update to version 2.0.8

add acceleration inference docs (#1371 )
* add acceleration inference docs * minor fix
2026-04-14 13:48:20 +00:00 · 2026-04-14 16:57:44 +08:00 · 2026-04-14 13:29:29 +08:00 · 2026-04-14 11:35:43 +08:00 · 2026-04-13 14:57:10 +08:00 · 2026-04-13 14:50:48 +08:00
49 changed files with 1875 additions and 150 deletions
--- a/README.md
+++ b/README.md
@@ -7,6 +7,7 @@
 [![open issues](https://isitmaintained.com/badge/open/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/issues)
 [![GitHub pull-requests](https://img.shields.io/github/issues-pr/modelscope/DiffSynth-Studio.svg)](https://GitHub.com/modelscope/DiffSynth-Studio/pull/)
 [![GitHub latest commit](https://badgen.net/github/last-commit/modelscope/DiffSynth-Studio)](https://GitHub.com/modelscope/DiffSynth-Studio/commit/)
+[![Discord](https://badgen.net//discord/members/Mm9suEeUDc)](https://discord.gg/Mm9suEeUDc)

 [切换到中文版](./README_zh.md)

@@ -32,7 +33,8 @@ We believe that a well-developed open-source code framework can lower the thresh
 > DiffSynth-Studio has undergone major version updates, and some old features are no longer maintained. If you need to use old features, please switch to the [last historical version](https://github.com/modelscope/DiffSynth-Studio/tree/afd101f3452c9ecae0c87b79adfa2e22d65ffdc3) before the major version update.

 > 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.
- **January 19, 2026**: Added support for [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) and [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) models, including training and inference capabilities. [Documentation](/docs/en/Model_Details/Wan.md) and [example code](/examples/mova/) are now available.
+
+- **March 19, 2026**: Added support for [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) and [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) models, including training and inference capabilities. [Documentation](/docs/en/Model_Details/Wan.md) and [example code](/examples/mova/) are now available.

 - **March 12, 2026**: We have added support for the [LTX-2.3](https://modelscope.cn/models/Lightricks/LTX-2.3) audio-video generation model. The features includes text-to-audio/video, image-to-audio/video, IC-LoRA control, audio-to-video, and audio-video inpainting. We have supported the complete inference and training functionalities. For details, please refer to the [documentation](/docs/en/Model_Details/LTX-2.md) and [code](/examples/ltx2/).

@@ -875,6 +877,67 @@ Example code for Wan is available at: [/examples/wanvideo/](/examples/wanvideo/)

 </details>

+#### ERNIE-Image: [/docs/en/Model_Details/ERNIE-Image.md](/docs/en/Model_Details/ERNIE-Image.md)
+
+<details>
+
+<summary>Quick Start</summary>
+
+Running the following code will quickly load the [PaddlePaddle/ERNIE-Image](https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image) 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 3GB VRAM.
+
+```python
+from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": torch.bfloat16,
+    "offload_device": "cpu",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = ErnieImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device='cuda',
+    model_configs=[
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors", **vram_config),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="tokenizer/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+image = pipe(
+    prompt="一只黑白相间的中华田园犬",
+    negative_prompt="",
+    height=1024,
+    width=1024,
+    seed=42,
+    num_inference_steps=50,
+    cfg_scale=4.0,
+)
+image.save("output.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>Examples</summary>
+
+Example code for ERNIE-Image is available at: [/examples/ernie_image/](/examples/ernie_image/)
+
+| Model ID | Inference | Low VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
+|-|-|-|-|-|-|-|
+|[PaddlePaddle/ERNIE-Image](https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image)|[code](/examples/ernie_image/model_inference/ERNIE-Image.py)|[code](/examples/ernie_image/model_inference_low_vram/ERNIE-Image.py)|[code](/examples/ernie_image/model_training/full/ERNIE-Image.sh)|[code](/examples/ernie_image/model_training/validate_full/ERNIE-Image.py)|[code](/examples/ernie_image/model_training/lora/ERNIE-Image.sh)|[code](/examples/ernie_image/model_training/validate_lora/ERNIE-Image.py)|
+|[PaddlePaddle/ERNIE-Image-Turbo](https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image-Turbo)|[code](/examples/ernie_image/model_inference/ERNIE-Image-Turbo.py)|[code](/examples/ernie_image/model_inference_low_vram/ERNIE-Image-Turbo.py)|—|—|—|—|
+
+</details>
+
 ## Innovative Achievements

 DiffSynth-Studio is not just an engineered model framework, but also an incubator for innovative achievements.
@@ -1029,3 +1092,9 @@ https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-47
 https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea

 </details>
+
+## Contact Us
+
+|Discord：https://discord.gg/Mm9suEeUDc|
+|-|
+|<img width="160" height="160" alt="Image" src="https://github.com/user-attachments/assets/29bdc97b-e35d-4fea-88d6-32e35182e458" />|
--- a/README_zh.md
+++ b/README_zh.md
@@ -7,6 +7,7 @@
 [![open issues](https://isitmaintained.com/badge/open/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/issues)
 [![GitHub pull-requests](https://img.shields.io/github/issues-pr/modelscope/DiffSynth-Studio.svg)](https://GitHub.com/modelscope/DiffSynth-Studio/pull/)
 [![GitHub latest commit](https://badgen.net/github/last-commit/modelscope/DiffSynth-Studio)](https://GitHub.com/modelscope/DiffSynth-Studio/commit/)
+[![Discord](https://badgen.net//discord/members/Mm9suEeUDc)](https://discord.gg/Mm9suEeUDc)

 [Switch to English](./README.md)

@@ -33,7 +34,7 @@ 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1月19日** 新增对 [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) 和 [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) 模型的支持，包括完整的训练和推理功能。[文档](/docs/zh/Model_Details/Wan.md)和[示例代码](/examples/mova/)现已可用。
+- **2026年3月19日** 新增对 [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) 和 [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) 模型的支持，包括完整的训练和推理功能。[文档](/docs/zh/Model_Details/Wan.md)和[示例代码](/examples/mova/)现已可用。

 - **2026年3月12日** 我们新增了 [LTX-2.3](https://modelscope.cn/models/Lightricks/LTX-2.3) 音视频生成模型的支持，模型支持的功能包括文生音视频、图生音视频、IC-LoRA控制、音频生视频、音视频局部Inpainting，框架支持完整的推理和训练功能。详细信息请参考 [文档](/docs/zh/Model_Details/LTX-2.md) 和 [示例代码](/examples/ltx2/)。

@@ -876,6 +877,67 @@ Wan 的示例代码位于：[/examples/wanvideo/](/examples/wanvideo/)

 </details>

+#### ERNIE-Image: [/docs/zh/Model_Details/ERNIE-Image.md](/docs/zh/Model_Details/ERNIE-Image.md)
+
+<details>
+
+<summary>快速开始</summary>
+
+运行以下代码可以快速加载 [PaddlePaddle/ERNIE-Image](https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image) 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 3G 显存即可运行。
+
+```python
+from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": torch.bfloat16,
+    "offload_device": "cpu",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = ErnieImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device='cuda',
+    model_configs=[
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors", **vram_config),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="tokenizer/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+image = pipe(
+    prompt="一只黑白相间的中华田园犬",
+    negative_prompt="",
+    height=1024,
+    width=1024,
+    seed=42,
+    num_inference_steps=50,
+    cfg_scale=4.0,
+)
+image.save("output.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>示例代码</summary>
+
+ERNIE-Image 的示例代码位于：[/examples/ernie_image/](/examples/ernie_image/)
+
+| 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 |
+|-|-|-|-|-|-|-|
+|[PaddlePaddle/ERNIE-Image](https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image)|[code](/examples/ernie_image/model_inference/ERNIE-Image.py)|[code](/examples/ernie_image/model_inference_low_vram/ERNIE-Image.py)|[code](/examples/ernie_image/model_training/full/ERNIE-Image.sh)|[code](/examples/ernie_image/model_training/validate_full/ERNIE-Image.py)|[code](/examples/ernie_image/model_training/lora/ERNIE-Image.sh)|[code](/examples/ernie_image/model_training/validate_lora/ERNIE-Image.py)|
+|[PaddlePaddle/ERNIE-Image-Turbo](https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image-Turbo)|[code](/examples/ernie_image/model_inference/ERNIE-Image-Turbo.py)|[code](/examples/ernie_image/model_inference_low_vram/ERNIE-Image-Turbo.py)|—|—|—|—|
+
+</details>
+
 ## 创新成果

 DiffSynth-Studio 不仅仅是一个工程化的模型框架，更是创新成果的孵化器。
@@ -1032,3 +1094,9 @@ https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-47
 https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea

 </details>
+
+## 联系我们
+
+|Discord：https://discord.gg/Mm9suEeUDc|
+|-|
+|<img width="160" height="160" alt="Image" src="https://github.com/user-attachments/assets/29bdc97b-e35d-4fea-88d6-32e35182e458" />|
--- a/diffsynth/configs/model_configs.py
+++ b/diffsynth/configs/model_configs.py
@@ -541,6 +541,22 @@ flux2_series = [
    },
 ]

