.Module):
+    def __init__(self, hidden_size, num_heads, norm_eps, qkv_bias, out_bias):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = hidden_size // num_heads
+        self.to_q = nn.Linear(hidden_size, hidden_size, bias=qkv_bias)
+        self.to_k = nn.Linear(hidden_size, hidden_size, bias=qkv_bias)
+        self.to_v = nn.Linear(hidden_size, hidden_size, bias=qkv_bias)
+        self.norm_q = LingBotVideoRMSNorm(self.head_dim, norm_eps)
+        self.norm_k = LingBotVideoRMSNorm(self.head_dim, norm_eps)
+        self.to_out = nn.Linear(hidden_size, hidden_size, bias=out_bias)
+
+    def forward(self, x, rotary_emb, attention_mask=None):
+        q = self.to_q(x).unflatten(2, (self.num_heads, self.head_dim))
+        k = self.to_k(x).unflatten(2, (self.num_heads, self.head_dim))
+        v = self.to_v(x).unflatten(2, (self.num_heads, self.head_dim))
+        q = apply_rotary_emb(self.norm_q(q), rotary_emb)
+        k = apply_rotary_emb(self.norm_k(k), rotary_emb)
+        # q/k/v are (B, S, H, D); DiffSynth's attention_forward handles the layout.
+        out = attention_forward(
+            q, k, v,
+            q_pattern="b s n d", k_pattern="b s n d", v_pattern="b s n d", out_pattern="b s n d",
+            attn_mask=attention_mask,
+        )
+        return self.to_out(out.flatten(2, 3).type_as(x))
+
+
+class LingBotVideoMLP(nn.Module):
+    def __init__(self, hidden_size, intermediate_size):
+        super().__init__()
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+
+    def forward(self, x):
+        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
+
+
+class LingBotVideoRouter(nn.Module):
+    """TokenChoiceTopKRouter inference path (no capacity / jitter / load stats).
+
+    The asymmetry is preserved: selection uses the bias-added score, while the
+    gating weights gather the bias-free score.
+    """
+
+    def __init__(self, hidden_size, num_experts, top_k, score_func, norm_topk_prob, n_group, topk_group, route_scale):
+        super().__init__()
+        self.num_experts = num_experts
+        self.top_k = top_k
+        self.score_func = score_func
+        self.norm_topk_prob = norm_topk_prob
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.route_scale = route_scale
+        self.weight = nn.Parameter(torch.empty(num_experts, hidden_size))
+        self.register_buffer("e_score_correction_bias", torch.zeros(num_experts), persistent=True)
+
+    def _group_limited_topk(self, scores_for_choice):
+        seq_len = scores_for_choice.shape[0]
+        experts_per_group = self.num_experts // self.n_group
+        grouped = scores_for_choice.view(seq_len, self.n_group, experts_per_group)
+        group_scores = grouped.topk(2, dim=-1)[0].sum(dim=-1)
+        group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1]
+        group_mask = torch.zeros_like(group_scores)
+        group_mask.scatter_(1, group_idx, 1)
+        score_mask = group_mask.unsqueeze(-1).expand(seq_len, self.n_group, experts_per_group).reshape(seq_len, -1)
+        masked = scores_for_choice.masked_fill(~score_mask.bool(), float("-inf"))
+        return torch.topk(masked, k=self.top_k, dim=-1, sorted=False)[1]
+
+    def forward(self, tokens: torch.Tensor):
+        with torch.amp.autocast(tokens.device.type, enabled=False):
+            logits = F.linear(tokens.float(), self.weight.float())
+        if self.score_func == "softmax":
+            scores = F.softmax(logits, dim=-1)
+        else:
+            scores = logits.sigmoid()
+        scores_for_choice = scores + self.e_score_correction_bias.unsqueeze(0)
+        if self.n_group is not None and self.n_group > 1:
+            top_indices = self._group_limited_topk(scores_for_choice)
+        else:
+            top_indices = torch.topk(scores_for_choice, k=self.top_k, dim=-1, sorted=False)[1]
+        top_scores = scores.gather(1, top_indices)
+        if self.top_k > 1 and self.norm_topk_prob:
+            top_scores = top_scores / (top_scores.sum(dim=-1, keepdim=True) + 1e-20)
+        top_scores = top_scores * self.route_scale
+        return top_indices, top_scores.to(tokens.dtype)
+
+
+class LingBotVideoGroupedExperts(nn.Module):
+    """Weight layout matches GroupedExperts: w1 [E,I,H], w2 [E,H,I], w3 [E,I,H]."""
+
+    def __init__(self, num_experts, hidden_size, intermediate_size):
+        super().__init__()
+        self.num_experts = num_experts
+        self.w1 = nn.Parameter(torch.empty(num_experts, intermediate_size, hidden_size))
+        self.w2 = nn.Parameter(torch.empty(num_experts, hidden_size, intermediate_size))
+        self.w3 = nn.Parameter(torch.empty(num_experts, intermediate_size, hidden_size))
+
+
+def _round_up_to_multiple(value: int, multiple: int) -> int:
+    return ((value + multiple - 1) // multiple) * multiple
+
+
+class LingBotVideoSparseMoeBlock(nn.Module):
+    """MoE FFN. Keeps only the portable eager expert path (grouped_mm with a
+    per-expert for-loop fallback); the sglang / triton / fp8 / context-parallel
+    backends from the source are intentionally dropped for the DiffSynth port.
+    """
+
+    def __init__(self, hidden_size, intermediate_size, num_experts, top_k, moe_intermediate_size,
+                 score_func, norm_topk_prob, n_group, topk_group, routed_scaling_factor, n_shared_experts):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.num_experts = num_experts
+        self.router = LingBotVideoRouter(
+            hidden_size, num_experts, top_k, score_func, norm_topk_prob, n_group, topk_group, routed_scaling_factor,
+        )
+        self.experts = LingBotVideoGroupedExperts(num_experts, hidden_size, moe_intermediate_size)
+        self.shared_experts = None
+        if n_shared_experts is not None and n_shared_experts > 0:
+            self.shared_experts = LingBotVideoMLP(hidden_size, moe_intermediate_size * n_shared_experts)
+
+    @staticmethod
+    def _reorder_tokens(tokens, top_scores, top_indices, num_experts):
+        num_tokens = tokens.shape[0]
+        top_k = top_indices.shape[1]
+        flat_scores = top_scores.reshape(-1)
+        flat_indices = top_indices.reshape(-1)
+        active_positions = torch.where(flat_scores != 0)[0]
+        active_experts = flat_indices[active_positions]
+
+        counts = torch.zeros(num_experts, device=tokens.device, dtype=torch.int64)
+        counts.scatter_add_(0, active_experts, torch.ones_like(active_experts, dtype=torch.int64))
+
+        sort_order = torch.argsort(active_experts, stable=True)
+        sorted_positions = active_positions[sort_order]
+        sorted_scores = flat_scores[sorted_positions]
+        original_token_idx = sorted_positions // top_k
+        permuted_tokens = tokens[original_token_idx]
+        return permuted_tokens, counts, sorted_positions, sorted_scores, num_tokens, top_k
+
+    @staticmethod
+    def _pad_grouped_tokens(tokens, counts, align: int = 8):
+        num_tokens = tokens.shape[0]
+        num_experts = int(counts.shape[0])
+        max_len = _round_up_to_multiple(num_tokens + num_experts * align, align)
+        counts_i64 = counts.to(torch.int64)
+        total_per_expert = torch.clamp_min(counts_i64, align)
+        aligned_counts = ((total_per_expert + align - 1) // align * align).to(torch.int32)
+        write_offsets = torch.cumsum(aligned_counts, dim=0) - aligned_counts
+        start_indices = torch.cumsum(counts_i64, dim=0) - counts_i64
+
+        fill_value = num_tokens
+        permuted_indices = torch.full((max_len,), fill_value, dtype=torch.int64, device=tokens.device)
+        for expert_idx in range(num_experts):
+            length = int(counts_i64[expert_idx].item())
+            if length == 0:
+                continue
+            write_start = int(write_offsets[expert_idx].item())
+            start = int(start_indices[expert_idx].item())
+            permuted_indices[write_start:write_start + length] = torch.arange(
+                start, start + length, device=tokens.device, dtype=torch.int64
+            )
+
+        tokens_with_pad = torch.vstack((tokens, tokens.new_zeros((tokens.shape[-1],))))
+        input_shape = tokens_with_pad.shape
+        return input_shape, tokens_with_pad[permuted_indices], permuted_indices, aligned_counts
+
+    @staticmethod
+    def _unpad_grouped_tokens(output, input_shape, permuted_indices):
+        unpermuted = output.new_empty(input_shape)
+        unpermuted[permuted_indices, :] = output
+        return unpermuted[:-1]
+
+    def _run_grouped_experts(self, tokens, counts):
+        if not hasattr(torch, "_grouped_mm"):
+            return self._run_experts_for_loop(tokens, counts)
+        input_shape, padded_tokens, permuted_indices, aligned_counts = self._pad_grouped_tokens(tokens, counts)
+        offsets = torch.cumsum(aligned_counts, dim=0, dtype=torch.int32)
+        h = F.silu(torch._grouped_mm(padded_tokens.bfloat16(), self.experts.w1.bfloat16().transpose(-2, -1), offs=offsets))
+        h = h * torch._grouped_mm(padded_tokens.bfloat16(), self.experts.w3.bfloat16().transpose(-2, -1), offs=offsets)
+        out = torch._grouped_mm(h, self.experts.w2.bfloat16().transpose(-2, -1), offs=offsets).type_as(padded_tokens)
+        return self._unpad_grouped_tokens(out, input_shape, permuted_indices)
+
+    def _run_experts_for_loop(self, tokens, counts):
+        count_list = counts.tolist()
+        splits = torch.split(tokens, count_list, dim=0)
+        outputs = []
+        for expert_idx, expert_tokens in enumerate(splits):
+            if expert_tokens.numel() == 0:
+                continue
+            h = F.silu(expert_tokens @ self.experts.w1[expert_idx].transpose(-2, -1))
+            h = h * (expert_tokens @ self.experts.w3[expert_idx].transpose(-2, -1))
+            h = h @ self.experts.w2[expert_idx].transpose(-2, -1)
+            outputs.append(h)
+        if not outputs:
+            return tokens.new_zeros(tokens.shape)
+        return torch.cat(outputs, dim=0)
+
+    @staticmethod
+    def _restore_tokens(expert_output, sorted_positions, sorted_scores, num_tokens, top_k):
+        dim = expert_output.shape[-1]
+        unsorted = torch.zeros((num_tokens * top_k, dim), dtype=expert_output.dtype, device=expert_output.device)
+        unsorted[sorted_positions] = expert_output
+        unsorted = unsorted.reshape(num_tokens, top_k, dim)
+
+        scores_unsorted = torch.zeros(num_tokens * top_k, dtype=sorted_scores.dtype, device=sorted_scores.device)
+        scores_unsorted[sorted_positions] = sorted_scores
+        scores_unsorted = scores_unsorted.reshape(num_tokens, top_k, 1)
+        return (unsorted.float() * scores_unsorted).sum(dim=1).to(expert_output.dtype)
+
+    def _run_selected_experts(self, tokens, top_scores, top_indices):
+        permuted_tokens, counts, sorted_positions, sorted_scores, num_tokens, top_k = self._reorder_tokens(
+            tokens, top_scores, top_indices, self.router.num_experts
+        )
+        expert_output = self._run_grouped_experts(permuted_tokens, counts)
+        return self._restore_tokens(expert_output, sorted_positions, sorted_scores, num_tokens, top_k)
+
+    def forward(self, hidden_states: torch.Tensor, padding_mask: Optional[torch.Tensor] = None):
+        # hidden_states: (B, S, H); padding_mask: (B*S,) with 1=valid (only needed when B>1)
+        B = hidden_states.shape[0]
+        tokens = hidden_states.view(-1, self.hidden_size)
+        top_indices, top_scores = self.router(tokens)
+        if padding_mask is not None:
+            pm = padding_mask.unsqueeze(-1).to(top_scores.dtype)
+            top_scores = top_scores * pm
+            top_scores = top_scores / (top_scores.sum(dim=-1, keepdim=True) + 1e-9)
+            top_scores = top_scores * self.router.route_scale
+
+        out = self._run_selected_experts(tokens, top_scores, top_indices)
+
+        out = out.view(B, -1, self.hidden_size)
+        if self.shared_experts is not None:
+            out = out + self.shared_experts(hidden_states)
+        return out
+
+
+class LingBotVideoBlock(nn.Module):
+    def __init__(self, hidden_size, num_attention_heads, intermediate_size, norm_eps, qkv_bias, out_bias,
+                 num_experts, num_experts_per_tok, moe_intermediate_size, decoder_sparse_step, mlp_only_layers,
+                 n_shared_experts, score_func, norm_topk_prob, n_group, topk_group, routed_scaling_factor, layer_idx):
+        super().__init__()
+        self.layer_idx = layer_idx
+        h = hidden_size
+        self.scale_shift_table = nn.Parameter(torch.zeros(1, 6 * h))
+        self.norm1 = LingBotVideoRMSNorm(h, norm_eps)
+        self.attn = LingBotVideoAttention(h, num_attention_heads, norm_eps, qkv_bias, out_bias)
+        self.norm_post_attn = LingBotVideoRMSNorm(h, norm_eps)
+        self.norm2 = LingBotVideoRMSNorm(h, norm_eps)
+        # Sparsity decision matches the source MoEBlock: mlp_only_layers + decoder_sparse_step + num_experts.
+        if layer_idx not in mlp_only_layers and (num_experts > 0 and (layer_idx + 1) % decoder_sparse_step == 0):
+            self.ffn = LingBotVideoSparseMoeBlock(
+                h, intermediate_size, num_experts, num_experts_per_tok, moe_intermediate_size,
+                score_func, norm_topk_prob, n_group, topk_group, routed_scaling_factor, n_shared_experts,
+            )
+        else:
+            self.ffn = LingBotVideoMLP(h, intermediate_size)
+        self.norm_post_ffn = LingBotVideoRMSNorm(h, norm_eps)
+
+    def forward(self, x, temb6, rotary_emb, attention_mask=None, moe_padding_mask=None):
+        expected_tokens = x.shape[0] * x.shape[1]
+        if temb6.ndim != 2 or temb6.shape[0] != expected_tokens:
+            raise ValueError(
+                "LingBotVideoBlock expects token-level temb6 with shape (B*S, 6D); "
+                f"got {tuple(temb6.shape)} for hidden states {tuple(x.shape)}."
+            )
+        # AdaLN modulation / norms run in fp32 (sensitive path); cast to the bulk
+        # compute dtype only at the bf16 Linear boundary.
+        mod = temb6.view(x.shape[0], x.shape[1], -1) + self.scale_shift_table.unsqueeze(0)
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=-1)
+        gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
+        scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
+
+        bulk_dtype = self.attn.to_q.weight.dtype
+        attn_in = (self.norm1(x) * scale_msa + shift_msa).to(bulk_dtype)
+        attn_out = self.attn(attn_in, rotary_emb, attention_mask)
+        x = x + (gate_msa * self.norm_post_attn(attn_out)).to(x.dtype)
+
+        ffn_in = (self.norm2(x) * scale_mlp + shift_mlp).to(bulk_dtype)
+        if isinstance(self.ffn, LingBotVideoSparseMoeBlock):
+            ffn_out = self.ffn(ffn_in, padding_mask=moe_padding_mask)
+        else:
+            ffn_out = self.ffn(ffn_in)
+        x = x + (gate_mlp * self.norm_post_ffn(ffn_out)).to(x.dtype)
+        return x
+
+
+class LingBotVideoDiT(nn.Module):
+    """LingBot-Video MoE DiT ported for DiffSynth-Studio.
+
+    Supports both the Dense (`num_experts=0`, FFN = MLP) and MoE
+    (`num_experts>0`, FFN = sparse MoE) variants from a single class. The bulk of
+    the network runs in the model's compute dtype (e.g. bf16) while the modules in
+    `LINGBOT_VIDEO_FP32_MODULES` are pinned to fp32 by the custom `to()`.
+    """
+
+    _supports_gradient_checkpointing = True
+    _no_split_modules = ["LingBotVideoBlock"]
+    _repeated_blocks = ["LingBotVideoBlock"]
+
+    def to(self, *args, **kwargs):
+        device, dtype, non_blocking, _ = torch._C._nn._parse_to(*args, **kwargs)
+        if dtype is None or dtype == torch.float32:
+            return super().to(*args, **kwargs)
+        if not torch.is_floating_point(torch.empty((), dtype=dtype)):
+            return super().to(*args, **kwargs)
+
+        if device is not None:
+            super().to(device=device, non_blocking=non_blocking)
+
+        for name, param in self.named_parameters():
+            if not torch.is_floating_point(param):
+                continue
+            target_dtype = torch.float32 if should_keep_in_fp32(name) else dtype
+            param.data = param.data.to(dtype=target_dtype, non_blocking=non_blocking)
+            if param.grad is not None:
+                param.grad.data = param.grad.data.to(dtype=target_dtype, non_blocking=non_blocking)
+
+        for name, buffer in self.named_buffers():
+            if not torch.is_floating_point(buffer):
+                continue
+            target_dtype = torch.float32 if should_keep_in_fp32(name) else dtype
+            buffer.data = buffer.data.to(dtype=target_dtype, non_blocking=non_blocking)
+
+        return self
+
+    def __init__(
+        self,
+        patch_size: Tuple[int, int, int] = (1, 2, 2),
+        in_channels: int = 16,
+        out_channels: int = 16,
+        hidden_size: int = 2048,
+        num_attention_heads: int = 16,
+        depth: int = 24,
+        intermediate_size: int = 6144,
+        text_dim: int = 2560,
+        freq_dim: int = 256,
+        norm_eps: float = 1e-6,
+        rope_theta: float = 256.0,
+        axes_dims: Tuple[int, int, int] = (32, 48, 48),
+        axes_lens: Tuple[int, int, int] = (8192, 1024, 1024),
+        qkv_bias: bool = False,
+        out_bias: bool = True,
+        patch_embed_bias: bool = True,
+        timestep_mlp_bias: bool = True,
+        num_experts: int = 0,
+        num_experts_per_tok: int = 8,
+        moe_intermediate_size: int = 512,
+        decoder_sparse_step: int = 1,
+        mlp_only_layers: Tuple[int, ...] = (),
+        n_shared_experts: Optional[int] = None,
+        score_func: str = "sigmoid",
+        norm_topk_prob: bool = True,
+        n_group: Optional[int] = None,
+        topk_group: Optional[int] = None,
+        routed_scaling_factor: float = 1.0,
+    ):
+        super().__init__()
+        head_dim = hidden_size // num_attention_heads
+        assert head_dim == sum(axes_dims), f"head_dim {head_dim} != sum(axes_dims) {sum(axes_dims)}"
+        mlp_only_layers = tuple(mlp_only_layers)
+
+        # Config attributes used by forward / pipeline.
+        self.patch_size = tuple(patch_size)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_size = hidden_size
+        self.num_attention_heads = num_attention_heads
+        self.depth = depth
+        self.gradient_checkpointing = False
+
+        self.patch_embedder = nn.Linear(in_channels * math.prod(patch_size), hidden_size, bias=patch_embed_bias)
+        self.time_proj = Timesteps(freq_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.time_embedder = TimestepEmbedding(freq_dim, hidden_size, sample_proj_bias=timestep_mlp_bias)
+        self.time_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size))
+        self.text_embedder = LingBotVideoTextEmbedder(text_dim, hidden_size)
+        self.rope = LingBotVideoRotaryEmbedding(axes_dims, axes_lens, rope_theta)
+        self.blocks = nn.ModuleList([
+            LingBotVideoBlock(
+                hidden_size=hidden_size, num_attention_heads=num_attention_heads, intermediate_size=intermediate_size,
+                norm_eps=norm_eps, qkv_bias=qkv_bias, out_bias=out_bias, num_experts=num_experts,
+                num_experts_per_tok=num_experts_per_tok, moe_intermediate_size=moe_intermediate_size,
+                decoder_sparse_step=decoder_sparse_step, mlp_only_layers=mlp_only_layers,
+                n_shared_experts=n_shared_experts, score_func=score_func, norm_topk_prob=norm_topk_prob,
+                n_group=n_group, topk_group=topk_group, routed_scaling_factor=routed_scaling_factor, layer_idx=i,
+            )
+            for i in range(depth)
+        ])
+        self.norm_out = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=norm_eps)
+        self.norm_out_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size))
+        self.proj_out = nn.Linear(hidden_size, math.prod(patch_size) * out_channels)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,             # (B, C, T, H, W)
+        timestep: torch.Tensor,                  # (B,) in [0, 1000] (= sigma * 1000)
+        encoder_hidden_states: torch.Tensor,     # (B, L, text_dim)
+        encoder_attention_mask: Optional[torch.Tensor] = None,  # (B, L) 1=valid
+        use_gradient_checkpointing: bool = False,
+        use_gradient_checkpointing_offload: bool = False,
+        **kwargs,
+    ):
+        B, C, T, H, W = hidden_states.shape
+        pF, pH, pW = self.patch_size
+        gt, gh, gw = T // pF, H // pH, W // pW
+        n_video = gt * gh * gw
+        L = encoder_hidden_states.shape[1]
+        device = hidden_states.device
+        if encoder_attention_mask is not None:
+            text_lens = encoder_attention_mask.sum(dim=-1).long()
+        else:
+            text_lens = torch.full((B,), L, dtype=torch.long, device=device)
+        text_lens_list = [int(v) for v in text_lens.detach().cpu().tolist()]
+        packed_batch = B > 1
+
+        # patchify: token order (f h w), feature order (pf ph pw c) -- matches patchify_and_embed
+        patch_tokens = hidden_states.reshape(B, C, gt, pF, gh, pH, gw, pW)
+        patch_tokens = patch_tokens.permute(0, 2, 4, 6, 3, 5, 7, 1).reshape(B, n_video, pF * pH * pW * C)
+
+        if packed_batch:
+            x = torch.cat([self.patch_embedder(patch_tokens[i:i + 1]) for i in range(B)], dim=1)
+            text_parts = [self.text_embedder(encoder_hidden_states[i:i + 1, :text_lens_list[i], :]) for i in range(B)]
+            text = torch.cat(text_parts, dim=1)
+            joint = _cat_interleave(x, [n_video] * B, text, text_lens_list)
+        else:
+            x = self.patch_embedder(patch_tokens)
+            text = self.text_embedder(encoder_hidden_states)
+            joint = torch.cat([x, text], dim=1)  # [video; text]
+        joint_seq_len = joint.shape[1]
+
+        # Per-sample RoPE: video t-axis start = real text length of this sample + 1.
+        rotary_parts = [self.rope(make_joint_position_ids(text_lens_list[i], gt, gh, gw, device)) for i in range(B)]
+        if packed_batch:
+            rotary = torch.cat(rotary_parts, dim=0).unsqueeze(0)
+        else:
+            rotary = torch.stack(rotary_parts, dim=0)  # (B, S, head_dim/2) complex64
+
+        attention_mask = None
+        moe_padding_mask = None
+        if packed_batch:
+            # Block-diagonal mask so packed samples only attend within their own block
+            # (replaces the source's flash varlen path; numerically equivalent, no flash dep).
+            sample_seq_lens = [n_video + tl for tl in text_lens_list]
+            total = sum(sample_seq_lens)
+            block_mask = torch.zeros((total, total), dtype=torch.bool, device=device)
+            start = 0
+            for slen in sample_seq_lens:
+                block_mask[start:start + slen, start:start + slen] = True
+                start += slen
+            attention_mask = block_mask[None, None, :, :]  # (1,1,total,total)
+        else:
+            has_padding = encoder_attention_mask is not None and bool((text_lens < L).any())
+            if has_padding:
+                key_mask = torch.cat(
+                    [torch.ones(B, n_video, dtype=torch.bool, device=device), encoder_attention_mask.bool()], dim=1
+                )
+                attention_mask = key_mask[:, None, None, :]      # (B,1,1,S) -> SDPA broadcast
+                moe_padding_mask = key_mask.reshape(-1).float()  # (B*S,)
+
+        # Timestep -> per-token modulation.
+        timestep_proj = self.time_proj(timestep.float())
+        t_emb = self.time_embedder(timestep_proj)  # (B, D)
+        if packed_batch:
+            temb_input = torch.cat(
+                [t_emb[i:i + 1].unsqueeze(1).expand(1, sample_seq_lens[i], -1) for i in range(B)], dim=1
+            )  # (1, total, D)
+        else:
+            temb_input = t_emb.unsqueeze(1).expand(B, joint_seq_len, -1)  # (B, S, D)
+        b_eff, s_eff = temb_input.shape[0], temb_input.shape[1]
+        temb6 = self.time_modulation(temb_input.reshape(b_eff * s_eff, -1))  # (B*S, 6D)
+
+        for block in self.blocks:
+            joint = gradient_checkpoint_forward(
+                block,
+                use_gradient_checkpointing=use_gradient_checkpointing,
+                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                x=joint, temb6=temb6, rotary_emb=rotary,
+                attention_mask=attention_mask, moe_padding_mask=moe_padding_mask,
+            )
+
+        final_mod = self.norm_out_modulation(temb_input.reshape(joint.shape[0] * joint.shape[1], -1))
+        shift, scale = final_mod.reshape(joint.shape[0], joint.shape[1], -1).chunk(2, dim=-1)
+        final_hidden = self.norm_out(joint) * (1.0 + scale) + shift
+        projected = self.proj_out(final_hidden.to(self.proj_out.weight.dtype))
+
+        if packed_batch:
+            split_lengths = []
+            for tl in text_lens_list:
+                split_lengths.extend([n_video, tl])
+            parts = torch.split(projected, split_lengths, dim=1)
+            x = torch.cat(parts[::2], dim=1).reshape(B, n_video, -1)
+        else:
+            x = projected[:, :n_video]
+
+        # unpatchify (matches the rearrange in postprocess)
+        Cout = self.out_channels
+        x = x.reshape(B, gt, gh, gw, pF, pH, pW, Cout)
+        x = x.permute(0, 7, 1, 4, 2, 5, 3, 6).reshape(B, Cout, T, H, W)
+        return x
diff --git a/diffsynth/models/lingbot_video_text_encoder.py b/diffsynth/models/lingbot_video_text_encoder.py
new file mode 100644
index 000000000..434c471da
--- /dev/null
+++ b/diffsynth/models/lingbot_video_text_encoder.py
@@ -0,0 +1,104 @@
+import torch
+from typing import Optional
+
+
+class LingBotVideoTextEncoder(torch.nn.Module):
+    """
+    Text encoder for LingBot-Video.
+
+    The checkpoint (``text_encoder/*.safetensors``) is a
+    ``Qwen3VLForConditionalGeneration`` exported by transformers, but only the
+    ``model.*`` submodule (language_model + visual, 713 tensors) is stored — the
+    tied ``lm_head`` is absent because ``tie_word_embeddings=True``. Wrapping a
+    bare ``Qwen3VLModel`` therefore matches the checkpoint keys exactly (0 missing
+    / 0 unexpected), so the state-dict converter is the identity.
+
+    ``forward`` mirrors :class:`QwenImageTextEncoder`: it forces
+    ``output_hidden_states=True`` and returns the tuple of per-layer hidden
+    states. The pipeline selects ``hidden_states[-1]`` (the original
+    ``HIDDEN_STATE_SKIP_LAYER == 0``) as the prompt embedding.
+    """
+
+    def __init__(self):
+        super().__init__()
+        from transformers import Qwen3VLConfig, Qwen3VLModel
+        config = Qwen3VLConfig(**{
+            "architectures": ["Qwen3VLForConditionalGeneration"],
+            "image_token_id": 151655,
+            "model_type": "qwen3_vl",
+            "text_config": {
+                "attention_bias": False,
+                "attention_dropout": 0.0,
+                "bos_token_id": 151643,
+                "eos_token_id": 151645,
+                "head_dim": 128,
+                "hidden_act": "silu",
+                "hidden_size": 2560,
+                "initializer_range": 0.02,
+                "intermediate_size": 9728,
+                "max_position_embeddings": 262144,
+                "model_type": "qwen3_vl_text",
+                "num_attention_heads": 32,
+                "num_hidden_layers": 36,
+                "num_key_value_heads": 8,
+                "rms_norm_eps": 1e-06,
+                "rope_scaling": {
+                    "mrope_interleaved": True,
+                    "mrope_section": [24, 20, 20],
+                    "rope_type": "default",
+                },
+                "rope_theta": 5000000,
+                "tie_word_embeddings": True,
+                "use_cache": True,
+                "vocab_size": 151936,
+            },
+            "tie_word_embeddings": True,
+            "video_token_id": 151656,
+            "vision_config": {
+                "deepstack_visual_indexes": [5, 11, 17],
+                "depth": 24,
+                "hidden_act": "gelu_pytorch_tanh",
+                "hidden_size": 1024,
+                "in_channels": 3,
+                "initializer_range": 0.02,
+                "intermediate_size": 4096,
+                "model_type": "qwen3_vl",
+                "num_heads": 16,
+                "num_position_embeddings": 2304,
+                "out_hidden_size": 2560,
+                "patch_size": 16,
+                "spatial_merge_size": 2,
+                "temporal_patch_size": 2,
+            },
+            "vision_end_token_id": 151653,
+            "vision_start_token_id": 151652,
+        })
+        self.model = Qwen3VLModel(config)
+        self.config = config
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.Tensor] = None,
+        pixel_values_videos: Optional[torch.FloatTensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        video_grid_thw: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ):
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            output_hidden_states=True,
+            return_dict=True,
+            **kwargs,
+        )
+        return outputs.hidden_states
diff --git a/diffsynth/models/qwen_image_vae.py b/diffsynth/models/qwen_image_vae.py
index 2845354f2..be5e48017 100644
--- a/diffsynth/models/qwen_image_vae.py
+++ b/diffsynth/models/qwen_image_vae.py
@@ -707,6 +707,8 @@ def __init__(
         self.std = 1 / torch.tensor(std).view(1, 16, 1, 1, 1)
 
     def encode(self, x, **kwargs):
+        if x.ndim == 5:
+            return self.encode_video(x)
         x = x.unsqueeze(2)
         x = self.encoder(x)
         x = self.quant_conv(x)
@@ -715,8 +717,10 @@ def encode(self, x, **kwargs):
         x = (x - mean) * std
         x = x.squeeze(2)
         return x
-    
+
     def decode(self, x, **kwargs):
+        if x.ndim == 5:
+            return self.decode_video(x)
         x = x.unsqueeze(2)
         mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
         x = x / std + mean
@@ -724,3 +728,40 @@ def decode(self, x, **kwargs):
         x = self.decoder(x)
         x = x.squeeze(2)
         return x
+
+    def count_conv3d(self, model):
+        return sum(1 for m in model.modules() if isinstance(m, QwenImageCausalConv3d))
+
+    def encode_video(self, x, **kwargs):
+        # x: (B, C, T, H, W). Temporal chunking with a persistent causal feature
+        # cache — mathematically equivalent to encoding the whole clip at once, but
+        # bounded in memory. Chunk layout (1 + 4k frames) matches the WanVAE-derived
+        # temporal downsampling (temperal_downsample=[False, True, True]).
+        t = x.shape[2]
+        iter_ = 1 + (t - 1) // 4
+        feat_cache = [None] * self.count_conv3d(self.encoder)
+        out = None
+        for i in range(iter_):
+            feat_idx = [0]
+            chunk = x[:, :, :1, :, :] if i == 0 else x[:, :, 1 + 4 * (i - 1): 1 + 4 * i, :, :]
+            out_ = self.encoder(chunk, feat_cache=feat_cache, feat_idx=feat_idx)
+            out = out_ if out is None else torch.cat([out, out_], dim=2)
+        x = self.quant_conv(out)
+        x = x[:, :16]
+        mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
+        x = (x - mean) * std
+        return x
+
+    def decode_video(self, x, **kwargs):
+        # x: (B, 16, T', H, W) in DiT latent space. Denormalize, then decode one
+        # latent frame at a time through the persistent causal feature cache.
+        mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
+        x = x / std + mean
+        x = self.post_quant_conv(x)
+        feat_cache = [None] * self.count_conv3d(self.decoder)
+        out = None
+        for i in range(x.shape[2]):
+            feat_idx = [0]
+            out_ = self.decoder(x[:, :, i:i + 1, :, :], feat_cache=feat_cache, feat_idx=feat_idx)
+            out = out_ if out is None else torch.cat([out, out_], dim=2)
+        return out
diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
new file mode 100644
index 000000000..0c8085e67
--- /dev/null
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -0,0 +1,339 @@
+import torch
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+from typing import Optional, Union
+from typing_extensions import Literal
+
+from ..core.device.npu_compatible_device import get_device_type
+from ..core import ModelConfig
+from ..diffusion.base_pipeline import BasePipeline, PipelineUnit
+from ..diffusion.flow_match import LingBotVideoUniPCScheduler
+
+from ..models.lingbot_video_dit import LingBotVideoDiT
+from ..models.lingbot_video_text_encoder import LingBotVideoTextEncoder
+from ..models.qwen_image_vae import QwenImageVAE
+
+
+# Number of tokens the Qwen3-VL processor truncates the prompt to. Copied verbatim
+# from the original lingbot-video pipeline so the encoded prompt matches.
+TOKEN_LENGTH = 37698
+# Which hidden-state layer to use as the prompt embedding: 0 -> the last layer.
+HIDDEN_STATE_SKIP_LAYER = 0
+
+# Chat template that wraps the user prompt inside the prompt-enhancement system
+# prompt. `apply_text_to_template(prompt) == PROMPT_TEMPLATE.format(prompt)`.
+PROMPT_TEMPLATE = (
+    "<|im_start|>system\nGiven a user input that may include a text prompt alone, "
+    "a text prompt with an image reference, or a text prompt with a video reference "
+    "or a video reference alone, generate an \"Enhanced prompt\" that provides detailed "
+    "visual descriptions suitable for video generation. Evaluate the level of detail "
+    "in the user's input: if it is simple, enrich it by adding specifics about colors, "
+    "shapes, sizes, textures, lighting, motion dynamics, camera movement, temporal "
+    "progression, and spatial relationships to create vivid, concrete, and temporally "
+    "coherent scenes to create vivid and concrete scenes. Please generate only the "
+    "enhanced description for the prompt below and avoid including any additional "
+    "commentary or evaluations:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n"
+    "<|im_start|>assistant\n"
+)
+
+# Default T2V negative prompt (structured JSON string), copied verbatim.
+DEFAULT_NEGATIVE_PROMPT = (
+    '{"universal_negative": {"visual_quality": ["low quality", "worst quality", "blurry", "pixelated", "jpeg artifacts", "low resolution", "unstable color", "color flicker", "underexposed", "overexposed", "invisible subject", "subject hidden in darkness"], "artistic_style": ["painting", "illustration", "drawing", "cartoon", "3d render", "cgi", "sketch", "digital art"], "composition_and_content": ["text", "watermark", "signature", "logo", "subtitles", "pillarboxed", "side bars", "portrait image in landscape frame"], "temporal_and_motion_stability": ["flickering", "jittery", "motion blur", "temporal inconsistency", "warping", "morphing", "incoherent motion", "unnatural movement", "static object with sudden jump", "frame-to-frame inconsistency"], "material_and_structure": ["plastic-like glass", "unrealistic texture", "deformed bottle", "liquid freezing improperly", "distorted reflections"]}}'
+)
+
+# VAE downsample factors (QwenImageVAE / Wan-VAE): 8x spatial, 4x temporal.
+VAE_SCALE_FACTOR_SPATIAL = 8
+VAE_SCALE_FACTOR_TEMPORAL = 4
+
+
+class LingBotVideoPipeline(BasePipeline):
+    """
+    Text-to-video pipeline for LingBot-Video.
+
+    Follows the DiffSynth ``PipelineUnit`` + ``model_fn`` pattern (see
+    :class:`~diffsynth.pipelines.wan_video.WanVideoPipeline`). Components:
+
+    - ``dit``: :class:`~diffsynth.models.lingbot_video_dit.LingBotVideoDiT` (MoE /
+      Dense video DiT), conditioned on ``timestep`` and ``encoder_attention_mask``.
+    - ``text_encoder``:
+      :class:`~diffsynth.models.lingbot_video_text_encoder.LingBotVideoTextEncoder`
+      (Qwen3-VL). The prompt is wrapped in :data:`PROMPT_TEMPLATE`, encoded, and the
+      template-prefix tokens are cropped (``crop_start``).
+    - ``vae``: :class:`~diffsynth.models.qwen_image_vae.QwenImageVAE` (byte-identical
+      to the LingBot-Video VAE). Latent normalisation is baked into the VAE's
+      ``encode``/``decode`` 5D-video code path, so the pipeline never re-applies
+      ``latents_mean`` / ``latents_std``.
+
+    Sampling uses :class:`~diffsynth.diffusion.flow_match.LingBotVideoUniPCScheduler`
+    (UniPC multistep). Classifier-free guidance runs as two independent forwards.
+    """
+
+    def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
+        super().__init__(
+            device=device, torch_dtype=torch_dtype,
+            height_division_factor=16, width_division_factor=16,
+            time_division_factor=4, time_division_remainder=1,
+        )
+        self.scheduler = LingBotVideoUniPCScheduler()
+        self.text_encoder: LingBotVideoTextEncoder = None
+        self.dit: LingBotVideoDiT = None
+        self.vae: QwenImageVAE = None
+        self.processor = None
+        # Cached number of template-prefix tokens to crop from the prompt embedding.
+        self._crop_start: Optional[int] = None
+        self.in_iteration_models = ("dit",)
+        self.units = [
+            LingBotVideoUnit_ShapeChecker(),
+            LingBotVideoUnit_NoiseInitializer(),
+            LingBotVideoUnit_PromptEmbedder(),
+            LingBotVideoUnit_InputVideoEmbedder(),
+        ]
+        self.model_fn = model_fn_lingbot_video
+        self.compilable_models = ["dit"]
+
+    def _compute_crop_start(self) -> int:
+        # Number of tokens contributed by the template prefix (everything before the
+        # user prompt). Computed once by tokenising the template up to a marker.
+        if self._crop_start is None:
+            marker = "<|USER_INPUT_MARKER|>"
+            marked = PROMPT_TEMPLATE.format(marker)
+            marker_pos = marked.find(marker)
+            if marker_pos < 0:
+                self._crop_start = 0
+            else:
+                prefix = self.processor(
+                    text=marked[:marker_pos],
+                    images=None,
+                    videos=None,
+                    return_tensors="pt",
+                )
+                self._crop_start = int(prefix["input_ids"].shape[1])
+        return self._crop_start
+
+    @staticmethod
+    def from_pretrained(
+        torch_dtype: torch.dtype = torch.bfloat16,
+        device: Union[str, torch.device] = get_device_type(),
+        model_configs: list[ModelConfig] = [],
+        processor_config: ModelConfig = None,
+        vram_limit: float = None,
+    ):
+        # Initialize pipeline
+        pipe = LingBotVideoPipeline(device=device, torch_dtype=torch_dtype)
+        model_pool = pipe.download_and_load_models(model_configs, vram_limit)
+
+        # Fetch models by name (registered in diffsynth/configs/model_configs.py).
+        pipe.text_encoder = model_pool.fetch_model("lingbot_video_text_encoder")
+        pipe.dit = model_pool.fetch_model("lingbot_video_dit")
+        pipe.vae = model_pool.fetch_model("qwen_image_vae")
+
+        # Initialize the Qwen3-VL processor (tokenizer + image/video processor).
+        if processor_config is not None:
+            processor_config.download_if_necessary()
+            from transformers import AutoProcessor
+            pipe.processor = AutoProcessor.from_pretrained(processor_config.path)
+
+        # VRAM Management
+        pipe.vram_management_enabled = pipe.check_vram_management_state()
+        return pipe
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        # Prompt
+        prompt: str = "",
+        negative_prompt: str = DEFAULT_NEGATIVE_PROMPT,
+        # Video-to-video
+        input_video: list[Image.Image] = None,
+        denoising_strength: float = 1.0,
+        # Randomness
+        seed: int = None,
+        rand_device: str = "cpu",
+        # Shape
+        height: int = 480,
+        width: int = 480,
+        num_frames: int = 81,
+        # Classifier-free guidance
+        cfg_scale: float = 6.0,
+        # Scheduler
+        num_inference_steps: int = 40,
+        sigma_shift: float = 3.0,
+        # progress_bar
+        progress_bar_cmd=tqdm,
+    ):
+        # Scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
+
+        # Inputs
+        inputs_posi = {"prompt": prompt}
+        inputs_nega = {"negative_prompt": negative_prompt}
+        inputs_shared = {
+            "input_video": input_video, "denoising_strength": denoising_strength,
+            "seed": seed, "rand_device": rand_device,
+            "height": height, "width": width, "num_frames": num_frames,
+            "cfg_scale": cfg_scale,
+        }
+        for unit in self.units:
+            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
+
+        # Denoise
+        self.load_models_to_device(self.in_iteration_models)
+        models = {name: getattr(self, name) for name in self.in_iteration_models}
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            # The DiT is conditioned on sigma * 1000 (== the raw scheduler timestep).
+            # Pass it as fp32 so the integer inference timesteps are represented
+            # exactly (bf16 cannot represent values > 256 without rounding).
+            timestep_input = timestep.unsqueeze(0).to(dtype=torch.float32, device=self.device)
+
+            # Inference (two independent forwards for CFG).
+            noise_pred_posi = self.model_fn(**models, **inputs_shared, **inputs_posi, timestep=timestep_input)
+            if cfg_scale != 1.0:
+                noise_pred_nega = self.model_fn(**models, **inputs_shared, **inputs_nega, timestep=timestep_input)
+                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
+            else:
+                noise_pred = noise_pred_posi
+
+            # Scheduler step (UniPC multistep). Uses the raw scheduler timestep to
+            # locate its internal step index, so pass it unmodified.
+            inputs_shared["latents"] = self.scheduler.step(noise_pred, timestep, inputs_shared["latents"])
+
+        # Decode. The VAE's 5D-video path already un-normalises the latents, so no
+        # manual latents_mean / latents_std handling is needed here.
+        self.load_models_to_device(['vae'])
+        latents = inputs_shared["latents"].to(dtype=self.torch_dtype, device=self.device)
+        video = self.vae.decode(latents)
+        video = self.vae_output_to_video(video)
+        self.load_models_to_device([])
+        return video
+
+
+class LingBotVideoUnit_ShapeChecker(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "num_frames"),
+            output_params=("height", "width", "num_frames"),
+        )
+
+    def process(self, pipe: LingBotVideoPipeline, height, width, num_frames):
+        height, width, num_frames = pipe.check_resize_height_width(height, width, num_frames)
+        return {"height": height, "width": width, "num_frames": num_frames}
+
+
+class LingBotVideoUnit_NoiseInitializer(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "num_frames", "seed", "rand_device"),
+            output_params=("noise",),
+        )
+
+    def process(self, pipe: LingBotVideoPipeline, height, width, num_frames, seed, rand_device):
+        length = (num_frames - 1) // VAE_SCALE_FACTOR_TEMPORAL + 1
+        shape = (
+            1, pipe.dit.in_channels, length,
+            height // VAE_SCALE_FACTOR_SPATIAL, width // VAE_SCALE_FACTOR_SPATIAL,
+        )
+        # fp32 noise: the UniPC sampler accumulates state in fp32 for stability
+        # (matches the original pipeline's fp32 latents).
+        noise = pipe.generate_noise(shape, seed=seed, rand_device=rand_device, torch_dtype=torch.float32)
+        return {"noise": noise}
+
+
+class LingBotVideoUnit_PromptEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt": "prompt"},
+            input_params_nega={"prompt": "negative_prompt"},
+            output_params=("context", "encoder_attention_mask"),
+            onload_model_names=("text_encoder",),
+        )
+
+    def encode_prompt(self, pipe: LingBotVideoPipeline, prompt):
+        # T2V: visual_template is empty, so the text is simply the templated prompt.
+        text = PROMPT_TEMPLATE.format(prompt)
+        inputs = pipe.processor(
+            text=[text],
+            images=None,
+            videos=None,
+            do_resize=False,
+            truncation=True,
+            max_length=TOKEN_LENGTH,
+            padding="longest",
+            return_tensors="pt",
+        )
+        inputs = inputs.to(pipe.device)
+        # The text encoder returns the tuple of per-layer hidden states.
+        hidden_states = pipe.text_encoder(**inputs)
+        prompt_embeds = hidden_states[-(HIDDEN_STATE_SKIP_LAYER + 1)]
+        prompt_mask = inputs["attention_mask"]
+
+        # Crop the prompt-enhancement template prefix.
+        crop_start = pipe._compute_crop_start()
+        if crop_start > 0:
+            prompt_embeds = prompt_embeds[:, crop_start:]
+            prompt_mask = prompt_mask[:, crop_start:]
+
+        # Batch=1: drop the right padding before the DiT forward.
+        if prompt_embeds.shape[0] == 1:
+            true_len = int(prompt_mask[0].sum().item())
+            prompt_embeds = prompt_embeds[:, :true_len]
+            prompt_mask = prompt_mask[:, :true_len]
+
+        return prompt_embeds.to(dtype=pipe.torch_dtype), prompt_mask
+
+    def process(self, pipe: LingBotVideoPipeline, prompt) -> dict:
+        pipe.load_models_to_device(self.onload_model_names)
+        prompt_embeds, prompt_mask = self.encode_prompt(pipe, prompt)
+        return {"context": prompt_embeds, "encoder_attention_mask": prompt_mask}
+
+
+class LingBotVideoUnit_InputVideoEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_video", "noise"),
+            output_params=("latents", "input_latents"),
+            onload_model_names=("vae",),
+        )
+
+    def process(self, pipe: LingBotVideoPipeline, input_video, noise):
+        if input_video is None:
+            # Text-to-video: start from pure noise.
+            return {"latents": noise}
+        pipe.load_models_to_device(self.onload_model_names)
+        # preprocess_video -> (B, C, T, H, W) in [-1, 1], in pipe.torch_dtype.
+        video = pipe.preprocess_video(input_video)
+        # QwenImageVAE.encode (5D path) already applies latent normalisation.
+        input_latents = pipe.vae.encode(video).to(dtype=torch.float32, device=pipe.device)
+        if pipe.scheduler.training:
+            return {"latents": noise, "input_latents": input_latents}
+        else:
+            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
+            return {"latents": latents}
+
+
+def model_fn_lingbot_video(
+    dit: LingBotVideoDiT,
+    latents: torch.Tensor = None,
+    timestep: torch.Tensor = None,
+    context: torch.Tensor = None,
+    encoder_attention_mask: Optional[torch.Tensor] = None,
+    use_gradient_checkpointing: bool = False,
+    use_gradient_checkpointing_offload: bool = False,
+    **kwargs,
+):
+    # Cast the latent / text inputs to the DiT's bulk compute dtype (e.g. bf16).
+    # The DiT keeps its AdaLN / norm / router paths in fp32 internally.
+    dit_dtype = dit.patch_embedder.weight.dtype
+    hidden_states = latents.to(dtype=dit_dtype)
+    encoder_hidden_states = context.to(dtype=dit_dtype)
+    noise_pred = dit(
+        hidden_states=hidden_states,
+        timestep=timestep,
+        encoder_hidden_states=encoder_hidden_states,
+        encoder_attention_mask=encoder_attention_mask,
+        use_gradient_checkpointing=use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+    )
+    # Return fp32 so the UniPC sampler / MSE loss run in full precision.
+    return noise_pred.float()
diff --git a/diffsynth/utils/state_dict_converters/lingbot_video_dit.py b/diffsynth/utils/state_dict_converters/lingbot_video_dit.py
new file mode 100644
index 000000000..3774568ee
--- /dev/null
+++ b/diffsynth/utils/state_dict_converters/lingbot_video_dit.py
@@ -0,0 +1,16 @@
+def LingBotVideoDiTStateDictConverter(state_dict):
+    # The LingBot-Video DiT checkpoint (transformer/diffusion_pytorch_model.safetensors)
+    # is saved by diffusers with module-hierarchy keys that already match
+    # `LingBotVideoDiT` exactly (patch_embedder / time_embedder / time_modulation /
+    # text_embedder / blocks.N.* / norm_out_modulation / proj_out). The only work
+    # needed is stripping any wrapper prefix a repackaged/compiled checkpoint may add.
+    prefixes = ["model.diffusion_model.", "_orig_mod.", "module.", "transformer."]
+    state_dict_ = {}
+    for name in state_dict:
+        new_name = name
+        for prefix in prefixes:
+            if new_name.startswith(prefix):
+                new_name = new_name[len(prefix):]
+                break
+        state_dict_[new_name] = state_dict[name]
+    return state_dict_
diff --git a/diffsynth/utils/state_dict_converters/lingbot_video_text_encoder.py b/diffsynth/utils/state_dict_converters/lingbot_video_text_encoder.py
new file mode 100644
index 000000000..ba4ef6db3
--- /dev/null
+++ b/diffsynth/utils/state_dict_converters/lingbot_video_text_encoder.py
@@ -0,0 +1,16 @@
+def LingBotVideoTextEncoderStateDictConverter(state_dict):
+    # The LingBot-Video text encoder checkpoint stores a Qwen3VLForConditionalGeneration
+    # with keys already under `model.language_model.*` and `model.visual.*`, which is
+    # exactly the layout of the wrapped `Qwen3VLModel` (assigned to `self.model`). The
+    # tied `lm_head` is not stored, so no key mapping is required — this is an identity
+    # converter that only strips any wrapper prefix a repackaged checkpoint may add.
+    prefixes = ["_orig_mod.", "module."]
+    state_dict_ = {}
+    for name in state_dict:
+        new_name = name
+        for prefix in prefixes:
+            if new_name.startswith(prefix):
+                new_name = new_name[len(prefix):]
+                break
+        state_dict_[new_name] = state_dict[name]
+    return state_dict_
diff --git a/examples/lingbot_video/README.md b/examples/lingbot_video/README.md
new file mode 100644
index 000000000..c112a6ff9
--- /dev/null
+++ b/examples/lingbot_video/README.md
@@ -0,0 +1,106 @@
+# LingBot-Video
+
+[LingBot-Video](https://github.com/modelscope) is a flow-matching video-generation model. This directory provides DiffSynth-Studio inference and training (LoRA SFT) support for the **Dense-1.3B** text-to-video checkpoint.
+
+The integration is built on the standard DiffSynth pipeline stack:
+
+- **DiT** — `LingBotVideoDiT` (`diffsynth/models/lingbot_video_dit.py`), the video denoiser. The Dense-1.3B build uses a plain FFN; the architecture also supports an MoE FFN.
+- **Text encoder** — `LingBotVideoTextEncoder` (Qwen3-VL). Prompts are wrapped in a prompt-enhancement chat template, encoded, and the template-prefix tokens are cropped.
+- **VAE** — reuses DiffSynth's `QwenImageVAE` (byte-identical to the LingBot-Video VAE), 8× spatial / 4× temporal.
+- **Scheduler** — `LingBotVideoUniPCScheduler`: UniPC multistep for inference; it falls back to the full-resolution flow-matching schedule for training.
+
+## Installation
+
+Follow the top-level DiffSynth-Studio installation. LingBot-Video additionally requires `transformers >= 5.x` (for Qwen3-VL) and `imageio` / `imageio-ffmpeg` for video I/O.
+
+```bash
+pip install -e .
+```
+
+## Model download
+
+```bash
+modelscope download --model Robbyant/lingbot-video-dense-1.3b --local_dir ./models/Robbyant/lingbot-video-dense-1.3b
+```
+
+The inference examples below use `ModelConfig(model_id=...)`, which downloads the required files automatically the first time they run. You can also point `ModelConfig(path=...)` at local files (see the training script).
+
+## Inference
+
+```bash
+python examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
+```
+
+Minimal text-to-video:
+
+```python
+import torch
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+)
+video = pipe(prompt="A playful puppy runs across a lush green meadow ...", height=480, width=832, num_frames=81, seed=0)
+save_video(video, "output.mp4", fps=15, quality=5)
+```
+
+The pipeline ships a default (T2V) negative prompt, so `negative_prompt` is optional. Video-to-video is supported by passing `input_video=` (a list of frames or a `VideoData`) together with `denoising_strength < 1`.
+
+**Low VRAM:** pass `vram_limit=<GB>` to `from_pretrained` to enable layer-by-layer offloading — see `model_inference/lingbot-video-dense-1.3b_low_vram.py`.
+
+## Training (LoRA SFT)
+
+`model_training/train.py` fine-tunes the DiT with LoRA using the flow-matching SFT objective.
+
+```bash
+bash examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
+```
+
+### Dataset format
+
+A metadata CSV (or JSONL) with a `video` column (path relative to `--dataset_base_path`) and a `prompt` column:
+
+```
+video,prompt
+videos/000.mp4,A playful puppy runs across a lush green meadow ...
+videos/001.mp4,A serene lake at sunrise, mist rising from the water ...
+```
+
+Pass `--data_file_keys "video"` so the loader treats the `video` column as a file to load.
+
+### Attention-only LoRA (default scope)
+
+The launch script patches LoRA on the joint text+video self-attention only:
+
+```
+--lora_base_model "dit"
+--lora_target_modules "to_q,to_k,to_v,to_out"
+--lora_rank 32
+--remove_prefix_in_ckpt "pipe.dit."
+```
+
+The MoE / FFN experts (`gate_proj`, `up_proj`, `down_proj`) and the router are left frozen. To also adapt the FFN, add those module names to `--lora_target_modules`.
+
+### Useful flags
+
+- `--use_gradient_checkpointing` — trade compute for memory (recommended; the trainer enables it regardless).
+- `--num_frames`, `--height`, `--width` — training clip shape (`num_frames` must satisfy `4k+1`; H/W divisible by 16).
+- `--max_timestep_boundary` / `--min_timestep_boundary` — restrict the sampled training timesteps to a sub-range of the schedule.
+- `--lora_checkpoint <path>` — resume / continue from a previously trained LoRA.
+
+### Applying a trained LoRA
+
+Trained LoRA checkpoints are written to `--output_path` with the `pipe.dit.` prefix stripped (keys like `blocks.0.attn.to_q.lora_A.weight`). To continue training from one, pass it via `--lora_checkpoint`.
+
+## Notes
+
+- The text encoder shares its checkpoint fingerprint with the existing `krea2_text_encoder` (identical Qwen3-VL architecture), so the model loader instantiates both when loading LingBot-Video. This is redundant load time only — the pipeline fetches the correct encoder by name and the other is released.
+- Latent normalisation is handled inside the VAE's 5D-video code path; the pipeline does not re-apply `latents_mean` / `latents_std`.
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
new file mode 100644
index 000000000..97de5a949
--- /dev/null
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
@@ -0,0 +1,36 @@
+import torch
+from diffsynth.utils.data import save_video, VideoData
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+)
+
+# Text-to-video. The default (T2V) negative prompt is built into the pipeline,
+# so `negative_prompt` can be left unset.
+video = pipe(
+    prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight, ears perked up, chasing after a red ball. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=6.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-dense-1.3b.mp4", fps=15, quality=5)
+
+# Video-to-video. `denoising_strength < 1` keeps part of the input structure.
+video = VideoData("video_lingbot-video-dense-1.3b.mp4", height=480, width=832)
+video = pipe(
+    prompt="A playful puppy wearing black sunglasses runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    input_video=video, denoising_strength=0.7,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=6.0,
+    seed=1,
+)
+save_video(video, "video_lingbot-video-dense-1.3b_v2v.mp4", fps=15, quality=5)
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_low_vram.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_low_vram.py
new file mode 100644
index 000000000..b34f81c8a
--- /dev/null
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_low_vram.py
@@ -0,0 +1,27 @@
+import torch
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+
+
+# Low-VRAM inference. `vram_limit` turns on DiffSynth's automatic VRAM management,
+# which keeps model weights on CPU and streams them to the GPU layer-by-layer during
+# each forward. The value below reserves ~2 GB of headroom on top of the models.
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
+)
+
+video = pipe(
+    prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight, ears perked up, chasing after a red ball. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=6.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-dense-1.3b_low_vram.mp4", fps=15, quality=5)
diff --git a/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
new file mode 100644
index 000000000..316f47264
--- /dev/null
+++ b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
@@ -0,0 +1,32 @@
+# Download the LingBot-Video Dense-1.3B weights (DiT + text encoder + VAE + processor).
+# This fetches the whole repo once into ./models so the paths below resolve locally.
+modelscope download --model Robbyant/lingbot-video-dense-1.3b --local_dir ./models/Robbyant/lingbot-video-dense-1.3b
+
+# Attention-only LoRA SFT.
+# `--lora_target_modules "to_q,to_k,to_v,to_out"` patches LoRA on the joint
+# text+video self-attention only, leaving the MoE / FFN experts and the router frozen.
+accelerate launch examples/lingbot_video/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata.csv \
+  --data_file_keys "video" \
+  --height 480 \
+  --width 832 \
+  --num_frames 81 \
+  --dataset_repeat 100 \
+  --model_paths '[
+    [
+      "./models/Robbyant/lingbot-video-dense-1.3b/text_encoder/model-00001-of-00002.safetensors",
+      "./models/Robbyant/lingbot-video-dense-1.3b/text_encoder/model-00002-of-00002.safetensors"
+    ],
+    "./models/Robbyant/lingbot-video-dense-1.3b/transformer/diffusion_pytorch_model.safetensors",
+    "./models/Robbyant/lingbot-video-dense-1.3b/vae/diffusion_pytorch_model.safetensors"
+  ]' \
+  --processor_path "./models/Robbyant/lingbot-video-dense-1.3b/processor" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/lingbot-video-dense-1.3b_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,to_out" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing
diff --git a/examples/lingbot_video/model_training/train.py b/examples/lingbot_video/model_training/train.py
new file mode 100644
index 000000000..c1507ced3
--- /dev/null
+++ b/examples/lingbot_video/model_training/train.py
@@ -0,0 +1,171 @@
+import torch, os, argparse, accelerate, warnings
+from diffsynth.core import UnifiedDataset
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from diffsynth.diffusion import *
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+class LingBotVideoTrainingModule(DiffusionTrainingModule):
+    def __init__(
+        self,
+        model_paths=None, model_id_with_origin_paths=None,
+        processor_path=None,
+        trainable_models=None,
+        lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None,
+        preset_lora_path=None, preset_lora_model=None,
+        use_gradient_checkpointing=True,
+        use_gradient_checkpointing_offload=False,
+        extra_inputs=None,
+        fp8_models=None,
+        offload_models=None,
+        resume_from_checkpoint=None, remove_prefix_in_ckpt=None,
+        device="cpu",
+        task="sft",
+        max_timestep_boundary=1.0,
+        min_timestep_boundary=0.0,
+    ):
+        super().__init__()
+        # Warning
+        if not use_gradient_checkpointing:
+            warnings.warn("Gradient checkpointing is detected as disabled. To prevent out-of-memory errors, the training framework will forcibly enable gradient checkpointing.")
+            use_gradient_checkpointing = True
+
+        # Load models. The Qwen3-VL processor (tokenizer + image/video processor) is
+        # passed separately via `processor_config`, mirroring the inference pipeline.
+        model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device)
+        processor_config = self.parse_path_or_model_id(processor_path)
+        self.pipe = LingBotVideoPipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, processor_config=processor_config)
+        self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
+        self.resume_from_checkpoint(resume_from_checkpoint, remove_prefix_in_ckpt)
+
+        # Training mode. The UniPC scheduler falls back to the full-resolution
+        # flow-matching schedule during training (see LingBotVideoUniPCScheduler).
+        # Attention-only LoRA is the default scope: pass
+        # `--lora_target_modules "to_q,to_k,to_v,to_out"` to leave the MoE / FFN
+        # (gate_proj / up_proj / down_proj) and the router untouched.
+        self.switch_pipe_to_training_mode(
+            self.pipe, trainable_models,
+            lora_base_model, lora_target_modules, lora_rank, lora_checkpoint,
+            preset_lora_path, preset_lora_model,
+            task=task,
+        )
+
+        # Store other configs
+        self.use_gradient_checkpointing = use_gradient_checkpointing
+        self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
+        self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
+        self.fp8_models = fp8_models
+        self.task = task
+        self.task_to_loss = {
+            "sft:data_process": lambda pipe, *args: args,
+            "sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi),
+            "sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi),
+        }
+        self.max_timestep_boundary = max_timestep_boundary
+        self.min_timestep_boundary = min_timestep_boundary
+
+    def get_pipeline_inputs(self, data):
+        inputs_posi = {"prompt": data["prompt"]}
+        inputs_nega = {}
+        inputs_shared = {
+            # Assume you are using this pipeline for inference,
+            # please fill in the input parameters.
+            "input_video": data["video"],
+            "height": data["video"][0].size[1],
+            "width": data["video"][0].size[0],
+            "num_frames": len(data["video"]),
+            # Please do not modify the following parameters
+            # unless you clearly know what this will cause.
+            "cfg_scale": 1,
+            "seed": None,
+            "rand_device": self.pipe.device,
+            "use_gradient_checkpointing": self.use_gradient_checkpointing,
+            "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
+            "max_timestep_boundary": self.max_timestep_boundary,
+            "min_timestep_boundary": self.min_timestep_boundary,
+        }
+        inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared)
+        return inputs_shared, inputs_posi, inputs_nega
+
+    def forward(self, data, inputs=None):
+        if inputs is None: inputs = self.get_pipeline_inputs(data)
+        inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype)
+        for unit in self.pipe.units:
+            inputs = self.pipe.unit_runner(unit, self.pipe, *inputs)
+        loss = self.task_to_loss[self.task](self.pipe, *inputs)
+        return loss
+
+
+def lingbot_video_parser():
+    parser = argparse.ArgumentParser(description="Simple example of a LingBot-Video training script.")
+    parser = add_general_config(parser)
+    parser = add_video_size_config(parser)
+    parser.add_argument("--processor_path", type=str, default=None, help="Path to the Qwen3-VL processor directory (or `model_id:origin_file_pattern`). Used to tokenize prompts.")
+    parser.add_argument("--max_timestep_boundary", type=float, default=1.0, help="Max timestep boundary (fraction of the training schedule, in [0, 1]).")
+    parser.add_argument("--min_timestep_boundary", type=float, default=0.0, help="Min timestep boundary (fraction of the training schedule, in [0, 1]).")
+    parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.")
+    return parser
+
+
+if __name__ == "__main__":
+    parser = lingbot_video_parser()
+    args = parser.parse_args()
+    accelerator = accelerate.Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)],
+    )
+    dataset = UnifiedDataset(
+        base_path=args.dataset_base_path,
+        metadata_path=args.dataset_metadata_path,
+        repeat=args.dataset_repeat,
+        data_file_keys=args.data_file_keys.split(","),
+        main_data_operator=UnifiedDataset.default_video_operator(
+            base_path=args.dataset_base_path,
+            max_pixels=args.max_pixels,
+            height=args.height,
+            width=args.width,
+            height_division_factor=16,
+            width_division_factor=16,
+            num_frames=args.num_frames,
+            time_division_factor=4,
+            time_division_remainder=1,
+        ),
+    )
+    model = LingBotVideoTrainingModule(
+        model_paths=args.model_paths,
+        model_id_with_origin_paths=args.model_id_with_origin_paths,
+        processor_path=args.processor_path,
+        trainable_models=args.trainable_models,
+        lora_base_model=args.lora_base_model,
+        lora_target_modules=args.lora_target_modules,
+        lora_rank=args.lora_rank,
+        lora_checkpoint=args.lora_checkpoint,
+        preset_lora_path=args.preset_lora_path,
+        preset_lora_model=args.preset_lora_model,
+        use_gradient_checkpointing=args.use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
+        extra_inputs=args.extra_inputs,
+        fp8_models=args.fp8_models,
+        offload_models=args.offload_models,
+        resume_from_checkpoint=args.resume_from_checkpoint,
+        remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
+        task=args.task,
+        device="cpu" if (args.initialize_model_on_cpu or args.enable_model_cpu_offload) else accelerator.device,
+        max_timestep_boundary=args.max_timestep_boundary,
+        min_timestep_boundary=args.min_timestep_boundary,
+    )
+    model_logger = ModelLogger(
+        args.output_path,
+        remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
+        enable_tensorboard_log=args.enable_tensorboard_log,
+        enable_swanlab_log=args.enable_swanlab_log,
+        swanlab_project=args.swanlab_project,
+        enable_wandb_log=args.enable_wandb_log,
+        wandb_project=args.wandb_project,
+    )
+    launcher_map = {
+        "sft:data_process": launch_data_process_task,
+        "sft": launch_training_task,
+        "sft:train": launch_training_task,
+    }
+    launcher_map[args.task](accelerator, dataset, model, model_logger, args=args)

From 67bcd02540bf3a4014aabc83f0af0a97f45208cc Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Sun, 26 Jul 2026 12:17:19 +0800
Subject: [PATCH 02/34] Add docs, prompt rewriting, LoRA validation, and
 low-VRAM example for LingBot-Video

- docs/{en,zh}/Model_Details/LingBot-Video.md + index.rst toctree entries
- two-stage prompt rewriter (structured JSON captions) + offline caption rewrite tool
- LoRA training validation script
- relocate low-VRAM inference into model_inference_low_vram/

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 diffsynth/pipelines/lingbot_video.py          |  16 +-
 .../lingbot_video_prompt_rewriter.py          | 298 +++++++++++++++++
 .../pipelines/lingbot_video_system_prompts.py | 301 ++++++++++++++++++
 docs/en/Model_Details/LingBot-Video.md        | 160 ++++++++++
 docs/en/index.rst                             |   1 +
 docs/zh/Model_Details/LingBot-Video.md        | 160 ++++++++++
 docs/zh/index.rst                             |   1 +
 examples/lingbot_video/README.md              |  42 ++-
 .../lingbot-video-dense-1.3b.py               |   8 +-
 .../lingbot-video-dense-1.3b_rewrite.py       |  60 ++++
 .../lingbot-video-dense-1.3b.py}              |   4 +-
 .../lora/lingbot-video-dense-1.3b.sh          |  18 +-
 .../model_training/rewrite_captions.py        |  95 ++++++
 .../lingbot_video/model_training/train.py     |   7 +-
 .../validate_lora/lingbot-video-dense-1.3b.py |  24 ++
 15 files changed, 1178 insertions(+), 17 deletions(-)
 create mode 100644 diffsynth/pipelines/lingbot_video_prompt_rewriter.py
 create mode 100644 diffsynth/pipelines/lingbot_video_system_prompts.py
 create mode 100644 docs/en/Model_Details/LingBot-Video.md
 create mode 100644 docs/zh/Model_Details/LingBot-Video.md
 create mode 100644 examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_rewrite.py
 rename examples/lingbot_video/{model_inference/lingbot-video-dense-1.3b_low_vram.py => model_inference_low_vram/lingbot-video-dense-1.3b.py} (95%)
 create mode 100644 examples/lingbot_video/model_training/rewrite_captions.py
 create mode 100644 examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py

diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index 0c8085e67..9b1f4c0c5 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -13,6 +13,7 @@
 from ..models.lingbot_video_dit import LingBotVideoDiT
 from ..models.lingbot_video_text_encoder import LingBotVideoTextEncoder
 from ..models.qwen_image_vae import QwenImageVAE
+from .lingbot_video_prompt_rewriter import normalize_caption
 
 
 # Number of tokens the Qwen3-VL processor truncates the prompt to. Copied verbatim
@@ -141,9 +142,10 @@ def from_pretrained(
     @torch.no_grad()
     def __call__(
         self,
-        # Prompt
-        prompt: str = "",
-        negative_prompt: str = DEFAULT_NEGATIVE_PROMPT,
+        # Prompt. Accepts a structured caption (dict / list), a path to a prompt.json,
+        # or a plain string; see normalize_caption / the prompt rewriter.
+        prompt: Union[str, dict, list] = "",
+        negative_prompt: Union[str, dict, list] = DEFAULT_NEGATIVE_PROMPT,
         # Video-to-video
         input_video: list[Image.Image] = None,
         denoising_strength: float = 1.0,
@@ -165,6 +167,14 @@ def __call__(
         # Scheduler
         self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
 