+ernie_image_series = [
+    {
+        # Example: ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors")
+        "model_hash": "584c13713849f1af4e03d5f1858b8b7b",
+        "model_name": "ernie_image_dit",
+        "model_class": "diffsynth.models.ernie_image_dit.ErnieImageDiT",
+    },
+    {
+        # Example: ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors")
+        "model_hash": "404ed9f40796a38dd34c1620f1920207",
+        "model_name": "ernie_image_text_encoder",
+        "model_class": "diffsynth.models.ernie_image_text_encoder.ErnieImageTextEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ernie_image_text_encoder.ErnieImageTextEncoderStateDictConverter",
+    },
+]
+
 z_image_series = [
    {
        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="transformer/*.safetensors")
@@ -604,6 +620,13 @@ z_image_series = [
        "extra_kwargs": {"model_size": "0.6B"},
        "state_dict_converter": "diffsynth.utils.state_dict_converters.z_image_text_encoder.ZImageTextEncoderStateDictConverter",
    },
+    {
+        # To ensure compatibility with the `model.diffusion_model` prefix introduced by other frameworks.
+        "model_hash": "8cf241a0d32f93d5de368502a086852f",
+        "model_name": "z_image_dit",
+        "model_class": "diffsynth.models.z_image_dit.ZImageDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.z_image_dit.ZImageDiTStateDictConverter",
+    },
 ]
 """
 Offical model repo: https://www.modelscope.cn/models/Lightricks/LTX-2
@@ -877,4 +900,4 @@ mova_series = [
        "model_class": "diffsynth.models.mova_dual_tower_bridge.DualTowerConditionalBridge",
    },
 ]
-MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series + ltx2_series + anima_series + mova_series
+MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + ernie_image_series + z_image_series + ltx2_series + anima_series + mova_series
--- a/diffsynth/configs/vram_management_module_maps.py
+++ b/diffsynth/configs/vram_management_module_maps.py
@@ -267,6 +267,18 @@ VRAM_MANAGEMENT_MODULE_MAPS = {
        "torch.nn.Conv1d": "diffsynth.core.vram.layers.AutoWrappedModule",
        "torch.nn.ConvTranspose1d": "diffsynth.core.vram.layers.AutoWrappedModule",
    },
+    "diffsynth.models.ernie_image_dit.ErnieImageDiT": {
+        "diffsynth.models.ernie_image_dit.ErnieImageRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.ernie_image_text_encoder.ErnieImageTextEncoder": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.ministral3.modeling_ministral3.Ministral3RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
 }

 def QwenImageTextEncoder_Module_Map_Updater():
--- a/diffsynth/diffusion/base_pipeline.py
+++ b/diffsynth/diffusion/base_pipeline.py
@@ -339,6 +339,38 @@ class BasePipeline(torch.nn.Module):
            noise_pred = noise_pred_posi
        return noise_pred

+    def compile_pipeline(self, mode: str = "default", dynamic: bool = True, fullgraph: bool = False, compile_models: list = None, **kwargs):
+        """
+        compile the pipeline with torch.compile. The models that will be compiled are determined by the `compilable_models` attribute of the pipeline.
+        If a model has `_repeated_blocks` attribute, we will compile these blocks with regional compilation. Otherwise, we will compile the whole model.
+        See https://docs.pytorch.org/docs/stable/generated/torch.compile.html#torch.compile for details about compilation arguments.
+        Args:
+            mode: The compilation mode, which will be passed to `torch.compile`, options are "default", "reduce-overhead", "max-autotune" and "max-autotune-no-cudagraphs. Default to "default".
+            dynamic: Whether to enable dynamic graph compilation to support dynamic input shapes, which will be passed to `torch.compile`. Default to True (recommended).
+            fullgraph: Whether to use full graph compilation, which will be passed to `torch.compile`. Default to False (recommended).
+            compile_models: The list of model names to be compiled. If None, we will compile the models in `pipeline.compilable_models`. Default to None.
+            **kwargs: Other arguments for `torch.compile`.
+        """
+        compile_models = compile_models or getattr(self, "compilable_models", [])
+        if len(compile_models) == 0:
+            print("No compilable models in the pipeline. Skip compilation.")
+            return
+        for name in compile_models:
+            model = getattr(self, name, None)
+            if model is None:
+                print(f"Model '{name}' not found in the pipeline.")
+                continue
+            repeated_blocks = getattr(model, "_repeated_blocks", None)
+            # regional compilation for repeated blocks.
+            if repeated_blocks is not None:
+                for submod in model.modules():
+                    if submod.__class__.__name__ in repeated_blocks:
+                        submod.compile(mode=mode, dynamic=dynamic, fullgraph=fullgraph, **kwargs)
+            # compile the whole model.
+            else:
+                model.compile(mode=mode, dynamic=dynamic, fullgraph=fullgraph, **kwargs)
+            print(f"{name} is compiled with mode={mode}, dynamic={dynamic}, fullgraph={fullgraph}.")
+

 class PipelineUnitGraph:
    def __init__(self):
--- a/diffsynth/diffusion/flow_match.py
+++ b/diffsynth/diffusion/flow_match.py
@@ -4,7 +4,7 @@ from typing_extensions import Literal

 class FlowMatchScheduler():

-    def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image", "LTX-2", "Qwen-Image-Lightning"] = "FLUX.1"):
+    def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image", "LTX-2", "Qwen-Image-Lightning", "ERNIE-Image"] = "FLUX.1"):
        self.set_timesteps_fn = {
            "FLUX.1": FlowMatchScheduler.set_timesteps_flux,
            "Wan": FlowMatchScheduler.set_timesteps_wan,
@@ -13,6 +13,7 @@ class FlowMatchScheduler():
            "Z-Image": FlowMatchScheduler.set_timesteps_z_image,
            "LTX-2": FlowMatchScheduler.set_timesteps_ltx2,
            "Qwen-Image-Lightning": FlowMatchScheduler.set_timesteps_qwen_image_lightning,
+            "ERNIE-Image": FlowMatchScheduler.set_timesteps_ernie_image,
        }.get(template, FlowMatchScheduler.set_timesteps_flux)
        self.num_train_timesteps = 1000

@@ -129,6 +130,18 @@ class FlowMatchScheduler():
        timesteps = sigmas * num_train_timesteps
        return sigmas, timesteps

+    @staticmethod
+    def set_timesteps_ernie_image(num_inference_steps=50, denoising_strength=1.0, shift=3.0):
+        sigma_min = 0.0
+        sigma_max = 1.0
+        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]
+        if shift is not None and shift != 1.0:
+            sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+
    @staticmethod
    def set_timesteps_z_image(num_inference_steps=100, denoising_strength=1.0, shift=None, target_timesteps=None):
        sigma_min = 0.0
@@ -185,7 +198,7 @@ class FlowMatchScheduler():
            bsmntw_weighing = bsmntw_weighing * (len(self.timesteps) / steps)
            bsmntw_weighing = bsmntw_weighing + bsmntw_weighing[1]
        self.linear_timesteps_weights = bsmntw_weighing
-        
+
    def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False, **kwargs):
        self.sigmas, self.timesteps = self.set_timesteps_fn(
            num_inference_steps=num_inference_steps,
--- a/diffsynth/models/anima_dit.py
+++ b/diffsynth/models/anima_dit.py
@@ -1270,6 +1270,9 @@ class LLMAdapter(nn.Module):


 class AnimaDiT(MiniTrainDIT):
+
+    _repeated_blocks = ["Block"]
+
    def __init__(self):
        kwargs = {'image_model': 'anima', 'max_img_h': 240, 'max_img_w': 240, 'max_frames': 128, 'in_channels': 16, 'out_channels': 16, 'patch_spatial': 2, 'patch_temporal': 1, 'model_channels': 2048, 'concat_padding_mask': True, 'crossattn_emb_channels': 1024, 'pos_emb_cls': 'rope3d', 'pos_emb_learnable': True, 'pos_emb_interpolation': 'crop', 'min_fps': 1, 'max_fps': 30, 'use_adaln_lora': True, 'adaln_lora_dim': 256, 'num_blocks': 28, 'num_heads': 16, 'extra_per_block_abs_pos_emb': False, 'rope_h_extrapolation_ratio': 4.0, 'rope_w_extrapolation_ratio': 4.0, 'rope_t_extrapolation_ratio': 1.0, 'extra_h_extrapolation_ratio': 1.0, 'extra_w_extrapolation_ratio': 1.0, 'extra_t_extrapolation_ratio': 1.0, 'rope_enable_fps_modulation': False, 'dtype': torch.bfloat16, 'device': None, 'operations': torch.nn}
        super().__init__(**kwargs)
--- a/diffsynth/models/ernie_image_dit.py
+++ b/diffsynth/models/ernie_image_dit.py
@@ -0,0 +1,362 @@
+"""
+Ernie-Image DiT for DiffSynth-Studio.
+
+Refactored from diffusers ErnieImageTransformer2DModel to use DiffSynth core modules.
+Default parameters from actual checkpoint config.json (PaddlePaddle/ERNIE-Image transformer).
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple
+
+from ..core.attention import attention_forward
+from ..core.gradient import gradient_checkpoint_forward
+from .flux2_dit import Timesteps, TimestepEmbedding
+
+
+def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
+    assert dim % 2 == 0
+    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+    omega = 1.0 / (theta ** scale)
+    out = torch.einsum("...n,d->...nd", pos, omega)
+    return out.float()
+
+
+class ErnieImageEmbedND3(nn.Module):
+    def __init__(self, dim: int, theta: int, axes_dim: Tuple[int, int, int]):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = list(axes_dim)
+
+    def forward(self, ids: torch.Tensor) -> torch.Tensor:
+        emb = torch.cat([rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(3)], dim=-1)
+        emb = emb.unsqueeze(2)
+        return torch.stack([emb, emb], dim=-1).reshape(*emb.shape[:-1], -1)
+
+
+class ErnieImagePatchEmbedDynamic(nn.Module):
+    def __init__(self, in_channels: int, embed_dim: int, patch_size: int):
+        super().__init__()
+        self.patch_size = patch_size
+        self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size, bias=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        batch_size, dim, height, width = x.shape
+        return x.reshape(batch_size, dim, height * width).transpose(1, 2).contiguous()
+
+
+class ErnieImageSingleStreamAttnProcessor:
+    def __call__(
+        self,
+        attn: "ErnieImageAttention",
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        freqs_cis: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        query = query.unflatten(-1, (attn.heads, -1))
+        key = key.unflatten(-1, (attn.heads, -1))
+        value = value.unflatten(-1, (attn.heads, -1))
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        def apply_rotary_emb(x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
+            rot_dim = freqs_cis.shape[-1]
+            x, x_pass = x_in[..., :rot_dim], x_in[..., rot_dim:]
+            cos_ = torch.cos(freqs_cis).to(x.dtype)
+            sin_ = torch.sin(freqs_cis).to(x.dtype)
+            x1, x2 = x.chunk(2, dim=-1)
+            x_rotated = torch.cat((-x2, x1), dim=-1)
+            return torch.cat((x * cos_ + x_rotated * sin_, x_pass), dim=-1)
+
+        if freqs_cis is not None:
+            query = apply_rotary_emb(query, freqs_cis)
+            key = apply_rotary_emb(key, freqs_cis)
+
+        if attention_mask is not None and attention_mask.ndim == 2:
+            attention_mask = attention_mask[:, None, None, :]
+
+        hidden_states = attention_forward(
+            query, key, value,
+            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,
+        )
+
+        hidden_states = hidden_states.flatten(2, 3)
+        hidden_states = hidden_states.to(query.dtype)
+        output = attn.to_out[0](hidden_states)
+
+        return output
+
+
+class ErnieImageAttention(nn.Module):
+    def __init__(
+        self,
+        query_dim: int,
+        heads: int = 8,
+        dim_head: int = 64,
+        dropout: float = 0.0,
+        bias: bool = False,
+        qk_norm: str = "rms_norm",
+        out_bias: bool = True,
+        eps: float = 1e-5,
+        out_dim: int = None,
+        elementwise_affine: bool = True,
+    ):
+        super().__init__()
+
+        self.head_dim = dim_head
+        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
+        self.query_dim = query_dim
+        self.out_dim = out_dim if out_dim is not None else query_dim
+        self.heads = out_dim // dim_head if out_dim is not None else heads
+
+        self.use_bias = bias
+        self.dropout = dropout
+
+        self.to_q = nn.Linear(query_dim, self.inner_dim, bias=bias)
+        self.to_k = nn.Linear(query_dim, self.inner_dim, bias=bias)
+        self.to_v = nn.Linear(query_dim, self.inner_dim, bias=bias)
+
+        if qk_norm == "layer_norm":
+            self.norm_q = nn.LayerNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
+            self.norm_k = nn.LayerNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
+        elif qk_norm == "rms_norm":
+            self.norm_q = nn.RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
+            self.norm_k = nn.RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
+        else:
+            raise ValueError(
+                f"unknown qk_norm: {qk_norm}. Should be one of None, 'layer_norm', 'rms_norm'."
+            )
+
+        self.to_out = nn.ModuleList([])
+        self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
+
+        self.processor = ErnieImageSingleStreamAttnProcessor()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return self.processor(self, hidden_states, attention_mask, image_rotary_emb)
+
+
+class ErnieImageFeedForward(nn.Module):
+    def __init__(self, hidden_size: int, ffn_hidden_size: int):
+        super().__init__()
+        self.gate_proj = nn.Linear(hidden_size, ffn_hidden_size, bias=False)
+        self.up_proj = nn.Linear(hidden_size, ffn_hidden_size, bias=False)
+        self.linear_fc2 = nn.Linear(ffn_hidden_size, hidden_size, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.linear_fc2(self.up_proj(x) * F.gelu(self.gate_proj(x)))
+
+
+class ErnieImageRMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
+        hidden_states = hidden_states * self.weight
+        return hidden_states.to(input_dtype)
+
+
+class ErnieImageSharedAdaLNBlock(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        ffn_hidden_size: int,
+        eps: float = 1e-6,
+        qk_layernorm: bool = True,
+    ):
+        super().__init__()
+        self.adaLN_sa_ln = ErnieImageRMSNorm(hidden_size, eps=eps)
+        self.self_attention = ErnieImageAttention(
+            query_dim=hidden_size,
+            dim_head=hidden_size // num_heads,
+            heads=num_heads,
+            qk_norm="rms_norm" if qk_layernorm else None,
+            eps=eps,
+            bias=False,
+            out_bias=False,
+        )
+        self.adaLN_mlp_ln = ErnieImageRMSNorm(hidden_size, eps=eps)
+        self.mlp = ErnieImageFeedForward(hidden_size, ffn_hidden_size)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        rotary_pos_emb: torch.Tensor,
+        temb: Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = temb
+        residual = x
+        x = self.adaLN_sa_ln(x)
+        x = (x.float() * (1 + scale_msa.float()) + shift_msa.float()).to(x.dtype)
+        x_bsh = x.permute(1, 0, 2)
+        attn_out = self.self_attention(x_bsh, attention_mask=attention_mask, image_rotary_emb=rotary_pos_emb)
+        attn_out = attn_out.permute(1, 0, 2)
+        x = residual + (gate_msa.float() * attn_out.float()).to(x.dtype)
+        residual = x
+        x = self.adaLN_mlp_ln(x)
+        x = (x.float() * (1 + scale_mlp.float()) + shift_mlp.float()).to(x.dtype)
+        return residual + (gate_mlp.float() * self.mlp(x).float()).to(x.dtype)
+
+
+class ErnieImageAdaLNContinuous(nn.Module):
+    def __init__(self, hidden_size: int, eps: float = 1e-6):
+        super().__init__()
+        self.norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=eps)
+        self.linear = nn.Linear(hidden_size, hidden_size * 2)
+
+    def forward(self, x: torch.Tensor, conditioning: torch.Tensor) -> torch.Tensor:
+        scale, shift = self.linear(conditioning).chunk(2, dim=-1)
+        x = self.norm(x)
+        x = x * (1 + scale.unsqueeze(0)) + shift.unsqueeze(0)
+        return x
+
+
+class ErnieImageDiT(nn.Module):
+    """
+    Ernie-Image DiT model for DiffSynth-Studio.
+
+    Architecture: SharedAdaLN + RoPE 3D + Joint Image-Text Attention.
+    Internal format: [S, B, H] for transformer blocks, [B, S, H] for attention.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int = 4096,
+        num_attention_heads: int = 32,
+        num_layers: int = 36,
+        ffn_hidden_size: int = 12288,
+        in_channels: int = 128,
+        out_channels: int = 128,
+        patch_size: int = 1,
+        text_in_dim: int = 3072,
+        rope_theta: int = 256,
+        rope_axes_dim: Tuple[int, int, int] = (32, 48, 48),
+        eps: float = 1e-6,
+        qk_layernorm: bool = True,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.num_heads = num_attention_heads
+        self.head_dim = hidden_size // num_attention_heads
+        self.num_layers = num_layers
+        self.patch_size = patch_size
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.text_in_dim = text_in_dim
+
+        self.x_embedder = ErnieImagePatchEmbedDynamic(in_channels, hidden_size, patch_size)
+        self.text_proj = nn.Linear(text_in_dim, hidden_size, bias=False) if text_in_dim != hidden_size else None
+        self.time_proj = Timesteps(hidden_size, flip_sin_to_cos=False, downscale_freq_shift=0)
+        self.time_embedding = TimestepEmbedding(hidden_size, hidden_size)
+        self.pos_embed = ErnieImageEmbedND3(dim=self.head_dim, theta=rope_theta, axes_dim=rope_axes_dim)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size))
+        nn.init.zeros_(self.adaLN_modulation[-1].weight)
+        nn.init.zeros_(self.adaLN_modulation[-1].bias)
+        self.layers = nn.ModuleList([
+            ErnieImageSharedAdaLNBlock(hidden_size, num_attention_heads, ffn_hidden_size, eps, qk_layernorm=qk_layernorm)
+            for _ in range(num_layers)
+        ])
+        self.final_norm = ErnieImageAdaLNContinuous(hidden_size, eps)
+        self.final_linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels)
+        nn.init.zeros_(self.final_linear.weight)
+        nn.init.zeros_(self.final_linear.bias)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.Tensor,
+        text_bth: torch.Tensor,
+        text_lens: torch.Tensor,
+        use_gradient_checkpointing: bool = False,
+        use_gradient_checkpointing_offload: bool = False,
+    ) -> torch.Tensor:
+        device, dtype = hidden_states.device, hidden_states.dtype
+        B, C, H, W = hidden_states.shape
+        p, Hp, Wp = self.patch_size, H // self.patch_size, W // self.patch_size
+        N_img = Hp * Wp
+
+        img_sbh = self.x_embedder(hidden_states).transpose(0, 1).contiguous()
+
+        if self.text_proj is not None and text_bth.numel() > 0:
+            text_bth = self.text_proj(text_bth)
+        Tmax = text_bth.shape[1]
+        text_sbh = text_bth.transpose(0, 1).contiguous()
+
+        x = torch.cat([img_sbh, text_sbh], dim=0)
+        S = x.shape[0]
+
+        text_ids = torch.cat([
+            torch.arange(Tmax, device=device, dtype=torch.float32).view(1, Tmax, 1).expand(B, -1, -1),
+            torch.zeros((B, Tmax, 2), device=device)
+        ], dim=-1) if Tmax > 0 else torch.zeros((B, 0, 3), device=device)
+        grid_yx = torch.stack(
+            torch.meshgrid(torch.arange(Hp, device=device, dtype=torch.float32),
+                           torch.arange(Wp, device=device, dtype=torch.float32), indexing="ij"),
+            dim=-1
+        ).reshape(-1, 2)
+        image_ids = torch.cat([
+            text_lens.float().view(B, 1, 1).expand(-1, N_img, -1),
+            grid_yx.view(1, N_img, 2).expand(B, -1, -1)
+        ], dim=-1)
+        rotary_pos_emb = self.pos_embed(torch.cat([image_ids, text_ids], dim=1))
+
+        valid_text = torch.arange(Tmax, device=device).view(1, Tmax) < text_lens.view(B, 1) if Tmax > 0 else torch.zeros((B, 0), device=device, dtype=torch.bool)
+        attention_mask = torch.cat([
+            torch.ones((B, N_img), device=device, dtype=torch.bool),
+            valid_text
+        ], dim=1)[:, None, None, :]
+
+        sample = self.time_proj(timestep.to(dtype))
+        sample = sample.to(self.time_embedding.linear_1.weight.dtype)
+        c = self.time_embedding(sample)
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = [
+            t.unsqueeze(0).expand(S, -1, -1).contiguous()
+            for t in self.adaLN_modulation(c).chunk(6, dim=-1)
+        ]
+
+        for layer in self.layers:
+            temb = [shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp]
+            if torch.is_grad_enabled() and use_gradient_checkpointing:
+                x = gradient_checkpoint_forward(
+                    layer,
+                    use_gradient_checkpointing,
+                    use_gradient_checkpointing_offload,
+                    x,
+                    rotary_pos_emb,
+                    temb,
+                    attention_mask,
+                )
+            else:
+                x = layer(x, rotary_pos_emb, temb, attention_mask)
+
+        x = self.final_norm(x, c).type_as(x)
+        patches = self.final_linear(x)[:N_img].transpose(0, 1).contiguous()
+        output = patches.view(B, Hp, Wp, p, p, self.out_channels).permute(0, 5, 1, 3, 2, 4).contiguous().view(B, self.out_channels, H, W)
+
+        return output
--- a/diffsynth/models/ernie_image_text_encoder.py
+++ b/diffsynth/models/ernie_image_text_encoder.py
@@ -0,0 +1,76 @@
+"""
+Ernie-Image TextEncoder for DiffSynth-Studio.
+
+Wraps transformers Ministral3Model to output text embeddings.
+Pattern: lazy import + manual config dict + torch.nn.Module wrapper.
+Only loads the text (language) model, ignoring vision components.
+"""
+
+import torch
+
+
+class ErnieImageTextEncoder(torch.nn.Module):
+    """
+    Text encoder using Ministral3Model (transformers).
+    Only the text_config portion of the full Mistral3Model checkpoint.
+    Uses the base model (no lm_head) since the checkpoint only has embeddings.
+    """
+
+    def __init__(self):
+        super().__init__()
+        from transformers import Ministral3Config, Ministral3Model
+
+        text_config = {
+            "attention_dropout": 0.0,
+            "bos_token_id": 1,
+            "dtype": "bfloat16",
+            "eos_token_id": 2,
+            "head_dim": 128,
+            "hidden_act": "silu",
+            "hidden_size": 3072,
+            "initializer_range": 0.02,
+            "intermediate_size": 9216,
+            "max_position_embeddings": 262144,
+            "model_type": "ministral3",
+            "num_attention_heads": 32,
+            "num_hidden_layers": 26,
+            "num_key_value_heads": 8,
+            "pad_token_id": 11,
+            "rms_norm_eps": 1e-05,
+            "rope_parameters": {
+                "beta_fast": 32.0,
+                "beta_slow": 1.0,
+                "factor": 16.0,
+                "llama_4_scaling_beta": 0.1,
+                "mscale": 1.0,
+                "mscale_all_dim": 1.0,
+                "original_max_position_embeddings": 16384,
+                "rope_theta": 1000000.0,
+                "rope_type": "yarn",
+                "type": "yarn",
+            },
+            "sliding_window": None,
+            "tie_word_embeddings": True,
+            "use_cache": True,
+            "vocab_size": 131072,
+        }
+        config = Ministral3Config(**text_config)
+        self.model = Ministral3Model(config)
+        self.config = config
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        **kwargs,
+    ):
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_hidden_states=True,
+            return_dict=True,
+            **kwargs,
+        )
+        return (outputs.hidden_states,)
--- a/diffsynth/models/flux2_dit.py
+++ b/diffsynth/models/flux2_dit.py
@@ -879,6 +879,9 @@ class Flux2Modulation(nn.Module):