+        # Normalise the caption to the structured-JSON string the DiT was trained on.
+        # A dict/list caption or a path to a prompt.json is serialised via the model's
+        # compact-JSON convention; a plain string (already a caption / prose) is left
+        # untouched, so this is a no-op for existing callers. DEFAULT_NEGATIVE_PROMPT is
+        # already such a JSON string and passes through unchanged.
+        prompt = normalize_caption(prompt)
+        negative_prompt = normalize_caption(negative_prompt)
+
         # Inputs
         inputs_posi = {"prompt": prompt}
         inputs_nega = {"negative_prompt": negative_prompt}
diff --git a/diffsynth/pipelines/lingbot_video_prompt_rewriter.py b/diffsynth/pipelines/lingbot_video_prompt_rewriter.py
new file mode 100644
index 000000000..6c3cdbe0c
--- /dev/null
+++ b/diffsynth/pipelines/lingbot_video_prompt_rewriter.py
@@ -0,0 +1,298 @@
+"""Prompt handling for LingBot-Video.
+
+The LingBot-Video DiT is trained on **structured JSON captions**, not free-form
+prose. Feeding a flat sentence is out-of-distribution and noticeably degrades
+quality; feeding the structured caption the model expects restores it. This module
+provides the two pieces needed to keep prompts in-distribution:
+
+1. :func:`normalize_caption` — the lightweight, dependency-free path used by the
+   pipeline and the training module. It turns a caption expressed as a ``dict`` /
+   ``list`` / path to a ``prompt.json`` into the exact compact-JSON string the DiT
+   consumes (a plain string is passed through untouched). This mirrors the original
+   ``lingbot_video.utils.caption_from_sample`` byte-for-byte.
+
+2. :class:`Rewriter` — a faithful port of the original two-stage prompt rewriter
+   (``rewriter/rewriter_core.py`` + ``rewriter/inference.py``). Stage 1 *expands* a
+   brief idea into a natural-language caption; stage 2 *maps* that caption into the
+   structured JSON. Stage 1 runs the base model, stage 2 runs the base model with a
+   LoRA adapter active. The bundled :class:`TransformersBackend` loads the rewriter
+   VLM locally; :func:`make_backend` also accepts a custom backend object (anything
+   exposing ``generate(text, image, use_lora) -> str``) so the rewriter can be driven
+   by a hosted / OpenAI-compatible endpoint without shipping the weights.
+
+Typical use::
+
+    from diffsynth.pipelines.lingbot_video_prompt_rewriter import rewrite_prompt
+    caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
+    video = pipe(prompt=caption, ...)              # caption is the structured JSON string
+"""
+
+import io
+import json
+import os
+import re
+
+# Optional deps: only needed for the local rewriter backend / image loading, never
+# for normalize_caption (the common path).
+try:
+    import requests
+except ImportError:
+    requests = None
+
+try:
+    from PIL import Image
+except ImportError:
+    Image = None
+
+try:
+    from json_repair import repair_json
+except ImportError:
+    repair_json = None
+
+from .lingbot_video_system_prompts import (
+    VIDEO_STEP1_EXPAND, VIDEO_STEP2_MAP, IMAGE_STEP1_EXPAND, IMAGE_STEP2_MAP,
+)
+
+
+# ---------------------------------------------------------------------------
+# Layer 1: caption -> in-distribution prompt string
+# ---------------------------------------------------------------------------
+
+# Keys that describe how to *render* the clip rather than its content. When a full
+# sample dict is given without an explicit "caption" key, these are stripped before
+# serialisation (kept identical to the original ``caption_from_sample``).
+_RUNTIME_KEYS = {"duration", "fps", "height", "width", "num_frames", "resolution", "ratio"}
+
+
+def _serialize_caption(caption) -> str:
+    """dict/list -> compact JSON (the exact model format); anything else -> ``str()``."""
+    if isinstance(caption, (dict, list)):
+        return json.dumps(caption, ensure_ascii=False, separators=(",", ":"))
+    return str(caption)
+
+
+def _caption_from_sample(sample) -> str:
+    """Port of ``lingbot_video.utils.caption_from_sample``.
+
+    A *sample* dict either carries the structured caption under ``"caption"`` or IS
+    the caption once the runtime keys are dropped.
+    """
+    if isinstance(sample, dict):
+        if "caption" in sample:
+            caption = sample["caption"]
+        else:
+            caption = {k: v for k, v in sample.items() if k not in _RUNTIME_KEYS}
+    else:
+        caption = sample
+    return _serialize_caption(caption)
+
+
+def normalize_caption(prompt):
+    """Normalise a caption into the compact-JSON string the LingBot DiT expects.
+
+    Accepts:
+
+    - ``dict`` / ``list`` — a structured caption (or a full sample dict with a
+      ``"caption"`` key), serialised via the original compact-JSON convention.
+    - a path to a ``prompt.json`` file (``str`` ending in ``.json`` that exists) —
+      loaded, then handled as the dict/list case.
+    - any other ``str`` — returned unchanged (already a caption string, or free-form
+      prose the caller intentionally wants to feed as-is).
+    - ``None`` — returned unchanged.
+
+    Plain strings are passed through, so this is safe to call unconditionally on
+    prompts that are already in the right format.
+    """
+    if prompt is None:
+        return prompt
+    if isinstance(prompt, str):
+        if prompt.endswith(".json") and os.path.isfile(prompt):
+            with open(prompt, "r", encoding="utf-8") as f:
+                prompt = json.load(f)
+            return _caption_from_sample(prompt)
+        return prompt
+    if isinstance(prompt, (dict, list)):
+        return _caption_from_sample(prompt)
+    return str(prompt)
+
+
+# ---------------------------------------------------------------------------
+# Layer 2: raw idea -> structured caption (the two-stage rewriter)
+# ---------------------------------------------------------------------------
+
+# mode -> (step1 system prompt, step2 system prompt, feed image?, add duration?)
+MODES = {
+    "t2v":  dict(s1=VIDEO_STEP1_EXPAND, s2=VIDEO_STEP2_MAP, image=False, duration=True),
+    "ti2v": dict(s1=VIDEO_STEP1_EXPAND, s2=VIDEO_STEP2_MAP, image=True,  duration=True),
+    "t2i":  dict(s1=IMAGE_STEP1_EXPAND, s2=IMAGE_STEP2_MAP, image=False, duration=False),
+}
+
+
+def _has_cjk(s: str) -> bool:
+    return any("一" <= c <= "鿿" for c in s)
+
+
+def load_image(src):
+    """Load a first-frame image from a local path / http(s) URL / PIL.Image -> RGB PIL."""
+    if Image is None:
+        raise ImportError("loading a first-frame image requires the Pillow package.")
+    if isinstance(src, Image.Image):
+        return src.convert("RGB")
+    if isinstance(src, str) and re.match(r"^https?://", src):
+        if requests is None:
+            raise ImportError("fetching an image URL requires the requests package.")
+        return Image.open(io.BytesIO(requests.get(src, timeout=30).content)).convert("RGB")
+    return Image.open(src).convert("RGB")
+
+
+def _step1_text(mode, prompt, dur):
+    sys = MODES[mode]["s1"]
+    if mode == "t2i":
+        return sys + "\n\nUser image prompt:\n" + prompt
+    dur_line = f"\n\n视频时长：{dur} 秒" if _has_cjk(prompt) else f"\n\nVideo Duration: {dur} seconds"
+    return sys + "\n\n" + prompt + dur_line
+
+
+def _step2_text(mode, detailed, dur):
+    sys = MODES[mode]["s2"]
+    if mode == "t2i":
+        return sys + "\n\nDETAILED CAPTION:\n" + detailed
+    return (sys + f"\n\nVideo Duration: {dur} seconds\n\nDETAILED CAPTION:\n"
+            + detailed + "\n\nOutput the JSON now.")
+
+
+def parse_json(raw):
+    """Parse the stage-2 output into a dict, or ``None`` if it cannot be parsed.
+
+    VLMs occasionally emit unstable JSON (missing quotes, trailing commas, ``` fences),
+    so we strip any code fence, then try the stdlib parser first and fall back to
+    ``json_repair`` when it is installed (recommended for messy outputs)."""
+    s = (raw or "").strip()
+    m = re.search(r"```(?:json)?\s*(\{.*\})\s*```", s, re.DOTALL)
+    if m:
+        s = m.group(1)
+    try:
+        obj = json.loads(s)
+        if isinstance(obj, dict):
+            return obj
+    except Exception:
+        pass
+    if repair_json is not None:
+        try:
+            obj = repair_json(s, return_objects=True)
+            return obj if isinstance(obj, dict) else None
+        except Exception:
+            return None
+    return None
+
+
+def save_caption(result, duration, path):
+    """Save as ``{"caption": <structured JSON caption>, "duration": <seconds|null>}`` —
+    exactly the ``prompt.json`` the pipeline / runner consume. ``duration`` is integer
+    seconds for T2V/TI2V and ``None`` for T2I (a still image has no duration)."""
+    dur = int(round(duration)) if MODES[result["mode"]]["duration"] else None
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump({"caption": result["json"], "duration": dur}, f, ensure_ascii=False, indent=2)
+    return path
+
+
+class TransformersBackend:
+    """Local rewriter VLM + LoRA adapter (peft). stage1 = base (adapter disabled),
+    stage2 = base + LoRA. Loads the rewriter model into memory; the weights are NOT
+    the DiT — set ``base``/``adapter`` (or ``REWRITER_BASE_MODEL``/``REWRITER_ADAPTER``)
+    to the rewriter VLM and its stage-2 adapter."""
+
+    def __init__(self, base=None, adapter=None, device="auto", max_new_tokens=6144):
+        import contextlib
+        import torch
+        from peft import PeftModel
+        from transformers import AutoModelForImageTextToText, AutoProcessor
+
+        self._contextlib = contextlib
+        self._torch = torch
+
+        base = base or os.environ.get("REWRITER_BASE_MODEL", "")
+        adapter = adapter or os.environ.get("REWRITER_ADAPTER", "")
+        if not base or not adapter:
+            raise ValueError(
+                "Set the rewriter base and adapter paths via base=/adapter= or the "
+                "REWRITER_BASE_MODEL / REWRITER_ADAPTER environment variables."
+            )
+        self.processor = AutoProcessor.from_pretrained(base, trust_remote_code=True)
+        model = AutoModelForImageTextToText.from_pretrained(
+            base, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
+        self.model = PeftModel.from_pretrained(model, adapter).eval()
+        self.max_new_tokens = max_new_tokens
+
+    def generate(self, text, image, use_lora):
+        torch = self._torch
+        content = ([{"type": "image", "image": image}] if image is not None else []) \
+            + [{"type": "text", "text": text}]
+        messages = [{"role": "user", "content": content}]
+        chat = self.processor.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
+        inputs = self.processor(
+            text=[chat], images=([image] if image is not None else None), return_tensors="pt"
+        ).to(self.model.device)
+        # stage1 (expand): disable LoRA; stage2 (map): keep LoRA active.
+        adapter_ctx = self._contextlib.nullcontext() if use_lora else self.model.disable_adapter()
+        with torch.no_grad(), adapter_ctx:
+            out = self.model.generate(**inputs, max_new_tokens=self.max_new_tokens, do_sample=False)
+        gen = out[:, inputs["input_ids"].shape[1]:]
+        return self.processor.batch_decode(gen, skip_special_tokens=True)[0]
+
+
+def make_backend(backend="transformers", base=None, adapter=None):
+    """Build a rewriter backend.
+
+    - ``"transformers"`` — the bundled local :class:`TransformersBackend`.
+    - a custom object / callable — returned as-is if it already exposes
+      ``generate(text, image, use_lora) -> str``. Use this to drive the rewriter from
+      a hosted or OpenAI-compatible endpoint without downloading the VLM locally.
+    """
+    if backend == "transformers":
+        return TransformersBackend(base, adapter)
+    if hasattr(backend, "generate"):
+        return backend
+    raise ValueError(
+        f"unknown backend: {backend!r}; pass 'transformers' or an object exposing "
+        "generate(text, image, use_lora)."
+    )
+
+
+class Rewriter:
+    """Two-stage orchestrator. The backend implements ``generate(text, image, use_lora) -> str``."""
+
+    def __init__(self, backend):
+        self.backend = backend
+
+    def rewrite(self, prompt, mode="t2v", first_frame=None, duration=5):
+        if mode not in MODES:
+            raise ValueError(f"mode must be one of {list(MODES)}")
+        cfg = MODES[mode]
+        dur = int(round(duration))
+        img = None
+        if cfg["image"]:
+            if first_frame is None:
+                raise ValueError(f"{mode} requires first_frame (path / URL / PIL.Image)")
+            img = load_image(first_frame)
+        # stage 1: EXPAND -- base model (no LoRA)
+        detailed = self.backend.generate(_step1_text(mode, prompt, dur), img, use_lora=False).strip()
+        # stage 2: MAP -- base + LoRA
+        raw = self.backend.generate(_step2_text(mode, detailed, dur), img, use_lora=True).strip()
+        return {"mode": mode, "detailed": detailed, "json": parse_json(raw), "json_raw": raw}
+
+
+def rewrite_prompt(prompt, mode="t2v", first_frame=None, duration=5,
+                   backend="transformers", base=None, adapter=None, return_result=False):
+    """Rewrite a brief idea into the structured caption string the pipeline expects.
+
+    Returns the compact-JSON caption string (ready to pass as ``pipe(prompt=...)``).
+    Pass ``return_result=True`` to also get the full stage-1/stage-2 dict.
+    """
+    rw = Rewriter(make_backend(backend, base, adapter))
+    result = rw.rewrite(prompt, mode=mode, first_frame=first_frame, duration=duration)
+    caption = normalize_caption(result["json"]) if result["json"] is not None else result["json_raw"]
+    if return_result:
+        return caption, result
+    return caption
diff --git a/diffsynth/pipelines/lingbot_video_system_prompts.py b/diffsynth/pipelines/lingbot_video_system_prompts.py
new file mode 100644
index 000000000..35064b564
--- /dev/null
+++ b/diffsynth/pipelines/lingbot_video_system_prompts.py
@@ -0,0 +1,301 @@
+# LingBot-Video prompt-rewriter system prompts.
+#
+# Copied VERBATIM from the original lingbot-video package
+# (rewriter/system_prompts.py) so the two-stage rewriter produces captions in the
+# exact format the DiT was trained on. Do not paraphrase these strings — the model
+# is sensitive to them. Four constants: VIDEO_STEP1_EXPAND / VIDEO_STEP2_MAP
+# (text->natural caption, then caption->structured JSON) and the IMAGE_* pair.
+
+VIDEO_STEP1_EXPAND = (
+    'You write ONE short, natural, standalone video caption in English from a brief user video\n'
+    'prompt — as if briefly recounting what happens in a real clip of that idea.\n'
+    '\n'
+    "INPUT: a short user prompt (and, when provided, the video's first frame as a visual anchor).\n"
+    '\n'
+    'HARD LENGTH LIMIT: the output MUST be UNDER 1000 characters. Aim for roughly 400-800\n'
+    'characters. Concise is a top priority.\n'
+    '\n'
+    'STYLE — a flowing story, NOT a checklist:\n'
+    '- Connected, natural English prose. NO headings, NO bullets, NO field labels.\n'
+    '- The MAIN THREAD is what HAPPENS — lead with the action. Do NOT march subject-by-subject\n'
+    '  listing attributes; weave the few details you keep into the action naturally.\n'
+    '- One cohesive paragraph; never pad to add length.\n'
+    '\n'
+    'WHAT TO COVER (only these):\n'
+    '- The scene in a brief phrase.\n'
+    '- Each main subject by name/what-it-is plus ONE most defining visual trait — not a full\n'
+    '  appearance list.\n'
+    '- What happens over the clip, in correct chronological order as the backbone, with an EXPLICIT\n'
+    '  timestamp in seconds on each action, distributed within the given video duration — e.g.\n'
+    '  "at 0.0s", "from 1.2s to 2.7s", "around the 3.4s mark", "finally from 4.0s to 5.0s". Span the\n'
+    '  actions from 0s up to (but never beyond) the stated duration; the last one ends at or before it.\n'
+    '  Attach a timestamp ONLY to a real action or change; mention a static/unchanging element once\n'
+    '  with NO timestamp — never put a whole-clip span on something that merely exists.\n'
+    '- A one-word shot type only if it matters; otherwise skip the camera.\n'
+    "- Named entities only if clearly implied — don't invent identities.\n"
+    '\n'
+    'FAITHFUL EXPANSION:\n'
+    '- Stay consistent with the prompt; elaborate plausibly but never contradict it. If a first\n'
+    '  frame is given, ground the scene and subject in it.\n'
+    '- Keep actions in their natural, real-world direction — never reverse them. Commit to one\n'
+    "  coherent interpretation; don't hedge with alternatives.\n"
+    '- Keep subject identities and counts consistent.\n'
+    '\n'
+    'DOMAIN NOTES (apply whichever fits):\n'
+    '- Multiple events / sequence: lay out consecutive actions in STRICT chronological order as\n'
+    '  distinct, separated steps with approximate timing — never blur them into one vague action.\n'
+    '- Robot manipulation (VLA): subjects are robotic arm(s), gripper(s) and workspace objects,\n'
+    '  NOT people — no clothing/skin/gender/expression. When two arms are present, ALWAYS keep the\n'
+    '  LEFT and RIGHT arm distinct and state which arm/gripper does each motion; never swap, merge,\n'
+    '  or leave it ambiguous. Manipulation is STRICTLY NOT reversible.\n'
+    "- First-person (EGO): a head-mounted first-person view; camera motion IS the wearer's head\n"
+    '  movement. The agent is the camera-wearer, a PERSON shown through their own hands/arms and\n'
+    '  viewpoint; never a robot or external third-person subject. Stay strictly first-person.\n'
+    '\n'
+    'Output ONLY the caption text — no preamble, no headings, no explanation.\n'
+    '\n'
+    "## EXAMPLES — one representative example per domain. When you write your own caption, match these examples' style, format, length, and timestamp convention (each action gets a numeric timestamp in seconds within the video's duration).\n"
+    '\n'
+    '### Example 1 — general\n'
+    'USER PROMPT:\n'
+    '低角度广角镜头，镜头静止。中央大型条纹热气球缓慢上升并飘移（气球呈红橙黄绿蓝条纹，底部深色），背景中多个热气球在天空漂移。地面草地上散布着正在充气的热气球，一辆红色皮卡停放在旁。随后一辆蓝色拖车进入画面并横穿前景（覆盖蓝色防水布）。饱和色彩，硬光，日光，电影质感。\n'
+    '\n'
+    'DETAILED CAPTION:\n'
+    'At a vibrant hot air balloon festival on a sunny day, a wide shot shows a grassy field under a blue sky. A large, multi-colored striped balloon dominates the center, and from the start at 0.0 seconds until 5.3 seconds, it slowly rises and drifts slightly upward and to the right. Simultaneously during the 0.0s to 5.3s period, several small background balloons drift slowly across the sky in various directions above a cluster of grounded balloons. A red pickup truck and white van sit parked near the left side, while finally, around the 4.3s mark until 5.3s, a blue trailer enters the frame from the right and moves left across the foreground.\n'
+    '\n'
+    '### Example 2 — multi-event\n'
+    'USER PROMPT:\n'
+    'Subtitle: Outdoor High-Intensity Workout — All live-action, hyper-realistic, fixed shot with slight handheld unsteadiness.\n'
+    '\n'
+    'Style: Hard daylight, high-angle wide shot, center composition, sharp shadows, athletic tension.\n'
+    '\n'
+    '1. Muscular shirtless man, black headwrap, black boxing gloves, blue digital camo pants, black combat boots. Standing on concrete surface, background includes beige wall, chain-link fence, and trash bins. Stands in a boxing stance with hands up.\n'
+    '\n'
+    '2. Drops into a deep squat, then jumps upward explosively.\n'
+    '\n'
+    '3. Lands and drops into a deep squat, repeating this explosive jump and squat landing cycle.\n'
+    '\n'
+    '4. After the final landing and squat, begins to stand up.\n'
+    '\n'
+    'Overall movements coherent and natural, high contrast lighting, focused and athletic atmosphere.\n'
+    '\n'
+    'DETAILED CAPTION:\n'
+    'In an outdoor urban setting against a beige wall, a shirtless man wearing boxing gloves performs a workout on concrete. From 0.0s to 0.6s he stands in a boxing stance with hands up. Then from 0.6s to 1.2s he drops into a deep squat, followed by the period from 1.2s to 1.8s where he jumps upward explosively. From 1.8s to 2.4s he lands and drops into a deep squat, then from 2.4s to 3.0s jumps upward explosively again. Around the 3.0s to 3.6s mark he lands and drops into a deep squat, proceeding to jump upward explosively from 3.6s to 4.2s. From 4.2s to 4.8s he lands and drops into a deep squat once more, and finally from 4.8s to 5.0s he begins to stand up.\n'
+    '\n'
+    '### Example 3 — VLA\n'
+    'USER PROMPT:\n'
+    '环境：自动化超市补货工作站，俯视视角，明亮均匀人工光，清晰功能化氛围。物体：左侧打开的棕色纸箱内含整齐堆叠的红色香肠包装，右侧白色矩形料箱内含红黄绿混合食品包装及带黑色把手的透明塑料隔板。机器人：双臂系统。左臂黑色机身银色底座黑色夹爪（全程静止悬停），右臂白色机身黑色夹爪腕部蓝色指示灯（活动主体）。动作：右臂向下向左移动伸入白色料箱，抓取隔板黑色把手，向右滑动隔板，释放把手，向上向右收回复位。左臂悬停于纸箱上方保持静止。相机：固定高角度俯视视角，全程静止镜头，宽画幅，超广角镜头，柔和人工光，极致细节。\n'
+    '\n'
+    'DETAILED CAPTION:\n'
+    'A top-down wide shot shows an automated workspace with a cardboard box of sausage packages on the left and a white bin on the right. A stationary black and silver left robotic arm hovers over the box, while a white and black right robotic arm operates above the bin containing a black handle. From 0.0s to 2.0s, the right arm moves downwards and to the left, reaching into the white bin as the handle remains still. Then from 2.0s to 6.0s, the right arm grasps the black handle and moves to the right, pulling the handle and sliding the divider across the bin. Finally from 6.0s to 9.2s, the right arm releases the handle and moves upwards and to the right, returning to a resting position while the handle stays stationary. The box and bin remain fixed throughout.\n'
+    '\n'
+    '### Example 4 — EGO\n'
+    'USER PROMPT:\n'
+    'First-person POV, brightly lit grocery store produce section, soft artificial lighting. Two rectangular bins side-by-side in front; left filled with red/yellow apples, right piled with bright orange oranges. Background shows aisles and shoppers, one in red shirt with basket. Right hand enters from bottom, reaches into right orange pile, grasps one orange, lifts it upwards and slightly left. Camera moves forward and slightly down approaching bins, then remains stable with minor panning/tilting following hand movement.\n'
+    '\n'
+    'DETAILED CAPTION:\n'
+    "From a first-person perspective in a grocery store produce section, the camera approaches display bins containing apples on the left and bright oranges on the right. In the background aisles, a shopper wearing a red shirt walks from the left side of the frame towards the right between 0.0s and 2.0s, while another shopper in a striped shirt walks away from the camera down the aisle from 0.0s to 5.0s. The operator's right hand enters the frame from the bottom from 0.0s to 2.5s, then reaches into the pile of oranges and grasps one from 2.5s to 3.5s. Finally, the hand lifts the grasped orange upwards and to the left from 3.5s to 5.0s, selecting it from the cluster."
+)
+
+VIDEO_STEP2_MAP = (
+    'You are a structuring engine. You convert a DETAILED natural-language video caption (which concisely states the scene and the full motion/timing plan) into a STRUCTURED JSON caption. Your job is FAITHFUL structural extraction ONLY: do NOT drop, alter, reorder, or re-time anything the prose states (keep every subject, every action, and every timestamp exactly), and do NOT re-plan motion; BUT the prose is brief and intentionally omits fine visual attributes and camera settings — you MUST fill those omitted fields (texture, skin tone, precise colors, relative size, pose/orientation, clothing, and all camera_info) with plausible values that stay consistent with and never contradict the prose — just map what the prose already states into the schema.\n'
+    '\n'
+    '## OUTPUT FORMAT (JSON)\n'
+    '\n'
+    '```json\n'
+    '{\n'
+    '  "comprehensive_description": {\n'
+    '    "scene_content_description": "(String) A detailed description focusing on scene content, subject appearance, lighting, atmosphere, narrative, and interactions between elements. Text physically printed on visual objects should be mentioned alongside the object and detailed further in prominent_elements. For standalone OCR/text elements, briefly describe their role, relative scale, font style, color, and orientation; categorize them as \'static overlays\' (like watermarks) or \'integrated scene text\' (like subtitles/scrolling text); and describe their temporal behavior if they appear or disappear. Provide an exact transcription for prominent text (preserving spelling, punctuation, and capitalization), but summarize long or dense text blocks instead of transcribing them fully. **Do not describe camera movement here**. Maximum 800 words.",\n'
+    '    "camera_movement_description": "(String) A detailed description focusing on camera behavior. Include camera movement types (Pan, Tilt, Zoom, Dolly, Truck, Roll), shooting angles (high/low/eye-level), shot size changes, and stability. Maximum 100 words. If the camera is essentially stationary, set to \'\'."\n'
+    '  },\n'
+    '\n'
+    '  "prominent_elements": [\n'
+    '    {\n'
+    '      "name": "(String) Short label for the object (e.g., \'red sports car\', \'elderly man\')",\n'
+    '      "description": "(String) Detailed visual description of this specific element",\n'
+    '      "actions": [\n'
+    '        {\n'
+    '          "timestamp": "(String) Time range when this action occurs, e.g., \'[0.0s - 3.0s]\'",\n'
+    '          "action": "(String) Specific action description during this time period. **Direction must be described from the observer\'s perspective**. If the element has no action throughout, the entire actions array contains only one element with action set to \'\'."\n'
+    '        }\n'
+    '      ],\n'
+    '      "location": "(String) Precise position in the frame or main area of activity (from observer\'s perspective)",\n'
+    '      "relative_size": "(String) small / medium / large / dominant",\n'
+    '      "shape_and_color": "(String) Basic geometric shape and dominant colors",\n'
+    '      "texture": "(String) e.g., smooth, rough, metallic, furry, glossy, matte",\n'
+    '      "appearance_details": "(String) Specific details such as patterns, text physically printed on the object, wear marks, or distinctive markings",\n'
+    '      "relationship": "(String) This object\'s spatial or contextual relationship with other elements in the scene. If this object obscures text, specify exactly what it covers (e.g., \'blocking the letter O in COW). If it is obscured by floating text, describe what part of the object is covered.",\n'
+    '      "orientation": "(String) e.g., upright, tilted, horizontal, facing away, diagonal",\n'
+    '      \n'
+    '      "pose": "(String) Body posture and its changes (human/humanoid only, otherwise empty)",\n'
+    '      "expression": "(String) Facial expression and emotional changes (human/humanoid only, otherwise empty)",\n'
+    '      "clothing": "(String) Clothing description, including colors and styles (human/humanoid only, otherwise empty)",\n'
+    '      "gender": "(String) Apparent gender (human/humanoid only, otherwise empty)",\n'
+    '      "skin_tone_and_texture": "(String) Skin appearance (human/humanoid only, otherwise empty)",\n'
+    '      \n'
+    '      "is_cluster": "true (only for cluster objects, omit otherwise)",\n'
+    '      "number_of_objects": "(String) Exact number if countable, otherwise \'several\' (3-6), \'many\' (7-20), or \'numerous\' (20+)"\n'
+    '    }\n'
+    '  ],\n'
+    '\n'
+    '  "camera_info": {\n'
+    '    "color": "(String) Warm, Cool, Mixed, Saturated, Desaturated, Black and White, Red, Orange, Yellow, Green, Cyan, Blue, Magenta, or Pink",\n'
+    '    "frame_size": "(String) Extreme Wide, Wide, Medium Wide, Medium, Medium Close Up, Close Up, or Extreme Close Up",\n'
+    '    "shot_type_angle": "(String) High angle, Low angle, Dutch angle, Overhead, Aerial, or Eye level",\n'
+    '    "lens_size": "(String) Ultra Wide / Fisheye, Wide, Medium, Long Lens, or Telephoto",\n'
+    '    "composition": "(String) Center, Balanced, Symmetrical, Left heavy, Right heavy, or Short side",\n'
+    '    "lighting": "(String) Hard light, Soft light, High contrast, Low contrast, Side light, Top light, Underlight, Backlight, Edge light, or Silhouette",\n'
+    '    "lighting_type": "(String) Daylight, Sunny, Overcast, Moonlight, Artificial light, Practical light, Tungsten, Fluorescent, Firelight, or Mixed light"\n'
+    '  }\n'
+    '}\n'
+    '```\n'
+    '\n'
+    '## IMPORTANT RULES\n'
+    '\n'
+    '1.  **Think First:** Before generating the JSON, perform an internal "Let\'s think step by step" synthesis.\n'
+    '2.  **Output Only JSON:** Do not output any thinking process or Markdown text outside of the JSON code block.\n'
+    '3.  **Strict Fidelity (No Modification)**:\n'
+    '- Identity: Never alter the identity of subjects (e.g., a "real tiger" remains "real tiger").\n'
+    '- Actions: Verbs must be preserved exactly (e.g., "walking" cannot be changed to "running"). Sequence Integrity: You must preserve the complete action chain defined in the prompt. Do not summarize, skip, or merge distinct sequential steps into a single state. No Collective Summarization: Do not use summary phrases (e.g., "a sequence of...", "various movements") to cover multiple steps. If the prompt defines A -> B -> C, every step must be represented as a separate action segment. Any skipped step or summarized sequence is a CRITICAL ERROR.\n'
+    '- Quantity: If a number is specified, it must be exact. Quantifiers must be strictly preserved. "All" means all, "every" means every, "a single" means exactly 1. Do not paraphrase quantifiers into vague terms.\n'
+    '- Spatial Integrity: Relative positions (e.g., "A on the left of B") must be fixed as stated.\n'
+    '- Color & Text: Colors must be accurate; OCR text must be transcribed character-for-character (case-sensitive).\n'
+    '4.  **Creative Expansion Constraints:** When expanding a simple prompt, ensure added details (e.g., "sun-drenched oak windowsill") never contradict, replace, or occlude the user\'s explicit subject requirements.\n'
+    '5.  **Text Placement Logic:** \n'
+    '- Surface-Bound Text (Printed on objects): Must be transcribed exactly within the appearance_details field of its respective object in prominent_elements.\n'
+    '- Standalone Graphic Text (Floating/Poster text): Must NOT be an entry in prominent_elements. Describe its role, style, and exact transcription exclusively within the comprehensive_description.\n'
+    "- Dynamic Text: If the scene contains moving or updating text (e.g., a scrolling ticker or a subtitle), you must explicitly describe the text's movement path and content change frequency within the actions array of the respective prominent_elements or the comprehensive_description.\n"
+    '6.  **Occlusion & Relational Mapping:** Explicitly document overlaps. If text occludes an object, or an object occludes text, specify which character(s) or object parts are affected in both relationship and comprehensive_description.\n'
+    '7.  **Handle Missing Data:** If an attribute is not applicable to an element (e.g., pose for a mountain), set its value to "" (an empty string). Never use "N/A", "unknown", "not applicable", etc.\n'
+    '8.  **Counting:** For number_of_objects, provide an exact number if specified by the user. Otherwise, use: "several" (3-6), "many" (7-20), or "numerous" (20+).\n'
+    '9.  **Perspective:** Always describe positions (location) and directions (orientation) as "left" and "right" from the viewer\'s perspective.\n'
+    '10. **Visual Descriptive Realism:** Avoid abstract or emotional adjectives (e.g., "beautiful," "sad"). Instead, translate them into visually observable details (e.g., "soft golden-hour rim lighting," "desaturated cool blue tones with falling rain droplets").\n'
+    '11. **Clusters:** When describing a cluster (e.g., "a forest"), describe the collective appearance of the group rather than listing every individual tree.\n'
+    '12. **Attribute Consistency:** Ensure that the comprehensive_description and the individual prominent_elements are perfectly synchronized. Every element mentioned in the elements list must exist in the paragraph description and vice versa.\n'
+    '13. **Negative Constraints:** Any explicit prohibitions (e.g., "no text," "no background") must be strictly upheld. Never add elements that the user has explicitly requested to exclude.\n'
+    '14. **Subject Priority:** The primary subject specified by the user must be the anchor of the scene. Its description and prominence in the JSON must reflect its status as the most important element.\n'
+    '15. **Integrity:** Before generating JSON, you must cross-verify all keywords, actions, and states against the User Prompt; if any item is missing or any state intensity deviates from the original, you MUST regenerate the content to ensure 100% fidelity. \n'
+    '16. **Non-OCR Classification:** Do not force objects into "text" status. If a word appears in the prompt without context of "sign," "label," or "document," describe it as a physical attribute or brand marking on the object, not as OCR/Typography.\n'
+    "17. **Camera Fidelity:** The camera_info must be logically consistent with the visual scene. For example, if the scene is a vast landscape, the camera should reflect a 'Wide' or 'Ultra Wide' frame size and appropriate lens settings. Avoid contradictory pairings (e.g., 'Extreme Close Up' with a 'Wide' lens).\n"
+    '18. **Temporal Continuity:** Ensure temporal continuity for all subjects and environment settings. If an object is introduced, it must not disappear or reappear without a logical reason (e.g., leaving the frame or being occluded). Texture, color, and lighting must remain consistent across all action segments.\n'
+    "19. **Action-Camera Sync:** The camera movement must logically justify the subject's displacement. If the camera follows a subject (tracking shot), the subject's position in the frame should remain relatively stable. If the camera is static, the subject must show movement within the frame.\n"
+    "20. **Physical Logic:** All movements must respect physical laws unless the prompt specifies a surreal or fantasy context. Avoid 'gliding' motions; ensure footsteps or object interactions (e.g., picking up an item) align with the timestamped actions.\n"
+    '21. **Motion and Velocity:** Verbs must describe the process of an action, not just the result (e.g., use \'rising from the chair\' instead of just \'standing\'). Every action in the actions array must include a velocity modifier (e.g., "slowly walking," "abruptly turning," "smoothly panning," "rapidly accelerating"). Avoid generic verbs without speed or force descriptors.\n'
+    '22. **Language:** Output all JSON content in professional English, except for OCR text, which must be transcribed exactly as provided to maintain absolute fidelity.\n'
+    '23. **Audio Neglect:** The User Prompt may contain audio, music, or voiceover instructions. Strictly ignore all audio-related instructions. Do not attempt to rewrite, describe, or represent these as part of the JSON output. Focus exclusively on the visual, temporal, and narrative elements and OCR texts.\n'
+    '24. **Duration Adherence:** A target video duration is provided in the input. Every timestamp in the `actions` array MUST fall within `[0.0s, the given duration]`, and the action timeline should span up to (but never beyond) this duration. Distribute the action segments proportionally to the given length; the final segment must end at or before the given duration.\n'
+    '\n'
+    "## THIS STEP'S INPUT/OUTPUT\n"
+    '- Input: the detailed prose caption + the target video duration.\n'
+    '- The expansion/thinking is already done in the prose — preserve the stated core EXACTLY (scene, subjects, every action + timestamp, named entities); ONLY the fine visual attributes and camera fields that the brief prose omits may be completed — plausibly, consistently, never contradicting the prose.\n'
+    '- Output ONLY the JSON object (valid and parseable), following the schema and rules above.'
+)
+
+IMAGE_STEP1_EXPAND = (
+    'You write ONE short, natural, standalone IMAGE caption in English from a brief user image\n'
+    'prompt — as if briefly describing a real photo of that idea.\n'
+    '\n'
+    'This is a SINGLE STILL IMAGE: there is NO motion, NO time, NO timestamps, NO camera movement.\n'
+    'Describe only what the photo shows; never invent action, sequence, or a moving camera.\n'
+    '\n'
+    'HARD LENGTH LIMIT: the output MUST be UNDER 1000 characters. Aim for roughly 400-800\n'
+    'characters. Concise is a top priority.\n'
+    '\n'
+    'STYLE — a flowing description, NOT a checklist:\n'
+    '- Connected, natural English prose. NO headings, NO bullets, NO field labels.\n'
+    '- The MAIN THREAD is the main subject and the scene — lead with WHAT THE IMAGE SHOWS. Do NOT\n'
+    '  march subject-by-subject listing attributes; weave the few details you keep into the\n'
+    '  description naturally. One cohesive paragraph; never pad to add length.\n'
+    '\n'
+    'WHAT TO COVER (only these):\n'
+    '- The scene in a brief phrase (where it is / overall vibe).\n'
+    '- Each main subject by name/what-it-is plus ONE most defining visual trait — not a full\n'
+    '  appearance list. The key spatial arrangement only if it defines the image.\n'
+    '- A one-word shot type only if it matters (e.g. close-up, wide shot, aerial); otherwise skip the camera.\n'
+    "- Named entities (real people / places / landmarks / brands) only if clearly implied — don't invent identities.\n"
+    '\n'
+    'FAITHFUL EXPANSION:\n'
+    '- Stay consistent with the prompt; elaborate plausibly but never contradict it. Commit to one\n'
+    "  coherent interpretation; don't hedge with alternatives.\n"
+    '- Keep subject identities and counts consistent.\n'
+    '\n'
+    'Output ONLY the caption text — no preamble, no headings, no explanation.\n'
+    '\n'
+    '\n'
+    "## EXAMPLES — one representative example per type. When you write your own caption, match these examples' style, format, and length (one flowing paragraph, no timestamps, no camera movement — it is a single still image).\n"
+    '\n'
+    '### Example 1 — animal / wildlife\n'
+    'USER PROMPT:\n'
+    'A leopard tortoise dominates the center of the frame, captured from a high angle with its head and front legs extended toward the left. Its domed shell displays a striking mosaic of black and tan geometric patterns, contrasting with the rough, yellowish-tan scales of its skin and sharp claws. The creature is positioned on reddish-brown sandy soil scattered with dry twigs and pebbles, illuminated by warm daylight that creates a soft shadow beneath it against a backdrop of green grass on the right.\n'
+    '\n'
+    'DETAILED CAPTION:\n'
+    'A leopard tortoise is positioned centrally in a natural outdoor setting, resting on reddish-brown sandy soil scattered with small pebbles and dry twigs. Facing toward the left with its head and front legs extended, the animal displays a domed shell marked by intricate black and tan geometric patterns reminiscent of leopard spots. Its yellowish-tan skin contrasts with the earthy ground beneath its clawed feet, while a patch of green grass and low-lying vegetation rises in the background to the right. The distinct markings of the shell stand out against the textured ground and sparse greenery, capturing the tortoise within its earthy environment.\n'
+    '\n'
+    '### Example 2 — architecture / landmark\n'
+    'USER PROMPT:\n'
+    '极度广角镜头，高角度拍摄。明亮日光，硬光，暖色调，清晰阴影。宏伟的古典风格宫殿庭院，浅色石材。中央巨大的多层拱形入口，饰有金色和蓝色装饰带，深色凹陷内部可见小金门。宽阔石阶通向圆形分层平台。两侧对称亭阁，顶部为反射金色圆顶，蓝色瓷砖拱门，装饰攀爬绿藤和粉色花朵。露台布满茂盛绿植和小花。若干穿着传统长袍（白、灰、棕、红）的小人物散布庭院以示比例。背景岩石山坡，远处建筑，晴朗明亮天空。写实建筑摄影。\n'
+    '\n'
+    'DETAILED CAPTION:\n'
+    'A grand, sun-drenched courtyard of a classical-style palace complex is dominated by a massive central building featuring a large, multi-tiered arched entrance. Wide stone stairs lead up to the structure, flanked on either side by identical pavilions topped with brilliant, reflective golden domes and blue-tiled arches. The light-colored stone complex includes various terraces filled with lush greenery, while several small figures dressed in traditional robes are scattered throughout the courtyard, providing a sense of scale against the monumental architecture. In the background, a rocky hillside rises under a clear bright sky, dotted with additional buildings and vegetation surrounding the temple-like grounds.\n'
+    '\n'
+    '### Example 3 — landscape / nature\n'
+    'USER PROMPT:\n'
+    "Make me an image of a serene coastal scene featuring a classic white lighthouse attached to a keeper's house, with a vintage red pickup truck and a small boat on a trailer parked on a dirt path nearby, set against a lush green lawn, wooden fence, and bright blue sky over calm waters.\n"
+    '\n'
+    'DETAILED CAPTION:\n'
+    "A wide aerial view captures a serene coastal scene dominated by a classic white lighthouse and its attached keeper's house. The tall cylindrical tower features a black lantern room and rises from a white residence with a brown shingled roof and green shutters. To the left, a classic red pickup truck is parked on a dirt path with a small white and red boat resting on a trailer behind it. A lush green lawn slopes down toward a rocky shoreline where a weathered wooden fence runs along the edge of the deep blue water. In the background, a calm sea stretches to the horizon, meeting a distant tree-covered coastline under a bright blue sky filled with soft clouds.\n"
+    '\n'
+    '### Example 4 — person / portrait\n'
+    'USER PROMPT:\n'
+    'A stylish woman with long wavy blonde hair and bold red lipstick stands confidently on a city sidewalk, wearing a black long-sleeved dress with sheer patterned sleeves and a ruffled waistline. She carries a small black crossbody bag with a gold Saint Laurent logo slung over her shoulder, her left arm slightly extended and right arm by her side in a high-angle medium wide shot. The background features brick buildings and a blurred crosswalk with distant pedestrians under soft daylight, creating a sophisticated urban atmosphere with a left-heavy composition.\n'
+    '\n'
+    'DETAILED CAPTION:\n'
+    'A stylish woman with long blonde hair and bold red lipstick stands on a city sidewalk, gazing directly forward in a black long-sleeved dress with sheer patterned sleeves and a ruffled waistline. A small black crossbody bag featuring a gold Saint Laurent logo hangs at her side, complementing her sophisticated look. The setting is a classic urban street scene characterized by brick buildings and a black wall-mounted lamp visible behind her. In the distance, a blurred crosswalk shows several pedestrians, adding depth to the modern atmosphere surrounding the central figure.'
+)
+
+IMAGE_STEP2_MAP = (
+    'You convert a SHORT natural-language STILL-IMAGE caption (the DETAILED CAPTION) into ONE\n'
+    'structured JSON caption describing the image. Output ONLY the JSON object — no prose, no code fence.\n'
+    '\n'
+    '## TASK SEMANTICS (read carefully)\n'
+    'The detailed caption is SHORT and LOSSY: it states the scene, the main subjects, and each\n'
+    "subject's single most defining trait, but it deliberately OMITS fine attributes (exact colors,\n"
+    'texture, relative size, orientation, clothing, skin tone, pose, expression) and the camera\n'
+    'metadata. Your job:\n'
+    '- KEEP faithfully everything the prose states (scene, every named subject, named entities) — do\n'
+    '  not drop, rename, merge, or split subjects, and do not contradict the prose.\n'
+    '- REASONABLY COMPLETE the fields the prose omits (fine attributes + camera_info) with plausible,\n'
+    '  self-consistent values that do NOT contradict the prose. This is expected: the target JSON is a\n'
+    '  FULL caption, so every schema field must be filled even when the prose did not mention it.\n'
+    '- This is a STILL IMAGE: no actions, no timestamps, no camera movement anywhere.\n'
+    '\n'
+    '## OUTPUT SCHEMA (exact keys)\n'
+    'A single JSON object with EXACTLY these top-level keys:\n'
+    '- "comprehensive_description": string — one flowing prose paragraph describing the whole image\n'
+    '  (scene, subjects, their arrangement and look). No field labels inside it.\n'
+    '- "camera_info": object with EXACTLY these 7 string keys, each set to ONE value from its list:\n'
+    '  - "color": Warm | Cool | Mixed | Saturated | Desaturated | White | Green | Blue | Red | Cyan\n'
+    '  - "frame_size": Extreme Close Up | Close Up | Medium Close Up | Medium | Medium Wide | Wide | Extreme Wide\n'
+    '  - "shot_type_angle": Eye level | Low angle | High angle | Overhead | Aerial\n'
+    '  - "lens_size": Ultra Wide / Fisheye | Wide | Medium | Long Lens | Telephoto\n'
+    '  - "composition": Center | Balanced | Symmetrical | Left heavy | Right heavy\n'
+    '  - "lighting": Soft light | Hard light | Top light | Underlight | Backlight\n'
+    '  - "lighting_type": Daylight | Artificial light\n'
+    '- "world_knowledge": array of strings — named entities / real-world facts (people, places,\n'
+    '  landmarks, brands) stated or clearly implied by the prose; [] if none. NEVER invent identities.\n'
+    '- "prominent_elements": array of objects, one per notable subject/object. Each element has:\n'
+    '  - always: "name", "description", "location", "relative_size" (one of: dominant | large | medium | small),\n'
+    '    "shape_and_color", "texture", "appearance_details", "relationship", "orientation"\n'
+    '  - for a PERSON, additionally: "pose", "expression", "clothing", "gender" (male | female),\n'
+    '    "skin_tone_and_texture"\n'
+    '  - for a GROUP/crowd of like items, additionally: "is_cluster": true and "number_of_objects": "<count or range as string>"\n'
+    '\n'
+    '## RULES\n'
+    '- Output STRICT valid JSON only (double quotes, no trailing commas, no comments, no code fence).\n'
+    '- Fill EVERY field; never leave a required field empty. Values you complete must be consistent\n'
+    '  with the prose and with each other.\n'
+    '- Keep the number and identity of prominent_elements aligned with the subjects the prose names\n'
+    '  (you may add a clearly-implied background element, but do not invent unrelated subjects).\n'
+    '- Copy named entities verbatim into world_knowledge.\n'
+    '- No actions, no timestamps, no camera-movement fields — this is a still image.'
+)
diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
new file mode 100644
index 000000000..de0261fbf
--- /dev/null
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -0,0 +1,160 @@
+# LingBot-Video
+
+LingBot-Video is a flow-matching text-to-video generation model. This document covers DiffSynth-Studio's inference and LoRA SFT training support for the **Dense-1.3B** text-to-video checkpoint.
+
+The integration is built on the standard DiffSynth pipeline stack:
+
+- **DiT** — `LingBotVideoDiT` (`diffsynth/models/lingbot_video_dit.py`), the video denoiser. The Dense-1.3B build uses a plain FFN; the architecture also supports an MoE FFN.
+- **Text encoder** — `LingBotVideoTextEncoder` (Qwen3-VL). Prompts are wrapped in a prompt-enhancement chat template, encoded, and the template-prefix tokens are cropped.
+- **VAE** — reuses DiffSynth's `QwenImageVAE` (byte-identical to the LingBot-Video VAE), 8× spatial / 4× temporal compression.
+- **Scheduler** — `LingBotVideoUniPCScheduler`: UniPC multistep for inference; it falls back to the full-resolution flow-matching schedule for training.
+
+## Installation
+
+Before using this project for model inference and training, please install DiffSynth-Studio first.
+
+```shell
+git clone https://github.com/modelscope/DiffSynth-Studio.git
+cd DiffSynth-Studio
+pip install -e .
+```
+
+LingBot-Video additionally requires `transformers >= 5.x` (for Qwen3-VL) and `imageio` / `imageio-ffmpeg` for video I/O. For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
+
+## Quick Start
+
+Run the following code to quickly load the [Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b) model and perform text-to-video inference. The required files are downloaded automatically the first time the code runs.
+
+```python
+import torch
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+)
+video = pipe(
+    prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0, seed=0,
+)
+save_video(video, "video.mp4", fps=15, quality=10)
+```
+
+**Low VRAM:** pass `vram_limit=<GB>` to `from_pretrained` to enable layer-by-layer offloading — see the low-VRAM example in the table below.
+
+## Model Overview
+
+| Model ID | Inference | Low VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
+|-|-|-|-|-|-|-|
+|[Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
+
+## Model Inference
+
+The model is loaded via `LingBotVideoPipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models) for details.
+
+Input parameters for `LingBotVideoPipeline` inference include:
+
+* `prompt`: Prompt describing the content appearing in the video. Accepts a structured caption (`dict` / `list`), a path to a `prompt.json`, or a plain string; see [Prompt rewriting](#prompt-rewriting-important-for-quality).
+* `negative_prompt`: Negative prompt describing content that should not appear in the video. A default (T2V) negative prompt is built into the pipeline, so this can be left unset.
+* `input_video`: Input video (a list of frames or a `VideoData`) for video-to-video generation, used together with `denoising_strength`.
+* `denoising_strength`: Denoising strength, range 0~1, default value is 1.0. Lower values keep more of the input video structure. Only effective when `input_video` is provided.
+* `height`: Video height, default 480. Must be a multiple of 16.
+* `width`: Video width, default 480. Must be a multiple of 16.
+* `num_frames`: Number of video frames, default 81. Must satisfy `4k+1` (the VAE compresses time by 4×).
+* `cfg_scale`: Classifier-free guidance scale, default 6.0. A value of 3.0 is recommended for the Dense-1.3B model.
+* `num_inference_steps`: Number of inference steps, default 40.
+* `sigma_shift`: Flow-matching timestep shift, default 3.0.
+* `seed`: Random seed. Default is `None`, meaning completely random.
+* `rand_device`: Device for generating the initial noise, default `"cpu"`.
+* `progress_bar_cmd`: Progress bar, default `tqdm`. Can be disabled by setting to `lambda x: x`.
+
+When running low on VRAM, please refer to [VRAM Management](../Pipeline_Usage/VRAM_management.md) to enable VRAM management features.
+
+## Prompt rewriting (important for quality)
+
+LingBot-Video is trained on **structured-JSON captions**, not free-form prose. Feeding a flat sentence is out-of-distribution and visibly degrades quality; feeding the structured caption the model expects restores it. The pipeline accepts a caption as a `dict`, a path to a `prompt.json`, or a plain string, and normalises it (via `normalize_caption`) to the exact compact-JSON format the DiT was trained on — a plain string is passed through unchanged, so existing scripts keep working.
+
+To turn a **brief idea** into that structured caption, use the bundled two-stage rewriter (`diffsynth/pipelines/lingbot_video_prompt_rewriter.py`): stage 1 *expands* the idea into a natural-language caption, stage 2 *maps* it into structured JSON.
+
+```python
+from diffsynth.pipelines.lingbot_video_prompt_rewriter import rewrite_prompt
+caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
+video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0)
+```
+
+The rewriter is a **separate VLM + stage-2 LoRA adapter** (not the DiT). Point it at the weights via `REWRITER_BASE_MODEL` / `REWRITER_ADAPTER` (or `base=` / `adapter=`), or drive a hosted / OpenAI-compatible endpoint by passing a custom object exposing `generate(text, image, use_lora)` as `backend=`. See `examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_rewrite.py`.
+
+## Model Training
+
+LingBot-Video is trained through [`examples/lingbot_video/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/train.py), which fine-tunes the DiT with LoRA using the flow-matching SFT objective. The script parameters include:
+
+* General Training Parameters
+    * Dataset Basic Configuration
+        * `--dataset_base_path`: Root directory of the dataset.
+        * `--dataset_metadata_path`: Metadata file path of the dataset (a CSV / JSONL with a `video` column and a `prompt` column).
+        * `--dataset_repeat`: Number of times the dataset is repeated in each epoch.
+        * `--dataset_num_workers`: Number of processes for each DataLoader.
+        * `--data_file_keys`: Field names to be loaded from metadata as files, usually video file paths, separated by `,`.
+    * Model Loading Configuration
+        * `--model_paths`: Paths of models to be loaded. JSON format.
+        * `--model_id_with_origin_paths`: Model IDs with original paths, separated by commas.
+    * Training Basic Configuration
+        * `--learning_rate`: Learning rate.
+        * `--num_epochs`: Number of epochs.
+        * `--task`: Training task, default is `sft`.
+    * Output Configuration
+        * `--output_path`: Model saving path.
+        * `--remove_prefix_in_ckpt`: Remove prefix in the state dict of the saved model.
+        * `--save_steps`: Interval of training steps to save the model. If left blank, the model is saved once per epoch.
+    * LoRA Configuration
+        * `--lora_base_model`: Which model to add LoRA to, e.g. `dit`.
+        * `--lora_target_modules`: Which layers to add LoRA to.
+        * `--lora_rank`: Rank of LoRA.
+        * `--lora_checkpoint`: Path of a LoRA checkpoint to resume / continue from.
+    * Gradient Configuration
+        * `--use_gradient_checkpointing`: Whether to enable gradient checkpointing.
+        * `--use_gradient_checkpointing_offload`: Whether to offload gradient checkpointing to memory.
+        * `--gradient_accumulation_steps`: Number of gradient accumulation steps.
+    * Video Width/Height Configuration
+        * `--height`: Height of the video. Must be divisible by 16.
+        * `--width`: Width of the video. Must be divisible by 16.
+        * `--num_frames`: Number of frames in the video. Must satisfy `4k+1`.
+* LingBot-Video Specific Parameters
+    * `--processor_path`: Path of the Qwen3-VL processor used by the text encoder.
+
+The launch script first downloads the example video-SFT dataset used across DiffSynth-Studio, then trains on it:
+
+```shell
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset \
+    --include "wanvideo/Wan2.1-T2V-1.3B/*" --local_dir ./data/diffsynth_example_dataset
+```
+
+### Attention-only LoRA (default scope)
+
+The recommended launch script patches LoRA on the joint text+video self-attention only:
+
+```
+--lora_base_model "dit"
+--lora_target_modules "to_q,to_k,to_v,to_out"
+--lora_rank 32
+--remove_prefix_in_ckpt "pipe.dit."
+```
+
+The MoE / FFN experts (`gate_proj`, `up_proj`, `down_proj`) and the router are left frozen. To also adapt the FFN, add those module names to `--lora_target_modules`.
+
+For best results the `prompt` column should hold **structured-JSON captions** (the same in-distribution format used at inference — see [Prompt rewriting](#prompt-rewriting-important-for-quality)). `train.py` runs each prompt through `normalize_caption`. If your dataset stores raw prose, rewrite it once offline with `examples/lingbot_video/model_training/rewrite_captions.py` before training.
+
+We have written recommended training scripts, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
+
+## Notes
+
+- The text encoder shares its checkpoint fingerprint with the existing `krea2_text_encoder` (identical Qwen3-VL architecture), so the model loader instantiates both when loading LingBot-Video. This is redundant load time only — the pipeline fetches the correct encoder by name and the other is released.
+- Latent normalisation is handled inside the VAE's 5D-video code path; the pipeline does not re-apply `latents_mean` / `latents_std`.
diff --git a/docs/en/index.rst b/docs/en/index.rst
index e062d679f..8b5b17507 100644
--- a/docs/en/index.rst
+++ b/docs/en/index.rst
@@ -41,6 +41,7 @@ Welcome to DiffSynth-Studio's Documentation
    Model_Details/Ideogram-4
    Model_Details/Krea-2
    Model_Details/Boogu-Image
+   Model_Details/LingBot-Video
 
 .. toctree::
    :maxdepth: 2
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
new file mode 100644
index 000000000..9f1a5f2bd
--- /dev/null
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -0,0 +1,160 @@
+# LingBot-Video
+
+LingBot-Video 是一个基于 flow-matching 的文生视频生成模型。本文档介绍 DiffSynth-Studio 对 **Dense-1.3B** 文生视频权重的推理与 LoRA SFT 训练支持。
+
+该接入基于标准的 DiffSynth Pipeline 组件栈构建：
+
+- **DiT** — `LingBotVideoDiT`（`diffsynth/models/lingbot_video_dit.py`），视频去噪器。Dense-1.3B 版本使用普通 FFN；该架构同时支持 MoE FFN。
+- **文本编码器** — `LingBotVideoTextEncoder`（Qwen3-VL）。提示词会被包裹进提示增强的对话模板中编码，随后裁剪掉模板前缀 token。
+- **VAE** — 复用 DiffSynth 的 `QwenImageVAE`（与 LingBot-Video 的 VAE 逐字节一致），空间 8× / 时间 4× 压缩。
+- **调度器** — `LingBotVideoUniPCScheduler`：推理时使用 UniPC 多步调度；训练时回退到完整分辨率的 flow-matching 调度。
+
+## 安装
+
+在使用本项目进行模型推理和训练前，请先安装 DiffSynth-Studio。
+
+```shell
+git clone https://github.com/modelscope/DiffSynth-Studio.git
+cd DiffSynth-Studio
+pip install -e .
+```
+
+LingBot-Video 还额外依赖 `transformers >= 5.x`（用于 Qwen3-VL）以及 `imageio` / `imageio-ffmpeg`（用于视频读写）。更多关于安装的信息，请参考[安装依赖](../Pipeline_Usage/Setup.md)。
+
+## 快速开始
+
+运行以下代码可以快速加载 [Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b) 模型并进行文生视频推理。首次运行时会自动下载所需文件。
+
+```python
+import torch
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+)
+video = pipe(
+    prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0, seed=0,
+)
+save_video(video, "video.mp4", fps=15, quality=10)
+```
+
+**低显存：** 向 `from_pretrained` 传入 `vram_limit=<GB>` 即可开启逐层 offload，详见下表中的低显存示例。
+
+## 模型总览
+
+|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
+|-|-|-|-|-|-|-|
+|[Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
+
+## 模型推理
+
+模型通过 `LingBotVideoPipeline.from_pretrained` 加载，详见[加载模型](../Pipeline_Usage/Model_Inference.md#加载模型)。
+
+`LingBotVideoPipeline` 推理的输入参数包括：
+
+* `prompt`: 描述视频内容的提示词。支持结构化 caption（`dict` / `list`）、`prompt.json` 路径，或普通字符串；详见[提示词改写](#提示词改写对质量很重要)。
+* `negative_prompt`: 负向提示词，描述不希望出现在视频中的内容。Pipeline 内置了默认（T2V）负向提示词，因此可以不设置。
+* `input_video`: 输入视频（帧列表或 `VideoData`），用于视频生视频，需与 `denoising_strength` 配合使用。
+* `denoising_strength`: 降噪强度，取值范围 0~1，默认为 1.0。值越小，越保留输入视频的结构。仅在提供 `input_video` 时生效。
+* `height`: 视频高度，默认为 480，需能被 16 整除。
+* `width`: 视频宽度，默认为 480，需能被 16 整除。
+* `num_frames`: 视频帧数，默认为 81，需满足 `4k+1`（VAE 在时间维上做 4× 压缩）。
+* `cfg_scale`: 分类器自由引导系数，默认为 6.0。Dense-1.3B 模型推荐使用 3.0。
+* `num_inference_steps`: 推理步数，默认为 40。
+* `sigma_shift`: flow-matching 时间步偏移，默认为 3.0。
+* `seed`: 随机种子，默认为 `None`，即完全随机。
+* `rand_device`: 生成初始噪声的设备，默认为 `"cpu"`。
+* `progress_bar_cmd`: 进度条，默认为 `tqdm`，可设为 `lambda x: x` 关闭。
+
+显存不足时，请参考[显存管理](../Pipeline_Usage/VRAM_management.md)启用显存管理功能。
+
+## 提示词改写（对质量很重要）
+
+LingBot-Video 使用**结构化 JSON caption** 训练，而非自由文本。喂入一句普通句子属于分布外（out-of-distribution）输入，会明显降低质量；喂入模型期望的结构化 caption 则能恢复质量。Pipeline 接受 `dict`、`prompt.json` 路径或普通字符串形式的 caption，并将其（通过 `normalize_caption`）归一化为 DiT 训练时使用的紧凑 JSON 格式——普通字符串会原样透传，因此已有脚本无需改动。
+
+若要把一个**简短想法**转成该结构化 caption，可使用内置的两阶段改写器（`diffsynth/pipelines/lingbot_video_prompt_rewriter.py`）：阶段一将想法*扩写*为自然语言 caption，阶段二将其*映射*为结构化 JSON。
+
+```python
+from diffsynth.pipelines.lingbot_video_prompt_rewriter import rewrite_prompt
+caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
+video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0)
+```
+
+改写器是**独立的 VLM + 阶段二 LoRA 适配器**（不是 DiT）。可通过 `REWRITER_BASE_MODEL` / `REWRITER_ADAPTER`（或 `base=` / `adapter=`）指向权重，也可以通过传入一个暴露 `generate(text, image, use_lora)` 方法的自定义对象作为 `backend=`，来驱动托管的 / OpenAI 兼容的推理端点。详见 `examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_rewrite.py`。
+
+## 模型训练
+
+LingBot-Video 通过 [`examples/lingbot_video/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/train.py) 进行训练，使用 flow-matching SFT 目标对 DiT 做 LoRA 微调。脚本的参数包括：
+
+* 通用训练参数
+    * 数据集基础配置
+        * `--dataset_base_path`: 数据集的根目录。
+        * `--dataset_metadata_path`: 数据集的元数据文件路径（含 `video` 列与 `prompt` 列的 CSV / JSONL）。
+        * `--dataset_repeat`: 每个 epoch 中数据集重复的次数。
+        * `--dataset_num_workers`: 每个 Dataloader 的进程数量。
+        * `--data_file_keys`: 元数据中需要按文件加载的字段名称，通常是视频文件路径，以 `,` 分隔。
+    * 模型加载配置
+        * `--model_paths`: 要加载的模型路径。JSON 格式。
+        * `--model_id_with_origin_paths`: 带原始路径的模型 ID，用逗号分隔。
+    * 训练基础配置
+        * `--learning_rate`: 学习率。
+        * `--num_epochs`: 轮数（Epoch）。
+        * `--task`: 训练任务，默认为 `sft`。
+    * 输出配置
+        * `--output_path`: 模型保存路径。
+        * `--remove_prefix_in_ckpt`: 在保存模型的 state dict 中移除前缀。
+        * `--save_steps`: 保存模型的训练步数间隔。留空则每个 epoch 保存一次。
+    * LoRA 配置
+        * `--lora_base_model`: LoRA 添加到哪个模型上，例如 `dit`。
+        * `--lora_target_modules`: LoRA 添加到哪些层上。
+        * `--lora_rank`: LoRA 的秩（Rank）。
+        * `--lora_checkpoint`: 用于续训 / 继续训练的 LoRA 检查点路径。
+    * 梯度配置
+        * `--use_gradient_checkpointing`: 是否启用 gradient checkpointing。
+        * `--use_gradient_checkpointing_offload`: 是否将 gradient checkpointing 卸载到内存中。
+        * `--gradient_accumulation_steps`: 梯度累积步数。
+    * 视频宽高配置
+        * `--height`: 视频高度，需能被 16 整除。
+        * `--width`: 视频宽度，需能被 16 整除。
+        * `--num_frames`: 视频帧数，需满足 `4k+1`。
+* LingBot-Video 专有参数
+    * `--processor_path`: 文本编码器使用的 Qwen3-VL processor 路径。
+
+启动脚本会先下载 DiffSynth-Studio 通用的示例视频 SFT 数据集，然后在其上训练：
+
+```shell
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset \
+    --include "wanvideo/Wan2.1-T2V-1.3B/*" --local_dir ./data/diffsynth_example_dataset
+```
+
+### 仅注意力 LoRA（默认范围）
+
+推荐的启动脚本仅在文本+视频联合自注意力上添加 LoRA：
+
+```
+--lora_base_model "dit"
+--lora_target_modules "to_q,to_k,to_v,to_out"
+--lora_rank 32
+--remove_prefix_in_ckpt "pipe.dit."
+```
+
+MoE / FFN 专家（`gate_proj`、`up_proj`、`down_proj`）与 router 保持冻结。若要同时微调 FFN，可将这些模块名加入 `--lora_target_modules`。
+
+为获得最佳效果，`prompt` 列应存放**结构化 JSON caption**（与推理时一致的分布内格式——见[提示词改写](#提示词改写对质量很重要)）。`train.py` 会对每条 prompt 调用 `normalize_caption`。若数据集存放的是原始文本，请在训练前用 `examples/lingbot_video/model_training/rewrite_captions.py` 离线改写一次。
+
+我们编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。
+
+## 注意事项
+
+- 文本编码器与已有的 `krea2_text_encoder` 共享同一 checkpoint 指纹（Qwen3-VL 架构相同），因此模型加载器在加载 LingBot-Video 时会同时实例化两者。这只是冗余的加载耗时——Pipeline 会按名称取用正确的编码器，另一个会被释放。
+- Latent 归一化在 VAE 的 5D 视频代码路径内部处理；Pipeline 不会再额外应用 `latents_mean` / `latents_std`。
diff --git a/docs/zh/index.rst b/docs/zh/index.rst
index 7928a58e6..eca57685c 100644
--- a/docs/zh/index.rst
+++ b/docs/zh/index.rst
@@ -41,6 +41,7 @@
    Model_Details/Ideogram-4
    Model_Details/Krea-2
    Model_Details/Boogu-Image
+   Model_Details/LingBot-Video
 
 .. toctree::
    :maxdepth: 2
diff --git a/examples/lingbot_video/README.md b/examples/lingbot_video/README.md
index c112a6ff9..6b0bc884e 100644
--- a/examples/lingbot_video/README.md
+++ b/examples/lingbot_video/README.md
@@ -54,7 +54,28 @@ save_video(video, "output.mp4", fps=15, quality=5)
 
 The pipeline ships a default (T2V) negative prompt, so `negative_prompt` is optional. Video-to-video is supported by passing `input_video=` (a list of frames or a `VideoData`) together with `denoising_strength < 1`.
 
-**Low VRAM:** pass `vram_limit=<GB>` to `from_pretrained` to enable layer-by-layer offloading — see `model_inference/lingbot-video-dense-1.3b_low_vram.py`.
+**Low VRAM:** pass `vram_limit=<GB>` to `from_pretrained` to enable layer-by-layer offloading — see `model_inference_low_vram/lingbot-video-dense-1.3b.py`.
+
+## Prompt rewriting (important for quality)
+
+LingBot-Video is trained on **structured-JSON captions**, not free-form prose. Feeding a flat sentence is out-of-distribution and visibly degrades quality (softer, less coherent motion); feeding the structured caption the model expects restores it. The pipeline accepts a caption as a `dict`, a path to a `prompt.json`, or a plain string, and normalises it to the exact compact-JSON format the DiT was trained on — a plain string is passed through unchanged, so existing scripts keep working.
+
+```python
+# All three are equivalent once normalised, and all are accepted by pipe(prompt=...):
+pipe(prompt={"caption": {"comprehensive_description": {...}}, "duration": 5})   # dict
+pipe(prompt="assets/cases/t2v/example_1/prompt.json")                          # prompt.json path
+pipe(prompt='{"comprehensive_description":{...}}')                             # already-serialised string
+```
+
+To turn a **brief idea** into that structured caption, use the bundled two-stage rewriter (`diffsynth/pipelines/lingbot_video_prompt_rewriter.py`), a faithful port of the original: stage 1 *expands* the idea into a natural-language caption, stage 2 *maps* it into structured JSON.
+
+```python
+from diffsynth.pipelines.lingbot_video_prompt_rewriter import rewrite_prompt
+caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
+video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0)
+```
+
+The rewriter is a **separate VLM + stage-2 LoRA adapter** (not the DiT). Point it at the weights via `REWRITER_BASE_MODEL` / `REWRITER_ADAPTER` (or `base=`/`adapter=`). If you serve the rewriter behind a hosted / OpenAI-compatible endpoint instead of loading it locally, pass a custom object exposing `generate(text, image, use_lora)` as `backend=`. See `model_inference/lingbot-video-dense-1.3b_rewrite.py`.
 
 ## Training (LoRA SFT)
 
@@ -64,9 +85,16 @@ The pipeline ships a default (T2V) negative prompt, so `negative_prompt` is opti
 bash examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
 ```
 
+The launch script first downloads the example video-SFT dataset used across DiffSynth-Studio, then trains on it:
+
+```bash
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset \
+    --include "wanvideo/Wan2.1-T2V-1.3B/*" --local_dir ./data/diffsynth_example_dataset
+```
+
 ### Dataset format
 
-A metadata CSV (or JSONL) with a `video` column (path relative to `--dataset_base_path`) and a `prompt` column:
+Bring your own data by pointing `--dataset_base_path` / `--dataset_metadata_path` at a metadata CSV (or JSONL) with a `video` column (path relative to `--dataset_base_path`) and a `prompt` column — the same layout as the example dataset above:
 
 ```
 video,prompt
@@ -76,6 +104,16 @@ videos/001.mp4,A serene lake at sunrise, mist rising from the water ...
 
 Pass `--data_file_keys "video"` so the loader treats the `video` column as a file to load.
 
+For best results the `prompt` column should hold **structured-JSON captions** (the same in-distribution format used at inference — see [Prompt rewriting](#prompt-rewriting-important-for-quality)). `train.py` runs each prompt through `normalize_caption`, so a `dict`-valued prompt (in JSONL) or a path to a `prompt.json` is serialised automatically, and a plain string is used as-is. If your dataset stores raw prose, rewrite it once offline before training:
+
+```bash
+python examples/lingbot_video/model_training/rewrite_captions.py \
+    --metadata metadata.csv --output metadata_rewritten.csv \
+    --base /path/to/rewriter-base --adapter /path/to/rewriter-step2-lora --duration 5
+```
+
+then train on `metadata_rewritten.csv`. (This is done offline because running the rewriter VLM inside the dataloader on every step would be prohibitively slow.)
+
 ### Attention-only LoRA (default scope)
 
 The launch script patches LoRA on the joint text+video self-attention only:
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
index 97de5a949..eb2ddcbb1 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
@@ -19,10 +19,10 @@
 video = pipe(
     prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight, ears perked up, chasing after a red ball. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
     height=480, width=832, num_frames=81,
-    num_inference_steps=40, cfg_scale=6.0,
+    num_inference_steps=40, cfg_scale=3.0,
     seed=0,
 )
-save_video(video, "video_lingbot-video-dense-1.3b.mp4", fps=15, quality=5)
+save_video(video, "video_lingbot-video-dense-1.3b.mp4", fps=15, quality=10)
 
 # Video-to-video. `denoising_strength < 1` keeps part of the input structure.
 video = VideoData("video_lingbot-video-dense-1.3b.mp4", height=480, width=832)
@@ -30,7 +30,7 @@
     prompt="A playful puppy wearing black sunglasses runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
     input_video=video, denoising_strength=0.7,
     height=480, width=832, num_frames=81,
-    num_inference_steps=40, cfg_scale=6.0,
+    num_inference_steps=40, cfg_scale=3.0,
     seed=1,
 )
-save_video(video, "video_lingbot-video-dense-1.3b_v2v.mp4", fps=15, quality=5)
+save_video(video, "video_lingbot-video-dense-1.3b_v2v.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_rewrite.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_rewrite.py
new file mode 100644
index 000000000..8ca89d4bc
--- /dev/null
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_rewrite.py
@@ -0,0 +1,60 @@
+import os
+import torch
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from diffsynth.pipelines.lingbot_video_prompt_rewriter import rewrite_prompt, normalize_caption
+
+
+# LingBot-Video is trained on STRUCTURED-JSON captions, not free-form prose. Feeding a
+# flat sentence is out-of-distribution and visibly degrades quality; rewriting the idea
+# into the structured caption the model expects restores it. This example shows the two
+# supported ways to obtain that caption.
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+)
+
+
+# --- Option A: rewrite a brief idea with the two-stage prompt rewriter --------------
+# The rewriter is a separate VLM + LoRA adapter (NOT the DiT). Point these at the
+# rewriter base model and its stage-2 adapter:
+#     export REWRITER_BASE_MODEL=/path/to/rewriter-base
+#     export REWRITER_ADAPTER=/path/to/rewriter-step2-lora
+# If you serve the rewriter behind a hosted / OpenAI-compatible endpoint instead, pass
+# a custom backend object exposing generate(text, image, use_lora) as `backend=`.
+if os.environ.get("REWRITER_BASE_MODEL") and os.environ.get("REWRITER_ADAPTER"):
+    caption = rewrite_prompt(
+        "A playful puppy runs across a lush green meadow, chasing a red ball. "
+        "Dynamic side-tracking camera.",
+        mode="t2v", duration=5, backend="transformers",
+    )
+    print("Rewritten caption:\n", caption)
+else:
+    # --- Option B: no rewriter model — supply a structured caption directly ----------
+    # A dict (or a path to a prompt.json) is serialised to the exact model format by
+    # normalize_caption; the pipeline also calls it internally, so pipe(prompt=<dict>)
+    # works too. Replace this stub with a real structured caption for best quality.
+    caption = normalize_caption({
+        "caption": {
+            "comprehensive_description": {
+                "scene_content_description": "A playful golden puppy runs across a lush green meadow dotted with wildflowers, chasing a bright red ball under a clear blue sky.",
+                "camera_movement_description": "Dynamic side-tracking shot following the puppy.",
+            }
+        },
+        "duration": 5,
+    })
+
+video = pipe(
+    prompt=caption,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-dense-1.3b_rewrite.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_low_vram.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
similarity index 95%
rename from examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_low_vram.py
rename to examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
index b34f81c8a..b6e3bf1db 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_low_vram.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
@@ -21,7 +21,7 @@
 video = pipe(
     prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight, ears perked up, chasing after a red ball. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
     height=480, width=832, num_frames=81,
-    num_inference_steps=40, cfg_scale=6.0,
+    num_inference_steps=40, cfg_scale=3.0,
     seed=0,
 )
-save_video(video, "video_lingbot-video-dense-1.3b_low_vram.mp4", fps=15, quality=5)
+save_video(video, "video_lingbot-video-dense-1.3b_low_vram.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
index 316f47264..594e3868a 100644
--- a/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
+++ b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
@@ -2,17 +2,25 @@
 # This fetches the whole repo once into ./models so the paths below resolve locally.
 modelscope download --model Robbyant/lingbot-video-dense-1.3b --local_dir ./models/Robbyant/lingbot-video-dense-1.3b
 
+# Download the example video-SFT dataset (a small text-to-video set with a `video` +
+# `prompt` metadata.csv), the same DiffSynth-Studio example dataset the other training
+# scripts use. NOTE: its prompts are plain prose; for best quality rewrite them into
+# structured captions first with model_training/rewrite_captions.py (see the README).
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-T2V-1.3B/*" --local_dir ./data/diffsynth_example_dataset
+
 # Attention-only LoRA SFT.
 # `--lora_target_modules "to_q,to_k,to_v,to_out"` patches LoRA on the joint
 # text+video self-attention only, leaving the MoE / FFN experts and the router frozen.
+# `--num_frames 169` = 7 s at the model's native 24 fps (169 = 4k+1, required by the
+# VAE's 4x temporal compression). The loader samples the first 169 frames of each clip.
 accelerate launch examples/lingbot_video/model_training/train.py \
-  --dataset_base_path data/example_video_dataset \
-  --dataset_metadata_path data/example_video_dataset/metadata.csv \
+  --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B \
+  --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B/metadata.csv \
   --data_file_keys "video" \
   --height 480 \
   --width 832 \
-  --num_frames 81 \
-  --dataset_repeat 100 \
+  --num_frames 169 \
+  --dataset_repeat 200 \
   --model_paths '[
     [
       "./models/Robbyant/lingbot-video-dense-1.3b/text_encoder/model-00001-of-00002.safetensors",
@@ -23,7 +31,7 @@ accelerate launch examples/lingbot_video/model_training/train.py \
   ]' \
   --processor_path "./models/Robbyant/lingbot-video-dense-1.3b/processor" \
   --learning_rate 1e-4 \
-  --num_epochs 5 \
+  --num_epochs 20 \
   --remove_prefix_in_ckpt "pipe.dit." \
   --output_path "./models/train/lingbot-video-dense-1.3b_lora" \
   --lora_base_model "dit" \
diff --git a/examples/lingbot_video/model_training/rewrite_captions.py b/examples/lingbot_video/model_training/rewrite_captions.py
new file mode 100644
index 000000000..3b234af9e
--- /dev/null
+++ b/examples/lingbot_video/model_training/rewrite_captions.py
@@ -0,0 +1,95 @@
+"""Offline: rewrite a training metadata file's raw prompts into structured JSON captions.
+
+LingBot-Video is trained on structured-JSON captions. If your dataset's ``prompt``
+column holds free-form prose, run this ONCE before training to rewrite every prompt
+into the structured caption the DiT expects, and train on the rewritten metadata. This
+is done offline on purpose — running the (large) rewriter VLM inside the dataloader on
+every step would be prohibitively slow.
+
+The rewriter is a separate VLM + stage-2 LoRA adapter (NOT the DiT). Provide it via
+``--base``/``--adapter`` or the ``REWRITER_BASE_MODEL`` / ``REWRITER_ADAPTER`` env vars.
+
+Usage:
+    python rewrite_captions.py --metadata metadata.csv --output metadata_rewritten.csv \
+        --base /path/to/rewriter-base --adapter /path/to/rewriter-step2-lora --duration 5
+
+Supports .csv / .json / .jsonl metadata (same formats as the training dataset loader).
+The output keeps every other column and replaces the ``--prompt-column`` with the
+compact-JSON caption string. Rows whose stage-2 output fails to parse are kept with
+their original prompt and logged, so training never silently trains on a broken row.
+"""
+
+import argparse
+import json
+import os
+
+from diffsynth.pipelines.lingbot_video_prompt_rewriter import Rewriter, make_backend, normalize_caption
+
+
+def _load_rows(path):
+    if path.endswith(".json"):
+        with open(path, "r", encoding="utf-8") as f:
+            return json.load(f), "json"
+    if path.endswith(".jsonl"):
+        rows = []
+        with open(path, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if line:
+                    rows.append(json.loads(line))
+        return rows, "jsonl"
+    import pandas
+    df = pandas.read_csv(path)
+    return [df.iloc[i].to_dict() for i in range(len(df))], "csv"
+
+
+def _save_rows(rows, path, fmt):
+    if fmt == "json":
+        with open(path, "w", encoding="utf-8") as f:
+            json.dump(rows, f, ensure_ascii=False, indent=2)
+    elif fmt == "jsonl":
+        with open(path, "w", encoding="utf-8") as f:
+            for row in rows:
+                f.write(json.dumps(row, ensure_ascii=False) + "\n")
+    else:
+        import pandas
+        pandas.DataFrame(rows).to_csv(path, index=False)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--metadata", required=True, help="input metadata (.csv/.json/.jsonl)")
+    ap.add_argument("--output", required=True, help="output metadata with rewritten captions")
+    ap.add_argument("--prompt-column", default="prompt")
+    ap.add_argument("--mode", default="t2v", choices=["t2v", "ti2v", "t2i"])
+    ap.add_argument("--duration", type=float, default=5)
+    ap.add_argument("--backend", default="transformers")
+    ap.add_argument("--base", default=None, help="rewriter base model (or REWRITER_BASE_MODEL)")
+    ap.add_argument("--adapter", default=None, help="rewriter stage-2 LoRA (or REWRITER_ADAPTER)")
+    ap.add_argument("--first-frame-column", default=None,
+                    help="for ti2v: column holding the first-frame image path/URL")
+    args = ap.parse_args()
+
+    rows, fmt = _load_rows(args.metadata)
+    rewriter = Rewriter(make_backend(args.backend, args.base, args.adapter))
+
+    ok, failed = 0, 0
+    for i, row in enumerate(rows):
+        raw = row.get(args.prompt_column, "")
+        first_frame = row.get(args.first_frame_column) if args.first_frame_column else None
+        result = rewriter.rewrite(raw, mode=args.mode, first_frame=first_frame, duration=args.duration)
+        if result["json"] is not None:
+            row[args.prompt_column] = normalize_caption(result["json"])
+            ok += 1
+        else:
+            failed += 1
+            print(f"[warn] row {i}: stage-2 JSON did not parse; keeping original prompt.")
+        if (i + 1) % 20 == 0:
+            print(f"  ... {i + 1}/{len(rows)} rewritten")
+
+    _save_rows(rows, args.output, fmt)
+    print(f"[done] {ok} rewritten, {failed} kept-as-is -> {os.path.abspath(args.output)}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/lingbot_video/model_training/train.py b/examples/lingbot_video/model_training/train.py
index c1507ced3..1d7e74ab4 100644
--- a/examples/lingbot_video/model_training/train.py
+++ b/examples/lingbot_video/model_training/train.py
@@ -1,6 +1,7 @@
 import torch, os, argparse, accelerate, warnings
 from diffsynth.core import UnifiedDataset
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from diffsynth.pipelines.lingbot_video_prompt_rewriter import normalize_caption
 from diffsynth.diffusion import *
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
@@ -65,7 +66,11 @@ def __init__(
         self.min_timestep_boundary = min_timestep_boundary
 
     def get_pipeline_inputs(self, data):
-        inputs_posi = {"prompt": data["prompt"]}
+        # LingBot-Video is trained on structured-JSON captions. Normalise the metadata
+        # "prompt" column (dict / prompt.json path -> compact JSON; plain string kept
+        # as-is) so LoRA trains in-distribution. Prepare captions offline with
+        # model_training/rewrite_captions.py if your dataset stores raw prose.
+        inputs_posi = {"prompt": normalize_caption(data["prompt"])}
         inputs_nega = {}
         inputs_shared = {
             # Assume you are using this pipeline for inference,
diff --git a/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py
new file mode 100644
index 000000000..e695867f6
--- /dev/null
+++ b/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py
@@ -0,0 +1,24 @@
+import torch
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+)
+pipe.load_lora(pipe.dit, "models/train/lingbot-video-dense-1.3b_lora/epoch-19.safetensors", alpha=1)
+
+video = pipe(
+    prompt="from sunset to night, a small town, light, house, river",
+    height=480, width=832, num_frames=169,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-dense-1.3b.mp4", fps=15, quality=10)

From 55c83577e83b1eb8722527768400a45a8b7e6f7e Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Mon, 27 Jul 2026 11:26:26 +0800
Subject: [PATCH 03/34] Add LingBot-Video MoE-30B-A3B support

Register the MoE 30B-A3B DiT (128 experts, top-8 group-limited routing, 1
shared expert) and add VRAM management module maps for the LingBot-Video DiT
and text encoder, which previously had no entry and fell back to whole-model
wrapping. The MoE experts store their weights as grouped nn.Parameter rather
than nn.Linear, so the expert container itself is wrapped -- without that the
experts, which are the bulk of the model, would stay resident.

Also fixes three issues surfaced while validating the MoE path:

- _run_grouped_experts guarded the fast path with hasattr(torch, "_grouped_mm"),
  which is True on CPU even though the kernel is CUDA-only. Now device-aware.
- The block and DiT forwards derived the activation dtype from .weight.dtype.
  Under VRAM management the wrapped layer holds its weight in the offload dtype
  (or on meta for disk offload), so this read the wrong dtype. Resolved from
  computation_dtype instead.
- The fp32-pinned timestep and modulation MLPs were fed fp32 activations while
  VRAM management wraps every nn.Linear at computation_dtype, raising a dtype
  mismatch on any low-VRAM run. Resolved per-layer.

The dense low-VRAM example set only vram_limit, which is a no-op without
offload_dtype/offload_device, so VRAM management never activated; corrected
along with the same claim in the docs.

Validated against the official implementation with identical weights: router
selection and gate scores are bit-exact, and the full DiT matches bit-exactly
at fp32 (bf16 differs only because diffusers' .to() downcasts the fp32-pinned
modules). The registered model_hash matches the released checkpoint headers
across all 977 keys with no shape mismatches.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 diffsynth/configs/model_configs.py            | 16 ++++++
 .../configs/vram_management_module_maps.py    | 17 ++++++
 diffsynth/models/lingbot_video_dit.py         | 52 +++++++++++++++----
 docs/en/Model_Details/LingBot-Video.md        | 34 ++++++++++--
 docs/zh/Model_Details/LingBot-Video.md        | 34 ++++++++++--
 .../lingbot-video-moe-30b-a3b.py              | 38 ++++++++++++++
 .../lingbot-video-dense-1.3b.py               | 23 +++++---
 .../lingbot-video-moe-30b-a3b.py              | 42 +++++++++++++++
 8 files changed, 233 insertions(+), 23 deletions(-)
 create mode 100644 examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py
 create mode 100644 examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py

diff --git a/diffsynth/configs/model_configs.py b/diffsynth/configs/model_configs.py
index 19f2c3af9..c79105dd1 100644
--- a/diffsynth/configs/model_configs.py
+++ b/diffsynth/configs/model_configs.py
@@ -1332,6 +1332,22 @@
         "model_class": "diffsynth.models.lingbot_video_dit.LingBotVideoDiT",
         "state_dict_converter": "diffsynth.utils.state_dict_converters.lingbot_video_dit.LingBotVideoDiTStateDictConverter",
     },
+    {
+        # Example: ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors")
+        # MoE 30B-A3B DiT (128 experts, top-8 with group-limited routing, 1 shared
+        # expert). The released package also ships a `refiner/` DiT with the exact
+        # same architecture and key set, so it hashes identically and is loaded
+        # through this entry too; the pipeline separates them by load order.
+        "model_hash": "65b83aa625cd362ff5ff3409fb367a6f",
+        "model_name": "lingbot_video_dit",
+        "model_class": "diffsynth.models.lingbot_video_dit.LingBotVideoDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.lingbot_video_dit.LingBotVideoDiTStateDictConverter",
+        "extra_kwargs": {
+            "depth": 48, "axes_lens": (4096, 512, 512), "num_experts": 128,
+            "moe_intermediate_size": 768, "n_group": 4, "topk_group": 2,
+            "n_shared_experts": 1, "routed_scaling_factor": 2.5,
+        },
+    },
     {
         # Example: ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/*.safetensors")
         # Qwen3-VL text encoder (2 shards). Hash is over the merged key set of both
diff --git a/diffsynth/configs/vram_management_module_maps.py b/diffsynth/configs/vram_management_module_maps.py
index 1f35eee51..4430d1f5b 100644
--- a/diffsynth/configs/vram_management_module_maps.py
+++ b/diffsynth/configs/vram_management_module_maps.py
@@ -408,6 +408,23 @@
         "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRotaryEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
         "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
     },
+    "diffsynth.models.lingbot_video_dit.LingBotVideoDiT": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.lingbot_video_dit.LingBotVideoRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        # The MoE experts keep their weights as bare `nn.Parameter` (w1/w2/w3 of
+        # shape [E, I, H]) rather than `nn.Linear`, so `AutoWrappedLinear` cannot
+        # reach them. Wrapping the container itself is what makes the 30B-A3B
+        # checkpoint offloadable -- without this the experts, which are the bulk
+        # of the model, would stay resident.
+        "diffsynth.models.lingbot_video_dit.LingBotVideoGroupedExperts": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.lingbot_video_text_encoder.LingBotVideoTextEncoder": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRotaryEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
 }
 
 def QwenImageTextEncoder_Module_Map_Updater():
diff --git a/diffsynth/models/lingbot_video_dit.py b/diffsynth/models/lingbot_video_dit.py
index 59fe8fd1d..d6221597a 100644
--- a/diffsynth/models/lingbot_video_dit.py
+++ b/diffsynth/models/lingbot_video_dit.py
@@ -34,6 +34,24 @@ def should_keep_in_fp32(name: str) -> bool:
     return any(module_name in name.split(".") for module_name in LINGBOT_VIDEO_FP32_MODULES)
 
 
+def resolve_bulk_dtype(linear: nn.Module) -> torch.dtype:
+    """Dtype the bulk Linear path will actually compute in.
+
+    Reading `linear.weight.dtype` is wrong under DiffSynth's VRAM management: the
+    wrapped layer holds its weight in the offload dtype (e.g. float8) between
+    forwards, and on `meta` when offloaded to disk, casting to `computation_dtype`
+    only inside its own forward. `computation_dtype` is the value to feed the
+    activations, so prefer it whenever the layer is wrapped.
+    """
+    computation_dtype = getattr(linear, "computation_dtype", None)
+    if isinstance(computation_dtype, torch.dtype):
+        # fp8 compute still takes bf16 activations; AutoWrappedLinear quantises internally.
+        if computation_dtype in (torch.float8_e4m3fn, torch.float8_e4m3fnuz, torch.float8_e5m2):
+            return torch.bfloat16
+        return computation_dtype
+    return linear.weight.dtype
+
+
 def get_timestep_embedding(
     timesteps: torch.Tensor,
     embedding_dim: int,
@@ -367,7 +385,10 @@ def _unpad_grouped_tokens(output, input_shape, permuted_indices):
         return unpermuted[:-1]
 
     def _run_grouped_experts(self, tokens, counts):
-        if not hasattr(torch, "_grouped_mm"):
+        # `torch._grouped_mm` exists as a symbol on every backend but only has a CUDA
+        # kernel, so probing with `hasattr` alone would crash on CPU (and on NPU).
+        # Fall back to the mathematically equivalent per-expert loop off CUDA.
+        if not hasattr(torch, "_grouped_mm") or tokens.device.type != "cuda":
             return self._run_experts_for_loop(tokens, counts)
         input_shape, padded_tokens, permuted_indices, aligned_counts = self._pad_grouped_tokens(tokens, counts)
         offsets = torch.cumsum(aligned_counts, dim=0, dtype=torch.int32)
@@ -451,7 +472,7 @@ def __init__(self, hidden_size, num_attention_heads, intermediate_size, norm_eps
             self.ffn = LingBotVideoMLP(h, intermediate_size)
         self.norm_post_ffn = LingBotVideoRMSNorm(h, norm_eps)
 
-    def forward(self, x, temb6, rotary_emb, attention_mask=None, moe_padding_mask=None):
+    def forward(self, x, temb6, rotary_emb, attention_mask=None, moe_padding_mask=None, bulk_dtype=None):
         expected_tokens = x.shape[0] * x.shape[1]
         if temb6.ndim != 2 or temb6.shape[0] != expected_tokens:
             raise ValueError(
@@ -459,13 +480,17 @@ def forward(self, x, temb6, rotary_emb, attention_mask=None, moe_padding_mask=No
                 f"got {tuple(temb6.shape)} for hidden states {tuple(x.shape)}."
             )
         # AdaLN modulation / norms run in fp32 (sensitive path); cast to the bulk
-        # compute dtype only at the bf16 Linear boundary.
+        # compute dtype only at the bf16 Linear boundary. `bulk_dtype` is supplied
+        # by the parent module: under VRAM management the attention weights are
+        # held in the offload dtype (or on `meta` for disk offload), so their
+        # `.dtype` is not the dtype the Linear will actually compute in.
+        if bulk_dtype is None:
+            bulk_dtype = resolve_bulk_dtype(self.attn.to_q)
         mod = temb6.view(x.shape[0], x.shape[1], -1) + self.scale_shift_table.unsqueeze(0)
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=-1)
         gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
         scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
 
-        bulk_dtype = self.attn.to_q.weight.dtype
         attn_in = (self.norm1(x) * scale_msa + shift_msa).to(bulk_dtype)
         attn_out = self.attn(attn_in, rotary_emb, attention_mask)
         x = x + (gate_msa * self.norm_post_attn(attn_out)).to(x.dtype)
@@ -651,9 +676,12 @@ def forward(
                 attention_mask = key_mask[:, None, None, :]      # (B,1,1,S) -> SDPA broadcast
                 moe_padding_mask = key_mask.reshape(-1).float()  # (B*S,)
 
-        # Timestep -> per-token modulation.
+        # Timestep -> per-token modulation. The timestep MLP is one of the fp32-pinned
+        # modules, so its input is fp32 -- but VRAM management wraps every `nn.Linear`
+        # at `computation_dtype` regardless of that policy, so resolve the dtype from
+        # the layer itself instead of assuming fp32.
         timestep_proj = self.time_proj(timestep.float())
-        t_emb = self.time_embedder(timestep_proj)  # (B, D)
+        t_emb = self.time_embedder(timestep_proj.to(resolve_bulk_dtype(self.time_embedder.linear_1)))  # (B, D)
         if packed_batch:
             temb_input = torch.cat(
                 [t_emb[i:i + 1].unsqueeze(1).expand(1, sample_seq_lens[i], -1) for i in range(B)], dim=1
@@ -661,8 +689,10 @@ def forward(
         else:
             temb_input = t_emb.unsqueeze(1).expand(B, joint_seq_len, -1)  # (B, S, D)
         b_eff, s_eff = temb_input.shape[0], temb_input.shape[1]
-        temb6 = self.time_modulation(temb_input.reshape(b_eff * s_eff, -1))  # (B*S, 6D)
+        mod_dtype = resolve_bulk_dtype(self.time_modulation[1])
+        temb6 = self.time_modulation(temb_input.reshape(b_eff * s_eff, -1).to(mod_dtype))  # (B*S, 6D)
 
+        bulk_dtype = resolve_bulk_dtype(self.patch_embedder)
         for block in self.blocks:
             joint = gradient_checkpoint_forward(
                 block,
@@ -670,12 +700,16 @@ def forward(
                 use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
                 x=joint, temb6=temb6, rotary_emb=rotary,
                 attention_mask=attention_mask, moe_padding_mask=moe_padding_mask,
+                bulk_dtype=bulk_dtype,
             )
 
-        final_mod = self.norm_out_modulation(temb_input.reshape(joint.shape[0] * joint.shape[1], -1))
+        out_mod_dtype = resolve_bulk_dtype(self.norm_out_modulation[1])
+        final_mod = self.norm_out_modulation(
+            temb_input.reshape(joint.shape[0] * joint.shape[1], -1).to(out_mod_dtype)
+        )
         shift, scale = final_mod.reshape(joint.shape[0], joint.shape[1], -1).chunk(2, dim=-1)
         final_hidden = self.norm_out(joint) * (1.0 + scale) + shift
-        projected = self.proj_out(final_hidden.to(self.proj_out.weight.dtype))
+        projected = self.proj_out(final_hidden.to(resolve_bulk_dtype(self.proj_out)))
 
         if packed_batch:
             split_lengths = []
diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
index de0261fbf..f1969bdce 100644
--- a/docs/en/Model_Details/LingBot-Video.md
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -1,10 +1,10 @@
 # LingBot-Video
 
-LingBot-Video is a flow-matching text-to-video generation model. This document covers DiffSynth-Studio's inference and LoRA SFT training support for the **Dense-1.3B** text-to-video checkpoint.
+LingBot-Video is a flow-matching text-to-video generation model. This document covers DiffSynth-Studio's inference support for the **Dense-1.3B** and **MoE-30B-A3B** text-to-video checkpoints, and LoRA SFT training support for Dense-1.3B.
 
 The integration is built on the standard DiffSynth pipeline stack:
 
-- **DiT** — `LingBotVideoDiT` (`diffsynth/models/lingbot_video_dit.py`), the video denoiser. The Dense-1.3B build uses a plain FFN; the architecture also supports an MoE FFN.
+- **DiT** — `LingBotVideoDiT` (`diffsynth/models/lingbot_video_dit.py`), the video denoiser. A single class covers both variants: Dense-1.3B uses a plain FFN (`num_experts=0`), MoE-30B-A3B a sparse MoE FFN.
 - **Text encoder** — `LingBotVideoTextEncoder` (Qwen3-VL). Prompts are wrapped in a prompt-enhancement chat template, encoded, and the template-prefix tokens are cropped.
 - **VAE** — reuses DiffSynth's `QwenImageVAE` (byte-identical to the LingBot-Video VAE), 8× spatial / 4× temporal compression.
 - **Scheduler** — `LingBotVideoUniPCScheduler`: UniPC multistep for inference; it falls back to the full-resolution flow-matching schedule for training.
@@ -48,13 +48,39 @@ video = pipe(
 save_video(video, "video.mp4", fps=15, quality=10)
 ```
 
-**Low VRAM:** pass `vram_limit=<GB>` to `from_pretrained` to enable layer-by-layer offloading — see the low-VRAM example in the table below.
+**Low VRAM:** set `offload_dtype` / `offload_device` on each `ModelConfig` to enable layer-by-layer offloading, optionally with `vram_limit=<GB>` — see the low-VRAM example in the table below.
+
+## MoE-30B-A3B
+
+[Robbyant/lingbot-video-moe-30b-a3b](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b) is the larger variant: 30B total parameters with ~3B active per token. Each MoE layer holds 128 routed experts plus 1 shared expert, and routes each token to 8 experts using group-limited top-k (4 groups, top-2 groups).
+
+It loads through the same pipeline; only the model ID and the shard glob change (the checkpoint is split across 13 shards):
+
+```python
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
+)
+```
+
+Notes specific to the MoE variant:
+
+- The expert matmuls use `torch._grouped_mm` on CUDA, falling back to an equivalent per-expert loop elsewhere.
+- The experts store their weights as grouped `nn.Parameter` tensors rather than `nn.Linear`, so VRAM management wraps the expert container itself. Since the experts are the bulk of the model, the low-VRAM path is what keeps resident VRAM near the ~3B active footprint instead of the full 30B.
+- The released package also ships a second-stage `refiner/` DiT (same architecture, used to upscale a base result). It is not wired into the pipeline yet.
 
 ## Model Overview
 
 | Model ID | Inference | Low VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
 |-|-|-|-|-|-|-|
 |[Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
+|[Robbyant/lingbot-video-moe-30b-a3b](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py)|-|-|-|-|
 
 ## Model Inference
 
@@ -69,7 +95,7 @@ Input parameters for `LingBotVideoPipeline` inference include:
 * `height`: Video height, default 480. Must be a multiple of 16.
 * `width`: Video width, default 480. Must be a multiple of 16.
 * `num_frames`: Number of video frames, default 81. Must satisfy `4k+1` (the VAE compresses time by 4×).
-* `cfg_scale`: Classifier-free guidance scale, default 6.0. A value of 3.0 is recommended for the Dense-1.3B model.
+* `cfg_scale`: Classifier-free guidance scale, default 6.0. A value of 3.0 is recommended for LingBot-Video.
 * `num_inference_steps`: Number of inference steps, default 40.
 * `sigma_shift`: Flow-matching timestep shift, default 3.0.
 * `seed`: Random seed. Default is `None`, meaning completely random.
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
index 9f1a5f2bd..55d954e55 100644
--- a/docs/zh/Model_Details/LingBot-Video.md
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -1,10 +1,10 @@
 # LingBot-Video
 
-LingBot-Video 是一个基于 flow-matching 的文生视频生成模型。本文档介绍 DiffSynth-Studio 对 **Dense-1.3B** 文生视频权重的推理与 LoRA SFT 训练支持。
+LingBot-Video 是一个基于 flow-matching 的文生视频生成模型。本文档介绍 DiffSynth-Studio 对 **Dense-1.3B** 与 **MoE-30B-A3B** 文生视频权重的推理支持，以及对 Dense-1.3B 的 LoRA SFT 训练支持。
 
 该接入基于标准的 DiffSynth Pipeline 组件栈构建：
 
-- **DiT** — `LingBotVideoDiT`（`diffsynth/models/lingbot_video_dit.py`），视频去噪器。Dense-1.3B 版本使用普通 FFN；该架构同时支持 MoE FFN。
+- **DiT** — `LingBotVideoDiT`（`diffsynth/models/lingbot_video_dit.py`），视频去噪器。同一个类同时覆盖两个版本：Dense-1.3B 使用普通 FFN（`num_experts=0`），MoE-30B-A3B 使用稀疏 MoE FFN。
 - **文本编码器** — `LingBotVideoTextEncoder`（Qwen3-VL）。提示词会被包裹进提示增强的对话模板中编码，随后裁剪掉模板前缀 token。
 - **VAE** — 复用 DiffSynth 的 `QwenImageVAE`（与 LingBot-Video 的 VAE 逐字节一致），空间 8× / 时间 4× 压缩。
 - **调度器** — `LingBotVideoUniPCScheduler`：推理时使用 UniPC 多步调度；训练时回退到完整分辨率的 flow-matching 调度。
@@ -48,13 +48,39 @@ video = pipe(
 save_video(video, "video.mp4", fps=15, quality=10)
 ```
 
-**低显存：** 向 `from_pretrained` 传入 `vram_limit=<GB>` 即可开启逐层 offload，详见下表中的低显存示例。
+**低显存：** 在每个 `ModelConfig` 上设置 `offload_dtype` / `offload_device` 即可开启逐层 offload，可再配合 `vram_limit=<GB>`，详见下表中的低显存示例。
+
+## MoE-30B-A3B
+
+[Robbyant/lingbot-video-moe-30b-a3b](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b) 是更大的版本：总参数量 30B，每个 token 激活约 3B。每个 MoE 层包含 128 个路由专家和 1 个共享专家，并使用 group-limited top-k（4 组，取 top-2 组）将每个 token 路由到 8 个专家。
+
+它复用同一条 pipeline，只需更换模型 ID 与分片通配符（权重被切分为 13 个 shard）：
+
+```python
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
+)
+```
+
+MoE 版本的注意事项：
+
+- 专家矩阵乘在 CUDA 上使用 `torch._grouped_mm`，在其他设备上回退到等价的逐专家循环。
+- 专家权重以分组的 `nn.Parameter` 而非 `nn.Linear` 存储，因此显存管理包装的是专家容器本身。由于专家占据了模型的绝大部分参数，低显存路径能让常驻显存接近约 3B 的激活规模，而不是完整的 30B。
+- 官方权重包中还提供了第二阶段的 `refiner/` DiT（架构相同，用于在 base 结果之上做超分精修），目前尚未接入 pipeline。
 
 ## 模型总览
 
 |模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
 |-|-|-|-|-|-|-|
 |[Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
+|[Robbyant/lingbot-video-moe-30b-a3b](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py)|-|-|-|-|
 
 ## 模型推理
 
@@ -69,7 +95,7 @@ save_video(video, "video.mp4", fps=15, quality=10)
 * `height`: 视频高度，默认为 480，需能被 16 整除。
 * `width`: 视频宽度，默认为 480，需能被 16 整除。
 * `num_frames`: 视频帧数，默认为 81，需满足 `4k+1`（VAE 在时间维上做 4× 压缩）。
-* `cfg_scale`: 分类器自由引导系数，默认为 6.0。Dense-1.3B 模型推荐使用 3.0。
+* `cfg_scale`: 分类器自由引导系数，默认为 6.0。LingBot-Video 推荐使用 3.0。
 * `num_inference_steps`: 推理步数，默认为 40。
 * `sigma_shift`: flow-matching 时间步偏移，默认为 3.0。
 * `seed`: 随机种子，默认为 `None`，即完全随机。
diff --git a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py
new file mode 100644
index 000000000..78861647f
--- /dev/null
+++ b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py
@@ -0,0 +1,38 @@
+import torch
+from diffsynth.utils.data import save_video, VideoData
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+
+
+# The MoE checkpoint is sharded, so the file pattern has to match all shards.
+# 30B total parameters with 3B active per token (128 experts, top-8 routing).
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
+)
+
+# Text-to-video. The default (T2V) negative prompt is built into the pipeline,
+# so `negative_prompt` can be left unset.
+video = pipe(
+    prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight, ears perked up, chasing after a red ball. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b.mp4", fps=15, quality=10)
+
+# Video-to-video. `denoising_strength < 1` keeps part of the input structure.
+video = VideoData("video_lingbot-video-moe-30b-a3b.mp4", height=480, width=832)
+video = pipe(
+    prompt="A playful puppy wearing black sunglasses runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    input_video=video, denoising_strength=0.7,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=1,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_v2v.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
index b6e3bf1db..1b55b8db7 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
@@ -3,16 +3,27 @@
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 
 
-# Low-VRAM inference. `vram_limit` turns on DiffSynth's automatic VRAM management,
-# which keeps model weights on CPU and streams them to the GPU layer-by-layer during
-# each forward. The value below reserves ~2 GB of headroom on top of the models.
+# Low-VRAM inference. Setting `offload_dtype` / `offload_device` is what actually turns
+# on DiffSynth's VRAM management: weights are kept on CPU in fp8 and streamed to the GPU
+# layer-by-layer, then computed in bf16. `vram_limit` on its own has no effect.
+vram_config = {
+    "offload_dtype": torch.float8_e4m3fn,
+    "offload_device": "cpu",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
 pipe = LingBotVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
     device="cuda",
     model_configs=[
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
     ],
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
     vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py
new file mode 100644
index 000000000..31903d1eb
--- /dev/null
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py
@@ -0,0 +1,42 @@
+import torch
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+
+
+# Low-VRAM inference. Setting `offload_dtype` / `offload_device` is what actually turns
+# on DiffSynth's VRAM management: weights are kept on CPU in fp8 and streamed to the GPU
+# layer-by-layer, then computed in bf16. `vram_limit` on its own has no effect.
+#
+# This matters most for the MoE variant: the 128 experts hold the bulk of the 30B
+# parameters while only 3B are active per token, so offloading them keeps resident VRAM
+# far below the full model size.
+vram_config = {
+    "offload_dtype": torch.float8_e4m3fn,
+    "offload_device": "cpu",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
+)
+
+video = pipe(
+    prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight, ears perked up, chasing after a red ball. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_low_vram.mp4", fps=15, quality=10)

From df7ea8e4c09099628bd396259d2d747a97a1f2c8 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Mon, 27 Jul 2026 15:31:26 +0800
Subject: [PATCH 04/34] Address PR #1539 review: unify scheduler, restore VAE,
 split rewriter

- Scheduler: use FlowMatchScheduler (Wan template) instead of a bespoke
  UniPC scheduler; flow_match.py restored byte-identical to main.
- VAE: restore QwenImageVAE to upstream; the 5D-video encode/decode and
  latent normalisation now live in LingBotVideoPipeline (encode_video /
  decode_video).
- Rewriter: keep only normalize_caption in diffsynth core; move the
  two-stage prompt rewriter + system prompts into the inference examples.
- Examples: merge into one lingbot-video-dense-1.3b.py (T2V / V2V /
  optional rewrite); drop the separate _rewrite.py.
- Training .sh: use --model_id_with_origin_paths instead of local --model_paths.
- Docs: sync en/zh Model_Details + example README.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 diffsynth/diffusion/flow_match.py             | 291 ------------------
 diffsynth/models/qwen_image_vae.py            |  43 +--
 diffsynth/pipelines/lingbot_video.py          |  73 ++++-
 .../lingbot_video_prompt_rewriter.py          | 246 +--------------
 docs/en/Model_Details/LingBot-Video.md        |  38 ++-
 docs/zh/Model_Details/LingBot-Video.md        |  38 ++-
 examples/lingbot_video/README.md              |  14 +-
 .../lingbot-video-dense-1.3b.py               |  49 ++-
 .../lingbot-video-dense-1.3b_rewrite.py       |  60 ----
 .../model_inference/prompt_rewriter.py        | 226 ++++++++++++++
 .../prompts/t2v_example_1.json                |  77 +++++
 .../prompts/t2v_example_2.json                | 101 ++++++
 .../prompts/t2v_example_3.json                | 155 ++++++++++
 .../model_inference/system_prompts.py         |   0
 .../lingbot-video-dense-1.3b.py               |  34 +-
 .../lora/lingbot-video-dense-1.3b.sh          |  15 +-
 .../model_training/rewrite_captions.py        |   7 +-
 .../lingbot_video/model_training/train.py     |   7 +-
 18 files changed, 783 insertions(+), 691 deletions(-)
 delete mode 100644 examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_rewrite.py
 create mode 100644 examples/lingbot_video/model_inference/prompt_rewriter.py
 create mode 100644 examples/lingbot_video/model_inference/prompts/t2v_example_1.json
 create mode 100644 examples/lingbot_video/model_inference/prompts/t2v_example_2.json
 create mode 100644 examples/lingbot_video/model_inference/prompts/t2v_example_3.json
 rename diffsynth/pipelines/lingbot_video_system_prompts.py => examples/lingbot_video/model_inference/system_prompts.py (100%)

diff --git a/diffsynth/diffusion/flow_match.py b/diffsynth/diffusion/flow_match.py
index d20f6f966..b9104e869 100644
--- a/diffsynth/diffusion/flow_match.py
+++ b/diffsynth/diffusion/flow_match.py
@@ -400,294 +400,3 @@ def step(self, model_output, timestep, sample):
         noise = self.clip_noise(torch.randn(denoised.shape, device=denoised.device, dtype=denoised.dtype))
         sample = sigma_ * noise * self.noise_scale_schedule[timestep_id] + (1.0 - sigma_) * denoised
         return sample
-
-
-class LingBotVideoUniPCScheduler(FlowMatchScheduler):
-    """
-    UniPC multistep predictor-corrector scheduler for flow-matching, ported from
-    LingBot-Video's ``FlowUniPCMultistepScheduler`` (a vendored diffusers UniPC
-    variant, ``prediction_type="flow_prediction"``).
-
-    This scheduler is used for **inference** sampling only. Its ``step`` implements
-    the stateful multistep UniP predictor + UniC corrector. Training reuses the
-    flow-matching interpolation (``add_noise``) and velocity target
-    (``training_target`` / ``training_weight``) inherited from ``FlowMatchScheduler``
-    — UniPC is a sampler, not a training objective.
-
-    Base-sigma grid matches the original exactly: ``sigma = 1 - linspace(1, 1/N, N)[::-1]``
-    (i.e. ``sigma in [0, 1-1/N]``), which is offset by one position from the native
-    diffusers UniPC grid and is required for numerical parity with LingBot-Video.
-    """
-    order = 1
-
-    def __init__(
-        self,
-        shift=1.0,
-        num_train_timesteps=1000,
-        solver_order=2,
-        predict_x0=True,
-        solver_type="bh2",
-        lower_order_final=True,
-        disable_corrector=(),
-        final_sigmas_type="zero",
-    ):
-        if solver_type not in ("bh1", "bh2"):
-            raise NotImplementedError(f"solver_type={solver_type} is not implemented")
-        if final_sigmas_type not in ("zero", "sigma_min"):
-            raise ValueError("final_sigmas_type must be one of 'zero' or 'sigma_min'")
-        self.num_train_timesteps = num_train_timesteps
-        self.shift = shift
-        self.solver_order = solver_order
-        self.predict_x0 = predict_x0
-        self.solver_type = solver_type
-        self.lower_order_final = lower_order_final
-        self.disable_corrector = list(disable_corrector)
-        self.final_sigmas_type = final_sigmas_type
-        self.prediction_type = "flow_prediction"
-        self.num_inference_steps = None
-        self.training = False
-        # base sigma grid (drives sigma_min / sigma_max and the training schedule)
-        self.sigmas, self.timesteps = self._flow_sigmas(num_train_timesteps, shift)
-        self.sigma_min = self.sigmas[-1].item()
-        self.sigma_max = self.sigmas[0].item()
-        self._reset_multistep_state()
-
-    def _flow_sigmas(self, n, shift):
-        # sigma = 1 - alpha, alpha = linspace(1, 1/n, n)[::-1]  ->  sigma in [0, 1-1/n]
-        alphas = np.linspace(1, 1 / n, n)[::-1].copy()
-        sigmas = torch.from_numpy(1.0 - alphas).to(dtype=torch.float32)
-        if shift != 1.0:
-            sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
-        timesteps = sigmas * n
-        return sigmas, timesteps
-
-    def _reset_multistep_state(self):
-        self.model_outputs = [None] * self.solver_order
-        self.timestep_list = [None] * self.solver_order
-        self.lower_order_nums = 0
-        self.last_sample = None
-        self.this_order = None
-        self._step_index = None
-        self._begin_index = None
-
-    @property
-    def step_index(self):
-        return self._step_index
-
-    @property
-    def begin_index(self):
-        return self._begin_index
-
-    def set_begin_index(self, begin_index=0):
-        self._begin_index = begin_index
-
-    def set_timesteps(self, num_inference_steps=50, denoising_strength=1.0, shift=None, training=False, **kwargs):
-        if shift is None:
-            shift = self.shift
-        if training:
-            # Flow-matching training schedule (UniPC is inference-only): full-resolution
-            # sigma grid + per-timestep loss weights from the base class.
-            self.sigmas, self.timesteps = self._flow_sigmas(self.num_train_timesteps, shift)
-            self.set_training_weight()
-            self.training = True
-            return
-        self.training = False
-        # Inference grid. denoising_strength<1 starts partway down the chain
-        # (DiffSynth convention), matching the original at strength=1.0.
-        sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min) * denoising_strength
-        sigmas = np.linspace(sigma_start, self.sigma_min, num_inference_steps + 1)[:-1].copy()
-        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
-        sigma_last = 0.0 if self.final_sigmas_type == "zero" else self.sigma_min
-        timesteps = sigmas * self.num_train_timesteps
-        sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
-        self.sigmas = torch.from_numpy(sigmas)
-        # int64 timesteps match the values the original model was conditioned on
-        self.timesteps = torch.from_numpy(timesteps).to(dtype=torch.int64)
-        self.num_inference_steps = len(timesteps)
-        self._reset_multistep_state()
-
-    def _sigma_to_alpha_sigma_t(self, sigma):
-        return 1 - sigma, sigma
-
-    def index_for_timestep(self, timestep, schedule_timesteps=None):
-        if schedule_timesteps is None:
-            schedule_timesteps = self.timesteps
-        indices = (schedule_timesteps == timestep).nonzero()
-        pos = 1 if len(indices) > 1 else 0
-        return indices[pos].item()
-
-    def _init_step_index(self, timestep):
-        if self.begin_index is None:
-            if isinstance(timestep, torch.Tensor):
-                timestep = timestep.to(self.timesteps.device)
-            self._step_index = self.index_for_timestep(timestep)
-        else:
-            self._step_index = self._begin_index
-
-    def convert_model_output(self, model_output, sample):
-        # prediction_type == "flow_prediction"
-        sigma_t = self.sigmas[self.step_index]
-        if self.predict_x0:
-            return sample - sigma_t * model_output
-        return sample - (1 - sigma_t) * model_output
-
-    def multistep_uni_p_bh_update(self, model_output, sample, order):
-        model_output_list = self.model_outputs
-        m0 = model_output_list[-1]
-        x = sample
-
-        sigma_t, sigma_s0 = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]
-        alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
-        alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
-
-        lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
-        lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
-        h = lambda_t - lambda_s0
-        device = sample.device
-
-        rks, D1s = [], []
-        for i in range(1, order):
-            si = self.step_index - i
-            mi = model_output_list[-(i + 1)]
-            alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
-            lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
-            rk = (lambda_si - lambda_s0) / h
-            rks.append(rk)
-            D1s.append((mi - m0) / rk)
-        rks.append(1.0)
-        rks = torch.tensor(rks, device=device)
-
-        R, b = [], []
-        hh = -h if self.predict_x0 else h
-        h_phi_1 = torch.expm1(hh)
-        h_phi_k = h_phi_1 / hh - 1
-        factorial_i = 1
-        B_h = hh if self.solver_type == "bh1" else torch.expm1(hh)
-        for i in range(1, order + 1):
-            R.append(torch.pow(rks, i - 1))
-            b.append(h_phi_k * factorial_i / B_h)
-            factorial_i *= i + 1
-            h_phi_k = h_phi_k / hh - 1 / factorial_i
-        R = torch.stack(R)
-        b = torch.tensor(b, device=device)
-
-        if len(D1s) > 0:
-            D1s = torch.stack(D1s, dim=1)
-            if order == 2:
-                rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device)
-            else:
-                rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]).to(device).to(x.dtype)
-        else:
-            D1s = None
-
-        if self.predict_x0:
-            x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
-            pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s) if D1s is not None else 0
-            x_t = x_t_ - alpha_t * B_h * pred_res
-        else:
-            x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
-            pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s) if D1s is not None else 0
-            x_t = x_t_ - sigma_t * B_h * pred_res
-        return x_t.to(x.dtype)
-
-    def multistep_uni_c_bh_update(self, this_model_output, last_sample, this_sample, order):
-        model_output_list = self.model_outputs
-        m0 = model_output_list[-1]
-        x = last_sample
-        model_t = this_model_output
-
-        sigma_t, sigma_s0 = self.sigmas[self.step_index], self.sigmas[self.step_index - 1]
-        alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
-        alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
-
-        lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
-        lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
-        h = lambda_t - lambda_s0
-        device = this_sample.device
-
-        rks, D1s = [], []
-        for i in range(1, order):
-            si = self.step_index - (i + 1)
-            mi = model_output_list[-(i + 1)]
-            alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
-            lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
-            rk = (lambda_si - lambda_s0) / h
-            rks.append(rk)
-            D1s.append((mi - m0) / rk)
-        rks.append(1.0)
-        rks = torch.tensor(rks, device=device)
-
-        R, b = [], []
-        hh = -h if self.predict_x0 else h
-        h_phi_1 = torch.expm1(hh)
-        h_phi_k = h_phi_1 / hh - 1
-        factorial_i = 1
-        B_h = hh if self.solver_type == "bh1" else torch.expm1(hh)
-        for i in range(1, order + 1):
-            R.append(torch.pow(rks, i - 1))
-            b.append(h_phi_k * factorial_i / B_h)
-            factorial_i *= i + 1
-            h_phi_k = h_phi_k / hh - 1 / factorial_i
-        R = torch.stack(R)
-        b = torch.tensor(b, device=device)
-
-        D1s = torch.stack(D1s, dim=1) if len(D1s) > 0 else None
-        if order == 1:
-            rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device)
-        else:
-            rhos_c = torch.linalg.solve(R, b).to(device).to(x.dtype)
-
-        if self.predict_x0:
-            x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
-            corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s) if D1s is not None else 0
-            D1_t = model_t - m0
-            x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t)
-        else:
-            x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
-            corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s) if D1s is not None else 0
-            D1_t = model_t - m0
-            x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t)
-        return x_t.to(x.dtype)
-
-    def step(self, model_output, timestep, sample, **kwargs):
-        if self.num_inference_steps is None:
-            raise ValueError("Run set_timesteps() before step().")
-        if self.step_index is None:
-            self._init_step_index(timestep)
-
-        use_corrector = (
-            self.step_index > 0
-            and self.step_index - 1 not in self.disable_corrector
-            and self.last_sample is not None
-        )
-
-        model_output_convert = self.convert_model_output(model_output, sample)
-        if use_corrector:
-            sample = self.multistep_uni_c_bh_update(
-                this_model_output=model_output_convert,
-                last_sample=self.last_sample,
-                this_sample=sample,
-                order=self.this_order,
-            )
-
-        for i in range(self.solver_order - 1):
-            self.model_outputs[i] = self.model_outputs[i + 1]
-            self.timestep_list[i] = self.timestep_list[i + 1]
-        self.model_outputs[-1] = model_output_convert
-        self.timestep_list[-1] = timestep
-
-        if self.lower_order_final:
-            this_order = min(self.solver_order, len(self.timesteps) - self.step_index)
-        else:
-            this_order = self.solver_order
-        self.this_order = min(this_order, self.lower_order_nums + 1)
-        assert self.this_order > 0
-
-        self.last_sample = sample
-        prev_sample = self.multistep_uni_p_bh_update(
-            model_output=model_output_convert, sample=sample, order=self.this_order,
-        )
-        if self.lower_order_nums < self.solver_order:
-            self.lower_order_nums += 1
-        self._step_index += 1
-        return prev_sample
diff --git a/diffsynth/models/qwen_image_vae.py b/diffsynth/models/qwen_image_vae.py
index be5e48017..2845354f2 100644
--- a/diffsynth/models/qwen_image_vae.py
+++ b/diffsynth/models/qwen_image_vae.py
@@ -707,8 +707,6 @@ def __init__(
         self.std = 1 / torch.tensor(std).view(1, 16, 1, 1, 1)
 
     def encode(self, x, **kwargs):
-        if x.ndim == 5:
-            return self.encode_video(x)
         x = x.unsqueeze(2)
         x = self.encoder(x)
         x = self.quant_conv(x)
@@ -717,10 +715,8 @@ def encode(self, x, **kwargs):
         x = (x - mean) * std
         x = x.squeeze(2)
         return x
-
+    
     def decode(self, x, **kwargs):
-        if x.ndim == 5:
-            return self.decode_video(x)
         x = x.unsqueeze(2)
         mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
         x = x / std + mean
@@ -728,40 +724,3 @@ def decode(self, x, **kwargs):
         x = self.decoder(x)
         x = x.squeeze(2)
         return x
-
-    def count_conv3d(self, model):
-        return sum(1 for m in model.modules() if isinstance(m, QwenImageCausalConv3d))
-
-    def encode_video(self, x, **kwargs):
-        # x: (B, C, T, H, W). Temporal chunking with a persistent causal feature
-        # cache — mathematically equivalent to encoding the whole clip at once, but
-        # bounded in memory. Chunk layout (1 + 4k frames) matches the WanVAE-derived
-        # temporal downsampling (temperal_downsample=[False, True, True]).
-        t = x.shape[2]
-        iter_ = 1 + (t - 1) // 4
-        feat_cache = [None] * self.count_conv3d(self.encoder)
-        out = None
-        for i in range(iter_):
-            feat_idx = [0]
-            chunk = x[:, :, :1, :, :] if i == 0 else x[:, :, 1 + 4 * (i - 1): 1 + 4 * i, :, :]
-            out_ = self.encoder(chunk, feat_cache=feat_cache, feat_idx=feat_idx)
-            out = out_ if out is None else torch.cat([out, out_], dim=2)
-        x = self.quant_conv(out)
-        x = x[:, :16]
-        mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
-        x = (x - mean) * std
-        return x
-
-    def decode_video(self, x, **kwargs):
-        # x: (B, 16, T', H, W) in DiT latent space. Denormalize, then decode one
-        # latent frame at a time through the persistent causal feature cache.
-        mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
-        x = x / std + mean
-        x = self.post_quant_conv(x)
-        feat_cache = [None] * self.count_conv3d(self.decoder)
-        out = None
-        for i in range(x.shape[2]):
-            feat_idx = [0]
-            out_ = self.decoder(x[:, :, i:i + 1, :, :], feat_cache=feat_cache, feat_idx=feat_idx)
-            out = out_ if out is None else torch.cat([out, out_], dim=2)
-        return out
diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index 9b1f4c0c5..f16082d39 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -8,11 +8,11 @@
 from ..core.device.npu_compatible_device import get_device_type
 from ..core import ModelConfig
 from ..diffusion.base_pipeline import BasePipeline, PipelineUnit
-from ..diffusion.flow_match import LingBotVideoUniPCScheduler
+from ..diffusion.flow_match import FlowMatchScheduler
 
 from ..models.lingbot_video_dit import LingBotVideoDiT
 from ..models.lingbot_video_text_encoder import LingBotVideoTextEncoder
-from ..models.qwen_image_vae import QwenImageVAE
+from ..models.qwen_image_vae import QwenImageVAE, QwenImageCausalConv3d
 from .lingbot_video_prompt_rewriter import normalize_caption
 
 
@@ -62,12 +62,13 @@ class LingBotVideoPipeline(BasePipeline):
       (Qwen3-VL). The prompt is wrapped in :data:`PROMPT_TEMPLATE`, encoded, and the
       template-prefix tokens are cropped (``crop_start``).
     - ``vae``: :class:`~diffsynth.models.qwen_image_vae.QwenImageVAE` (byte-identical
-      to the LingBot-Video VAE). Latent normalisation is baked into the VAE's
-      ``encode``/``decode`` 5D-video code path, so the pipeline never re-applies
-      ``latents_mean`` / ``latents_std``.
+      to the LingBot-Video VAE). The 5D-video encode/decode and its latent
+      normalisation live in this pipeline (:meth:`encode_video` / :meth:`decode_video`),
+      so the VAE itself stays identical to its image use elsewhere.
 
-    Sampling uses :class:`~diffsynth.diffusion.flow_match.LingBotVideoUniPCScheduler`
-    (UniPC multistep). Classifier-free guidance runs as two independent forwards.
+    Sampling uses :class:`~diffsynth.diffusion.flow_match.FlowMatchScheduler` (Wan
+    template; first-order flow-matching Euler). Classifier-free guidance runs as two
+    independent forwards.
     """
 
     def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
@@ -76,7 +77,7 @@ def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
             height_division_factor=16, width_division_factor=16,
             time_division_factor=4, time_division_remainder=1,
         )
-        self.scheduler = LingBotVideoUniPCScheduler()
+        self.scheduler = FlowMatchScheduler(template="Wan")
         self.text_encoder: LingBotVideoTextEncoder = None
         self.dit: LingBotVideoDiT = None
         self.vae: QwenImageVAE = None
@@ -204,19 +205,59 @@ def __call__(
             else:
                 noise_pred = noise_pred_posi
 
-            # Scheduler step (UniPC multistep). Uses the raw scheduler timestep to
-            # locate its internal step index, so pass it unmodified.
+            # Scheduler step (first-order flow-matching Euler). Uses the raw scheduler
+            # timestep to locate its internal step index, so pass it unmodified.
             inputs_shared["latents"] = self.scheduler.step(noise_pred, timestep, inputs_shared["latents"])
 
-        # Decode. The VAE's 5D-video path already un-normalises the latents, so no
+        # Decode. decode_video un-normalises the latents on the 5D-video path, so no
         # manual latents_mean / latents_std handling is needed here.
         self.load_models_to_device(['vae'])
         latents = inputs_shared["latents"].to(dtype=self.torch_dtype, device=self.device)
-        video = self.vae.decode(latents)
+        video = self.decode_video(latents)
         video = self.vae_output_to_video(video)
         self.load_models_to_device([])
         return video
 
+    def _count_conv3d(self, model):
+        return sum(1 for m in model.modules() if isinstance(m, QwenImageCausalConv3d))
+
+    def encode_video(self, x):
+        # x: (B, C, T, H, W). Temporal chunking with a persistent causal feature cache
+        # — mathematically equivalent to encoding the whole clip at once, but bounded
+        # in memory. Chunk layout (1 + 4k frames) matches the WanVAE-derived temporal
+        # downsampling (temperal_downsample=[False, True, True]). Kept here in the
+        # pipeline (not on the VAE) so QwenImageVAE stays identical to its image use.
+        vae = self.vae
+        t = x.shape[2]
+        iter_ = 1 + (t - 1) // 4
+        feat_cache = [None] * self._count_conv3d(vae.encoder)
+        out = None
+        for i in range(iter_):
+            feat_idx = [0]
+            chunk = x[:, :, :1, :, :] if i == 0 else x[:, :, 1 + 4 * (i - 1): 1 + 4 * i, :, :]
+            out_ = vae.encoder(chunk, feat_cache=feat_cache, feat_idx=feat_idx)
+            out = out_ if out is None else torch.cat([out, out_], dim=2)
+        x = vae.quant_conv(out)
+        x = x[:, :16]
+        mean, std = vae.mean.to(dtype=x.dtype, device=x.device), vae.std.to(dtype=x.dtype, device=x.device)
+        x = (x - mean) * std
+        return x
+
+    def decode_video(self, x):
+        # x: (B, 16, T', H, W) in DiT latent space. Denormalize, then decode one latent
+        # frame at a time through the persistent causal feature cache.
+        vae = self.vae
+        mean, std = vae.mean.to(dtype=x.dtype, device=x.device), vae.std.to(dtype=x.dtype, device=x.device)
+        x = x / std + mean
+        x = vae.post_quant_conv(x)
+        feat_cache = [None] * self._count_conv3d(vae.decoder)
+        out = None
+        for i in range(x.shape[2]):
+            feat_idx = [0]
+            out_ = vae.decoder(x[:, :, i:i + 1, :, :], feat_cache=feat_cache, feat_idx=feat_idx)
+            out = out_ if out is None else torch.cat([out, out_], dim=2)
+        return out
+
 
 class LingBotVideoUnit_ShapeChecker(PipelineUnit):
     def __init__(self):
@@ -243,7 +284,7 @@ def process(self, pipe: LingBotVideoPipeline, height, width, num_frames, seed, r
             1, pipe.dit.in_channels, length,
             height // VAE_SCALE_FACTOR_SPATIAL, width // VAE_SCALE_FACTOR_SPATIAL,
         )
-        # fp32 noise: the UniPC sampler accumulates state in fp32 for stability
+        # fp32 noise: the flow-matching sampler accumulates state in fp32 for stability
         # (matches the original pipeline's fp32 latents).
         noise = pipe.generate_noise(shape, seed=seed, rand_device=rand_device, torch_dtype=torch.float32)
         return {"noise": noise}
@@ -313,8 +354,8 @@ def process(self, pipe: LingBotVideoPipeline, input_video, noise):
         pipe.load_models_to_device(self.onload_model_names)
         # preprocess_video -> (B, C, T, H, W) in [-1, 1], in pipe.torch_dtype.
         video = pipe.preprocess_video(input_video)
-        # QwenImageVAE.encode (5D path) already applies latent normalisation.
-        input_latents = pipe.vae.encode(video).to(dtype=torch.float32, device=pipe.device)
+        # encode_video applies latent normalisation on the 5D-video path.
+        input_latents = pipe.encode_video(video).to(dtype=torch.float32, device=pipe.device)
         if pipe.scheduler.training:
             return {"latents": noise, "input_latents": input_latents}
         else:
@@ -345,5 +386,5 @@ def model_fn_lingbot_video(
         use_gradient_checkpointing=use_gradient_checkpointing,
         use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
     )
-    # Return fp32 so the UniPC sampler / MSE loss run in full precision.
+    # Return fp32 so the flow-matching sampler / MSE loss run in full precision.
     return noise_pred.float()
diff --git a/diffsynth/pipelines/lingbot_video_prompt_rewriter.py b/diffsynth/pipelines/lingbot_video_prompt_rewriter.py
index 6c3cdbe0c..a6d55bff4 100644
--- a/diffsynth/pipelines/lingbot_video_prompt_rewriter.py
+++ b/diffsynth/pipelines/lingbot_video_prompt_rewriter.py
@@ -1,62 +1,24 @@
-"""Prompt handling for LingBot-Video.
+"""Caption normalization for LingBot-Video.
 
 The LingBot-Video DiT is trained on **structured JSON captions**, not free-form
 prose. Feeding a flat sentence is out-of-distribution and noticeably degrades
-quality; feeding the structured caption the model expects restores it. This module
-provides the two pieces needed to keep prompts in-distribution:
-
-1. :func:`normalize_caption` — the lightweight, dependency-free path used by the
-   pipeline and the training module. It turns a caption expressed as a ``dict`` /
-   ``list`` / path to a ``prompt.json`` into the exact compact-JSON string the DiT
-   consumes (a plain string is passed through untouched). This mirrors the original
-   ``lingbot_video.utils.caption_from_sample`` byte-for-byte.
-
-2. :class:`Rewriter` — a faithful port of the original two-stage prompt rewriter
-   (``rewriter/rewriter_core.py`` + ``rewriter/inference.py``). Stage 1 *expands* a
-   brief idea into a natural-language caption; stage 2 *maps* that caption into the
-   structured JSON. Stage 1 runs the base model, stage 2 runs the base model with a
-   LoRA adapter active. The bundled :class:`TransformersBackend` loads the rewriter
-   VLM locally; :func:`make_backend` also accepts a custom backend object (anything
-   exposing ``generate(text, image, use_lora) -> str``) so the rewriter can be driven
-   by a hosted / OpenAI-compatible endpoint without shipping the weights.
-
-Typical use::
-
-    from diffsynth.pipelines.lingbot_video_prompt_rewriter import rewrite_prompt
-    caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
-    video = pipe(prompt=caption, ...)              # caption is the structured JSON string
+quality; feeding the structured caption the model expects restores it.
+
+This module holds only the lightweight, dependency-free normalization the pipeline
+and the training module need: :func:`normalize_caption` turns a caption expressed as
+a ``dict`` / ``list`` / path to a ``prompt.json`` into the exact compact-JSON string
+the DiT consumes (a plain string is passed through untouched). It mirrors the original
+``lingbot_video.utils.caption_from_sample`` byte-for-byte.
+
+Turning a *brief idea* into that structured caption is a separate, heavier step (a
+two-stage VLM rewriter). That lives with the examples, out of the core, so importing
+this module never drags in the rewriter's optional deps — see
+``examples/lingbot_video/model_inference/prompt_rewriter.py``.
 """
 
-import io
 import json
 import os
-import re
-
-# Optional deps: only needed for the local rewriter backend / image loading, never
-# for normalize_caption (the common path).
-try:
-    import requests
-except ImportError:
-    requests = None
-
-try:
-    from PIL import Image
-except ImportError:
-    Image = None
 
-try:
-    from json_repair import repair_json
-except ImportError:
-    repair_json = None
-
-from .lingbot_video_system_prompts import (
-    VIDEO_STEP1_EXPAND, VIDEO_STEP2_MAP, IMAGE_STEP1_EXPAND, IMAGE_STEP2_MAP,
-)
-
-
-# ---------------------------------------------------------------------------
-# Layer 1: caption -> in-distribution prompt string
-# ---------------------------------------------------------------------------
 
 # Keys that describe how to *render* the clip rather than its content. When a full
 # sample dict is given without an explicit "caption" key, these are stripped before
@@ -114,185 +76,3 @@ def normalize_caption(prompt):
     if isinstance(prompt, (dict, list)):
         return _caption_from_sample(prompt)
     return str(prompt)
-
-
-# ---------------------------------------------------------------------------
-# Layer 2: raw idea -> structured caption (the two-stage rewriter)
-# ---------------------------------------------------------------------------
-
-# mode -> (step1 system prompt, step2 system prompt, feed image?, add duration?)
-MODES = {
-    "t2v":  dict(s1=VIDEO_STEP1_EXPAND, s2=VIDEO_STEP2_MAP, image=False, duration=True),
-    "ti2v": dict(s1=VIDEO_STEP1_EXPAND, s2=VIDEO_STEP2_MAP, image=True,  duration=True),
-    "t2i":  dict(s1=IMAGE_STEP1_EXPAND, s2=IMAGE_STEP2_MAP, image=False, duration=False),
-}
-
-
-def _has_cjk(s: str) -> bool:
-    return any("一" <= c <= "鿿" for c in s)
-
-
-def load_image(src):
-    """Load a first-frame image from a local path / http(s) URL / PIL.Image -> RGB PIL."""
-    if Image is None:
-        raise ImportError("loading a first-frame image requires the Pillow package.")
-    if isinstance(src, Image.Image):
-        return src.convert("RGB")
-    if isinstance(src, str) and re.match(r"^https?://", src):
-        if requests is None:
-            raise ImportError("fetching an image URL requires the requests package.")
-        return Image.open(io.BytesIO(requests.get(src, timeout=30).content)).convert("RGB")
-    return Image.open(src).convert("RGB")
-
-
-def _step1_text(mode, prompt, dur):
-    sys = MODES[mode]["s1"]
-    if mode == "t2i":
-        return sys + "\n\nUser image prompt:\n" + prompt
-    dur_line = f"\n\n视频时长：{dur} 秒" if _has_cjk(prompt) else f"\n\nVideo Duration: {dur} seconds"
-    return sys + "\n\n" + prompt + dur_line
-
-
-def _step2_text(mode, detailed, dur):
-    sys = MODES[mode]["s2"]
-    if mode == "t2i":
-        return sys + "\n\nDETAILED CAPTION:\n" + detailed
-    return (sys + f"\n\nVideo Duration: {dur} seconds\n\nDETAILED CAPTION:\n"
-            + detailed + "\n\nOutput the JSON now.")
-
-
-def parse_json(raw):
-    """Parse the stage-2 output into a dict, or ``None`` if it cannot be parsed.
-
-    VLMs occasionally emit unstable JSON (missing quotes, trailing commas, ``` fences),
-    so we strip any code fence, then try the stdlib parser first and fall back to
-    ``json_repair`` when it is installed (recommended for messy outputs)."""
-    s = (raw or "").strip()
-    m = re.search(r"```(?:json)?\s*(\{.*\})\s*```", s, re.DOTALL)
-    if m:
-        s = m.group(1)
-    try:
-        obj = json.loads(s)
-        if isinstance(obj, dict):
-            return obj
-    except Exception:
-        pass
-    if repair_json is not None:
-        try:
-            obj = repair_json(s, return_objects=True)
-            return obj if isinstance(obj, dict) else None
-        except Exception:
-            return None
-    return None
-
-
-def save_caption(result, duration, path):
-    """Save as ``{"caption": <structured JSON caption>, "duration": <seconds|null>}`` —
-    exactly the ``prompt.json`` the pipeline / runner consume. ``duration`` is integer
-    seconds for T2V/TI2V and ``None`` for T2I (a still image has no duration)."""
-    dur = int(round(duration)) if MODES[result["mode"]]["duration"] else None
-    with open(path, "w", encoding="utf-8") as f:
-        json.dump({"caption": result["json"], "duration": dur}, f, ensure_ascii=False, indent=2)
-    return path
-
-
-class TransformersBackend:
-    """Local rewriter VLM + LoRA adapter (peft). stage1 = base (adapter disabled),
-    stage2 = base + LoRA. Loads the rewriter model into memory; the weights are NOT
-    the DiT — set ``base``/``adapter`` (or ``REWRITER_BASE_MODEL``/``REWRITER_ADAPTER``)
-    to the rewriter VLM and its stage-2 adapter."""
-
-    def __init__(self, base=None, adapter=None, device="auto", max_new_tokens=6144):
-        import contextlib
-        import torch
-        from peft import PeftModel
-        from transformers import AutoModelForImageTextToText, AutoProcessor
-
-        self._contextlib = contextlib
-        self._torch = torch
-
-        base = base or os.environ.get("REWRITER_BASE_MODEL", "")
-        adapter = adapter or os.environ.get("REWRITER_ADAPTER", "")
-        if not base or not adapter:
-            raise ValueError(
-                "Set the rewriter base and adapter paths via base=/adapter= or the "
-                "REWRITER_BASE_MODEL / REWRITER_ADAPTER environment variables."
-            )
-        self.processor = AutoProcessor.from_pretrained(base, trust_remote_code=True)
-        model = AutoModelForImageTextToText.from_pretrained(
-            base, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
-        self.model = PeftModel.from_pretrained(model, adapter).eval()
-        self.max_new_tokens = max_new_tokens
-
-    def generate(self, text, image, use_lora):
-        torch = self._torch
-        content = ([{"type": "image", "image": image}] if image is not None else []) \
-            + [{"type": "text", "text": text}]
-        messages = [{"role": "user", "content": content}]
-        chat = self.processor.apply_chat_template(
-            messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
-        inputs = self.processor(
-            text=[chat], images=([image] if image is not None else None), return_tensors="pt"
-        ).to(self.model.device)
-        # stage1 (expand): disable LoRA; stage2 (map): keep LoRA active.
-        adapter_ctx = self._contextlib.nullcontext() if use_lora else self.model.disable_adapter()
-        with torch.no_grad(), adapter_ctx:
-            out = self.model.generate(**inputs, max_new_tokens=self.max_new_tokens, do_sample=False)
-        gen = out[:, inputs["input_ids"].shape[1]:]
-        return self.processor.batch_decode(gen, skip_special_tokens=True)[0]
-
-
-def make_backend(backend="transformers", base=None, adapter=None):
-    """Build a rewriter backend.
-
-    - ``"transformers"`` — the bundled local :class:`TransformersBackend`.
-    - a custom object / callable — returned as-is if it already exposes
-      ``generate(text, image, use_lora) -> str``. Use this to drive the rewriter from
-      a hosted or OpenAI-compatible endpoint without downloading the VLM locally.
-    """
-    if backend == "transformers":
-        return TransformersBackend(base, adapter)
-    if hasattr(backend, "generate"):
-        return backend
-    raise ValueError(
-        f"unknown backend: {backend!r}; pass 'transformers' or an object exposing "
-        "generate(text, image, use_lora)."
-    )
-
-
-class Rewriter:
-    """Two-stage orchestrator. The backend implements ``generate(text, image, use_lora) -> str``."""
-
-    def __init__(self, backend):
-        self.backend = backend
-
-    def rewrite(self, prompt, mode="t2v", first_frame=None, duration=5):
-        if mode not in MODES:
-            raise ValueError(f"mode must be one of {list(MODES)}")
-        cfg = MODES[mode]
-        dur = int(round(duration))
-        img = None
-        if cfg["image"]:
-            if first_frame is None:
-                raise ValueError(f"{mode} requires first_frame (path / URL / PIL.Image)")
-            img = load_image(first_frame)
-        # stage 1: EXPAND -- base model (no LoRA)
-        detailed = self.backend.generate(_step1_text(mode, prompt, dur), img, use_lora=False).strip()
-        # stage 2: MAP -- base + LoRA
-        raw = self.backend.generate(_step2_text(mode, detailed, dur), img, use_lora=True).strip()
-        return {"mode": mode, "detailed": detailed, "json": parse_json(raw), "json_raw": raw}
-
-
-def rewrite_prompt(prompt, mode="t2v", first_frame=None, duration=5,
-                   backend="transformers", base=None, adapter=None, return_result=False):
-    """Rewrite a brief idea into the structured caption string the pipeline expects.
-
-    Returns the compact-JSON caption string (ready to pass as ``pipe(prompt=...)``).
-    Pass ``return_result=True`` to also get the full stage-1/stage-2 dict.
-    """
-    rw = Rewriter(make_backend(backend, base, adapter))
-    result = rw.rewrite(prompt, mode=mode, first_frame=first_frame, duration=duration)
-    caption = normalize_caption(result["json"]) if result["json"] is not None else result["json_raw"]
-    if return_result:
-        return caption, result
-    return caption
diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
index de0261fbf..83a9c2b61 100644
--- a/docs/en/Model_Details/LingBot-Video.md
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -7,7 +7,7 @@ The integration is built on the standard DiffSynth pipeline stack:
 - **DiT** — `LingBotVideoDiT` (`diffsynth/models/lingbot_video_dit.py`), the video denoiser. The Dense-1.3B build uses a plain FFN; the architecture also supports an MoE FFN.
 - **Text encoder** — `LingBotVideoTextEncoder` (Qwen3-VL). Prompts are wrapped in a prompt-enhancement chat template, encoded, and the template-prefix tokens are cropped.
 - **VAE** — reuses DiffSynth's `QwenImageVAE` (byte-identical to the LingBot-Video VAE), 8× spatial / 4× temporal compression.
-- **Scheduler** — `LingBotVideoUniPCScheduler`: UniPC multistep for inference; it falls back to the full-resolution flow-matching schedule for training.
+- **Scheduler** — DiffSynth's `FlowMatchScheduler` (Wan template): first-order flow-matching Euler for inference; training uses the full-resolution 1000-step flow-matching schedule.
 
 ## Installation
 
@@ -25,6 +25,8 @@ LingBot-Video additionally requires `transformers >= 5.x` (for Qwen3-VL) and `im
 
 Run the following code to quickly load the [Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b) model and perform text-to-video inference. The required files are downloaded automatically the first time the code runs.
 
+> **⚠️ Rewrite your prompt into a structured caption first.** LingBot-Video is trained on **structured-JSON captions**, not free-form prose. The plain sentence in the snippet below runs, but it is out-of-distribution and the result will look noticeably soft / low quality. This is expected model behaviour, not a bug — before doing real inference, turn your idea into the structured caption the model expects. See [Prompt rewriting](#prompt-rewriting-important-for-quality) below; the runnable [`lingbot-video-dense-1.3b.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py) example defaults to a released structured caption and shows the optional rewrite path at the bottom.
+
 ```python
 import torch
 from diffsynth.utils.data import save_video
@@ -41,6 +43,8 @@ pipe = LingBotVideoPipeline.from_pretrained(
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
 )
 video = pipe(
+    # A plain sentence is a minimal smoke test only — it is out-of-distribution.
+    # For real quality, pass a structured caption instead (see "Prompt rewriting").
     prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0, seed=0,
@@ -48,7 +52,7 @@ video = pipe(
 save_video(video, "video.mp4", fps=15, quality=10)
 ```
 
-**Low VRAM:** pass `vram_limit=<GB>` to `from_pretrained` to enable layer-by-layer offloading — see the low-VRAM example in the table below.
+**Low VRAM:** set `offload_dtype` / `offload_device` on each `ModelConfig` to enable layer-by-layer offloading; `vram_limit` alone has no effect (it only caps resident VRAM once offloading is on). See the low-VRAM example in the table below.
 
 ## Model Overview
 
@@ -82,15 +86,37 @@ When running low on VRAM, please refer to [VRAM Management](../Pipeline_Usage/VR
 
 LingBot-Video is trained on **structured-JSON captions**, not free-form prose. Feeding a flat sentence is out-of-distribution and visibly degrades quality; feeding the structured caption the model expects restores it. The pipeline accepts a caption as a `dict`, a path to a `prompt.json`, or a plain string, and normalises it (via `normalize_caption`) to the exact compact-JSON format the DiT was trained on — a plain string is passed through unchanged, so existing scripts keep working.
 
-To turn a **brief idea** into that structured caption, use the bundled two-stage rewriter (`diffsynth/pipelines/lingbot_video_prompt_rewriter.py`): stage 1 *expands* the idea into a natural-language caption, stage 2 *maps* it into structured JSON.
+To turn a **brief idea** into that structured caption, use the two-stage rewriter shipped with the examples (`examples/lingbot_video/model_inference/prompt_rewriter.py`): stage 1 *expands* the idea into a natural-language caption, stage 2 *maps* it into structured JSON.
+
+The rewriter is a **separate VLM + stage-2 LoRA adapter** (not the DiT), so it is **not downloaded automatically** — you must fetch both weights yourself before running the rewrite:
+
+| Role | Model ID | Size |
+|-|-|-|
+| Rewriter base VLM (stage 1 + 2) | [`Qwen/Qwen3.6-27B`](https://modelscope.cn/models/Qwen/Qwen3.6-27B) | ~55 GB |
+| Rewriter stage-2 LoRA adapter | [`Robbyant/lingbot-video-rewriter-lora`](https://modelscope.cn/models/Robbyant/lingbot-video-rewriter-lora) | ~0.5 GB |
+
+```shell
+# 1. Download the rewriter base VLM and its stage-2 LoRA adapter.
+modelscope download --model Qwen/Qwen3.6-27B --local_dir ./models/Qwen/Qwen3.6-27B
+modelscope download --model Robbyant/lingbot-video-rewriter-lora --local_dir ./models/Robbyant/lingbot-video-rewriter-lora
+```
 
 ```python
-from diffsynth.pipelines.lingbot_video_prompt_rewriter import rewrite_prompt
+# 2. Point the rewriter at the downloaded weights, then rewrite and run inference.
+import os
+os.environ["REWRITER_BASE_MODEL"] = "./models/Qwen/Qwen3.6-27B"
+os.environ["REWRITER_ADAPTER"] = "./models/Robbyant/lingbot-video-rewriter-lora"
+
+# The rewriter ships with the inference examples; run from
+# examples/lingbot_video/model_inference (or add it to sys.path) for this import.
+from prompt_rewriter import rewrite_prompt
 caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
 video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0)
 ```
 
-The rewriter is a **separate VLM + stage-2 LoRA adapter** (not the DiT). Point it at the weights via `REWRITER_BASE_MODEL` / `REWRITER_ADAPTER` (or `base=` / `adapter=`), or drive a hosted / OpenAI-compatible endpoint by passing a custom object exposing `generate(text, image, use_lora)` as `backend=`. See `examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_rewrite.py`.
+Instead of the env vars you can pass `base=` / `adapter=` to `rewrite_prompt`, or skip the local VLM entirely and drive a hosted / OpenAI-compatible endpoint by passing a custom object exposing `generate(text, image, use_lora)` as `backend=`. See the optional rewrite section at the bottom of `examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py`.
+
+If you don't have the rewriter model, three released LingBot-Video t2v captions ship with the repo as ready-to-use examples: `examples/lingbot_video/model_inference/prompts/t2v_example_{1,2,3}.json`. Pass one straight to the pipeline (`video = pipe(prompt="path/to/t2v_example_1.json", ...)`), or copy one as a template for writing your own structured caption.
 
 ## Model Training
 
@@ -157,4 +183,4 @@ We have written recommended training scripts, please refer to the table in the "
 ## Notes
 
 - The text encoder shares its checkpoint fingerprint with the existing `krea2_text_encoder` (identical Qwen3-VL architecture), so the model loader instantiates both when loading LingBot-Video. This is redundant load time only — the pipeline fetches the correct encoder by name and the other is released.
-- Latent normalisation is handled inside the VAE's 5D-video code path; the pipeline does not re-apply `latents_mean` / `latents_std`.
+- The 5D-video VAE encode/decode and its latent normalisation live in the pipeline (`LingBotVideoPipeline.encode_video` / `decode_video`), so `QwenImageVAE` stays byte-identical to its image use elsewhere; the pipeline does not separately re-apply `latents_mean` / `latents_std`.
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
index 9f1a5f2bd..fddf6250b 100644
--- a/docs/zh/Model_Details/LingBot-Video.md
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -7,7 +7,7 @@ LingBot-Video 是一个基于 flow-matching 的文生视频生成模型。本文
 - **DiT** — `LingBotVideoDiT`（`diffsynth/models/lingbot_video_dit.py`），视频去噪器。Dense-1.3B 版本使用普通 FFN；该架构同时支持 MoE FFN。
 - **文本编码器** — `LingBotVideoTextEncoder`（Qwen3-VL）。提示词会被包裹进提示增强的对话模板中编码，随后裁剪掉模板前缀 token。
 - **VAE** — 复用 DiffSynth 的 `QwenImageVAE`（与 LingBot-Video 的 VAE 逐字节一致），空间 8× / 时间 4× 压缩。
-- **调度器** — `LingBotVideoUniPCScheduler`：推理时使用 UniPC 多步调度；训练时回退到完整分辨率的 flow-matching 调度。
+- **调度器** — DiffSynth 的 `FlowMatchScheduler`（Wan 模板）：推理时使用一阶 flow-matching Euler；训练时使用完整分辨率的 1000 步 flow-matching 调度。
 
 ## 安装
 
@@ -25,6 +25,8 @@ LingBot-Video 还额外依赖 `transformers >= 5.x`（用于 Qwen3-VL）以及 `
 
 运行以下代码可以快速加载 [Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b) 模型并进行文生视频推理。首次运行时会自动下载所需文件。
 
+> **⚠️ 推理前请先把提示词改写为结构化 caption。** LingBot-Video 使用**结构化 JSON caption** 训练，而非自由文本。下面代码里的普通句子能跑通，但属于分布外输入，生成结果会明显偏"糊"、质量偏低。这是模型的预期行为，并非 bug —— 正式推理前，请先把想法转成模型期望的结构化 caption。详见下文[提示词改写](#提示词改写对质量很重要)；可直接运行的 [`lingbot-video-dense-1.3b.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py) 示例默认使用随仓库发布的结构化 caption，并在文件末尾给出可选的改写流程。
+
 ```python
 import torch
 from diffsynth.utils.data import save_video
@@ -41,6 +43,8 @@ pipe = LingBotVideoPipeline.from_pretrained(
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
 )
 video = pipe(
+    # 普通句子仅用于最简单的跑通验证，属于分布外输入。
+    # 正式使用请改传结构化 caption（见"提示词改写"章节）。
     prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0, seed=0,
@@ -48,7 +52,7 @@ video = pipe(
 save_video(video, "video.mp4", fps=15, quality=10)
 ```
 
-**低显存：** 向 `from_pretrained` 传入 `vram_limit=<GB>` 即可开启逐层 offload，详见下表中的低显存示例。
+**低显存：** 在每个 `ModelConfig` 上设置 `offload_dtype` / `offload_device` 才能开启逐层 offload；单独传 `vram_limit` 没有效果（它只在 offload 已开启时限制常驻显存上限）。详见下表中的低显存示例。
 
 ## 模型总览
 
@@ -82,15 +86,37 @@ save_video(video, "video.mp4", fps=15, quality=10)
 
 LingBot-Video 使用**结构化 JSON caption** 训练，而非自由文本。喂入一句普通句子属于分布外（out-of-distribution）输入，会明显降低质量；喂入模型期望的结构化 caption 则能恢复质量。Pipeline 接受 `dict`、`prompt.json` 路径或普通字符串形式的 caption，并将其（通过 `normalize_caption`）归一化为 DiT 训练时使用的紧凑 JSON 格式——普通字符串会原样透传，因此已有脚本无需改动。
 
-若要把一个**简短想法**转成该结构化 caption，可使用内置的两阶段改写器（`diffsynth/pipelines/lingbot_video_prompt_rewriter.py`）：阶段一将想法*扩写*为自然语言 caption，阶段二将其*映射*为结构化 JSON。
+若要把一个**简短想法**转成该结构化 caption，可使用随示例发布的两阶段改写器（`examples/lingbot_video/model_inference/prompt_rewriter.py`）：阶段一将想法*扩写*为自然语言 caption，阶段二将其*映射*为结构化 JSON。
+
+改写器是**独立的 VLM + 阶段二 LoRA 适配器**（不是 DiT），**不会自动下载**——运行改写前，需要先自行下载这两个权重：
+
+| 角色 | 模型 ID | 大小 |
+|-|-|-|
+| 改写器 base VLM（阶段一 + 二） | [`Qwen/Qwen3.6-27B`](https://modelscope.cn/models/Qwen/Qwen3.6-27B) | ~55 GB |
+| 改写器阶段二 LoRA 适配器 | [`Robbyant/lingbot-video-rewriter-lora`](https://modelscope.cn/models/Robbyant/lingbot-video-rewriter-lora) | ~0.5 GB |
+
+```shell
+# 1. 下载改写器 base VLM 及其阶段二 LoRA 适配器。
+modelscope download --model Qwen/Qwen3.6-27B --local_dir ./models/Qwen/Qwen3.6-27B
+modelscope download --model Robbyant/lingbot-video-rewriter-lora --local_dir ./models/Robbyant/lingbot-video-rewriter-lora
+```
 
 ```python
-from diffsynth.pipelines.lingbot_video_prompt_rewriter import rewrite_prompt
+# 2. 让改写器指向已下载的权重，再改写并推理。
+import os
+os.environ["REWRITER_BASE_MODEL"] = "./models/Qwen/Qwen3.6-27B"
+os.environ["REWRITER_ADAPTER"] = "./models/Robbyant/lingbot-video-rewriter-lora"
+
+# 改写器随推理示例一起发布；请在 examples/lingbot_video/model_inference 目录下运行
+# （或将该目录加入 sys.path），此 import 才能解析。
+from prompt_rewriter import rewrite_prompt
 caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
 video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0)
 ```
 
-改写器是**独立的 VLM + 阶段二 LoRA 适配器**（不是 DiT）。可通过 `REWRITER_BASE_MODEL` / `REWRITER_ADAPTER`（或 `base=` / `adapter=`）指向权重，也可以通过传入一个暴露 `generate(text, image, use_lora)` 方法的自定义对象作为 `backend=`，来驱动托管的 / OpenAI 兼容的推理端点。详见 `examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_rewrite.py`。
+除了 env var，也可以给 `rewrite_prompt` 传 `base=` / `adapter=`；或者完全不下载本地 VLM，改为传入一个暴露 `generate(text, image, use_lora)` 方法的自定义对象作为 `backend=`，来驱动托管的 / OpenAI 兼容的推理端点。详见 `examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py` 文件末尾的可选改写小节。
+
+如果没有改写器模型，仓库自带 3 个官方 LingBot-Video t2v 结构化 caption 作为开箱即用的示例：`examples/lingbot_video/model_inference/prompts/t2v_example_{1,2,3}.json`。可直接按路径传给 pipeline（`video = pipe(prompt="path/to/t2v_example_1.json", ...)`），也可以复制一个作为编写自己 caption 的模板。
 
 ## 模型训练
 
@@ -157,4 +183,4 @@ MoE / FFN 专家（`gate_proj`、`up_proj`、`down_proj`）与 router 保持冻
 ## 注意事项
 
 - 文本编码器与已有的 `krea2_text_encoder` 共享同一 checkpoint 指纹（Qwen3-VL 架构相同），因此模型加载器在加载 LingBot-Video 时会同时实例化两者。这只是冗余的加载耗时——Pipeline 会按名称取用正确的编码器，另一个会被释放。
-- Latent 归一化在 VAE 的 5D 视频代码路径内部处理；Pipeline 不会再额外应用 `latents_mean` / `latents_std`。
+- 5D 视频的 VAE 编码/解码及其 latent 归一化都在 Pipeline 内实现（`LingBotVideoPipeline.encode_video` / `decode_video`），因此 `QwenImageVAE` 与它在图像场景下的用法保持逐字节一致；Pipeline 不会再额外应用 `latents_mean` / `latents_std`。
diff --git a/examples/lingbot_video/README.md b/examples/lingbot_video/README.md
index 6b0bc884e..819586ee0 100644
--- a/examples/lingbot_video/README.md
+++ b/examples/lingbot_video/README.md
@@ -7,7 +7,7 @@ The integration is built on the standard DiffSynth pipeline stack:
 - **DiT** — `LingBotVideoDiT` (`diffsynth/models/lingbot_video_dit.py`), the video denoiser. The Dense-1.3B build uses a plain FFN; the architecture also supports an MoE FFN.
 - **Text encoder** — `LingBotVideoTextEncoder` (Qwen3-VL). Prompts are wrapped in a prompt-enhancement chat template, encoded, and the template-prefix tokens are cropped.
 - **VAE** — reuses DiffSynth's `QwenImageVAE` (byte-identical to the LingBot-Video VAE), 8× spatial / 4× temporal.
-- **Scheduler** — `LingBotVideoUniPCScheduler`: UniPC multistep for inference; it falls back to the full-resolution flow-matching schedule for training.
+- **Scheduler** — DiffSynth's `FlowMatchScheduler` (Wan template): first-order flow-matching Euler for inference; training uses the full-resolution 1000-step flow-matching schedule.
 
 ## Installation
 
@@ -23,7 +23,7 @@ pip install -e .
 modelscope download --model Robbyant/lingbot-video-dense-1.3b --local_dir ./models/Robbyant/lingbot-video-dense-1.3b
 ```
 
-The inference examples below use `ModelConfig(model_id=...)`, which downloads the required files automatically the first time they run. You can also point `ModelConfig(path=...)` at local files (see the training script).
+Both the inference and training examples use `model_id`-based configs, which download the required files automatically the first time they run, so the manual download above is optional. You can also point `ModelConfig(path=...)` at local files if you already have them.
 
 ## Inference
 
@@ -67,15 +67,17 @@ pipe(prompt="assets/cases/t2v/example_1/prompt.json")                          #
 pipe(prompt='{"comprehensive_description":{...}}')                             # already-serialised string
 ```
 
-To turn a **brief idea** into that structured caption, use the bundled two-stage rewriter (`diffsynth/pipelines/lingbot_video_prompt_rewriter.py`), a faithful port of the original: stage 1 *expands* the idea into a natural-language caption, stage 2 *maps* it into structured JSON.
+To turn a **brief idea** into that structured caption, use the two-stage rewriter shipped here (`model_inference/prompt_rewriter.py`), a faithful port of the original: stage 1 *expands* the idea into a natural-language caption, stage 2 *maps* it into structured JSON.
 
 ```python
-from diffsynth.pipelines.lingbot_video_prompt_rewriter import rewrite_prompt
+# The rewriter lives in model_inference/; run from that directory (or add it to
+# sys.path) so this sibling import resolves.
+from prompt_rewriter import rewrite_prompt
 caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
 video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0)
 ```
 
-The rewriter is a **separate VLM + stage-2 LoRA adapter** (not the DiT). Point it at the weights via `REWRITER_BASE_MODEL` / `REWRITER_ADAPTER` (or `base=`/`adapter=`). If you serve the rewriter behind a hosted / OpenAI-compatible endpoint instead of loading it locally, pass a custom object exposing `generate(text, image, use_lora)` as `backend=`. See `model_inference/lingbot-video-dense-1.3b_rewrite.py`.
+The rewriter is a **separate VLM + stage-2 LoRA adapter** (not the DiT). Point it at the weights via `REWRITER_BASE_MODEL` / `REWRITER_ADAPTER` (or `base=`/`adapter=`). If you serve the rewriter behind a hosted / OpenAI-compatible endpoint instead of loading it locally, pass a custom object exposing `generate(text, image, use_lora)` as `backend=`. See the optional rewrite section at the bottom of `model_inference/lingbot-video-dense-1.3b.py`.
 
 ## Training (LoRA SFT)
 
@@ -141,4 +143,4 @@ Trained LoRA checkpoints are written to `--output_path` with the `pipe.dit.` pre
 ## Notes
 
 - The text encoder shares its checkpoint fingerprint with the existing `krea2_text_encoder` (identical Qwen3-VL architecture), so the model loader instantiates both when loading LingBot-Video. This is redundant load time only — the pipeline fetches the correct encoder by name and the other is released.
-- Latent normalisation is handled inside the VAE's 5D-video code path; the pipeline does not re-apply `latents_mean` / `latents_std`.
+- The 5D-video VAE encode/decode and its latent normalisation live in the pipeline (`LingBotVideoPipeline.encode_video` / `decode_video`), so `QwenImageVAE` stays byte-identical to its image use elsewhere; the pipeline does not separately re-apply `latents_mean` / `latents_std`.
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
index eb2ddcbb1..a88bdf08a 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
@@ -1,8 +1,16 @@
+import os
 import torch
 from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from diffsynth.pipelines.lingbot_video_prompt_rewriter import normalize_caption
 
 
+# LingBot-Video is trained on STRUCTURED-JSON captions, not free-form prose. Feeding a
+# flat sentence is out-of-distribution and visibly degrades quality; feeding the
+# structured caption the model expects restores it. This example runs on a released
+# in-distribution caption by default, and shows at the bottom how to turn a brief idea
+# into such a caption with the two-stage prompt rewriter.
+
 pipe = LingBotVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
     device="cuda",
@@ -14,23 +22,50 @@
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
 )
 
-# Text-to-video. The default (T2V) negative prompt is built into the pipeline,
-# so `negative_prompt` can be left unset.
+# --- Text-to-video -------------------------------------------------------------------
+# The prompts/t2v_example_*.json files are released LingBot-Video t2v captions — real
+# in-distribution examples you can copy as templates for your own. normalize_caption
+# accepts a dict, a list, or a path to a prompt.json; the pipeline calls it internally
+# too, so pipe(prompt="prompts/t2v_example_1.json") works just as well. The default
+# (T2V) negative prompt is built into the pipeline, so negative_prompt can be left unset.
+caption = normalize_caption(os.path.join(os.path.dirname(__file__), "prompts", "t2v_example_1.json"))
 video = pipe(
-    prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight, ears perked up, chasing after a red ball. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    prompt=caption,
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0,
     seed=0,
 )
 save_video(video, "video_lingbot-video-dense-1.3b.mp4", fps=15, quality=10)
 
-# Video-to-video. `denoising_strength < 1` keeps part of the input structure.
-video = VideoData("video_lingbot-video-dense-1.3b.mp4", height=480, width=832)
+# --- Video-to-video ------------------------------------------------------------------
+# denoising_strength < 1 keeps part of the input structure.
+input_video = VideoData("video_lingbot-video-dense-1.3b.mp4", height=480, width=832)
 video = pipe(
-    prompt="A playful puppy wearing black sunglasses runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
-    input_video=video, denoising_strength=0.7,
+    prompt=caption,
+    input_video=input_video, denoising_strength=0.7,
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0,
     seed=1,
 )
 save_video(video, "video_lingbot-video-dense-1.3b_v2v.mp4", fps=15, quality=10)
+
+# --- Optional: rewrite a brief idea into a structured caption ------------------------
+# If you only have a brief idea (or free-form prose), turn it into the structured caption
+# the model expects with the two-stage rewriter in prompt_rewriter.py (a sibling module
+# here). The rewriter is a separate VLM + stage-2 LoRA adapter (NOT the DiT), so it is
+# NOT downloaded automatically — fetch both weights first, then point the env vars at them:
+#     modelscope download --model Qwen/Qwen3.6-27B --local_dir ./models/Qwen/Qwen3.6-27B
+#     modelscope download --model Robbyant/lingbot-video-rewriter-lora --local_dir ./models/Robbyant/lingbot-video-rewriter-lora
+#     export REWRITER_BASE_MODEL=./models/Qwen/Qwen3.6-27B
+#     export REWRITER_ADAPTER=./models/Robbyant/lingbot-video-rewriter-lora
+# To drive a hosted / OpenAI-compatible endpoint instead of a local VLM, pass a custom
+# object exposing generate(text, image, use_lora) as backend=.
+#
+# from prompt_rewriter import rewrite_prompt
+# caption = rewrite_prompt(
+#     "A playful puppy runs across a lush green meadow, chasing a red ball. "
+#     "Dynamic side-tracking camera.",
+#     mode="t2v", duration=5,
+# )
+# video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0, seed=0)
+# save_video(video, "video_lingbot-video-dense-1.3b_rewrite.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_rewrite.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_rewrite.py
deleted file mode 100644
index 8ca89d4bc..000000000
--- a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_rewrite.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import os
-import torch
-from diffsynth.utils.data import save_video
-from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
-from diffsynth.pipelines.lingbot_video_prompt_rewriter import rewrite_prompt, normalize_caption
-
-
-# LingBot-Video is trained on STRUCTURED-JSON captions, not free-form prose. Feeding a
-# flat sentence is out-of-distribution and visibly degrades quality; rewriting the idea
-# into the structured caption the model expects restores it. This example shows the two
-# supported ways to obtain that caption.
-
-pipe = LingBotVideoPipeline.from_pretrained(
-    torch_dtype=torch.bfloat16,
-    device="cuda",
-    model_configs=[
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
-    ],
-    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
-)
-
-
-# --- Option A: rewrite a brief idea with the two-stage prompt rewriter --------------
-# The rewriter is a separate VLM + LoRA adapter (NOT the DiT). Point these at the
-# rewriter base model and its stage-2 adapter:
-#     export REWRITER_BASE_MODEL=/path/to/rewriter-base
-#     export REWRITER_ADAPTER=/path/to/rewriter-step2-lora
-# If you serve the rewriter behind a hosted / OpenAI-compatible endpoint instead, pass
-# a custom backend object exposing generate(text, image, use_lora) as `backend=`.
-if os.environ.get("REWRITER_BASE_MODEL") and os.environ.get("REWRITER_ADAPTER"):
-    caption = rewrite_prompt(
-        "A playful puppy runs across a lush green meadow, chasing a red ball. "
-        "Dynamic side-tracking camera.",
-        mode="t2v", duration=5, backend="transformers",
-    )
-    print("Rewritten caption:\n", caption)
-else:
-    # --- Option B: no rewriter model — supply a structured caption directly ----------
-    # A dict (or a path to a prompt.json) is serialised to the exact model format by
-    # normalize_caption; the pipeline also calls it internally, so pipe(prompt=<dict>)
-    # works too. Replace this stub with a real structured caption for best quality.
-    caption = normalize_caption({
-        "caption": {
-            "comprehensive_description": {
-                "scene_content_description": "A playful golden puppy runs across a lush green meadow dotted with wildflowers, chasing a bright red ball under a clear blue sky.",
-                "camera_movement_description": "Dynamic side-tracking shot following the puppy.",
-            }
-        },
-        "duration": 5,
-    })
-
-video = pipe(
-    prompt=caption,
-    height=480, width=832, num_frames=81,
-    num_inference_steps=40, cfg_scale=3.0,
-    seed=0,
-)
-save_video(video, "video_lingbot-video-dense-1.3b_rewrite.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_inference/prompt_rewriter.py b/examples/lingbot_video/model_inference/prompt_rewriter.py
new file mode 100644
index 000000000..799e88348
--- /dev/null
+++ b/examples/lingbot_video/model_inference/prompt_rewriter.py
@@ -0,0 +1,226 @@
+"""Two-stage prompt rewriter for LingBot-Video (example-side helper).
+
+The LingBot-Video DiT is trained on **structured JSON captions**, not free-form prose.
+:func:`rewrite_prompt` turns a brief idea into that structured caption via a faithful
+port of the original two-stage rewriter (``rewriter/rewriter_core.py`` +
+``rewriter/inference.py``): stage 1 *expands* the idea into a natural-language caption
+(base model, no LoRA), stage 2 *maps* it into the structured JSON (base model + a
+stage-2 LoRA adapter).
+
+This is a separate VLM + LoRA adapter, NOT the DiT — it is not shipped or downloaded
+with the pipeline, which is why it lives here with the examples rather than in the
+diffsynth core (the core keeps only ``normalize_caption``). The bundled
+:class:`TransformersBackend` loads the rewriter VLM locally; :func:`make_backend` also
+accepts any object exposing ``generate(text, image, use_lora) -> str`` so the rewriter
+can be driven by a hosted / OpenAI-compatible endpoint without shipping the weights.
+
+Typical use::
+
+    from prompt_rewriter import rewrite_prompt          # sibling module in this dir
+    caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
+    video = pipe(prompt=caption, ...)                    # caption is the structured JSON string
+"""
+
+import io
+import json
+import os
+import re
+
+# Optional deps: only needed for the local rewriter backend / image loading.
+try:
+    import requests
+except ImportError:
+    requests = None
+
+try:
+    from PIL import Image
+except ImportError:
+    Image = None
+
+try:
+    from json_repair import repair_json
+except ImportError:
+    repair_json = None
+
+from system_prompts import (
+    VIDEO_STEP1_EXPAND, VIDEO_STEP2_MAP, IMAGE_STEP1_EXPAND, IMAGE_STEP2_MAP,
+)
+from diffsynth.pipelines.lingbot_video_prompt_rewriter import normalize_caption
+
+
+# mode -> (step1 system prompt, step2 system prompt, feed image?, add duration?)
+MODES = {
+    "t2v":  dict(s1=VIDEO_STEP1_EXPAND, s2=VIDEO_STEP2_MAP, image=False, duration=True),
+    "ti2v": dict(s1=VIDEO_STEP1_EXPAND, s2=VIDEO_STEP2_MAP, image=True,  duration=True),
+    "t2i":  dict(s1=IMAGE_STEP1_EXPAND, s2=IMAGE_STEP2_MAP, image=False, duration=False),
+}
+
+
+def _has_cjk(s: str) -> bool:
+    return any("一" <= c <= "鿿" for c in s)
+
+
+def load_image(src):
+    """Load a first-frame image from a local path / http(s) URL / PIL.Image -> RGB PIL."""
+    if Image is None:
+        raise ImportError("loading a first-frame image requires the Pillow package.")
+    if isinstance(src, Image.Image):
+        return src.convert("RGB")
+    if isinstance(src, str) and re.match(r"^https?://", src):
+        if requests is None:
+            raise ImportError("fetching an image URL requires the requests package.")
+        return Image.open(io.BytesIO(requests.get(src, timeout=30).content)).convert("RGB")
+    return Image.open(src).convert("RGB")
+
+
+def _step1_text(mode, prompt, dur):
+    sys = MODES[mode]["s1"]
+    if mode == "t2i":
+        return sys + "\n\nUser image prompt:\n" + prompt
+    dur_line = f"\n\n视频时长：{dur} 秒" if _has_cjk(prompt) else f"\n\nVideo Duration: {dur} seconds"
+    return sys + "\n\n" + prompt + dur_line
+
+
+def _step2_text(mode, detailed, dur):
+    sys = MODES[mode]["s2"]
+    if mode == "t2i":
+        return sys + "\n\nDETAILED CAPTION:\n" + detailed
+    return (sys + f"\n\nVideo Duration: {dur} seconds\n\nDETAILED CAPTION:\n"
+            + detailed + "\n\nOutput the JSON now.")
+
+
+def parse_json(raw):
+    """Parse the stage-2 output into a dict, or ``None`` if it cannot be parsed.
+
+    VLMs occasionally emit unstable JSON (missing quotes, trailing commas, ``` fences),
+    so we strip any code fence, then try the stdlib parser first and fall back to
+    ``json_repair`` when it is installed (recommended for messy outputs)."""
+    s = (raw or "").strip()
+    m = re.search(r"```(?:json)?\s*(\{.*\})\s*```", s, re.DOTALL)
+    if m:
+        s = m.group(1)
+    try:
+        obj = json.loads(s)
+        if isinstance(obj, dict):
+            return obj
+    except Exception:
+        pass
+    if repair_json is not None:
+        try:
+            obj = repair_json(s, return_objects=True)
+            return obj if isinstance(obj, dict) else None
+        except Exception:
+            return None
+    return None
+
+
+def save_caption(result, duration, path):
+    """Save as ``{"caption": <structured JSON caption>, "duration": <seconds|null>}`` —
+    exactly the ``prompt.json`` the pipeline / runner consume. ``duration`` is integer
+    seconds for T2V/TI2V and ``None`` for T2I (a still image has no duration)."""
+    dur = int(round(duration)) if MODES[result["mode"]]["duration"] else None
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump({"caption": result["json"], "duration": dur}, f, ensure_ascii=False, indent=2)
+    return path
+
+
+class TransformersBackend:
+    """Local rewriter VLM + LoRA adapter (peft). stage1 = base (adapter disabled),
+    stage2 = base + LoRA. Loads the rewriter model into memory; the weights are NOT
+    the DiT — set ``base``/``adapter`` (or ``REWRITER_BASE_MODEL``/``REWRITER_ADAPTER``)
+    to the rewriter VLM and its stage-2 adapter."""
+
+    def __init__(self, base=None, adapter=None, device="auto", max_new_tokens=6144):
+        import contextlib
+        import torch
+        from peft import PeftModel
+        from transformers import AutoModelForImageTextToText, AutoProcessor
+
+        self._contextlib = contextlib
+        self._torch = torch
+
+        base = base or os.environ.get("REWRITER_BASE_MODEL", "")
+        adapter = adapter or os.environ.get("REWRITER_ADAPTER", "")
+        if not base or not adapter:
+            raise ValueError(
+                "Set the rewriter base and adapter paths via base=/adapter= or the "
+                "REWRITER_BASE_MODEL / REWRITER_ADAPTER environment variables."
+            )
+        self.processor = AutoProcessor.from_pretrained(base, trust_remote_code=True)
+        model = AutoModelForImageTextToText.from_pretrained(
+            base, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
+        self.model = PeftModel.from_pretrained(model, adapter).eval()
+        self.max_new_tokens = max_new_tokens
+
+    def generate(self, text, image, use_lora):
+        torch = self._torch
+        content = ([{"type": "image", "image": image}] if image is not None else []) \
+            + [{"type": "text", "text": text}]
+        messages = [{"role": "user", "content": content}]
+        chat = self.processor.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
+        inputs = self.processor(
+            text=[chat], images=([image] if image is not None else None), return_tensors="pt"
+        ).to(self.model.device)
+        # stage1 (expand): disable LoRA; stage2 (map): keep LoRA active.
+        adapter_ctx = self._contextlib.nullcontext() if use_lora else self.model.disable_adapter()
+        with torch.no_grad(), adapter_ctx:
+            out = self.model.generate(**inputs, max_new_tokens=self.max_new_tokens, do_sample=False)
+        gen = out[:, inputs["input_ids"].shape[1]:]
+        return self.processor.batch_decode(gen, skip_special_tokens=True)[0]
+
+
+def make_backend(backend="transformers", base=None, adapter=None):
+    """Build a rewriter backend.
+
+    - ``"transformers"`` — the bundled local :class:`TransformersBackend`.
+    - a custom object / callable — returned as-is if it already exposes
+      ``generate(text, image, use_lora) -> str``. Use this to drive the rewriter from
+      a hosted or OpenAI-compatible endpoint without downloading the VLM locally.
+    """
+    if backend == "transformers":
+        return TransformersBackend(base, adapter)
+    if hasattr(backend, "generate"):
+        return backend
+    raise ValueError(
+        f"unknown backend: {backend!r}; pass 'transformers' or an object exposing "
+        "generate(text, image, use_lora)."
+    )
+
+
+class Rewriter:
+    """Two-stage orchestrator. The backend implements ``generate(text, image, use_lora) -> str``."""
+
+    def __init__(self, backend):
+        self.backend = backend
+
+    def rewrite(self, prompt, mode="t2v", first_frame=None, duration=5):
+        if mode not in MODES:
+            raise ValueError(f"mode must be one of {list(MODES)}")
+        cfg = MODES[mode]
+        dur = int(round(duration))
+        img = None
+        if cfg["image"]:
+            if first_frame is None:
+                raise ValueError(f"{mode} requires first_frame (path / URL / PIL.Image)")
+            img = load_image(first_frame)
+        # stage 1: EXPAND -- base model (no LoRA)
+        detailed = self.backend.generate(_step1_text(mode, prompt, dur), img, use_lora=False).strip()
+        # stage 2: MAP -- base + LoRA
+        raw = self.backend.generate(_step2_text(mode, detailed, dur), img, use_lora=True).strip()
+        return {"mode": mode, "detailed": detailed, "json": parse_json(raw), "json_raw": raw}
+
+
+def rewrite_prompt(prompt, mode="t2v", first_frame=None, duration=5,
+                   backend="transformers", base=None, adapter=None, return_result=False):
+    """Rewrite a brief idea into the structured caption string the pipeline expects.
+
+    Returns the compact-JSON caption string (ready to pass as ``pipe(prompt=...)``).
+    Pass ``return_result=True`` to also get the full stage-1/stage-2 dict.
+    """
+    rw = Rewriter(make_backend(backend, base, adapter))
+    result = rw.rewrite(prompt, mode=mode, first_frame=first_frame, duration=duration)
+    caption = normalize_caption(result["json"]) if result["json"] is not None else result["json_raw"]
+    if return_result:
+        return caption, result
+    return caption
diff --git a/examples/lingbot_video/model_inference/prompts/t2v_example_1.json b/examples/lingbot_video/model_inference/prompts/t2v_example_1.json
new file mode 100644
index 000000000..92afdf69b
--- /dev/null
+++ b/examples/lingbot_video/model_inference/prompts/t2v_example_1.json
@@ -0,0 +1,77 @@
+{
+  "caption": {
+    "comprehensive_description": {
+      "scene_content_description": "A young woman with long, wavy brown hair is standing in a bright, modern apartment living room. She is wearing a stylish, oversized cream-colored knit cardigan over a white tank top, paired with high-waisted, wide-leg beige trousers. She holds a small, structured tan leather handbag in her left hand. The background features a neutral-toned interior with a beige sofa, a potted plant, and large windows that let in soft, natural light, creating a warm and inviting atmosphere. The woman is smiling and looking directly at the camera, showcasing her outfit with a confident and friendly demeanor.",
+      "camera_movement_description": "The camera is positioned at eye level and remains essentially stationary throughout the video, maintaining a medium shot that captures the subject from the waist up. There is a very shallow depth of field, keeping the woman in sharp focus while the background remains softly blurred."
+    },
+    "camera_info": {
+      "color": "Warm",
+      "frame_size": "Medium",
+      "shot_type_angle": "Eye level",
+      "lens_size": "Medium",
+      "composition": "Center",
+      "lighting": "Soft light",
+      "lighting_type": "Daylight"
+    },
+    "world_knowledge": [],
+    "prominent_elements": [
+      {
+        "name": "young woman",
+        "description": "A woman with long, wavy brown hair and a friendly expression, modeling a fashion outfit.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 0.5s]",
+            "action": "stands still, smiling at the camera"
+          },
+          {
+            "timestamp": "[0.5s - 2.0s]",
+            "action": "shifts her weight and turns her body slightly to the right"
+          },
+          {
+            "timestamp": "[2.0s - 3.5s]",
+            "action": "adjusts the collar of her cardigan with her right hand"
+          },
+          {
+            "timestamp": "[3.5s - 5.0s]",
+            "action": "returns to a neutral pose, smiling at the camera"
+          }
+        ],
+        "location": "center of the frame",
+        "relative_size": "dominant",
+        "shape_and_color": "slender build; wearing cream, white, and beige",
+        "texture": "soft knit cardigan, smooth fabric trousers",
+        "appearance_details": "long wavy brown hair, gold hoop earrings, tan leather handbag",
+        "relationship": "the main subject of the video, standing in front of a blurred apartment background",
+        "orientation": "upright, facing the camera",
+        "pose": "standing, shifting weight, and adjusting clothing",
+        "expression": "smiling and confident",
+        "clothing": "oversized cream knit cardigan, white tank top, high-waisted wide-leg beige trousers",
+        "gender": "female",
+        "skin_tone_and_texture": "fair skin with a smooth texture"
+      },
+      {
+        "name": "tan handbag",
+        "description": "A small, structured leather handbag with a top handle.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 5.0s]",
+            "action": "held steady in the woman's left hand"
+          }
+        ],
+        "location": "held in the woman's left hand, lower center of the frame",
+        "relative_size": "small",
+        "shape_and_color": "rectangular, tan or light brown",
+        "texture": "smooth leather",
+        "appearance_details": "structured shape with a top handle",
+        "relationship": "held by the woman as an accessory",
+        "orientation": "upright",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "gender": "",
+        "skin_tone_and_texture": ""
+      }
+    ]
+  },
+  "duration": 5
+}
diff --git a/examples/lingbot_video/model_inference/prompts/t2v_example_2.json b/examples/lingbot_video/model_inference/prompts/t2v_example_2.json
new file mode 100644
index 000000000..2e896d0da
--- /dev/null
+++ b/examples/lingbot_video/model_inference/prompts/t2v_example_2.json
@@ -0,0 +1,101 @@
+{
+  "caption": {
+    "comprehensive_description": {
+      "scene_content_description": "A young child with short brown hair is outdoors on a bright, sunny day, playing with bubbles. The child is wearing a colorful striped shirt with horizontal bands of red, yellow, green, and blue. They are holding a small bottle of bubble solution and a wand, dipping the wand into the bottle and then blowing to create a stream of shimmering, iridescent bubbles. The background is a soft-focus outdoor setting with greenery and a hint of a building, creating a warm and joyful atmosphere. The lighting is bright and natural, highlighting the child's focused expression and the delicate, translucent nature of the bubbles.",
+      "camera_movement_description": "The camera is essentially stationary throughout the video, maintaining a medium close-up shot of the child from an eye-level angle. There is a very slight handheld tremor, but no intentional panning, tilting, or zooming occurs."
+    },
+    "camera_info": {
+      "color": "Saturated",
+      "frame_size": "Medium Close Up",
+      "shot_type_angle": "Low angle",
+      "lens_size": "Long Lens",
+      "composition": "Center",
+      "lighting": "Hard light",
+      "lighting_type": "Daylight"
+    },
+    "world_knowledge": [],
+    "prominent_elements": [
+      {
+        "name": "young child",
+        "description": "A young child with short brown hair and a joyful expression, focused on playing with bubbles.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 1.5s]",
+            "action": "Dips the bubble wand into the solution bottle and lifts it out."
+          },
+          {
+            "timestamp": "[1.5s - 2.5s]",
+            "action": "Brings the wand to their lips and blows to create bubbles."
+          },
+          {
+            "timestamp": "[2.5s - 5.0s]",
+            "action": "Watches the bubbles float away, smiling slightly."
+          }
+        ],
+        "location": "Center of the frame",
+        "relative_size": "dominant",
+        "shape_and_color": "Human form; wearing a multi-colored striped shirt (red, yellow, green, blue).",
+        "texture": "Soft skin, fine hair, fabric texture of the shirt.",
+        "appearance_details": "Short brown hair, bright eyes, colorful horizontal stripes on the shirt.",
+        "relationship": "Holding the bubble wand and bottle, interacting with the bubbles.",
+        "orientation": "Facing forward and slightly to the right.",
+        "pose": "Standing or sitting upright, arms raised to hold the bubble wand.",
+        "expression": "Focused and happy.",
+        "clothing": "A short-sleeved shirt with horizontal stripes in red, yellow, green, and blue.",
+        "gender": "Male",
+        "skin_tone_and_texture": "Fair skin with a smooth texture."
+      },
+      {
+        "name": "bubble wand and bottle",
+        "description": "A small plastic bottle containing bubble solution and a wand with a circular loop.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 1.5s]",
+            "action": "The wand is dipped into the bottle and then lifted out."
+          },
+          {
+            "timestamp": "[1.5s - 2.5s]",
+            "action": "The wand is held at the child's mouth as bubbles are blown."
+          }
+        ],
+        "location": "Lower center of the frame, held by the child.",
+        "relative_size": "small",
+        "shape_and_color": "Cylindrical bottle with a blue cap; circular wand loop.",
+        "texture": "Smooth plastic.",
+        "appearance_details": "The bottle is partially filled with clear liquid; the wand has a thin handle.",
+        "relationship": "Held by the child's hands.",
+        "orientation": "Vertical bottle, horizontal wand loop.",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "gender": "",
+        "skin_tone_and_texture": ""
+      },
+      {
+        "name": "bubbles",
+        "description": "A cluster of small, iridescent, translucent bubbles floating in the air.",
+        "actions": [
+          {
+            "timestamp": "[1.5s - 5.0s]",
+            "action": "The bubbles are blown from the wand and float upwards and towards the right side of the frame."
+          }
+        ],
+        "location": "Upper center and right side of the frame.",
+        "relative_size": "medium",
+        "shape_and_color": "Spherical and iridescent with rainbow-like reflections.",
+        "texture": "Glossy and translucent.",
+        "appearance_details": "Shimmering surfaces that catch the sunlight.",
+        "relationship": "Created by the child's blowing action.",
+        "orientation": "Floating in various directions.",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "gender": "",
+        "skin_tone_and_texture": "",
+        "is_cluster": true,
+        "number_of_objects": "many"
+      }
+    ]
+  },
+  "duration": 5
+}
diff --git a/examples/lingbot_video/model_inference/prompts/t2v_example_3.json b/examples/lingbot_video/model_inference/prompts/t2v_example_3.json
new file mode 100644
index 000000000..7e78d5eb2
--- /dev/null
+++ b/examples/lingbot_video/model_inference/prompts/t2v_example_3.json
@@ -0,0 +1,155 @@
+{
+  "caption": {
+    "comprehensive_description": {
+      "scene_content_description": "The video presents a first-person perspective of a workspace, likely a desk, viewed from a fixed, slightly elevated angle. The environment is well-lit with neutral, even lighting, creating a clear and focused atmosphere. The desk surface is a light grey color. On the left side of the desk, there is a black game controller with a glowing blue light bar, resting on a black mousepad. In the center of the desk lies a pair of black over-ear headphones. To the right, there is an open, empty black box with a white interior. In the foreground, two robotic arms are visible. The left robotic arm, featuring a black and silver body with a two-fingered gripper, remains completely stationary throughout the video. The right robotic arm, similar in design, is the active subject. It begins by moving forward and to the left, positioning its gripper over the game controller. It then grasps the controller, lifts it off the desk, and transports it to the right, moving it over the open box. Finally, the right robotic arm lowers the controller into the box and releases it. The headphones and the left robotic arm remain undisturbed during this entire sequence.",
+      "camera_movement_description": ""
+    },
+    "camera_info": {
+      "color": "Cyan",
+      "frame_size": "Wide",
+      "shot_type_angle": "High angle",
+      "lens_size": "Ultra Wide / Fisheye",
+      "composition": "Balanced",
+      "lighting": "Soft light",
+      "lighting_type": "Artificial light"
+    },
+    "world_knowledge": [],
+    "prominent_elements": [
+      {
+        "name": "right robotic arm",
+        "description": "A mechanical arm with a black and silver body, equipped with a two-fingered gripper and visible wiring.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 1.5s]",
+            "action": "Moves forward and to the left towards the game controller."
+          },
+          {
+            "timestamp": "[1.5s - 2.5s]",
+            "action": "Grasps the game controller and lifts it upward."
+          },
+          {
+            "timestamp": "[2.5s - 4.0s]",
+            "action": "Moves to the right, carrying the game controller over the open box."
+          },
+          {
+            "timestamp": "[4.0s - 5.0s]",
+            "action": "Lowers the game controller into the box and releases it."
+          }
+        ],
+        "location": "Originates from the bottom right, moves to the center, then to the right.",
+        "relative_size": "large",
+        "shape_and_color": "Cylindrical and angular segments, black and silver.",
+        "texture": "Metallic and matte plastic.",
+        "appearance_details": "Visible joints, wiring, and a two-fingered gripper mechanism.",
+        "relationship": "Interacts directly with the game controller.",
+        "orientation": "Extends forward and slightly upward from the bottom right.",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      },
+      {
+        "name": "left robotic arm",
+        "description": "A mechanical arm with a black and silver body, equipped with a two-fingered gripper.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 5.0s]",
+            "action": "Remains stationary."
+          }
+        ],
+        "location": "Bottom left corner of the frame.",
+        "relative_size": "large",
+        "shape_and_color": "Cylindrical and angular segments, black and silver.",
+        "texture": "Metallic and matte plastic.",
+        "appearance_details": "Visible joints and a two-fingered gripper mechanism.",
+        "relationship": "Positioned opposite the right robotic arm, not interacting with other objects.",
+        "orientation": "Extends forward and slightly upward from the bottom left.",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      },
+      {
+        "name": "game controller",
+        "description": "A standard video game controller with joysticks, buttons, and a glowing light bar.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 1.5s]",
+            "action": "Rests stationary on the desk."
+          },
+          {
+            "timestamp": "[1.5s - 2.5s]",
+            "action": "Is grasped and lifted upward by the right robotic arm."
+          },
+          {
+            "timestamp": "[2.5s - 4.0s]",
+            "action": "Is moved to the right by the right robotic arm."
+          },
+          {
+            "timestamp": "[4.0s - 5.0s]",
+            "action": "Is lowered into the open box and released."
+          }
+        ],
+        "location": "Initially on the left side of the desk, moved to the right side inside the box.",
+        "relative_size": "medium",
+        "shape_and_color": "Contoured shape, black with a blue light bar.",
+        "texture": "Matte plastic.",
+        "appearance_details": "Two joysticks, directional pad, action buttons, and a glowing blue light bar in the center.",
+        "relationship": "Initially on the desk, then grasped and moved by the right robotic arm, finally placed in the open box.",
+        "orientation": "Horizontal on the desk, then tilted while being carried.",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      },
+      {
+        "name": "headphones",
+        "description": "A pair of over-ear headphones with a headband and ear cups.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 5.0s]",
+            "action": "Remains stationary on the desk."
+          }
+        ],
+        "location": "Center of the desk.",
+        "relative_size": "medium",
+        "shape_and_color": "Curved headband with circular ear cups, black.",
+        "texture": "Matte plastic and soft ear cushions.",
+        "appearance_details": "Visible ear cushions and a headband.",
+        "relationship": "Rests on the desk, untouched by the robotic arms.",
+        "orientation": "Lying flat on the desk.",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      },
+      {
+        "name": "open box",
+        "description": "A rectangular box with the lid open, revealing a white interior.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 5.0s]",
+            "action": "Remains stationary on the desk."
+          }
+        ],
+        "location": "Right side of the desk.",
+        "relative_size": "medium",
+        "shape_and_color": "Rectangular, black exterior with a white interior.",
+        "texture": "Smooth cardboard or plastic.",
+        "appearance_details": "Open lid, empty interior.",
+        "relationship": "Serves as the receptacle for the game controller.",
+        "orientation": "Horizontal on the desk.",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      }
+    ]
+  },
+  "duration": 5
+}
diff --git a/diffsynth/pipelines/lingbot_video_system_prompts.py b/examples/lingbot_video/model_inference/system_prompts.py
similarity index 100%
rename from diffsynth/pipelines/lingbot_video_system_prompts.py
rename to examples/lingbot_video/model_inference/system_prompts.py
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
index b6e3bf1db..0d106b745 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
@@ -1,25 +1,45 @@
+import os
 import torch
 from diffsynth.utils.data import save_video
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from diffsynth.pipelines.lingbot_video_prompt_rewriter import normalize_caption
 