 class Flux2DiT(torch.nn.Module):
+
+    _repeated_blocks = ["Flux2TransformerBlock", "Flux2SingleTransformerBlock"]
+
    def __init__(
        self,
        patch_size: int = 1,
--- a/diffsynth/models/flux_dit.py
+++ b/diffsynth/models/flux_dit.py
@@ -275,6 +275,9 @@ class AdaLayerNormContinuous(torch.nn.Module):


 class FluxDiT(torch.nn.Module):
+
+    _repeated_blocks = ["FluxJointTransformerBlock", "FluxSingleTransformerBlock"]
+
    def __init__(self, disable_guidance_embedder=False, input_dim=64, num_blocks=19):
        super().__init__()
        self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])
--- a/diffsynth/models/ltx2_dit.py
+++ b/diffsynth/models/ltx2_dit.py
@@ -1280,6 +1280,7 @@ class LTXModel(torch.nn.Module):
    LTX model transformer implementation.
    This class implements the transformer blocks for the LTX model.
    """
+    _repeated_blocks = ["BasicAVTransformerBlock"]

    def __init__(  # noqa: PLR0913
        self,
--- a/diffsynth/models/qwen_image_dit.py
+++ b/diffsynth/models/qwen_image_dit.py
@@ -549,6 +549,9 @@ class QwenImageTransformerBlock(nn.Module):


 class QwenImageDiT(torch.nn.Module):
+
+    _repeated_blocks = ["QwenImageTransformerBlock"]
+
    def __init__(
        self,
        num_layers: int = 60,
--- a/diffsynth/models/wan_video_dit.py
+++ b/diffsynth/models/wan_video_dit.py
@@ -336,6 +336,9 @@ class WanToDanceInjector(nn.Module):


 class WanModel(torch.nn.Module):
+
+    _repeated_blocks = ["DiTBlock"]
+
    def __init__(
        self,
        dim: int,
--- a/diffsynth/models/z_image_dit.py
+++ b/diffsynth/models/z_image_dit.py
@@ -326,6 +326,7 @@ class RopeEmbedder:
 class ZImageDiT(nn.Module):
    _supports_gradient_checkpointing = True
    _no_split_modules = ["ZImageTransformerBlock"]
+    _repeated_blocks = ["ZImageTransformerBlock"]

    def __init__(
        self,
--- a/diffsynth/pipelines/anima_image.py
+++ b/diffsynth/pipelines/anima_image.py
@@ -39,6 +39,7 @@ class AnimaImagePipeline(BasePipeline):
            AnimaUnit_PromptEmbedder(),
        ]
        self.model_fn = model_fn_anima
+        self.compilable_models = ["dit"]
    
    
    @staticmethod
--- a/diffsynth/pipelines/ernie_image.py
+++ b/diffsynth/pipelines/ernie_image.py
@@ -0,0 +1,266 @@
+"""
+ERNIE-Image Text-to-Image Pipeline for DiffSynth-Studio.
+
+Architecture: SharedAdaLN DiT + RoPE 3D + Joint Image-Text Attention.
+"""
+
+import torch
+from typing import Union, Optional
+from tqdm import tqdm
+from transformers import AutoTokenizer
+
+from ..core.device.npu_compatible_device import get_device_type
+from ..diffusion import FlowMatchScheduler
+from ..core import ModelConfig
+from ..diffusion.base_pipeline import BasePipeline, PipelineUnit
+from ..models.ernie_image_text_encoder import ErnieImageTextEncoder
+from ..models.ernie_image_dit import ErnieImageDiT
+from ..models.flux2_vae import Flux2VAE
+
+
+class ErnieImagePipeline(BasePipeline):
+
+    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,
+        )
+        self.scheduler = FlowMatchScheduler("ERNIE-Image")
+        self.text_encoder: ErnieImageTextEncoder = None
+        self.dit: ErnieImageDiT = None
+        self.vae: Flux2VAE = None
+        self.tokenizer: AutoTokenizer = None
+
+        self.in_iteration_models = ("dit",)
+        self.units = [
+            ErnieImageUnit_ShapeChecker(),
+            ErnieImageUnit_PromptEmbedder(),
+            ErnieImageUnit_NoiseInitializer(),
+            ErnieImageUnit_InputImageEmbedder(),
+        ]
+        self.model_fn = model_fn_ernie_image
+        self.compilable_models = ["dit"]
+
+    @staticmethod
+    def from_pretrained(
+        torch_dtype: torch.dtype = torch.bfloat16,
+        device: Union[str, torch.device] = get_device_type(),
+        model_configs: list[ModelConfig] = [],
+        tokenizer_config: ModelConfig = ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="tokenizer/"),
+        vram_limit: float = None,
+    ):
+        pipe = ErnieImagePipeline(device=device, torch_dtype=torch_dtype)
+        model_pool = pipe.download_and_load_models(model_configs, vram_limit)
+
+        pipe.text_encoder = model_pool.fetch_model("ernie_image_text_encoder")
+        pipe.dit = model_pool.fetch_model("ernie_image_dit")
+        pipe.vae = model_pool.fetch_model("flux2_vae")
+
+        if tokenizer_config is not None:
+            tokenizer_config.download_if_necessary()
+            pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
+
+        pipe.vram_management_enabled = pipe.check_vram_management_state()
+        return pipe
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        # Prompt
+        prompt: str,
+        negative_prompt: str = "",
+        cfg_scale: float = 4.0,
+        # Shape
+        height: int = 1024,
+        width: int = 1024,
+        # Randomness
+        seed: int = None,
+        rand_device: str = "cuda",
+        # Steps
+        num_inference_steps: int = 50,
+        sigma_shift: float = 3.0,
+        # Progress bar
+        progress_bar_cmd=tqdm,
+    ):
+        # Scheduler
+        self.scheduler.set_timesteps(num_inference_steps=num_inference_steps, shift=sigma_shift)
+
+        # Parameters
+        inputs_posi = {"prompt": prompt}
+        inputs_nega = {"negative_prompt": negative_prompt}
+        inputs_shared = {
+            "height": height, "width": width, "seed": seed,
+            "cfg_scale": cfg_scale, "num_inference_steps": num_inference_steps,
+            "rand_device": rand_device,
+        }
+        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)):
+            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, 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)
+
+        # Decode
+        self.load_models_to_device(['vae'])
+        latents = inputs_shared["latents"]
+        image = self.vae.decode(latents)
+        image = self.vae_output_to_image(image)
+        self.load_models_to_device([])
+        return image
+
+
+class ErnieImageUnit_ShapeChecker(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width"),
+            output_params=("height", "width"),
+        )
+
+    def process(self, pipe: ErnieImagePipeline, height, width):
+        height, width = pipe.check_resize_height_width(height, width)
+        return {"height": height, "width": width}
+
+
+class ErnieImageUnit_PromptEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt": "prompt"},
+            input_params_nega={"prompt": "negative_prompt"},
+            output_params=("prompt_embeds", "prompt_embeds_mask"),
+            onload_model_names=("text_encoder",)
+        )
+
+    def encode_prompt(self, pipe: ErnieImagePipeline, prompt):
+        if isinstance(prompt, str):
+            prompt = [prompt]
+
+        text_hiddens = []
+        text_lens_list = []
+        for p in prompt:
+            ids = pipe.tokenizer(
+                p,
+                add_special_tokens=True,
+                truncation=True,
+                padding=False,
+            )["input_ids"]
+
+            if len(ids) == 0:
+                if pipe.tokenizer.bos_token_id is not None:
+                    ids = [pipe.tokenizer.bos_token_id]
+                else:
+                    ids = [0]
+
+            input_ids = torch.tensor([ids], device=pipe.device)
+            outputs = pipe.text_encoder(
+                input_ids=input_ids,
+            )
+            # Text encoder returns tuple of (hidden_states_tuple,) where each layer's hidden state is included
+            all_hidden_states = outputs[0]
+            hidden = all_hidden_states[-2][0]  # [T, H] - second to last layer
+            text_hiddens.append(hidden)
+            text_lens_list.append(hidden.shape[0])
+
+        # Pad to uniform length
+        if len(text_hiddens) == 0:
+            text_in_dim = pipe.text_encoder.config.hidden_size if hasattr(pipe.text_encoder, 'config') else 3072
+            return {
+                "prompt_embeds": torch.zeros((0, 0, text_in_dim), device=pipe.device, dtype=pipe.torch_dtype),
+                "prompt_embeds_mask": torch.zeros((0,), device=pipe.device, dtype=torch.long),
+            }
+
+        normalized = [th.to(pipe.device).to(pipe.torch_dtype) for th in text_hiddens]
+        text_lens = torch.tensor([t.shape[0] for t in normalized], device=pipe.device, dtype=torch.long)
+        Tmax = int(text_lens.max().item())
+        text_in_dim = normalized[0].shape[1]
+        text_bth = torch.zeros((len(normalized), Tmax, text_in_dim), device=pipe.device, dtype=pipe.torch_dtype)
+        for i, t in enumerate(normalized):
+            text_bth[i, :t.shape[0], :] = t
+
+        return {"prompt_embeds": text_bth, "prompt_embeds_mask": text_lens}
+
+    def process(self, pipe: ErnieImagePipeline, prompt):
+        pipe.load_models_to_device(self.onload_model_names)
+        if pipe.text_encoder is not None:
+            return self.encode_prompt(pipe, prompt)
+        return {}
+
+
+class ErnieImageUnit_NoiseInitializer(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "seed", "rand_device"),
+            output_params=("noise",),
+        )
+
+    def process(self, pipe: ErnieImagePipeline, height, width, seed, rand_device):
+        latent_h = height // pipe.height_division_factor
+        latent_w = width // pipe.width_division_factor
+        latent_channels = pipe.dit.in_channels
+
+        # Use pipeline device if rand_device is not specified
+        if rand_device is None:
+            rand_device = str(pipe.device)
+
+        noise = pipe.generate_noise(
+            (1, latent_channels, latent_h, latent_w),
+            seed=seed,
+            rand_device=rand_device,
+            rand_torch_dtype=pipe.torch_dtype,
+        )
+        return {"noise": noise}
+
+
+class ErnieImageUnit_InputImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_image", "noise"),
+            output_params=("latents", "input_latents"),
+            onload_model_names=("vae",)
+        )
+
+    def process(self, pipe: ErnieImagePipeline, input_image, noise):
+        if input_image is None:
+            # T2I path: use noise directly as initial latents
+            return {"latents": noise, "input_latents": None}
+
+        # I2I path: VAE encode input image
+        pipe.load_models_to_device(['vae'])
+        image = pipe.preprocess_image(input_image).to(device=pipe.device, dtype=pipe.torch_dtype)
+        input_latents = pipe.vae.encode(image)
+
+        if pipe.scheduler.training:
+            return {"latents": noise, "input_latents": input_latents}
+        else:
+            # In inference mode, add noise to encoded latents
+            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
+            return {"latents": latents}
+
+
+def model_fn_ernie_image(
+    dit: ErnieImageDiT,
+    latents=None,
+    timestep=None,
+    prompt_embeds=None,
+    prompt_embeds_mask=None,
+    use_gradient_checkpointing=False,
+    use_gradient_checkpointing_offload=False,
+    **kwargs,
+):
+    output = dit(
+        hidden_states=latents,
+        timestep=timestep,
+        text_bth=prompt_embeds,
+        text_lens=prompt_embeds_mask,
+        use_gradient_checkpointing=use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+    )
+    return output
--- a/diffsynth/pipelines/flux2_image.py
+++ b/diffsynth/pipelines/flux2_image.py
@@ -42,6 +42,7 @@ class Flux2ImagePipeline(BasePipeline):
            Flux2Unit_ImageIDs(),
        ]
        self.model_fn = model_fn_flux2
+        self.compilable_models = ["dit"]
    
    
    @staticmethod
--- a/diffsynth/pipelines/flux_image.py
+++ b/diffsynth/pipelines/flux_image.py
@@ -103,6 +103,7 @@ class FluxImagePipeline(BasePipeline):
            FluxImageUnit_LoRAEncode(),
        ]
        self.model_fn = model_fn_flux_image
+        self.compilable_models = ["dit"]
        self.lora_loader = FluxLoRALoader

    def enable_lora_merger(self):
--- a/diffsynth/pipelines/ltx2_audio_video.py
+++ b/diffsynth/pipelines/ltx2_audio_video.py
@@ -76,6 +76,7 @@ class LTX2AudioVideoPipeline(BasePipeline):
            LTX2AudioVideoUnit_SetScheduleStage2(),
        ]
        self.model_fn = model_fn_ltx2
+        self.compilable_models = ["dit"]

        self.default_negative_prompt = {
            "LTX-2": (
--- a/diffsynth/pipelines/mova_audio_video.py
+++ b/diffsynth/pipelines/mova_audio_video.py
@@ -52,6 +52,7 @@ class MovaAudioVideoPipeline(BasePipeline):
            MovaAudioVideoUnit_UnifiedSequenceParallel(),
        ]
        self.model_fn = model_fn_mova_audio_video
+        self.compilable_models = ["video_dit", "video_dit2", "audio_dit"]

    def enable_usp(self):
        from ..utils.xfuser import get_sequence_parallel_world_size, usp_attn_forward
--- a/diffsynth/pipelines/qwen_image.py
+++ b/diffsynth/pipelines/qwen_image.py
@@ -56,6 +56,7 @@ class QwenImagePipeline(BasePipeline):
            QwenImageUnit_BlockwiseControlNet(),
        ]
        self.model_fn = model_fn_qwen_image
+        self.compilable_models = ["dit"]
    