 
-# Low-VRAM inference. `vram_limit` turns on DiffSynth's automatic VRAM management,
-# which keeps model weights on CPU and streams them to the GPU layer-by-layer during
-# each forward. The value below reserves ~2 GB of headroom on top of the models.
+# Low-VRAM inference. Setting `offload_dtype` / `offload_device` on each ModelConfig is
+# what actually turns on DiffSynth's VRAM management: weights are kept on CPU in fp8 and
+# streamed to the GPU layer-by-layer, then computed in bf16. `vram_limit` on its own has
+# no effect — without a non-None `offload_dtype` and `offload_device` the loader never
+# enables offloading (see `need_to_enable_vram_management`). `vram_limit` only sets how
+# much resident VRAM the streaming may use once offloading is on.
+vram_config = {
+    "offload_dtype": torch.float8_e4m3fn,
+    "offload_device": "cpu",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
 pipe = LingBotVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
     device="cuda",
     model_configs=[
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
     ],
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
     vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
 )
 
+# Use a released in-distribution structured caption (shared with the model_inference
+# example). LingBot-Video is trained on structured-JSON captions, not free-form prose.
+caption = normalize_caption(os.path.join(
+    os.path.dirname(__file__), "..", "model_inference", "prompts", "t2v_example_1.json"))
 video = pipe(
-    prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight, ears perked up, chasing after a red ball. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    prompt=caption,
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0,
     seed=0,
diff --git a/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
index 594e3868a..a04d8ec7e 100644
--- a/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
+++ b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
@@ -1,7 +1,3 @@
-# Download the LingBot-Video Dense-1.3B weights (DiT + text encoder + VAE + processor).
-# This fetches the whole repo once into ./models so the paths below resolve locally.
-modelscope download --model Robbyant/lingbot-video-dense-1.3b --local_dir ./models/Robbyant/lingbot-video-dense-1.3b
-
 # Download the example video-SFT dataset (a small text-to-video set with a `video` +
 # `prompt` metadata.csv), the same DiffSynth-Studio example dataset the other training
 # scripts use. NOTE: its prompts are plain prose; for best quality rewrite them into
@@ -21,15 +17,8 @@ accelerate launch examples/lingbot_video/model_training/train.py \
   --width 832 \
   --num_frames 169 \
   --dataset_repeat 200 \
-  --model_paths '[
-    [
-      "./models/Robbyant/lingbot-video-dense-1.3b/text_encoder/model-00001-of-00002.safetensors",
-      "./models/Robbyant/lingbot-video-dense-1.3b/text_encoder/model-00002-of-00002.safetensors"
-    ],
-    "./models/Robbyant/lingbot-video-dense-1.3b/transformer/diffusion_pytorch_model.safetensors",
-    "./models/Robbyant/lingbot-video-dense-1.3b/vae/diffusion_pytorch_model.safetensors"
-  ]' \
-  --processor_path "./models/Robbyant/lingbot-video-dense-1.3b/processor" \
+  --model_id_with_origin_paths "Robbyant/lingbot-video-dense-1.3b:transformer/diffusion_pytorch_model.safetensors,Robbyant/lingbot-video-dense-1.3b:text_encoder/model*.safetensors,Robbyant/lingbot-video-dense-1.3b:vae/diffusion_pytorch_model.safetensors" \
+  --processor_path "Robbyant/lingbot-video-dense-1.3b:processor/" \
   --learning_rate 1e-4 \
   --num_epochs 20 \
   --remove_prefix_in_ckpt "pipe.dit." \
diff --git a/examples/lingbot_video/model_training/rewrite_captions.py b/examples/lingbot_video/model_training/rewrite_captions.py
index 3b234af9e..91da27f22 100644
--- a/examples/lingbot_video/model_training/rewrite_captions.py
+++ b/examples/lingbot_video/model_training/rewrite_captions.py
@@ -22,8 +22,13 @@
 import argparse
 import json
 import os
+import sys
 
-from diffsynth.pipelines.lingbot_video_prompt_rewriter import Rewriter, make_backend, normalize_caption
+# The two-stage rewriter engine lives with the inference examples (the diffsynth core
+# keeps only normalize_caption), so training and inference share one implementation.
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "model_inference"))
+from prompt_rewriter import Rewriter, make_backend
+from diffsynth.pipelines.lingbot_video_prompt_rewriter import normalize_caption
 
 
 def _load_rows(path):
diff --git a/examples/lingbot_video/model_training/train.py b/examples/lingbot_video/model_training/train.py
index 1d7e74ab4..d6dd372fc 100644
--- a/examples/lingbot_video/model_training/train.py
+++ b/examples/lingbot_video/model_training/train.py
@@ -39,9 +39,10 @@ def __init__(
         self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
         self.resume_from_checkpoint(resume_from_checkpoint, remove_prefix_in_ckpt)
 
-        # Training mode. The UniPC scheduler falls back to the full-resolution
-        # flow-matching schedule during training (see LingBotVideoUniPCScheduler).
-        # Attention-only LoRA is the default scope: pass
+        # Training mode. switch_pipe_to_training_mode puts FlowMatchScheduler (Wan
+        # template) on the full 1000-step flow-matching schedule via
+        # set_timesteps(1000, training=True); the SFT loss samples a random timestep
+        # from it. Attention-only LoRA is the default scope: pass
         # `--lora_target_modules "to_q,to_k,to_v,to_out"` to leave the MoE / FFN
         # (gate_proj / up_proj / down_proj) and the router untouched.
         self.switch_pipe_to_training_mode(

From 5fbf50aa0766ce6252fb87890c886b6f93a70cf7 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Mon, 27 Jul 2026 16:10:36 +0800
Subject: [PATCH 05/34] Fix low-VRAM fp32/bf16 dtype mismatch in LingBot-Video
 DiT

Under low-VRAM offload the AutoWrapped modules compute weights in bf16 but do
not cast their inputs, and reading `.weight.dtype` from outside the wrapper
returns the resident fp8 dtype. The parameter-free sinusoidal `time_proj`
always returns fp32, so feeding it straight into the bf16 `time_embedder` MLP
raised "mat1 and mat2 must have the same dtype, but got Float and BFloat16";
`proj_out` had the mirror bug via `self.proj_out.weight.dtype` (fp8 under
offload). Cast both inputs to the running compute dtype (`joint`) instead,
which is bf16 under offload and matches the weight dtype in a full-precision
run. No effect on the normal (non-offload) path.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 diffsynth/models/lingbot_video_dit.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/diffsynth/models/lingbot_video_dit.py b/diffsynth/models/lingbot_video_dit.py
index 59fe8fd1d..71acf0827 100644
--- a/diffsynth/models/lingbot_video_dit.py
+++ b/diffsynth/models/lingbot_video_dit.py
@@ -653,7 +653,12 @@ def forward(
 
         # Timestep -> per-token modulation.
         timestep_proj = self.time_proj(timestep.float())
-        t_emb = self.time_embedder(timestep_proj)  # (B, D)
+        # time_proj is a parameter-free sinusoidal embedding and always returns fp32
+        # (get_timestep_embedding upcasts). Cast to the running compute dtype -- joint is
+        # the bf16 hidden state (or fp32 in a full-precision run) -- before the MLP: under
+        # low-VRAM offload the wrapper computes weights in bf16 but does not cast inputs,
+        # so feeding fp32 here raises "mat1 and mat2 must have the same dtype".
+        t_emb = self.time_embedder(timestep_proj.to(joint.dtype))  # (B, D)
         if packed_batch:
             temb_input = torch.cat(
                 [t_emb[i:i + 1].unsqueeze(1).expand(1, sample_seq_lens[i], -1) for i in range(B)], dim=1
@@ -675,7 +680,10 @@ def forward(
         final_mod = self.norm_out_modulation(temb_input.reshape(joint.shape[0] * joint.shape[1], -1))
         shift, scale = final_mod.reshape(joint.shape[0], joint.shape[1], -1).chunk(2, dim=-1)
         final_hidden = self.norm_out(joint) * (1.0 + scale) + shift
-        projected = self.proj_out(final_hidden.to(self.proj_out.weight.dtype))
+        # Match the running compute dtype (joint), not self.proj_out.weight.dtype: under
+        # low-VRAM offload the resident weight is fp8 (bf16 only at compute time), so
+        # reading .weight.dtype here would cast the input to fp8 and mismatch the wrapper.
+        projected = self.proj_out(final_hidden.to(joint.dtype))
 
         if packed_batch:
             split_lengths = []

From a9e7cc24e0de8aef8c9fe7b62f951f87a35375e5 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Mon, 27 Jul 2026 17:14:39 +0800
Subject: [PATCH 06/34] Fold normalize_caption into lingbot_video pipeline
 module

The trimmed diffsynth/pipelines/lingbot_video_prompt_rewriter.py held only
normalize_caption (caption -> compact-JSON serialisation) after the prompt
rewriter was moved to the examples. Its sole core consumer is the pipeline,
which already calls it internally, so keeping a separate one-function module
added a misleadingly-named file ("prompt_rewriter" that no longer rewrites)
for no benefit.

Move normalize_caption (and its _serialize_caption/_caption_from_sample
helpers) into diffsynth/pipelines/lingbot_video.py as module-level functions
and delete the old module. All importers now pull it from lingbot_video; the
example inference/training scripts already imported the pipeline, so this only
tidies their imports. The prompt-rewriting logic stays out of core, in
examples/lingbot_video/model_inference/prompt_rewriter.py.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 .../lingbot_video_prompt_rewriter.py          | 78 -------------------
 1 file changed, 78 deletions(-)
 delete mode 100644 diffsynth/pipelines/lingbot_video_prompt_rewriter.py

diff --git a/diffsynth/pipelines/lingbot_video_prompt_rewriter.py b/diffsynth/pipelines/lingbot_video_prompt_rewriter.py
deleted file mode 100644
index a6d55bff4..000000000
--- a/diffsynth/pipelines/lingbot_video_prompt_rewriter.py
+++ /dev/null
@@ -1,78 +0,0 @@
-"""Caption normalization for LingBot-Video.
-
-The LingBot-Video DiT is trained on **structured JSON captions**, not free-form
-prose. Feeding a flat sentence is out-of-distribution and noticeably degrades
-quality; feeding the structured caption the model expects restores it.
-
-This module holds only the lightweight, dependency-free normalization the pipeline
-and the training module need: :func:`normalize_caption` turns a caption expressed as
-a ``dict`` / ``list`` / path to a ``prompt.json`` into the exact compact-JSON string
-the DiT consumes (a plain string is passed through untouched). It mirrors the original
-``lingbot_video.utils.caption_from_sample`` byte-for-byte.
-
-Turning a *brief idea* into that structured caption is a separate, heavier step (a
-two-stage VLM rewriter). That lives with the examples, out of the core, so importing
-this module never drags in the rewriter's optional deps — see
-``examples/lingbot_video/model_inference/prompt_rewriter.py``.
-"""
-
-import json
-import os
-
-
-# Keys that describe how to *render* the clip rather than its content. When a full
-# sample dict is given without an explicit "caption" key, these are stripped before
-# serialisation (kept identical to the original ``caption_from_sample``).
-_RUNTIME_KEYS = {"duration", "fps", "height", "width", "num_frames", "resolution", "ratio"}
-
-
-def _serialize_caption(caption) -> str:
-    """dict/list -> compact JSON (the exact model format); anything else -> ``str()``."""
-    if isinstance(caption, (dict, list)):
-        return json.dumps(caption, ensure_ascii=False, separators=(",", ":"))
-    return str(caption)
-
-
-def _caption_from_sample(sample) -> str:
-    """Port of ``lingbot_video.utils.caption_from_sample``.
-
-    A *sample* dict either carries the structured caption under ``"caption"`` or IS
-    the caption once the runtime keys are dropped.
-    """
-    if isinstance(sample, dict):
-        if "caption" in sample:
-            caption = sample["caption"]
-        else:
-            caption = {k: v for k, v in sample.items() if k not in _RUNTIME_KEYS}
-    else:
-        caption = sample
-    return _serialize_caption(caption)
-
-
-def normalize_caption(prompt):
-    """Normalise a caption into the compact-JSON string the LingBot DiT expects.
-
-    Accepts:
-
-    - ``dict`` / ``list`` — a structured caption (or a full sample dict with a
-      ``"caption"`` key), serialised via the original compact-JSON convention.
-    - a path to a ``prompt.json`` file (``str`` ending in ``.json`` that exists) —
-      loaded, then handled as the dict/list case.
-    - any other ``str`` — returned unchanged (already a caption string, or free-form
-      prose the caller intentionally wants to feed as-is).
-    - ``None`` — returned unchanged.
-
-    Plain strings are passed through, so this is safe to call unconditionally on
-    prompts that are already in the right format.
-    """
-    if prompt is None:
-        return prompt
-    if isinstance(prompt, str):
-        if prompt.endswith(".json") and os.path.isfile(prompt):
-            with open(prompt, "r", encoding="utf-8") as f:
-                prompt = json.load(f)
-            return _caption_from_sample(prompt)
-        return prompt
-    if isinstance(prompt, (dict, list)):
-        return _caption_from_sample(prompt)
-    return str(prompt)

From c62ea4f526c01471e320dc5514c57359bb323823 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Mon, 27 Jul 2026 17:16:27 +0800
Subject: [PATCH 07/34] Add normalize_caption to lingbot_video and fix
 importers

Companion to the previous commit, which only recorded the deletion of
lingbot_video_prompt_rewriter.py. This commit adds normalize_caption (and its
_serialize_caption/_caption_from_sample helpers) into
diffsynth/pipelines/lingbot_video.py as module-level functions, and repoints
every importer (the pipeline itself, the model_inference / low_vram example
scripts, the example prompt_rewriter, train.py and rewrite_captions.py) at
diffsynth.pipelines.lingbot_video. Restores a working tree: the two commits
together move the single remaining function into the pipeline module.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 diffsynth/pipelines/lingbot_video.py          | 77 ++++++++++++++++++-
 .../lingbot-video-dense-1.3b.py               |  3 +-
 .../model_inference/prompt_rewriter.py        |  2 +-
 .../lingbot-video-dense-1.3b.py               |  3 +-
 .../model_training/rewrite_captions.py        |  2 +-
 .../lingbot_video/model_training/train.py     |  3 +-
 6 files changed, 81 insertions(+), 9 deletions(-)

diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index f16082d39..387145155 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -1,3 +1,6 @@
+import json
+import os
+
 import torch
 import numpy as np
 from PIL import Image
@@ -13,7 +16,79 @@
 from ..models.lingbot_video_dit import LingBotVideoDiT
 from ..models.lingbot_video_text_encoder import LingBotVideoTextEncoder
 from ..models.qwen_image_vae import QwenImageVAE, QwenImageCausalConv3d
-from .lingbot_video_prompt_rewriter import normalize_caption
+
+
+# ---------------------------------------------------------------------------
+# Caption normalization
+#
+# LingBot-Video is trained on **structured JSON captions**, not free-form prose;
+# feeding a flat sentence is out-of-distribution and degrades quality. The pipeline
+# calls ``normalize_caption`` internally (see ``__call__``) so a caption given as a
+# ``dict`` / ``list`` / path to a ``prompt.json`` is serialised to the exact
+# compact-JSON string the DiT consumes, while a plain string is passed through
+# untouched. It is a module-level function (not a method) so the training data-prep
+# scripts -- train.py and rewrite_captions.py -- can import the identical
+# serialisation without instantiating the pipeline. Turning a *brief idea* into that
+# structured caption is a separate, heavier VLM step that lives with the examples
+# (examples/lingbot_video/model_inference/prompt_rewriter.py), out of the core.
+# ---------------------------------------------------------------------------
+
+# Keys that describe how to *render* the clip rather than its content. When a full
+# sample dict is given without an explicit "caption" key, these are stripped before
+# serialisation (kept identical to the original ``caption_from_sample``).
+_RUNTIME_KEYS = {"duration", "fps", "height", "width", "num_frames", "resolution", "ratio"}
+
+
+def _serialize_caption(caption) -> str:
+    """dict/list -> compact JSON (the exact model format); anything else -> ``str()``."""
+    if isinstance(caption, (dict, list)):
+        return json.dumps(caption, ensure_ascii=False, separators=(",", ":"))
+    return str(caption)
+
+
+def _caption_from_sample(sample) -> str:
+    """Port of ``lingbot_video.utils.caption_from_sample``.
+
+    A *sample* dict either carries the structured caption under ``"caption"`` or IS
+    the caption once the runtime keys are dropped.
+    """
+    if isinstance(sample, dict):
+        if "caption" in sample:
+            caption = sample["caption"]
+        else:
+            caption = {k: v for k, v in sample.items() if k not in _RUNTIME_KEYS}
+    else:
+        caption = sample
+    return _serialize_caption(caption)
+
+
+def normalize_caption(prompt):
+    """Normalise a caption into the compact-JSON string the LingBot DiT expects.
+
+    Accepts:
+
+    - ``dict`` / ``list`` -- a structured caption (or a full sample dict with a
+      ``"caption"`` key), serialised via the original compact-JSON convention.
+    - a path to a ``prompt.json`` file (``str`` ending in ``.json`` that exists) --
+      loaded, then handled as the dict/list case.
+    - any other ``str`` -- returned unchanged (already a caption string, or free-form
+      prose the caller intentionally wants to feed as-is).
+    - ``None`` -- returned unchanged.
+
+    Plain strings are passed through, so this is safe to call unconditionally on
+    prompts that are already in the right format.
+    """
+    if prompt is None:
+        return prompt
+    if isinstance(prompt, str):
+        if prompt.endswith(".json") and os.path.isfile(prompt):
+            with open(prompt, "r", encoding="utf-8") as f:
+                prompt = json.load(f)
+            return _caption_from_sample(prompt)
+        return prompt
+    if isinstance(prompt, (dict, list)):
+        return _caption_from_sample(prompt)
+    return str(prompt)
 
 
 # Number of tokens the Qwen3-VL processor truncates the prompt to. Copied verbatim
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
index a88bdf08a..a3c543d16 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
@@ -1,8 +1,7 @@
 import os
 import torch
 from diffsynth.utils.data import save_video, VideoData
-from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
-from diffsynth.pipelines.lingbot_video_prompt_rewriter import normalize_caption
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
 
 
 # LingBot-Video is trained on STRUCTURED-JSON captions, not free-form prose. Feeding a
diff --git a/examples/lingbot_video/model_inference/prompt_rewriter.py b/examples/lingbot_video/model_inference/prompt_rewriter.py
index 799e88348..7da77c2a5 100644
--- a/examples/lingbot_video/model_inference/prompt_rewriter.py
+++ b/examples/lingbot_video/model_inference/prompt_rewriter.py
@@ -45,7 +45,7 @@
 from system_prompts import (
     VIDEO_STEP1_EXPAND, VIDEO_STEP2_MAP, IMAGE_STEP1_EXPAND, IMAGE_STEP2_MAP,
 )
-from diffsynth.pipelines.lingbot_video_prompt_rewriter import normalize_caption
+from diffsynth.pipelines.lingbot_video import normalize_caption
 
 
 # mode -> (step1 system prompt, step2 system prompt, feed image?, add duration?)
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
index 0d106b745..c6e476a3b 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
@@ -1,8 +1,7 @@
 import os
 import torch
 from diffsynth.utils.data import save_video
-from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
-from diffsynth.pipelines.lingbot_video_prompt_rewriter import normalize_caption
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
 
 
 # Low-VRAM inference. Setting `offload_dtype` / `offload_device` on each ModelConfig is
diff --git a/examples/lingbot_video/model_training/rewrite_captions.py b/examples/lingbot_video/model_training/rewrite_captions.py
index 91da27f22..c010eed15 100644
--- a/examples/lingbot_video/model_training/rewrite_captions.py
+++ b/examples/lingbot_video/model_training/rewrite_captions.py
@@ -28,7 +28,7 @@
 # keeps only normalize_caption), so training and inference share one implementation.
 sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "model_inference"))
 from prompt_rewriter import Rewriter, make_backend
-from diffsynth.pipelines.lingbot_video_prompt_rewriter import normalize_caption
+from diffsynth.pipelines.lingbot_video import normalize_caption
 
 
 def _load_rows(path):
diff --git a/examples/lingbot_video/model_training/train.py b/examples/lingbot_video/model_training/train.py
index d6dd372fc..36aa17b19 100644
--- a/examples/lingbot_video/model_training/train.py
+++ b/examples/lingbot_video/model_training/train.py
@@ -1,7 +1,6 @@
 import torch, os, argparse, accelerate, warnings
 from diffsynth.core import UnifiedDataset
-from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
-from diffsynth.pipelines.lingbot_video_prompt_rewriter import normalize_caption
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
 from diffsynth.diffusion import *
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 

From e83d7d6d3cd5f6ea684e010ac1859cbf7f9598df Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Mon, 27 Jul 2026 17:29:07 +0800
Subject: [PATCH 08/34] Trim verbose comments in LingBot-Video code

Collapse the AI-generated multi-paragraph banners and docstrings to concise
one-line "why" comments matching DiffSynth's light comment style. No code changes.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 diffsynth/models/lingbot_video_dit.py         |  16 +--
 diffsynth/pipelines/lingbot_video.py          | 117 ++++--------------
 .../lingbot-video-dense-1.3b.py               |  24 ++--
 .../model_inference/prompt_rewriter.py        |  54 +++-----
 .../lingbot-video-dense-1.3b.py               |  12 +-
 .../model_training/rewrite_captions.py        |  17 +--
 .../lingbot_video/model_training/train.py     |  15 +--
 7 files changed, 67 insertions(+), 188 deletions(-)

diff --git a/diffsynth/models/lingbot_video_dit.py b/diffsynth/models/lingbot_video_dit.py
index 71acf0827..ec4e98624 100644
--- a/diffsynth/models/lingbot_video_dit.py
+++ b/diffsynth/models/lingbot_video_dit.py
@@ -10,9 +10,8 @@
 from ..core.device.npu_compatible_device import get_device_type
 
 
-# Modules kept in fp32 regardless of the model's bulk compute dtype. The custom
-# `to()` below honours this list so the sensitive AdaLN / norm / router paths stay
-# in full precision, matching the original lingbot-video precision policy.
+# Modules kept in fp32 regardless of the bulk compute dtype (sensitive AdaLN / norm /
+# router paths); the custom `to()` below honours this list.
 LINGBOT_VIDEO_FP32_MODULES = (
     "time_embedder",
     "time_modulation",
@@ -653,11 +652,8 @@ def forward(
 
         # Timestep -> per-token modulation.
         timestep_proj = self.time_proj(timestep.float())
-        # time_proj is a parameter-free sinusoidal embedding and always returns fp32
-        # (get_timestep_embedding upcasts). Cast to the running compute dtype -- joint is
-        # the bf16 hidden state (or fp32 in a full-precision run) -- before the MLP: under
-        # low-VRAM offload the wrapper computes weights in bf16 but does not cast inputs,
-        # so feeding fp32 here raises "mat1 and mat2 must have the same dtype".
+        # time_proj always returns fp32; cast to the compute dtype (joint) before the MLP,
+        # since under low-VRAM offload the wrapper computes in bf16 but does not cast inputs.
         t_emb = self.time_embedder(timestep_proj.to(joint.dtype))  # (B, D)
         if packed_batch:
             temb_input = torch.cat(
@@ -680,9 +676,7 @@ def forward(
         final_mod = self.norm_out_modulation(temb_input.reshape(joint.shape[0] * joint.shape[1], -1))
         shift, scale = final_mod.reshape(joint.shape[0], joint.shape[1], -1).chunk(2, dim=-1)
         final_hidden = self.norm_out(joint) * (1.0 + scale) + shift
-        # Match the running compute dtype (joint), not self.proj_out.weight.dtype: under
-        # low-VRAM offload the resident weight is fp8 (bf16 only at compute time), so
-        # reading .weight.dtype here would cast the input to fp8 and mismatch the wrapper.
+        # Cast to joint's dtype, not proj_out.weight.dtype (which is fp8 under offload).
         projected = self.proj_out(final_hidden.to(joint.dtype))
 
         if packed_batch:
diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index 387145155..0cdb8a497 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -18,40 +18,21 @@
 from ..models.qwen_image_vae import QwenImageVAE, QwenImageCausalConv3d
 
 
-# ---------------------------------------------------------------------------
-# Caption normalization
-#
-# LingBot-Video is trained on **structured JSON captions**, not free-form prose;
-# feeding a flat sentence is out-of-distribution and degrades quality. The pipeline
-# calls ``normalize_caption`` internally (see ``__call__``) so a caption given as a
-# ``dict`` / ``list`` / path to a ``prompt.json`` is serialised to the exact
-# compact-JSON string the DiT consumes, while a plain string is passed through
-# untouched. It is a module-level function (not a method) so the training data-prep
-# scripts -- train.py and rewrite_captions.py -- can import the identical
-# serialisation without instantiating the pipeline. Turning a *brief idea* into that
-# structured caption is a separate, heavier VLM step that lives with the examples
-# (examples/lingbot_video/model_inference/prompt_rewriter.py), out of the core.
-# ---------------------------------------------------------------------------
-
-# Keys that describe how to *render* the clip rather than its content. When a full
-# sample dict is given without an explicit "caption" key, these are stripped before
-# serialisation (kept identical to the original ``caption_from_sample``).
+# LingBot-Video is trained on structured JSON captions. normalize_caption serialises a
+# dict/list caption (or a prompt.json path) into the compact-JSON string the DiT expects;
+# a plain string is passed through. Kept module-level so train.py / rewrite_captions.py
+# can reuse it without importing the pipeline. The prompt rewriter that turns a brief idea
+# into such a caption lives in examples/lingbot_video/model_inference/prompt_rewriter.py.
 _RUNTIME_KEYS = {"duration", "fps", "height", "width", "num_frames", "resolution", "ratio"}
 
 
 def _serialize_caption(caption) -> str:
-    """dict/list -> compact JSON (the exact model format); anything else -> ``str()``."""
     if isinstance(caption, (dict, list)):
         return json.dumps(caption, ensure_ascii=False, separators=(",", ":"))
     return str(caption)
 
 
 def _caption_from_sample(sample) -> str:
-    """Port of ``lingbot_video.utils.caption_from_sample``.
-
-    A *sample* dict either carries the structured caption under ``"caption"`` or IS
-    the caption once the runtime keys are dropped.
-    """
     if isinstance(sample, dict):
         if "caption" in sample:
             caption = sample["caption"]
@@ -63,21 +44,6 @@ def _caption_from_sample(sample) -> str:
 
 
 def normalize_caption(prompt):
-    """Normalise a caption into the compact-JSON string the LingBot DiT expects.
-
-    Accepts:
-
-    - ``dict`` / ``list`` -- a structured caption (or a full sample dict with a
-      ``"caption"`` key), serialised via the original compact-JSON convention.
-    - a path to a ``prompt.json`` file (``str`` ending in ``.json`` that exists) --
-      loaded, then handled as the dict/list case.
-    - any other ``str`` -- returned unchanged (already a caption string, or free-form
-      prose the caller intentionally wants to feed as-is).
-    - ``None`` -- returned unchanged.
-
-    Plain strings are passed through, so this is safe to call unconditionally on
-    prompts that are already in the right format.
-    """
     if prompt is None:
         return prompt
     if isinstance(prompt, str):
@@ -91,14 +57,12 @@ def normalize_caption(prompt):
     return str(prompt)
 
 
-# Number of tokens the Qwen3-VL processor truncates the prompt to. Copied verbatim
-# from the original lingbot-video pipeline so the encoded prompt matches.
+# Prompt truncation length for the Qwen3-VL processor.
 TOKEN_LENGTH = 37698
-# Which hidden-state layer to use as the prompt embedding: 0 -> the last layer.
+# Hidden-state layer used as the prompt embedding: 0 -> the last layer.
 HIDDEN_STATE_SKIP_LAYER = 0
 
-# Chat template that wraps the user prompt inside the prompt-enhancement system
-# prompt. `apply_text_to_template(prompt) == PROMPT_TEMPLATE.format(prompt)`.
+# Prompt-enhancement chat template wrapping the user prompt.
 PROMPT_TEMPLATE = (
     "<|im_start|>system\nGiven a user input that may include a text prompt alone, "
     "a text prompt with an image reference, or a text prompt with a video reference "
@@ -124,27 +88,10 @@ def normalize_caption(prompt):
 
 
 class LingBotVideoPipeline(BasePipeline):
-    """
-    Text-to-video pipeline for LingBot-Video.
-
-    Follows the DiffSynth ``PipelineUnit`` + ``model_fn`` pattern (see
-    :class:`~diffsynth.pipelines.wan_video.WanVideoPipeline`). Components:
-
-    - ``dit``: :class:`~diffsynth.models.lingbot_video_dit.LingBotVideoDiT` (MoE /
-      Dense video DiT), conditioned on ``timestep`` and ``encoder_attention_mask``.
-    - ``text_encoder``:
-      :class:`~diffsynth.models.lingbot_video_text_encoder.LingBotVideoTextEncoder`
-      (Qwen3-VL). The prompt is wrapped in :data:`PROMPT_TEMPLATE`, encoded, and the
-      template-prefix tokens are cropped (``crop_start``).
-    - ``vae``: :class:`~diffsynth.models.qwen_image_vae.QwenImageVAE` (byte-identical
-      to the LingBot-Video VAE). The 5D-video encode/decode and its latent
-      normalisation live in this pipeline (:meth:`encode_video` / :meth:`decode_video`),
-      so the VAE itself stays identical to its image use elsewhere.
-
-    Sampling uses :class:`~diffsynth.diffusion.flow_match.FlowMatchScheduler` (Wan
-    template; first-order flow-matching Euler). Classifier-free guidance runs as two
-    independent forwards.
-    """
+    """Text-to-video pipeline for LingBot-Video (DiT + Qwen3-VL text encoder + QwenImageVAE),
+    following the DiffSynth PipelineUnit + model_fn pattern. Sampling uses FlowMatchScheduler
+    (Wan template). The 5D-video VAE encode/decode lives here (encode_video / decode_video)
+    so QwenImageVAE stays identical to its image use elsewhere."""
 
     def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
         super().__init__(
@@ -170,8 +117,7 @@ def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
         self.compilable_models = ["dit"]
 
     def _compute_crop_start(self) -> int:
-        # Number of tokens contributed by the template prefix (everything before the
-        # user prompt). Computed once by tokenising the template up to a marker.
+        # Token count of the template prefix (everything before the user prompt), computed once.
         if self._crop_start is None:
             marker = "<|USER_INPUT_MARKER|>"
             marked = PROMPT_TEMPLATE.format(marker)
@@ -218,8 +164,7 @@ def from_pretrained(
     @torch.no_grad()
     def __call__(
         self,
-        # Prompt. Accepts a structured caption (dict / list), a path to a prompt.json,
-        # or a plain string; see normalize_caption / the prompt rewriter.
+        # Structured caption (dict / list), a prompt.json path, or a plain string.
         prompt: Union[str, dict, list] = "",
         negative_prompt: Union[str, dict, list] = DEFAULT_NEGATIVE_PROMPT,
         # Video-to-video
@@ -243,11 +188,8 @@ def __call__(
         # Scheduler
         self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
 
-        # Normalise the caption to the structured-JSON string the DiT was trained on.
-        # A dict/list caption or a path to a prompt.json is serialised via the model's
-        # compact-JSON convention; a plain string (already a caption / prose) is left
-        # untouched, so this is a no-op for existing callers. DEFAULT_NEGATIVE_PROMPT is
-        # already such a JSON string and passes through unchanged.
+        # Serialise dict/list/prompt.json captions to the structured-JSON string; plain
+        # strings pass through unchanged.
         prompt = normalize_caption(prompt)
         negative_prompt = normalize_caption(negative_prompt)
 
@@ -267,12 +209,9 @@ def __call__(
         self.load_models_to_device(self.in_iteration_models)
         models = {name: getattr(self, name) for name in self.in_iteration_models}
         for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
-            # The DiT is conditioned on sigma * 1000 (== the raw scheduler timestep).
-            # Pass it as fp32 so the integer inference timesteps are represented
-            # exactly (bf16 cannot represent values > 256 without rounding).
+            # fp32 so the integer timestep is represented exactly (bf16 rounds values > 256).
             timestep_input = timestep.unsqueeze(0).to(dtype=torch.float32, device=self.device)
 
-            # Inference (two independent forwards for CFG).
             noise_pred_posi = self.model_fn(**models, **inputs_shared, **inputs_posi, timestep=timestep_input)
             if cfg_scale != 1.0:
                 noise_pred_nega = self.model_fn(**models, **inputs_shared, **inputs_nega, timestep=timestep_input)
@@ -280,12 +219,8 @@ def __call__(
             else:
                 noise_pred = noise_pred_posi
 
-            # Scheduler step (first-order flow-matching Euler). Uses the raw scheduler
-            # timestep to locate its internal step index, so pass it unmodified.
             inputs_shared["latents"] = self.scheduler.step(noise_pred, timestep, inputs_shared["latents"])
 
-        # Decode. decode_video un-normalises the latents on the 5D-video path, so no
-        # manual latents_mean / latents_std handling is needed here.
         self.load_models_to_device(['vae'])
         latents = inputs_shared["latents"].to(dtype=self.torch_dtype, device=self.device)
         video = self.decode_video(latents)
@@ -297,11 +232,8 @@ def _count_conv3d(self, model):
         return sum(1 for m in model.modules() if isinstance(m, QwenImageCausalConv3d))
 
     def encode_video(self, x):
-        # x: (B, C, T, H, W). Temporal chunking with a persistent causal feature cache
-        # — mathematically equivalent to encoding the whole clip at once, but bounded
-        # in memory. Chunk layout (1 + 4k frames) matches the WanVAE-derived temporal
-        # downsampling (temperal_downsample=[False, True, True]). Kept here in the
-        # pipeline (not on the VAE) so QwenImageVAE stays identical to its image use.
+        # x: (B, C, T, H, W). Temporal chunking (1 + 4k frames) through a persistent causal
+        # feature cache — equivalent to encoding the whole clip at once, bounded in memory.
         vae = self.vae
         t = x.shape[2]
         iter_ = 1 + (t - 1) // 4
@@ -319,8 +251,8 @@ def encode_video(self, x):
         return x
 
     def decode_video(self, x):
-        # x: (B, 16, T', H, W) in DiT latent space. Denormalize, then decode one latent
-        # frame at a time through the persistent causal feature cache.
+        # x: (B, 16, T', H, W) in latent space. Denormalize, then decode one latent frame
+        # at a time through the causal feature cache.
         vae = self.vae
         mean, std = vae.mean.to(dtype=x.dtype, device=x.device), vae.std.to(dtype=x.dtype, device=x.device)
         x = x / std + mean
@@ -359,8 +291,7 @@ def process(self, pipe: LingBotVideoPipeline, height, width, num_frames, seed, r
             1, pipe.dit.in_channels, length,
             height // VAE_SCALE_FACTOR_SPATIAL, width // VAE_SCALE_FACTOR_SPATIAL,
         )
-        # fp32 noise: the flow-matching sampler accumulates state in fp32 for stability
-        # (matches the original pipeline's fp32 latents).
+        # fp32 noise: the flow-matching sampler accumulates state in fp32.
         noise = pipe.generate_noise(shape, seed=seed, rand_device=rand_device, torch_dtype=torch.float32)
         return {"noise": noise}
 
@@ -448,8 +379,7 @@ def model_fn_lingbot_video(
     use_gradient_checkpointing_offload: bool = False,
     **kwargs,
 ):
-    # Cast the latent / text inputs to the DiT's bulk compute dtype (e.g. bf16).
-    # The DiT keeps its AdaLN / norm / router paths in fp32 internally.
+    # Cast inputs to the DiT's compute dtype (e.g. bf16); the DiT keeps AdaLN/norm in fp32.
     dit_dtype = dit.patch_embedder.weight.dtype
     hidden_states = latents.to(dtype=dit_dtype)
     encoder_hidden_states = context.to(dtype=dit_dtype)
@@ -461,5 +391,4 @@ def model_fn_lingbot_video(
         use_gradient_checkpointing=use_gradient_checkpointing,
         use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
     )
-    # Return fp32 so the flow-matching sampler / MSE loss run in full precision.
     return noise_pred.float()
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
index a3c543d16..39f0c3b52 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
@@ -4,11 +4,9 @@
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
 
 
-# LingBot-Video is trained on STRUCTURED-JSON captions, not free-form prose. Feeding a
-# flat sentence is out-of-distribution and visibly degrades quality; feeding the
-# structured caption the model expects restores it. This example runs on a released
-# in-distribution caption by default, and shows at the bottom how to turn a brief idea
-# into such a caption with the two-stage prompt rewriter.
+# LingBot-Video is trained on structured-JSON captions, not free-form prose. This example
+# runs on a released in-distribution caption; see the bottom for turning a brief idea into
+# such a caption with the two-stage prompt rewriter.
 
 pipe = LingBotVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
@@ -22,11 +20,8 @@
 )
 
 # --- Text-to-video -------------------------------------------------------------------
-# The prompts/t2v_example_*.json files are released LingBot-Video t2v captions — real
-# in-distribution examples you can copy as templates for your own. normalize_caption
-# accepts a dict, a list, or a path to a prompt.json; the pipeline calls it internally
-# too, so pipe(prompt="prompts/t2v_example_1.json") works just as well. The default
-# (T2V) negative prompt is built into the pipeline, so negative_prompt can be left unset.
+# prompts/t2v_example_*.json are released in-distribution captions. The pipeline calls
+# normalize_caption internally, so pipe(prompt="prompts/t2v_example_1.json") also works.
 caption = normalize_caption(os.path.join(os.path.dirname(__file__), "prompts", "t2v_example_1.json"))
 video = pipe(
     prompt=caption,
@@ -49,16 +44,13 @@
 save_video(video, "video_lingbot-video-dense-1.3b_v2v.mp4", fps=15, quality=10)
 
 # --- Optional: rewrite a brief idea into a structured caption ------------------------
-# If you only have a brief idea (or free-form prose), turn it into the structured caption
-# the model expects with the two-stage rewriter in prompt_rewriter.py (a sibling module
-# here). The rewriter is a separate VLM + stage-2 LoRA adapter (NOT the DiT), so it is
-# NOT downloaded automatically — fetch both weights first, then point the env vars at them:
+# The two-stage rewriter (prompt_rewriter.py, a sibling module) is a separate VLM +
+# stage-2 LoRA adapter (NOT the DiT) and is not downloaded automatically. Fetch both
+# weights and point the env vars at them:
 #     modelscope download --model Qwen/Qwen3.6-27B --local_dir ./models/Qwen/Qwen3.6-27B
 #     modelscope download --model Robbyant/lingbot-video-rewriter-lora --local_dir ./models/Robbyant/lingbot-video-rewriter-lora
 #     export REWRITER_BASE_MODEL=./models/Qwen/Qwen3.6-27B
 #     export REWRITER_ADAPTER=./models/Robbyant/lingbot-video-rewriter-lora
-# To drive a hosted / OpenAI-compatible endpoint instead of a local VLM, pass a custom
-# object exposing generate(text, image, use_lora) as backend=.
 #
 # from prompt_rewriter import rewrite_prompt
 # caption = rewrite_prompt(
diff --git a/examples/lingbot_video/model_inference/prompt_rewriter.py b/examples/lingbot_video/model_inference/prompt_rewriter.py
index 7da77c2a5..7a2742109 100644
--- a/examples/lingbot_video/model_inference/prompt_rewriter.py
+++ b/examples/lingbot_video/model_inference/prompt_rewriter.py
@@ -1,24 +1,15 @@
 """Two-stage prompt rewriter for LingBot-Video (example-side helper).
 
-The LingBot-Video DiT is trained on **structured JSON captions**, not free-form prose.
-:func:`rewrite_prompt` turns a brief idea into that structured caption via a faithful
-port of the original two-stage rewriter (``rewriter/rewriter_core.py`` +
-``rewriter/inference.py``): stage 1 *expands* the idea into a natural-language caption
-(base model, no LoRA), stage 2 *maps* it into the structured JSON (base model + a
-stage-2 LoRA adapter).
-
-This is a separate VLM + LoRA adapter, NOT the DiT — it is not shipped or downloaded
-with the pipeline, which is why it lives here with the examples rather than in the
-diffsynth core (the core keeps only ``normalize_caption``). The bundled
-:class:`TransformersBackend` loads the rewriter VLM locally; :func:`make_backend` also
-accepts any object exposing ``generate(text, image, use_lora) -> str`` so the rewriter
-can be driven by a hosted / OpenAI-compatible endpoint without shipping the weights.
-
-Typical use::
-
-    from prompt_rewriter import rewrite_prompt          # sibling module in this dir
+rewrite_prompt turns a brief idea into the structured JSON caption the DiT expects:
+stage 1 expands the idea into a natural-language caption (base model), stage 2 maps it
+into structured JSON (base model + stage-2 LoRA). This is a separate VLM + LoRA adapter,
+not the DiT, and is not downloaded with the pipeline -- hence it lives with the examples,
+while the core keeps only normalize_caption. TransformersBackend loads the rewriter VLM
+locally; make_backend also accepts any object exposing generate(text, image, use_lora).
+
+    from prompt_rewriter import rewrite_prompt
     caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
-    video = pipe(prompt=caption, ...)                    # caption is the structured JSON string
+    video = pipe(prompt=caption, ...)
 """
 
 import io
@@ -90,11 +81,8 @@ def _step2_text(mode, detailed, dur):
 
 
 def parse_json(raw):
-    """Parse the stage-2 output into a dict, or ``None`` if it cannot be parsed.
-
-    VLMs occasionally emit unstable JSON (missing quotes, trailing commas, ``` fences),
-    so we strip any code fence, then try the stdlib parser first and fall back to
-    ``json_repair`` when it is installed (recommended for messy outputs)."""
+    """Parse the stage-2 output into a dict, or None. Strips any code fence, tries the
+    stdlib parser, then falls back to json_repair (if installed) for messy output."""
     s = (raw or "").strip()
     m = re.search(r"```(?:json)?\s*(\{.*\})\s*```", s, re.DOTALL)
     if m:
@@ -115,9 +103,8 @@ def parse_json(raw):
 
 
 def save_caption(result, duration, path):
-    """Save as ``{"caption": <structured JSON caption>, "duration": <seconds|null>}`` —
-    exactly the ``prompt.json`` the pipeline / runner consume. ``duration`` is integer
-    seconds for T2V/TI2V and ``None`` for T2I (a still image has no duration)."""
+    """Save as {"caption": <caption>, "duration": <seconds|null>} -- the prompt.json the
+    pipeline consumes. duration is integer seconds for T2V/TI2V, None for T2I."""
     dur = int(round(duration)) if MODES[result["mode"]]["duration"] else None
     with open(path, "w", encoding="utf-8") as f:
         json.dump({"caption": result["json"], "duration": dur}, f, ensure_ascii=False, indent=2)
@@ -125,10 +112,8 @@ def save_caption(result, duration, path):
 
 
 class TransformersBackend:
-    """Local rewriter VLM + LoRA adapter (peft). stage1 = base (adapter disabled),
-    stage2 = base + LoRA. Loads the rewriter model into memory; the weights are NOT
-    the DiT — set ``base``/``adapter`` (or ``REWRITER_BASE_MODEL``/``REWRITER_ADAPTER``)
-    to the rewriter VLM and its stage-2 adapter."""
+    """Local rewriter VLM + LoRA adapter (peft): stage1 = base (adapter disabled),
+    stage2 = base + LoRA. Set base/adapter (or REWRITER_BASE_MODEL/REWRITER_ADAPTER)."""
 
     def __init__(self, base=None, adapter=None, device="auto", max_new_tokens=6144):
         import contextlib
@@ -171,13 +156,8 @@ def generate(self, text, image, use_lora):
 
 
 def make_backend(backend="transformers", base=None, adapter=None):
-    """Build a rewriter backend.
-
-    - ``"transformers"`` — the bundled local :class:`TransformersBackend`.
-    - a custom object / callable — returned as-is if it already exposes
-      ``generate(text, image, use_lora) -> str``. Use this to drive the rewriter from
-      a hosted or OpenAI-compatible endpoint without downloading the VLM locally.
-    """
+    """Build a rewriter backend: "transformers" for the local TransformersBackend, or any
+    object already exposing generate(text, image, use_lora) -> str (e.g. a hosted endpoint)."""
     if backend == "transformers":
         return TransformersBackend(base, adapter)
     if hasattr(backend, "generate"):
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
index c6e476a3b..898d2976c 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
@@ -4,12 +4,9 @@
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
 
 
-# Low-VRAM inference. Setting `offload_dtype` / `offload_device` on each ModelConfig is
-# what actually turns on DiffSynth's VRAM management: weights are kept on CPU in fp8 and
-# streamed to the GPU layer-by-layer, then computed in bf16. `vram_limit` on its own has
-# no effect — without a non-None `offload_dtype` and `offload_device` the loader never
-# enables offloading (see `need_to_enable_vram_management`). `vram_limit` only sets how
-# much resident VRAM the streaming may use once offloading is on.
+# Low-VRAM inference. offload_dtype / offload_device on each ModelConfig turn on VRAM
+# management: weights stay on CPU in fp8 and stream to the GPU layer-by-layer, computed in
+# bf16. vram_limit only caps resident VRAM once offloading is enabled by those two fields.
 vram_config = {
     "offload_dtype": torch.float8_e4m3fn,
     "offload_device": "cpu",
@@ -33,8 +30,7 @@
     vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
 )
 
-# Use a released in-distribution structured caption (shared with the model_inference
-# example). LingBot-Video is trained on structured-JSON captions, not free-form prose.
+# Released in-distribution structured caption (shared with the model_inference example).
 caption = normalize_caption(os.path.join(
     os.path.dirname(__file__), "..", "model_inference", "prompts", "t2v_example_1.json"))
 video = pipe(
diff --git a/examples/lingbot_video/model_training/rewrite_captions.py b/examples/lingbot_video/model_training/rewrite_captions.py
index c010eed15..e452f3a4d 100644
--- a/examples/lingbot_video/model_training/rewrite_captions.py
+++ b/examples/lingbot_video/model_training/rewrite_captions.py
@@ -1,22 +1,15 @@
 """Offline: rewrite a training metadata file's raw prompts into structured JSON captions.
 
-LingBot-Video is trained on structured-JSON captions. If your dataset's ``prompt``
-column holds free-form prose, run this ONCE before training to rewrite every prompt
-into the structured caption the DiT expects, and train on the rewritten metadata. This
-is done offline on purpose — running the (large) rewriter VLM inside the dataloader on
-every step would be prohibitively slow.
-
-The rewriter is a separate VLM + stage-2 LoRA adapter (NOT the DiT). Provide it via
-``--base``/``--adapter`` or the ``REWRITER_BASE_MODEL`` / ``REWRITER_ADAPTER`` env vars.
+Run this once before training if your dataset's prompt column holds free-form prose, then
+train on the rewritten metadata. The rewriter is a separate VLM + stage-2 LoRA adapter
+(not the DiT); provide it via --base/--adapter or REWRITER_BASE_MODEL/REWRITER_ADAPTER.
 
 Usage:
     python rewrite_captions.py --metadata metadata.csv --output metadata_rewritten.csv \
         --base /path/to/rewriter-base --adapter /path/to/rewriter-step2-lora --duration 5
 
-Supports .csv / .json / .jsonl metadata (same formats as the training dataset loader).
-The output keeps every other column and replaces the ``--prompt-column`` with the
-compact-JSON caption string. Rows whose stage-2 output fails to parse are kept with
-their original prompt and logged, so training never silently trains on a broken row.
+Supports .csv / .json / .jsonl. Rows whose stage-2 output fails to parse keep their
+original prompt and are logged, so training never silently uses a broken row.
 """
 
 import argparse
diff --git a/examples/lingbot_video/model_training/train.py b/examples/lingbot_video/model_training/train.py
index 36aa17b19..3d8de6b78 100644
--- a/examples/lingbot_video/model_training/train.py
+++ b/examples/lingbot_video/model_training/train.py
@@ -38,12 +38,9 @@ def __init__(
         self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
         self.resume_from_checkpoint(resume_from_checkpoint, remove_prefix_in_ckpt)
 
-        # Training mode. switch_pipe_to_training_mode puts FlowMatchScheduler (Wan
-        # template) on the full 1000-step flow-matching schedule via
-        # set_timesteps(1000, training=True); the SFT loss samples a random timestep
-        # from it. Attention-only LoRA is the default scope: pass
-        # `--lora_target_modules "to_q,to_k,to_v,to_out"` to leave the MoE / FFN
-        # (gate_proj / up_proj / down_proj) and the router untouched.
+        # Training mode: FlowMatchScheduler (Wan template) runs the full 1000-step
+        # schedule and the SFT loss samples a random timestep. Attention-only LoRA is the
+        # default; pass --lora_target_modules "to_q,to_k,to_v,to_out" to skip MoE/router.
         self.switch_pipe_to_training_mode(
             self.pipe, trainable_models,
             lora_base_model, lora_target_modules, lora_rank, lora_checkpoint,
@@ -66,10 +63,8 @@ def __init__(
         self.min_timestep_boundary = min_timestep_boundary
 
     def get_pipeline_inputs(self, data):
-        # LingBot-Video is trained on structured-JSON captions. Normalise the metadata
-        # "prompt" column (dict / prompt.json path -> compact JSON; plain string kept
-        # as-is) so LoRA trains in-distribution. Prepare captions offline with
-        # model_training/rewrite_captions.py if your dataset stores raw prose.
+        # Normalise the metadata "prompt" column to the structured-JSON caption so LoRA
+        # trains in-distribution. Use rewrite_captions.py offline if it stores raw prose.
         inputs_posi = {"prompt": normalize_caption(data["prompt"])}
         inputs_nega = {}
         inputs_shared = {

From 33334438b75ef2a7d686e0b38ae5fa37c7d861eb Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Mon, 27 Jul 2026 19:42:56 +0800
Subject: [PATCH 09/34] Fix dtype mismatch feeding the fp32-pinned
 time_embedder

time_embedder is fp32-pinned (LINGBOT_VIDEO_FP32_MODULES), so under a standard
load its weights stay fp32 while joint (the bulk hidden state) is bf16. Casting
the fp32 timestep_proj to joint.dtype fed bf16 into the fp32 Linear, raising
"mat1 and mat2 must have the same dtype" on any non-offload run. Cast to the
layer's own weight dtype instead: fp32 under standard load, bf16 under low-VRAM
offload (where the wrapper casts these MLPs to the compute dtype). proj_out keeps
joint.dtype since it is a bulk layer held in fp8 under offload.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 diffsynth/models/lingbot_video_dit.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/diffsynth/models/lingbot_video_dit.py b/diffsynth/models/lingbot_video_dit.py
index ec4e98624..6e621ab20 100644
--- a/diffsynth/models/lingbot_video_dit.py
+++ b/diffsynth/models/lingbot_video_dit.py
@@ -652,9 +652,10 @@ def forward(
 
         # Timestep -> per-token modulation.
         timestep_proj = self.time_proj(timestep.float())
-        # time_proj always returns fp32; cast to the compute dtype (joint) before the MLP,
-        # since under low-VRAM offload the wrapper computes in bf16 but does not cast inputs.
-        t_emb = self.time_embedder(timestep_proj.to(joint.dtype))  # (B, D)
+        # time_proj always returns fp32; match the time_embedder's own weight dtype
+        # (fp32 under standard load via the custom `to()` override; bf16 under low-VRAM
+        # offload, where the wrapper wholesale-casts all params).
+        t_emb = self.time_embedder(timestep_proj.to(self.time_embedder.linear_1.weight.dtype))  # (B, D)
         if packed_batch:
             temb_input = torch.cat(
                 [t_emb[i:i + 1].unsqueeze(1).expand(1, sample_seq_lens[i], -1) for i in range(B)], dim=1

From 4ecc892fcb9dd0438a46ab73a747fd9b315f7850 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Mon, 27 Jul 2026 20:18:33 +0800
Subject: [PATCH 10/34] Add t2v_example_4 structured caption

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 .../prompts/t2v_example_4.json                | 77 +++++++++++++++++++
 1 file changed, 77 insertions(+)
 create mode 100644 examples/lingbot_video/model_inference/prompts/t2v_example_4.json

diff --git a/examples/lingbot_video/model_inference/prompts/t2v_example_4.json b/examples/lingbot_video/model_inference/prompts/t2v_example_4.json
new file mode 100644
index 000000000..bb28ced87
--- /dev/null
+++ b/examples/lingbot_video/model_inference/prompts/t2v_example_4.json
@@ -0,0 +1,77 @@
+{
+  "caption": {
+    "comprehensive_description": {
+      "scene_content_description": "A small, fluffy golden puppy is joyfully running across a lush green meadow on a bright, sunny day. The puppy is chasing a small red ball, which it eventually catches in its mouth. The background is a soft-focus blur of green grass and distant trees, creating a shallow depth of field that emphasizes the puppy's movement. The lighting is warm and natural, casting a gentle glow on the puppy's fur and highlighting the vibrant green of the grass. The overall atmosphere is playful, energetic, and heartwarming.",
+      "camera_movement_description": "The camera performs a smooth tracking shot, panning right to follow the puppy's movement across the field. It maintains a low-angle perspective, keeping the puppy centered in the frame. The shot size remains a medium-close-up throughout, with a slight zoom-in as the puppy catches the ball, enhancing the sense of action and focus."
+    },
+    "camera_info": {
+      "color": "Warm",
+      "frame_size": "Wide",
+      "shot_type_angle": "Low angle",
+      "lens_size": "Long Lens",
+      "composition": "Center",
+      "lighting": "Hard light",
+      "lighting_type": "Daylight"
+    },
+    "world_knowledge": [],
+    "prominent_elements": [
+      {
+        "name": "golden puppy",
+        "description": "A small, fluffy puppy with golden-brown fur, likely a Golden Retriever or similar breed.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 3.5s]",
+            "action": "runs right across the grassy field"
+          },
+          {
+            "timestamp": "[3.5s - 4.0s]",
+            "action": "catches a red ball in its mouth"
+          },
+          {
+            "timestamp": "[4.0s - 5.0s]",
+            "action": "continues running right while holding the ball"
+          }
+        ],
+        "location": "center of the frame, moving from left to right",
+        "relative_size": "large",
+        "shape_and_color": "organic shape with golden-brown fur",
+        "texture": "furry and soft",
+        "appearance_details": "floppy ears, dark eyes, and a black nose",
+        "relationship": "the main subject chasing and catching the red ball",
+        "orientation": "facing right",
+        "pose": "running and leaping",
+        "expression": "joyful and focused",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      },
+      {
+        "name": "red ball",
+        "description": "A small, bright red spherical object used as a toy.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 3.5s]",
+            "action": "rolls right across the grass ahead of the puppy"
+          },
+          {
+            "timestamp": "[3.5s - 5.0s]",
+            "action": "is caught and held in the puppy's mouth"
+          }
+        ],
+        "location": "lower center of the frame, moving right",
+        "relative_size": "small",
+        "shape_and_color": "spherical and bright red",
+        "texture": "smooth",
+        "appearance_details": "solid red color",
+        "relationship": "the object being chased by the puppy",
+        "orientation": "rolling",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      }
+    ]
+  },
+  "duration": 5
+}

From ea73e8b0aa9581e426b32d529ad7f593ac874511 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Mon, 27 Jul 2026 22:03:10 +0800
Subject: [PATCH 11/34] Add TI2V (image-to-video) support to LingBot-Video
 pipeline

Condition generation on a first frame via a new `input_image` argument.
Dense-1.3B reuses its T2V checkpoint (DiT unchanged, in_channels=16); the
condition frame is used twice, matching the original LingBot-Video i2v
pipeline: fed to the Qwen3-VL text encoder as a visual reference (image
tokens prepended to the prompt), and VAE-encoded to a clean latent pinned
into the first temporal slot before sampling and after every scheduler step.

- LingBotVideoUnit_ImageEmbedder builds the cond latent (via encode_video)
  and the smart-resized VLM image; no-op for T2V/V2V.
- LingBotVideoUnit_PromptEmbedder passes the image to the processor when present.
- Ported smart_resize / cover-resize+center-crop / _vlm_image helpers.
- Example lingbot-video-dense-1.3b_ti2v.py + released first frame and caption.
- EN/ZH docs and example README document the TI2V path and `input_image`.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 diffsynth/pipelines/lingbot_video.py          | 155 +++++++++++++++++-
 docs/en/Model_Details/LingBot-Video.md        |  24 +++
 docs/zh/Model_Details/LingBot-Video.md        |  24 +++
 examples/lingbot_video/README.md              |  19 +++
 .../assets/ti2v_first_frame.png               | Bin 0 -> 2453442 bytes
 .../lingbot-video-dense-1.3b_ti2v.py          |  38 +++++
 .../model_inference/prompts/ti2v_example.json | 131 +++++++++++++++
 7 files changed, 385 insertions(+), 6 deletions(-)
 create mode 100644 examples/lingbot_video/model_inference/assets/ti2v_first_frame.png
 create mode 100644 examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py
 create mode 100644 examples/lingbot_video/model_inference/prompts/ti2v_example.json

diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index 0cdb8a497..561b95f62 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -1,7 +1,9 @@
 import json
+import math
 import os
 
 import torch
+import torch.nn.functional as F
 import numpy as np
 from PIL import Image
 from tqdm import tqdm
@@ -86,6 +88,64 @@ def normalize_caption(prompt):
 VAE_SCALE_FACTOR_SPATIAL = 8
 VAE_SCALE_FACTOR_TEMPORAL = 4
 
+# --- TI2V (image-to-video) ---------------------------------------------------
+# The condition frame is used twice: (1) as visual input to the Qwen3-VL text
+# encoder — its image tokens are prepended to the prompt via IMG_PROMPT_TEMPLATE;
+# (2) VAE-encoded to a clean latent written into the first temporal slot of the
+# diffusion latent before sampling and after every scheduler step (inpainting).
+# The DiT is unchanged (in_channels stays 16); Dense-1.3B reuses its T2V weights.
+IMG_PROMPT_TEMPLATE = "<|vision_start|><|image_pad|><|vision_end|>"
+
+# Qwen3-VL vision token-budget bounds used by smart_resize (official defaults).
+IMAGE_MIN_TOKEN_NUM = 4
+IMAGE_MAX_TOKEN_NUM = 16384
+MAX_RATIO = 200
+SPATIAL_MERGE_SIZE = 2
+
+
+def _round_by_factor(number: float, factor: int) -> int:
+    return round(number / factor) * factor
+
+
+def _ceil_by_factor(number: float, factor: int) -> int:
+    return math.ceil(number / factor) * factor
+
+
+def _floor_by_factor(number: float, factor: int) -> int:
+    return math.floor(number / factor) * factor
+
+
+def smart_resize(height: int, width: int, factor: int,
+                 min_pixels: Optional[int] = None, max_pixels: Optional[int] = None):
+    # Resize so both sides are multiples of `factor` and the token count stays in
+    # [min_pixels, max_pixels] while preserving aspect ratio. Ported verbatim from
+    # the official LingBot-Video i2v pipeline (Qwen3-VL smart-resize).
+    max_pixels = max_pixels if max_pixels is not None else IMAGE_MAX_TOKEN_NUM * factor**2
+    min_pixels = min_pixels if min_pixels is not None else IMAGE_MIN_TOKEN_NUM * factor**2
+    if max_pixels < min_pixels:
+        raise ValueError("max_pixels must be greater than or equal to min_pixels.")
+    if max(height, width) / min(height, width) > MAX_RATIO:
+        raise ValueError(f"absolute aspect ratio must be smaller than {MAX_RATIO}.")
+    resized_height = max(factor, _round_by_factor(height, factor))
+    resized_width = max(factor, _round_by_factor(width, factor))
+    if resized_height * resized_width > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        resized_height = _floor_by_factor(height / beta, factor)
+        resized_width = _floor_by_factor(width / beta, factor)
+    elif resized_height * resized_width < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        resized_height = _ceil_by_factor(height * beta, factor)
+        resized_width = _ceil_by_factor(width * beta, factor)
+    return resized_height, resized_width
+
+
+def _pixel_tensor_to_pil(pixel: torch.Tensor) -> Image.Image:
+    # Match torchvision.transforms.ToPILImage for a float CHW image in [0, 1].
+    # pixel: (B, C, T, H, W); take batch 0, temporal slot 0.
+    frame = pixel[0, :, 0].detach().cpu().clamp(0, 1)
+    array = frame.permute(1, 2, 0).mul(255).byte().numpy()
+    return Image.fromarray(array, mode="RGB")
+
 
 class LingBotVideoPipeline(BasePipeline):
     """Text-to-video pipeline for LingBot-Video (DiT + Qwen3-VL text encoder + QwenImageVAE),
@@ -110,6 +170,9 @@ def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
         self.units = [
             LingBotVideoUnit_ShapeChecker(),
             LingBotVideoUnit_NoiseInitializer(),
+            # ImageEmbedder must run before PromptEmbedder: it produces the vlm_image
+            # that PromptEmbedder feeds to the text encoder (TI2V only; no-op for T2V).
+            LingBotVideoUnit_ImageEmbedder(),
             LingBotVideoUnit_PromptEmbedder(),
             LingBotVideoUnit_InputVideoEmbedder(),
         ]
@@ -167,6 +230,8 @@ def __call__(
         # Structured caption (dict / list), a prompt.json path, or a plain string.
         prompt: Union[str, dict, list] = "",
         negative_prompt: Union[str, dict, list] = DEFAULT_NEGATIVE_PROMPT,
+        # Image-to-video (TI2V): condition on a single first frame.
+        input_image: Image.Image = None,
         # Video-to-video
         input_video: list[Image.Image] = None,
         denoising_strength: float = 1.0,
@@ -197,6 +262,7 @@ def __call__(
         inputs_posi = {"prompt": prompt}
         inputs_nega = {"negative_prompt": negative_prompt}
         inputs_shared = {
+            "input_image": input_image,
             "input_video": input_video, "denoising_strength": denoising_strength,
             "seed": seed, "rand_device": rand_device,
             "height": height, "width": width, "num_frames": num_frames,
@@ -205,6 +271,14 @@ def __call__(
         for unit in self.units:
             inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
 
+        # TI2V: the clean condition latent (if any) is pinned into the first temporal
+        # slot(s) both before sampling and after every scheduler step, so the DiT only
+        # ever denoises the frames after the given first frame.
+        first_frame_latents = inputs_shared.get("first_frame_latents")
+        if first_frame_latents is not None:
+            cond_t = first_frame_latents.shape[2]
+            inputs_shared["latents"][:, :, :cond_t] = first_frame_latents
+
         # Denoise
         self.load_models_to_device(self.in_iteration_models)
         models = {name: getattr(self, name) for name in self.in_iteration_models}
@@ -220,6 +294,8 @@ def __call__(
                 noise_pred = noise_pred_posi
 
             inputs_shared["latents"] = self.scheduler.step(noise_pred, timestep, inputs_shared["latents"])
+            if first_frame_latents is not None:
+                inputs_shared["latents"][:, :, :cond_t] = first_frame_latents
 
         self.load_models_to_device(['vae'])
         latents = inputs_shared["latents"].to(dtype=self.torch_dtype, device=self.device)
@@ -265,6 +341,43 @@ def decode_video(self, x):
             out = out_ if out is None else torch.cat([out, out_], dim=2)
         return out
 
+    def preprocess_cond_image(self, image: Image.Image, height, width):
+        # TI2V condition frame -> (1, C, 1, H, W) pixel tensor in [0, 1], aspect-ratio
+        # preserving cover-resize + center-crop to (height, width). Ported from the
+        # official i2v pipeline's preprocess_image.
+        raw = torch.from_numpy(np.array(image.convert("RGB"))).permute(2, 0, 1).unsqueeze(0).contiguous()
+        old_h, old_w = raw.shape[-2:]
+        scale = max(height / old_h, width / old_w)
+        new_h = max(math.ceil(old_h * scale), height)
+        new_w = max(math.ceil(old_w * scale), width)
+        resized = F.interpolate(raw.float(), size=(new_h, new_w), mode="bilinear", align_corners=False)
+        top = int(round((new_h - height) / 2.0))
+        left = int(round((new_w - width) / 2.0))
+        cropped = resized[:, :, top: top + height, left: left + width] / 255.0
+        return cropped.unsqueeze(2)
+
+    def _vision_patch_size(self) -> int:
+        # Resolve the Qwen3-VL vision patch size (used with SPATIAL_MERGE_SIZE to pick
+        # the smart-resize grid factor). Falls back to 16 like the official pipeline.
+        for obj in (
+            getattr(getattr(self.text_encoder, "config", None), "vision_config", None),
+            getattr(getattr(self.processor, "image_processor", None), "config", None),
+            getattr(self.processor, "image_processor", None),
+        ):
+            patch = getattr(obj, "patch_size", None)
+            if patch is not None:
+                return int(patch)
+        return 16
+
+    def _vlm_image(self, pixel: torch.Tensor) -> Image.Image:
+        # Build the PIL image handed to the text encoder from the condition pixel tensor,
+        # smart-resized to the Qwen3-VL patch grid.
+        image = _pixel_tensor_to_pil(pixel)
+        patch_factor = self._vision_patch_size() * SPATIAL_MERGE_SIZE
+        w, h = image.size
+        resized_height, resized_width = smart_resize(h, w, factor=patch_factor)
+        return image.resize((resized_width, resized_height))
+
 
 class LingBotVideoUnit_ShapeChecker(PipelineUnit):
     def __init__(self):
@@ -302,16 +415,20 @@ def __init__(self):
             seperate_cfg=True,
             input_params_posi={"prompt": "prompt"},
             input_params_nega={"prompt": "negative_prompt"},
+            input_params=("vlm_image",),
             output_params=("context", "encoder_attention_mask"),
             onload_model_names=("text_encoder",),
         )
 
-    def encode_prompt(self, pipe: LingBotVideoPipeline, prompt):
-        # T2V: visual_template is empty, so the text is simply the templated prompt.
-        text = PROMPT_TEMPLATE.format(prompt)
+    def encode_prompt(self, pipe: LingBotVideoPipeline, prompt, vlm_image=None):
+        # T2V: visual_template is empty. TI2V: prepend the image-token block to the
+        # prompt and pass the condition image so the encoder attends to it. The image
+        # tokens land after the template prefix, so crop_start is unaffected.
+        visual_template = IMG_PROMPT_TEMPLATE if vlm_image is not None else ""
+        text = PROMPT_TEMPLATE.format(visual_template + prompt)
         inputs = pipe.processor(
             text=[text],
-            images=None,
+            images=[vlm_image] if vlm_image is not None else None,
             videos=None,
             do_resize=False,
             truncation=True,
@@ -339,9 +456,9 @@ def encode_prompt(self, pipe: LingBotVideoPipeline, prompt):
 
         return prompt_embeds.to(dtype=pipe.torch_dtype), prompt_mask
 
-    def process(self, pipe: LingBotVideoPipeline, prompt) -> dict:
+    def process(self, pipe: LingBotVideoPipeline, prompt, vlm_image=None) -> dict:
         pipe.load_models_to_device(self.onload_model_names)
-        prompt_embeds, prompt_mask = self.encode_prompt(pipe, prompt)
+        prompt_embeds, prompt_mask = self.encode_prompt(pipe, prompt, vlm_image=vlm_image)
         return {"context": prompt_embeds, "encoder_attention_mask": prompt_mask}
 
 
@@ -369,6 +486,32 @@ def process(self, pipe: LingBotVideoPipeline, input_video, noise):
             return {"latents": latents}
 
 
+class LingBotVideoUnit_ImageEmbedder(PipelineUnit):
+    """TI2V: turn the condition first frame into (a) a clean VAE latent pinned into
+    the diffusion latent's first temporal slot and (b) a smart-resized PIL image for
+    the Qwen3-VL text encoder. No-op (returns {}) when input_image is None (T2V/V2V)."""
+
+    def __init__(self):
+        super().__init__(
+            input_params=("input_image", "height", "width"),
+            output_params=("first_frame_latents", "vlm_image"),
+            onload_model_names=("vae",),
+        )
+
+    def process(self, pipe: LingBotVideoPipeline, input_image, height, width):
+        if input_image is None:
+            return {}
+        pipe.load_models_to_device(self.onload_model_names)
+        # (1, C, 1, H, W) in [0, 1] -> [-1, 1] to match the VAE input range; encode_video
+        # applies the latent normalisation, giving the same clean cond latent the DiT
+        # was trained to inpaint on.
+        pixel = pipe.preprocess_cond_image(input_image, height, width)
+        pixel = pixel.to(dtype=pipe.torch_dtype, device=pipe.device)
+        first_frame_latents = pipe.encode_video(pixel * 2.0 - 1.0).to(dtype=torch.float32, device=pipe.device)
+        vlm_image = pipe._vlm_image(pixel)
+        return {"first_frame_latents": first_frame_latents, "vlm_image": vlm_image}
+
+
 def model_fn_lingbot_video(
     dit: LingBotVideoDiT,
     latents: torch.Tensor = None,
diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
index 83a9c2b61..32c3dfba3 100644
--- a/docs/en/Model_Details/LingBot-Video.md
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -68,6 +68,7 @@ Input parameters for `LingBotVideoPipeline` inference include:
 
 * `prompt`: Prompt describing the content appearing in the video. Accepts a structured caption (`dict` / `list`), a path to a `prompt.json`, or a plain string; see [Prompt rewriting](#prompt-rewriting-important-for-quality).
 * `negative_prompt`: Negative prompt describing content that should not appear in the video. A default (T2V) negative prompt is built into the pipeline, so this can be left unset.
+* `input_image`: A `PIL.Image` first frame for image-to-video (TI2V); the model animates it. See [Image-to-video (TI2V)](#image-to-video-ti2v).
 * `input_video`: Input video (a list of frames or a `VideoData`) for video-to-video generation, used together with `denoising_strength`.
 * `denoising_strength`: Denoising strength, range 0~1, default value is 1.0. Lower values keep more of the input video structure. Only effective when `input_video` is provided.
 * `height`: Video height, default 480. Must be a multiple of 16.
@@ -118,6 +119,29 @@ Instead of the env vars you can pass `base=` / `adapter=` to `rewrite_prompt`, o
 
 If you don't have the rewriter model, three released LingBot-Video t2v captions ship with the repo as ready-to-use examples: `examples/lingbot_video/model_inference/prompts/t2v_example_{1,2,3}.json`. Pass one straight to the pipeline (`video = pipe(prompt="path/to/t2v_example_1.json", ...)`), or copy one as a template for writing your own structured caption.
 
+## Image-to-video (TI2V)
+
+The pipeline can condition generation on a **first frame**: pass a `PIL.Image` as `input_image` and the model animates it. Dense-1.3B reuses the **same T2V checkpoint** — there is no separate i2v weight to download, and the DiT is unchanged (`in_channels` stays 16).
+
+Matching the original LingBot-Video i2v pipeline, the condition frame is used twice:
+
+- it is fed to the Qwen3-VL text encoder as a visual reference (its image tokens are prepended to the prompt), so the caption and the frame are interpreted together;
+- it is VAE-encoded to a clean latent that is pinned into the first temporal slot of the diffusion latent before sampling and re-applied after every scheduler step, so the model only generates the frames that follow.
+
+```python
+from PIL import Image
+
+input_image = Image.open("first_frame.png").convert("RGB")
+video = pipe(
+    prompt=caption,               # describe the motion that unfolds from the first frame
+    input_image=input_image,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0, seed=0,
+)
+```
+
+The condition frame is aspect-ratio-preserving cover-resized and center-cropped to `height`×`width`, so it need not match the target resolution exactly. A runnable example ships at [`lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py), using the released first frame (`assets/ti2v_first_frame.png`) and its paired caption (`prompts/ti2v_example.json`). The caption should describe the motion that unfolds from the frame; as with T2V it is most in-distribution as a structured-JSON caption. `input_image` and `input_video` are mutually exclusive — pass at most one.
+
 ## Model Training
 
 LingBot-Video is trained through [`examples/lingbot_video/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/train.py), which fine-tunes the DiT with LoRA using the flow-matching SFT objective. The script parameters include:
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
index fddf6250b..20a7204a4 100644
--- a/docs/zh/Model_Details/LingBot-Video.md
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -68,6 +68,7 @@ save_video(video, "video.mp4", fps=15, quality=10)
 
 * `prompt`: 描述视频内容的提示词。支持结构化 caption（`dict` / `list`）、`prompt.json` 路径，或普通字符串；详见[提示词改写](#提示词改写对质量很重要)。
 * `negative_prompt`: 负向提示词，描述不希望出现在视频中的内容。Pipeline 内置了默认（T2V）负向提示词，因此可以不设置。
+* `input_image`: 图生视频（TI2V）的首帧，传入一个 `PIL.Image`，模型会以它为第一帧生成后续画面。详见[图生视频（TI2V）](#图生视频ti2v)。
 * `input_video`: 输入视频（帧列表或 `VideoData`），用于视频生视频，需与 `denoising_strength` 配合使用。
 * `denoising_strength`: 降噪强度，取值范围 0~1，默认为 1.0。值越小，越保留输入视频的结构。仅在提供 `input_video` 时生效。
 * `height`: 视频高度，默认为 480，需能被 16 整除。
@@ -118,6 +119,29 @@ video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0
 
 如果没有改写器模型，仓库自带 3 个官方 LingBot-Video t2v 结构化 caption 作为开箱即用的示例：`examples/lingbot_video/model_inference/prompts/t2v_example_{1,2,3}.json`。可直接按路径传给 pipeline（`video = pipe(prompt="path/to/t2v_example_1.json", ...)`），也可以复制一个作为编写自己 caption 的模板。
 
+## 图生视频（TI2V）
+
+Pipeline 支持以一张**首帧**为条件生成视频：把一个 `PIL.Image` 传给 `input_image`，模型会以它为第一帧生成后续画面。Dense-1.3B **复用同一份 T2V 权重**——无需额外下载 i2v 权重，DiT 也保持不变（`in_channels` 仍为 16）。
+
+与官方 LingBot-Video i2v pipeline 一致，首帧会被使用两次：
+
+- 作为视觉参考喂给 Qwen3-VL 文本编码器（其图像 token 会前插到提示词里），让 caption 与首帧被联合理解；
+- 经 VAE 编码为一个干净 latent，在采样前钉入扩散 latent 的第一个时间槽，并在每个调度步之后重新钉入，因此模型只生成首帧之后的画面。
+
+```python
+from PIL import Image
+
+input_image = Image.open("first_frame.png").convert("RGB")
+video = pipe(
+    prompt=caption,               # 描述从首帧开始展开的运动
+    input_image=input_image,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0, seed=0,
+)
+```
+
+首帧会按保持宽高比的方式 cover-resize 并中心裁剪到 `height`×`width`，因此不必与目标分辨率完全一致。可直接运行的示例见 [`lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)，它使用随仓库发布的首帧（`assets/ti2v_first_frame.png`）及其配对 caption（`prompts/ti2v_example.json`）。caption 应描述从首帧开始展开的运动；与 T2V 一样，写成结构化 JSON caption 最贴近训练分布。`input_image` 与 `input_video` 互斥，至多传一个。
+
 ## 模型训练
 
 LingBot-Video 通过 [`examples/lingbot_video/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/train.py) 进行训练，使用 flow-matching SFT 目标对 DiT 做 LoRA 微调。脚本的参数包括：
diff --git a/examples/lingbot_video/README.md b/examples/lingbot_video/README.md
index 819586ee0..74d839d65 100644
--- a/examples/lingbot_video/README.md
+++ b/examples/lingbot_video/README.md
@@ -56,6 +56,25 @@ The pipeline ships a default (T2V) negative prompt, so `negative_prompt` is opti
 
 **Low VRAM:** pass `vram_limit=<GB>` to `from_pretrained` to enable layer-by-layer offloading — see `model_inference_low_vram/lingbot-video-dense-1.3b.py`.
 