    
    @staticmethod
--- a/diffsynth/pipelines/wan_video.py
+++ b/diffsynth/pipelines/wan_video.py
@@ -83,10 +83,11 @@ class WanVideoPipeline(BasePipeline):
            WanVideoPostUnit_S2V(),
        ]
        self.model_fn = model_fn_wan_video
+        self.compilable_models = ["dit", "dit2"]


    def enable_usp(self):
-        from ..utils.xfuser import get_sequence_parallel_world_size, usp_attn_forward, usp_dit_forward
+        from ..utils.xfuser import get_sequence_parallel_world_size, usp_attn_forward, usp_dit_forward, usp_vace_forward

        for block in self.dit.blocks:
            block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn)
@@ -95,6 +96,14 @@ class WanVideoPipeline(BasePipeline):
            for block in self.dit2.blocks:
                block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn)
            self.dit2.forward = types.MethodType(usp_dit_forward, self.dit2)
+        if self.vace is not None:
+            for block in self.vace.vace_blocks:
+                block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn)
+            self.vace.forward = types.MethodType(usp_vace_forward, self.vace)
+        if self.vace2 is not None:
+            for block in self.vace2.vace_blocks:
+                block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn)
+            self.vace2.forward = types.MethodType(usp_vace_forward, self.vace2)
        self.sp_size = get_sequence_parallel_world_size()
        self.use_unified_sequence_parallel = True

@@ -1450,13 +1459,6 @@ def model_fn_wan_video(
        tea_cache_update = tea_cache.check(dit, x, t_mod)
    else:
        tea_cache_update = False
-        
-    if vace_context is not None:
-        vace_hints = vace(
-            x, vace_context, context, t_mod, freqs,
-            use_gradient_checkpointing=use_gradient_checkpointing,
-            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload
-        )

    # WanToDance
    if hasattr(dit, "wantodance_enable_global") and dit.wantodance_enable_global:
@@ -1519,6 +1521,13 @@ def model_fn_wan_video(
            pad_shape = chunks[0].shape[1] - chunks[-1].shape[1]
            chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in chunks]
            x = chunks[get_sequence_parallel_rank()]
+
+    if vace_context is not None:
+        vace_hints = vace(
+            x, vace_context, context, t_mod, freqs,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload
+        )
    if tea_cache_update:
        x = tea_cache.update(x)
    else:
@@ -1561,9 +1570,6 @@ def model_fn_wan_video(
            # VACE
            if vace_context is not None and block_id in vace.vace_layers_mapping:
                current_vace_hint = vace_hints[vace.vace_layers_mapping[block_id]]
-                if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1:
-                    current_vace_hint = torch.chunk(current_vace_hint, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()]
-                    current_vace_hint = torch.nn.functional.pad(current_vace_hint, (0, 0, 0, chunks[0].shape[1] - current_vace_hint.shape[1]), value=0)
                x = x + current_vace_hint * vace_scale
            
            # Animate
--- a/diffsynth/pipelines/z_image.py
+++ b/diffsynth/pipelines/z_image.py
@@ -54,6 +54,7 @@ class ZImagePipeline(BasePipeline):
            ZImageUnit_PAIControlNet(),
        ]
        self.model_fn = model_fn_z_image
+        self.compilable_models = ["dit"]
    
    
    @staticmethod
--- a/diffsynth/utils/state_dict_converters/ernie_image_text_encoder.py
+++ b/diffsynth/utils/state_dict_converters/ernie_image_text_encoder.py
@@ -0,0 +1,21 @@
+def ErnieImageTextEncoderStateDictConverter(state_dict):
+    """
+    Maps checkpoint keys from multimodal Mistral3Model format
+    to text-only Ministral3Model format.
+
+    Checkpoint keys (Mistral3Model):
+        language_model.model.layers.0.input_layernorm.weight
+        language_model.model.norm.weight
+
+    Model keys (ErnieImageTextEncoder → self.model = Ministral3Model):
+        model.layers.0.input_layernorm.weight
+        model.norm.weight
+
+    Mapping: language_model. → model.
+    """
+    new_state_dict = {}
+    for key in state_dict:
+        if key.startswith("language_model.model."):
+            new_key = key.replace("language_model.model.", "model.", 1)
+            new_state_dict[new_key] = state_dict[key]
+    return new_state_dict
--- a/diffsynth/utils/state_dict_converters/z_image_dit.py
+++ b/diffsynth/utils/state_dict_converters/z_image_dit.py
@@ -0,0 +1,3 @@
+def ZImageDiTStateDictConverter(state_dict):
+    state_dict_ = {name.replace("model.diffusion_model.", ""): state_dict[name] for name in state_dict}
+    return state_dict_
--- a/diffsynth/utils/xfuser/init.py
+++ b/diffsynth/utils/xfuser/init.py
@@ -1 +1 @@
-from .xdit_context_parallel import usp_attn_forward, usp_dit_forward, get_sequence_parallel_world_size, initialize_usp, get_current_chunk, gather_all_chunks
+from .xdit_context_parallel import usp_attn_forward, usp_dit_forward, usp_vace_forward, get_sequence_parallel_world_size, initialize_usp, get_current_chunk, gather_all_chunks
--- a/diffsynth/utils/xfuser/xdit_context_parallel.py
+++ b/diffsynth/utils/xfuser/xdit_context_parallel.py
@@ -117,6 +117,39 @@ def usp_dit_forward(self,
    return x


+def usp_vace_forward(
+    self, x, vace_context, context, t_mod, freqs,
+    use_gradient_checkpointing: bool = False,
+    use_gradient_checkpointing_offload: bool = False,
+):
+    # Compute full sequence length from the sharded x
+    full_seq_len = x.shape[1] * get_sequence_parallel_world_size()
+
+    # Embed vace_context via patch embedding
+    c = [self.vace_patch_embedding(u.unsqueeze(0)) for u in vace_context]
+    c = [u.flatten(2).transpose(1, 2) for u in c]
+    c = torch.cat([
+        torch.cat([u, u.new_zeros(1, full_seq_len - u.size(1), u.size(2))],
+                  dim=1) for u in c
+    ])
+
+    # Chunk VACE context along sequence dim BEFORE processing through blocks
+    c = torch.chunk(c, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()]
+
+    # Process through vace_blocks (self_attn already monkey-patched to usp_attn_forward)
+    for block in self.vace_blocks:
+        c = gradient_checkpoint_forward(
+            block,
+            use_gradient_checkpointing,
+            use_gradient_checkpointing_offload,
+            c, x, context, t_mod, freqs
+        )
+
+    # Hints are already sharded per-rank
+    hints = torch.unbind(c)[:-1]
+    return hints
+
+
 def usp_attn_forward(self, x, freqs):
    q = self.norm_q(self.q(x))
    k = self.norm_k(self.k(x))
--- a/docs/en/Model_Details/ERNIE-Image.md
+++ b/docs/en/Model_Details/ERNIE-Image.md
@@ -0,0 +1,134 @@
+# ERNIE-Image
+
+ERNIE-Image is a powerful image generation model with 8B parameters developed by Baidu, featuring a compact and efficient architecture with strong instruction-following capability. Based on an 8B DiT backbone, it delivers performance comparable to larger (20B+) models in certain scenarios while maintaining parameter efficiency. It offers reliable performance in instruction understanding and execution, text generation (English/Chinese/Japanese), and overall stability.
+
+## Installation
+
+Before performing 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 .
+```
+
+For more information on installation, please refer to [Setup Dependencies](../Pipeline_Usage/Setup.md).
+
+## Quick Start
+
+Running the following code will load the [PaddlePaddle/ERNIE-Image](https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image) model for inference. VRAM management is enabled, the framework automatically controls parameter loading based on available VRAM, requiring a minimum of 3G VRAM.
+
+```python
+from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": torch.bfloat16,
+    "offload_device": "cpu",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = ErnieImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device='cuda',
+    model_configs=[
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors", **vram_config),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="tokenizer/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+image = pipe(
+    prompt="一只黑白相间的中华田园犬",
+    negative_prompt="",
+    height=1024,
+    width=1024,
+    seed=42,
+    num_inference_steps=50,
+    cfg_scale=4.0,
+)
+image.save("output.jpg")
+```
+
+## Model Overview
+
+|Model ID|Inference|Low VRAM Inference|Full Training|Full Training Validation|LoRA Training|LoRA Training Validation|
+|-|-|-|-|-|-|-|
+|[PaddlePaddle/ERNIE-Image](https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_inference/ERNIE-Image.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_inference_low_vram/ERNIE-Image.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_training/full/ERNIE-Image.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_training/validate_full/ERNIE-Image.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_training/lora/ERNIE-Image.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_training/validate_lora/ERNIE-Image.py)|
+|[PaddlePaddle/ERNIE-Image-Turbo](https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image-Turbo)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_inference/ERNIE-Image-Turbo.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_inference_low_vram/ERNIE-Image-Turbo.py)|—|—|—|—|
+
+## Model Inference
+
+The model is loaded via `ErnieImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models) for details.
+
+The input parameters for `ErnieImagePipeline` inference include:
+
+* `prompt`: The prompt describing the content to appear in the image.
+* `negative_prompt`: The negative prompt describing what should not appear in the image, default value is `""`.
+* `cfg_scale`: Classifier-free guidance parameter, default value is 4.0.
+* `height`: Image height, must be a multiple of 16, default value is 1024.
+* `width`: Image width, must be a multiple of 16, default value is 1024.
+* `seed`: Random seed. Default is `None`, meaning completely random.
+* `rand_device`: The computing device for generating random Gaussian noise matrices, default is `"cuda"`. When set to `cuda`, different GPUs will produce different results.
+* `num_inference_steps`: Number of inference steps, default value is 50.
+
+If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low-VRAM configurations for each model in the "Model Overview" table above.
+
+## Model Training
+
+ERNIE-Image series models are trained uniformly via [`examples/ernie_image/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_training/train.py). The script parameters include:
+
+* General Training Parameters
+    * Dataset Configuration
+        * `--dataset_base_path`: Root directory of the dataset.
+        * `--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 to load models from, in JSON format.
+        * `--model_id_with_origin_paths`: Model IDs with original paths, e.g., `"PaddlePaddle/ERNIE-Image:transformer/diffusion_pytorch_model*.safetensors"`, separated by commas.
+        * `--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.
+        * `--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`: 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.
+        * `--lora_target_modules`: Which layers to add LoRA to.
+        * `--lora_rank`: Rank of LoRA.
+        * `--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 CPU memory.
+        * `--gradient_accumulation_steps`: Number of gradient accumulation steps.
+    * Resolution Configuration
+        * `--height`: Height of the image. Leave empty to enable dynamic resolution.
+        * `--width`: Width of the image. Leave empty to enable dynamic resolution.
+        * `--max_pixels`: Maximum pixel area, images larger than this will be scaled down during dynamic resolution.
+* ERNIE-Image Specific Parameters
+    * `--tokenizer_path`: Path to the tokenizer, leave empty to auto-download from remote.
+
+We provide an example image dataset for testing, which can be downloaded with the following command:
+
+```shell
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --local_dir ./data/diffsynth_example_dataset
+```
+
+We provide recommended training scripts for each model, 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/).
--- a/docs/en/Model_Details/LTX-2.md
+++ b/docs/en/Model_Details/LTX-2.md
@@ -16,7 +16,7 @@ For more information about installation, please refer to [Installation Dependenc

 ## Quick Start

-Run the following code to quickly load the [Lightricks/LTX-2](https://www.modelscope.cn/models/Lightricks/LTX-2) model and perform inference. VRAM management has been enabled, and the framework will automatically control model parameter loading based on remaining VRAM. It can run with a minimum of 8GB VRAM.
+Run the following code to quickly load the [Lightricks/LTX-2.3](https://www.modelscope.cn/models/Lightricks/LTX-2.3) model and perform inference. VRAM management has been enabled, and the framework will automatically control model parameter loading based on remaining VRAM. It can run with a minimum of 8GB VRAM.

 ```python
 import torch
@@ -24,88 +24,36 @@ from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelCo
 from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2

 vram_config = {
-    "offload_dtype": torch.float8_e5m2,
+    "offload_dtype": torch.bfloat16,
    "offload_device": "cpu",
-    "onload_dtype": torch.float8_e5m2,
-    "onload_device": "cpu",
-    "preparing_dtype": torch.float8_e5m2,
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cuda",
+    "preparing_dtype": torch.bfloat16,
    "preparing_device": "cuda",
    "computation_dtype": torch.bfloat16,
    "computation_device": "cuda",
 }
-"""
-Offical model repo: https://www.modelscope.cn/models/Lightricks/LTX-2
-Repackaged model repo: https://www.modelscope.cn/models/DiffSynth-Studio/LTX-2-Repackage
-For base models of LTX-2, offical checkpoint (with model config ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors"))
-and repackaged checkpoints (with model config ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="*.safetensors")) are both supported.
-We have repackeged the official checkpoints in DiffSynth-Studio/LTX-2-Repackage repo to support separate loading of different submodules,
-and avoid redundant memory usage when users only want to use part of the model.
-"""
-# use the repackaged modelconfig from "DiffSynth-Studio/LTX-2-Repackage" to avoid redundant model loading
 pipe = LTX2AudioVideoPipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config),
-        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="transformer.safetensors", **vram_config),
-        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config),
-        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config),
-        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config),
-        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config),
-        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_encoder.safetensors", **vram_config),
-        ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-spatial-upscaler-x2-1.0.safetensors", **vram_config),
+        ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors", **vram_config),
+        ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-spatial-upscaler-x2-1.0.safetensors", **vram_config),
    ],
    tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"),
-    stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-distilled-lora-384.safetensors"),
-    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+    stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-distilled-lora-384.safetensors"),
 )
-
-# use the following modelconfig if you want to initialize model from offical checkpoints from "Lightricks/LTX-2"
-# pipe = LTX2AudioVideoPipeline.from_pretrained(
-#     torch_dtype=torch.bfloat16,
-#     device="cuda",
-#     model_configs=[
-#         ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config),
-#         ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors", **vram_config),
-#         ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-spatial-upscaler-x2-1.0.safetensors", **vram_config),
-#     ],
-#     tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"),
-#     stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-distilled-lora-384.safetensors"),
-#     vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
-# )
-
-prompt = "A girl is very happy, she is speaking: \"I enjoy working with Diffsynth-Studio, it's a perfect framework.\""
-negative_prompt = (
-    "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, "
-    "grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, "
-    "deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, "
-    "wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of "
-    "field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent "
-    "lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny "
-    "valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, "
-    "mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, "
-    "off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward "
-    "pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, "
-    "inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts."
-)
-height, width, num_frames = 512 * 2, 768 * 2, 121
+prompt = "Two cute orange cats, wearing boxing gloves, stand in a boxing ring and fight each other. They are punching each other fast and yelling: 'I will win!'"
+negative_prompt = pipe.default_negative_prompt["LTX-2.3"]
 video, audio = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    seed=43,
-    height=height,
-    width=width,
-    num_frames=num_frames,
-    tiled=True,
-    use_two_stage_pipeline=True,
-)
-write_video_audio_ltx2(
-    video=video,
-    audio=audio,
-    output_path='ltx2_twostage.mp4',
-    fps=24,
-    audio_sample_rate=24000,
+    height=1024, width=1536, num_frames=121,
+    tiled=True, use_two_stage_pipeline=True,
 )
+write_video_audio_ltx2(video=video, audio=audio, output_path='video.mp4', fps=24, audio_sample_rate=pipe.audio_vocoder.output_sampling_rate)
 ```