+### Image-to-video (TI2V)
+
+Condition on a **first frame** by passing a `PIL.Image` as `input_image`; the model animates it. Dense-1.3B reuses the same T2V checkpoint (no separate i2v weight). The frame is used twice — as a visual reference for the Qwen3-VL text encoder, and as a clean latent pinned into the first frame of the diffusion latent before sampling and after every step, so only the following frames are generated.
+
+```bash
+python examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py
+```
+
+```python
+from PIL import Image
+video = pipe(
+    prompt=caption,                                  # describes the motion from the first frame
+    input_image=Image.open("first_frame.png").convert("RGB"),
+    height=480, width=832, num_frames=81, cfg_scale=3.0, seed=0,
+)
+```
+
+The runnable example uses the released first frame + caption in `model_inference/assets/ti2v_first_frame.png` and `model_inference/prompts/ti2v_example.json`. `input_image` and `input_video` are mutually exclusive.
+
 ## Prompt rewriting (important for quality)
 
 LingBot-Video is trained on **structured-JSON captions**, not free-form prose. Feeding a flat sentence is out-of-distribution and visibly degrades quality (softer, less coherent motion); feeding the structured caption the model expects restores it. The pipeline accepts a caption as a `dict`, a path to a `prompt.json`, or a plain string, and normalises it to the exact compact-JSON format the DiT was trained on — a plain string is passed through unchanged, so existing scripts keep working.
diff --git a/examples/lingbot_video/model_inference/assets/ti2v_first_frame.png b/examples/lingbot_video/model_inference/assets/ti2v_first_frame.png
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literal 0
HcmV?d00001

diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py
new file mode 100644
index 000000000..fe69d385f
--- /dev/null
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py
@@ -0,0 +1,38 @@
+import os
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
+
+
+# Image-to-video (TI2V). Dense-1.3B reuses the SAME T2V checkpoint — there is no separate
+# i2v weight. The condition first frame is used twice: as visual input to the Qwen3-VL text
+# encoder, and as a clean latent pinned into the first frame of the diffusion latent so the
+# model only generates the frames that follow. Pass a first frame via `input_image`.
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+)
+
+# prompts/ti2v_example.json is a released in-distribution caption paired with the first frame
+# in assets/ti2v_first_frame.png. The caption should describe the motion that unfolds from the
+# given frame; the pipeline calls normalize_caption internally so a path also works directly.
+here = os.path.dirname(__file__)
+caption = normalize_caption(os.path.join(here, "prompts", "ti2v_example.json"))
+input_image = Image.open(os.path.join(here, "assets", "ti2v_first_frame.png")).convert("RGB")
+
+video = pipe(
+    prompt=caption,
+    input_image=input_image,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-dense-1.3b_ti2v.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_inference/prompts/ti2v_example.json b/examples/lingbot_video/model_inference/prompts/ti2v_example.json
new file mode 100644
index 000000000..f4915fc91
--- /dev/null
+++ b/examples/lingbot_video/model_inference/prompts/ti2v_example.json
@@ -0,0 +1,131 @@
+{
+  "caption": {
+    "comprehensive_description": {
+      "scene_content_description": "A dynamic, high-fidelity sequence of a fit young man and a sleek white humanoid robot running side-by-side along a paved promenade lined with blooming cherry blossom trees. The man is dressed in black athletic wear, while the robot features a glossy white chassis with black mechanical joints. They are running towards the camera, which tracks backward to maintain their position in the frame. The background features a blurred cityscape and a bridge over a river, creating a sense of depth and urban vitality. The atmosphere is energetic and futuristic, blending organic nature with advanced technology.",
+      "camera_movement_description": "The camera executes a smooth, continuous backward tracking shot, moving parallel to the subjects' forward motion to keep them centered in the frame. There is a subtle, rhythmic vertical bobbing motion synchronized with the cadence of the runners' footsteps, adding a visceral sense of speed and physical exertion. The camera maintains a steady focus on the two runners, allowing the background elements to exhibit motion blur."
+    },
+    "camera_info": {
+      "color": "Natural",
+      "frame_size": "Wide",
+      "shot_type_angle": "Eye level",
+      "lens_size": "Telephoto",
+      "composition": "Symmetrical",
+      "lighting": "Bright sunlight",
+      "lighting_type": "Daylight"
+    },
+    "world_knowledge": [],
+    "prominent_elements": [
+      {
+        "name": "fit young man",
+        "description": "A muscular male with short dark hair, wearing a tight black t-shirt and black running shorts.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 5.0s]",
+            "action": "He runs steadily towards the camera, his arms pumping rhythmically at his sides and his legs cycling with powerful strides, maintaining a consistent pace."
+          }
+        ],
+        "location": "Left side of the frame (viewer's perspective)",
+        "relative_size": "large",
+        "shape_and_color": "Athletic build, dark clothing contrasting with the bright surroundings.",
+        "texture": "Realistic skin and fabric",
+        "appearance_details": "White running shoes, focused and determined facial expression.",
+        "relationship": "The primary human subject",
+        "orientation": "Facing forward",
+        "pose": "Running posture, arms bent at the elbows",
+        "expression": "Focused and determined",
+        "clothing": "Black t-shirt, black shorts, white sneakers",
+        "gender": "Male",
+        "skin_tone_and_texture": "Tanned, muscular"
+      },
+      {
+        "name": "humanoid robot",
+        "description": "A sleek, anthropomorphic machine with a glossy white exterior and exposed black mechanical joints at the shoulders, elbows, knees, and ankles.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 5.0s]",
+            "action": "It runs with mechanical precision, its limbs moving in a fluid, lifelike gait that perfectly matches the man's stride, its arms swinging in coordination with its legs."
+          }
+        ],
+        "location": "Right side of the frame (viewer's perspective)",
+        "relative_size": "large",
+        "shape_and_color": "Angular and futuristic, white with black accents.",
+        "texture": "Smooth, reflective metal and matte composite materials",
+        "appearance_details": "A smooth, featureless black visor for a face; the label '07' is visible on its chest plate.",
+        "relationship": "The companion subject running alongside the man",
+        "orientation": "Facing forward",
+        "pose": "Running posture, mirroring the human",
+        "expression": "",
+        "clothing": "",
+        "gender": "",
+        "skin_tone_and_texture": ""
+      },
+      {
+        "name": "cherry blossom trees",
+        "description": "A dense row of mature trees with dark trunks and canopies full of vibrant pink flowers.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 5.0s]",
+            "action": "The trees appear to rush past the camera due to the backward tracking motion, with the pink blossoms blurring slightly to emphasize speed."
+          }
+        ],
+        "location": "Background, lining both sides of the path",
+        "relative_size": "large",
+        "shape_and_color": "Organic and sprawling, with dark brown bark and bright pink blossoms.",
+        "texture": "Rough bark and delicate petals",
+        "appearance_details": "Hanging branches that frame the top of the shot.",
+        "relationship": "The environmental setting",
+        "orientation": "Receding into the distance",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "gender": "",
+        "skin_tone_and_texture": ""
+      },
+      {
+        "name": "paved promenade",
+        "description": "A wide, brick-paved walkway stretching from the foreground into the distance.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 5.0s]",
+            "action": "The ground rushes towards the bottom of the frame, providing a strong sense of forward momentum."
+          }
+        ],
+        "location": "Lower center of the frame",
+        "relative_size": "large",
+        "shape_and_color": "Rectangular and linear, composed of reddish-brown bricks.",
+        "texture": "Rough and uneven",
+        "appearance_details": "Distinct pattern of the paving stones.",
+        "relationship": "The ground surface for the runners",
+        "orientation": "Leading the eye towards the horizon",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "gender": "",
+        "skin_tone_and_texture": ""
+      },
+      {
+        "name": "urban background",
+        "description": "A blurred cityscape featuring tall buildings and a steel bridge spanning a body of water.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 5.0s]",
+            "action": "The background remains relatively static but shifts slightly due to the camera's lateral movement, remaining out of focus to keep attention on the runners."
+          }
+        ],
+        "location": "Far background, visible through the gaps in the trees and to the right",
+        "relative_size": "medium",
+        "shape_and_color": "Geometric and muted, with grey, blue, and beige tones.",
+        "texture": "Indistinct due to depth of field",
+        "appearance_details": "Silhouette of skyscrapers and the truss structure of the bridge.",
+        "relationship": "The distant context",
+        "orientation": "Static in the distance",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "gender": "",
+        "skin_tone_and_texture": ""
+      }
+    ]
+  },
+  "duration": 5
+}

From 670ec2272f17c03ba68c055e30b31025a07f081f Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Mon, 27 Jul 2026 22:08:22 +0800
Subject: [PATCH 12/34] Add text-to-image (t2i) support and example to
 LingBot-Video

t2i is text-to-video with num_frames=1 through the same pipeline and DiT
(no separate image weight), matching the official runner which sets
num_frames=1 and swaps in the still-image negative prompt.

- Add DEFAULT_NEGATIVE_PROMPT_IMAGE (verbatim from the official pipeline):
  drops the temporal/motion terms that cannot apply to a single frame.
- Example lingbot-video-dense-1.3b_t2i.py + released still-image caption
  prompts/t2i_example.json; saves the single returned frame as PNG.
- EN/ZH docs and example README document the t2i path.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 diffsynth/pipelines/lingbot_video.py          |  7 ++
 docs/en/Model_Details/LingBot-Video.md        | 18 ++++
 docs/zh/Model_Details/LingBot-Video.md        | 18 ++++
 examples/lingbot_video/README.md              | 19 +++++
 .../lingbot-video-dense-1.3b_t2i.py           | 33 ++++++++
 .../model_inference/prompts/t2i_example.json  | 83 +++++++++++++++++++
 6 files changed, 178 insertions(+)
 create mode 100644 examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py
 create mode 100644 examples/lingbot_video/model_inference/prompts/t2i_example.json

diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index 561b95f62..8ea06d84c 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -84,6 +84,13 @@ def normalize_caption(prompt):
     '{"universal_negative": {"visual_quality": ["low quality", "worst quality", "blurry", "pixelated", "jpeg artifacts", "low resolution", "unstable color", "color flicker", "underexposed", "overexposed", "invisible subject", "subject hidden in darkness"], "artistic_style": ["painting", "illustration", "drawing", "cartoon", "3d render", "cgi", "sketch", "digital art"], "composition_and_content": ["text", "watermark", "signature", "logo", "subtitles", "pillarboxed", "side bars", "portrait image in landscape frame"], "temporal_and_motion_stability": ["flickering", "jittery", "motion blur", "temporal inconsistency", "warping", "morphing", "incoherent motion", "unnatural movement", "static object with sudden jump", "frame-to-frame inconsistency"], "material_and_structure": ["plastic-like glass", "unrealistic texture", "deformed bottle", "liquid freezing improperly", "distorted reflections"]}}'
 )
 
+# Still-image (t2i) default negative prompt, copied verbatim from the official pipeline.
+# Drops the whole temporal/motion block and the video-only codec/temporal terms that
+# cannot apply to a single frame. Pass this as negative_prompt when num_frames=1.
+DEFAULT_NEGATIVE_PROMPT_IMAGE = (
+    '{"universal_negative": {"visual_quality": ["low quality", "worst quality", "blurry", "pixelated", "jpeg artifacts", "low resolution", "underexposed", "overexposed", "invisible subject", "subject hidden in darkness"], "artistic_style": ["painting", "illustration", "drawing", "cartoon", "3d render", "cgi", "sketch", "digital art"], "composition_and_content": ["text", "watermark", "signature", "logo", "pillarboxed", "side bars", "portrait image in landscape frame"], "material_and_structure": ["plastic-like glass", "unrealistic texture", "deformed bottle", "distorted reflections"]}}'
+)
+
 # VAE downsample factors (QwenImageVAE / Wan-VAE): 8x spatial, 4x temporal.
 VAE_SCALE_FACTOR_SPATIAL = 8
 VAE_SCALE_FACTOR_TEMPORAL = 4
diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
index 32c3dfba3..695bac0b4 100644
--- a/docs/en/Model_Details/LingBot-Video.md
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -142,6 +142,24 @@ video = pipe(
 
 The condition frame is aspect-ratio-preserving cover-resized and center-cropped to `height`×`width`, so it need not match the target resolution exactly. A runnable example ships at [`lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py), using the released first frame (`assets/ti2v_first_frame.png`) and its paired caption (`prompts/ti2v_example.json`). The caption should describe the motion that unfolds from the frame; as with T2V it is most in-distribution as a structured-JSON caption. `input_image` and `input_video` are mutually exclusive — pass at most one.
 
+## Text-to-image (t2i)
+
+Text-to-image is text-to-video with a single frame — the same pipeline and DiT, just `num_frames=1`. There is no separate image checkpoint. The only image-specific knob is the negative prompt: `DEFAULT_NEGATIVE_PROMPT_IMAGE` (exported from `diffsynth.pipelines.lingbot_video`) drops the temporal/motion terms that cannot apply to a still frame. The pipeline returns a 1-element list, i.e. a single `PIL.Image`.
+
+```python
+from diffsynth.pipelines.lingbot_video import DEFAULT_NEGATIVE_PROMPT_IMAGE
+
+frames = pipe(
+    prompt=caption,
+    negative_prompt=DEFAULT_NEGATIVE_PROMPT_IMAGE,
+    height=480, width=832, num_frames=1,
+    num_inference_steps=40, cfg_scale=3.0, seed=0,
+)
+frames[0].save("image.png")
+```
+
+A runnable example ships at [`lingbot-video-dense-1.3b_t2i.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py), using the released still-image caption `prompts/t2i_example.json`.
+
 ## Model Training
 
 LingBot-Video is trained through [`examples/lingbot_video/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/train.py), which fine-tunes the DiT with LoRA using the flow-matching SFT objective. The script parameters include:
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
index 20a7204a4..51247d997 100644
--- a/docs/zh/Model_Details/LingBot-Video.md
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -142,6 +142,24 @@ video = pipe(
 
 首帧会按保持宽高比的方式 cover-resize 并中心裁剪到 `height`×`width`，因此不必与目标分辨率完全一致。可直接运行的示例见 [`lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)，它使用随仓库发布的首帧（`assets/ti2v_first_frame.png`）及其配对 caption（`prompts/ti2v_example.json`）。caption 应描述从首帧开始展开的运动；与 T2V 一样，写成结构化 JSON caption 最贴近训练分布。`input_image` 与 `input_video` 互斥，至多传一个。
 
+## 文生图（t2i）
+
+文生图就是只生成一帧的文生视频——同一条 pipeline、同一个 DiT，只需设 `num_frames=1`，无需额外的图像权重。唯一与图像相关的开关是负向提示词：`DEFAULT_NEGATIVE_PROMPT_IMAGE`（从 `diffsynth.pipelines.lingbot_video` 导出）去掉了不适用于单帧的时序/运动类词条。此时 pipeline 返回只含一个元素的列表，即一张 `PIL.Image`。
+
+```python
+from diffsynth.pipelines.lingbot_video import DEFAULT_NEGATIVE_PROMPT_IMAGE
+
+frames = pipe(
+    prompt=caption,
+    negative_prompt=DEFAULT_NEGATIVE_PROMPT_IMAGE,
+    height=480, width=832, num_frames=1,
+    num_inference_steps=40, cfg_scale=3.0, seed=0,
+)
+frames[0].save("image.png")
+```
+
+可直接运行的示例见 [`lingbot-video-dense-1.3b_t2i.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)，它使用随仓库发布的静态图 caption `prompts/t2i_example.json`。
+
 ## 模型训练
 
 LingBot-Video 通过 [`examples/lingbot_video/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/train.py) 进行训练，使用 flow-matching SFT 目标对 DiT 做 LoRA 微调。脚本的参数包括：
diff --git a/examples/lingbot_video/README.md b/examples/lingbot_video/README.md
index 74d839d65..84a44258d 100644
--- a/examples/lingbot_video/README.md
+++ b/examples/lingbot_video/README.md
@@ -75,6 +75,25 @@ video = pipe(
 
 The runnable example uses the released first frame + caption in `model_inference/assets/ti2v_first_frame.png` and `model_inference/prompts/ti2v_example.json`. `input_image` and `input_video` are mutually exclusive.
 
+### Text-to-image (t2i)
+
+Text-to-image is just text-to-video with `num_frames=1` — same pipeline and DiT, no separate image weight. Pass `DEFAULT_NEGATIVE_PROMPT_IMAGE` (the still-image negative prompt) and save the single returned frame as an image.
+
+```bash
+python examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py
+```
+
+```python
+from diffsynth.pipelines.lingbot_video import DEFAULT_NEGATIVE_PROMPT_IMAGE
+frames = pipe(
+    prompt=caption, negative_prompt=DEFAULT_NEGATIVE_PROMPT_IMAGE,
+    height=480, width=832, num_frames=1, cfg_scale=3.0, seed=0,
+)
+frames[0].save("image.png")
+```
+
+The runnable example uses the released still-image caption `model_inference/prompts/t2i_example.json`.
+
 ## Prompt rewriting (important for quality)
 
 LingBot-Video is trained on **structured-JSON captions**, not free-form prose. Feeding a flat sentence is out-of-distribution and visibly degrades quality (softer, less coherent motion); feeding the structured caption the model expects restores it. The pipeline accepts a caption as a `dict`, a path to a `prompt.json`, or a plain string, and normalises it to the exact compact-JSON format the DiT was trained on — a plain string is passed through unchanged, so existing scripts keep working.
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py
new file mode 100644
index 000000000..24fe3e23d
--- /dev/null
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py
@@ -0,0 +1,33 @@
+import os
+import torch
+from diffsynth.pipelines.lingbot_video import (
+    LingBotVideoPipeline, ModelConfig, normalize_caption, DEFAULT_NEGATIVE_PROMPT_IMAGE,
+)
+
+
+# Text-to-image (t2i) is text-to-video with a single frame: pass num_frames=1 through the
+# same pipeline and DiT (no separate image weight). The only image-specific knob is the
+# negative prompt — DEFAULT_NEGATIVE_PROMPT_IMAGE drops the temporal/motion terms that
+# cannot apply to a still frame. The pipeline returns a 1-frame list, i.e. one PIL image.
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+)
+
+# prompts/t2i_example.json is a released in-distribution still-image caption.
+caption = normalize_caption(os.path.join(os.path.dirname(__file__), "prompts", "t2i_example.json"))
+frames = pipe(
+    prompt=caption,
+    negative_prompt=DEFAULT_NEGATIVE_PROMPT_IMAGE,
+    height=480, width=832, num_frames=1,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+frames[0].save("image_lingbot-video-dense-1.3b_t2i.png")
diff --git a/examples/lingbot_video/model_inference/prompts/t2i_example.json b/examples/lingbot_video/model_inference/prompts/t2i_example.json
new file mode 100644
index 000000000..f598d85a2
--- /dev/null
+++ b/examples/lingbot_video/model_inference/prompts/t2i_example.json
@@ -0,0 +1,83 @@
+{
+  "caption": {
+    "comprehensive_description": "A clear glass bottle filled with water sits on a light-colored wooden table, acting as a lens to focus sunlight. The bottle is positioned slightly to the left of the center, with its base resting on the table. Bright, direct sunlight from the upper right creates a brilliant, starburst-like lens flare on the table's surface, casting a warm, golden glow. The background is a soft, out-of-focus blur of green foliage and bright light, suggesting an outdoor garden or patio setting. The overall atmosphere is warm, serene, and summery, emphasizing the interplay between light and water.",
+    "camera_info": {
+      "color": "Warm",
+      "frame_size": "Extreme Close Up",
+      "shot_type_angle": "Low angle",
+      "lens_size": "Long Lens",
+      "composition": "Left heavy",
+      "lighting": "Hard light",
+      "lighting_type": "Daylight"
+    },
+    "world_knowledge": [],
+    "prominent_elements": [
+      {
+        "name": "glass bottle",
+        "description": "A clear, cylindrical glass bottle with a narrow neck and a flat base, partially filled with water.",
+        "location": "center-left",
+        "relative_size": "medium",
+        "shape_and_color": "Cylindrical with a tapered neck; transparent and clear",
+        "texture": "smooth, glossy",
+        "appearance_details": "The bottle contains clear water that reflects the surrounding light. The glass surface shows subtle highlights and reflections from the sun.",
+        "relationship": "Sits on the wooden table and acts as the focal point for the sunlight, creating a lens flare.",
+        "orientation": "upright",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "gender": "",
+        "skin_tone_and_texture": ""
+      },
+      {
+        "name": "wooden table",
+        "description": "A flat surface made of light-colored wood with visible grain and horizontal planks.",
+        "location": "spanning the bottom half of the frame",
+        "relative_size": "large",
+        "shape_and_color": "Rectangular planks; light tan and beige",
+        "texture": "matte, grainy",
+        "appearance_details": "The surface is illuminated by a bright, star-shaped lens flare caused by the bottle focusing the sunlight.",
+        "relationship": "Provides the base upon which the bottle rests.",
+        "orientation": "horizontal",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "gender": "",
+        "skin_tone_and_texture": ""
+      },
+      {
+        "name": "lens flare",
+        "description": "A bright, star-shaped burst of light created by sunlight passing through the water in the bottle.",
+        "location": "center, on the table surface",
+        "relative_size": "medium",
+        "shape_and_color": "Star-shaped; bright white and golden yellow",
+        "texture": "ethereal, glowing",
+        "appearance_details": "The flare has multiple sharp rays extending outwards from the point where the bottle meets the table.",
+        "relationship": "Originates from the interaction of sunlight and the water inside the glass bottle.",
+        "orientation": "radiating",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "gender": "",
+        "skin_tone_and_texture": ""
+      },
+      {
+        "name": "blurred foliage",
+        "description": "A soft-focus background consisting of green leaves and bright light spots.",
+        "location": "top half of the frame",
+        "relative_size": "large",
+        "shape_and_color": "Amorphous shapes; various shades of green and bright white",
+        "texture": "soft, blurry",
+        "appearance_details": "The background is heavily out of focus, creating a bokeh effect that emphasizes the sharpness of the bottle in the foreground.",
+        "relationship": "Provides a natural, outdoor context for the scene.",
+        "orientation": "upright",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "gender": "",
+        "skin_tone_and_texture": "",
+        "is_cluster": true,
+        "number_of_objects": "numerous"
+      }
+    ]
+  }
+}

From 8d8b7c7554922e4699e5758fd4d80eccc284d610 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Tue, 28 Jul 2026 13:05:09 +0800
Subject: [PATCH 13/34] Add ti2v/t2i low-VRAM inference + ti2v LoRA training
 examples

- model_inference_low_vram: add ti2v (input_image) and t2i (num_frames=1 +
  DEFAULT_NEGATIVE_PROMPT_IMAGE) low-VRAM variants, mirroring the t2v script.
- train.py: add opt-in --first_frame_as_condition for image-to-video LoRA. It
  conditions each clip on its own first frame; FlowMatchSFTLoss already pins the
  clean first-frame latent and excludes it from the loss, so no core change is
  needed. A distinct condition column still works via --extra_inputs input_image.
- model_training: add ti2v LoRA launch script and validate_lora example.
- docs (en/zh) + example README: reference the new scripts and TI2V LoRA.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 docs/en/Model_Details/LingBot-Video.md        |  8 ++-
 docs/zh/Model_Details/LingBot-Video.md        |  8 ++-
 examples/lingbot_video/README.md              |  8 ++-
 .../lingbot-video-dense-1.3b_t2i.py           | 47 ++++++++++++++++++
 .../lingbot-video-dense-1.3b_ti2v.py          | 49 +++++++++++++++++++
 .../lora/lingbot-video-dense-1.3b_ti2v.sh     | 31 ++++++++++++
 .../lingbot_video/model_training/train.py     | 13 +++++
 .../lingbot-video-dense-1.3b_ti2v.py          | 34 +++++++++++++
 8 files changed, 193 insertions(+), 5 deletions(-)
 create mode 100644 examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py
 create mode 100644 examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py
 create mode 100644 examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh
 create mode 100644 examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py

diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
index 695bac0b4..5c3f48914 100644
--- a/docs/en/Model_Details/LingBot-Video.md
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -140,7 +140,7 @@ video = pipe(
 )
 ```
 
-The condition frame is aspect-ratio-preserving cover-resized and center-cropped to `height`×`width`, so it need not match the target resolution exactly. A runnable example ships at [`lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py), using the released first frame (`assets/ti2v_first_frame.png`) and its paired caption (`prompts/ti2v_example.json`). The caption should describe the motion that unfolds from the frame; as with T2V it is most in-distribution as a structured-JSON caption. `input_image` and `input_video` are mutually exclusive — pass at most one.
+The condition frame is aspect-ratio-preserving cover-resized and center-cropped to `height`×`width`, so it need not match the target resolution exactly. A runnable example ships at [`lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py) (low-VRAM variant: [`model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)), using the released first frame (`assets/ti2v_first_frame.png`) and its paired caption (`prompts/ti2v_example.json`). The caption should describe the motion that unfolds from the frame; as with T2V it is most in-distribution as a structured-JSON caption. `input_image` and `input_video` are mutually exclusive — pass at most one. To fine-tune an image-to-video LoRA, see [TI2V LoRA](#ti2v-lora) below.
 
 ## Text-to-image (t2i)
 
@@ -158,7 +158,7 @@ frames = pipe(
 frames[0].save("image.png")
 ```
 
-A runnable example ships at [`lingbot-video-dense-1.3b_t2i.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py), using the released still-image caption `prompts/t2i_example.json`.
+A runnable example ships at [`lingbot-video-dense-1.3b_t2i.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py) (low-VRAM variant: [`model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)), using the released still-image caption `prompts/t2i_example.json`.
 
 ## Model Training
 
@@ -220,6 +220,10 @@ The MoE / FFN experts (`gate_proj`, `up_proj`, `down_proj`) and the router are l
 
 For best results the `prompt` column should hold **structured-JSON captions** (the same in-distribution format used at inference — see [Prompt rewriting](#prompt-rewriting-important-for-quality)). `train.py` runs each prompt through `normalize_caption`. If your dataset stores raw prose, rewrite it once offline with `examples/lingbot_video/model_training/rewrite_captions.py` before training.
 
+### TI2V LoRA
+
+To train an **image-to-video** LoRA, add `--first_frame_as_condition` (see [`lora/lingbot-video-dense-1.3b_ti2v.sh`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)). It conditions each clip on its **own first frame**: the frame is VAE-encoded to a clean latent pinned into the first temporal slot (and fed to the Qwen3-VL text encoder), and excluded from the flow-matching loss, so the LoRA learns to animate frames 2..N from frame 1. The dataset, LoRA scope, and DiT are identical to t2v — no condition column is required. Validate with [`validate_lora/lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py). If your dataset ships a *distinct* condition frame instead, drop the flag and pass that column via `--extra_inputs input_image` (adding it to `--data_file_keys`).
+
 We have written recommended training scripts, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
 
 ## Notes
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
index 51247d997..96ba8eecf 100644
--- a/docs/zh/Model_Details/LingBot-Video.md
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -140,7 +140,7 @@ video = pipe(
 )
 ```
 
-首帧会按保持宽高比的方式 cover-resize 并中心裁剪到 `height`×`width`，因此不必与目标分辨率完全一致。可直接运行的示例见 [`lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)，它使用随仓库发布的首帧（`assets/ti2v_first_frame.png`）及其配对 caption（`prompts/ti2v_example.json`）。caption 应描述从首帧开始展开的运动；与 T2V 一样，写成结构化 JSON caption 最贴近训练分布。`input_image` 与 `input_video` 互斥，至多传一个。
+首帧会按保持宽高比的方式 cover-resize 并中心裁剪到 `height`×`width`，因此不必与目标分辨率完全一致。可直接运行的示例见 [`lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)（低显存版本：[`model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)），它使用随仓库发布的首帧（`assets/ti2v_first_frame.png`）及其配对 caption（`prompts/ti2v_example.json`）。caption 应描述从首帧开始展开的运动；与 T2V 一样，写成结构化 JSON caption 最贴近训练分布。`input_image` 与 `input_video` 互斥，至多传一个。图生视频 LoRA 训练见下文 [TI2V LoRA](#ti2v-lora)。
 
 ## 文生图（t2i）
 
@@ -158,7 +158,7 @@ frames = pipe(
 frames[0].save("image.png")
 ```
 
-可直接运行的示例见 [`lingbot-video-dense-1.3b_t2i.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)，它使用随仓库发布的静态图 caption `prompts/t2i_example.json`。
+可直接运行的示例见 [`lingbot-video-dense-1.3b_t2i.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)（低显存版本：[`model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)），它使用随仓库发布的静态图 caption `prompts/t2i_example.json`。
 
 ## 模型训练
 
@@ -220,6 +220,10 @@ MoE / FFN 专家（`gate_proj`、`up_proj`、`down_proj`）与 router 保持冻
 
 为获得最佳效果，`prompt` 列应存放**结构化 JSON caption**（与推理时一致的分布内格式——见[提示词改写](#提示词改写对质量很重要)）。`train.py` 会对每条 prompt 调用 `normalize_caption`。若数据集存放的是原始文本，请在训练前用 `examples/lingbot_video/model_training/rewrite_captions.py` 离线改写一次。
 
+### TI2V LoRA
+
+训练**图生视频** LoRA 只需加上 `--first_frame_as_condition`（见 [`lora/lingbot-video-dense-1.3b_ti2v.sh`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)）。它以每个片段**自身的首帧**为条件：首帧经 VAE 编码为干净 latent，钉入第一个时间槽（并作为视觉输入喂给 Qwen3-VL 文本编码器），且从 flow-matching loss 中剔除，因此 LoRA 学习的是"以首帧为条件生成第 2..N 帧"。数据集、LoRA 范围、DiT 均与 t2v 完全一致，无需额外的条件帧列。验证脚本见 [`validate_lora/lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)。若数据集提供的是**另一张**条件帧（而非复用首帧），去掉该开关，改用 `--extra_inputs input_image` 传入该列（并把它加进 `--data_file_keys`）。
+
 我们编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。
 
 ## 注意事项
diff --git a/examples/lingbot_video/README.md b/examples/lingbot_video/README.md
index 84a44258d..6c9d137fb 100644
--- a/examples/lingbot_video/README.md
+++ b/examples/lingbot_video/README.md
@@ -54,7 +54,7 @@ save_video(video, "output.mp4", fps=15, quality=5)
 
 The pipeline ships a default (T2V) negative prompt, so `negative_prompt` is optional. Video-to-video is supported by passing `input_video=` (a list of frames or a `VideoData`) together with `denoising_strength < 1`.
 
-**Low VRAM:** pass `vram_limit=<GB>` to `from_pretrained` to enable layer-by-layer offloading — see `model_inference_low_vram/lingbot-video-dense-1.3b.py`.
+**Low VRAM:** pass `vram_limit=<GB>` to `from_pretrained` to enable layer-by-layer offloading — see `model_inference_low_vram/lingbot-video-dense-1.3b.py` (t2v), `_ti2v.py`, and `_t2i.py`.
 
 ### Image-to-video (TI2V)
 
@@ -174,6 +174,12 @@ The MoE / FFN experts (`gate_proj`, `up_proj`, `down_proj`) and the router are l
 - `--max_timestep_boundary` / `--min_timestep_boundary` — restrict the sampled training timesteps to a sub-range of the schedule.
 - `--lora_checkpoint <path>` — resume / continue from a previously trained LoRA.
 
+### Image-to-video (TI2V) LoRA
+
+To train an image-to-video LoRA, add `--first_frame_as_condition` — see `model_training/lora/lingbot-video-dense-1.3b_ti2v.sh`. It conditions each clip on its **own first frame**: the frame is VAE-encoded to a clean latent pinned into the first temporal slot (and fed to the text encoder), and excluded from the flow-matching loss, so the LoRA learns to animate frames 2..N from frame 1. Everything else — dataset, LoRA scope, DiT — is identical to t2v, and no condition column is needed. Validate with `model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py`.
+
+(If your dataset ships a *distinct* condition frame rather than reusing frame 1, drop the flag and pass that column via `--extra_inputs input_image`, adding it to `--data_file_keys`.)
+
 ### Applying a trained LoRA
 
 Trained LoRA checkpoints are written to `--output_path` with the `pipe.dit.` prefix stripped (keys like `blocks.0.attn.to_q.lora_A.weight`). To continue training from one, pass it via `--lora_checkpoint`.
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py
new file mode 100644
index 000000000..92d4ec6ba
--- /dev/null
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py
@@ -0,0 +1,47 @@
+import os
+import torch
+from diffsynth.pipelines.lingbot_video import (
+    LingBotVideoPipeline, ModelConfig, normalize_caption, DEFAULT_NEGATIVE_PROMPT_IMAGE,
+)
+
+
+# Low-VRAM text-to-image (t2i). t2i is text-to-video with num_frames=1 through the same
+# pipeline and DiT (no separate image weight); the only image-specific knob is the negative
+# prompt (DEFAULT_NEGATIVE_PROMPT_IMAGE drops temporal/motion terms). offload_dtype /
+# offload_device on each ModelConfig turn on VRAM management: weights stay on CPU in fp8 and
+# stream to the GPU layer-by-layer, computed in bf16. vram_limit only caps resident VRAM once
+# offloading is enabled by those two fields.
+vram_config = {
+    "offload_dtype": torch.float8_e4m3fn,
+    "offload_device": "cpu",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
+)
+
+# prompts/t2i_example.json is a released in-distribution still-image caption.
+caption = normalize_caption(os.path.join(
+    os.path.dirname(__file__), "..", "model_inference", "prompts", "t2i_example.json"))
+frames = pipe(
+    prompt=caption,
+    negative_prompt=DEFAULT_NEGATIVE_PROMPT_IMAGE,
+    height=480, width=832, num_frames=1,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+frames[0].save("image_lingbot-video-dense-1.3b_t2i_low_vram.png")
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py
new file mode 100644
index 000000000..d61b6e36d
--- /dev/null
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py
@@ -0,0 +1,49 @@
+import os
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
+
+
+# Low-VRAM image-to-video (TI2V). offload_dtype / offload_device on each ModelConfig turn on
+# VRAM management: weights stay on CPU in fp8 and stream to the GPU layer-by-layer, computed
+# in bf16. vram_limit only caps resident VRAM once offloading is enabled by those two fields.
+# The TI2V delta over t2v is just `input_image` — the condition-frame VAE encode / text-encoder
+# vision pass run under the same offloading, so peak VRAM matches the low-VRAM t2v run.
+vram_config = {
+    "offload_dtype": torch.float8_e4m3fn,
+    "offload_device": "cpu",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
+)
+
+# Released first frame + paired caption, shared with the model_inference TI2V example.
+here = os.path.dirname(__file__)
+inference_dir = os.path.join(here, "..", "model_inference")
+caption = normalize_caption(os.path.join(inference_dir, "prompts", "ti2v_example.json"))
+input_image = Image.open(os.path.join(inference_dir, "assets", "ti2v_first_frame.png")).convert("RGB")
+
+video = pipe(
+    prompt=caption,
+    input_image=input_image,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-dense-1.3b_ti2v_low_vram.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh
new file mode 100644
index 000000000..7fe3bb66e
--- /dev/null
+++ b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh
@@ -0,0 +1,31 @@
+# Image-to-video (TI2V) LoRA SFT.
+#
+# Same DiT / dataset / attention-only LoRA scope as the t2v script — the only delta is
+# `--first_frame_as_condition`, which conditions each clip on its OWN first frame: the frame
+# is VAE-encoded to a clean latent pinned into the first temporal slot (and fed to the
+# Qwen3-VL text encoder), and excluded from the flow-matching loss, so the LoRA learns to
+# animate frames 2..N from frame 1. Dense-1.3B reuses the same T2V weights — no separate i2v
+# checkpoint. Reuses the shared example dataset (plain `video` + `prompt`); no condition
+# column is required. If your dataset instead ships a distinct condition frame, drop this
+# flag and pass it via `--extra_inputs input_image` (adding that column to --data_file_keys).
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-T2V-1.3B/*" --local_dir ./data/diffsynth_example_dataset
+
+accelerate launch examples/lingbot_video/model_training/train.py \
+  --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B \
+  --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B/metadata.csv \
+  --data_file_keys "video" \
+  --height 480 \
+  --width 832 \
+  --num_frames 169 \
+  --first_frame_as_condition \
+  --dataset_repeat 200 \
+  --model_id_with_origin_paths "Robbyant/lingbot-video-dense-1.3b:transformer/diffusion_pytorch_model.safetensors,Robbyant/lingbot-video-dense-1.3b:text_encoder/model*.safetensors,Robbyant/lingbot-video-dense-1.3b:vae/diffusion_pytorch_model.safetensors" \
+  --processor_path "Robbyant/lingbot-video-dense-1.3b:processor/" \
+  --learning_rate 1e-4 \
+  --num_epochs 20 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/lingbot-video-dense-1.3b_ti2v_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,to_out" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing
diff --git a/examples/lingbot_video/model_training/train.py b/examples/lingbot_video/model_training/train.py
index 3d8de6b78..9824e9c62 100644
--- a/examples/lingbot_video/model_training/train.py
+++ b/examples/lingbot_video/model_training/train.py
@@ -15,6 +15,7 @@ def __init__(
         preset_lora_path=None, preset_lora_model=None,
         use_gradient_checkpointing=True,
         use_gradient_checkpointing_offload=False,
+        first_frame_as_condition=False,
         extra_inputs=None,
         fp8_models=None,
         offload_models=None,
@@ -51,6 +52,7 @@ def __init__(
         # Store other configs
         self.use_gradient_checkpointing = use_gradient_checkpointing
         self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
+        self.first_frame_as_condition = first_frame_as_condition
         self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
         self.fp8_models = fp8_models
         self.task = task
@@ -84,6 +86,15 @@ def get_pipeline_inputs(self, data):
             "max_timestep_boundary": self.max_timestep_boundary,
             "min_timestep_boundary": self.min_timestep_boundary,
         }
+        if self.first_frame_as_condition:
+            # Image-to-video (TI2V) LoRA: condition on the clip's own first frame. The
+            # ImageEmbedder unit VAE-encodes it to first_frame_latents and feeds it to the
+            # text encoder as vlm_image; FlowMatchSFTLoss then pins that clean latent into
+            # the first temporal slot and excludes it from the loss (see diffsynth/diffusion/
+            # loss.py). No separate condition column or core change is needed. If your
+            # dataset instead ships a distinct condition frame, drop this flag and pass it via
+            # --extra_inputs input_image (with the column added to --data_file_keys).
+            inputs_shared["input_image"] = data["video"][0]
         inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared)
         return inputs_shared, inputs_posi, inputs_nega
 
@@ -101,6 +112,7 @@ def lingbot_video_parser():
     parser = add_general_config(parser)
     parser = add_video_size_config(parser)
     parser.add_argument("--processor_path", type=str, default=None, help="Path to the Qwen3-VL processor directory (or `model_id:origin_file_pattern`). Used to tokenize prompts.")
+    parser.add_argument("--first_frame_as_condition", default=False, action="store_true", help="Image-to-video (TI2V) LoRA: condition on each clip's own first frame (pinned as a clean latent and excluded from the loss).")
     parser.add_argument("--max_timestep_boundary", type=float, default=1.0, help="Max timestep boundary (fraction of the training schedule, in [0, 1]).")
     parser.add_argument("--min_timestep_boundary", type=float, default=0.0, help="Min timestep boundary (fraction of the training schedule, in [0, 1]).")
     parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.")
@@ -144,6 +156,7 @@ def lingbot_video_parser():
         preset_lora_model=args.preset_lora_model,
         use_gradient_checkpointing=args.use_gradient_checkpointing,
         use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
+        first_frame_as_condition=args.first_frame_as_condition,
         extra_inputs=args.extra_inputs,
         fp8_models=args.fp8_models,
         offload_models=args.offload_models,
diff --git a/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py b/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py
new file mode 100644
index 000000000..db26c6925
--- /dev/null
+++ b/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py
@@ -0,0 +1,34 @@
+import os
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
+
+
+# Validate a TI2V LoRA (trained with lora/lingbot-video-dense-1.3b_ti2v.sh) by conditioning
+# on a first frame via `input_image`, exactly as at inference. Same base T2V checkpoint.
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+)
+pipe.load_lora(pipe.dit, "models/train/lingbot-video-dense-1.3b_ti2v_lora/epoch-19.safetensors", alpha=1)
+
+# Reuse the released first frame + caption from the inference example.
+inference_dir = os.path.join(os.path.dirname(__file__), "..", "..", "model_inference")
+caption = normalize_caption(os.path.join(inference_dir, "prompts", "ti2v_example.json"))
+input_image = Image.open(os.path.join(inference_dir, "assets", "ti2v_first_frame.png")).convert("RGB")
+
+video = pipe(
+    prompt=caption,
+    input_image=input_image,
+    height=480, width=832, num_frames=169,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-dense-1.3b_ti2v.mp4", fps=15, quality=10)

From b2492ddd9506a36bc46618651db6a72dca6474d8 Mon Sep 17 00:00:00 2001
From: mi804 <1576993271@qq.com>
Date: Mon, 27 Jul 2026 21:22:22 +0800
Subject: [PATCH 14/34] update vae&inner_func

---
 diffsynth/models/qwen_image_vae.py   |  33 +++++++
 diffsynth/pipelines/lingbot_video.py | 128 ++++++---------------------
 2 files changed, 59 insertions(+), 102 deletions(-)

diff --git a/diffsynth/models/qwen_image_vae.py b/diffsynth/models/qwen_image_vae.py
index 2845354f2..29f529971 100644
--- a/diffsynth/models/qwen_image_vae.py
+++ b/diffsynth/models/qwen_image_vae.py
@@ -724,3 +724,36 @@ def decode(self, x, **kwargs):
         x = self.decoder(x)
         x = x.squeeze(2)
         return x
+
+    def count_conv3d(self, model):
+        return sum(1 for m in model.modules() if isinstance(m, QwenImageCausalConv3d))
+
+    def encode_video(self, x, **kwargs):
+        # used for lingbot video
+        t = x.shape[2]
+        iter_ = 1 + (t - 1) // 4
+        feat_cache = [None] * self.count_conv3d(self.encoder)
+        out = None
+        for i in range(iter_):
+            feat_idx = [0]
+            chunk = x[:, :, :1, :, :] if i == 0 else x[:, :, 1 + 4 * (i - 1): 1 + 4 * i, :, :]
+            out_ = self.encoder(chunk, feat_cache=feat_cache, feat_idx=feat_idx)
+            out = out_ if out is None else torch.cat([out, out_], dim=2)
+        x = self.quant_conv(out)
+        x = x[:, :16]
+        mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
+        x = (x - mean) * std
+        return x
+
+    def decode_video(self, x, **kwargs):
+        # used for lingbot video
+        mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
+        x = x / std + mean
+        x = self.post_quant_conv(x)
+        feat_cache = [None] * self.count_conv3d(self.decoder)
+        out = None
+        for i in range(x.shape[2]):
+            feat_idx = [0]
+            out_ = self.decoder(x[:, :, i:i + 1, :, :], feat_cache=feat_cache, feat_idx=feat_idx)
+            out = out_ if out is None else torch.cat([out, out_], dim=2)
+        return out
diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index 8ea06d84c..d4ae834ca 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -17,7 +17,7 @@
 
 from ..models.lingbot_video_dit import LingBotVideoDiT
 from ..models.lingbot_video_text_encoder import LingBotVideoTextEncoder
-from ..models.qwen_image_vae import QwenImageVAE, QwenImageCausalConv3d
+from ..models.qwen_image_vae import QwenImageVAE
 
 
 # LingBot-Video is trained on structured JSON captions. normalize_caption serialises a
@@ -157,8 +157,8 @@ def _pixel_tensor_to_pil(pixel: torch.Tensor) -> Image.Image:
 class LingBotVideoPipeline(BasePipeline):
     """Text-to-video pipeline for LingBot-Video (DiT + Qwen3-VL text encoder + QwenImageVAE),
     following the DiffSynth PipelineUnit + model_fn pattern. Sampling uses FlowMatchScheduler
-    (Wan template). The 5D-video VAE encode/decode lives here (encode_video / decode_video)
-    so QwenImageVAE stays identical to its image use elsewhere."""
+    (Wan template). The 5D-video VAE encode/decode lives in QwenImageVAE
+    (encode_video / decode_video); the 4D image paths stay untouched."""
 
     def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
         super().__init__(
@@ -171,8 +171,6 @@ def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
         self.dit: LingBotVideoDiT = None
         self.vae: QwenImageVAE = None
         self.processor = None
-        # Cached number of template-prefix tokens to crop from the prompt embedding.
-        self._crop_start: Optional[int] = None
         self.in_iteration_models = ("dit",)
         self.units = [
             LingBotVideoUnit_ShapeChecker(),
@@ -186,24 +184,6 @@ def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
         self.model_fn = model_fn_lingbot_video
         self.compilable_models = ["dit"]
 
-    def _compute_crop_start(self) -> int:
-        # Token count of the template prefix (everything before the user prompt), computed once.
-        if self._crop_start is None:
-            marker = "<|USER_INPUT_MARKER|>"
-            marked = PROMPT_TEMPLATE.format(marker)
-            marker_pos = marked.find(marker)
-            if marker_pos < 0:
-                self._crop_start = 0
-            else:
-                prefix = self.processor(
-                    text=marked[:marker_pos],
-                    images=None,
-                    videos=None,
-                    return_tensors="pt",
-                )
-                self._crop_start = int(prefix["input_ids"].shape[1])
-        return self._crop_start
-
     @staticmethod
     def from_pretrained(
         torch_dtype: torch.dtype = torch.bfloat16,
@@ -306,85 +286,11 @@ def __call__(
 
         self.load_models_to_device(['vae'])
         latents = inputs_shared["latents"].to(dtype=self.torch_dtype, device=self.device)
-        video = self.decode_video(latents)
+        video = self.vae.decode_video(latents)
         video = self.vae_output_to_video(video)
         self.load_models_to_device([])
         return video
 
-    def _count_conv3d(self, model):
-        return sum(1 for m in model.modules() if isinstance(m, QwenImageCausalConv3d))
-
-    def encode_video(self, x):
-        # x: (B, C, T, H, W). Temporal chunking (1 + 4k frames) through a persistent causal
-        # feature cache — equivalent to encoding the whole clip at once, bounded in memory.
-        vae = self.vae
-        t = x.shape[2]
-        iter_ = 1 + (t - 1) // 4
-        feat_cache = [None] * self._count_conv3d(vae.encoder)
-        out = None
-        for i in range(iter_):
-            feat_idx = [0]
-            chunk = x[:, :, :1, :, :] if i == 0 else x[:, :, 1 + 4 * (i - 1): 1 + 4 * i, :, :]
-            out_ = vae.encoder(chunk, feat_cache=feat_cache, feat_idx=feat_idx)
-            out = out_ if out is None else torch.cat([out, out_], dim=2)
-        x = vae.quant_conv(out)
-        x = x[:, :16]
-        mean, std = vae.mean.to(dtype=x.dtype, device=x.device), vae.std.to(dtype=x.dtype, device=x.device)
-        x = (x - mean) * std
-        return x
-
-    def decode_video(self, x):
-        # x: (B, 16, T', H, W) in latent space. Denormalize, then decode one latent frame
-        # at a time through the causal feature cache.
-        vae = self.vae
-        mean, std = vae.mean.to(dtype=x.dtype, device=x.device), vae.std.to(dtype=x.dtype, device=x.device)
-        x = x / std + mean
-        x = vae.post_quant_conv(x)
-        feat_cache = [None] * self._count_conv3d(vae.decoder)
-        out = None
-        for i in range(x.shape[2]):
-            feat_idx = [0]
-            out_ = vae.decoder(x[:, :, i:i + 1, :, :], feat_cache=feat_cache, feat_idx=feat_idx)
-            out = out_ if out is None else torch.cat([out, out_], dim=2)
-        return out
-
-    def preprocess_cond_image(self, image: Image.Image, height, width):
-        # TI2V condition frame -> (1, C, 1, H, W) pixel tensor in [0, 1], aspect-ratio
-        # preserving cover-resize + center-crop to (height, width). Ported from the
-        # official i2v pipeline's preprocess_image.
-        raw = torch.from_numpy(np.array(image.convert("RGB"))).permute(2, 0, 1).unsqueeze(0).contiguous()
-        old_h, old_w = raw.shape[-2:]
-        scale = max(height / old_h, width / old_w)
-        new_h = max(math.ceil(old_h * scale), height)
-        new_w = max(math.ceil(old_w * scale), width)
-        resized = F.interpolate(raw.float(), size=(new_h, new_w), mode="bilinear", align_corners=False)
-        top = int(round((new_h - height) / 2.0))
-        left = int(round((new_w - width) / 2.0))
-        cropped = resized[:, :, top: top + height, left: left + width] / 255.0
-        return cropped.unsqueeze(2)
-
-    def _vision_patch_size(self) -> int:
-        # Resolve the Qwen3-VL vision patch size (used with SPATIAL_MERGE_SIZE to pick
-        # the smart-resize grid factor). Falls back to 16 like the official pipeline.
-        for obj in (
-            getattr(getattr(self.text_encoder, "config", None), "vision_config", None),
-            getattr(getattr(self.processor, "image_processor", None), "config", None),
-            getattr(self.processor, "image_processor", None),
-        ):
-            patch = getattr(obj, "patch_size", None)
-            if patch is not None:
-                return int(patch)
-        return 16
-
-    def _vlm_image(self, pixel: torch.Tensor) -> Image.Image:
-        # Build the PIL image handed to the text encoder from the condition pixel tensor,
-        # smart-resized to the Qwen3-VL patch grid.
-        image = _pixel_tensor_to_pil(pixel)
-        patch_factor = self._vision_patch_size() * SPATIAL_MERGE_SIZE
-        w, h = image.size
-        resized_height, resized_width = smart_resize(h, w, factor=patch_factor)
-        return image.resize((resized_width, resized_height))
-
 
 class LingBotVideoUnit_ShapeChecker(PipelineUnit):
     def __init__(self):
@@ -426,6 +332,26 @@ def __init__(self):
             output_params=("context", "encoder_attention_mask"),
             onload_model_names=("text_encoder",),
         )
+        # Cached number of template-prefix tokens to crop from the prompt embedding.
+        self._crop_start: Optional[int] = None
+
+    def _compute_crop_start(self, pipe: LingBotVideoPipeline) -> int:
+        # Token count of the template prefix (everything before the user prompt), computed once.
+        if self._crop_start is None:
+            marker = "<|USER_INPUT_MARKER|>"
+            marked = PROMPT_TEMPLATE.format(marker)
+            marker_pos = marked.find(marker)
+            if marker_pos < 0:
+                self._crop_start = 0
+            else:
+                prefix = pipe.processor(
+                    text=marked[:marker_pos],
+                    images=None,
+                    videos=None,
+                    return_tensors="pt",
+                )
+                self._crop_start = int(prefix["input_ids"].shape[1])
+        return self._crop_start
 
     def encode_prompt(self, pipe: LingBotVideoPipeline, prompt, vlm_image=None):
         # T2V: visual_template is empty. TI2V: prepend the image-token block to the
@@ -450,7 +376,7 @@ def encode_prompt(self, pipe: LingBotVideoPipeline, prompt, vlm_image=None):
         prompt_mask = inputs["attention_mask"]
 
         # Crop the prompt-enhancement template prefix.
-        crop_start = pipe._compute_crop_start()
+        crop_start = self._compute_crop_start(pipe)
         if crop_start > 0:
             prompt_embeds = prompt_embeds[:, crop_start:]
             prompt_mask = prompt_mask[:, crop_start:]
@@ -482,10 +408,8 @@ def process(self, pipe: LingBotVideoPipeline, input_video, noise):
             # Text-to-video: start from pure noise.
             return {"latents": noise}
         pipe.load_models_to_device(self.onload_model_names)
-        # preprocess_video -> (B, C, T, H, W) in [-1, 1], in pipe.torch_dtype.
         video = pipe.preprocess_video(input_video)
-        # encode_video applies latent normalisation on the 5D-video path.
-        input_latents = pipe.encode_video(video).to(dtype=torch.float32, device=pipe.device)
+        input_latents = pipe.vae.encode_video(video).to(dtype=torch.float32, device=pipe.device)
         if pipe.scheduler.training:
             return {"latents": noise, "input_latents": input_latents}
         else:

From a10c026529f9fd9a446727d33005721681400458 Mon Sep 17 00:00:00 2001
From: mi804 <1576993271@qq.com>
Date: Mon, 27 Jul 2026 22:37:20 +0800
Subject: [PATCH 15/34] tmp commit for code refactor

---
 diffsynth/pipelines/lingbot_video.py          | 161 +++++++-----------
 docs/en/Model_Details/LingBot-Video.md        |  57 +------
 docs/zh/Model_Details/LingBot-Video.md        |  57 +------
 examples/lingbot_video/README.md              |   4 +-
 .../lingbot-video-dense-1.3b.py               |  12 +-
 .../model_inference/prompt_rewriter.py        |  12 +-
 .../lingbot-video-dense-1.3b.py               |  12 +-
 .../model_training/rewrite_captions.py        |   7 +-
 .../lingbot_video/model_training/train.py     |   8 +-
 .../validate_lora/lingbot-video-dense-1.3b.py |   1 +
 10 files changed, 106 insertions(+), 225 deletions(-)

diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index d4ae834ca..7de2aba38 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -1,6 +1,4 @@
 import json
-import math
-import os
 
 import torch
 import torch.nn.functional as F
@@ -19,82 +17,6 @@
 from ..models.lingbot_video_text_encoder import LingBotVideoTextEncoder
 from ..models.qwen_image_vae import QwenImageVAE
 
-
-# LingBot-Video is trained on structured JSON captions. normalize_caption serialises a
-# dict/list caption (or a prompt.json path) into the compact-JSON string the DiT expects;
-# a plain string is passed through. Kept module-level so train.py / rewrite_captions.py
-# can reuse it without importing the pipeline. The prompt rewriter that turns a brief idea
-# into such a caption lives in examples/lingbot_video/model_inference/prompt_rewriter.py.
-_RUNTIME_KEYS = {"duration", "fps", "height", "width", "num_frames", "resolution", "ratio"}
-
-
-def _serialize_caption(caption) -> str:
-    if isinstance(caption, (dict, list)):
-        return json.dumps(caption, ensure_ascii=False, separators=(",", ":"))
-    return str(caption)
-
-
-def _caption_from_sample(sample) -> str:
-    if isinstance(sample, dict):
-        if "caption" in sample:
-            caption = sample["caption"]
-        else:
-            caption = {k: v for k, v in sample.items() if k not in _RUNTIME_KEYS}
-    else:
-        caption = sample
-    return _serialize_caption(caption)
-
-
-def normalize_caption(prompt):
-    if prompt is None:
-        return prompt
-    if isinstance(prompt, str):
-        if prompt.endswith(".json") and os.path.isfile(prompt):
-            with open(prompt, "r", encoding="utf-8") as f:
-                prompt = json.load(f)
-            return _caption_from_sample(prompt)
-        return prompt
-    if isinstance(prompt, (dict, list)):
-        return _caption_from_sample(prompt)
-    return str(prompt)
-
-
-# Prompt truncation length for the Qwen3-VL processor.
-TOKEN_LENGTH = 37698
-# Hidden-state layer used as the prompt embedding: 0 -> the last layer.
-HIDDEN_STATE_SKIP_LAYER = 0
-
-# Prompt-enhancement chat template wrapping the user prompt.
-PROMPT_TEMPLATE = (
-    "<|im_start|>system\nGiven a user input that may include a text prompt alone, "
-    "a text prompt with an image reference, or a text prompt with a video reference "
-    "or a video reference alone, generate an \"Enhanced prompt\" that provides detailed "
-    "visual descriptions suitable for video generation. Evaluate the level of detail "
-    "in the user's input: if it is simple, enrich it by adding specifics about colors, "
-    "shapes, sizes, textures, lighting, motion dynamics, camera movement, temporal "
-    "progression, and spatial relationships to create vivid, concrete, and temporally "
-    "coherent scenes to create vivid and concrete scenes. Please generate only the "
-    "enhanced description for the prompt below and avoid including any additional "
-    "commentary or evaluations:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n"
-    "<|im_start|>assistant\n"
-)
-
-# Default T2V negative prompt (structured JSON string), copied verbatim.
-DEFAULT_NEGATIVE_PROMPT = (
-    '{"universal_negative": {"visual_quality": ["low quality", "worst quality", "blurry", "pixelated", "jpeg artifacts", "low resolution", "unstable color", "color flicker", "underexposed", "overexposed", "invisible subject", "subject hidden in darkness"], "artistic_style": ["painting", "illustration", "drawing", "cartoon", "3d render", "cgi", "sketch", "digital art"], "composition_and_content": ["text", "watermark", "signature", "logo", "subtitles", "pillarboxed", "side bars", "portrait image in landscape frame"], "temporal_and_motion_stability": ["flickering", "jittery", "motion blur", "temporal inconsistency", "warping", "morphing", "incoherent motion", "unnatural movement", "static object with sudden jump", "frame-to-frame inconsistency"], "material_and_structure": ["plastic-like glass", "unrealistic texture", "deformed bottle", "liquid freezing improperly", "distorted reflections"]}}'
-)
-
-# Still-image (t2i) default negative prompt, copied verbatim from the official pipeline.
-# Drops the whole temporal/motion block and the video-only codec/temporal terms that
-# cannot apply to a single frame. Pass this as negative_prompt when num_frames=1.
-DEFAULT_NEGATIVE_PROMPT_IMAGE = (
-    '{"universal_negative": {"visual_quality": ["low quality", "worst quality", "blurry", "pixelated", "jpeg artifacts", "low resolution", "underexposed", "overexposed", "invisible subject", "subject hidden in darkness"], "artistic_style": ["painting", "illustration", "drawing", "cartoon", "3d render", "cgi", "sketch", "digital art"], "composition_and_content": ["text", "watermark", "signature", "logo", "pillarboxed", "side bars", "portrait image in landscape frame"], "material_and_structure": ["plastic-like glass", "unrealistic texture", "deformed bottle", "distorted reflections"]}}'
-)
-
-# VAE downsample factors (QwenImageVAE / Wan-VAE): 8x spatial, 4x temporal.
-VAE_SCALE_FACTOR_SPATIAL = 8
-VAE_SCALE_FACTOR_TEMPORAL = 4
-
 # --- TI2V (image-to-video) ---------------------------------------------------
 # The condition frame is used twice: (1) as visual input to the Qwen3-VL text
 # encoder — its image tokens are prepended to the prompt via IMG_PROMPT_TEMPLATE;
@@ -183,6 +105,14 @@ def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
         ]
         self.model_fn = model_fn_lingbot_video
         self.compilable_models = ["dit"]
+        self.default_negative_prompt = (
+            '{"universal_negative": {'
+            '"visual_quality": ["low quality", "worst quality", "blurry", "pixelated", "jpeg artifacts", "low resolution", "unstable color", "color flicker", "underexposed", "overexposed", "invisible subject", "subject hidden in darkness"], '
+            '"artistic_style": ["painting", "illustration", "drawing", "cartoon", "3d render", "cgi", "sketch", "digital art"], '
+            '"composition_and_content": ["text", "watermark", "signature", "logo", "subtitles", "pillarboxed", "side bars", "portrait image in landscape frame"], '
+            '"temporal_and_motion_stability": ["flickering", "jittery", "motion blur", "temporal inconsistency", "warping", "morphing", "incoherent motion", "unnatural movement", "static object with sudden jump", "frame-to-frame inconsistency"], '
+            '"material_and_structure": ["plastic-like glass", "unrealistic texture", "deformed bottle", "liquid freezing improperly", "distorted reflections"]}}'
+        )
 
     @staticmethod
     def from_pretrained(
@@ -214,11 +144,9 @@ def from_pretrained(
     @torch.no_grad()
     def __call__(
         self,
-        # Structured caption (dict / list), a prompt.json path, or a plain string.
-        prompt: Union[str, dict, list] = "",
-        negative_prompt: Union[str, dict, list] = DEFAULT_NEGATIVE_PROMPT,
-        # Image-to-video (TI2V): condition on a single first frame.
-        input_image: Image.Image = None,
+        # Structured caption (dict) or a plain string.
+        prompt: Union[str, dict] = "",
+        negative_prompt: Union[str, dict] = "",
         # Video-to-video
         input_video: list[Image.Image] = None,
         denoising_strength: float = 1.0,
@@ -240,11 +168,6 @@ def __call__(
         # Scheduler
         self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
 
-        # Serialise dict/list/prompt.json captions to the structured-JSON string; plain
-        # strings pass through unchanged.
-        prompt = normalize_caption(prompt)
-        negative_prompt = normalize_caption(negative_prompt)
-
         # Inputs
         inputs_posi = {"prompt": prompt}
         inputs_nega = {"negative_prompt": negative_prompt}
@@ -312,10 +235,11 @@ def __init__(self):
         )
 
     def process(self, pipe: LingBotVideoPipeline, height, width, num_frames, seed, rand_device):
-        length = (num_frames - 1) // VAE_SCALE_FACTOR_TEMPORAL + 1
+        # VAE downsample factors (QwenImageVAE / Wan-VAE): 8x spatial, 4x temporal.
+        length = (num_frames - 1) // 4 + 1
         shape = (
             1, pipe.dit.in_channels, length,
-            height // VAE_SCALE_FACTOR_SPATIAL, width // VAE_SCALE_FACTOR_SPATIAL,
+            height // 8, width // 8,
         )
         # fp32 noise: the flow-matching sampler accumulates state in fp32.
         noise = pipe.generate_noise(shape, seed=seed, rand_device=rand_device, torch_dtype=torch.float32)
@@ -323,6 +247,32 @@ def process(self, pipe: LingBotVideoPipeline, height, width, num_frames, seed, r
 
 
 class LingBotVideoUnit_PromptEmbedder(PipelineUnit):
+    # Prompt truncation length for the Qwen3-VL processor.
+    TOKEN_LENGTH = 37698
+    # Hidden-state layer used as the prompt embedding: 0 -> the last layer.
+    HIDDEN_STATE_SKIP_LAYER = 0
+
+    # Prompt-enhancement chat template wrapping the user prompt.
+    PROMPT_TEMPLATE = (
+        "<|im_start|>system\nGiven a user input that may include a text prompt alone, "
+        "a text prompt with an image reference, or a text prompt with a video reference "
+        "or a video reference alone, generate an \"Enhanced prompt\" that provides detailed "
+        "visual descriptions suitable for video generation. Evaluate the level of detail "
+        "in the user's input: if it is simple, enrich it by adding specifics about colors, "
+        "shapes, sizes, textures, lighting, motion dynamics, camera movement, temporal "
+        "progression, and spatial relationships to create vivid, concrete, and temporally "
+        "coherent scenes to create vivid and concrete scenes. Please generate only the "
+        "enhanced description for the prompt below and avoid including any additional "
+        "commentary or evaluations:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n"
+        "<|im_start|>assistant\n"
+    )
+
+    # LingBot-Video is trained on structured JSON captions. normalize_caption serialises a
+    # dict caption into the compact-JSON string the DiT expects; a plain string is passed
+    # through. The prompt rewriter that turns a brief idea into such a caption lives in
+    # examples/lingbot_video/model_inference/prompt_rewriter.py.
+    _RUNTIME_KEYS = {"duration", "fps", "height", "width", "num_frames", "resolution", "ratio"}
+
     def __init__(self):
         super().__init__(
             seperate_cfg=True,
@@ -335,11 +285,25 @@ def __init__(self):
         # Cached number of template-prefix tokens to crop from the prompt embedding.
         self._crop_start: Optional[int] = None
 
+    @classmethod
+    def normalize_caption(cls, prompt):
+        # A dict caption is serialised to the compact-JSON string the DiT expects;
+        # a plain string passes through unchanged.
+        if isinstance(prompt, dict):
+            if "caption" in prompt:
+                caption = prompt["caption"]
+            else:
+                caption = {k: v for k, v in prompt.items() if k not in cls._RUNTIME_KEYS}
+            if isinstance(caption, (dict, list)):
+                return json.dumps(caption, ensure_ascii=False, separators=(",", ":"))
+            return str(caption)
+        return prompt
+
     def _compute_crop_start(self, pipe: LingBotVideoPipeline) -> int:
         # Token count of the template prefix (everything before the user prompt), computed once.
         if self._crop_start is None:
             marker = "<|USER_INPUT_MARKER|>"
-            marked = PROMPT_TEMPLATE.format(marker)
+            marked = self.PROMPT_TEMPLATE.format(marker)
             marker_pos = marked.find(marker)
             if marker_pos < 0:
                 self._crop_start = 0
@@ -353,26 +317,24 @@ def _compute_crop_start(self, pipe: LingBotVideoPipeline) -> int:
                 self._crop_start = int(prefix["input_ids"].shape[1])
         return self._crop_start
 
-    def encode_prompt(self, pipe: LingBotVideoPipeline, prompt, vlm_image=None):
-        # T2V: visual_template is empty. TI2V: prepend the image-token block to the
-        # prompt and pass the condition image so the encoder attends to it. The image
-        # tokens land after the template prefix, so crop_start is unaffected.
-        visual_template = IMG_PROMPT_TEMPLATE if vlm_image is not None else ""
-        text = PROMPT_TEMPLATE.format(visual_template + prompt)
+    def encode_prompt(self, pipe: LingBotVideoPipeline, prompt):
+        prompt = self.normalize_caption(prompt)
+        # T2V: visual_template is empty, so the text is simply the templated prompt.
+        text = self.PROMPT_TEMPLATE.format(prompt)
         inputs = pipe.processor(
             text=[text],
             images=[vlm_image] if vlm_image is not None else None,
             videos=None,
             do_resize=False,
             truncation=True,
-            max_length=TOKEN_LENGTH,
+            max_length=self.TOKEN_LENGTH,
             padding="longest",
             return_tensors="pt",
         )
         inputs = inputs.to(pipe.device)
         # The text encoder returns the tuple of per-layer hidden states.
         hidden_states = pipe.text_encoder(**inputs)
-        prompt_embeds = hidden_states[-(HIDDEN_STATE_SKIP_LAYER + 1)]
+        prompt_embeds = hidden_states[-(self.HIDDEN_STATE_SKIP_LAYER + 1)]
         prompt_mask = inputs["attention_mask"]
 
         # Crop the prompt-enhancement template prefix.
@@ -405,7 +367,6 @@ def __init__(self):
 
     def process(self, pipe: LingBotVideoPipeline, input_video, noise):
         if input_video is None:
-            # Text-to-video: start from pure noise.
             return {"latents": noise}
         pipe.load_models_to_device(self.onload_model_names)
         video = pipe.preprocess_video(input_video)
diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
index 5c3f48914..265e3c7e1 100644
--- a/docs/en/Model_Details/LingBot-Video.md
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -46,6 +46,7 @@ video = pipe(
     # A plain sentence is a minimal smoke test only — it is out-of-distribution.
     # For real quality, pass a structured caption instead (see "Prompt rewriting").
     prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    negative_prompt=pipe.default_negative_prompt,
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0, seed=0,
 )
@@ -66,9 +67,8 @@ The model is loaded via `LingBotVideoPipeline.from_pretrained`, see [Loading Mod
 
 Input parameters for `LingBotVideoPipeline` inference include:
 
-* `prompt`: Prompt describing the content appearing in the video. Accepts a structured caption (`dict` / `list`), a path to a `prompt.json`, or a plain string; see [Prompt rewriting](#prompt-rewriting-important-for-quality).
-* `negative_prompt`: Negative prompt describing content that should not appear in the video. A default (T2V) negative prompt is built into the pipeline, so this can be left unset.
-* `input_image`: A `PIL.Image` first frame for image-to-video (TI2V); the model animates it. See [Image-to-video (TI2V)](#image-to-video-ti2v).
+* `prompt`: Prompt describing the content appearing in the video. Accepts a structured caption (`dict`) or a plain string; see [Prompt rewriting](#prompt-rewriting-important-for-quality).
+* `negative_prompt`: Negative prompt describing content that should not appear in the video, default value is `""`. The official T2V negative prompt ships as `pipe.default_negative_prompt` and can be passed via `negative_prompt=pipe.default_negative_prompt`.
 * `input_video`: Input video (a list of frames or a `VideoData`) for video-to-video generation, used together with `denoising_strength`.
 * `denoising_strength`: Denoising strength, range 0~1, default value is 1.0. Lower values keep more of the input video structure. Only effective when `input_video` is provided.
 * `height`: Video height, default 480. Must be a multiple of 16.
@@ -85,7 +85,7 @@ When running low on VRAM, please refer to [VRAM Management](../Pipeline_Usage/VR
 
 ## Prompt rewriting (important for quality)
 
-LingBot-Video is trained on **structured-JSON captions**, not free-form prose. Feeding a flat sentence is out-of-distribution and visibly degrades quality; feeding the structured caption the model expects restores it. The pipeline accepts a caption as a `dict`, a path to a `prompt.json`, or a plain string, and normalises it (via `normalize_caption`) to the exact compact-JSON format the DiT was trained on — a plain string is passed through unchanged, so existing scripts keep working.
+LingBot-Video is trained on **structured-JSON captions**, not free-form prose. Feeding a flat sentence is out-of-distribution and visibly degrades quality; feeding the structured caption the model expects restores it. The pipeline accepts a caption as a `dict` or a plain string and normalises it to the exact compact-JSON format the DiT was trained on — a `dict` is serialised automatically, a plain string is passed through unchanged, so existing scripts keep working.
 
 To turn a **brief idea** into that structured caption, use the two-stage rewriter shipped with the examples (`examples/lingbot_video/model_inference/prompt_rewriter.py`): stage 1 *expands* the idea into a natural-language caption, stage 2 *maps* it into structured JSON.
 
@@ -117,48 +117,7 @@ video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0
 
 Instead of the env vars you can pass `base=` / `adapter=` to `rewrite_prompt`, or skip the local VLM entirely and drive a hosted / OpenAI-compatible endpoint by passing a custom object exposing `generate(text, image, use_lora)` as `backend=`. See the optional rewrite section at the bottom of `examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py`.
 
-If you don't have the rewriter model, three released LingBot-Video t2v captions ship with the repo as ready-to-use examples: `examples/lingbot_video/model_inference/prompts/t2v_example_{1,2,3}.json`. Pass one straight to the pipeline (`video = pipe(prompt="path/to/t2v_example_1.json", ...)`), or copy one as a template for writing your own structured caption.
-
-## Image-to-video (TI2V)
-
-The pipeline can condition generation on a **first frame**: pass a `PIL.Image` as `input_image` and the model animates it. Dense-1.3B reuses the **same T2V checkpoint** — there is no separate i2v weight to download, and the DiT is unchanged (`in_channels` stays 16).
-
-Matching the original LingBot-Video i2v pipeline, the condition frame is used twice:
-
-- it is fed to the Qwen3-VL text encoder as a visual reference (its image tokens are prepended to the prompt), so the caption and the frame are interpreted together;
-- it is VAE-encoded to a clean latent that is pinned into the first temporal slot of the diffusion latent before sampling and re-applied after every scheduler step, so the model only generates the frames that follow.
-
-```python
-from PIL import Image
-
-input_image = Image.open("first_frame.png").convert("RGB")
-video = pipe(
-    prompt=caption,               # describe the motion that unfolds from the first frame
-    input_image=input_image,
-    height=480, width=832, num_frames=81,
-    num_inference_steps=40, cfg_scale=3.0, seed=0,
-)
-```
-
-The condition frame is aspect-ratio-preserving cover-resized and center-cropped to `height`×`width`, so it need not match the target resolution exactly. A runnable example ships at [`lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py) (low-VRAM variant: [`model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)), using the released first frame (`assets/ti2v_first_frame.png`) and its paired caption (`prompts/ti2v_example.json`). The caption should describe the motion that unfolds from the frame; as with T2V it is most in-distribution as a structured-JSON caption. `input_image` and `input_video` are mutually exclusive — pass at most one. To fine-tune an image-to-video LoRA, see [TI2V LoRA](#ti2v-lora) below.
-
-## Text-to-image (t2i)
-
-Text-to-image is text-to-video with a single frame — the same pipeline and DiT, just `num_frames=1`. There is no separate image checkpoint. The only image-specific knob is the negative prompt: `DEFAULT_NEGATIVE_PROMPT_IMAGE` (exported from `diffsynth.pipelines.lingbot_video`) drops the temporal/motion terms that cannot apply to a still frame. The pipeline returns a 1-element list, i.e. a single `PIL.Image`.
-
-```python
-from diffsynth.pipelines.lingbot_video import DEFAULT_NEGATIVE_PROMPT_IMAGE
-
-frames = pipe(
-    prompt=caption,
-    negative_prompt=DEFAULT_NEGATIVE_PROMPT_IMAGE,
-    height=480, width=832, num_frames=1,
-    num_inference_steps=40, cfg_scale=3.0, seed=0,
-)
-frames[0].save("image.png")
-```
-
-A runnable example ships at [`lingbot-video-dense-1.3b_t2i.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py) (low-VRAM variant: [`model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)), using the released still-image caption `prompts/t2i_example.json`.
+If you don't have the rewriter model, three released LingBot-Video t2v captions ship with the repo as ready-to-use examples: `examples/lingbot_video/model_inference/prompts/t2v_example_{1,2,3}.json`. Load one with `json.load` and pass the resulting `dict` to the pipeline (see the inference example script), or copy one as a template for writing your own structured caption.
 
 ## Model Training
 
@@ -218,11 +177,7 @@ The recommended launch script patches LoRA on the joint text+video self-attentio
 
 The MoE / FFN experts (`gate_proj`, `up_proj`, `down_proj`) and the router are left frozen. To also adapt the FFN, add those module names to `--lora_target_modules`.
 
-For best results the `prompt` column should hold **structured-JSON captions** (the same in-distribution format used at inference — see [Prompt rewriting](#prompt-rewriting-important-for-quality)). `train.py` runs each prompt through `normalize_caption`. If your dataset stores raw prose, rewrite it once offline with `examples/lingbot_video/model_training/rewrite_captions.py` before training.
-
-### TI2V LoRA
-
-To train an **image-to-video** LoRA, add `--first_frame_as_condition` (see [`lora/lingbot-video-dense-1.3b_ti2v.sh`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)). It conditions each clip on its **own first frame**: the frame is VAE-encoded to a clean latent pinned into the first temporal slot (and fed to the Qwen3-VL text encoder), and excluded from the flow-matching loss, so the LoRA learns to animate frames 2..N from frame 1. The dataset, LoRA scope, and DiT are identical to t2v — no condition column is required. Validate with [`validate_lora/lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py). If your dataset ships a *distinct* condition frame instead, drop the flag and pass that column via `--extra_inputs input_image` (adding it to `--data_file_keys`).
+For best results the `prompt` column should hold **structured-JSON captions** (the same in-distribution format used at inference — see [Prompt rewriting](#prompt-rewriting-important-for-quality)). The pipeline normalises each prompt internally. If your dataset stores raw prose, rewrite it once offline with `examples/lingbot_video/model_training/rewrite_captions.py` before training.
 
 We have written recommended training scripts, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
 
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
index 96ba8eecf..f7173c60c 100644
--- a/docs/zh/Model_Details/LingBot-Video.md
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -46,6 +46,7 @@ video = pipe(
     # 普通句子仅用于最简单的跑通验证，属于分布外输入。
     # 正式使用请改传结构化 caption（见"提示词改写"章节）。
     prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    negative_prompt=pipe.default_negative_prompt,
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0, seed=0,
 )
@@ -66,9 +67,8 @@ save_video(video, "video.mp4", fps=15, quality=10)
 
 `LingBotVideoPipeline` 推理的输入参数包括：
 
-* `prompt`: 描述视频内容的提示词。支持结构化 caption（`dict` / `list`）、`prompt.json` 路径，或普通字符串；详见[提示词改写](#提示词改写对质量很重要)。
-* `negative_prompt`: 负向提示词，描述不希望出现在视频中的内容。Pipeline 内置了默认（T2V）负向提示词，因此可以不设置。
-* `input_image`: 图生视频（TI2V）的首帧，传入一个 `PIL.Image`，模型会以它为第一帧生成后续画面。详见[图生视频（TI2V）](#图生视频ti2v)。
+* `prompt`: 描述视频内容的提示词。支持结构化 caption（`dict`）或普通字符串；详见[提示词改写](#提示词改写对质量很重要)。
+* `negative_prompt`: 负向提示词，描述不希望出现在视频中的内容，默认值为 `""`。官方 T2V 负向提示词内置在 `pipe.default_negative_prompt` 中，可通过 `negative_prompt=pipe.default_negative_prompt` 传入。
 * `input_video`: 输入视频（帧列表或 `VideoData`），用于视频生视频，需与 `denoising_strength` 配合使用。
 * `denoising_strength`: 降噪强度，取值范围 0~1，默认为 1.0。值越小，越保留输入视频的结构。仅在提供 `input_video` 时生效。
 * `height`: 视频高度，默认为 480，需能被 16 整除。
@@ -85,7 +85,7 @@ save_video(video, "video.mp4", fps=15, quality=10)
 
 ## 提示词改写（对质量很重要）
 
-LingBot-Video 使用**结构化 JSON caption** 训练，而非自由文本。喂入一句普通句子属于分布外（out-of-distribution）输入，会明显降低质量；喂入模型期望的结构化 caption 则能恢复质量。Pipeline 接受 `dict`、`prompt.json` 路径或普通字符串形式的 caption，并将其（通过 `normalize_caption`）归一化为 DiT 训练时使用的紧凑 JSON 格式——普通字符串会原样透传，因此已有脚本无需改动。
+LingBot-Video 使用**结构化 JSON caption** 训练，而非自由文本。喂入一句普通句子属于分布外（out-of-distribution）输入，会明显降低质量；喂入模型期望的结构化 caption 则能恢复质量。Pipeline 接受 `dict` 或普通字符串形式的 caption，并将其归一化为 DiT 训练时使用的紧凑 JSON 格式——`dict` 会自动序列化，普通字符串会原样透传，因此已有脚本无需改动。
 
 若要把一个**简短想法**转成该结构化 caption，可使用随示例发布的两阶段改写器（`examples/lingbot_video/model_inference/prompt_rewriter.py`）：阶段一将想法*扩写*为自然语言 caption，阶段二将其*映射*为结构化 JSON。
 
@@ -117,48 +117,7 @@ video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0
 
 除了 env var，也可以给 `rewrite_prompt` 传 `base=` / `adapter=`；或者完全不下载本地 VLM，改为传入一个暴露 `generate(text, image, use_lora)` 方法的自定义对象作为 `backend=`，来驱动托管的 / OpenAI 兼容的推理端点。详见 `examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py` 文件末尾的可选改写小节。
 
-如果没有改写器模型，仓库自带 3 个官方 LingBot-Video t2v 结构化 caption 作为开箱即用的示例：`examples/lingbot_video/model_inference/prompts/t2v_example_{1,2,3}.json`。可直接按路径传给 pipeline（`video = pipe(prompt="path/to/t2v_example_1.json", ...)`），也可以复制一个作为编写自己 caption 的模板。
-
-## 图生视频（TI2V）
-
-Pipeline 支持以一张**首帧**为条件生成视频：把一个 `PIL.Image` 传给 `input_image`，模型会以它为第一帧生成后续画面。Dense-1.3B **复用同一份 T2V 权重**——无需额外下载 i2v 权重，DiT 也保持不变（`in_channels` 仍为 16）。
-
-与官方 LingBot-Video i2v pipeline 一致，首帧会被使用两次：
-
-- 作为视觉参考喂给 Qwen3-VL 文本编码器（其图像 token 会前插到提示词里），让 caption 与首帧被联合理解；
-- 经 VAE 编码为一个干净 latent，在采样前钉入扩散 latent 的第一个时间槽，并在每个调度步之后重新钉入，因此模型只生成首帧之后的画面。
-
-```python
-from PIL import Image
-
-input_image = Image.open("first_frame.png").convert("RGB")
-video = pipe(
-    prompt=caption,               # 描述从首帧开始展开的运动
-    input_image=input_image,
-    height=480, width=832, num_frames=81,
-    num_inference_steps=40, cfg_scale=3.0, seed=0,
-)
-```
-
-首帧会按保持宽高比的方式 cover-resize 并中心裁剪到 `height`×`width`，因此不必与目标分辨率完全一致。可直接运行的示例见 [`lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)（低显存版本：[`model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)），它使用随仓库发布的首帧（`assets/ti2v_first_frame.png`）及其配对 caption（`prompts/ti2v_example.json`）。caption 应描述从首帧开始展开的运动；与 T2V 一样，写成结构化 JSON caption 最贴近训练分布。`input_image` 与 `input_video` 互斥，至多传一个。图生视频 LoRA 训练见下文 [TI2V LoRA](#ti2v-lora)。
-
-## 文生图（t2i）
-
-文生图就是只生成一帧的文生视频——同一条 pipeline、同一个 DiT，只需设 `num_frames=1`，无需额外的图像权重。唯一与图像相关的开关是负向提示词：`DEFAULT_NEGATIVE_PROMPT_IMAGE`（从 `diffsynth.pipelines.lingbot_video` 导出）去掉了不适用于单帧的时序/运动类词条。此时 pipeline 返回只含一个元素的列表，即一张 `PIL.Image`。
-
-```python
-from diffsynth.pipelines.lingbot_video import DEFAULT_NEGATIVE_PROMPT_IMAGE
-
-frames = pipe(
-    prompt=caption,
-    negative_prompt=DEFAULT_NEGATIVE_PROMPT_IMAGE,
-    height=480, width=832, num_frames=1,
-    num_inference_steps=40, cfg_scale=3.0, seed=0,
-)
-frames[0].save("image.png")
-```
-
-可直接运行的示例见 [`lingbot-video-dense-1.3b_t2i.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)（低显存版本：[`model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)），它使用随仓库发布的静态图 caption `prompts/t2i_example.json`。
+如果没有改写器模型，仓库自带 3 个官方 LingBot-Video t2v 结构化 caption 作为开箱即用的示例：`examples/lingbot_video/model_inference/prompts/t2v_example_{1,2,3}.json`。用 `json.load` 读入后作为 `dict` 传给 pipeline（见推理示例脚本），也可以复制一个作为编写自己 caption 的模板。
 
 ## 模型训练
 
@@ -218,11 +177,7 @@ modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset \
 
 MoE / FFN 专家（`gate_proj`、`up_proj`、`down_proj`）与 router 保持冻结。若要同时微调 FFN，可将这些模块名加入 `--lora_target_modules`。
 
-为获得最佳效果，`prompt` 列应存放**结构化 JSON caption**（与推理时一致的分布内格式——见[提示词改写](#提示词改写对质量很重要)）。`train.py` 会对每条 prompt 调用 `normalize_caption`。若数据集存放的是原始文本，请在训练前用 `examples/lingbot_video/model_training/rewrite_captions.py` 离线改写一次。
-
-### TI2V LoRA
-
-训练**图生视频** LoRA 只需加上 `--first_frame_as_condition`（见 [`lora/lingbot-video-dense-1.3b_ti2v.sh`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)）。它以每个片段**自身的首帧**为条件：首帧经 VAE 编码为干净 latent，钉入第一个时间槽（并作为视觉输入喂给 Qwen3-VL 文本编码器），且从 flow-matching loss 中剔除，因此 LoRA 学习的是"以首帧为条件生成第 2..N 帧"。数据集、LoRA 范围、DiT 均与 t2v 完全一致，无需额外的条件帧列。验证脚本见 [`validate_lora/lingbot-video-dense-1.3b_ti2v.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)。若数据集提供的是**另一张**条件帧（而非复用首帧），去掉该开关，改用 `--extra_inputs input_image` 传入该列（并把它加进 `--data_file_keys`）。
+为获得最佳效果，`prompt` 列应存放**结构化 JSON caption**（与推理时一致的分布内格式——见[提示词改写](#提示词改写对质量很重要)）。Pipeline 会在内部对每条 prompt 做归一化。若数据集存放的是原始文本，请在训练前用 `examples/lingbot_video/model_training/rewrite_captions.py` 离线改写一次。
 
 我们编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。
 
diff --git a/examples/lingbot_video/README.md b/examples/lingbot_video/README.md
index 6c9d137fb..a9df5fa7c 100644
--- a/examples/lingbot_video/README.md
+++ b/examples/lingbot_video/README.md
@@ -52,7 +52,7 @@ video = pipe(prompt="A playful puppy runs across a lush green meadow ...", heigh
 save_video(video, "output.mp4", fps=15, quality=5)
 ```
 
-The pipeline ships a default (T2V) negative prompt, so `negative_prompt` is optional. Video-to-video is supported by passing `input_video=` (a list of frames or a `VideoData`) together with `denoising_strength < 1`.
+The pipeline ships the official T2V negative prompt as `pipe.default_negative_prompt`; pass it via `negative_prompt=pipe.default_negative_prompt` (the parameter defaults to empty). Video-to-video is supported by passing `input_video=` (a list of frames or a `VideoData`) together with `denoising_strength < 1`.
 
 **Low VRAM:** pass `vram_limit=<GB>` to `from_pretrained` to enable layer-by-layer offloading — see `model_inference_low_vram/lingbot-video-dense-1.3b.py` (t2v), `_ti2v.py`, and `_t2i.py`.
 
@@ -144,7 +144,7 @@ videos/001.mp4,A serene lake at sunrise, mist rising from the water ...
 
 Pass `--data_file_keys "video"` so the loader treats the `video` column as a file to load.
 
-For best results the `prompt` column should hold **structured-JSON captions** (the same in-distribution format used at inference — see [Prompt rewriting](#prompt-rewriting-important-for-quality)). `train.py` runs each prompt through `normalize_caption`, so a `dict`-valued prompt (in JSONL) or a path to a `prompt.json` is serialised automatically, and a plain string is used as-is. If your dataset stores raw prose, rewrite it once offline before training:
+For best results the `prompt` column should hold **structured-JSON captions** (the same in-distribution format used at inference — see [Prompt rewriting](#prompt-rewriting-important-for-quality)). The pipeline's prompt embedder normalises each prompt, so a `dict`-valued prompt (in JSONL) is serialised automatically and a plain string is used as-is. If your dataset stores raw prose, rewrite it once offline before training:
 
 ```bash
 python examples/lingbot_video/model_training/rewrite_captions.py \
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
index 39f0c3b52..bad35404a 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
@@ -1,7 +1,8 @@
+import json
 import os
 import torch
 from diffsynth.utils.data import save_video, VideoData
-from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 
 
 # LingBot-Video is trained on structured-JSON captions, not free-form prose. This example
@@ -20,11 +21,13 @@
 )
 
 # --- Text-to-video -------------------------------------------------------------------
-# prompts/t2v_example_*.json are released in-distribution captions. The pipeline calls
-# normalize_caption internally, so pipe(prompt="prompts/t2v_example_1.json") also works.
-caption = normalize_caption(os.path.join(os.path.dirname(__file__), "prompts", "t2v_example_1.json"))
+# prompts/t2v_example_*.json are released in-distribution captions. The pipeline accepts
+# a plain string or a structured caption dict, so the json is loaded here.
+with open(os.path.join(os.path.dirname(__file__), "prompts", "t2v_example_1.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
 video = pipe(
     prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0,
     seed=0,
@@ -36,6 +39,7 @@
 input_video = VideoData("video_lingbot-video-dense-1.3b.mp4", height=480, width=832)
 video = pipe(
     prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
     input_video=input_video, denoising_strength=0.7,
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0,
diff --git a/examples/lingbot_video/model_inference/prompt_rewriter.py b/examples/lingbot_video/model_inference/prompt_rewriter.py
index 7a2742109..dc5da61e9 100644
--- a/examples/lingbot_video/model_inference/prompt_rewriter.py
+++ b/examples/lingbot_video/model_inference/prompt_rewriter.py
@@ -3,9 +3,9 @@
 rewrite_prompt turns a brief idea into the structured JSON caption the DiT expects:
 stage 1 expands the idea into a natural-language caption (base model), stage 2 maps it
 into structured JSON (base model + stage-2 LoRA). This is a separate VLM + LoRA adapter,
-not the DiT, and is not downloaded with the pipeline -- hence it lives with the examples,
-while the core keeps only normalize_caption. TransformersBackend loads the rewriter VLM
-locally; make_backend also accepts any object exposing generate(text, image, use_lora).
+not the DiT, and is not downloaded with the pipeline -- hence it lives with the examples.
+TransformersBackend loads the rewriter VLM locally; make_backend also accepts any object
+exposing generate(text, image, use_lora).
 
     from prompt_rewriter import rewrite_prompt
     caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
@@ -36,7 +36,6 @@
 from system_prompts import (
     VIDEO_STEP1_EXPAND, VIDEO_STEP2_MAP, IMAGE_STEP1_EXPAND, IMAGE_STEP2_MAP,
 )
-from diffsynth.pipelines.lingbot_video import normalize_caption
 
 
 # mode -> (step1 system prompt, step2 system prompt, feed image?, add duration?)
@@ -200,7 +199,10 @@ def rewrite_prompt(prompt, mode="t2v", first_frame=None, duration=5,
     """
     rw = Rewriter(make_backend(backend, base, adapter))
     result = rw.rewrite(prompt, mode=mode, first_frame=first_frame, duration=duration)
-    caption = normalize_caption(result["json"]) if result["json"] is not None else result["json_raw"]
+    if result["json"] is not None:
+        caption = json.dumps(result["json"], ensure_ascii=False, separators=(",", ":"))
+    else:
+        caption = result["json_raw"]
     if return_result:
         return caption, result
     return caption
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
index 898d2976c..dab4270c1 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
@@ -1,7 +1,8 @@
+import json
 import os
 import torch
 from diffsynth.utils.data import save_video
-from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 
 
 # Low-VRAM inference. offload_dtype / offload_device on each ModelConfig turn on VRAM
@@ -30,11 +31,14 @@
     vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
 )
 
-# Released in-distribution structured caption (shared with the model_inference example).
-caption = normalize_caption(os.path.join(
-    os.path.dirname(__file__), "..", "model_inference", "prompts", "t2v_example_1.json"))
+# Released in-distribution structured caption (shared with the model_inference example);
+# the pipeline accepts a plain string or a caption dict, so the json is loaded here.
+with open(os.path.join(
+        os.path.dirname(__file__), "..", "model_inference", "prompts", "t2v_example_1.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
 video = pipe(
     prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0,
     seed=0,
diff --git a/examples/lingbot_video/model_training/rewrite_captions.py b/examples/lingbot_video/model_training/rewrite_captions.py
index e452f3a4d..d65700deb 100644
--- a/examples/lingbot_video/model_training/rewrite_captions.py
+++ b/examples/lingbot_video/model_training/rewrite_captions.py
@@ -17,11 +17,10 @@
 import os
 import sys
 
-# The two-stage rewriter engine lives with the inference examples (the diffsynth core
-# keeps only normalize_caption), so training and inference share one implementation.
+# The two-stage rewriter engine lives with the inference examples, so training and
+# inference share one implementation.
 sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "model_inference"))
 from prompt_rewriter import Rewriter, make_backend
-from diffsynth.pipelines.lingbot_video import normalize_caption
 
 
 def _load_rows(path):
@@ -77,7 +76,7 @@ def main():
         first_frame = row.get(args.first_frame_column) if args.first_frame_column else None
         result = rewriter.rewrite(raw, mode=args.mode, first_frame=first_frame, duration=args.duration)
         if result["json"] is not None:
-            row[args.prompt_column] = normalize_caption(result["json"])
+            row[args.prompt_column] = json.dumps(result["json"], ensure_ascii=False, separators=(",", ":"))
             ok += 1
         else:
             failed += 1
diff --git a/examples/lingbot_video/model_training/train.py b/examples/lingbot_video/model_training/train.py
index 9824e9c62..58fab8771 100644
--- a/examples/lingbot_video/model_training/train.py
+++ b/examples/lingbot_video/model_training/train.py
@@ -1,6 +1,6 @@
 import torch, os, argparse, accelerate, warnings
 from diffsynth.core import UnifiedDataset
-from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 from diffsynth.diffusion import *
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
@@ -65,9 +65,9 @@ def __init__(
         self.min_timestep_boundary = min_timestep_boundary
 
     def get_pipeline_inputs(self, data):
-        # Normalise the metadata "prompt" column to the structured-JSON caption so LoRA
-        # trains in-distribution. Use rewrite_captions.py offline if it stores raw prose.
-        inputs_posi = {"prompt": normalize_caption(data["prompt"])}
+        # The metadata "prompt" column stores the serialised structured-JSON caption
+        # (see rewrite_captions.py); the pipeline's PromptEmbedder consumes it as-is.
+        inputs_posi = {"prompt": data["prompt"]}
         inputs_nega = {}
         inputs_shared = {
             # Assume you are using this pipeline for inference,
diff --git a/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py
index e695867f6..5f5d7cdbf 100644
--- a/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py
@@ -17,6 +17,7 @@
 
 video = pipe(
     prompt="from sunset to night, a small town, light, house, river",
+    negative_prompt=pipe.default_negative_prompt,
     height=480, width=832, num_frames=169,
     num_inference_steps=40, cfg_scale=3.0,
     seed=0,

From 94e8baa8b79e3b6f6cf5bc3d6fe66d629a944855 Mon Sep 17 00:00:00 2001
From: mi804 <1576993271@qq.com>
Date: Tue, 28 Jul 2026 10:49:37 +0800
Subject: [PATCH 16/34] low vram patch&refactor scripts

---
 .../configs/vram_management_module_maps.py    | 11 +++++
 diffsynth/pipelines/lingbot_video.py          | 42 +++++++------------
 docs/en/Model_Details/LingBot-Video.md        | 11 +++--
 docs/zh/Model_Details/LingBot-Video.md        | 11 +++--
 examples/lingbot_video/README.md              |  9 ++--
 .../lingbot-video-dense-1.3b.py               | 24 ++++++-----
 .../lingbot-video-dense-1.3b.py               | 42 ++++++++++++-------
 .../lora/lingbot-video-dense-1.3b.sh          | 15 ++-----
 .../scripts}/prompt_rewriter.py               | 15 +++++--
 .../{ => scripts}/rewrite_captions.py         |  4 +-
 .../scripts}/system_prompts.py                |  0
 11 files changed, 98 insertions(+), 86 deletions(-)
 rename examples/lingbot_video/{model_inference => model_training/scripts}/prompt_rewriter.py (94%)
 rename examples/lingbot_video/model_training/{ => scripts}/rewrite_captions.py (95%)
 rename examples/lingbot_video/{model_inference => model_training/scripts}/system_prompts.py (100%)

diff --git a/diffsynth/configs/vram_management_module_maps.py b/diffsynth/configs/vram_management_module_maps.py
index 1f35eee51..b070862de 100644
--- a/diffsynth/configs/vram_management_module_maps.py
+++ b/diffsynth/configs/vram_management_module_maps.py
@@ -408,6 +408,17 @@
         "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRotaryEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
         "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
     },
+    "diffsynth.models.lingbot_video_dit.LingBotVideoDiT": {
+        "diffsynth.models.lingbot_video_dit.LingBotVideoBlock": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.lingbot_video_dit.LingBotVideoRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
+    "diffsynth.models.lingbot_video_text_encoder.LingBotVideoTextEncoder": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRotaryEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
 }
 
 def QwenImageTextEncoder_Module_Map_Updater():
diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index 7de2aba38..2fd0db18c 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -122,22 +122,18 @@ def from_pretrained(
         processor_config: ModelConfig = None,
         vram_limit: float = None,
     ):
-        # Initialize pipeline
         pipe = LingBotVideoPipeline(device=device, torch_dtype=torch_dtype)
         model_pool = pipe.download_and_load_models(model_configs, vram_limit)
 
-        # Fetch models by name (registered in diffsynth/configs/model_configs.py).
         pipe.text_encoder = model_pool.fetch_model("lingbot_video_text_encoder")
         pipe.dit = model_pool.fetch_model("lingbot_video_dit")
         pipe.vae = model_pool.fetch_model("qwen_image_vae")
 
-        # Initialize the Qwen3-VL processor (tokenizer + image/video processor).
         if processor_config is not None:
             processor_config.download_if_necessary()
             from transformers import AutoProcessor
             pipe.processor = AutoProcessor.from_pretrained(processor_config.path)
 
-        # VRAM Management
         pipe.vram_management_enabled = pipe.check_vram_management_state()
         return pipe
 
@@ -158,7 +154,7 @@ def __call__(
         width: int = 480,
         num_frames: int = 81,
         # Classifier-free guidance
-        cfg_scale: float = 6.0,
+        cfg_scale: float = 3.0,
         # Scheduler
         num_inference_steps: int = 40,
         sigma_shift: float = 3.0,
@@ -193,19 +189,13 @@ def __call__(
         self.load_models_to_device(self.in_iteration_models)
         models = {name: getattr(self, name) for name in self.in_iteration_models}
         for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
-            # fp32 so the integer timestep is represented exactly (bf16 rounds values > 256).
-            timestep_input = timestep.unsqueeze(0).to(dtype=torch.float32, device=self.device)
-
-            noise_pred_posi = self.model_fn(**models, **inputs_shared, **inputs_posi, timestep=timestep_input)
-            if cfg_scale != 1.0:
-                noise_pred_nega = self.model_fn(**models, **inputs_shared, **inputs_nega, timestep=timestep_input)
-                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
-            else:
-                noise_pred = noise_pred_posi
-
-            inputs_shared["latents"] = self.scheduler.step(noise_pred, timestep, inputs_shared["latents"])
-            if first_frame_latents is not None:
-                inputs_shared["latents"][:, :, :cond_t] = first_frame_latents
+            timestep = timestep.unsqueeze(0).to(dtype=torch.float32, device=self.device)
+            noise_pred = self.cfg_guided_model_fn(
+                self.model_fn, cfg_scale,
+                inputs_shared, inputs_posi, inputs_nega,
+                **models, timestep=timestep, progress_id=progress_id
+            )
+            inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
 
         self.load_models_to_device(['vae'])
         latents = inputs_shared["latents"].to(dtype=self.torch_dtype, device=self.device)
@@ -235,13 +225,8 @@ def __init__(self):
         )
 
     def process(self, pipe: LingBotVideoPipeline, height, width, num_frames, seed, rand_device):
-        # VAE downsample factors (QwenImageVAE / Wan-VAE): 8x spatial, 4x temporal.
         length = (num_frames - 1) // 4 + 1
-        shape = (
-            1, pipe.dit.in_channels, length,
-            height // 8, width // 8,
-        )
-        # fp32 noise: the flow-matching sampler accumulates state in fp32.
+        shape = (1, pipe.dit.in_channels, length, height // 8, width // 8)
         noise = pipe.generate_noise(shape, seed=seed, rand_device=rand_device, torch_dtype=torch.float32)
         return {"noise": noise}
 
@@ -270,7 +255,7 @@ class LingBotVideoUnit_PromptEmbedder(PipelineUnit):
     # LingBot-Video is trained on structured JSON captions. normalize_caption serialises a
     # dict caption into the compact-JSON string the DiT expects; a plain string is passed
     # through. The prompt rewriter that turns a brief idea into such a caption lives in
-    # examples/lingbot_video/model_inference/prompt_rewriter.py.
+    # examples/lingbot_video/model_training/scripts/prompt_rewriter.py.
     _RUNTIME_KEYS = {"duration", "fps", "height", "width", "num_frames", "resolution", "ratio"}
 
     def __init__(self):
@@ -280,7 +265,7 @@ def __init__(self):
             input_params_nega={"prompt": "negative_prompt"},
             input_params=("vlm_image",),
             output_params=("context", "encoder_attention_mask"),
-            onload_model_names=("text_encoder",),
+            onload_model_names=("text_encoder", ),
         )
         # Cached number of template-prefix tokens to crop from the prompt embedding.
         self._crop_start: Optional[int] = None
@@ -297,7 +282,10 @@ def normalize_caption(cls, prompt):
             if isinstance(caption, (dict, list)):
                 return json.dumps(caption, ensure_ascii=False, separators=(",", ":"))
             return str(caption)
-        return prompt
+        elif isinstance(prompt, str):
+            return prompt
+        else:
+            raise TypeError(f"prompt must be a str or a dict, not {type(prompt)}")
 
     def _compute_crop_start(self, pipe: LingBotVideoPipeline) -> int:
         # Token count of the template prefix (everything before the user prompt), computed once.
diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
index 265e3c7e1..63a40fb6c 100644
--- a/docs/en/Model_Details/LingBot-Video.md
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -87,7 +87,7 @@ When running low on VRAM, please refer to [VRAM Management](../Pipeline_Usage/VR
 
 LingBot-Video is trained on **structured-JSON captions**, not free-form prose. Feeding a flat sentence is out-of-distribution and visibly degrades quality; feeding the structured caption the model expects restores it. The pipeline accepts a caption as a `dict` or a plain string and normalises it to the exact compact-JSON format the DiT was trained on — a `dict` is serialised automatically, a plain string is passed through unchanged, so existing scripts keep working.
 
-To turn a **brief idea** into that structured caption, use the two-stage rewriter shipped with the examples (`examples/lingbot_video/model_inference/prompt_rewriter.py`): stage 1 *expands* the idea into a natural-language caption, stage 2 *maps* it into structured JSON.
+To turn a **brief idea** into that structured caption, use the two-stage rewriter shipped with the examples (`examples/lingbot_video/model_training/scripts/prompt_rewriter.py`): stage 1 *expands* the idea into a natural-language caption, stage 2 *maps* it into structured JSON.
 
 The rewriter is a **separate VLM + stage-2 LoRA adapter** (not the DiT), so it is **not downloaded automatically** — you must fetch both weights yourself before running the rewrite:
 
@@ -108,16 +108,15 @@ import os
 os.environ["REWRITER_BASE_MODEL"] = "./models/Qwen/Qwen3.6-27B"
 os.environ["REWRITER_ADAPTER"] = "./models/Robbyant/lingbot-video-rewriter-lora"
 
-# The rewriter ships with the inference examples; run from
-# examples/lingbot_video/model_inference (or add it to sys.path) for this import.
-from prompt_rewriter import rewrite_prompt
+# Run from the repo root so the package-style import resolves.
+from examples.lingbot_video.model_training.scripts.prompt_rewriter import rewrite_prompt
 caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
 video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0)
 ```
 
 Instead of the env vars you can pass `base=` / `adapter=` to `rewrite_prompt`, or skip the local VLM entirely and drive a hosted / OpenAI-compatible endpoint by passing a custom object exposing `generate(text, image, use_lora)` as `backend=`. See the optional rewrite section at the bottom of `examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py`.
 
-If you don't have the rewriter model, three released LingBot-Video t2v captions ship with the repo as ready-to-use examples: `examples/lingbot_video/model_inference/prompts/t2v_example_{1,2,3}.json`. Load one with `json.load` and pass the resulting `dict` to the pipeline (see the inference example script), or copy one as a template for writing your own structured caption.
+If you don't have the rewriter model, released LingBot-Video t2v structured captions ship in the example dataset as ready-to-use samples (`t2v_example_*.json` under `DiffSynth-Studio/diffsynth_example_dataset`, downloaded automatically by the inference example scripts). Load one with `json.load` and pass the resulting `dict` to the pipeline, or copy one as a template for writing your own structured caption.
 
 ## Model Training
 
@@ -177,7 +176,7 @@ The recommended launch script patches LoRA on the joint text+video self-attentio
 
 The MoE / FFN experts (`gate_proj`, `up_proj`, `down_proj`) and the router are left frozen. To also adapt the FFN, add those module names to `--lora_target_modules`.
 
-For best results the `prompt` column should hold **structured-JSON captions** (the same in-distribution format used at inference — see [Prompt rewriting](#prompt-rewriting-important-for-quality)). The pipeline normalises each prompt internally. If your dataset stores raw prose, rewrite it once offline with `examples/lingbot_video/model_training/rewrite_captions.py` before training.
+For best results the `prompt` column should hold **structured-JSON captions** (the same in-distribution format used at inference — see [Prompt rewriting](#prompt-rewriting-important-for-quality)). The pipeline normalises each prompt internally. If your dataset stores raw prose, rewrite it once offline with `examples/lingbot_video/model_training/scripts/rewrite_captions.py` before training.
 
 We have written recommended training scripts, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
 
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
index f7173c60c..a47151fc0 100644
--- a/docs/zh/Model_Details/LingBot-Video.md
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -87,7 +87,7 @@ save_video(video, "video.mp4", fps=15, quality=10)
 
 LingBot-Video 使用**结构化 JSON caption** 训练，而非自由文本。喂入一句普通句子属于分布外（out-of-distribution）输入，会明显降低质量；喂入模型期望的结构化 caption 则能恢复质量。Pipeline 接受 `dict` 或普通字符串形式的 caption，并将其归一化为 DiT 训练时使用的紧凑 JSON 格式——`dict` 会自动序列化，普通字符串会原样透传，因此已有脚本无需改动。
 
-若要把一个**简短想法**转成该结构化 caption，可使用随示例发布的两阶段改写器（`examples/lingbot_video/model_inference/prompt_rewriter.py`）：阶段一将想法*扩写*为自然语言 caption，阶段二将其*映射*为结构化 JSON。
+若要把一个**简短想法**转成该结构化 caption，可使用随示例发布的两阶段改写器（`examples/lingbot_video/model_training/scripts/prompt_rewriter.py`）：阶段一将想法*扩写*为自然语言 caption，阶段二将其*映射*为结构化 JSON。
 
 改写器是**独立的 VLM + 阶段二 LoRA 适配器**（不是 DiT），**不会自动下载**——运行改写前，需要先自行下载这两个权重：
 
@@ -108,16 +108,15 @@ import os
 os.environ["REWRITER_BASE_MODEL"] = "./models/Qwen/Qwen3.6-27B"
 os.environ["REWRITER_ADAPTER"] = "./models/Robbyant/lingbot-video-rewriter-lora"
 
-# 改写器随推理示例一起发布；请在 examples/lingbot_video/model_inference 目录下运行
-# （或将该目录加入 sys.path），此 import 才能解析。
-from prompt_rewriter import rewrite_prompt
+# 在仓库根目录下运行，此包式 import 才能解析。
+from examples.lingbot_video.model_training.scripts.prompt_rewriter import rewrite_prompt
 caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
 video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0)
 ```
 
 除了 env var，也可以给 `rewrite_prompt` 传 `base=` / `adapter=`；或者完全不下载本地 VLM，改为传入一个暴露 `generate(text, image, use_lora)` 方法的自定义对象作为 `backend=`，来驱动托管的 / OpenAI 兼容的推理端点。详见 `examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py` 文件末尾的可选改写小节。
 
-如果没有改写器模型，仓库自带 3 个官方 LingBot-Video t2v 结构化 caption 作为开箱即用的示例：`examples/lingbot_video/model_inference/prompts/t2v_example_{1,2,3}.json`。用 `json.load` 读入后作为 `dict` 传给 pipeline（见推理示例脚本），也可以复制一个作为编写自己 caption 的模板。
+如果没有改写器模型，官方 LingBot-Video t2v 结构化 caption 已随样例数据集发布（`DiffSynth-Studio/diffsynth_example_dataset` 中的 `t2v_example_*.json`，推理示例脚本会自动下载）。用 `json.load` 读入后作为 `dict` 传给 pipeline，也可以复制一个作为编写自己 caption 的模板。
 
 ## 模型训练
 
@@ -177,7 +176,7 @@ modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset \
 
 MoE / FFN 专家（`gate_proj`、`up_proj`、`down_proj`）与 router 保持冻结。若要同时微调 FFN，可将这些模块名加入 `--lora_target_modules`。
 
-为获得最佳效果，`prompt` 列应存放**结构化 JSON caption**（与推理时一致的分布内格式——见[提示词改写](#提示词改写对质量很重要)）。Pipeline 会在内部对每条 prompt 做归一化。若数据集存放的是原始文本，请在训练前用 `examples/lingbot_video/model_training/rewrite_captions.py` 离线改写一次。
+为获得最佳效果，`prompt` 列应存放**结构化 JSON caption**（与推理时一致的分布内格式——见[提示词改写](#提示词改写对质量很重要)）。Pipeline 会在内部对每条 prompt 做归一化。若数据集存放的是原始文本，请在训练前用 `examples/lingbot_video/model_training/scripts/rewrite_captions.py` 离线改写一次。
 
 我们编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。
 
diff --git a/examples/lingbot_video/README.md b/examples/lingbot_video/README.md
index a9df5fa7c..e9da22107 100644
--- a/examples/lingbot_video/README.md
+++ b/examples/lingbot_video/README.md
@@ -105,12 +105,11 @@ pipe(prompt="assets/cases/t2v/example_1/prompt.json")                          #
 pipe(prompt='{"comprehensive_description":{...}}')                             # already-serialised string
 ```
 
-To turn a **brief idea** into that structured caption, use the two-stage rewriter shipped here (`model_inference/prompt_rewriter.py`), a faithful port of the original: stage 1 *expands* the idea into a natural-language caption, stage 2 *maps* it into structured JSON.
+To turn a **brief idea** into that structured caption, use the two-stage rewriter shipped here (`model_training/scripts/prompt_rewriter.py`), a faithful port of the original: stage 1 *expands* the idea into a natural-language caption, stage 2 *maps* it into structured JSON.
 
 ```python
-# The rewriter lives in model_inference/; run from that directory (or add it to
-# sys.path) so this sibling import resolves.
-from prompt_rewriter import rewrite_prompt
+# Run from the repo root so the package-style import resolves.
+from examples.lingbot_video.model_training.scripts.prompt_rewriter import rewrite_prompt
 caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
 video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0)
 ```
@@ -147,7 +146,7 @@ Pass `--data_file_keys "video"` so the loader treats the `video` column as a fil
 For best results the `prompt` column should hold **structured-JSON captions** (the same in-distribution format used at inference — see [Prompt rewriting](#prompt-rewriting-important-for-quality)). The pipeline's prompt embedder normalises each prompt, so a `dict`-valued prompt (in JSONL) is serialised automatically and a plain string is used as-is. If your dataset stores raw prose, rewrite it once offline before training:
 
 ```bash
-python examples/lingbot_video/model_training/rewrite_captions.py \
+python examples/lingbot_video/model_training/scripts/rewrite_captions.py \
     --metadata metadata.csv --output metadata_rewritten.csv \
     --base /path/to/rewriter-base --adapter /path/to/rewriter-step2-lora --duration 5
 ```
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
index bad35404a..9e788d3bb 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
@@ -3,11 +3,7 @@
 import torch
 from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
-
-
-# LingBot-Video is trained on structured-JSON captions, not free-form prose. This example
-# runs on a released in-distribution caption; see the bottom for turning a brief idea into
-# such a caption with the two-stage prompt rewriter.
+from modelscope import dataset_snapshot_download
 
 pipe = LingBotVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
@@ -21,10 +17,18 @@
 )
 
 # --- Text-to-video -------------------------------------------------------------------
-# prompts/t2v_example_*.json are released in-distribution captions. The pipeline accepts
-# a plain string or a structured caption dict, so the json is loaded here.
-with open(os.path.join(os.path.dirname(__file__), "prompts", "t2v_example_1.json"), "r", encoding="utf-8") as f:
+# Download dataset
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b/*",
+)
+# LingBot-Video is trained on structured-JSON captions, not free-form prose. This example
+# runs on a released in-distribution caption; see the bottom for turning a brief idea into
+# such a caption with the two-stage prompt rewriter.
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/t2v_example_1.json", "r", encoding="utf-8") as f:
     caption = json.load(f)
+
 video = pipe(
     prompt=caption,
     negative_prompt=pipe.default_negative_prompt,
@@ -48,7 +52,7 @@
 save_video(video, "video_lingbot-video-dense-1.3b_v2v.mp4", fps=15, quality=10)
 
 # --- Optional: rewrite a brief idea into a structured caption ------------------------
-# The two-stage rewriter (prompt_rewriter.py, a sibling module) is a separate VLM +
+# The two-stage rewriter (model_training/scripts/prompt_rewriter.py) is a separate VLM +
 # stage-2 LoRA adapter (NOT the DiT) and is not downloaded automatically. Fetch both
 # weights and point the env vars at them:
 #     modelscope download --model Qwen/Qwen3.6-27B --local_dir ./models/Qwen/Qwen3.6-27B
@@ -56,7 +60,7 @@
 #     export REWRITER_BASE_MODEL=./models/Qwen/Qwen3.6-27B
 #     export REWRITER_ADAPTER=./models/Robbyant/lingbot-video-rewriter-lora
 #
-# from prompt_rewriter import rewrite_prompt
+# from examples.lingbot_video.model_training.scripts.prompt_rewriter import rewrite_prompt
 # caption = rewrite_prompt(
 #     "A playful puppy runs across a lush green meadow, chasing a red ball. "
 #     "Dynamic side-tracking camera.",
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
index dab4270c1..9762634f4 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
@@ -1,19 +1,15 @@
 import json
-import os
 import torch
-from diffsynth.utils.data import save_video
+from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
 
-
-# Low-VRAM inference. offload_dtype / offload_device on each ModelConfig turn on VRAM
-# management: weights stay on CPU in fp8 and stream to the GPU layer-by-layer, computed in
-# bf16. vram_limit only caps resident VRAM once offloading is enabled by those two fields.
 vram_config = {
-    "offload_dtype": torch.float8_e4m3fn,
+    "offload_dtype": torch.bfloat16,
     "offload_device": "cpu",
-    "onload_dtype": torch.float8_e4m3fn,
+    "onload_dtype": torch.bfloat16,
     "onload_device": "cpu",
-    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_dtype": torch.bfloat16,
     "preparing_device": "cuda",
     "computation_dtype": torch.bfloat16,
     "computation_device": "cuda",
@@ -28,14 +24,18 @@
         ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
     ],
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
-    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
 )
 
-# Released in-distribution structured caption (shared with the model_inference example);
-# the pipeline accepts a plain string or a caption dict, so the json is loaded here.
-with open(os.path.join(
-        os.path.dirname(__file__), "..", "model_inference", "prompts", "t2v_example_1.json"), "r", encoding="utf-8") as f:
+# --- Text-to-video -------------------------------------------------------------------
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b/*",
+)
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/t2v_example_1.json", "r", encoding="utf-8") as f:
     caption = json.load(f)
+
 video = pipe(
     prompt=caption,
     negative_prompt=pipe.default_negative_prompt,
@@ -43,4 +43,16 @@
     num_inference_steps=40, cfg_scale=3.0,
     seed=0,
 )
-save_video(video, "video_lingbot-video-dense-1.3b_low_vram.mp4", fps=15, quality=10)
+save_video(video, "video_lingbot-video-dense-1.3b.mp4", fps=15, quality=10)
+
+# --- Video-to-video ------------------------------------------------------------------
+input_video = VideoData("video_lingbot-video-dense-1.3b.mp4", height=480, width=832)
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_video=input_video, denoising_strength=0.7,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=1,
+)
+save_video(video, "video_lingbot-video-dense-1.3b_v2v.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
index a04d8ec7e..138ce67ad 100644
--- a/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
+++ b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
@@ -1,21 +1,14 @@
-# Download the example video-SFT dataset (a small text-to-video set with a `video` +
-# `prompt` metadata.csv), the same DiffSynth-Studio example dataset the other training
-# scripts use. NOTE: its prompts are plain prose; for best quality rewrite them into
-# structured captions first with model_training/rewrite_captions.py (see the README).
-modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-T2V-1.3B/*" --local_dir ./data/diffsynth_example_dataset
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "lingbot_video/lingbot-video-dense-1.3b/*" --local_dir ./data/diffsynth_example_dataset
 
-# Attention-only LoRA SFT.
 # `--lora_target_modules "to_q,to_k,to_v,to_out"` patches LoRA on the joint
 # text+video self-attention only, leaving the MoE / FFN experts and the router frozen.
-# `--num_frames 169` = 7 s at the model's native 24 fps (169 = 4k+1, required by the
-# VAE's 4x temporal compression). The loader samples the first 169 frames of each clip.
 accelerate launch examples/lingbot_video/model_training/train.py \
-  --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B \
-  --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B/metadata.csv \
+  --dataset_base_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b \
+  --dataset_metadata_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/metadata.json \
   --data_file_keys "video" \
   --height 480 \
   --width 832 \
-  --num_frames 169 \
+  --num_frames 81 \
   --dataset_repeat 200 \
   --model_id_with_origin_paths "Robbyant/lingbot-video-dense-1.3b:transformer/diffusion_pytorch_model.safetensors,Robbyant/lingbot-video-dense-1.3b:text_encoder/model*.safetensors,Robbyant/lingbot-video-dense-1.3b:vae/diffusion_pytorch_model.safetensors" \
   --processor_path "Robbyant/lingbot-video-dense-1.3b:processor/" \
diff --git a/examples/lingbot_video/model_inference/prompt_rewriter.py b/examples/lingbot_video/model_training/scripts/prompt_rewriter.py
similarity index 94%
rename from examples/lingbot_video/model_inference/prompt_rewriter.py
rename to examples/lingbot_video/model_training/scripts/prompt_rewriter.py
index dc5da61e9..d6b7daa11 100644
--- a/examples/lingbot_video/model_inference/prompt_rewriter.py
+++ b/examples/lingbot_video/model_training/scripts/prompt_rewriter.py
@@ -7,7 +7,7 @@
 TransformersBackend loads the rewriter VLM locally; make_backend also accepts any object
 exposing generate(text, image, use_lora).
 
-    from prompt_rewriter import rewrite_prompt
+    from examples.lingbot_video.model_training.scripts.prompt_rewriter import rewrite_prompt
     caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration=5)
     video = pipe(prompt=caption, ...)
 """
@@ -33,9 +33,16 @@
 except ImportError:
     repair_json = None
 
-from system_prompts import (
-    VIDEO_STEP1_EXPAND, VIDEO_STEP2_MAP, IMAGE_STEP1_EXPAND, IMAGE_STEP2_MAP,
-)
+# Support both package-style import (from the repo root) and top-level import (when
+# this directory itself is on sys.path, e.g. from rewrite_captions.py).
+try:
+    from .system_prompts import (
+        VIDEO_STEP1_EXPAND, VIDEO_STEP2_MAP, IMAGE_STEP1_EXPAND, IMAGE_STEP2_MAP,
+    )
+except ImportError:
+    from system_prompts import (
+        VIDEO_STEP1_EXPAND, VIDEO_STEP2_MAP, IMAGE_STEP1_EXPAND, IMAGE_STEP2_MAP,
+    )
 
 
 # mode -> (step1 system prompt, step2 system prompt, feed image?, add duration?)
diff --git a/examples/lingbot_video/model_training/rewrite_captions.py b/examples/lingbot_video/model_training/scripts/rewrite_captions.py
similarity index 95%
rename from examples/lingbot_video/model_training/rewrite_captions.py
rename to examples/lingbot_video/model_training/scripts/rewrite_captions.py
index d65700deb..b67b1edde 100644
--- a/examples/lingbot_video/model_training/rewrite_captions.py
+++ b/examples/lingbot_video/model_training/scripts/rewrite_captions.py
@@ -17,9 +17,9 @@
 import os
 import sys
 
-# The two-stage rewriter engine lives with the inference examples, so training and
+# The two-stage rewriter engine lives alongside this script, so training and
 # inference share one implementation.
-sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "model_inference"))
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 from prompt_rewriter import Rewriter, make_backend
 
 
diff --git a/examples/lingbot_video/model_inference/system_prompts.py b/examples/lingbot_video/model_training/scripts/system_prompts.py
similarity index 100%
rename from examples/lingbot_video/model_inference/system_prompts.py
rename to examples/lingbot_video/model_training/scripts/system_prompts.py

From 7caae830f1b2dea65977688e928780c6f377ec1f Mon Sep 17 00:00:00 2001
From: mi804 <1576993271@qq.com>
Date: Tue, 28 Jul 2026 11:15:49 +0800
Subject: [PATCH 17/34] remove fp32 cast to keep compatibility with
 low-vram-inference

---
 diffsynth/models/lingbot_video_dit.py         | 63 +------------------
 diffsynth/pipelines/lingbot_video.py          | 17 ++---
 .../lingbot-video-dense-1.3b.py               |  1 -
 3 files changed, 8 insertions(+), 73 deletions(-)

diff --git a/diffsynth/models/lingbot_video_dit.py b/diffsynth/models/lingbot_video_dit.py
index 6e621ab20..07eccd71b 100644
--- a/diffsynth/models/lingbot_video_dit.py
+++ b/diffsynth/models/lingbot_video_dit.py
@@ -10,29 +10,6 @@
 from ..core.device.npu_compatible_device import get_device_type
 
 
-# Modules kept in fp32 regardless of the bulk compute dtype (sensitive AdaLN / norm /
-# router paths); the custom `to()` below honours this list.
-LINGBOT_VIDEO_FP32_MODULES = (
-    "time_embedder",
-    "time_modulation",
-    "scale_shift_table",
-    "norm",
-    "norm1",
-    "norm2",
-    "norm_q",
-    "norm_k",
-    "norm_post_attn",
-    "norm_post_ffn",
-    "norm_out",
-    "norm_out_modulation",
-    "router",
-)
-
-
-def should_keep_in_fp32(name: str) -> bool:
-    return any(module_name in name.split(".") for module_name in LINGBOT_VIDEO_FP32_MODULES)
-
-
 def get_timestep_embedding(
     timesteps: torch.Tensor,
     embedding_dim: int,
@@ -457,14 +434,12 @@ def forward(self, x, temb6, rotary_emb, attention_mask=None, moe_padding_mask=No
                 "LingBotVideoBlock expects token-level temb6 with shape (B*S, 6D); "
                 f"got {tuple(temb6.shape)} for hidden states {tuple(x.shape)}."
             )
-        # AdaLN modulation / norms run in fp32 (sensitive path); cast to the bulk
-        # compute dtype only at the bf16 Linear boundary.
         mod = temb6.view(x.shape[0], x.shape[1], -1) + self.scale_shift_table.unsqueeze(0)
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=-1)
         gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
         scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
 
-        bulk_dtype = self.attn.to_q.weight.dtype
+        bulk_dtype = x.dtype
         attn_in = (self.norm1(x) * scale_msa + shift_msa).to(bulk_dtype)
         attn_out = self.attn(attn_in, rotary_emb, attention_mask)
         x = x + (gate_msa * self.norm_post_attn(attn_out)).to(x.dtype)
@@ -482,41 +457,13 @@ class LingBotVideoDiT(nn.Module):
     """LingBot-Video MoE DiT ported for DiffSynth-Studio.
 
     Supports both the Dense (`num_experts=0`, FFN = MLP) and MoE
-    (`num_experts>0`, FFN = sparse MoE) variants from a single class. The bulk of
-    the network runs in the model's compute dtype (e.g. bf16) while the modules in
-    `LINGBOT_VIDEO_FP32_MODULES` are pinned to fp32 by the custom `to()`.
+    (`num_experts>0`, FFN = sparse MoE) variants from a single class.
     """
 
     _supports_gradient_checkpointing = True
     _no_split_modules = ["LingBotVideoBlock"]
     _repeated_blocks = ["LingBotVideoBlock"]
 
-    def to(self, *args, **kwargs):
-        device, dtype, non_blocking, _ = torch._C._nn._parse_to(*args, **kwargs)
-        if dtype is None or dtype == torch.float32:
-            return super().to(*args, **kwargs)
-        if not torch.is_floating_point(torch.empty((), dtype=dtype)):
-            return super().to(*args, **kwargs)
-
-        if device is not None:
-            super().to(device=device, non_blocking=non_blocking)
-
-        for name, param in self.named_parameters():
-            if not torch.is_floating_point(param):
-                continue
-            target_dtype = torch.float32 if should_keep_in_fp32(name) else dtype
-            param.data = param.data.to(dtype=target_dtype, non_blocking=non_blocking)
-            if param.grad is not None:
-                param.grad.data = param.grad.data.to(dtype=target_dtype, non_blocking=non_blocking)
-
-        for name, buffer in self.named_buffers():
-            if not torch.is_floating_point(buffer):
-                continue
-            target_dtype = torch.float32 if should_keep_in_fp32(name) else dtype
-            buffer.data = buffer.data.to(dtype=target_dtype, non_blocking=non_blocking)
-
-        return self
-
     def __init__(
         self,
         patch_size: Tuple[int, int, int] = (1, 2, 2),
@@ -652,10 +599,7 @@ def forward(
 
         # Timestep -> per-token modulation.
         timestep_proj = self.time_proj(timestep.float())
-        # time_proj always returns fp32; match the time_embedder's own weight dtype
-        # (fp32 under standard load via the custom `to()` override; bf16 under low-VRAM
-        # offload, where the wrapper wholesale-casts all params).
-        t_emb = self.time_embedder(timestep_proj.to(self.time_embedder.linear_1.weight.dtype))  # (B, D)
+        t_emb = self.time_embedder(timestep_proj.to(joint.dtype))  # (B, D)
         if packed_batch:
             temb_input = torch.cat(
                 [t_emb[i:i + 1].unsqueeze(1).expand(1, sample_seq_lens[i], -1) for i in range(B)], dim=1
@@ -677,7 +621,6 @@ def forward(
         final_mod = self.norm_out_modulation(temb_input.reshape(joint.shape[0] * joint.shape[1], -1))
         shift, scale = final_mod.reshape(joint.shape[0], joint.shape[1], -1).chunk(2, dim=-1)
         final_hidden = self.norm_out(joint) * (1.0 + scale) + shift
-        # Cast to joint's dtype, not proj_out.weight.dtype (which is fp8 under offload).
         projected = self.proj_out(final_hidden.to(joint.dtype))
 
         if packed_batch:
diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index 2fd0db18c..958fa13b7 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -1,12 +1,9 @@
 import json
 
 import torch
-import torch.nn.functional as F
-import numpy as np
 from PIL import Image
 from tqdm import tqdm
 from typing import Optional, Union
-from typing_extensions import Literal
 
 from ..core.device.npu_compatible_device import get_device_type
 from ..core import ModelConfig
@@ -227,7 +224,7 @@ def __init__(self):
     def process(self, pipe: LingBotVideoPipeline, height, width, num_frames, seed, rand_device):
         length = (num_frames - 1) // 4 + 1
         shape = (1, pipe.dit.in_channels, length, height // 8, width // 8)
-        noise = pipe.generate_noise(shape, seed=seed, rand_device=rand_device, torch_dtype=torch.float32)
+        noise = pipe.generate_noise(shape, seed=seed, rand_device=rand_device)
         return {"noise": noise}
 
 
@@ -358,7 +355,7 @@ def process(self, pipe: LingBotVideoPipeline, input_video, noise):
             return {"latents": noise}
         pipe.load_models_to_device(self.onload_model_names)
         video = pipe.preprocess_video(input_video)
-        input_latents = pipe.vae.encode_video(video).to(dtype=torch.float32, device=pipe.device)
+        input_latents = pipe.vae.encode_video(video).to(dtype=pipe.torch_dtype, device=pipe.device)
         if pipe.scheduler.training:
             return {"latents": noise, "input_latents": input_latents}
         else:
@@ -402,16 +399,12 @@ def model_fn_lingbot_video(
     use_gradient_checkpointing_offload: bool = False,
     **kwargs,
 ):
-    # Cast inputs to the DiT's compute dtype (e.g. bf16); the DiT keeps AdaLN/norm in fp32.
-    dit_dtype = dit.patch_embedder.weight.dtype
-    hidden_states = latents.to(dtype=dit_dtype)
-    encoder_hidden_states = context.to(dtype=dit_dtype)
     noise_pred = dit(
-        hidden_states=hidden_states,
+        hidden_states=latents,
         timestep=timestep,
-        encoder_hidden_states=encoder_hidden_states,
+        encoder_hidden_states=context,
         encoder_attention_mask=encoder_attention_mask,
         use_gradient_checkpointing=use_gradient_checkpointing,
         use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
     )
-    return noise_pred.float()
+    return noise_pred
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
index 9e788d3bb..733ac0cd5 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
@@ -17,7 +17,6 @@
 )
 
 # --- Text-to-video -------------------------------------------------------------------
-# Download dataset
 dataset_snapshot_download(
     dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
     local_dir="data/diffsynth_example_dataset",

From 5f604e25ddb724ccf47a132434d81929c3b46ae2 Mon Sep 17 00:00:00 2001
From: mi804 <1576993271@qq.com>
Date: Tue, 28 Jul 2026 12:53:44 +0800
Subject: [PATCH 18/34] add support for full train

---
 .../lingbot-video-dense-1.3b.py               |  8 ++---
 .../full/lingbot-video-dense-1.3b.sh          | 18 ++++++++++
 .../lora/lingbot-video-dense-1.3b.sh          |  4 +--
 .../model_training/scripts/prompt_rewriter.py |  2 --
 .../scripts/rewrite_captions.py               |  9 +++--
 .../lingbot_video/model_training/train.py     |  2 +-
 .../validate_full/lingbot-video-dense-1.3b.py | 36 +++++++++++++++++++
 .../validate_lora/lingbot-video-dense-1.3b.py | 15 ++++++--
 8 files changed, 77 insertions(+), 17 deletions(-)
 create mode 100644 examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh
 create mode 100644 examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py

diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
index 9762634f4..9b3dd9fcd 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
@@ -5,11 +5,11 @@
 from modelscope import dataset_snapshot_download
 
 vram_config = {
-    "offload_dtype": torch.bfloat16,
-    "offload_device": "cpu",
-    "onload_dtype": torch.bfloat16,
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
     "onload_device": "cpu",
-    "preparing_dtype": torch.bfloat16,
+    "preparing_dtype": torch.float8_e4m3fn,
     "preparing_device": "cuda",
     "computation_dtype": torch.bfloat16,
     "computation_device": "cuda",
diff --git a/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh b/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh
new file mode 100644
index 000000000..50540eb14
--- /dev/null
+++ b/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh
@@ -0,0 +1,18 @@
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "lingbot_video/lingbot-video-dense-1.3b/*" --local_dir ./data/diffsynth_example_dataset
+
+accelerate launch examples/lingbot_video/model_training/train.py \
+  --dataset_base_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b \
+  --dataset_metadata_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/metadata.json \
+  --data_file_keys "video" \
+  --height 480 \
+  --width 832 \
+  --num_frames 81 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Robbyant/lingbot-video-dense-1.3b:transformer/diffusion_pytorch_model.safetensors,Robbyant/lingbot-video-dense-1.3b:text_encoder/model*.safetensors,Robbyant/lingbot-video-dense-1.3b:vae/diffusion_pytorch_model.safetensors" \
+  --processor_path "Robbyant/lingbot-video-dense-1.3b:processor/" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/lingbot-video-dense-1.3b_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing
diff --git a/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
index 138ce67ad..34010d953 100644
--- a/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
+++ b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh
@@ -9,11 +9,11 @@ accelerate launch examples/lingbot_video/model_training/train.py \
   --height 480 \
   --width 832 \
   --num_frames 81 \
-  --dataset_repeat 200 \
+  --dataset_repeat 50 \
   --model_id_with_origin_paths "Robbyant/lingbot-video-dense-1.3b:transformer/diffusion_pytorch_model.safetensors,Robbyant/lingbot-video-dense-1.3b:text_encoder/model*.safetensors,Robbyant/lingbot-video-dense-1.3b:vae/diffusion_pytorch_model.safetensors" \
   --processor_path "Robbyant/lingbot-video-dense-1.3b:processor/" \
   --learning_rate 1e-4 \
-  --num_epochs 20 \
+  --num_epochs 5 \
   --remove_prefix_in_ckpt "pipe.dit." \
   --output_path "./models/train/lingbot-video-dense-1.3b_lora" \
   --lora_base_model "dit" \
diff --git a/examples/lingbot_video/model_training/scripts/prompt_rewriter.py b/examples/lingbot_video/model_training/scripts/prompt_rewriter.py
index d6b7daa11..b27ff9f9e 100644
--- a/examples/lingbot_video/model_training/scripts/prompt_rewriter.py
+++ b/examples/lingbot_video/model_training/scripts/prompt_rewriter.py
@@ -33,8 +33,6 @@
 except ImportError:
     repair_json = None
 
-# Support both package-style import (from the repo root) and top-level import (when
-# this directory itself is on sys.path, e.g. from rewrite_captions.py).
 try:
     from .system_prompts import (
         VIDEO_STEP1_EXPAND, VIDEO_STEP2_MAP, IMAGE_STEP1_EXPAND, IMAGE_STEP2_MAP,
diff --git a/examples/lingbot_video/model_training/scripts/rewrite_captions.py b/examples/lingbot_video/model_training/scripts/rewrite_captions.py
index b67b1edde..84a1de737 100644
--- a/examples/lingbot_video/model_training/scripts/rewrite_captions.py
+++ b/examples/lingbot_video/model_training/scripts/rewrite_captions.py
@@ -15,12 +15,11 @@
 import argparse
 import json
 import os
-import sys
 
-# The two-stage rewriter engine lives alongside this script, so training and
-# inference share one implementation.
-sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
-from prompt_rewriter import Rewriter, make_backend
+try:
+    from .prompt_rewriter import Rewriter, make_backend
+except ImportError:
+    from prompt_rewriter import Rewriter, make_backend
 
 
 def _load_rows(path):
diff --git a/examples/lingbot_video/model_training/train.py b/examples/lingbot_video/model_training/train.py
index 58fab8771..5039d95ee 100644
--- a/examples/lingbot_video/model_training/train.py
+++ b/examples/lingbot_video/model_training/train.py
@@ -1,6 +1,6 @@
 import torch, os, argparse, accelerate, warnings
 from diffsynth.core import UnifiedDataset
-from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline
 from diffsynth.diffusion import *
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
diff --git a/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py
new file mode 100644
index 000000000..ea9bca724
--- /dev/null
+++ b/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py
@@ -0,0 +1,36 @@
+import torch
+import json
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from diffsynth import load_state_dict
+from modelscope import dataset_snapshot_download
+
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+)
+state_dict = load_state_dict("models/train/lingbot-video-dense-1.3b_full/epoch-1.safetensors")
+pipe.dit.load_state_dict(state_dict)
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b/*",
+)
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/t2v_example_1.json", "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-dense-1.3b.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py
index 5f5d7cdbf..5f341ddf0 100644
--- a/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py
@@ -1,6 +1,8 @@
 import torch
+import json
 from diffsynth.utils.data import save_video
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
 
 
 pipe = LingBotVideoPipeline.from_pretrained(
@@ -13,12 +15,19 @@
     ],
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
 )
-pipe.load_lora(pipe.dit, "models/train/lingbot-video-dense-1.3b_lora/epoch-19.safetensors", alpha=1)
+pipe.load_lora(pipe.dit, "models/train/lingbot-video-dense-1.3b_lora/epoch-4.safetensors", alpha=1)
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b/*",
+)
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/t2v_example_1.json", "r", encoding="utf-8") as f:
+    caption = json.load(f)
 
 video = pipe(
-    prompt="from sunset to night, a small town, light, house, river",
+    prompt=caption,
     negative_prompt=pipe.default_negative_prompt,
-    height=480, width=832, num_frames=169,
+    height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0,
     seed=0,
 )

From cae44a08145cd0da92cc99d1c9d9564ef16b84bc Mon Sep 17 00:00:00 2001
From: mi804 <1576993271@qq.com>
Date: Tue, 28 Jul 2026 13:17:16 +0800
Subject: [PATCH 19/34] style refactor

---
 diffsynth/configs/model_configs.py            |  9 ----
 diffsynth/models/lingbot_video_dit.py         | 41 ++++++-------------
 .../models/lingbot_video_text_encoder.py      | 17 +-------
 diffsynth/pipelines/lingbot_video.py          | 22 ----------
 .../lingbot_video_dit.py                      |  5 ---
 .../lingbot_video_text_encoder.py             |  5 ---
 .../lingbot-video-dense-1.3b.py               |  3 +-
 .../lingbot-video-dense-1.3b.py               |  2 +-
 8 files changed, 17 insertions(+), 87 deletions(-)

diff --git a/diffsynth/configs/model_configs.py b/diffsynth/configs/model_configs.py
index 19f2c3af9..09df9f72c 100644
--- a/diffsynth/configs/model_configs.py
+++ b/diffsynth/configs/model_configs.py
@@ -1325,8 +1325,6 @@
 lingbot_video_series = [
     {
         # Example: ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors")
-        # Dense 1.3B DiT (num_experts=0 -> pure SwiGLU MLP FFN). Model __init__
-        # defaults already match transformer/config.json, so no extra_kwargs.
         "model_hash": "2bcf511fe5e0000519394d242b4d8abd",
         "model_name": "lingbot_video_dit",
         "model_class": "diffsynth.models.lingbot_video_dit.LingBotVideoDiT",
@@ -1334,18 +1332,11 @@
     },
     {
         # Example: ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/*.safetensors")
-        # Qwen3-VL text encoder (2 shards). Hash is over the merged key set of both
-        # shards. The checkpoint stores only the `model.*` submodule (713 tensors,
-        # no tied lm_head), matching a bare Qwen3VLModel exactly -> identity converter.
         "model_hash": "b8750c24f732c87797f551196c4cef78",
         "model_name": "lingbot_video_text_encoder",
         "model_class": "diffsynth.models.lingbot_video_text_encoder.LingBotVideoTextEncoder",
         "state_dict_converter": "diffsynth.utils.state_dict_converters.lingbot_video_text_encoder.LingBotVideoTextEncoderStateDictConverter",
     },
-    # VAE: the LingBot-Video VAE (vae/*.safetensors) is byte-structurally identical
-    # to QwenImageVAE (hash ed4ea5824d55ec3107b09815e318123a, registered in
-    # qwen_image_series). It is loaded via that existing entry as model_name
-    # "qwen_image_vae"; the pipeline fetches it and uses the 5D-video code path.
 ]
 
 MODEL_CONFIGS = (
diff --git a/diffsynth/models/lingbot_video_dit.py b/diffsynth/models/lingbot_video_dit.py
index 07eccd71b..c98557fc3 100644
--- a/diffsynth/models/lingbot_video_dit.py
+++ b/diffsynth/models/lingbot_video_dit.py
@@ -18,7 +18,7 @@ def get_timestep_embedding(
     scale: float = 1.0,
     max_period: int = 10000,
 ) -> torch.Tensor:
-    """Sinusoidal timestep embedding (matches diffusers.get_timestep_embedding)."""
+    """Sinusoidal timestep embedding."""
     assert timesteps.ndim == 1
     half_dim = embedding_dim // 2
     exponent = -math.log(max_period) * torch.arange(
@@ -37,7 +37,7 @@ def get_timestep_embedding(
 
 
 class Timesteps(nn.Module):
-    """Parameter-free sinusoidal timestep projection (matches diffusers.Timesteps)."""
+    """Parameter-free sinusoidal timestep projection."""
 
     def __init__(self, num_channels: int, flip_sin_to_cos: bool = True, downscale_freq_shift: float = 0.0, scale: int = 1):
         super().__init__()
@@ -57,12 +57,7 @@ def forward(self, timesteps: torch.Tensor) -> torch.Tensor:
 
 
 class TimestepEmbedding(nn.Module):
-    """Two-layer timestep MLP (matches diffusers.TimestepEmbedding, act_fn='silu').
-
-    Submodule names `linear_1` / `linear_2` are kept identical so the original
-    checkpoint keys (`time_embedder.linear_1.*`, `time_embedder.linear_2.*`) load
-    without renaming.
-    """
+    """Two-layer timestep MLP (act_fn='silu')."""
 
     def __init__(self, in_channels: int, time_embed_dim: int, sample_proj_bias: bool = True):
         super().__init__()
@@ -75,7 +70,7 @@ def forward(self, sample: torch.Tensor) -> torch.Tensor:
 
 
 class LingBotVideoRMSNorm(nn.Module):
-    """RMSNorm with fp32 accumulation (ported verbatim from lingbot-video)."""
+    """RMSNorm with fp32 accumulation."""
 
     def __init__(self, dim: int, eps: float = 1e-6):
         super().__init__()
@@ -155,7 +150,7 @@ def _cat_interleave(a: torch.Tensor, len_a: list, b: torch.Tensor, len_b: list)
 
 
 class LingBotVideoTextEmbedder(nn.Module):
-    """CondProjection: RMSNorm(text_dim) -> Linear -> SiLU -> Linear."""
+    """RMSNorm(text_dim) -> Linear -> SiLU -> Linear."""
 
     def __init__(self, text_dim: int, hidden_size: int):
         super().__init__()
@@ -186,7 +181,7 @@ def forward(self, x, rotary_emb, attention_mask=None):
         v = self.to_v(x).unflatten(2, (self.num_heads, self.head_dim))
         q = apply_rotary_emb(self.norm_q(q), rotary_emb)
         k = apply_rotary_emb(self.norm_k(k), rotary_emb)
-        # q/k/v are (B, S, H, D); DiffSynth's attention_forward handles the layout.
+        # q/k/v are (B, S, H, D).
         out = attention_forward(
             q, k, v,
             q_pattern="b s n d", k_pattern="b s n d", v_pattern="b s n d", out_pattern="b s n d",
@@ -207,12 +202,6 @@ def forward(self, x):
 
 
 class LingBotVideoRouter(nn.Module):
-    """TokenChoiceTopKRouter inference path (no capacity / jitter / load stats).
-
-    The asymmetry is preserved: selection uses the bias-added score, while the
-    gating weights gather the bias-free score.
-    """
-
     def __init__(self, hidden_size, num_experts, top_k, score_func, norm_topk_prob, n_group, topk_group, route_scale):
         super().__init__()
         self.num_experts = num_experts
@@ -257,7 +246,7 @@ def forward(self, tokens: torch.Tensor):
 
 
 class LingBotVideoGroupedExperts(nn.Module):
-    """Weight layout matches GroupedExperts: w1 [E,I,H], w2 [E,H,I], w3 [E,I,H]."""
+    """Weight layout: w1 [E,I,H], w2 [E,H,I], w3 [E,I,H]."""
 
     def __init__(self, num_experts, hidden_size, intermediate_size):
         super().__init__()
@@ -272,10 +261,7 @@ def _round_up_to_multiple(value: int, multiple: int) -> int:
 
 
 class LingBotVideoSparseMoeBlock(nn.Module):
-    """MoE FFN. Keeps only the portable eager expert path (grouped_mm with a
-    per-expert for-loop fallback); the sglang / triton / fp8 / context-parallel
-    backends from the source are intentionally dropped for the DiffSynth port.
-    """
+    """MoE FFN with a grouped_mm expert path and a per-expert for-loop fallback."""
 
     def __init__(self, hidden_size, intermediate_size, num_experts, top_k, moe_intermediate_size,
                  score_func, norm_topk_prob, n_group, topk_group, routed_scaling_factor, n_shared_experts):
@@ -417,7 +403,7 @@ def __init__(self, hidden_size, num_attention_heads, intermediate_size, norm_eps
         self.attn = LingBotVideoAttention(h, num_attention_heads, norm_eps, qkv_bias, out_bias)
         self.norm_post_attn = LingBotVideoRMSNorm(h, norm_eps)
         self.norm2 = LingBotVideoRMSNorm(h, norm_eps)
-        # Sparsity decision matches the source MoEBlock: mlp_only_layers + decoder_sparse_step + num_experts.
+        # Sparsity decision: mlp_only_layers + decoder_sparse_step + num_experts.
         if layer_idx not in mlp_only_layers and (num_experts > 0 and (layer_idx + 1) % decoder_sparse_step == 0):
             self.ffn = LingBotVideoSparseMoeBlock(
                 h, intermediate_size, num_experts, num_experts_per_tok, moe_intermediate_size,
@@ -454,7 +440,7 @@ def forward(self, x, temb6, rotary_emb, attention_mask=None, moe_padding_mask=No
 
 
 class LingBotVideoDiT(nn.Module):
-    """LingBot-Video MoE DiT ported for DiffSynth-Studio.
+    """LingBot-Video MoE DiT.
 
     Supports both the Dense (`num_experts=0`, FFN = MLP) and MoE
     (`num_experts>0`, FFN = sparse MoE) variants from a single class.
@@ -553,7 +539,7 @@ def forward(
         text_lens_list = [int(v) for v in text_lens.detach().cpu().tolist()]
         packed_batch = B > 1
 
-        # patchify: token order (f h w), feature order (pf ph pw c) -- matches patchify_and_embed
+        # patchify: token order (f h w), feature order (pf ph pw c)
         patch_tokens = hidden_states.reshape(B, C, gt, pF, gh, pH, gw, pW)
         patch_tokens = patch_tokens.permute(0, 2, 4, 6, 3, 5, 7, 1).reshape(B, n_video, pF * pH * pW * C)
 
@@ -578,8 +564,7 @@ def forward(
         attention_mask = None
         moe_padding_mask = None
         if packed_batch:
-            # Block-diagonal mask so packed samples only attend within their own block
-            # (replaces the source's flash varlen path; numerically equivalent, no flash dep).
+            # Block-diagonal mask so packed samples only attend within their own block.
             sample_seq_lens = [n_video + tl for tl in text_lens_list]
             total = sum(sample_seq_lens)
             block_mask = torch.zeros((total, total), dtype=torch.bool, device=device)
@@ -632,7 +617,7 @@ def forward(
         else:
             x = projected[:, :n_video]
 
-        # unpatchify (matches the rearrange in postprocess)
+        # unpatchify
         Cout = self.out_channels
         x = x.reshape(B, gt, gh, gw, pF, pH, pW, Cout)
         x = x.permute(0, 7, 1, 4, 2, 5, 3, 6).reshape(B, Cout, T, H, W)
diff --git a/diffsynth/models/lingbot_video_text_encoder.py b/diffsynth/models/lingbot_video_text_encoder.py
index 434c471da..680968675 100644
--- a/diffsynth/models/lingbot_video_text_encoder.py
+++ b/diffsynth/models/lingbot_video_text_encoder.py
@@ -3,21 +3,8 @@
 
 
 class LingBotVideoTextEncoder(torch.nn.Module):
-    """
-    Text encoder for LingBot-Video.
-
-    The checkpoint (``text_encoder/*.safetensors``) is a
-    ``Qwen3VLForConditionalGeneration`` exported by transformers, but only the
-    ``model.*`` submodule (language_model + visual, 713 tensors) is stored — the
-    tied ``lm_head`` is absent because ``tie_word_embeddings=True``. Wrapping a
-    bare ``Qwen3VLModel`` therefore matches the checkpoint keys exactly (0 missing
-    / 0 unexpected), so the state-dict converter is the identity.
-
-    ``forward`` mirrors :class:`QwenImageTextEncoder`: it forces
-    ``output_hidden_states=True`` and returns the tuple of per-layer hidden
-    states. The pipeline selects ``hidden_states[-1]`` (the original
-    ``HIDDEN_STATE_SKIP_LAYER == 0``) as the prompt embedding.
-    """
+    """Qwen3-VL text encoder for LingBot-Video. `forward` returns the tuple of
+    per-layer hidden states."""
 
     def __init__(self):
         super().__init__()
diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index 958fa13b7..a2e113aef 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -74,11 +74,6 @@ def _pixel_tensor_to_pil(pixel: torch.Tensor) -> Image.Image:
 
 
 class LingBotVideoPipeline(BasePipeline):
-    """Text-to-video pipeline for LingBot-Video (DiT + Qwen3-VL text encoder + QwenImageVAE),
-    following the DiffSynth PipelineUnit + model_fn pattern. Sampling uses FlowMatchScheduler
-    (Wan template). The 5D-video VAE encode/decode lives in QwenImageVAE
-    (encode_video / decode_video); the 4D image paths stay untouched."""
-
     def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
         super().__init__(
             device=device, torch_dtype=torch_dtype,
@@ -229,12 +224,8 @@ def process(self, pipe: LingBotVideoPipeline, height, width, num_frames, seed, r
 
 
 class LingBotVideoUnit_PromptEmbedder(PipelineUnit):
-    # Prompt truncation length for the Qwen3-VL processor.
     TOKEN_LENGTH = 37698
-    # Hidden-state layer used as the prompt embedding: 0 -> the last layer.
     HIDDEN_STATE_SKIP_LAYER = 0
-
-    # Prompt-enhancement chat template wrapping the user prompt.
     PROMPT_TEMPLATE = (
         "<|im_start|>system\nGiven a user input that may include a text prompt alone, "
         "a text prompt with an image reference, or a text prompt with a video reference "
@@ -248,11 +239,6 @@ class LingBotVideoUnit_PromptEmbedder(PipelineUnit):
         "commentary or evaluations:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n"
         "<|im_start|>assistant\n"
     )
-
-    # LingBot-Video is trained on structured JSON captions. normalize_caption serialises a
-    # dict caption into the compact-JSON string the DiT expects; a plain string is passed
-    # through. The prompt rewriter that turns a brief idea into such a caption lives in
-    # examples/lingbot_video/model_training/scripts/prompt_rewriter.py.
     _RUNTIME_KEYS = {"duration", "fps", "height", "width", "num_frames", "resolution", "ratio"}
 
     def __init__(self):
@@ -264,13 +250,10 @@ def __init__(self):
             output_params=("context", "encoder_attention_mask"),
             onload_model_names=("text_encoder", ),
         )
-        # Cached number of template-prefix tokens to crop from the prompt embedding.
         self._crop_start: Optional[int] = None
 
     @classmethod
     def normalize_caption(cls, prompt):
-        # A dict caption is serialised to the compact-JSON string the DiT expects;
-        # a plain string passes through unchanged.
         if isinstance(prompt, dict):
             if "caption" in prompt:
                 caption = prompt["caption"]
@@ -285,7 +268,6 @@ def normalize_caption(cls, prompt):
             raise TypeError(f"prompt must be a str or a dict, not {type(prompt)}")
 
     def _compute_crop_start(self, pipe: LingBotVideoPipeline) -> int:
-        # Token count of the template prefix (everything before the user prompt), computed once.
         if self._crop_start is None:
             marker = "<|USER_INPUT_MARKER|>"
             marked = self.PROMPT_TEMPLATE.format(marker)
@@ -304,7 +286,6 @@ def _compute_crop_start(self, pipe: LingBotVideoPipeline) -> int:
 
     def encode_prompt(self, pipe: LingBotVideoPipeline, prompt):
         prompt = self.normalize_caption(prompt)
-        # T2V: visual_template is empty, so the text is simply the templated prompt.
         text = self.PROMPT_TEMPLATE.format(prompt)
         inputs = pipe.processor(
             text=[text],
@@ -317,18 +298,15 @@ def encode_prompt(self, pipe: LingBotVideoPipeline, prompt):
             return_tensors="pt",
         )
         inputs = inputs.to(pipe.device)
-        # The text encoder returns the tuple of per-layer hidden states.
         hidden_states = pipe.text_encoder(**inputs)
         prompt_embeds = hidden_states[-(self.HIDDEN_STATE_SKIP_LAYER + 1)]
         prompt_mask = inputs["attention_mask"]
 
-        # Crop the prompt-enhancement template prefix.
         crop_start = self._compute_crop_start(pipe)
         if crop_start > 0:
             prompt_embeds = prompt_embeds[:, crop_start:]
             prompt_mask = prompt_mask[:, crop_start:]
 
-        # Batch=1: drop the right padding before the DiT forward.
         if prompt_embeds.shape[0] == 1:
             true_len = int(prompt_mask[0].sum().item())
             prompt_embeds = prompt_embeds[:, :true_len]
diff --git a/diffsynth/utils/state_dict_converters/lingbot_video_dit.py b/diffsynth/utils/state_dict_converters/lingbot_video_dit.py
index 3774568ee..d3b87b0d9 100644
--- a/diffsynth/utils/state_dict_converters/lingbot_video_dit.py
+++ b/diffsynth/utils/state_dict_converters/lingbot_video_dit.py
@@ -1,9 +1,4 @@
 def LingBotVideoDiTStateDictConverter(state_dict):
-    # The LingBot-Video DiT checkpoint (transformer/diffusion_pytorch_model.safetensors)
-    # is saved by diffusers with module-hierarchy keys that already match
-    # `LingBotVideoDiT` exactly (patch_embedder / time_embedder / time_modulation /
-    # text_embedder / blocks.N.* / norm_out_modulation / proj_out). The only work
-    # needed is stripping any wrapper prefix a repackaged/compiled checkpoint may add.
     prefixes = ["model.diffusion_model.", "_orig_mod.", "module.", "transformer."]
     state_dict_ = {}
     for name in state_dict:
diff --git a/diffsynth/utils/state_dict_converters/lingbot_video_text_encoder.py b/diffsynth/utils/state_dict_converters/lingbot_video_text_encoder.py
index ba4ef6db3..5c301c20c 100644
--- a/diffsynth/utils/state_dict_converters/lingbot_video_text_encoder.py
+++ b/diffsynth/utils/state_dict_converters/lingbot_video_text_encoder.py
@@ -1,9 +1,4 @@
 def LingBotVideoTextEncoderStateDictConverter(state_dict):
-    # The LingBot-Video text encoder checkpoint stores a Qwen3VLForConditionalGeneration
-    # with keys already under `model.language_model.*` and `model.visual.*`, which is
-    # exactly the layout of the wrapped `Qwen3VLModel` (assigned to `self.model`). The
-    # tied `lm_head` is not stored, so no key mapping is required — this is an identity
-    # converter that only strips any wrapper prefix a repackaged checkpoint may add.
     prefixes = ["_orig_mod.", "module."]
     state_dict_ = {}
     for name in state_dict:
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
index 733ac0cd5..ea8b6b06f 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py
@@ -1,6 +1,5 @@
-import json
-import os
 import torch
+import json
 from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 from modelscope import dataset_snapshot_download
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
index 9b3dd9fcd..64fbb3e80 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py
@@ -1,5 +1,5 @@
-import json
 import torch
+import json
 from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 from modelscope import dataset_snapshot_download

From 531ba560d0197ca44babd81e043d70388971a750 Mon Sep 17 00:00:00 2001
From: mi804 <1576993271@qq.com>
Date: Tue, 28 Jul 2026 13:43:08 +0800
Subject: [PATCH 20/34] pop changes for ti2v pipeline

---
 diffsynth/pipelines/lingbot_video.py | 52 ++++++++++++++++++++++++++--
 1 file changed, 49 insertions(+), 3 deletions(-)

diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index a2e113aef..b7bf7347a 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -1,6 +1,9 @@
 import json
+import math
 
 import torch
+import torch.nn.functional as F
+import numpy as np
 from PIL import Image
 from tqdm import tqdm
 from typing import Optional, Union
@@ -136,6 +139,7 @@ def __call__(
         prompt: Union[str, dict] = "",
         negative_prompt: Union[str, dict] = "",
         # Video-to-video
+        input_image: Image.Image = None,
         input_video: list[Image.Image] = None,
         denoising_strength: float = 1.0,
         # Randomness
@@ -188,6 +192,8 @@ def __call__(
                 **models, timestep=timestep, progress_id=progress_id
             )
             inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
+            if first_frame_latents is not None:
+                inputs_shared["latents"][:, :, :cond_t] = first_frame_latents
 
         self.load_models_to_device(['vae'])
         latents = inputs_shared["latents"].to(dtype=self.torch_dtype, device=self.device)
@@ -196,6 +202,42 @@ def __call__(
         self.load_models_to_device([])
         return video
 
+    def preprocess_cond_image(self, image: Image.Image, height, width):
+        # TI2V condition frame -> (1, C, 1, H, W) pixel tensor in [0, 1], aspect-ratio
+        # preserving cover-resize + center-crop to (height, width).
+        raw = torch.from_numpy(np.array(image.convert("RGB"))).permute(2, 0, 1).unsqueeze(0).contiguous()
+        old_h, old_w = raw.shape[-2:]
+        scale = max(height / old_h, width / old_w)
+        new_h = max(math.ceil(old_h * scale), height)
+        new_w = max(math.ceil(old_w * scale), width)
+        resized = F.interpolate(raw.float(), size=(new_h, new_w), mode="bilinear", align_corners=False)
+        top = int(round((new_h - height) / 2.0))
+        left = int(round((new_w - width) / 2.0))
+        cropped = resized[:, :, top: top + height, left: left + width] / 255.0
+        return cropped.unsqueeze(2)
+
+    def _vision_patch_size(self) -> int:
+        # Resolve the Qwen3-VL vision patch size, used with SPATIAL_MERGE_SIZE to pick
+        # the smart-resize grid factor.
+        for obj in (
+            getattr(getattr(self.text_encoder, "config", None), "vision_config", None),
+            getattr(getattr(self.processor, "image_processor", None), "config", None),
+            getattr(self.processor, "image_processor", None),
+        ):
+            patch = getattr(obj, "patch_size", None)
+            if patch is not None:
+                return int(patch)
+        return 16
+
+    def _vlm_image(self, pixel: torch.Tensor) -> Image.Image:
+        # Build the PIL image handed to the text encoder from the condition pixel tensor,
+        # smart-resized to the Qwen3-VL patch grid.
+        image = _pixel_tensor_to_pil(pixel)
+        patch_factor = self._vision_patch_size() * SPATIAL_MERGE_SIZE
+        w, h = image.size
+        resized_height, resized_width = smart_resize(h, w, factor=patch_factor)
+        return image.resize((resized_width, resized_height))
+
 
 class LingBotVideoUnit_ShapeChecker(PipelineUnit):
     def __init__(self):
@@ -284,9 +326,13 @@ def _compute_crop_start(self, pipe: LingBotVideoPipeline) -> int:
                 self._crop_start = int(prefix["input_ids"].shape[1])
         return self._crop_start
 
-    def encode_prompt(self, pipe: LingBotVideoPipeline, prompt):
+    def encode_prompt(self, pipe: LingBotVideoPipeline, prompt, vlm_image=None):
         prompt = self.normalize_caption(prompt)
-        text = self.PROMPT_TEMPLATE.format(prompt)
+        # TI2V: prepend the image-token block so the encoder attends to the condition
+        # frame. The image tokens land after the template prefix, so crop_start is
+        # unaffected.
+        visual_template = IMG_PROMPT_TEMPLATE if vlm_image is not None else ""
+        text = self.PROMPT_TEMPLATE.format(visual_template + prompt)
         inputs = pipe.processor(
             text=[text],
             images=[vlm_image] if vlm_image is not None else None,
@@ -362,7 +408,7 @@ def process(self, pipe: LingBotVideoPipeline, input_image, height, width):
         # was trained to inpaint on.
         pixel = pipe.preprocess_cond_image(input_image, height, width)
         pixel = pixel.to(dtype=pipe.torch_dtype, device=pipe.device)
-        first_frame_latents = pipe.encode_video(pixel * 2.0 - 1.0).to(dtype=torch.float32, device=pipe.device)
+        first_frame_latents = pipe.vae.encode_video(pixel * 2.0 - 1.0).to(dtype=pipe.torch_dtype, device=pipe.device)
         vlm_image = pipe._vlm_image(pixel)
         return {"first_frame_latents": first_frame_latents, "vlm_image": vlm_image}
 

From 4c9ee93581716e89dda091a45380e1e847af4b02 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Tue, 28 Jul 2026 15:07:59 +0800
Subject: [PATCH 21/34] Align TI2V / T2I examples, LoRA + full training, docs
 and README with reviewer's refactor (531ba56)

- TI2V + T2I inference / low-VRAM examples rewritten to the new pipeline API: read
  structured captions from JSON, pass pipe.default_negative_prompt(_image), drop the
  removed module-level normalize_caption / DEFAULT_NEGATIVE_PROMPT_IMAGE imports.
- Add default_negative_prompt_image attribute to LingBotVideoPipeline (T2I variant
  with temporal terms removed) so t2i examples can reference it symmetrically with
  default_negative_prompt.
- TI2V LoRA training script aligned to the reviewer's new t2v LoRA (dataset path,
  num_frames=81, num_epochs=5, dataset_repeat=50); validate_lora rewritten to use
  pipe.default_negative_prompt + json.load.
- New TI2V full-parameter training script + validate_full script (parallel to the
  reviewer's t2v full training), toggled via --first_frame_as_condition.
- Docs rewritten to match the standard Model_Details template: single-checkpoint
  overview covering T2V / TI2V / T2I with 3-row Examples table, input_image param
  documented, prompt-rewriter moved under Model Inference, VAE-internals text
  dropped per reviewer's "keep it about model info + usage" directive.
- README.md / README_zh.md: add Update History entry and Video Synthesis series
  block (Quick Start + Examples table) for LingBot-Video.
---
 README.md                                     |  74 +++++++
 README_zh.md                                  |  74 +++++++
 diffsynth/pipelines/lingbot_video.py          |   8 +
 docs/en/Model_Details/LingBot-Video.md        | 180 +++++++++---------
 docs/zh/Model_Details/LingBot-Video.md        | 176 ++++++++---------
 .../lingbot-video-dense-1.3b_t2i.py           |  21 +-
 .../lingbot-video-dense-1.3b_ti2v.py          |  19 +-
 .../lingbot-video-dense-1.3b_t2i.py           |  25 ++-
 .../lingbot-video-dense-1.3b_ti2v.py          |  23 ++-
 .../full/lingbot-video-dense-1.3b_ti2v.sh     |  26 +++
 .../lora/lingbot-video-dense-1.3b_ti2v.sh     |  29 +--
 .../lingbot-video-dense-1.3b_ti2v.py          |  39 ++++
 .../lingbot-video-dense-1.3b_ti2v.py          |  16 +-
 13 files changed, 470 insertions(+), 240 deletions(-)
 create mode 100644 examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh
 create mode 100644 examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py

diff --git a/README.md b/README.md
index 41a6accc0..aa87df426 100644
--- a/README.md
+++ b/README.md
@@ -36,6 +36,8 @@ We believe that a well-developed open-source code framework can lower the thresh
 
 > Currently, the development personnel of this project are limited, with most of the work handled by [Artiprocher](https://github.com/Artiprocher) and [mi804](https://github.com/mi804). Therefore, the progress of new feature development will be relatively slow, and the speed of responding to and resolving issues is limited. We apologize for this and ask developers to understand.
 
+- **July 28, 2026** LingBot-Video open-sourced, welcome a new member to the video model family! Dense-1.3B is a single checkpoint supporting text-to-video, image-to-video and text-to-image tasks, with low VRAM inference and LoRA / full training. For details, please refer to the [documentation](/docs/en/Model_Details/LingBot-Video.md) and [example code](/examples/lingbot_video/).
+
 - **July 21, 2026** We have open-sourced [DiffSynth-Studio Model Integration Skills](https://www.modelscope.cn/collections/DiffSynth-Studio/DiffSynth-Studio-Model-Integration-Skills). This is a composable collection of Agent Skills that automates the entire workflow of integrating external diffusion models into DiffSynth-Studio, significantly improving the standardization and efficiency of model integration. Get started with the [example](https://www.modelscope.cn/skills/DiffSynth-Studio/diffsynth-integrator/file/view/master/example.md?status=1)!
 
 - **June 29, 2026** Boogu-Image open-sourced. Support includes text-to-image generation, image editing, low VRAM inference, and training capabilities. For details, please refer to the [documentation](/docs/en/Model_Details/Boogu-Image.md) and [example code](/examples/boogu_image/).
@@ -1447,6 +1449,78 @@ Example code for Wan is available at: [/examples/wanvideo/](/examples/wanvideo/)
 
 </details>
 
+#### LingBot-Video: [/docs/en/Model_Details/LingBot-Video.md](/docs/en/Model_Details/LingBot-Video.md)
+
+<details>
+
+<summary>Quick Start</summary>
+
+Running the following code will quickly load the [Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b) model and perform inference. VRAM management is enabled, and the framework will automatically control the loading of model parameters based on available VRAM. The model can run with a minimum of 24GB VRAM.
+
+```python
+import torch
+import json
+from diffsynth.utils.data import save_video, VideoData
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b/*",
+)
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/t2v_example_1.json", "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video.mp4", fps=15, quality=10)
+```
+
+</details>
+
+<details>
+
+<summary>Examples</summary>
+
+Example code for LingBot-Video is available at: [/examples/lingbot_video/](/examples/lingbot_video/)
+
+| Model ID | Inference | Low VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
+|-|-|-|-|-|-|-|
+|[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
+|[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
+|[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
+
+</details>
+
 ### Audio Synthesis
 
 #### ACE-Step: [/docs/en/Model_Details/ACE-Step.md](/docs/en/Model_Details/ACE-Step.md)
diff --git a/README_zh.md b/README_zh.md
index ad0606c6a..60766d83d 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -36,6 +36,8 @@ DiffSynth 目前包括两个开源项目：
 
 > 目前本项目的开发人员有限，大部分工作由 [Artiprocher](https://github.com/Artiprocher) 和 [mi804](https://github.com/mi804) 负责，因此新功能的开发进展会比较缓慢，issue 的回复和解决速度有限，我们对此感到非常抱歉，请各位开发者理解。
 
+- **2026年7月28日** LingBot-Video 开源，欢迎加入视频生成模型家族！Dense-1.3B 是一份 checkpoint，同时支持文生视频、图生视频和文生图任务，并配备低显存推理和 LoRA / 全量训练能力。详情请参考[文档](/docs/zh/Model_Details/LingBot-Video.md)和[示例代码](/examples/lingbot_video/)。
+
 - **2026年7月21日** 我们开源了 [DiffSynth-Studio Model Integration Skills](https://www.modelscope.cn/collections/DiffSynth-Studio/DiffSynth-Studio-Model-Integration-Skills)。这是一套可组合的 Agent Skill 合集，将外部扩散模型接入 DiffSynth-Studio 的全流程自动化，大幅提升模型接入标准化程度与效率。从[使用示例](https://www.modelscope.cn/skills/DiffSynth-Studio/diffsynth-integrator/file/view/master/example.md?status=1)开始体验吧！
 
 - **2026年6月29日** Boogu-Image 开源，已支持文生图推理、图像编辑、低显存推理和训练能力。详情请参考[文档](/docs/zh/Model_Details/Boogu-Image.md)和[示例代码](/examples/boogu_image/)。
@@ -1447,6 +1449,78 @@ Wan 的示例代码位于：[/examples/wanvideo/](/examples/wanvideo/)
 
 </details>
 
+#### LingBot-Video: [/docs/zh/Model_Details/LingBot-Video.md](/docs/zh/Model_Details/LingBot-Video.md)
+
+<details>
+
+<summary>快速开始</summary>
+
+运行以下代码可以快速加载 [Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b) 模型并进行推理。显存管理已启动，框架会自动根据剩余显存控制模型参数的加载，最低 24G 显存即可运行。
+
+```python
+import torch
+import json
+from diffsynth.utils.data import save_video, VideoData
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b/*",
+)
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/t2v_example_1.json", "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video.mp4", fps=15, quality=10)
+```
+
+</details>
+
+<details>
+
+<summary>示例代码</summary>
+
+LingBot-Video 的示例代码位于：[/examples/lingbot_video/](/examples/lingbot_video/)
+
+| 模型 ID | 推理 | 低显存推理 | 全量训练 | 全量训练后验证 | LoRA 训练 | LoRA 训练后验证 |
+|-|-|-|-|-|-|-|
+|[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
+|[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
+|[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
+
+</details>
+
 ### 音频生成模型
 
 #### ACE-Step: [/docs/zh/Model_Details/ACE-Step.md](/docs/zh/Model_Details/ACE-Step.md)
diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index b7bf7347a..bbb94ed2b 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -108,6 +108,14 @@ def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
             '"temporal_and_motion_stability": ["flickering", "jittery", "motion blur", "temporal inconsistency", "warping", "morphing", "incoherent motion", "unnatural movement", "static object with sudden jump", "frame-to-frame inconsistency"], '
             '"material_and_structure": ["plastic-like glass", "unrealistic texture", "deformed bottle", "liquid freezing improperly", "distorted reflections"]}}'
         )
+        # T2I variant: video-only temporal/motion group dropped (cannot apply to a still).
+        self.default_negative_prompt_image = (
+            '{"universal_negative": {'
+            '"visual_quality": ["low quality", "worst quality", "blurry", "pixelated", "jpeg artifacts", "low resolution", "unstable color", "underexposed", "overexposed", "invisible subject", "subject hidden in darkness"], '
+            '"artistic_style": ["painting", "illustration", "drawing", "cartoon", "3d render", "cgi", "sketch", "digital art"], '
+            '"composition_and_content": ["text", "watermark", "signature", "logo", "subtitles", "pillarboxed", "side bars", "portrait image in landscape frame"], '
+            '"material_and_structure": ["plastic-like glass", "unrealistic texture", "deformed bottle", "liquid freezing improperly", "distorted reflections"]}}'
+        )
 
     @staticmethod
     def from_pretrained(
diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
index 63a40fb6c..0f4d5f8de 100644
--- a/docs/en/Model_Details/LingBot-Video.md
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -1,17 +1,10 @@
 # LingBot-Video
 
-LingBot-Video is a flow-matching text-to-video generation model. This document covers DiffSynth-Studio's inference and LoRA SFT training support for the **Dense-1.3B** text-to-video checkpoint.
-
-The integration is built on the standard DiffSynth pipeline stack:
-
-- **DiT** — `LingBotVideoDiT` (`diffsynth/models/lingbot_video_dit.py`), the video denoiser. The Dense-1.3B build uses a plain FFN; the architecture also supports an MoE FFN.
-- **Text encoder** — `LingBotVideoTextEncoder` (Qwen3-VL). Prompts are wrapped in a prompt-enhancement chat template, encoded, and the template-prefix tokens are cropped.
-- **VAE** — reuses DiffSynth's `QwenImageVAE` (byte-identical to the LingBot-Video VAE), 8× spatial / 4× temporal compression.
-- **Scheduler** — DiffSynth's `FlowMatchScheduler` (Wan template): first-order flow-matching Euler for inference; training uses the full-resolution 1000-step flow-matching schedule.
+LingBot-Video is a flow-matching video generation model developed by the LingBot team, supporting text-to-video, image-to-video and text-to-image tasks with a single checkpoint.
 
 ## Installation
 
-Before using this project for model inference and training, please install DiffSynth-Studio first.
+Before performing model inference and training, please install DiffSynth-Studio first.
 
 ```shell
 git clone https://github.com/modelscope/DiffSynth-Studio.git
@@ -19,91 +12,107 @@ cd DiffSynth-Studio
 pip install -e .
 ```
 
-LingBot-Video additionally requires `transformers >= 5.x` (for Qwen3-VL) and `imageio` / `imageio-ffmpeg` for video I/O. For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
+For more information on installation, please refer to [Setup Dependencies](../Pipeline_Usage/Setup.md).
 
 ## Quick Start
 
-Run the following code to quickly load the [Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b) model and perform text-to-video inference. The required files are downloaded automatically the first time the code runs.
-
-> **⚠️ Rewrite your prompt into a structured caption first.** LingBot-Video is trained on **structured-JSON captions**, not free-form prose. The plain sentence in the snippet below runs, but it is out-of-distribution and the result will look noticeably soft / low quality. This is expected model behaviour, not a bug — before doing real inference, turn your idea into the structured caption the model expects. See [Prompt rewriting](#prompt-rewriting-important-for-quality) below; the runnable [`lingbot-video-dense-1.3b.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py) example defaults to a released structured caption and shows the optional rewrite path at the bottom.
+Running the following code will load the [Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b) model for inference. VRAM management is enabled, the framework automatically controls parameter loading based on available VRAM, requiring a minimum of 24GB VRAM.
 
 ```python
 import torch
-from diffsynth.utils.data import save_video
+import json
+from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
 
 pipe = LingBotVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
     device="cuda",
     model_configs=[
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
     ],
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b/*",
 )
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/t2v_example_1.json", "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
 video = pipe(
-    # A plain sentence is a minimal smoke test only — it is out-of-distribution.
-    # For real quality, pass a structured caption instead (see "Prompt rewriting").
-    prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    prompt=caption,
     negative_prompt=pipe.default_negative_prompt,
     height=480, width=832, num_frames=81,
-    num_inference_steps=40, cfg_scale=3.0, seed=0,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
 )
 save_video(video, "video.mp4", fps=15, quality=10)
 ```
 
-**Low VRAM:** set `offload_dtype` / `offload_device` on each `ModelConfig` to enable layer-by-layer offloading; `vram_limit` alone has no effect (it only caps resident VRAM once offloading is on). See the low-VRAM example in the table below.
-
 ## Model Overview
 
-| Model ID | Inference | Low VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
+Dense-1.3B is a single checkpoint that serves three tasks — text-to-video, image-to-video, and text-to-image — through the same pipeline. Each task ships its own inference / low-VRAM / training / validation scripts.
+
+|Model ID|Inference|Low VRAM Inference|Full Training|Full Training Validation|LoRA Training|LoRA Training Validation|
 |-|-|-|-|-|-|-|
-|[Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
+|[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
+|[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
+|[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
 
 ## Model Inference
 
 The model is loaded via `LingBotVideoPipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models) for details.
 
-Input parameters for `LingBotVideoPipeline` inference include:
+The input parameters for `LingBotVideoPipeline` inference include:
 
-* `prompt`: Prompt describing the content appearing in the video. Accepts a structured caption (`dict`) or a plain string; see [Prompt rewriting](#prompt-rewriting-important-for-quality).
-* `negative_prompt`: Negative prompt describing content that should not appear in the video, default value is `""`. The official T2V negative prompt ships as `pipe.default_negative_prompt` and can be passed via `negative_prompt=pipe.default_negative_prompt`.
+* `prompt`: Structured-JSON caption (`dict`) or a plain string describing the content. LingBot-Video is trained on structured captions; the pipeline normalises a `dict` automatically. Released structured captions ship in the example dataset (see [Prompt rewriting](#prompt-rewriting) below).
+* `negative_prompt`: Negative prompt describing content that should not appear. `pipe.default_negative_prompt` ships the official T2V/V2V/TI2V negative prompt; `pipe.default_negative_prompt_image` is the T2I variant with temporal terms removed.
+* `input_image`: First-frame PIL image for image-to-video (TI2V). The frame is VAE-encoded to a clean latent pinned into the first temporal slot after every scheduler step, so the model only generates the frames that follow. Leave `None` for T2V / V2V / T2I.
 * `input_video`: Input video (a list of frames or a `VideoData`) for video-to-video generation, used together with `denoising_strength`.
-* `denoising_strength`: Denoising strength, range 0~1, default value is 1.0. Lower values keep more of the input video structure. Only effective when `input_video` is provided.
-* `height`: Video height, default 480. Must be a multiple of 16.
-* `width`: Video width, default 480. Must be a multiple of 16.
-* `num_frames`: Number of video frames, default 81. Must satisfy `4k+1` (the VAE compresses time by 4×).
-* `cfg_scale`: Classifier-free guidance scale, default 6.0. A value of 3.0 is recommended for the Dense-1.3B model.
-* `num_inference_steps`: Number of inference steps, default 40.
-* `sigma_shift`: Flow-matching timestep shift, default 3.0.
+* `denoising_strength`: Denoising strength in `[0, 1]`, default `1.0`. Lower values keep more of the input video structure. Only effective when `input_video` is provided.
+* `height`: Video / image height, default `480`. Must be a multiple of 16.
+* `width`: Video / image width, default `480`. Must be a multiple of 16.
+* `num_frames`: Number of frames, default `81`. Must satisfy `4k+1` (the VAE compresses time by 4×). Use `num_frames=1` for text-to-image.
+* `cfg_scale`: Classifier-free guidance scale, default `3.0`.
+* `num_inference_steps`: Number of inference steps, default `40`.
+* `sigma_shift`: Flow-matching timestep shift, default `3.0`.
 * `seed`: Random seed. Default is `None`, meaning completely random.
 * `rand_device`: Device for generating the initial noise, default `"cpu"`.
 * `progress_bar_cmd`: Progress bar, default `tqdm`. Can be disabled by setting to `lambda x: x`.
 
-When running low on VRAM, please refer to [VRAM Management](../Pipeline_Usage/VRAM_management.md) to enable VRAM management features.
+If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low-VRAM configurations for each task in the example code, see the table in the "Model Overview" section above.
 
-## Prompt rewriting (important for quality)
+### Prompt rewriting
 
-LingBot-Video is trained on **structured-JSON captions**, not free-form prose. Feeding a flat sentence is out-of-distribution and visibly degrades quality; feeding the structured caption the model expects restores it. The pipeline accepts a caption as a `dict` or a plain string and normalises it to the exact compact-JSON format the DiT was trained on — a `dict` is serialised automatically, a plain string is passed through unchanged, so existing scripts keep working.
+LingBot-Video is trained on **structured-JSON captions**, not free-form prose. Feeding a flat sentence is out-of-distribution and visibly degrades quality. The pipeline accepts a caption as a `dict` (the format used at training time) or a plain string, and normalises the `dict` internally.
 
-To turn a **brief idea** into that structured caption, use the two-stage rewriter shipped with the examples (`examples/lingbot_video/model_training/scripts/prompt_rewriter.py`): stage 1 *expands* the idea into a natural-language caption, stage 2 *maps* it into structured JSON.
+Released structured captions ship in the example dataset (`t2v_example_*.json`, `ti2v_example.json`, `t2i_example.json` under `DiffSynth-Studio/diffsynth_example_dataset`, downloaded automatically by the inference example scripts). Load one with `json.load` and pass the resulting `dict` to the pipeline, or use one as a template.
 
-The rewriter is a **separate VLM + stage-2 LoRA adapter** (not the DiT), so it is **not downloaded automatically** — you must fetch both weights yourself before running the rewrite:
+To turn a brief idea into a structured caption, use the two-stage rewriter shipped under `examples/lingbot_video/model_training/scripts/prompt_rewriter.py` — stage 1 expands the idea into a natural-language caption, stage 2 maps it into structured JSON. The rewriter is a **separate VLM + stage-2 LoRA adapter** and is not downloaded automatically:
 
 | Role | Model ID | Size |
 |-|-|-|
 | Rewriter base VLM (stage 1 + 2) | [`Qwen/Qwen3.6-27B`](https://modelscope.cn/models/Qwen/Qwen3.6-27B) | ~55 GB |
 | Rewriter stage-2 LoRA adapter | [`Robbyant/lingbot-video-rewriter-lora`](https://modelscope.cn/models/Robbyant/lingbot-video-rewriter-lora) | ~0.5 GB |
 
-```shell
-# 1. Download the rewriter base VLM and its stage-2 LoRA adapter.
-modelscope download --model Qwen/Qwen3.6-27B --local_dir ./models/Qwen/Qwen3.6-27B
-modelscope download --model Robbyant/lingbot-video-rewriter-lora --local_dir ./models/Robbyant/lingbot-video-rewriter-lora
-```
-
 ```python
-# 2. Point the rewriter at the downloaded weights, then rewrite and run inference.
 import os
 os.environ["REWRITER_BASE_MODEL"] = "./models/Qwen/Qwen3.6-27B"
 os.environ["REWRITER_ADAPTER"] = "./models/Robbyant/lingbot-video-rewriter-lora"
@@ -114,73 +123,64 @@ caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration
 video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0)
 ```
 
-Instead of the env vars you can pass `base=` / `adapter=` to `rewrite_prompt`, or skip the local VLM entirely and drive a hosted / OpenAI-compatible endpoint by passing a custom object exposing `generate(text, image, use_lora)` as `backend=`. See the optional rewrite section at the bottom of `examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py`.
-
-If you don't have the rewriter model, released LingBot-Video t2v structured captions ship in the example dataset as ready-to-use samples (`t2v_example_*.json` under `DiffSynth-Studio/diffsynth_example_dataset`, downloaded automatically by the inference example scripts). Load one with `json.load` and pass the resulting `dict` to the pipeline, or copy one as a template for writing your own structured caption.
+Instead of the env vars you can pass `base=` / `adapter=` to `rewrite_prompt`, or drive a hosted / OpenAI-compatible endpoint by passing a custom object exposing `generate(text, image, use_lora)` as `backend=`.
 
 ## Model Training
 
-LingBot-Video is trained through [`examples/lingbot_video/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/train.py), which fine-tunes the DiT with LoRA using the flow-matching SFT objective. The script parameters include:
+Models in the LingBot-Video series are trained uniformly via [`examples/lingbot_video/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/train.py). The script parameters include:
 
 * General Training Parameters
-    * Dataset Basic Configuration
+    * Dataset Configuration
         * `--dataset_base_path`: Root directory of the dataset.
-        * `--dataset_metadata_path`: Metadata file path of the dataset (a CSV / JSONL with a `video` column and a `prompt` column).
-        * `--dataset_repeat`: Number of times the dataset is repeated in each epoch.
-        * `--dataset_num_workers`: Number of processes for each DataLoader.
-        * `--data_file_keys`: Field names to be loaded from metadata as files, usually video file paths, separated by `,`.
+        * `--dataset_metadata_path`: Path to the dataset metadata file.
+        * `--dataset_repeat`: Number of dataset repeats per epoch.
+        * `--dataset_num_workers`: Number of processes per DataLoader.
+        * `--data_file_keys`: Field names to load from metadata, typically paths to image or video files, separated by `,`.
     * Model Loading Configuration
-        * `--model_paths`: Paths of models to be loaded. JSON format.
+        * `--model_paths`: Paths to load models from, in JSON format.
         * `--model_id_with_origin_paths`: Model IDs with original paths, separated by commas.
-    * Training Basic Configuration
+        * `--extra_inputs`: Additional input parameters required by the model Pipeline, separated by `,`.
+        * `--fp8_models`: Models to load in FP8 format, currently only supported for models whose parameters are not updated by gradients.
+    * Basic Training Configuration
         * `--learning_rate`: Learning rate.
         * `--num_epochs`: Number of epochs.
-        * `--task`: Training task, default is `sft`.
+        * `--trainable_models`: Trainable models, e.g., `dit`, `vae`, `text_encoder`.
+        * `--find_unused_parameters`: Whether unused parameters exist in DDP training.
+        * `--weight_decay`: Weight decay magnitude.
+        * `--task`: Training task, defaults to `sft`.
     * Output Configuration
-        * `--output_path`: Model saving path.
-        * `--remove_prefix_in_ckpt`: Remove prefix in the state dict of the saved model.
-        * `--save_steps`: Interval of training steps to save the model. If left blank, the model is saved once per epoch.
+        * `--output_path`: Path to save the model.
+        * `--remove_prefix_in_ckpt`: Remove prefix in the model's state dict.
+        * `--save_steps`: Interval in training steps to save the model.
     * LoRA Configuration
-        * `--lora_base_model`: Which model to add LoRA to, e.g. `dit`.
+        * `--lora_base_model`: Which model to add LoRA to.
         * `--lora_target_modules`: Which layers to add LoRA to.
         * `--lora_rank`: Rank of LoRA.
-        * `--lora_checkpoint`: Path of a LoRA checkpoint to resume / continue from.
+        * `--lora_checkpoint`: Path to LoRA checkpoint.
+        * `--preset_lora_path`: Path to preset LoRA checkpoint for LoRA differential training.
+        * `--preset_lora_model`: Which model to integrate preset LoRA into, e.g., `dit`.
     * Gradient Configuration
         * `--use_gradient_checkpointing`: Whether to enable gradient checkpointing.
-        * `--use_gradient_checkpointing_offload`: Whether to offload gradient checkpointing to memory.
+        * `--use_gradient_checkpointing_offload`: Whether to offload gradient checkpointing to CPU memory.
         * `--gradient_accumulation_steps`: Number of gradient accumulation steps.
-    * Video Width/Height Configuration
+    * Resolution Configuration
         * `--height`: Height of the video. Must be divisible by 16.
         * `--width`: Width of the video. Must be divisible by 16.
+        * `--max_pixels`: Maximum pixel area, images larger than this will be scaled down during dynamic resolution.
         * `--num_frames`: Number of frames in the video. Must satisfy `4k+1`.
 * LingBot-Video Specific Parameters
-    * `--processor_path`: Path of the Qwen3-VL processor used by the text encoder.
+    * `--processor_path`: Path to the Qwen3-VL processor directory (or `model_id:origin_file_pattern`). Used to tokenize prompts.
+    * `--first_frame_as_condition`: Enable image-to-video (TI2V) LoRA / full training. Each clip is conditioned on its own first frame: the frame is VAE-encoded to a clean latent pinned into the first temporal slot (and fed to the Qwen3-VL text encoder as vision input), and excluded from the flow-matching loss.
+    * `--max_timestep_boundary`: Max timestep boundary as a fraction of the training schedule, in `[0, 1]`.
+    * `--min_timestep_boundary`: Min timestep boundary as a fraction of the training schedule, in `[0, 1]`.
+    * `--initialize_model_on_cpu`: Whether to initialize models on CPU.
 
-The launch script first downloads the example video-SFT dataset used across DiffSynth-Studio, then trains on it:
+We provide a sample dataset for your testing. You can download it with the following command:
 
 ```shell
-modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset \
-    --include "wanvideo/Wan2.1-T2V-1.3B/*" --local_dir ./data/diffsynth_example_dataset
-```
-
-### Attention-only LoRA (default scope)
-
-The recommended launch script patches LoRA on the joint text+video self-attention only:
-
-```
---lora_base_model "dit"
---lora_target_modules "to_q,to_k,to_v,to_out"
---lora_rank 32
---remove_prefix_in_ckpt "pipe.dit."
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "lingbot_video/lingbot-video-dense-1.3b/*" --local_dir ./data/diffsynth_example_dataset
 ```
 
-The MoE / FFN experts (`gate_proj`, `up_proj`, `down_proj`) and the router are left frozen. To also adapt the FFN, add those module names to `--lora_target_modules`.
-
-For best results the `prompt` column should hold **structured-JSON captions** (the same in-distribution format used at inference — see [Prompt rewriting](#prompt-rewriting-important-for-quality)). The pipeline normalises each prompt internally. If your dataset stores raw prose, rewrite it once offline with `examples/lingbot_video/model_training/scripts/rewrite_captions.py` before training.
-
-We have written recommended training scripts, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
-
-## Notes
+Training captions should be **structured-JSON captions** (the same in-distribution format used at inference). If your dataset stores raw prose, rewrite it once offline with [`examples/lingbot_video/model_training/scripts/rewrite_captions.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/scripts/rewrite_captions.py) before training.
 
-- The text encoder shares its checkpoint fingerprint with the existing `krea2_text_encoder` (identical Qwen3-VL architecture), so the model loader instantiates both when loading LingBot-Video. This is redundant load time only — the pipeline fetches the correct encoder by name and the other is released.
-- The 5D-video VAE encode/decode and its latent normalisation live in the pipeline (`LingBotVideoPipeline.encode_video` / `decode_video`), so `QwenImageVAE` stays byte-identical to its image use elsewhere; the pipeline does not separately re-apply `latents_mean` / `latents_std`.
+We provide recommended training scripts for each task, please refer to the table in "Model Overview" above. For guidance on writing model training scripts, see [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, see [Training Framework Overview](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
index a47151fc0..3564a41cf 100644
--- a/docs/zh/Model_Details/LingBot-Video.md
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -1,13 +1,6 @@
 # LingBot-Video
 
-LingBot-Video 是一个基于 flow-matching 的文生视频生成模型。本文档介绍 DiffSynth-Studio 对 **Dense-1.3B** 文生视频权重的推理与 LoRA SFT 训练支持。
-
-该接入基于标准的 DiffSynth Pipeline 组件栈构建：
-
-- **DiT** — `LingBotVideoDiT`（`diffsynth/models/lingbot_video_dit.py`），视频去噪器。Dense-1.3B 版本使用普通 FFN；该架构同时支持 MoE FFN。
-- **文本编码器** — `LingBotVideoTextEncoder`（Qwen3-VL）。提示词会被包裹进提示增强的对话模板中编码，随后裁剪掉模板前缀 token。
-- **VAE** — 复用 DiffSynth 的 `QwenImageVAE`（与 LingBot-Video 的 VAE 逐字节一致），空间 8× / 时间 4× 压缩。
-- **调度器** — DiffSynth 的 `FlowMatchScheduler`（Wan 模板）：推理时使用一阶 flow-matching Euler；训练时使用完整分辨率的 1000 步 flow-matching 调度。
+LingBot-Video 是由 LingBot 团队研发的 flow-matching 视频生成模型，同一份 checkpoint 支持文生视频、图生视频和文生图三种任务。
 
 ## 安装
 
@@ -19,47 +12,69 @@ cd DiffSynth-Studio
 pip install -e .
 ```
 
-LingBot-Video 还额外依赖 `transformers >= 5.x`（用于 Qwen3-VL）以及 `imageio` / `imageio-ffmpeg`（用于视频读写）。更多关于安装的信息，请参考[安装依赖](../Pipeline_Usage/Setup.md)。
+更多关于安装的信息，请参考[安装依赖](../Pipeline_Usage/Setup.md)。
 
 ## 快速开始
 
-运行以下代码可以快速加载 [Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b) 模型并进行文生视频推理。首次运行时会自动下载所需文件。
-
-> **⚠️ 推理前请先把提示词改写为结构化 caption。** LingBot-Video 使用**结构化 JSON caption** 训练，而非自由文本。下面代码里的普通句子能跑通，但属于分布外输入，生成结果会明显偏"糊"、质量偏低。这是模型的预期行为，并非 bug —— 正式推理前，请先把想法转成模型期望的结构化 caption。详见下文[提示词改写](#提示词改写对质量很重要)；可直接运行的 [`lingbot-video-dense-1.3b.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py) 示例默认使用随仓库发布的结构化 caption，并在文件末尾给出可选的改写流程。
+运行以下代码可以快速加载 [Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b) 模型并进行推理。显存管理已启动，框架会自动根据剩余显存控制模型参数的加载，最低 24G 显存即可运行。
 
 ```python
 import torch
-from diffsynth.utils.data import save_video
+import json
+from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
 
 pipe = LingBotVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
     device="cuda",
     model_configs=[
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
     ],
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b/*",
 )
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/t2v_example_1.json", "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
 video = pipe(
-    # 普通句子仅用于最简单的跑通验证，属于分布外输入。
-    # 正式使用请改传结构化 caption（见"提示词改写"章节）。
-    prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    prompt=caption,
     negative_prompt=pipe.default_negative_prompt,
     height=480, width=832, num_frames=81,
-    num_inference_steps=40, cfg_scale=3.0, seed=0,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
 )
 save_video(video, "video.mp4", fps=15, quality=10)
 ```
 
-**低显存：** 在每个 `ModelConfig` 上设置 `offload_dtype` / `offload_device` 才能开启逐层 offload；单独传 `vram_limit` 没有效果（它只在 offload 已开启时限制常驻显存上限）。详见下表中的低显存示例。
-
 ## 模型总览
 
+Dense-1.3B 是一份 checkpoint，通过同一个 Pipeline 支持文生视频、图生视频、文生图三种任务，每种任务都配有推理 / 低显存 / 训练 / 验证脚本。
+
 |模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
 |-|-|-|-|-|-|-|
-|[Robbyant/lingbot-video-dense-1.3b](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
+|[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
+|[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
+|[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
 
 ## 模型推理
 
@@ -67,43 +82,37 @@ save_video(video, "video.mp4", fps=15, quality=10)
 
 `LingBotVideoPipeline` 推理的输入参数包括：
 
-* `prompt`: 描述视频内容的提示词。支持结构化 caption（`dict`）或普通字符串；详见[提示词改写](#提示词改写对质量很重要)。
-* `negative_prompt`: 负向提示词，描述不希望出现在视频中的内容，默认值为 `""`。官方 T2V 负向提示词内置在 `pipe.default_negative_prompt` 中，可通过 `negative_prompt=pipe.default_negative_prompt` 传入。
-* `input_video`: 输入视频（帧列表或 `VideoData`），用于视频生视频，需与 `denoising_strength` 配合使用。
-* `denoising_strength`: 降噪强度，取值范围 0~1，默认为 1.0。值越小，越保留输入视频的结构。仅在提供 `input_video` 时生效。
-* `height`: 视频高度，默认为 480，需能被 16 整除。
-* `width`: 视频宽度，默认为 480，需能被 16 整除。
-* `num_frames`: 视频帧数，默认为 81，需满足 `4k+1`（VAE 在时间维上做 4× 压缩）。
-* `cfg_scale`: 分类器自由引导系数，默认为 6.0。Dense-1.3B 模型推荐使用 3.0。
-* `num_inference_steps`: 推理步数，默认为 40。
-* `sigma_shift`: flow-matching 时间步偏移，默认为 3.0。
-* `seed`: 随机种子，默认为 `None`，即完全随机。
-* `rand_device`: 生成初始噪声的设备，默认为 `"cpu"`。
-* `progress_bar_cmd`: 进度条，默认为 `tqdm`，可设为 `lambda x: x` 关闭。
+* `prompt`: 描述视频内容的提示词，接受结构化 JSON caption（`dict`）或纯字符串。LingBot-Video 在结构化 caption 上训练，Pipeline 会自动对 `dict` 进行归一化。示例数据集中提供了发布版的结构化 caption（见下方[提示词改写](#提示词改写)）。
+* `negative_prompt`: 描述不应出现内容的负向提示词。`pipe.default_negative_prompt` 提供了官方 T2V/V2V/TI2V 负向提示词；`pipe.default_negative_prompt_image` 是移除时序项后的 T2I 版本。
+* `input_image`: 图生视频（TI2V）的首帧 PIL 图像。该帧被 VAE 编码成 clean latent，在每个采样步之后重新写入第一个时间槽，使模型只生成后续帧。T2V / V2V / T2I 时留 `None`。
+* `input_video`: 视频到视频生成的输入视频（帧列表或 `VideoData`），与 `denoising_strength` 配合使用。
+* `denoising_strength`: 去噪强度，范围 `[0, 1]`，默认 `1.0`。较小值保留更多输入视频结构。仅当 `input_video` 提供时生效。
+* `height`: 视频 / 图像高度，默认 `480`，必须是 16 的倍数。
+* `width`: 视频 / 图像宽度，默认 `480`，必须是 16 的倍数。
+* `num_frames`: 帧数，默认 `81`，须满足 `4k+1`（VAE 时间上 4× 压缩）。文生图使用 `num_frames=1`。
+* `cfg_scale`: 无分类器指导强度，默认 `3.0`。
+* `num_inference_steps`: 推理步数，默认 `40`。
+* `sigma_shift`: Flow-matching 时间步 shift，默认 `3.0`。
+* `seed`: 随机种子，默认 `None`（完全随机）。
+* `rand_device`: 生成初始噪声的设备，默认 `"cpu"`。
+* `progress_bar_cmd`: 进度条，默认 `tqdm`，可设为 `lambda x: x` 关闭。
 
-显存不足时，请参考[显存管理](../Pipeline_Usage/VRAM_management.md)启用显存管理功能。
+显存不足时请参考[显存管理](../Pipeline_Usage/VRAM_management.md)启用显存管理功能。我们在示例代码中提供了每个任务的推荐低显存配置，见上方"模型总览"中的表格。
 
-## 提示词改写（对质量很重要）
+### 提示词改写
 
-LingBot-Video 使用**结构化 JSON caption** 训练，而非自由文本。喂入一句普通句子属于分布外（out-of-distribution）输入，会明显降低质量；喂入模型期望的结构化 caption 则能恢复质量。Pipeline 接受 `dict` 或普通字符串形式的 caption，并将其归一化为 DiT 训练时使用的紧凑 JSON 格式——`dict` 会自动序列化，普通字符串会原样透传，因此已有脚本无需改动。
+LingBot-Video 训练时使用的是**结构化 JSON caption**，直接喂平铺句子属于分布外输入，会明显降低生成质量。Pipeline 接受 `dict` 形式的 caption（与训练一致的格式）或纯字符串，`dict` 会被内部归一化。
 
-若要把一个**简短想法**转成该结构化 caption，可使用随示例发布的两阶段改写器（`examples/lingbot_video/model_training/scripts/prompt_rewriter.py`）：阶段一将想法*扩写*为自然语言 caption，阶段二将其*映射*为结构化 JSON。
+发布版的结构化 caption 已通过 `DiffSynth-Studio/diffsynth_example_dataset` 示例数据集提供（`t2v_example_*.json`、`ti2v_example.json`、`t2i_example.json`，推理示例脚本会自动下载）。用 `json.load` 读入后作为 `dict` 传入 Pipeline，或作为编写自定义 caption 的模板。
 
-改写器是**独立的 VLM + 阶段二 LoRA 适配器**（不是 DiT），**不会自动下载**——运行改写前，需要先自行下载这两个权重：
+如需将一段简短描述改写为结构化 caption，可使用 `examples/lingbot_video/model_training/scripts/prompt_rewriter.py` 中的两阶段改写器：阶段 1 将想法扩展为自然语言描述，阶段 2 将其映射为结构化 JSON。改写器是**独立的 VLM + 阶段二 LoRA 适配器**，需要另外下载：
 
 | 角色 | 模型 ID | 大小 |
 |-|-|-|
-| 改写器 base VLM（阶段一 + 二） | [`Qwen/Qwen3.6-27B`](https://modelscope.cn/models/Qwen/Qwen3.6-27B) | ~55 GB |
+| 改写器 base VLM（阶段 1 + 2） | [`Qwen/Qwen3.6-27B`](https://modelscope.cn/models/Qwen/Qwen3.6-27B) | ~55 GB |
 | 改写器阶段二 LoRA 适配器 | [`Robbyant/lingbot-video-rewriter-lora`](https://modelscope.cn/models/Robbyant/lingbot-video-rewriter-lora) | ~0.5 GB |
 
-```shell
-# 1. 下载改写器 base VLM 及其阶段二 LoRA 适配器。
-modelscope download --model Qwen/Qwen3.6-27B --local_dir ./models/Qwen/Qwen3.6-27B
-modelscope download --model Robbyant/lingbot-video-rewriter-lora --local_dir ./models/Robbyant/lingbot-video-rewriter-lora
-```
-
 ```python
-# 2. 让改写器指向已下载的权重，再改写并推理。
 import os
 os.environ["REWRITER_BASE_MODEL"] = "./models/Qwen/Qwen3.6-27B"
 os.environ["REWRITER_ADAPTER"] = "./models/Robbyant/lingbot-video-rewriter-lora"
@@ -114,73 +123,64 @@ caption = rewrite_prompt("a puppy running across a meadow", mode="t2v", duration
 video = pipe(prompt=caption, height=480, width=832, num_frames=81, cfg_scale=3.0)
 ```
 
-除了 env var，也可以给 `rewrite_prompt` 传 `base=` / `adapter=`；或者完全不下载本地 VLM，改为传入一个暴露 `generate(text, image, use_lora)` 方法的自定义对象作为 `backend=`，来驱动托管的 / OpenAI 兼容的推理端点。详见 `examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py` 文件末尾的可选改写小节。
-
-如果没有改写器模型，官方 LingBot-Video t2v 结构化 caption 已随样例数据集发布（`DiffSynth-Studio/diffsynth_example_dataset` 中的 `t2v_example_*.json`，推理示例脚本会自动下载）。用 `json.load` 读入后作为 `dict` 传给 pipeline，也可以复制一个作为编写自己 caption 的模板。
+除环境变量外，也可以直接向 `rewrite_prompt` 传 `base=` / `adapter=`；或者提供一个实现了 `generate(text, image, use_lora)` 方法的自定义对象作为 `backend=`，从而对接托管服务或 OpenAI-compatible 端点。
 
 ## 模型训练
 
-LingBot-Video 通过 [`examples/lingbot_video/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/train.py) 进行训练，使用 flow-matching SFT 目标对 DiT 做 LoRA 微调。脚本的参数包括：
+LingBot-Video 系列模型统一通过 [`examples/lingbot_video/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/train.py) 进行训练，脚本的参数包括：
 
 * 通用训练参数
     * 数据集基础配置
         * `--dataset_base_path`: 数据集的根目录。
-        * `--dataset_metadata_path`: 数据集的元数据文件路径（含 `video` 列与 `prompt` 列的 CSV / JSONL）。
+        * `--dataset_metadata_path`: 数据集的元数据文件路径。
         * `--dataset_repeat`: 每个 epoch 中数据集重复的次数。
         * `--dataset_num_workers`: 每个 Dataloader 的进程数量。
-        * `--data_file_keys`: 元数据中需要按文件加载的字段名称，通常是视频文件路径，以 `,` 分隔。
+        * `--data_file_keys`: 元数据中需要加载的字段名称，通常是图像或视频文件的路径，以 `,` 分隔。
     * 模型加载配置
         * `--model_paths`: 要加载的模型路径。JSON 格式。
-        * `--model_id_with_origin_paths`: 带原始路径的模型 ID，用逗号分隔。
+        * `--model_id_with_origin_paths`: 带原始路径的模型 ID。用逗号分隔。
+        * `--extra_inputs`: 模型 Pipeline 所需的额外输入参数，以 `,` 分隔。
+        * `--fp8_models`: 以 FP8 格式加载的模型，目前仅支持参数不被梯度更新的模型。
     * 训练基础配置
         * `--learning_rate`: 学习率。
         * `--num_epochs`: 轮数（Epoch）。
+        * `--trainable_models`: 可训练的模型，例如 `dit`、`vae`、`text_encoder`。
+        * `--find_unused_parameters`: DDP 训练中是否存在未使用的参数。
+        * `--weight_decay`: 权重衰减大小。
         * `--task`: 训练任务，默认为 `sft`。
     * 输出配置
         * `--output_path`: 模型保存路径。
-        * `--remove_prefix_in_ckpt`: 在保存模型的 state dict 中移除前缀。
-        * `--save_steps`: 保存模型的训练步数间隔。留空则每个 epoch 保存一次。
+        * `--remove_prefix_in_ckpt`: 在模型文件的 state dict 中移除前缀。
+        * `--save_steps`: 保存模型的训练步数间隔。
     * LoRA 配置
-        * `--lora_base_model`: LoRA 添加到哪个模型上，例如 `dit`。
+        * `--lora_base_model`: LoRA 添加到哪个模型上。
         * `--lora_target_modules`: LoRA 添加到哪些层上。
         * `--lora_rank`: LoRA 的秩（Rank）。
-        * `--lora_checkpoint`: 用于续训 / 继续训练的 LoRA 检查点路径。
+        * `--lora_checkpoint`: LoRA 检查点的路径。
+        * `--preset_lora_path`: 预置 LoRA 检查点路径，用于 LoRA 差分训练。
+        * `--preset_lora_model`: 预置 LoRA 融入的模型，例如 `dit`。
     * 梯度配置
         * `--use_gradient_checkpointing`: 是否启用 gradient checkpointing。
         * `--use_gradient_checkpointing_offload`: 是否将 gradient checkpointing 卸载到内存中。
         * `--gradient_accumulation_steps`: 梯度累积步数。
-    * 视频宽高配置
-        * `--height`: 视频高度，需能被 16 整除。
-        * `--width`: 视频宽度，需能被 16 整除。
-        * `--num_frames`: 视频帧数，需满足 `4k+1`。
+    * 分辨率配置
+        * `--height`: 视频的高度，必须能被 16 整除。
+        * `--width`: 视频的宽度，必须能被 16 整除。
+        * `--max_pixels`: 最大像素面积，动态分辨率时大于此值的图片会被缩小。
+        * `--num_frames`: 视频的帧数，须满足 `4k+1`。
 * LingBot-Video 专有参数
-    * `--processor_path`: 文本编码器使用的 Qwen3-VL processor 路径。
+    * `--processor_path`: Qwen3-VL processor 目录（或 `model_id:origin_file_pattern` 形式）路径，用于对 prompt 进行 tokenize。
+    * `--first_frame_as_condition`: 启用图生视频（TI2V）的 LoRA / 全量训练。每段视频以自己的第一帧作为条件：该帧被 VAE 编码为 clean latent 固定到第一个时间槽（同时作为视觉输入送入 Qwen3-VL 文本编码器），并从 flow-matching 损失中排除。
+    * `--max_timestep_boundary`: 训练时时间步的上边界，取值 `[0, 1]` 表示相对训练调度的比例。
+    * `--min_timestep_boundary`: 训练时时间步的下边界，取值 `[0, 1]` 表示相对训练调度的比例。
+    * `--initialize_model_on_cpu`: 是否在 CPU 上初始化模型。
 
-启动脚本会先下载 DiffSynth-Studio 通用的示例视频 SFT 数据集，然后在其上训练：
+我们构建了一个样例数据集供您测试，可通过以下命令下载：
 
 ```shell
-modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset \
-    --include "wanvideo/Wan2.1-T2V-1.3B/*" --local_dir ./data/diffsynth_example_dataset
-```
-
-### 仅注意力 LoRA（默认范围）
-
-推荐的启动脚本仅在文本+视频联合自注意力上添加 LoRA：
-
-```
---lora_base_model "dit"
---lora_target_modules "to_q,to_k,to_v,to_out"
---lora_rank 32
---remove_prefix_in_ckpt "pipe.dit."
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "lingbot_video/lingbot-video-dense-1.3b/*" --local_dir ./data/diffsynth_example_dataset
 ```
 
-MoE / FFN 专家（`gate_proj`、`up_proj`、`down_proj`）与 router 保持冻结。若要同时微调 FFN，可将这些模块名加入 `--lora_target_modules`。
-
-为获得最佳效果，`prompt` 列应存放**结构化 JSON caption**（与推理时一致的分布内格式——见[提示词改写](#提示词改写对质量很重要)）。Pipeline 会在内部对每条 prompt 做归一化。若数据集存放的是原始文本，请在训练前用 `examples/lingbot_video/model_training/scripts/rewrite_captions.py` 离线改写一次。
-
-我们编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。
-
-## 注意事项
+训练时 `prompt` 字段应存放**结构化 JSON caption**（与推理时使用的分布内格式一致）。如果数据集里存的是原始散文，可先使用 [`examples/lingbot_video/model_training/scripts/rewrite_captions.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/scripts/rewrite_captions.py) 离线改写一次。
 
-- 文本编码器与已有的 `krea2_text_encoder` 共享同一 checkpoint 指纹（Qwen3-VL 架构相同），因此模型加载器在加载 LingBot-Video 时会同时实例化两者。这只是冗余的加载耗时——Pipeline 会按名称取用正确的编码器，另一个会被释放。
-- 5D 视频的 VAE 编码/解码及其 latent 归一化都在 Pipeline 内实现（`LingBotVideoPipeline.encode_video` / `decode_video`），因此 `QwenImageVAE` 与它在图像场景下的用法保持逐字节一致；Pipeline 不会再额外应用 `latents_mean` / `latents_std`。
+我们为每个任务编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py
index 24fe3e23d..ad9a49495 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py
@@ -1,14 +1,14 @@
 import os
+import json
 import torch
-from diffsynth.pipelines.lingbot_video import (
-    LingBotVideoPipeline, ModelConfig, normalize_caption, DEFAULT_NEGATIVE_PROMPT_IMAGE,
-)
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 
 
-# Text-to-image (t2i) is text-to-video with a single frame: pass num_frames=1 through the
-# same pipeline and DiT (no separate image weight). The only image-specific knob is the
-# negative prompt — DEFAULT_NEGATIVE_PROMPT_IMAGE drops the temporal/motion terms that
-# cannot apply to a still frame. The pipeline returns a 1-frame list, i.e. one PIL image.
+# Text-to-image (t2i). t2i is text-to-video with a single frame: pass num_frames=1 through
+# the same pipeline and DiT (no separate image weight). The only image-specific knob is the
+# negative prompt -- `pipe.default_negative_prompt_image` drops the temporal/motion terms
+# that cannot apply to a still frame. The pipeline returns a 1-frame list, i.e. one PIL
+# image.
 
 pipe = LingBotVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
@@ -21,11 +21,12 @@
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
 )
 
-# prompts/t2i_example.json is a released in-distribution still-image caption.
-caption = normalize_caption(os.path.join(os.path.dirname(__file__), "prompts", "t2i_example.json"))
+with open(os.path.join(os.path.dirname(__file__), "prompts", "t2i_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
 frames = pipe(
     prompt=caption,
-    negative_prompt=DEFAULT_NEGATIVE_PROMPT_IMAGE,
+    negative_prompt=pipe.default_negative_prompt_image,
     height=480, width=832, num_frames=1,
     num_inference_steps=40, cfg_scale=3.0,
     seed=0,
diff --git a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py
index fe69d385f..8b7d6652a 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py
@@ -1,14 +1,16 @@
 import os
+import json
 import torch
 from PIL import Image
 from diffsynth.utils.data import save_video
-from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 
 
-# Image-to-video (TI2V). Dense-1.3B reuses the SAME T2V checkpoint — there is no separate
+# Image-to-video (TI2V). Dense-1.3B reuses the SAME T2V checkpoint -- there is no separate
 # i2v weight. The condition first frame is used twice: as visual input to the Qwen3-VL text
-# encoder, and as a clean latent pinned into the first frame of the diffusion latent so the
-# model only generates the frames that follow. Pass a first frame via `input_image`.
+# encoder, and VAE-encoded to a clean latent pinned into the first frame of the diffusion
+# latent (and re-pinned after every denoising step) so the model only generates the frames
+# that follow. Pass a first frame via `input_image`.
 
 pipe = LingBotVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
@@ -21,15 +23,16 @@
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
 )
 
-# prompts/ti2v_example.json is a released in-distribution caption paired with the first frame
-# in assets/ti2v_first_frame.png. The caption should describe the motion that unfolds from the
-# given frame; the pipeline calls normalize_caption internally so a path also works directly.
+# In-tree released ti2v caption + paired first frame; the reviewer will move these to the
+# diffsynth_example_dataset repo in a follow-up pass.
 here = os.path.dirname(__file__)
-caption = normalize_caption(os.path.join(here, "prompts", "ti2v_example.json"))
+with open(os.path.join(here, "prompts", "ti2v_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
 input_image = Image.open(os.path.join(here, "assets", "ti2v_first_frame.png")).convert("RGB")
 
 video = pipe(
     prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
     input_image=input_image,
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0,
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py
index 92d4ec6ba..c237ba8b0 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py
@@ -1,19 +1,16 @@
 import os
+import json
 import torch
-from diffsynth.pipelines.lingbot_video import (
-    LingBotVideoPipeline, ModelConfig, normalize_caption, DEFAULT_NEGATIVE_PROMPT_IMAGE,
-)
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 
 
 # Low-VRAM text-to-image (t2i). t2i is text-to-video with num_frames=1 through the same
 # pipeline and DiT (no separate image weight); the only image-specific knob is the negative
-# prompt (DEFAULT_NEGATIVE_PROMPT_IMAGE drops temporal/motion terms). offload_dtype /
-# offload_device on each ModelConfig turn on VRAM management: weights stay on CPU in fp8 and
-# stream to the GPU layer-by-layer, computed in bf16. vram_limit only caps resident VRAM once
-# offloading is enabled by those two fields.
+# prompt (`pipe.default_negative_prompt_image` drops temporal/motion terms). Uses the same
+# disk-offload VRAM profile as the low-VRAM t2v/ti2v examples.
 vram_config = {
-    "offload_dtype": torch.float8_e4m3fn,
-    "offload_device": "cpu",
+    "offload_dtype": "disk",
+    "offload_device": "disk",
     "onload_dtype": torch.float8_e4m3fn,
     "onload_device": "cpu",
     "preparing_dtype": torch.float8_e4m3fn,
@@ -31,15 +28,15 @@
         ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
     ],
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
-    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
 )
 
-# prompts/t2i_example.json is a released in-distribution still-image caption.
-caption = normalize_caption(os.path.join(
-    os.path.dirname(__file__), "..", "model_inference", "prompts", "t2i_example.json"))
+with open(os.path.join(os.path.dirname(__file__), "..", "model_inference", "prompts", "t2i_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
 frames = pipe(
     prompt=caption,
-    negative_prompt=DEFAULT_NEGATIVE_PROMPT_IMAGE,
+    negative_prompt=pipe.default_negative_prompt_image,
     height=480, width=832, num_frames=1,
     num_inference_steps=40, cfg_scale=3.0,
     seed=0,
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py
index d61b6e36d..3963cfb46 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py
@@ -1,18 +1,19 @@
 import os
+import json
 import torch
 from PIL import Image
 from diffsynth.utils.data import save_video
-from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 
 
-# Low-VRAM image-to-video (TI2V). offload_dtype / offload_device on each ModelConfig turn on
-# VRAM management: weights stay on CPU in fp8 and stream to the GPU layer-by-layer, computed
-# in bf16. vram_limit only caps resident VRAM once offloading is enabled by those two fields.
-# The TI2V delta over t2v is just `input_image` — the condition-frame VAE encode / text-encoder
-# vision pass run under the same offloading, so peak VRAM matches the low-VRAM t2v run.
+# Low-VRAM image-to-video (TI2V). Uses the reviewer's disk-offload VRAM profile from the
+# low-VRAM t2v example: weights live on disk, stream to CPU (fp8) then to GPU (bf16 compute)
+# one layer at a time. The TI2V delta over t2v is just `input_image` -- the condition-frame
+# VAE encode / text-encoder vision pass run under the same offloading, so peak VRAM matches
+# the low-VRAM t2v run.
 vram_config = {
-    "offload_dtype": torch.float8_e4m3fn,
-    "offload_device": "cpu",
+    "offload_dtype": "disk",
+    "offload_device": "disk",
     "onload_dtype": torch.float8_e4m3fn,
     "onload_device": "cpu",
     "preparing_dtype": torch.float8_e4m3fn,
@@ -30,17 +31,19 @@
         ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
     ],
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
-    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
 )
 
 # Released first frame + paired caption, shared with the model_inference TI2V example.
 here = os.path.dirname(__file__)
 inference_dir = os.path.join(here, "..", "model_inference")
-caption = normalize_caption(os.path.join(inference_dir, "prompts", "ti2v_example.json"))
+with open(os.path.join(inference_dir, "prompts", "ti2v_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
 input_image = Image.open(os.path.join(inference_dir, "assets", "ti2v_first_frame.png")).convert("RGB")
 
 video = pipe(
     prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
     input_image=input_image,
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0,
diff --git a/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh b/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh
new file mode 100644
index 000000000..aa372e3ac
--- /dev/null
+++ b/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh
@@ -0,0 +1,26 @@
+# Image-to-video (TI2V) full-parameter SFT.
+#
+# Same DiT + dataset as the t2v full-parameter script; adds `--first_frame_as_condition`
+# to condition each clip on its OWN first frame (VAE-encoded latent pinned into the first
+# temporal slot and excluded from the flow-matching loss). Dense-1.3B reuses the T2V
+# weights, so there is no separate i2v checkpoint to load. `--trainable_models "dit"`
+# unfreezes the whole DiT for a full-parameter update.
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "lingbot_video/lingbot-video-dense-1.3b/*" --local_dir ./data/diffsynth_example_dataset
+
+accelerate launch examples/lingbot_video/model_training/train.py \
+  --dataset_base_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b \
+  --dataset_metadata_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/metadata.json \
+  --data_file_keys "video" \
+  --height 480 \
+  --width 832 \
+  --num_frames 81 \
+  --first_frame_as_condition \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Robbyant/lingbot-video-dense-1.3b:transformer/diffusion_pytorch_model.safetensors,Robbyant/lingbot-video-dense-1.3b:text_encoder/model*.safetensors,Robbyant/lingbot-video-dense-1.3b:vae/diffusion_pytorch_model.safetensors" \
+  --processor_path "Robbyant/lingbot-video-dense-1.3b:processor/" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/lingbot-video-dense-1.3b_ti2v_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing
diff --git a/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh
index 7fe3bb66e..e877d0952 100644
--- a/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh
+++ b/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh
@@ -1,28 +1,29 @@
 # Image-to-video (TI2V) LoRA SFT.
 #
-# Same DiT / dataset / attention-only LoRA scope as the t2v script — the only delta is
-# `--first_frame_as_condition`, which conditions each clip on its OWN first frame: the frame
-# is VAE-encoded to a clean latent pinned into the first temporal slot (and fed to the
-# Qwen3-VL text encoder), and excluded from the flow-matching loss, so the LoRA learns to
-# animate frames 2..N from frame 1. Dense-1.3B reuses the same T2V weights — no separate i2v
-# checkpoint. Reuses the shared example dataset (plain `video` + `prompt`); no condition
-# column is required. If your dataset instead ships a distinct condition frame, drop this
-# flag and pass it via `--extra_inputs input_image` (adding that column to --data_file_keys).
-modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.1-T2V-1.3B/*" --local_dir ./data/diffsynth_example_dataset
+# Same DiT / dataset / attention-only LoRA scope as the t2v script -- the only delta is
+# `--first_frame_as_condition`, which conditions each clip on its OWN first frame: the
+# frame is VAE-encoded to a clean latent pinned into the first temporal slot (and fed to
+# the Qwen3-VL text encoder), and excluded from the flow-matching loss, so the LoRA learns
+# to animate frames 2..N from frame 1. Dense-1.3B reuses the same T2V weights -- no
+# separate i2v checkpoint. Reuses the shared example dataset (plain `video` + `prompt`);
+# no condition column is required. If your dataset instead ships a distinct condition
+# frame, drop this flag and pass it via `--extra_inputs input_image` (adding that column
+# to --data_file_keys).
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "lingbot_video/lingbot-video-dense-1.3b/*" --local_dir ./data/diffsynth_example_dataset
 
 accelerate launch examples/lingbot_video/model_training/train.py \
-  --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B \
-  --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.1-T2V-1.3B/metadata.csv \
+  --dataset_base_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b \
+  --dataset_metadata_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/metadata.json \
   --data_file_keys "video" \
   --height 480 \
   --width 832 \
-  --num_frames 169 \
+  --num_frames 81 \
   --first_frame_as_condition \
-  --dataset_repeat 200 \
+  --dataset_repeat 50 \
   --model_id_with_origin_paths "Robbyant/lingbot-video-dense-1.3b:transformer/diffusion_pytorch_model.safetensors,Robbyant/lingbot-video-dense-1.3b:text_encoder/model*.safetensors,Robbyant/lingbot-video-dense-1.3b:vae/diffusion_pytorch_model.safetensors" \
   --processor_path "Robbyant/lingbot-video-dense-1.3b:processor/" \
   --learning_rate 1e-4 \
-  --num_epochs 20 \
+  --num_epochs 5 \
   --remove_prefix_in_ckpt "pipe.dit." \
   --output_path "./models/train/lingbot-video-dense-1.3b_ti2v_lora" \
   --lora_base_model "dit" \
diff --git a/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py b/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py
new file mode 100644
index 000000000..42f92877d
--- /dev/null
+++ b/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py
@@ -0,0 +1,39 @@
+import os
+import json
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from diffsynth import load_state_dict
+
+
+# TI2V full-parameter validation: load the freshly-trained DiT weights and generate from
+# the same caption + first frame the TI2V inference example uses. Adjust
+# `epoch-N.safetensors` to whichever epoch you want to validate.
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
+)
+state_dict = load_state_dict("models/train/lingbot-video-dense-1.3b_ti2v_full/epoch-1.safetensors")
+pipe.dit.load_state_dict(state_dict)
+
+inference_dir = os.path.join(os.path.dirname(__file__), "..", "..", "model_inference")
+with open(os.path.join(inference_dir, "prompts", "ti2v_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+input_image = Image.open(os.path.join(inference_dir, "assets", "ti2v_first_frame.png")).convert("RGB")
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_image=input_image,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-dense-1.3b_ti2v.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py b/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py
index db26c6925..eb0b65e5e 100644
--- a/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py
+++ b/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py
@@ -1,12 +1,14 @@
 import os
+import json
 import torch
 from PIL import Image
 from diffsynth.utils.data import save_video
-from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig, normalize_caption
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 
 
-# Validate a TI2V LoRA (trained with lora/lingbot-video-dense-1.3b_ti2v.sh) by conditioning
-# on a first frame via `input_image`, exactly as at inference. Same base T2V checkpoint.
+# TI2V LoRA validation: load the fresh LoRA checkpoint and generate from the same caption
+# + first frame the TI2V inference example uses. Adjust `epoch-N.safetensors` to whichever
+# epoch you want to validate (matches the .sh's `--num_epochs`).
 pipe = LingBotVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
     device="cuda",
@@ -17,17 +19,19 @@
     ],
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="processor/"),
 )
-pipe.load_lora(pipe.dit, "models/train/lingbot-video-dense-1.3b_ti2v_lora/epoch-19.safetensors", alpha=1)
+pipe.load_lora(pipe.dit, "models/train/lingbot-video-dense-1.3b_ti2v_lora/epoch-4.safetensors", alpha=1)
 
 # Reuse the released first frame + caption from the inference example.
 inference_dir = os.path.join(os.path.dirname(__file__), "..", "..", "model_inference")
-caption = normalize_caption(os.path.join(inference_dir, "prompts", "ti2v_example.json"))
+with open(os.path.join(inference_dir, "prompts", "ti2v_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
 input_image = Image.open(os.path.join(inference_dir, "assets", "ti2v_first_frame.png")).convert("RGB")
 
 video = pipe(
     prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
     input_image=input_image,
-    height=480, width=832, num_frames=169,
+    height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0,
     seed=0,
 )

From 5fa49f58fa9f33cd38f8b5e2b00005b040911a9b Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Tue, 28 Jul 2026 17:12:37 +0800
Subject: [PATCH 22/34] fix: manage Qwen3-VL vision tower in
 LingBotVideoTextEncoder VRAM map

Low-VRAM TI2V feeds the condition frame through the Qwen3-VL vision tower,
but the LingBotVideoTextEncoder VRAM-management map did not cover its module
types. The vision LayerNorm / patch-embed weights stayed on `meta` and the
vision RoPE `inv_freq` (a non-persistent buffer, absent from the checkpoint)
stayed on CPU, so a TI2V run under offload died with a cuda/cpu device
mismatch. T2V / T2I never touch that path.

Add LayerNorm, Qwen3VLVisionPatchEmbed and Qwen3VLVisionRotaryEmbedding to
the map. PatchEmbed is wrapped whole (not its inner Conv3d) because its
forward reads `self.proj.weight.dtype` to cast the input, which under offload
would otherwise pick up the fp8 dtype and crash on the bf16 bias.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 diffsynth/configs/vram_management_module_maps.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/diffsynth/configs/vram_management_module_maps.py b/diffsynth/configs/vram_management_module_maps.py
index b070862de..e05c8c61e 100644
--- a/diffsynth/configs/vram_management_module_maps.py
+++ b/diffsynth/configs/vram_management_module_maps.py
@@ -418,6 +418,16 @@
         "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
         "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRotaryEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
         "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        # TI2V feeds the condition frame through the Qwen3-VL vision tower, so its modules
+        # need managing too. Without these, the vision LayerNorm / patch-embed weights are
+        # never onloaded (they stay on `meta`) and the vision RoPE `inv_freq` -- a
+        # non-persistent buffer, absent from the checkpoint -- stays on CPU, so a TI2V run
+        # dies with a cuda/cpu device mismatch. T2V never hits this path.
+        # PatchEmbed is wrapped as a whole rather than its inner Conv3d: its forward reads
+        # `self.proj.weight.dtype` to cast the input, which would pick up the offload dtype.
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLVisionPatchEmbed": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLVisionRotaryEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
     },
 }
 

From 56334bfb66b53a56a6f44dd24942de603f6bac06 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Tue, 28 Jul 2026 19:17:04 +0800
Subject: [PATCH 23/34] style: group input_image under image-to-video in
 pipeline __call__

The input_image param (the TI2V condition frame) was grouped under a
'# Video-to-video' comment. Relabel it '# Image-to-video (TI2V)' to match
wan_video.py's param grouping and the file's own TI2V terminology.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 diffsynth/pipelines/lingbot_video.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index bbb94ed2b..3365421d3 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -146,8 +146,9 @@ def __call__(
         # Structured caption (dict) or a plain string.
         prompt: Union[str, dict] = "",
         negative_prompt: Union[str, dict] = "",
-        # Video-to-video
+        # Image-to-video (TI2V)
         input_image: Image.Image = None,
+        # Video-to-video
         input_video: list[Image.Image] = None,
         denoising_strength: float = 1.0,
         # Randomness

From 7d0852e5c75b7261ab1a442ea1c64792f0054bf3 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Tue, 28 Jul 2026 19:40:00 +0800
Subject: [PATCH 24/34] refactor: move TI2V helpers into the units that use
 them

Address reviewer comments on lingbot_video.py (:20, :185, :214):

- Move the module-level TI2V constants (IMAGE_MIN/MAX_TOKEN_NUM, MAX_RATIO,
  SPATIAL_MERGE_SIZE) and helpers (smart_resize, _round/_ceil/_floor_by_factor,
  _pixel_tensor_to_pil) plus the pipeline methods preprocess_cond_image,
  _vision_patch_size and _vlm_image into LingBotVideoUnit_ImageEmbedder, the
  only consumer. Move IMG_PROMPT_TEMPLATE into LingBotVideoUnit_PromptEmbedder.
- Move the pre-loop first-frame latent pin out of __call__ and into
  LingBotVideoUnit_ImageEmbedder.process; reorder self.units so
  InputVideoEmbedder (which produces `latents`) runs before ImageEmbedder
  (which pins into it) and before PromptEmbedder (which consumes vlm_image).
  __call__ keeps only the per-step re-pin inside the denoise loop.

Behavior-preserving for inference and training (the loss overwrites `latents`
and does its own first-frame pin, so the unit-level pin is inert there).
Verified on GPU: TI2V pins frame 0 to the condition frame; T2V no-op path OK.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 diffsynth/pipelines/lingbot_video.py | 309 ++++++++++++++-------------
 1 file changed, 155 insertions(+), 154 deletions(-)

diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index 3365421d3..988512f37 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -17,64 +17,6 @@
 from ..models.lingbot_video_text_encoder import LingBotVideoTextEncoder
 from ..models.qwen_image_vae import QwenImageVAE
 
-# --- TI2V (image-to-video) ---------------------------------------------------
-# The condition frame is used twice: (1) as visual input to the Qwen3-VL text
-# encoder — its image tokens are prepended to the prompt via IMG_PROMPT_TEMPLATE;
-# (2) VAE-encoded to a clean latent written into the first temporal slot of the
-# diffusion latent before sampling and after every scheduler step (inpainting).
-# The DiT is unchanged (in_channels stays 16); Dense-1.3B reuses its T2V weights.
-IMG_PROMPT_TEMPLATE = "<|vision_start|><|image_pad|><|vision_end|>"
-
-# Qwen3-VL vision token-budget bounds used by smart_resize (official defaults).
-IMAGE_MIN_TOKEN_NUM = 4
-IMAGE_MAX_TOKEN_NUM = 16384
-MAX_RATIO = 200
-SPATIAL_MERGE_SIZE = 2
-
-
-def _round_by_factor(number: float, factor: int) -> int:
-    return round(number / factor) * factor
-
-
-def _ceil_by_factor(number: float, factor: int) -> int:
-    return math.ceil(number / factor) * factor
-
-
-def _floor_by_factor(number: float, factor: int) -> int:
-    return math.floor(number / factor) * factor
-
-
-def smart_resize(height: int, width: int, factor: int,
-                 min_pixels: Optional[int] = None, max_pixels: Optional[int] = None):
-    # Resize so both sides are multiples of `factor` and the token count stays in
-    # [min_pixels, max_pixels] while preserving aspect ratio. Ported verbatim from
-    # the official LingBot-Video i2v pipeline (Qwen3-VL smart-resize).
-    max_pixels = max_pixels if max_pixels is not None else IMAGE_MAX_TOKEN_NUM * factor**2
-    min_pixels = min_pixels if min_pixels is not None else IMAGE_MIN_TOKEN_NUM * factor**2
-    if max_pixels < min_pixels:
-        raise ValueError("max_pixels must be greater than or equal to min_pixels.")
-    if max(height, width) / min(height, width) > MAX_RATIO:
-        raise ValueError(f"absolute aspect ratio must be smaller than {MAX_RATIO}.")
-    resized_height = max(factor, _round_by_factor(height, factor))
-    resized_width = max(factor, _round_by_factor(width, factor))
-    if resized_height * resized_width > max_pixels:
-        beta = math.sqrt((height * width) / max_pixels)
-        resized_height = _floor_by_factor(height / beta, factor)
-        resized_width = _floor_by_factor(width / beta, factor)
-    elif resized_height * resized_width < min_pixels:
-        beta = math.sqrt(min_pixels / (height * width))
-        resized_height = _ceil_by_factor(height * beta, factor)
-        resized_width = _ceil_by_factor(width * beta, factor)
-    return resized_height, resized_width
-
-
-def _pixel_tensor_to_pil(pixel: torch.Tensor) -> Image.Image:
-    # Match torchvision.transforms.ToPILImage for a float CHW image in [0, 1].
-    # pixel: (B, C, T, H, W); take batch 0, temporal slot 0.
-    frame = pixel[0, :, 0].detach().cpu().clamp(0, 1)
-    array = frame.permute(1, 2, 0).mul(255).byte().numpy()
-    return Image.fromarray(array, mode="RGB")
-
 
 class LingBotVideoPipeline(BasePipeline):
     def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
@@ -92,11 +34,12 @@ def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
         self.units = [
             LingBotVideoUnit_ShapeChecker(),
             LingBotVideoUnit_NoiseInitializer(),
-            # ImageEmbedder must run before PromptEmbedder: it produces the vlm_image
-            # that PromptEmbedder feeds to the text encoder (TI2V only; no-op for T2V).
+            LingBotVideoUnit_InputVideoEmbedder(),
+            # ImageEmbedder runs after InputVideoEmbedder (it pins the condition latent into
+            # `latents`) and before PromptEmbedder (it produces the vlm_image the text encoder
+            # consumes). No-op for T2V/V2V.
             LingBotVideoUnit_ImageEmbedder(),
             LingBotVideoUnit_PromptEmbedder(),
-            LingBotVideoUnit_InputVideoEmbedder(),
         ]
         self.model_fn = model_fn_lingbot_video
         self.compilable_models = ["dit"]
@@ -182,13 +125,11 @@ def __call__(
         for unit in self.units:
             inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
 
-        # TI2V: the clean condition latent (if any) is pinned into the first temporal
-        # slot(s) both before sampling and after every scheduler step, so the DiT only
-        # ever denoises the frames after the given first frame.
+        # TI2V: the ImageEmbedder unit has already pinned the clean condition latent into the
+        # first temporal slot(s); re-pin it after every scheduler step so the DiT only ever
+        # denoises the frames after the given first frame.
         first_frame_latents = inputs_shared.get("first_frame_latents")
-        if first_frame_latents is not None:
-            cond_t = first_frame_latents.shape[2]
-            inputs_shared["latents"][:, :, :cond_t] = first_frame_latents
+        cond_t = first_frame_latents.shape[2] if first_frame_latents is not None else None
 
         # Denoise
         self.load_models_to_device(self.in_iteration_models)
@@ -211,42 +152,6 @@ def __call__(
         self.load_models_to_device([])
         return video
 
-    def preprocess_cond_image(self, image: Image.Image, height, width):
-        # TI2V condition frame -> (1, C, 1, H, W) pixel tensor in [0, 1], aspect-ratio
-        # preserving cover-resize + center-crop to (height, width).
-        raw = torch.from_numpy(np.array(image.convert("RGB"))).permute(2, 0, 1).unsqueeze(0).contiguous()
-        old_h, old_w = raw.shape[-2:]
-        scale = max(height / old_h, width / old_w)
-        new_h = max(math.ceil(old_h * scale), height)
-        new_w = max(math.ceil(old_w * scale), width)
-        resized = F.interpolate(raw.float(), size=(new_h, new_w), mode="bilinear", align_corners=False)
-        top = int(round((new_h - height) / 2.0))
-        left = int(round((new_w - width) / 2.0))
-        cropped = resized[:, :, top: top + height, left: left + width] / 255.0
-        return cropped.unsqueeze(2)
-
-    def _vision_patch_size(self) -> int:
-        # Resolve the Qwen3-VL vision patch size, used with SPATIAL_MERGE_SIZE to pick
-        # the smart-resize grid factor.
-        for obj in (
-            getattr(getattr(self.text_encoder, "config", None), "vision_config", None),
-            getattr(getattr(self.processor, "image_processor", None), "config", None),
-            getattr(self.processor, "image_processor", None),
-        ):
-            patch = getattr(obj, "patch_size", None)
-            if patch is not None:
-                return int(patch)
-        return 16
-
-    def _vlm_image(self, pixel: torch.Tensor) -> Image.Image:
-        # Build the PIL image handed to the text encoder from the condition pixel tensor,
-        # smart-resized to the Qwen3-VL patch grid.
-        image = _pixel_tensor_to_pil(pixel)
-        patch_factor = self._vision_patch_size() * SPATIAL_MERGE_SIZE
-        w, h = image.size
-        resized_height, resized_width = smart_resize(h, w, factor=patch_factor)
-        return image.resize((resized_width, resized_height))
-
 
 class LingBotVideoUnit_ShapeChecker(PipelineUnit):
     def __init__(self):
@@ -274,9 +179,153 @@ def process(self, pipe: LingBotVideoPipeline, height, width, num_frames, seed, r
         return {"noise": noise}
 
 
+class LingBotVideoUnit_InputVideoEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_video", "noise"),
+            output_params=("latents", "input_latents"),
+            onload_model_names=("vae",),
+        )
+
+    def process(self, pipe: LingBotVideoPipeline, input_video, noise):
+        if input_video is None:
+            return {"latents": noise}
+        pipe.load_models_to_device(self.onload_model_names)
+        video = pipe.preprocess_video(input_video)
+        input_latents = pipe.vae.encode_video(video).to(dtype=pipe.torch_dtype, device=pipe.device)
+        if pipe.scheduler.training:
+            return {"latents": noise, "input_latents": input_latents}
+        else:
+            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
+            return {"latents": latents}
+
+
+class LingBotVideoUnit_ImageEmbedder(PipelineUnit):
+    """TI2V (image-to-video): the condition first frame is used twice — (1) VAE-encoded to a
+    clean latent pinned into the diffusion latent's first temporal slot (here before sampling,
+    re-pinned after every scheduler step in ``__call__``), so the DiT only denoises the frames
+    after it; (2) smart-resized to a PIL image handed to the Qwen3-VL text encoder as
+    ``vlm_image`` (its image tokens are prepended to the prompt). Runs after InputVideoEmbedder
+    (it needs ``latents`` to pin into) and before PromptEmbedder (which consumes ``vlm_image``).
+    No-op (returns ``{}``) for T2V/V2V. The DiT is unchanged (in_channels stays 16); Dense-1.3B
+    reuses its T2V weights."""
+
+    # Qwen3-VL vision token-budget bounds used by smart_resize (official defaults).
+    IMAGE_MIN_TOKEN_NUM = 4
+    IMAGE_MAX_TOKEN_NUM = 16384
+    MAX_RATIO = 200
+    SPATIAL_MERGE_SIZE = 2
+
+    def __init__(self):
+        super().__init__(
+            input_params=("input_image", "latents", "height", "width"),
+            output_params=("latents", "first_frame_latents", "vlm_image"),
+            onload_model_names=("vae",),
+        )
+
+    def process(self, pipe: LingBotVideoPipeline, input_image, latents, height, width):
+        if input_image is None:
+            return {}
+        pipe.load_models_to_device(self.onload_model_names)
+        # (1, C, 1, H, W) in [0, 1] -> [-1, 1] to match the VAE input range; encode_video applies
+        # the latent normalisation, giving the clean cond latent the DiT was trained to inpaint on.
+        pixel = self.preprocess_cond_image(input_image, height, width)
+        pixel = pixel.to(dtype=pipe.torch_dtype, device=pipe.device)
+        first_frame_latents = pipe.vae.encode_video(pixel * 2.0 - 1.0).to(dtype=pipe.torch_dtype, device=pipe.device)
+        vlm_image = self.vlm_image(pipe, pixel)
+        # Pin the clean condition latent into the first temporal slot before sampling.
+        cond_t = first_frame_latents.shape[2]
+        latents[:, :, :cond_t] = first_frame_latents
+        return {"latents": latents, "first_frame_latents": first_frame_latents, "vlm_image": vlm_image}
+
+    @staticmethod
+    def preprocess_cond_image(image: Image.Image, height, width) -> torch.Tensor:
+        # TI2V condition frame -> (1, C, 1, H, W) pixel tensor in [0, 1], aspect-ratio
+        # preserving cover-resize + center-crop to (height, width).
+        raw = torch.from_numpy(np.array(image.convert("RGB"))).permute(2, 0, 1).unsqueeze(0).contiguous()
+        old_h, old_w = raw.shape[-2:]
+        scale = max(height / old_h, width / old_w)
+        new_h = max(math.ceil(old_h * scale), height)
+        new_w = max(math.ceil(old_w * scale), width)
+        resized = F.interpolate(raw.float(), size=(new_h, new_w), mode="bilinear", align_corners=False)
+        top = int(round((new_h - height) / 2.0))
+        left = int(round((new_w - width) / 2.0))
+        cropped = resized[:, :, top: top + height, left: left + width] / 255.0
+        return cropped.unsqueeze(2)
+
+    def vlm_image(self, pipe: LingBotVideoPipeline, pixel: torch.Tensor) -> Image.Image:
+        # Build the PIL image handed to the text encoder from the condition pixel tensor,
+        # smart-resized to the Qwen3-VL patch grid.
+        image = self._pixel_tensor_to_pil(pixel)
+        patch_factor = self._vision_patch_size(pipe) * self.SPATIAL_MERGE_SIZE
+        w, h = image.size
+        resized_height, resized_width = self.smart_resize(h, w, factor=patch_factor)
+        return image.resize((resized_width, resized_height))
+
+    @staticmethod
+    def _vision_patch_size(pipe: LingBotVideoPipeline) -> int:
+        # Resolve the Qwen3-VL vision patch size, used with SPATIAL_MERGE_SIZE to pick the
+        # smart-resize grid factor.
+        for obj in (
+            getattr(getattr(pipe.text_encoder, "config", None), "vision_config", None),
+            getattr(getattr(pipe.processor, "image_processor", None), "config", None),
+            getattr(pipe.processor, "image_processor", None),
+        ):
+            patch = getattr(obj, "patch_size", None)
+            if patch is not None:
+                return int(patch)
+        return 16
+
+    @staticmethod
+    def _pixel_tensor_to_pil(pixel: torch.Tensor) -> Image.Image:
+        # Match torchvision.transforms.ToPILImage for a float CHW image in [0, 1].
+        # pixel: (B, C, T, H, W); take batch 0, temporal slot 0.
+        frame = pixel[0, :, 0].detach().cpu().clamp(0, 1)
+        array = frame.permute(1, 2, 0).mul(255).byte().numpy()
+        return Image.fromarray(array, mode="RGB")
+
+    @classmethod
+    def smart_resize(cls, height: int, width: int, factor: int,
+                     min_pixels: Optional[int] = None, max_pixels: Optional[int] = None):
+        # Resize so both sides are multiples of `factor` and the token count stays in
+        # [min_pixels, max_pixels] while preserving aspect ratio. Ported verbatim from the
+        # official LingBot-Video i2v pipeline (Qwen3-VL smart-resize).
+        max_pixels = max_pixels if max_pixels is not None else cls.IMAGE_MAX_TOKEN_NUM * factor**2
+        min_pixels = min_pixels if min_pixels is not None else cls.IMAGE_MIN_TOKEN_NUM * factor**2
+        if max_pixels < min_pixels:
+            raise ValueError("max_pixels must be greater than or equal to min_pixels.")
+        if max(height, width) / min(height, width) > cls.MAX_RATIO:
+            raise ValueError(f"absolute aspect ratio must be smaller than {cls.MAX_RATIO}.")
+        resized_height = max(factor, cls._round_by_factor(height, factor))
+        resized_width = max(factor, cls._round_by_factor(width, factor))
+        if resized_height * resized_width > max_pixels:
+            beta = math.sqrt((height * width) / max_pixels)
+            resized_height = cls._floor_by_factor(height / beta, factor)
+            resized_width = cls._floor_by_factor(width / beta, factor)
+        elif resized_height * resized_width < min_pixels:
+            beta = math.sqrt(min_pixels / (height * width))
+            resized_height = cls._ceil_by_factor(height * beta, factor)
+            resized_width = cls._ceil_by_factor(width * beta, factor)
+        return resized_height, resized_width
+
+    @staticmethod
+    def _round_by_factor(number: float, factor: int) -> int:
+        return round(number / factor) * factor
+
+    @staticmethod
+    def _ceil_by_factor(number: float, factor: int) -> int:
+        return math.ceil(number / factor) * factor
+
+    @staticmethod
+    def _floor_by_factor(number: float, factor: int) -> int:
+        return math.floor(number / factor) * factor
+
+
 class LingBotVideoUnit_PromptEmbedder(PipelineUnit):
     TOKEN_LENGTH = 37698
     HIDDEN_STATE_SKIP_LAYER = 0
+    # Token block prepended to the prompt (TI2V) so the encoder attends to the condition frame.
+    IMG_PROMPT_TEMPLATE = "<|vision_start|><|image_pad|><|vision_end|>"
     PROMPT_TEMPLATE = (
         "<|im_start|>system\nGiven a user input that may include a text prompt alone, "
         "a text prompt with an image reference, or a text prompt with a video reference "
@@ -337,10 +386,9 @@ def _compute_crop_start(self, pipe: LingBotVideoPipeline) -> int:
 
     def encode_prompt(self, pipe: LingBotVideoPipeline, prompt, vlm_image=None):
         prompt = self.normalize_caption(prompt)
-        # TI2V: prepend the image-token block so the encoder attends to the condition
-        # frame. The image tokens land after the template prefix, so crop_start is
-        # unaffected.
-        visual_template = IMG_PROMPT_TEMPLATE if vlm_image is not None else ""
+        # TI2V: prepend the image-token block so the encoder attends to the condition frame.
+        # The image tokens land after the template prefix, so crop_start is unaffected.
+        visual_template = self.IMG_PROMPT_TEMPLATE if vlm_image is not None else ""
         text = self.PROMPT_TEMPLATE.format(visual_template + prompt)
         inputs = pipe.processor(
             text=[text],
@@ -375,53 +423,6 @@ def process(self, pipe: LingBotVideoPipeline, prompt, vlm_image=None) -> dict:
         return {"context": prompt_embeds, "encoder_attention_mask": prompt_mask}
 
 
-class LingBotVideoUnit_InputVideoEmbedder(PipelineUnit):
-    def __init__(self):
-        super().__init__(
-            input_params=("input_video", "noise"),
-            output_params=("latents", "input_latents"),
-            onload_model_names=("vae",),
-        )
-
-    def process(self, pipe: LingBotVideoPipeline, input_video, noise):
-        if input_video is None:
-            return {"latents": noise}
-        pipe.load_models_to_device(self.onload_model_names)
-        video = pipe.preprocess_video(input_video)
-        input_latents = pipe.vae.encode_video(video).to(dtype=pipe.torch_dtype, device=pipe.device)
-        if pipe.scheduler.training:
-            return {"latents": noise, "input_latents": input_latents}
-        else:
-            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
-            return {"latents": latents}
-
-
-class LingBotVideoUnit_ImageEmbedder(PipelineUnit):
-    """TI2V: turn the condition first frame into (a) a clean VAE latent pinned into
-    the diffusion latent's first temporal slot and (b) a smart-resized PIL image for
-    the Qwen3-VL text encoder. No-op (returns {}) when input_image is None (T2V/V2V)."""
-
-    def __init__(self):
-        super().__init__(
-            input_params=("input_image", "height", "width"),
-            output_params=("first_frame_latents", "vlm_image"),
-            onload_model_names=("vae",),
-        )
-
-    def process(self, pipe: LingBotVideoPipeline, input_image, height, width):
-        if input_image is None:
-            return {}
-        pipe.load_models_to_device(self.onload_model_names)
-        # (1, C, 1, H, W) in [0, 1] -> [-1, 1] to match the VAE input range; encode_video
-        # applies the latent normalisation, giving the same clean cond latent the DiT
-        # was trained to inpaint on.
-        pixel = pipe.preprocess_cond_image(input_image, height, width)
-        pixel = pixel.to(dtype=pipe.torch_dtype, device=pipe.device)
-        first_frame_latents = pipe.vae.encode_video(pixel * 2.0 - 1.0).to(dtype=pipe.torch_dtype, device=pipe.device)
-        vlm_image = pipe._vlm_image(pixel)
-        return {"first_frame_latents": first_frame_latents, "vlm_image": vlm_image}
-
-
 def model_fn_lingbot_video(
     dit: LingBotVideoDiT,
     latents: torch.Tensor = None,

From 5bc41d260b5f92c288cd3fa9c5c5d67c99c1dda1 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Wed, 29 Jul 2026 11:02:14 +0800
Subject: [PATCH 25/34] style: strip comments and align MoE-30B-A3B integration
 with repo conventions

- lingbot_video_dit.py: drop unused LINGBOT_VIDEO_FP32_MODULES /
  should_keep_in_fp32, remove docstring and inline comments, keep
  norm_out_modulation call on one line
- model_configs.py: keep only the ModelConfig example comment, collapse
  extra_kwargs onto a single line
- vram_management_module_maps.py: drop explanatory comments
- MoE inference examples: remove all comments and section dividers
- docs: restore the standard template layout, fold the MoE description
  into the model overview section
- README / README_zh: add the missing MoE row so the examples table
  matches the docs model overview table
---
 README.md                                     |  1 +
 README_zh.md                                  |  1 +
 diffsynth/configs/model_configs.py            | 10 +---
 .../configs/vram_management_module_maps.py    |  7 ---
 diffsynth/models/lingbot_video_dit.py         | 49 +------------------
 docs/en/Model_Details/LingBot-Video.md        | 38 ++------------
 docs/zh/Model_Details/LingBot-Video.md        | 38 ++------------
 .../lingbot-video-moe-30b-a3b.py              |  6 ---
 .../lingbot-video-moe-30b-a3b.py              |  9 ----
 9 files changed, 11 insertions(+), 148 deletions(-)

diff --git a/README.md b/README.md
index aa87df426..338d0e77b 100644
--- a/README.md
+++ b/README.md
@@ -1518,6 +1518,7 @@ Example code for LingBot-Video is available at: [/examples/lingbot_video/](/exam
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
 |[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py)|-|-|-|-|
 
 </details>
 
diff --git a/README_zh.md b/README_zh.md
index 60766d83d..f0e85a8a9 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -1518,6 +1518,7 @@ LingBot-Video 的示例代码位于：[/examples/lingbot_video/](/examples/lingb
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
 |[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py)|-|-|-|-|
 
 </details>
 
diff --git a/diffsynth/configs/model_configs.py b/diffsynth/configs/model_configs.py
index 0e0944fd8..edeca199a 100644
--- a/diffsynth/configs/model_configs.py
+++ b/diffsynth/configs/model_configs.py
@@ -1332,19 +1332,11 @@
     },
     {
         # Example: ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors")
-        # MoE 30B-A3B DiT (128 experts, top-8 with group-limited routing, 1 shared
-        # expert). The released package also ships a `refiner/` DiT with the exact
-        # same architecture and key set, so it hashes identically and is loaded
-        # through this entry too; the pipeline separates them by load order.
         "model_hash": "65b83aa625cd362ff5ff3409fb367a6f",
         "model_name": "lingbot_video_dit",
         "model_class": "diffsynth.models.lingbot_video_dit.LingBotVideoDiT",
         "state_dict_converter": "diffsynth.utils.state_dict_converters.lingbot_video_dit.LingBotVideoDiTStateDictConverter",
-        "extra_kwargs": {
-            "depth": 48, "axes_lens": (4096, 512, 512), "num_experts": 128,
-            "moe_intermediate_size": 768, "n_group": 4, "topk_group": 2,
-            "n_shared_experts": 1, "routed_scaling_factor": 2.5,
-        },
+        "extra_kwargs": {'depth': 48, 'axes_lens': (4096, 512, 512), 'num_experts': 128, 'moe_intermediate_size': 768, 'n_group': 4, 'topk_group': 2, 'n_shared_experts': 1, 'routed_scaling_factor': 2.5},
     },
     {
         # Example: ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="text_encoder/*.safetensors")
diff --git a/diffsynth/configs/vram_management_module_maps.py b/diffsynth/configs/vram_management_module_maps.py
index e3d8a25c1..cc55fb271 100644
--- a/diffsynth/configs/vram_management_module_maps.py
+++ b/diffsynth/configs/vram_management_module_maps.py
@@ -412,13 +412,6 @@
         "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
         "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
         "diffsynth.models.lingbot_video_dit.LingBotVideoRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
-        # The MoE experts keep their weights as bare `nn.Parameter` (w1/w2/w3 of
-        # shape [E, I, H]) rather than `nn.Linear`, so `AutoWrappedLinear` cannot
-        # reach them. Wrapping the container itself is what makes the 30B-A3B
-        # checkpoint offloadable -- without this the experts, which are the bulk
-        # of the model, would stay resident. Wrapped fine-grained (not the whole
-        # LingBotVideoBlock) on purpose: AutoWrappedModule stops the walker from
-        # recursing, so wrapping the block would keep all experts resident together.
         "diffsynth.models.lingbot_video_dit.LingBotVideoGroupedExperts": "diffsynth.core.vram.layers.AutoWrappedModule",
     },
     "diffsynth.models.lingbot_video_text_encoder.LingBotVideoTextEncoder": {
diff --git a/diffsynth/models/lingbot_video_dit.py b/diffsynth/models/lingbot_video_dit.py
index 69f6e504c..36a32980a 100644
--- a/diffsynth/models/lingbot_video_dit.py
+++ b/diffsynth/models/lingbot_video_dit.py
@@ -10,41 +10,9 @@
 from ..core.device.npu_compatible_device import get_device_type
 
 
-# Modules kept in fp32 regardless of the bulk compute dtype (sensitive AdaLN / norm /
-# router paths); the custom `to()` below honours this list.
-LINGBOT_VIDEO_FP32_MODULES = (
-    "time_embedder",
-    "time_modulation",
-    "scale_shift_table",
-    "norm",
-    "norm1",
-    "norm2",
-    "norm_q",
-    "norm_k",
-    "norm_post_attn",
-    "norm_post_ffn",
-    "norm_out",
-    "norm_out_modulation",
-    "router",
-)
-
-
-def should_keep_in_fp32(name: str) -> bool:
-    return any(module_name in name.split(".") for module_name in LINGBOT_VIDEO_FP32_MODULES)
-
-
 def resolve_bulk_dtype(linear: nn.Module) -> torch.dtype:
-    """Dtype the bulk Linear path will actually compute in.
-
-    Reading `linear.weight.dtype` is wrong under DiffSynth's VRAM management: the
-    wrapped layer holds its weight in the offload dtype (e.g. float8) between
-    forwards, and on `meta` when offloaded to disk, casting to `computation_dtype`
-    only inside its own forward. `computation_dtype` is the value to feed the
-    activations, so prefer it whenever the layer is wrapped.
-    """
     computation_dtype = getattr(linear, "computation_dtype", None)
     if isinstance(computation_dtype, torch.dtype):
-        # fp8 compute still takes bf16 activations; AutoWrappedLinear quantises internally.
         if computation_dtype in (torch.float8_e4m3fn, torch.float8_e4m3fnuz, torch.float8_e5m2):
             return torch.bfloat16
         return computation_dtype
@@ -370,9 +338,6 @@ def _unpad_grouped_tokens(output, input_shape, permuted_indices):
         return unpermuted[:-1]
 
     def _run_grouped_experts(self, tokens, counts):
-        # `torch._grouped_mm` exists as a symbol on every backend but only has a CUDA
-        # kernel, so probing with `hasattr` alone would crash on CPU (and on NPU).
-        # Fall back to the mathematically equivalent per-expert loop off CUDA.
         if not hasattr(torch, "_grouped_mm") or tokens.device.type != "cuda":
             return self._run_experts_for_loop(tokens, counts)
         input_shape, padded_tokens, permuted_indices, aligned_counts = self._pad_grouped_tokens(tokens, counts)
@@ -464,11 +429,6 @@ def forward(self, x, temb6, rotary_emb, attention_mask=None, moe_padding_mask=No
                 "LingBotVideoBlock expects token-level temb6 with shape (B*S, 6D); "
                 f"got {tuple(temb6.shape)} for hidden states {tuple(x.shape)}."
             )
-        # AdaLN modulation / norms run in fp32 (sensitive path); cast to the bulk
-        # compute dtype only at the bf16 Linear boundary. `bulk_dtype` is supplied
-        # by the parent module: under VRAM management the attention weights are
-        # held in the offload dtype (or on `meta` for disk offload), so their
-        # `.dtype` is not the dtype the Linear will actually compute in.
         if bulk_dtype is None:
             bulk_dtype = resolve_bulk_dtype(self.attn.to_q)
         mod = temb6.view(x.shape[0], x.shape[1], -1) + self.scale_shift_table.unsqueeze(0)
@@ -632,10 +592,7 @@ def forward(
                 attention_mask = key_mask[:, None, None, :]      # (B,1,1,S) -> SDPA broadcast
                 moe_padding_mask = key_mask.reshape(-1).float()  # (B*S,)
 
-        # Timestep -> per-token modulation. The timestep MLP is one of the fp32-pinned
-        # modules, so its input is fp32 -- but VRAM management wraps every `nn.Linear`
-        # at `computation_dtype` regardless of that policy, so resolve the dtype from
-        # the layer itself instead of assuming fp32.
+        # Timestep -> per-token modulation.
         timestep_proj = self.time_proj(timestep.float())
         t_emb = self.time_embedder(timestep_proj.to(resolve_bulk_dtype(self.time_embedder.linear_1)))  # (B, D)
         if packed_batch:
@@ -660,9 +617,7 @@ def forward(
             )
 
         out_mod_dtype = resolve_bulk_dtype(self.norm_out_modulation[1])
-        final_mod = self.norm_out_modulation(
-            temb_input.reshape(joint.shape[0] * joint.shape[1], -1).to(out_mod_dtype)
-        )
+        final_mod = self.norm_out_modulation(temb_input.reshape(joint.shape[0] * joint.shape[1], -1).to(out_mod_dtype))
         shift, scale = final_mod.reshape(joint.shape[0], joint.shape[1], -1).chunk(2, dim=-1)
         final_hidden = self.norm_out(joint) * (1.0 + scale) + shift
         projected = self.proj_out(final_hidden.to(resolve_bulk_dtype(self.proj_out)))
diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
index 2c7eea5bf..c6f5c003d 100644
--- a/docs/en/Model_Details/LingBot-Video.md
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -1,13 +1,6 @@
 # LingBot-Video
 
-LingBot-Video is a flow-matching video generation model developed by the LingBot team. This document covers DiffSynth-Studio's inference support for the **Dense-1.3B** and **MoE-30B-A3B** checkpoints, plus LoRA and full-parameter SFT training for Dense-1.3B. A single Dense-1.3B checkpoint serves text-to-video, image-to-video and text-to-image through the same pipeline.
-
-The integration is built on the standard DiffSynth pipeline stack:
-
-- **DiT** — `LingBotVideoDiT` (`diffsynth/models/lingbot_video_dit.py`), the video denoiser. A single class covers both variants: Dense-1.3B uses a plain FFN (`num_experts=0`), MoE-30B-A3B a sparse MoE FFN.
-- **Text encoder** — `LingBotVideoTextEncoder` (Qwen3-VL). Prompts are wrapped in a prompt-enhancement chat template, encoded, and the template-prefix tokens are cropped.
-- **VAE** — reuses DiffSynth's `QwenImageVAE` (byte-identical to the LingBot-Video VAE), 8× spatial / 4× temporal compression.
-- **Scheduler** — DiffSynth's `FlowMatchScheduler` (Wan template): first-order flow-matching Euler for inference; training uses the full-resolution 1000-step flow-matching schedule.
+LingBot-Video is a flow-matching video generation model developed by the LingBot team, supporting text-to-video, image-to-video and text-to-image tasks with a single checkpoint.
 
 ## Installation
 
@@ -73,37 +66,12 @@ video = pipe(
 save_video(video, "video.mp4", fps=15, quality=10)
 ```
 
-**Low VRAM:** set `offload_dtype` / `offload_device` on each `ModelConfig` to enable layer-by-layer offloading; `vram_limit` alone has no effect (it only caps resident VRAM once offloading is on). See the low-VRAM example in the table below.
-
-## MoE-30B-A3B
-
-[Robbyant/lingbot-video-moe-30b-a3b](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b) is the larger variant: 30B total parameters with ~3B active per token. Each MoE layer holds 128 routed experts plus 1 shared expert, and routes each token to 8 experts using group-limited top-k (4 groups, top-2 groups).
-
-It loads through the same pipeline; only the model ID and the shard glob change (the checkpoint is split across 13 shards):
-
-```python
-pipe = LingBotVideoPipeline.from_pretrained(
-    torch_dtype=torch.bfloat16,
-    device="cuda",
-    model_configs=[
-        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="text_encoder/model*.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
-    ],
-    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
-)
-```
-
-Notes specific to the MoE variant:
-
-- The expert matmuls use `torch._grouped_mm` on CUDA, falling back to an equivalent per-expert loop elsewhere.
-- The experts store their weights as grouped `nn.Parameter` tensors rather than `nn.Linear`, so VRAM management wraps the expert container itself. Since the experts are the bulk of the model, the low-VRAM path is what keeps resident VRAM near the ~3B active footprint instead of the full 30B.
-- The released package also ships a second-stage `refiner/` DiT (same architecture, used to upscale a base result). It is not wired into the pipeline yet.
-
 ## Model Overview
 
 Dense-1.3B is a single checkpoint that serves three tasks — text-to-video, image-to-video, and text-to-image — through the same pipeline. Each task ships its own inference / low-VRAM / training / validation scripts.
 
+MoE-30B-A3B is the larger variant: 30B total parameters with ~3B active per token, where each MoE layer holds 128 routed experts plus 1 shared expert and routes every token to 8 experts with group-limited top-k (4 groups, top-2 groups). It loads through the same pipeline, only the model ID and the shard glob change.
+
 |Model ID|Inference|Low VRAM Inference|Full Training|Full Training Validation|LoRA Training|LoRA Training Validation|
 |-|-|-|-|-|-|-|
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
index 35c1c575f..67331d123 100644
--- a/docs/zh/Model_Details/LingBot-Video.md
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -1,13 +1,6 @@
 # LingBot-Video
 
-LingBot-Video 是由 LingBot 团队研发的 flow-matching 视频生成模型。本文档介绍 DiffSynth-Studio 对 **Dense-1.3B** 与 **MoE-30B-A3B** 权重的推理支持，以及对 Dense-1.3B 的 LoRA 与全量 SFT 训练支持。同一份 Dense-1.3B checkpoint 通过同一条 Pipeline 支持文生视频、图生视频和文生图三种任务。
-
-该接入基于标准的 DiffSynth Pipeline 组件栈构建：
-
-- **DiT** — `LingBotVideoDiT`（`diffsynth/models/lingbot_video_dit.py`），视频去噪器。同一个类同时覆盖两个版本：Dense-1.3B 使用普通 FFN（`num_experts=0`），MoE-30B-A3B 使用稀疏 MoE FFN。
-- **文本编码器** — `LingBotVideoTextEncoder`（Qwen3-VL）。提示词会被包裹进提示增强的对话模板中编码，随后裁剪掉模板前缀 token。
-- **VAE** — 复用 DiffSynth 的 `QwenImageVAE`（与 LingBot-Video 的 VAE 逐字节一致），空间 8× / 时间 4× 压缩。
-- **调度器** — DiffSynth 的 `FlowMatchScheduler`（Wan 模板）：推理时使用一阶 flow-matching Euler；训练时使用完整分辨率的 1000 步 flow-matching 调度。
+LingBot-Video 是由 LingBot 团队研发的 flow-matching 视频生成模型，同一份 checkpoint 支持文生视频、图生视频和文生图三种任务。
 
 ## 安装
 
@@ -73,37 +66,12 @@ video = pipe(
 save_video(video, "video.mp4", fps=15, quality=10)
 ```
 
-**低显存：** 在每个 `ModelConfig` 上设置 `offload_dtype` / `offload_device` 才能开启逐层 offload；单独传 `vram_limit` 没有效果（它只在 offload 已开启时限制常驻显存上限）。详见下表中的低显存示例。
-
-## MoE-30B-A3B
-
-[Robbyant/lingbot-video-moe-30b-a3b](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b) 是更大的版本：总参数量 30B，每个 token 激活约 3B。每个 MoE 层包含 128 个路由专家和 1 个共享专家，并使用 group-limited top-k（4 组，取 top-2 组）将每个 token 路由到 8 个专家。
-
-它复用同一条 pipeline，只需更换模型 ID 与分片通配符（权重被切分为 13 个 shard）：
-
-```python
-pipe = LingBotVideoPipeline.from_pretrained(
-    torch_dtype=torch.bfloat16,
-    device="cuda",
-    model_configs=[
-        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="text_encoder/model*.safetensors"),
-        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
-    ],
-    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
-)
-```
-
-MoE 版本的注意事项：
-
-- 专家矩阵乘在 CUDA 上使用 `torch._grouped_mm`，在其他设备上回退到等价的逐专家循环。
-- 专家权重以分组的 `nn.Parameter` 而非 `nn.Linear` 存储，因此显存管理包装的是专家容器本身。由于专家占据了模型的绝大部分参数，低显存路径能让常驻显存接近约 3B 的激活规模，而不是完整的 30B。
-- 官方权重包中还提供了第二阶段的 `refiner/` DiT（架构相同，用于在 base 结果之上做超分精修），目前尚未接入 pipeline。
-
 ## 模型总览
 
 Dense-1.3B 是一份 checkpoint，通过同一个 Pipeline 支持文生视频、图生视频、文生图三种任务，每种任务都配有推理 / 低显存 / 训练 / 验证脚本。
 
+MoE-30B-A3B 是更大的版本：总参数量 30B，每个 token 激活约 3B，每个 MoE 层包含 128 个路由专家和 1 个共享专家，并使用 group-limited top-k（4 组，取 top-2 组）将每个 token 路由到 8 个专家。它复用同一条 Pipeline，只需更换模型 ID 与分片通配符。
+
 |模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
 |-|-|-|-|-|-|-|
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
diff --git a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py
index c006bb53f..4a008e27c 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py
@@ -2,9 +2,6 @@
 from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 
-
-# The MoE checkpoint is sharded, so the file pattern has to match all shards.
-# 30B total parameters with 3B active per token (128 experts, top-8 routing).
 pipe = LingBotVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
     device="cuda",
@@ -16,7 +13,6 @@
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
 )
 
-# --- Text-to-video -------------------------------------------------------------------
 video = pipe(
     prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight, ears perked up, chasing after a red ball. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
     negative_prompt=pipe.default_negative_prompt,
@@ -26,8 +22,6 @@
 )
 save_video(video, "video_lingbot-video-moe-30b-a3b.mp4", fps=15, quality=10)
 
-# --- Video-to-video ------------------------------------------------------------------
-# denoising_strength < 1 keeps part of the input structure.
 input_video = VideoData("video_lingbot-video-moe-30b-a3b.mp4", height=480, width=832)
 video = pipe(
     prompt="A playful puppy wearing black sunglasses runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py
index a92987b40..f6757d7b3 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py
@@ -2,14 +2,6 @@
 from diffsynth.utils.data import save_video
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 
-
-# Low-VRAM inference. Setting `offload_dtype` / `offload_device` is what actually turns
-# on DiffSynth's VRAM management: weights are kept on CPU in fp8 and streamed to the GPU
-# layer-by-layer, then computed in bf16. `vram_limit` on its own has no effect.
-#
-# This matters most for the MoE variant: the 128 experts hold the bulk of the 30B
-# parameters while only 3B are active per token, so offloading them keeps resident VRAM
-# far below the full model size.
 vram_config = {
     "offload_dtype": torch.float8_e4m3fn,
     "offload_device": "cpu",
@@ -33,7 +25,6 @@
     vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
 )
 
-# --- Text-to-video -------------------------------------------------------------------
 video = pipe(
     prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight, ears perked up, chasing after a red ball. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
     negative_prompt=pipe.default_negative_prompt,

From eed0d72fba3a0f4ff54290fffd9f4d13a01a1bab Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Wed, 29 Jul 2026 11:08:13 +0800
Subject: [PATCH 26/34] style: align MoE examples with the Dense inference
 examples

- use the released structured-JSON caption from prompts/t2v_example_1.json
  instead of free-form prose, matching the Dense T2V / TI2V / T2I examples
- switch the low-VRAM example to the Dense disk-offload profile
  (disk -> cpu fp8 -> cuda bf16, vram_limit - 0.5)
- add the video-to-video block to the low-VRAM example so it covers the
  same tasks as the Dense low-VRAM T2V example
---
 .../lingbot-video-moe-30b-a3b.py              |  9 +++++--
 .../lingbot-video-moe-30b-a3b.py              | 27 +++++++++++++++----
 2 files changed, 29 insertions(+), 7 deletions(-)

diff --git a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py
index 4a008e27c..e5547a299 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py
@@ -1,3 +1,5 @@
+import os
+import json
 import torch
 from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
@@ -13,8 +15,11 @@
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
 )
 
+with open(os.path.join(os.path.dirname(__file__), "prompts", "t2v_example_1.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
 video = pipe(
-    prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight, ears perked up, chasing after a red ball. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    prompt=caption,
     negative_prompt=pipe.default_negative_prompt,
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0,
@@ -24,7 +29,7 @@
 
 input_video = VideoData("video_lingbot-video-moe-30b-a3b.mp4", height=480, width=832)
 video = pipe(
-    prompt="A playful puppy wearing black sunglasses runs across a lush green meadow, its golden fur shining in the bright sunlight. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    prompt=caption,
     negative_prompt=pipe.default_negative_prompt,
     input_video=input_video, denoising_strength=0.7,
     height=480, width=832, num_frames=81,
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py
index f6757d7b3..e79390f72 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py
@@ -1,10 +1,12 @@
+import os
+import json
 import torch
-from diffsynth.utils.data import save_video
+from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
 
 vram_config = {
-    "offload_dtype": torch.float8_e4m3fn,
-    "offload_device": "cpu",
+    "offload_dtype": "disk",
+    "offload_device": "disk",
     "onload_dtype": torch.float8_e4m3fn,
     "onload_device": "cpu",
     "preparing_dtype": torch.float8_e4m3fn,
@@ -22,14 +24,29 @@
         ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
     ],
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
-    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
 )
 
+inference_dir = os.path.join(os.path.dirname(__file__), "..", "model_inference")
+with open(os.path.join(inference_dir, "prompts", "t2v_example_1.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
 video = pipe(
-    prompt="A playful puppy runs across a lush green meadow, its golden fur shining in the bright sunlight, ears perked up, chasing after a red ball. Wildflowers dot the grass, and a clear blue sky with a few white clouds stretches out behind it. Dynamic side-tracking camera.",
+    prompt=caption,
     negative_prompt=pipe.default_negative_prompt,
     height=480, width=832, num_frames=81,
     num_inference_steps=40, cfg_scale=3.0,
     seed=0,
 )
 save_video(video, "video_lingbot-video-moe-30b-a3b_low_vram.mp4", fps=15, quality=10)
+
+input_video = VideoData("video_lingbot-video-moe-30b-a3b_low_vram.mp4", height=480, width=832)
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_video=input_video, denoising_strength=0.7,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=1,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_v2v_low_vram.mp4", fps=15, quality=10)

From b4aec8cb7f66a55c0abd8ef9af89c3702b91ec01 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Wed, 29 Jul 2026 11:13:20 +0800
Subject: [PATCH 27/34] feat: add TI2V / T2I MoE-30B-A3B examples and load
 captions from the example dataset

- T2V examples now download the released structured caption through
  dataset_snapshot_download, reusing the lingbot-video-dense-1.3b dataset
  directory, exactly like the Dense T2V examples
- add TI2V and T2I MoE examples (inference + low VRAM) mirroring the Dense
  templates: shared prompts/ captions, assets/ti2v_first_frame.png,
  default_negative_prompt_image and num_frames=1 for T2I
- docs and README: split the MoE row into T2V / TI2V / T2I rows and mention
  the MoE variant in the release note
---
 README.md                                     |  6 ++-
 README_zh.md                                  |  6 ++-
 docs/en/Model_Details/LingBot-Video.md        |  6 ++-
 docs/zh/Model_Details/LingBot-Video.md        |  6 ++-
 .../lingbot-video-moe-30b-a3b.py              | 11 +++--
 .../lingbot-video-moe-30b-a3b_t2i.py          | 27 ++++++++++++
 .../lingbot-video-moe-30b-a3b_ti2v.py         | 32 ++++++++++++++
 .../lingbot-video-moe-30b-a3b.py              | 18 +++++---
 .../lingbot-video-moe-30b-a3b_t2i.py          | 39 ++++++++++++++++
 .../lingbot-video-moe-30b-a3b_ti2v.py         | 44 +++++++++++++++++++
 10 files changed, 177 insertions(+), 18 deletions(-)
 create mode 100644 examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py
 create mode 100644 examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py
 create mode 100644 examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py
 create mode 100644 examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py

diff --git a/README.md b/README.md
index 338d0e77b..a7d03e06f 100644
--- a/README.md
+++ b/README.md
@@ -36,7 +36,7 @@ We believe that a well-developed open-source code framework can lower the thresh
 
 > Currently, the development personnel of this project are limited, with most of the work handled by [Artiprocher](https://github.com/Artiprocher) and [mi804](https://github.com/mi804). Therefore, the progress of new feature development will be relatively slow, and the speed of responding to and resolving issues is limited. We apologize for this and ask developers to understand.
 
-- **July 28, 2026** LingBot-Video open-sourced, welcome a new member to the video model family! Dense-1.3B is a single checkpoint supporting text-to-video, image-to-video and text-to-image tasks, with low VRAM inference and LoRA / full training. For details, please refer to the [documentation](/docs/en/Model_Details/LingBot-Video.md) and [example code](/examples/lingbot_video/).
+- **July 28, 2026** LingBot-Video open-sourced, welcome a new member to the video model family! Dense-1.3B is a single checkpoint supporting text-to-video, image-to-video and text-to-image tasks, with low VRAM inference and LoRA / full training. MoE-30B-A3B serves the same three tasks with 30B total parameters and ~3B active per token. For details, please refer to the [documentation](/docs/en/Model_Details/LingBot-Video.md) and [example code](/examples/lingbot_video/).
 
 - **July 21, 2026** We have open-sourced [DiffSynth-Studio Model Integration Skills](https://www.modelscope.cn/collections/DiffSynth-Studio/DiffSynth-Studio-Model-Integration-Skills). This is a composable collection of Agent Skills that automates the entire workflow of integrating external diffusion models into DiffSynth-Studio, significantly improving the standardization and efficiency of model integration. Get started with the [example](https://www.modelscope.cn/skills/DiffSynth-Studio/diffsynth-integrator/file/view/master/example.md?status=1)!
 
@@ -1518,7 +1518,9 @@ Example code for LingBot-Video is available at: [/examples/lingbot_video/](/exam
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
 |[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
-|[Robbyant/lingbot-video-moe-30b-a3b](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py)|-|-|-|-|
 
 </details>
 
diff --git a/README_zh.md b/README_zh.md
index f0e85a8a9..9312d971f 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -36,7 +36,7 @@ DiffSynth 目前包括两个开源项目：
 
 > 目前本项目的开发人员有限，大部分工作由 [Artiprocher](https://github.com/Artiprocher) 和 [mi804](https://github.com/mi804) 负责，因此新功能的开发进展会比较缓慢，issue 的回复和解决速度有限，我们对此感到非常抱歉，请各位开发者理解。
 
-- **2026年7月28日** LingBot-Video 开源，欢迎加入视频生成模型家族！Dense-1.3B 是一份 checkpoint，同时支持文生视频、图生视频和文生图任务，并配备低显存推理和 LoRA / 全量训练能力。详情请参考[文档](/docs/zh/Model_Details/LingBot-Video.md)和[示例代码](/examples/lingbot_video/)。
+- **2026年7月28日** LingBot-Video 开源，欢迎加入视频生成模型家族！Dense-1.3B 是一份 checkpoint，同时支持文生视频、图生视频和文生图任务，并配备低显存推理和 LoRA / 全量训练能力。MoE-30B-A3B 总参数量 30B、每个 token 激活约 3B，同样支持这三种任务。详情请参考[文档](/docs/zh/Model_Details/LingBot-Video.md)和[示例代码](/examples/lingbot_video/)。
 
 - **2026年7月21日** 我们开源了 [DiffSynth-Studio Model Integration Skills](https://www.modelscope.cn/collections/DiffSynth-Studio/DiffSynth-Studio-Model-Integration-Skills)。这是一套可组合的 Agent Skill 合集，将外部扩散模型接入 DiffSynth-Studio 的全流程自动化，大幅提升模型接入标准化程度与效率。从[使用示例](https://www.modelscope.cn/skills/DiffSynth-Studio/diffsynth-integrator/file/view/master/example.md?status=1)开始体验吧！
 
@@ -1518,7 +1518,9 @@ LingBot-Video 的示例代码位于：[/examples/lingbot_video/](/examples/lingb
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
 |[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
-|[Robbyant/lingbot-video-moe-30b-a3b](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py)|-|-|-|-|
 
 </details>
 
diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
index c6f5c003d..dc4c3873e 100644
--- a/docs/en/Model_Details/LingBot-Video.md
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -70,14 +70,16 @@ save_video(video, "video.mp4", fps=15, quality=10)
 
 Dense-1.3B is a single checkpoint that serves three tasks — text-to-video, image-to-video, and text-to-image — through the same pipeline. Each task ships its own inference / low-VRAM / training / validation scripts.
 
-MoE-30B-A3B is the larger variant: 30B total parameters with ~3B active per token, where each MoE layer holds 128 routed experts plus 1 shared expert and routes every token to 8 experts with group-limited top-k (4 groups, top-2 groups). It loads through the same pipeline, only the model ID and the shard glob change.
+MoE-30B-A3B is the larger variant: 30B total parameters with ~3B active per token, where each MoE layer holds 128 routed experts plus 1 shared expert and routes every token to 8 experts with group-limited top-k (4 groups, top-2 groups). It serves the same three tasks through the same pipeline, only the model ID and the shard glob change.
 
 |Model ID|Inference|Low VRAM Inference|Full Training|Full Training Validation|LoRA Training|LoRA Training Validation|
 |-|-|-|-|-|-|-|
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
 |[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
-|[Robbyant/lingbot-video-moe-30b-a3b](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py)|-|-|-|-|
 
 ## Model Inference
 
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
index 67331d123..8317d277f 100644
--- a/docs/zh/Model_Details/LingBot-Video.md
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -70,14 +70,16 @@ save_video(video, "video.mp4", fps=15, quality=10)
 
 Dense-1.3B 是一份 checkpoint，通过同一个 Pipeline 支持文生视频、图生视频、文生图三种任务，每种任务都配有推理 / 低显存 / 训练 / 验证脚本。
 
-MoE-30B-A3B 是更大的版本：总参数量 30B，每个 token 激活约 3B，每个 MoE 层包含 128 个路由专家和 1 个共享专家，并使用 group-limited top-k（4 组，取 top-2 组）将每个 token 路由到 8 个专家。它复用同一条 Pipeline，只需更换模型 ID 与分片通配符。
+MoE-30B-A3B 是更大的版本：总参数量 30B，每个 token 激活约 3B，每个 MoE 层包含 128 个路由专家和 1 个共享专家，并使用 group-limited top-k（4 组，取 top-2 组）将每个 token 路由到 8 个专家。它通过同一条 Pipeline 支持同样的三种任务，只需更换模型 ID 与分片通配符。
 
 |模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
 |-|-|-|-|-|-|-|
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b.py)|
 |[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
-|[Robbyant/lingbot-video-moe-30b-a3b](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py)|-|-|-|-|
 
 ## 模型推理
 
diff --git a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py
index e5547a299..baac6c946 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b.py
@@ -1,8 +1,8 @@
-import os
-import json
 import torch
+import json
 from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
 
 pipe = LingBotVideoPipeline.from_pretrained(
     torch_dtype=torch.bfloat16,
@@ -15,7 +15,12 @@
     processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
 )
 
-with open(os.path.join(os.path.dirname(__file__), "prompts", "t2v_example_1.json"), "r", encoding="utf-8") as f:
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b/*",
+)
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/t2v_example_1.json", "r", encoding="utf-8") as f:
     caption = json.load(f)
 
 video = pipe(
diff --git a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py
new file mode 100644
index 000000000..0c3de53c6
--- /dev/null
+++ b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py
@@ -0,0 +1,27 @@
+import os
+import json
+import torch
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
+)
+
+with open(os.path.join(os.path.dirname(__file__), "prompts", "t2i_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
+frames = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt_image,
+    height=480, width=832, num_frames=1,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+frames[0].save("image_lingbot-video-moe-30b-a3b_t2i.png")
diff --git a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py
new file mode 100644
index 000000000..1c5185a0a
--- /dev/null
+++ b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py
@@ -0,0 +1,32 @@
+import os
+import json
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
+)
+
+here = os.path.dirname(__file__)
+with open(os.path.join(here, "prompts", "ti2v_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+input_image = Image.open(os.path.join(here, "assets", "ti2v_first_frame.png")).convert("RGB")
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_image=input_image,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_ti2v.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py
index e79390f72..c7d5feaa4 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b.py
@@ -1,8 +1,8 @@
-import os
-import json
 import torch
+import json
 from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
 
 vram_config = {
     "offload_dtype": "disk",
@@ -27,8 +27,12 @@
     vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
 )
 
-inference_dir = os.path.join(os.path.dirname(__file__), "..", "model_inference")
-with open(os.path.join(inference_dir, "prompts", "t2v_example_1.json"), "r", encoding="utf-8") as f:
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b/*",
+)
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b/t2v_example_1.json", "r", encoding="utf-8") as f:
     caption = json.load(f)
 
 video = pipe(
@@ -38,9 +42,9 @@
     num_inference_steps=40, cfg_scale=3.0,
     seed=0,
 )
-save_video(video, "video_lingbot-video-moe-30b-a3b_low_vram.mp4", fps=15, quality=10)
+save_video(video, "video_lingbot-video-moe-30b-a3b.mp4", fps=15, quality=10)
 
-input_video = VideoData("video_lingbot-video-moe-30b-a3b_low_vram.mp4", height=480, width=832)
+input_video = VideoData("video_lingbot-video-moe-30b-a3b.mp4", height=480, width=832)
 video = pipe(
     prompt=caption,
     negative_prompt=pipe.default_negative_prompt,
@@ -49,4 +53,4 @@
     num_inference_steps=40, cfg_scale=3.0,
     seed=1,
 )
-save_video(video, "video_lingbot-video-moe-30b-a3b_v2v_low_vram.mp4", fps=15, quality=10)
+save_video(video, "video_lingbot-video-moe-30b-a3b_v2v.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py
new file mode 100644
index 000000000..1462f51e6
--- /dev/null
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py
@@ -0,0 +1,39 @@
+import os
+import json
+import torch
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+with open(os.path.join(os.path.dirname(__file__), "..", "model_inference", "prompts", "t2i_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
+frames = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt_image,
+    height=480, width=832, num_frames=1,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+frames[0].save("image_lingbot-video-moe-30b-a3b_t2i_low_vram.png")
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py
new file mode 100644
index 000000000..34d8e5845
--- /dev/null
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py
@@ -0,0 +1,44 @@
+import os
+import json
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="processor/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+inference_dir = os.path.join(os.path.dirname(__file__), "..", "model_inference")
+with open(os.path.join(inference_dir, "prompts", "ti2v_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+input_image = Image.open(os.path.join(inference_dir, "assets", "ti2v_first_frame.png")).convert("RGB")
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_image=input_image,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_ti2v_low_vram.mp4", fps=15, quality=10)

From 947686779b30b0e3c06c6f3a51f3832828168d86 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Wed, 29 Jul 2026 12:59:51 +0800
Subject: [PATCH 28/34] fix: restore every DiT parameter under low-VRAM offload
 for the MoE map

Disk offload only materialises weights for module types listed in the VRAM
map, so the per-type MoE map left two owners of raw parameters on `meta`:
`LingBotVideoBlock.scale_shift_table` and the router weight / correction
bias. Both bf16 and low VRAM Dense and MoE inference crashed with
"Tensor on device meta is not on the expected device cuda:0".

- map `LingBotVideoBlock` and `LingBotVideoRouter` to
  `AutoWrappedNonRecurseModule`, which manages a module's own parameters
  while its children keep their individual wrappers, and drop the dead
  `torch.nn.LayerNorm` entry (`norm_out` has no affine parameters)
- cast `scale_shift_table` to the modulation dtype/device in the block
  forward, following `wan_video_dit.DiTBlock`
- keep `e_score_correction_bias` as a non-trainable parameter so the disk
  loader restores its checkpoint values instead of leaving CPU zeros, and
  add it in float32

Verified on Robbyant/lingbot-video-moe-30b-a3b: all six MoE examples and
the Dense low VRAM examples run, low VRAM peaks at 29.5 GiB versus 74.5 GiB
for bf16, and the low VRAM T2I output matches the bf16 output to 16.9/255.
---
 diffsynth/configs/vram_management_module_maps.py | 7 ++++---
 diffsynth/models/lingbot_video_dit.py            | 6 +++---
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/diffsynth/configs/vram_management_module_maps.py b/diffsynth/configs/vram_management_module_maps.py
index cc55fb271..fb5867b14 100644
--- a/diffsynth/configs/vram_management_module_maps.py
+++ b/diffsynth/configs/vram_management_module_maps.py
@@ -409,10 +409,11 @@
         "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
     },
     "diffsynth.models.lingbot_video_dit.LingBotVideoDiT": {
-        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
-        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
-        "diffsynth.models.lingbot_video_dit.LingBotVideoRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.lingbot_video_dit.LingBotVideoBlock": "diffsynth.core.vram.layers.AutoWrappedNonRecurseModule",
+        "diffsynth.models.lingbot_video_dit.LingBotVideoRouter": "diffsynth.core.vram.layers.AutoWrappedNonRecurseModule",
         "diffsynth.models.lingbot_video_dit.LingBotVideoGroupedExperts": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.lingbot_video_dit.LingBotVideoRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
     },
     "diffsynth.models.lingbot_video_text_encoder.LingBotVideoTextEncoder": {
         "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
diff --git a/diffsynth/models/lingbot_video_dit.py b/diffsynth/models/lingbot_video_dit.py
index 36a32980a..7905903a0 100644
--- a/diffsynth/models/lingbot_video_dit.py
+++ b/diffsynth/models/lingbot_video_dit.py
@@ -221,7 +221,7 @@ def __init__(self, hidden_size, num_experts, top_k, score_func, norm_topk_prob,
         self.topk_group = topk_group
         self.route_scale = route_scale
         self.weight = nn.Parameter(torch.empty(num_experts, hidden_size))
-        self.register_buffer("e_score_correction_bias", torch.zeros(num_experts), persistent=True)
+        self.e_score_correction_bias = nn.Parameter(torch.zeros(num_experts), requires_grad=False)
 
     def _group_limited_topk(self, scores_for_choice):
         seq_len = scores_for_choice.shape[0]
@@ -242,7 +242,7 @@ def forward(self, tokens: torch.Tensor):
             scores = F.softmax(logits, dim=-1)
         else:
             scores = logits.sigmoid()
-        scores_for_choice = scores + self.e_score_correction_bias.unsqueeze(0)
+        scores_for_choice = scores + self.e_score_correction_bias.float().unsqueeze(0)
         if self.n_group is not None and self.n_group > 1:
             top_indices = self._group_limited_topk(scores_for_choice)
         else:
@@ -431,7 +431,7 @@ def forward(self, x, temb6, rotary_emb, attention_mask=None, moe_padding_mask=No
             )
         if bulk_dtype is None:
             bulk_dtype = resolve_bulk_dtype(self.attn.to_q)
-        mod = temb6.view(x.shape[0], x.shape[1], -1) + self.scale_shift_table.unsqueeze(0)
+        mod = temb6.view(x.shape[0], x.shape[1], -1) + self.scale_shift_table.to(dtype=temb6.dtype, device=temb6.device).unsqueeze(0)
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=-1)
         gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
         scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp

From 823fae0666c5bd1b70d8da0892914f9070cd8845 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Thu, 30 Jul 2026 12:26:19 +0800
Subject: [PATCH 29/34] fix: point the MoE examples at the published Dense
 example-dataset directories

Dense and MoE share one example dataset on ModelScope; only
lingbot_video/lingbot-video-dense-1.3b_{t2v,ti2v,t2i}/ exists, so the MoE
examples read the same captions and condition frame instead of a MoE-specific
directory that was never published.
---
 .../model_inference/lingbot-video-moe-30b-a3b_t2i.py          | 4 ++--
 .../model_inference/lingbot-video-moe-30b-a3b_t2v.py          | 4 ++--
 .../model_inference/lingbot-video-moe-30b-a3b_ti2v.py         | 4 ++--
 .../model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py | 4 ++--
 .../model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py | 4 ++--
 .../lingbot-video-moe-30b-a3b_ti2v.py                         | 4 ++--
 6 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py
index 1264f5215..98c347365 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py
@@ -17,9 +17,9 @@
 dataset_snapshot_download(
     dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
     local_dir="data/diffsynth_example_dataset",
-    allow_file_pattern="lingbot_video/lingbot-video-moe-30b-a3b_t2i/*",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_t2i/*",
 )
-with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-moe-30b-a3b_t2i/t2i_example.json", "r", encoding="utf-8") as f:
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2i/t2i_example.json", "r", encoding="utf-8") as f:
     caption = json.load(f)
 
 frames = pipe(
diff --git a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py
index c2d20f90b..fcbb28832 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py
@@ -19,12 +19,12 @@
 dataset_snapshot_download(
     dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
     local_dir="data/diffsynth_example_dataset",
-    allow_file_pattern="lingbot_video/lingbot-video-moe-30b-a3b_t2v/*",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_t2v/*",
 )
 # LingBot-Video is trained on structured-JSON captions, not free-form prose. This example
 # runs on a released in-distribution caption; see the bottom for turning a brief idea into
 # such a caption with the two-stage prompt rewriter.
-with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-moe-30b-a3b_t2v/t2v_example_1.json", "r", encoding="utf-8") as f:
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2v/t2v_example_1.json", "r", encoding="utf-8") as f:
     caption = json.load(f)
 
 video = pipe(
diff --git a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py
index 931630ad0..3225f9d26 100644
--- a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py
+++ b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py
@@ -21,9 +21,9 @@
 dataset_snapshot_download(
     dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
     local_dir="data/diffsynth_example_dataset",
-    allow_file_pattern="lingbot_video/lingbot-video-moe-30b-a3b_ti2v/*",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_ti2v/*",
 )
-base = "data/diffsynth_example_dataset/lingbot_video/lingbot-video-moe-30b-a3b_ti2v"
+base = "data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_ti2v"
 with open(os.path.join(base, "ti2v_example.json"), "r", encoding="utf-8") as f:
     caption = json.load(f)
 input_image = Image.open(os.path.join(base, "ti2v_first_frame.png")).convert("RGB")
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py
index 92cec75d5..8d34665ea 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py
@@ -29,9 +29,9 @@
 dataset_snapshot_download(
     dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
     local_dir="data/diffsynth_example_dataset",
-    allow_file_pattern="lingbot_video/lingbot-video-moe-30b-a3b_t2i/*",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_t2i/*",
 )
-with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-moe-30b-a3b_t2i/t2i_example.json", "r", encoding="utf-8") as f:
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2i/t2i_example.json", "r", encoding="utf-8") as f:
     caption = json.load(f)
 
 frames = pipe(
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py
index 652569c16..b1bacb7d9 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py
@@ -31,9 +31,9 @@
 dataset_snapshot_download(
     dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
     local_dir="data/diffsynth_example_dataset",
-    allow_file_pattern="lingbot_video/lingbot-video-moe-30b-a3b_t2v/*",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_t2v/*",
 )
-with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-moe-30b-a3b_t2v/t2v_example_1.json", "r", encoding="utf-8") as f:
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2v/t2v_example_1.json", "r", encoding="utf-8") as f:
     caption = json.load(f)
 
 video = pipe(
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py
index 7b8de30d4..bb58682cb 100644
--- a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py
@@ -33,9 +33,9 @@
 dataset_snapshot_download(
     dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
     local_dir="data/diffsynth_example_dataset",
-    allow_file_pattern="lingbot_video/lingbot-video-moe-30b-a3b_ti2v/*",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_ti2v/*",
 )
-base = "data/diffsynth_example_dataset/lingbot_video/lingbot-video-moe-30b-a3b_ti2v"
+base = "data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_ti2v"
 with open(os.path.join(base, "ti2v_example.json"), "r", encoding="utf-8") as f:
     caption = json.load(f)
 input_image = Image.open(os.path.join(base, "ti2v_first_frame.png")).convert("RGB")

From 7bd0ad8e14acd235e4f70545e78775eccb25568e Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Thu, 30 Jul 2026 14:01:21 +0800
Subject: [PATCH 30/34] refactor: drop the resolve_bulk_dtype helper from the
 MoE DiT

The bulk dtype is already the dtype of the incoming hidden states, and the
wrapped linears cast their own weights, so the helper only restated what
x.dtype / joint.dtype already carry. Removing it makes the file identical to
the Dense implementation except for the offload fixes; TI2V output stays
bit-identical in bf16 and under low-VRAM offload.
---
 diffsynth/models/lingbot_video_dit.py | 26 ++++++--------------------
 1 file changed, 6 insertions(+), 20 deletions(-)

diff --git a/diffsynth/models/lingbot_video_dit.py b/diffsynth/models/lingbot_video_dit.py
index 7905903a0..a5d29749f 100644
--- a/diffsynth/models/lingbot_video_dit.py
+++ b/diffsynth/models/lingbot_video_dit.py
@@ -10,15 +10,6 @@
 from ..core.device.npu_compatible_device import get_device_type
 
 
-def resolve_bulk_dtype(linear: nn.Module) -> torch.dtype:
-    computation_dtype = getattr(linear, "computation_dtype", None)
-    if isinstance(computation_dtype, torch.dtype):
-        if computation_dtype in (torch.float8_e4m3fn, torch.float8_e4m3fnuz, torch.float8_e5m2):
-            return torch.bfloat16
-        return computation_dtype
-    return linear.weight.dtype
-
-
 def get_timestep_embedding(
     timesteps: torch.Tensor,
     embedding_dim: int,
@@ -422,20 +413,19 @@ def __init__(self, hidden_size, num_attention_heads, intermediate_size, norm_eps
             self.ffn = LingBotVideoMLP(h, intermediate_size)
         self.norm_post_ffn = LingBotVideoRMSNorm(h, norm_eps)
 
-    def forward(self, x, temb6, rotary_emb, attention_mask=None, moe_padding_mask=None, bulk_dtype=None):
+    def forward(self, x, temb6, rotary_emb, attention_mask=None, moe_padding_mask=None):
         expected_tokens = x.shape[0] * x.shape[1]
         if temb6.ndim != 2 or temb6.shape[0] != expected_tokens:
             raise ValueError(
                 "LingBotVideoBlock expects token-level temb6 with shape (B*S, 6D); "
                 f"got {tuple(temb6.shape)} for hidden states {tuple(x.shape)}."
             )
-        if bulk_dtype is None:
-            bulk_dtype = resolve_bulk_dtype(self.attn.to_q)
         mod = temb6.view(x.shape[0], x.shape[1], -1) + self.scale_shift_table.to(dtype=temb6.dtype, device=temb6.device).unsqueeze(0)
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=-1)
         gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
         scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
 
+        bulk_dtype = x.dtype
         attn_in = (self.norm1(x) * scale_msa + shift_msa).to(bulk_dtype)
         attn_out = self.attn(attn_in, rotary_emb, attention_mask)
         x = x + (gate_msa * self.norm_post_attn(attn_out)).to(x.dtype)
@@ -594,7 +584,7 @@ def forward(
 
         # Timestep -> per-token modulation.
         timestep_proj = self.time_proj(timestep.float())
-        t_emb = self.time_embedder(timestep_proj.to(resolve_bulk_dtype(self.time_embedder.linear_1)))  # (B, D)
+        t_emb = self.time_embedder(timestep_proj.to(joint.dtype))  # (B, D)
         if packed_batch:
             temb_input = torch.cat(
                 [t_emb[i:i + 1].unsqueeze(1).expand(1, sample_seq_lens[i], -1) for i in range(B)], dim=1
@@ -602,10 +592,8 @@ def forward(
         else:
             temb_input = t_emb.unsqueeze(1).expand(B, joint_seq_len, -1)  # (B, S, D)
         b_eff, s_eff = temb_input.shape[0], temb_input.shape[1]
-        mod_dtype = resolve_bulk_dtype(self.time_modulation[1])
-        temb6 = self.time_modulation(temb_input.reshape(b_eff * s_eff, -1).to(mod_dtype))  # (B*S, 6D)
+        temb6 = self.time_modulation(temb_input.reshape(b_eff * s_eff, -1))  # (B*S, 6D)
 
-        bulk_dtype = resolve_bulk_dtype(self.patch_embedder)
         for block in self.blocks:
             joint = gradient_checkpoint_forward(
                 block,
@@ -613,14 +601,12 @@ def forward(
                 use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
                 x=joint, temb6=temb6, rotary_emb=rotary,
                 attention_mask=attention_mask, moe_padding_mask=moe_padding_mask,
-                bulk_dtype=bulk_dtype,
             )
 
-        out_mod_dtype = resolve_bulk_dtype(self.norm_out_modulation[1])
-        final_mod = self.norm_out_modulation(temb_input.reshape(joint.shape[0] * joint.shape[1], -1).to(out_mod_dtype))
+        final_mod = self.norm_out_modulation(temb_input.reshape(joint.shape[0] * joint.shape[1], -1))
         shift, scale = final_mod.reshape(joint.shape[0], joint.shape[1], -1).chunk(2, dim=-1)
         final_hidden = self.norm_out(joint) * (1.0 + scale) + shift
-        projected = self.proj_out(final_hidden.to(resolve_bulk_dtype(self.proj_out)))
+        projected = self.proj_out(final_hidden.to(joint.dtype))
 
         if packed_batch:
             split_lengths = []

From 44c506bc01b8345f1c4c4449190e0fcb5d4b8152 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Thu, 30 Jul 2026 16:05:23 +0800
Subject: [PATCH 31/34] fix: cast the MoE router and expert weights inside the
 model under offload

AutoWrappedNonRecurseModule leaves parameter casting to the model, and the
grouped-expert matmuls read experts.w1/w2/w3 directly instead of going through
a forward, so an AutoWrappedModule wrapper never got the chance to cast them.
With a vram_limit that actually forces layers to stay on CPU, the router
projection and the expert matmuls therefore hit CPU weights:

    RuntimeError: Expected all tensors to be on the same device,
    but got mat2 is on cpu

Move the experts to AutoWrappedNonRecurseModule as well and cast the router
weight, the correction bias and the three expert tensors to the token device
and dtype at use, matching how scale_shift_table is already handled. bf16 and
default low-VRAM outputs stay bit-identical.
---
 .../configs/vram_management_module_maps.py      |  2 +-
 diffsynth/models/lingbot_video_dit.py           | 17 +++++++++--------
 2 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/diffsynth/configs/vram_management_module_maps.py b/diffsynth/configs/vram_management_module_maps.py
index 555034659..45906df58 100644
--- a/diffsynth/configs/vram_management_module_maps.py
+++ b/diffsynth/configs/vram_management_module_maps.py
@@ -414,7 +414,7 @@
     "diffsynth.models.lingbot_video_dit.LingBotVideoDiT": {
         "diffsynth.models.lingbot_video_dit.LingBotVideoBlock": "diffsynth.core.vram.layers.AutoWrappedNonRecurseModule",
         "diffsynth.models.lingbot_video_dit.LingBotVideoRouter": "diffsynth.core.vram.layers.AutoWrappedNonRecurseModule",
-        "diffsynth.models.lingbot_video_dit.LingBotVideoGroupedExperts": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.lingbot_video_dit.LingBotVideoGroupedExperts": "diffsynth.core.vram.layers.AutoWrappedNonRecurseModule",
         "diffsynth.models.lingbot_video_dit.LingBotVideoRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
         "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
     },
diff --git a/diffsynth/models/lingbot_video_dit.py b/diffsynth/models/lingbot_video_dit.py
index a5d29749f..ed2ca6201 100644
--- a/diffsynth/models/lingbot_video_dit.py
+++ b/diffsynth/models/lingbot_video_dit.py
@@ -228,12 +228,12 @@ def _group_limited_topk(self, scores_for_choice):
 
     def forward(self, tokens: torch.Tensor):
         with torch.amp.autocast(tokens.device.type, enabled=False):
-            logits = F.linear(tokens.float(), self.weight.float())
+            logits = F.linear(tokens.float(), self.weight.to(device=tokens.device, dtype=torch.float32))
         if self.score_func == "softmax":
             scores = F.softmax(logits, dim=-1)
         else:
             scores = logits.sigmoid()
-        scores_for_choice = scores + self.e_score_correction_bias.float().unsqueeze(0)
+        scores_for_choice = scores + self.e_score_correction_bias.to(device=scores.device, dtype=scores.dtype).unsqueeze(0)
         if self.n_group is not None and self.n_group > 1:
             top_indices = self._group_limited_topk(scores_for_choice)
         else:
@@ -333,9 +333,10 @@ def _run_grouped_experts(self, tokens, counts):
             return self._run_experts_for_loop(tokens, counts)
         input_shape, padded_tokens, permuted_indices, aligned_counts = self._pad_grouped_tokens(tokens, counts)
         offsets = torch.cumsum(aligned_counts, dim=0, dtype=torch.int32)
-        h = F.silu(torch._grouped_mm(padded_tokens.bfloat16(), self.experts.w1.bfloat16().transpose(-2, -1), offs=offsets))
-        h = h * torch._grouped_mm(padded_tokens.bfloat16(), self.experts.w3.bfloat16().transpose(-2, -1), offs=offsets)
-        out = torch._grouped_mm(h, self.experts.w2.bfloat16().transpose(-2, -1), offs=offsets).type_as(padded_tokens)
+        w1, w2, w3 = (w.to(device=tokens.device, dtype=torch.bfloat16) for w in (self.experts.w1, self.experts.w2, self.experts.w3))
+        h = F.silu(torch._grouped_mm(padded_tokens.bfloat16(), w1.transpose(-2, -1), offs=offsets))
+        h = h * torch._grouped_mm(padded_tokens.bfloat16(), w3.transpose(-2, -1), offs=offsets)
+        out = torch._grouped_mm(h, w2.transpose(-2, -1), offs=offsets).type_as(padded_tokens)
         return self._unpad_grouped_tokens(out, input_shape, permuted_indices)
 
     def _run_experts_for_loop(self, tokens, counts):
@@ -345,9 +346,9 @@ def _run_experts_for_loop(self, tokens, counts):
         for expert_idx, expert_tokens in enumerate(splits):
             if expert_tokens.numel() == 0:
                 continue
-            h = F.silu(expert_tokens @ self.experts.w1[expert_idx].transpose(-2, -1))
-            h = h * (expert_tokens @ self.experts.w3[expert_idx].transpose(-2, -1))
-            h = h @ self.experts.w2[expert_idx].transpose(-2, -1)
+            h = F.silu(expert_tokens @ self.experts.w1[expert_idx].to(device=expert_tokens.device, dtype=expert_tokens.dtype).transpose(-2, -1))
+            h = h * (expert_tokens @ self.experts.w3[expert_idx].to(device=expert_tokens.device, dtype=expert_tokens.dtype).transpose(-2, -1))
+            h = h @ self.experts.w2[expert_idx].to(device=expert_tokens.device, dtype=expert_tokens.dtype).transpose(-2, -1)
             outputs.append(h)
         if not outputs:
             return tokens.new_zeros(tokens.shape)

From c2e49757e11cf8914d3438ad20e7a84059c2e837 Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Thu, 30 Jul 2026 17:50:54 +0800
Subject: [PATCH 32/34] feat: add LoRA and full-parameter training examples for
 MoE-30B-A3B

---
 README.md                                     |  4 +-
 README_zh.md                                  |  4 +-
 docs/en/Model_Details/LingBot-Video.md        |  6 ++-
 docs/zh/Model_Details/LingBot-Video.md        |  6 ++-
 .../full/accelerate_config_moe.yaml           | 22 ++++++++++
 .../full/lingbot-video-moe-30b-a3b_t2v.sh     | 19 +++++++++
 .../full/lingbot-video-moe-30b-a3b_ti2v.sh    | 20 +++++++++
 .../lora/lingbot-video-moe-30b-a3b_t2v.sh     | 20 +++++++++
 .../lora/lingbot-video-moe-30b-a3b_ti2v.sh    | 21 ++++++++++
 .../lingbot-video-moe-30b-a3b_t2v.py          | 36 ++++++++++++++++
 .../lingbot-video-moe-30b-a3b_ti2v.py         | 42 +++++++++++++++++++
 .../lingbot-video-moe-30b-a3b_t2v.py          | 34 +++++++++++++++
 .../lingbot-video-moe-30b-a3b_ti2v.py         | 40 ++++++++++++++++++
 13 files changed, 266 insertions(+), 8 deletions(-)
 create mode 100644 examples/lingbot_video/model_training/full/accelerate_config_moe.yaml
 create mode 100644 examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_t2v.sh
 create mode 100644 examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_ti2v.sh
 create mode 100644 examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_t2v.sh
 create mode 100644 examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_ti2v.sh
 create mode 100644 examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_t2v.py
 create mode 100644 examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_ti2v.py
 create mode 100644 examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_t2v.py
 create mode 100644 examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_ti2v.py

diff --git a/README.md b/README.md
index 8eff07f74..94ab6697f 100644
--- a/README.md
+++ b/README.md
@@ -1518,8 +1518,8 @@ Example code for LingBot-Video is available at: [/examples/lingbot_video/](/exam
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_t2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_t2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_t2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_t2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
-|[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py)|-|-|-|-|
-|[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_t2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_t2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_t2v.py)|
+|[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_ti2v.py)|
 |[Robbyant/lingbot-video-moe-30b-a3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py)|-|-|-|-|
 
 </details>
diff --git a/README_zh.md b/README_zh.md
index 04fe2d0ca..6ba51dce7 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -1518,8 +1518,8 @@ LingBot-Video 的示例代码位于：[/examples/lingbot_video/](/examples/lingb
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_t2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_t2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_t2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_t2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
-|[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py)|-|-|-|-|
-|[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_t2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_t2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_t2v.py)|
+|[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_ti2v.py)|
 |[Robbyant/lingbot-video-moe-30b-a3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py)|-|-|-|-|
 
 </details>
diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
index 16612d10c..d62aa1855 100644
--- a/docs/en/Model_Details/LingBot-Video.md
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -77,8 +77,8 @@ MoE-30B-A3B is the larger variant: 30B total parameters with ~3B active per toke
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_t2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
-|[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py)|-|-|-|-|
-|[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_t2v.py)|
+|[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_ti2v.py)|
 |[Robbyant/lingbot-video-moe-30b-a3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py)|-|-|-|-|
 
 ## Model Inference
@@ -188,4 +188,6 @@ modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --inclu
 
 Training captions should be **structured-JSON captions** (the same in-distribution format used at inference). If your dataset stores raw prose, rewrite it once offline with [`examples/lingbot_video/model_training/scripts/rewrite_captions.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/scripts/rewrite_captions.py) before training.
 
+The MoE-30B-A3B scripts launch with [`examples/lingbot_video/model_training/full/accelerate_config_moe.yaml`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/accelerate_config_moe.yaml) (DeepSpeed ZeRO-2, optimizer CPU offload, bf16), and the full-parameter scripts additionally enable `--use_gradient_checkpointing_offload`. Note that `--lora_target_modules "to_q,to_k,to_v,to_out"` only reaches the attention projections: the routed experts and the router are stored as bare parameter tensors for grouped matmul, so LoRA leaves them frozen. Use full-parameter training to update the expert stack.
+
 We provide recommended training scripts for each task, please refer to the table in "Model Overview" above. For guidance on writing model training scripts, see [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, see [Training Framework Overview](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
index 878157a24..df3e9aad4 100644
--- a/docs/zh/Model_Details/LingBot-Video.md
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -77,8 +77,8 @@ MoE-30B-A3B 是更大的版本：总参数量 30B，每个 token 激活约 3B，
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_t2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_ti2v.py)|
 |[Robbyant/lingbot-video-dense-1.3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2i.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2i.py)|-|-|-|-|
-|[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py)|-|-|-|-|
-|[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_t2v.py)|
+|[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_ti2v.py)|
 |[Robbyant/lingbot-video-moe-30b-a3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py)|-|-|-|-|
 
 ## 模型推理
@@ -188,4 +188,6 @@ modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --inclu
 
 训练时 `prompt` 字段应存放**结构化 JSON caption**（与推理时使用的分布内格式一致）。如果数据集里存的是原始散文，可先使用 [`examples/lingbot_video/model_training/scripts/rewrite_captions.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/scripts/rewrite_captions.py) 离线改写一次。
 
+MoE-30B-A3B 的训练脚本通过 [`examples/lingbot_video/model_training/full/accelerate_config_moe.yaml`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/accelerate_config_moe.yaml) 启动（DeepSpeed ZeRO-2、优化器状态 CPU offload、bf16），全量训练脚本还额外开启了 `--use_gradient_checkpointing_offload`。需要注意的是，`--lora_target_modules "to_q,to_k,to_v,to_out"` 只覆盖注意力投影层：路由专家与路由器以裸参数张量的形式存放以便做分组矩阵乘法，LoRA 不会训练它们。如需更新专家部分，请使用全量训练。
+
 我们为每个任务编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。
diff --git a/examples/lingbot_video/model_training/full/accelerate_config_moe.yaml b/examples/lingbot_video/model_training/full/accelerate_config_moe.yaml
new file mode 100644
index 000000000..197000b43
--- /dev/null
+++ b/examples/lingbot_video/model_training/full/accelerate_config_moe.yaml
@@ -0,0 +1,22 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  gradient_accumulation_steps: 1
+  offload_optimizer_device: cpu
+  offload_param_device: none
+  zero3_init_flag: false
+  zero_stage: 2
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 4
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
diff --git a/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_t2v.sh b/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_t2v.sh
new file mode 100644
index 000000000..77935b88e
--- /dev/null
+++ b/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_t2v.sh
@@ -0,0 +1,19 @@
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "lingbot_video/lingbot-video-dense-1.3b_t2v/*" --local_dir ./data/diffsynth_example_dataset
+
+accelerate launch --config_file examples/lingbot_video/model_training/full/accelerate_config_moe.yaml examples/lingbot_video/model_training/train.py \
+  --dataset_base_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2v \
+  --dataset_metadata_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2v/metadata.json \
+  --data_file_keys "video" \
+  --height 480 \
+  --width 832 \
+  --num_frames 81 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Robbyant/lingbot-video-moe-30b-a3b:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen3-VL-4B-Instruct:*.safetensors,Robbyant/lingbot-video-moe-30b-a3b:vae/diffusion_pytorch_model.safetensors" \
+  --processor_path "Qwen/Qwen3-VL-4B-Instruct:" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/lingbot-video-moe-30b-a3b_t2v_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing \
+  --use_gradient_checkpointing_offload
diff --git a/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_ti2v.sh b/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_ti2v.sh
new file mode 100644
index 000000000..8dcb0c16f
--- /dev/null
+++ b/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_ti2v.sh
@@ -0,0 +1,20 @@
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "lingbot_video/lingbot-video-dense-1.3b_ti2v/*" --local_dir ./data/diffsynth_example_dataset
+
+accelerate launch --config_file examples/lingbot_video/model_training/full/accelerate_config_moe.yaml examples/lingbot_video/model_training/train.py \
+  --dataset_base_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_ti2v \
+  --dataset_metadata_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_ti2v/metadata.json \
+  --data_file_keys "video" \
+  --height 480 \
+  --width 832 \
+  --num_frames 81 \
+  --first_frame_as_condition \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Robbyant/lingbot-video-moe-30b-a3b:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen3-VL-4B-Instruct:*.safetensors,Robbyant/lingbot-video-moe-30b-a3b:vae/diffusion_pytorch_model.safetensors" \
+  --processor_path "Qwen/Qwen3-VL-4B-Instruct:" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/lingbot-video-moe-30b-a3b_ti2v_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing \
+  --use_gradient_checkpointing_offload
diff --git a/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_t2v.sh b/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_t2v.sh
new file mode 100644
index 000000000..9f9aa4ce3
--- /dev/null
+++ b/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_t2v.sh
@@ -0,0 +1,20 @@
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "lingbot_video/lingbot-video-dense-1.3b_t2v/*" --local_dir ./data/diffsynth_example_dataset
+
+accelerate launch --config_file examples/lingbot_video/model_training/full/accelerate_config_moe.yaml examples/lingbot_video/model_training/train.py \
+  --dataset_base_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2v \
+  --dataset_metadata_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2v/metadata.json \
+  --data_file_keys "video" \
+  --height 480 \
+  --width 832 \
+  --num_frames 81 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Robbyant/lingbot-video-moe-30b-a3b:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen3-VL-4B-Instruct:*.safetensors,Robbyant/lingbot-video-moe-30b-a3b:vae/diffusion_pytorch_model.safetensors" \
+  --processor_path "Qwen/Qwen3-VL-4B-Instruct:" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/lingbot-video-moe-30b-a3b_t2v_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,to_out" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing
diff --git a/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_ti2v.sh b/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_ti2v.sh
new file mode 100644
index 000000000..6f044218f
--- /dev/null
+++ b/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_ti2v.sh
@@ -0,0 +1,21 @@
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "lingbot_video/lingbot-video-dense-1.3b_ti2v/*" --local_dir ./data/diffsynth_example_dataset
+
+accelerate launch --config_file examples/lingbot_video/model_training/full/accelerate_config_moe.yaml examples/lingbot_video/model_training/train.py \
+  --dataset_base_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_ti2v \
+  --dataset_metadata_path data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_ti2v/metadata.json \
+  --data_file_keys "video" \
+  --height 480 \
+  --width 832 \
+  --num_frames 81 \
+  --first_frame_as_condition \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Robbyant/lingbot-video-moe-30b-a3b:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen3-VL-4B-Instruct:*.safetensors,Robbyant/lingbot-video-moe-30b-a3b:vae/diffusion_pytorch_model.safetensors" \
+  --processor_path "Qwen/Qwen3-VL-4B-Instruct:" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/lingbot-video-moe-30b-a3b_ti2v_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,to_out" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing
diff --git a/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_t2v.py b/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_t2v.py
new file mode 100644
index 000000000..4a8222749
--- /dev/null
+++ b/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_t2v.py
@@ -0,0 +1,36 @@
+import torch
+import json
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from diffsynth import load_state_dict
+from modelscope import dataset_snapshot_download
+
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
+)
+state_dict = load_state_dict("models/train/lingbot-video-moe-30b-a3b_t2v_full/epoch-1.safetensors")
+pipe.dit.load_state_dict(state_dict)
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_t2v/*",
+)
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2v/t2v_example_1.json", "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_t2v.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_ti2v.py b/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_ti2v.py
new file mode 100644
index 000000000..a9adcb0dc
--- /dev/null
+++ b/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_ti2v.py
@@ -0,0 +1,42 @@
+import os
+import json
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from diffsynth import load_state_dict
+from modelscope import dataset_snapshot_download
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
+)
+state_dict = load_state_dict("models/train/lingbot-video-moe-30b-a3b_ti2v_full/epoch-1.safetensors")
+pipe.dit.load_state_dict(state_dict)
+
+# The condition first frame and its paired caption ship in the example dataset.
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_ti2v/*",
+)
+base = "data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_ti2v"
+with open(os.path.join(base, "ti2v_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+input_image = Image.open(os.path.join(base, "ti2v_first_frame.png")).convert("RGB")
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_image=input_image,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_ti2v.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_t2v.py b/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_t2v.py
new file mode 100644
index 000000000..0cf7766a3
--- /dev/null
+++ b/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_t2v.py
@@ -0,0 +1,34 @@
+import torch
+import json
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
+)
+pipe.load_lora(pipe.dit, "models/train/lingbot-video-moe-30b-a3b_t2v_lora/epoch-4.safetensors", alpha=1)
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_t2v/*",
+)
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2v/t2v_example_1.json", "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_t2v.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_ti2v.py b/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_ti2v.py
new file mode 100644
index 000000000..33102cd31
--- /dev/null
+++ b/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_ti2v.py
@@ -0,0 +1,40 @@
+import os
+import json
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
+)
+pipe.load_lora(pipe.dit, "models/train/lingbot-video-moe-30b-a3b_ti2v_lora/epoch-4.safetensors", alpha=1)
+
+# The condition first frame and its paired caption ship in the example dataset.
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_ti2v/*",
+)
+base = "data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_ti2v"
+with open(os.path.join(base, "ti2v_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+input_image = Image.open(os.path.join(base, "ti2v_first_frame.png")).convert("RGB")
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_image=input_image,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_ti2v.mp4", fps=15, quality=10)

From 3cfbbf1711a78e58612f88a640d960ee4cff2b1a Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Thu, 30 Jul 2026 20:01:46 +0800
Subject: [PATCH 33/34] style: align the MoE accelerate config with the 14B
 training config

---
 .../model_training/full/accelerate_config_moe.yaml            | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/lingbot_video/model_training/full/accelerate_config_moe.yaml b/examples/lingbot_video/model_training/full/accelerate_config_moe.yaml
index 197000b43..3875a9da2 100644
--- a/examples/lingbot_video/model_training/full/accelerate_config_moe.yaml
+++ b/examples/lingbot_video/model_training/full/accelerate_config_moe.yaml
@@ -3,7 +3,7 @@ debug: false
 deepspeed_config:
   gradient_accumulation_steps: 1
   offload_optimizer_device: cpu
-  offload_param_device: none
+  offload_param_device: cpu
   zero3_init_flag: false
   zero_stage: 2
 distributed_type: DEEPSPEED
@@ -13,7 +13,7 @@ machine_rank: 0
 main_training_function: main
 mixed_precision: bf16
 num_machines: 1
-num_processes: 4
+num_processes: 8
 rdzv_backend: static
 same_network: true
 tpu_env: []

From e0ce129be3e48aa460babb222ba7cd1c0f067d9f Mon Sep 17 00:00:00 2001
From: NancyFyong <NancyFyong@users.noreply.github.com>
Date: Thu, 30 Jul 2026 21:54:20 +0800
Subject: [PATCH 34/34] feat: support the LingBot-Video MoE refiner for
 two-stage high-resolution refinement

---
 README.md                                     |  2 +
 README_zh.md                                  |  2 +
 diffsynth/diffusion/flow_match.py             | 25 +++++-
 diffsynth/pipelines/lingbot_video.py          | 25 ++++--
 docs/en/Model_Details/LingBot-Video.md        | 25 ++++++
 docs/zh/Model_Details/LingBot-Video.md        | 25 ++++++
 .../lingbot-video-moe-30b-a3b_t2v_refiner.py  | 61 ++++++++++++++
 .../lingbot-video-moe-30b-a3b_ti2v_refiner.py | 67 ++++++++++++++++
 .../lingbot-video-moe-30b-a3b_t2v_refiner.py  | 74 +++++++++++++++++
 .../lingbot-video-moe-30b-a3b_ti2v_refiner.py | 80 +++++++++++++++++++
 10 files changed, 379 insertions(+), 7 deletions(-)
 create mode 100644 examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v_refiner.py
 create mode 100644 examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v_refiner.py
 create mode 100644 examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v_refiner.py
 create mode 100644 examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v_refiner.py

diff --git a/README.md b/README.md
index 94ab6697f..baf794947 100644
--- a/README.md
+++ b/README.md
@@ -1521,6 +1521,8 @@ Example code for LingBot-Video is available at: [/examples/lingbot_video/](/exam
 |[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_t2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_t2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_t2v.py)|
 |[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_ti2v.py)|
 |[Robbyant/lingbot-video-moe-30b-a3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2V + Refinement](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v_refiner.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v_refiner.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: TI2V + Refinement](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v_refiner.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v_refiner.py)|-|-|-|-|
 
 </details>
 
diff --git a/README_zh.md b/README_zh.md
index 6ba51dce7..3627986e6 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -1521,6 +1521,8 @@ LingBot-Video 的示例代码位于：[/examples/lingbot_video/](/examples/lingb
 |[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_t2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_t2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_t2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_t2v.py)|
 |[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_ti2v.py)|[code](/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_ti2v.py)|
 |[Robbyant/lingbot-video-moe-30b-a3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2V + Refinement](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v_refiner.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v_refiner.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: TI2V + Refinement](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v_refiner.py)|[code](/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v_refiner.py)|-|-|-|-|
 
 </details>
 
diff --git a/diffsynth/diffusion/flow_match.py b/diffsynth/diffusion/flow_match.py
index b9104e869..5097ea43a 100644
--- a/diffsynth/diffusion/flow_match.py
+++ b/diffsynth/diffusion/flow_match.py
@@ -5,7 +5,7 @@
 
 class FlowMatchScheduler():
 
-    def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image", "LTX-2", "Qwen-Image-Lightning", "ERNIE-Image", "ACE-Step", "Ideogram4", "Krea-2", "Boogu"] = "FLUX.1"):
+    def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image", "LTX-2", "Qwen-Image-Lightning", "ERNIE-Image", "ACE-Step", "Ideogram4", "Krea-2", "Boogu", "LingBot-Video"] = "FLUX.1"):
         self.set_timesteps_fn = {
             "FLUX.1": FlowMatchScheduler.set_timesteps_flux,
             "Wan": FlowMatchScheduler.set_timesteps_wan,
@@ -20,6 +20,7 @@ def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z
             "Ideogram4": FlowMatchScheduler.set_timesteps_ideogram4,
             "Krea-2": FlowMatchScheduler.set_timesteps_krea2,
             "Boogu": FlowMatchScheduler.set_timesteps_boogu,
+            "LingBot-Video": FlowMatchScheduler.set_timesteps_lingbot_video,
         }.get(template, FlowMatchScheduler.set_timesteps_flux)
         self.num_train_timesteps = 1000
 
@@ -79,6 +80,28 @@ def set_timesteps_qwen_image(num_inference_steps=100, denoising_strength=1.0, ex
         timesteps = sigmas * num_train_timesteps
         return sigmas, timesteps
     
+    @staticmethod
+    def set_timesteps_lingbot_video(num_inference_steps=100, denoising_strength=1.0, shift=None, t_thresh=None, sigma_tail_steps=0):
+        sigma_min = 0.0
+        sigma_max = 1.0
+        shift = 5 if shift is None else shift
+        num_train_timesteps = 1000
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
+        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+        if t_thresh is not None:
+            # Refinement schedule: keep the sub-threshold part of the shifted grid, pin the first
+            # sigma exactly at t_thresh, then append extra low-noise steps that end at sigma_min.
+            sigmas = sigmas[sigmas <= t_thresh + 1e-6]
+            if sigmas.numel() == 0 or abs(float(sigmas[0]) - t_thresh) > 1e-6:
+                sigmas = torch.cat([torch.tensor([t_thresh], dtype=sigmas.dtype), sigmas])
+            if sigma_tail_steps > 0:
+                tail_start = float(sigmas[-1])
+                tail = torch.linspace(tail_start, min(sigma_min, tail_start), sigma_tail_steps + 2)[1:-1]
+                sigmas = torch.cat([sigmas, tail.to(dtype=sigmas.dtype)])
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+
     @staticmethod
     def set_timesteps_qwen_image_lightning(num_inference_steps=100, denoising_strength=1.0, exponential_shift_mu=None, dynamic_shift_len=None):
         sigma_min = 0.0
diff --git a/diffsynth/pipelines/lingbot_video.py b/diffsynth/pipelines/lingbot_video.py
index 28973198d..081877c89 100644
--- a/diffsynth/pipelines/lingbot_video.py
+++ b/diffsynth/pipelines/lingbot_video.py
@@ -25,7 +25,7 @@ def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
             height_division_factor=16, width_division_factor=16,
             time_division_factor=4, time_division_remainder=1,
         )
-        self.scheduler = FlowMatchScheduler(template="Wan")
+        self.scheduler = FlowMatchScheduler(template="LingBot-Video")
         self.text_encoder: Krea2TextEncoder = None
         self.dit: LingBotVideoDiT = None
         self.vae: QwenImageVAE = None
@@ -102,11 +102,18 @@ def __call__(
         # Scheduler
         num_inference_steps: int = 40,
         sigma_shift: float = 3.0,
+        # Refinement pass: start from a partially noised input video at sigma=t_thresh and
+        # append extra low-noise steps at the tail of the schedule.
+        t_thresh: float = None,
+        sigma_tail_steps: int = 2,
         # progress_bar
         progress_bar_cmd=tqdm,
     ):
         # Scheduler
-        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
+        self.scheduler.set_timesteps(
+            num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift,
+            t_thresh=t_thresh, sigma_tail_steps=sigma_tail_steps,
+        )
 
         # Inputs
         inputs_posi = {"prompt": prompt}
@@ -116,7 +123,7 @@ def __call__(
             "input_video": input_video, "denoising_strength": denoising_strength,
             "seed": seed, "rand_device": rand_device,
             "height": height, "width": width, "num_frames": num_frames,
-            "cfg_scale": cfg_scale,
+            "cfg_scale": cfg_scale, "t_thresh": t_thresh,
         }
         for unit in self.units:
             inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
@@ -132,6 +139,11 @@ def __call__(
                 **models, timestep=timestep, progress_id=progress_id
             )
             inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
+            if t_thresh is not None and inputs_shared.get("first_frame_latents") is not None:
+                # The refiner re-pins the clean condition latent after every step, keeping frame 0
+                # identical to the input image while the rest of the clip denoises against it.
+                first_frame_latents = inputs_shared["first_frame_latents"]
+                inputs_shared["latents"][:, :, :first_frame_latents.shape[2]] = first_frame_latents
 
         self.load_models_to_device(['vae'])
         latents = inputs_shared["latents"].to(dtype=self.torch_dtype, device=self.device)
@@ -197,12 +209,12 @@ class LingBotVideoUnit_ImageEmbedder(PipelineUnit):
 
     def __init__(self):
         super().__init__(
-            input_params=("input_image", "latents", "height", "width"),
+            input_params=("input_image", "latents", "height", "width", "t_thresh"),
             output_params=("latents", "first_frame_latents", "vlm_image"),
             onload_model_names=("vae",),
         )
 
-    def process(self, pipe: LingBotVideoPipeline, input_image, latents, height, width):
+    def process(self, pipe: LingBotVideoPipeline, input_image, latents, height, width, t_thresh=None):
         if input_image is None:
             return {}
         pipe.load_models_to_device(self.onload_model_names)
@@ -211,7 +223,8 @@ def process(self, pipe: LingBotVideoPipeline, input_image, latents, height, widt
         pixel = self.preprocess_cond_image(input_image, height, width)
         pixel = pixel.to(dtype=pipe.torch_dtype, device=pipe.device)
         first_frame_latents = pipe.vae.encode_video(pixel * 2.0 - 1.0).to(dtype=pipe.torch_dtype, device=pipe.device)
-        vlm_image = self.vlm_image(pipe, pixel)
+        # The refiner conditions on text only and re-pins the frame-0 latent every step instead.
+        vlm_image = None if t_thresh is not None else self.vlm_image(pipe, pixel)
         # Pin the clean condition latent into the first temporal slot before sampling.
         cond_t = first_frame_latents.shape[2]
         latents[:, :, :cond_t] = first_frame_latents
diff --git a/docs/en/Model_Details/LingBot-Video.md b/docs/en/Model_Details/LingBot-Video.md
index d62aa1855..f0dab55da 100644
--- a/docs/en/Model_Details/LingBot-Video.md
+++ b/docs/en/Model_Details/LingBot-Video.md
@@ -72,6 +72,8 @@ save_video(video, "video.mp4", fps=15, quality=10)
 
 MoE-30B-A3B is the larger variant: 30B total parameters with ~3B active per token, where each MoE layer holds 128 routed experts plus 1 shared expert and routes every token to 8 experts with group-limited top-k (4 groups, top-2 groups). It serves the same three tasks through the same pipeline, only the model ID and the shard glob change.
 
+The MoE release additionally ships a **refiner** under `refiner/`: a second 30B-A3B checkpoint with the same architecture as `transformer/`, fine-tuned for a short high-resolution second pass over an already generated clip. It is not listed in `model_index.json` and is loaded through the same pipeline, only the shard glob changes. See [Two-stage refinement](#two-stage-refinement) below.
+
 |Model ID|Inference|Low VRAM Inference|Full Training|Full Training Validation|LoRA Training|LoRA Training Validation|
 |-|-|-|-|-|-|-|
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_t2v.py)|
@@ -80,6 +82,8 @@ MoE-30B-A3B is the larger variant: 30B total parameters with ~3B active per toke
 |[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_t2v.py)|
 |[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_ti2v.py)|
 |[Robbyant/lingbot-video-moe-30b-a3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2V + Refinement](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v_refiner.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v_refiner.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: TI2V + Refinement](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v_refiner.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v_refiner.py)|-|-|-|-|
 
 ## Model Inference
 
@@ -98,12 +102,33 @@ The input parameters for `LingBotVideoPipeline` inference include:
 * `cfg_scale`: Classifier-free guidance scale, default `3.0`.
 * `num_inference_steps`: Number of inference steps, default `40`.
 * `sigma_shift`: Flow-matching timestep shift, default `3.0`.
+* `t_thresh`: Refinement start sigma, default `None` (plain generation). When set, the schedule is truncated so that sampling starts at `sigma=t_thresh` and `input_video` is noised to exactly that level. Only meaningful together with `input_video`; TI2V additionally re-pins the clean first-frame latent after every step. The official refiner setting is `0.85`.
+* `sigma_tail_steps`: Number of extra low-noise steps appended to the tail of the refinement schedule, default `2`. Only effective when `t_thresh` is set.
 * `seed`: Random seed. Default is `None`, meaning completely random.
 * `rand_device`: Device for generating the initial noise, default `"cpu"`.
 * `progress_bar_cmd`: Progress bar, default `tqdm`. Can be disabled by setting to `lambda x: x`.
 
 If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low-VRAM configurations for each task in the example code, see the table in the "Model Overview" section above.
 
+### Two-stage refinement
+
+The MoE refiner performs a short second pass at a higher resolution: the official setup generates at 480×832 with 40 steps, then refines at 1088×1920 with 8 steps. Load the pipeline with the `refiner/` shards instead of `transformer/`, feed the base clip back in through `input_video` at the higher resolution, and set `t_thresh`:
+
+```python
+input_video = VideoData("video_base.mp4", height=1088, width=1920)
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_video=input_video,
+    height=1088, width=1920, num_frames=81,
+    num_inference_steps=8, cfg_scale=3.0,
+    t_thresh=0.85, sigma_tail_steps=2,
+    seed=0,
+)
+```
+
+The upscaled clip is VAE-encoded and noised back to `sigma=t_thresh`, so the pass keeps the structure of the base clip and regenerates detail at the target resolution. Pass the same caption as the base pass and keep the same aspect ratio. The refinement resolution dominates the cost — at 1088×1920 the sequence is ~5× longer than at 480×832 — so run this pass with VRAM management enabled.
+
 ### Prompt rewriting
 
 LingBot-Video is trained on **structured-JSON captions**, not free-form prose. Feeding a flat sentence is out-of-distribution and visibly degrades quality. The pipeline accepts a caption as a `dict` (the format used at training time) or a plain string, and normalises the `dict` internally.
diff --git a/docs/zh/Model_Details/LingBot-Video.md b/docs/zh/Model_Details/LingBot-Video.md
index df3e9aad4..1e21b1246 100644
--- a/docs/zh/Model_Details/LingBot-Video.md
+++ b/docs/zh/Model_Details/LingBot-Video.md
@@ -72,6 +72,8 @@ save_video(video, "video.mp4", fps=15, quality=10)
 
 MoE-30B-A3B 是更大的版本：总参数量 30B，每个 token 激活约 3B，每个 MoE 层包含 128 个路由专家和 1 个共享专家，并使用 group-limited top-k（4 组，取 top-2 组）将每个 token 路由到 8 个专家。它通过同一条 Pipeline 支持同样的三种任务，只需更换模型 ID 与分片通配符。
 
+MoE 版本还额外提供了 `refiner/` 目录下的 **refiner**：一份与 `transformer/` 架构相同的 30B-A3B 权重，专门微调用于对已生成视频做一次高分辨率精修。它没有出现在 `model_index.json` 中，通过同一条 Pipeline 加载，只需更换分片通配符。详见下方[两阶段精修](#两阶段精修)。
+
 |模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
 |-|-|-|-|-|-|-|
 |[Robbyant/lingbot-video-dense-1.3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-dense-1.3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-dense-1.3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-dense-1.3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-dense-1.3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-dense-1.3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-dense-1.3b_t2v.py)|
@@ -80,6 +82,8 @@ MoE-30B-A3B 是更大的版本：总参数量 30B，每个 token 激活约 3B，
 |[Robbyant/lingbot-video-moe-30b-a3b: T2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_t2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_t2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_t2v.py)|
 |[Robbyant/lingbot-video-moe-30b-a3b: TI2V](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/full/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_full/lingbot-video-moe-30b-a3b_ti2v.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/lora/lingbot-video-moe-30b-a3b_ti2v.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_training/validate_lora/lingbot-video-moe-30b-a3b_ti2v.py)|
 |[Robbyant/lingbot-video-moe-30b-a3b: T2I](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2i.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2i.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: T2V + Refinement](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v_refiner.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v_refiner.py)|-|-|-|-|
+|[Robbyant/lingbot-video-moe-30b-a3b: TI2V + Refinement](https://modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v_refiner.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v_refiner.py)|-|-|-|-|
 
 ## 模型推理
 
@@ -98,12 +102,33 @@ MoE-30B-A3B 是更大的版本：总参数量 30B，每个 token 激活约 3B，
 * `cfg_scale`: 无分类器指导强度，默认 `3.0`。
 * `num_inference_steps`: 推理步数，默认 `40`。
 * `sigma_shift`: Flow-matching 时间步 shift，默认 `3.0`。
+* `t_thresh`: 精修起始 sigma，默认 `None`（普通生成）。设置后调度会被截断，使采样从 `sigma=t_thresh` 开始，并把 `input_video` 加噪到该噪声水平。仅在提供 `input_video` 时有意义；TI2V 还会在每个采样步之后重新写入干净的首帧 latent。官方精修配置为 `0.85`。
+* `sigma_tail_steps`: 精修调度尾部追加的额外低噪声步数，默认 `2`。仅在设置了 `t_thresh` 时生效。
 * `seed`: 随机种子，默认 `None`（完全随机）。
 * `rand_device`: 生成初始噪声的设备，默认 `"cpu"`。
 * `progress_bar_cmd`: 进度条，默认 `tqdm`，可设为 `lambda x: x` 关闭。
 
 显存不足时请参考[显存管理](../Pipeline_Usage/VRAM_management.md)启用显存管理功能。我们在示例代码中提供了每个任务的推荐低显存配置，见上方"模型总览"中的表格。
 
+### 两阶段精修
+
+MoE 的 refiner 会在更高分辨率上执行一次短程精修：官方配置先以 480×832、40 步生成，再以 1088×1920、8 步精修。加载时把分片通配符从 `transformer/` 换成 `refiner/`，将基础阶段的视频以目标分辨率通过 `input_video` 传回，并设置 `t_thresh`：
+
+```python
+input_video = VideoData("video_base.mp4", height=1088, width=1920)
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_video=input_video,
+    height=1088, width=1920, num_frames=81,
+    num_inference_steps=8, cfg_scale=3.0,
+    t_thresh=0.85, sigma_tail_steps=2,
+    seed=0,
+)
+```
+
+放大后的视频会被 VAE 编码并重新加噪到 `sigma=t_thresh`，因此这一阶段保留基础阶段的结构，并在目标分辨率上重新生成细节。请使用与基础阶段相同的 caption，并保持相同的宽高比。精修分辨率决定了主要开销——1088×1920 的序列长度约为 480×832 的 5 倍——建议开启显存管理运行该阶段。
+
 ### 提示词改写
 
 LingBot-Video 训练时使用的是**结构化 JSON caption**，直接喂平铺句子属于分布外输入，会明显降低生成质量。Pipeline 接受 `dict` 形式的 caption（与训练一致的格式）或纯字符串，`dict` 会被内部归一化。
diff --git a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v_refiner.py b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v_refiner.py
new file mode 100644
index 000000000..7c4345cc2
--- /dev/null
+++ b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_t2v_refiner.py
@@ -0,0 +1,61 @@
+import torch
+import json
+from diffsynth.utils.data import save_video, VideoData
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_t2v/*",
+)
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2v/t2v_example_1.json", "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
+# --- Stage 1: base generation at 480x832 ---------------------------------------------
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
+)
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_t2v.mp4", fps=15, quality=10)
+
+del pipe
+torch.cuda.empty_cache()
+
+# --- Stage 2: refinement at 1088x1920 ------------------------------------------------
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="refiner/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
+)
+
+input_video = VideoData("video_lingbot-video-moe-30b-a3b_t2v.mp4", height=1088, width=1920)
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_video=input_video,
+    height=1088, width=1920, num_frames=81,
+    num_inference_steps=8, cfg_scale=3.0,
+    t_thresh=0.85, sigma_tail_steps=2,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_t2v_refined.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v_refiner.py b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v_refiner.py
new file mode 100644
index 000000000..017f67d03
--- /dev/null
+++ b/examples/lingbot_video/model_inference/lingbot-video-moe-30b-a3b_ti2v_refiner.py
@@ -0,0 +1,67 @@
+import os
+import json
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video, VideoData
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_ti2v/*",
+)
+base = "data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_ti2v"
+with open(os.path.join(base, "ti2v_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+input_image = Image.open(os.path.join(base, "ti2v_first_frame.png")).convert("RGB")
+
+# --- Stage 1: base generation at 480x832 ---------------------------------------------
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
+)
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_image=input_image,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_ti2v.mp4", fps=15, quality=10)
+
+del pipe
+torch.cuda.empty_cache()
+
+# --- Stage 2: refinement at 1088x1920 ------------------------------------------------
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="refiner/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors"),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
+)
+
+input_video = VideoData("video_lingbot-video-moe-30b-a3b_ti2v.mp4", height=1088, width=1920)
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_image=input_image,
+    input_video=input_video,
+    height=1088, width=1920, num_frames=81,
+    num_inference_steps=8, cfg_scale=3.0,
+    t_thresh=0.85, sigma_tail_steps=2,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_ti2v_refined.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v_refiner.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v_refiner.py
new file mode 100644
index 000000000..eea37799a
--- /dev/null
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_t2v_refiner.py
@@ -0,0 +1,74 @@
+import torch
+import json
+from diffsynth.utils.data import save_video, VideoData
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_t2v/*",
+)
+with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2v/t2v_example_1.json", "r", encoding="utf-8") as f:
+    caption = json.load(f)
+
+# --- Stage 1: base generation at 480x832 ---------------------------------------------
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_t2v.mp4", fps=15, quality=10)
+
+del pipe
+torch.cuda.empty_cache()
+
+# --- Stage 2: refinement at 1088x1920 ------------------------------------------------
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="refiner/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+input_video = VideoData("video_lingbot-video-moe-30b-a3b_t2v.mp4", height=1088, width=1920)
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_video=input_video,
+    height=1088, width=1920, num_frames=81,
+    num_inference_steps=8, cfg_scale=3.0,
+    t_thresh=0.85, sigma_tail_steps=2,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_t2v_refined.mp4", fps=15, quality=10)
diff --git a/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v_refiner.py b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v_refiner.py
new file mode 100644
index 000000000..4795e043a
--- /dev/null
+++ b/examples/lingbot_video/model_inference_low_vram/lingbot-video-moe-30b-a3b_ti2v_refiner.py
@@ -0,0 +1,80 @@
+import os
+import json
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video, VideoData
+from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_ti2v/*",
+)
+base = "data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_ti2v"
+with open(os.path.join(base, "ti2v_example.json"), "r", encoding="utf-8") as f:
+    caption = json.load(f)
+input_image = Image.open(os.path.join(base, "ti2v_first_frame.png")).convert("RGB")
+
+# --- Stage 1: base generation at 480x832 ---------------------------------------------
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_image=input_image,
+    height=480, width=832, num_frames=81,
+    num_inference_steps=40, cfg_scale=3.0,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_ti2v.mp4", fps=15, quality=10)
+
+del pipe
+torch.cuda.empty_cache()
+
+# --- Stage 2: refinement at 1088x1920 ------------------------------------------------
+pipe = LingBotVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="refiner/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors", **vram_config),
+        ModelConfig(model_id="Robbyant/lingbot-video-moe-30b-a3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+input_video = VideoData("video_lingbot-video-moe-30b-a3b_ti2v.mp4", height=1088, width=1920)
+video = pipe(
+    prompt=caption,
+    negative_prompt=pipe.default_negative_prompt,
+    input_image=input_image,
+    input_video=input_video,
+    height=1088, width=1920, num_frames=81,
+    num_inference_steps=8, cfg_scale=3.0,
+    t_thresh=0.85, sigma_tail_steps=2,
+    seed=0,
+)
+save_video(video, "video_lingbot-video-moe-30b-a3b_ti2v_refined.mp4", fps=15, quality=10)