 ## Model Overview
--- a/docs/en/Pipeline_Usage/Accelerated_Inference.md
+++ b/docs/en/Pipeline_Usage/Accelerated_Inference.md
@@ -0,0 +1,94 @@
+# Inference Acceleration
+
+The denoising process of diffusion models is typically time-consuming. To improve inference speed, various acceleration techniques can be applied, including lossless acceleration solutions such as multi-GPU parallel inference and computation graph compilation, as well as lossy acceleration solutions like Cache and quantization.
+
+Currently, most diffusion models are built on Diffusion Transformers (DiT), and efficient attention mechanisms are also a common acceleration method. DiffSynth-Studio currently supports certain lossless acceleration inference features. This section focuses on introducing acceleration methods from two dimensions: multi-GPU parallel inference and computation graph compilation.
+
+## Efficient Attention Mechanisms
+
+For details on the acceleration of attention mechanisms, please refer to [Attention Mechanism Implementation](../API_Reference/core/attention.md).
+
+## Multi-GPU Parallel Inference
+
+DiffSynth-Studio adopts a multi-GPU inference solution using Unified Sequence Parallel (USP). It splits the token sequence in the DiT across multiple GPUs for parallel processing. The underlying implementation is based on [xDiT](https://github.com/xdit-project/xDiT). Please note that unified sequence parallelism introduces additional communication overhead, so the actual speedup ratio is usually lower than the number of GPUs.
+
+Currently, DiffSynth-Studio supports unified sequence parallel acceleration for the [Wan](../Model_Details/Wan.md) and [MOVA](../Model_Details/Wan.md) models.
+
+First, install the `xDiT` dependency.
+
+```bash
+pip install "xfuser[flash-attn]>=0.4.3"
+```
+
+Then, use `torchrun` to launch multi-GPU inference.
+
+```bash
+torchrun --standalone --nproc_per_node=8 examples/wanvideo/acceleration/unified_sequence_parallel.py
+```
+
+When building the pipeline, simply configure `use_usp=True` to enable USP parallel inference. A code example is shown below.
+
+```python
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
+import torch.distributed as dist
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    use_usp=True,
+    model_configs=[
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"),
+)
+
+# Text-to-video
+video = pipe(
+    prompt="一名宇航员身穿太空服，面朝镜头骑着一匹机械马在火星表面驰骋。红色的荒凉地表延伸至远方，点缀着巨大的陨石坑和奇特的岩石结构。机械马的步伐稳健，扬起微弱的尘埃，展现出未来科技与原始探索的完美结合。宇航员手持操控装置，目光坚定，仿佛正在开辟人类的新疆域。背景是深邃的宇宙和蔚蓝的地球，画面既科幻又充满希望，让人不禁畅想未来的星际生活。",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    seed=0, tiled=True,
+)
+if dist.get_rank() == 0:
+    save_video(video, "video1.mp4", fps=15, quality=5)
+```
+
+## Computation Graph Compilation
+
+PyTorch 2.0 provides an automatic computation graph compilation interface, [torch.compile](https://docs.pytorch.org/tutorials/intermediate/torch_compile_tutorial.html), which can just-in-time (JIT) compile PyTorch code into optimized kernels, thereby improving execution speed. Since the inference time of diffusion models is concentrated in the multi-step denoising phase of the DiT, and the DiT is primarily stacked with basic blocks, DiffSynth's compile feature uses a [regional compilation](https://docs.pytorch.org/tutorials/recipes/regional_compilation.html) strategy targeting only the basic Transformer blocks to reduce compilation time.
+
+### Compile Usage Example
+
+Compared to standard inference, you simply need to execute `pipe.compile_pipeline()` before calling the pipeline to enable compilation acceleration. For the specific function definition, please refer to the [source code](https://github.com/modelscope/DiffSynth-Studio/blob/166e6d2d38764209f66a74dd0fe468226536ad0f/diffsynth/diffusion/base_pipeline.py#L342).
+
+The input parameters for `compile_pipeline` consist mainly of two types.
+
+The first type is the compiled model parameters, `compile_models`. Taking the Qwen-Image Pipeline as an example, if you only want to compile the DiT model, you can keep this parameter empty. If you need to additionally compile models like the VAE, you can pass `compile_models=["vae", "dit"]`. Aside from DiT, all other models use a full-graph compilation strategy, meaning the model's forward function is completely compiled into a computation graph.
+
+The second type is the compilation strategy parameters. This covers `mode`, `dynamic`, `fullgraph`, and other custom options. These parameters are directly passed to the `torch.compile` interface. If you are not deeply familiar with the specific mechanics of these parameters, it is recommended to keep the default settings.
+
+  * `mode` specifies the compilation mode, including `"default"`, `"reduce-overhead"`, `"max-autotune"`, and `"max-autotune-no-cudagraphs"`. Because cudagraph has stricter requirements on computation graphs (for example, it might need to be used in conjunction with `torch.compiler.cudagraph_mark_step_begin()`), the `"reduce-overhead"` and `"max-autotune"` modes might fail to compile.
+  * `dynamic` determines whether to enable dynamic shapes. For most generative models, modifying the prompt, enabling CFG, or adjusting the resolution will change the shape of the input tensors to the computation graph. Setting `dynamic=True` will increase the compilation time of the first run, but it supports dynamic shapes, meaning no recompilation is needed when shapes change. When set to `dynamic=False`, the first compilation is faster, but any operation that alters the input shape will trigger a recompilation. For most scenarios, setting it to `dynamic=True` is recommended.
+  * `fullgraph`, when set to `True`, makes the underlying system attempt to compile the target model into a single computation graph, throwing an error if it fails. When set to `False`, the underlying system will set breakpoints where connections cannot be made, compiling the model into multiple independent computation graphs. Developers can set it to `True` to optimize compilation performance, but regular users are advised to only use `False`.
+  * For other parameter configurations, please consult the [API documentation](https://docs.pytorch.org/docs/stable/generated/torch.compile.html).
+
+### Compile Feature Developer Documentation
+
+If you need to provide compile support for a newly integrated pipeline, you should configure the `compilable_models` attribute in the pipeline to specify the default models to compile. For the DiT model class of that pipeline, you also need to configure `_repeated_blocks` to specify the types of basic blocks that will participate in regional compilation.
+
+Taking Qwen-Image as an example, its pipeline configuration is as follows:
+
+```python
+self.compilable_models = ["dit"]
+```
+
+Its DiT configuration is as follows:
+
+```python
+class QwenImageDiT(torch.nn.Module):
+    _repeated_blocks = ["QwenImageTransformerBlock"]
+```
--- a/docs/en/index.rst
+++ b/docs/en/index.rst
@@ -13,6 +13,7 @@ Welcome to DiffSynth-Studio's Documentation

   Pipeline_Usage/Setup
   Pipeline_Usage/Model_Inference
+   Pipeline_Usage/Accelerated_Inference
   Pipeline_Usage/VRAM_management
   Pipeline_Usage/Model_Training
   Pipeline_Usage/Environment_Variables
@@ -29,6 +30,7 @@ Welcome to DiffSynth-Studio's Documentation
   Model_Details/Z-Image
   Model_Details/Anima
   Model_Details/LTX-2
+   Model_Details/ERNIE-Image

 .. toctree::
   :maxdepth: 2
--- a/docs/zh/Model_Details/ERNIE-Image.md
+++ b/docs/zh/Model_Details/ERNIE-Image.md
@@ -0,0 +1,134 @@
+# ERNIE-Image
+
+ERNIE-Image 是百度推出的拥有 8B 参数的图像生成模型，具有紧凑高效的架构和出色的指令跟随能力。基于 8B DiT 主干网络，其在某些场景下的性能可与 20B 以上的更大模型相媲美，同时保持了良好的参数效率。该模型在指令理解与执行、文本生成（如英文/中文/日文）以及整体稳定性方面提供了较为可靠的表现。
+
+## 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 .
+```
+
+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 [PaddlePaddle/ERNIE-Image](https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image) 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 3G 显存即可运行。
+
+```python
+from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": torch.bfloat16,
+    "offload_device": "cpu",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = ErnieImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device='cuda',
+    model_configs=[
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors", **vram_config),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="tokenizer/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+image = pipe(
+    prompt="一只黑白相间的中华田园犬",
+    negative_prompt="",
+    height=1024,
+    width=1024,
+    seed=42,
+    num_inference_steps=50,
+    cfg_scale=4.0,
+)
+image.save("output.jpg")
+```
+
+## 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|
+|-|-|-|-|-|-|-|
+|[PaddlePaddle/ERNIE-Image](https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_inference/ERNIE-Image.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_inference_low_vram/ERNIE-Image.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_training/full/ERNIE-Image.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_training/validate_full/ERNIE-Image.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_training/lora/ERNIE-Image.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_training/validate_lora/ERNIE-Image.py)|
+|[PaddlePaddle/ERNIE-Image-Turbo](https://www.modelscope.cn/models/PaddlePaddle/ERNIE-Image-Turbo)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_inference/ERNIE-Image-Turbo.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/model_inference_low_vram/ERNIE-Image-Turbo.py)|—|—|—|—|
+
+## 模型推理
+
+模型通过 `ErnieImagePipeline.from_pretrained` 加载，详见[加载模型](../Pipeline_Usage/Model_Inference.md#加载模型)。
+
+`ErnieImagePipeline` 推理的输入参数包括：
+
+* `prompt`: 提示词，描述画面中出现的内容。
+* `negative_prompt`: 负向提示词，描述画面中不应该出现的内容，默认值为 `""`。
+* `cfg_scale`: Classifier-free guidance 的参数，默认值为 4.0。
+* `height`: 图像高度，需保证高度为 16 的倍数，默认值为 1024。
+* `width`: 图像宽度，需保证宽度为 16 的倍数，默认值为 1024。
+* `seed`: 随机种子。默认为 `None`，即完全随机。
+* `rand_device`: 生成随机高斯噪声矩阵的计算设备，默认为 `"cuda"`。当设置为 `cuda` 时，在不同 GPU 上会导致不同的生成结果。
+* `num_inference_steps`: 推理步数，默认值为 50。
+
+如果显存不足，请开启[显存管理](../Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"模型总览"中的表格。
+
+## 模型训练
+
+ERNIE-Image 系列模型统一通过 [`examples/ernie_image/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ernie_image/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。
+        * `--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，例如 `"PaddlePaddle/ERNIE-Image:transformer/diffusion_pytorch_model*.safetensors"`。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。
+        * `--lora_target_modules`: LoRA 添加到哪些层上。
+        * `--lora_rank`: LoRA 的秩（Rank）。
+        * `--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。
+    * 分辨率配置
+        * `--height`: 图像的高度。留空启用动态分辨率。
+        * `--width`: 图像的宽度。留空启用动态分辨率。
+        * `--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。
+* ERNIE-Image 专有参数
+    * `--tokenizer_path`: tokenizer 的路径，留空则自动从远程下载。
+
+我们构建了一个样例图像数据集，以方便您进行测试，通过以下命令可以下载这个数据集：
+
+```shell
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --local_dir ./data/diffsynth_example_dataset
+```
+
+我们为每个模型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/)。
--- a/docs/zh/Model_Details/LTX-2.md
+++ b/docs/zh/Model_Details/LTX-2.md
@@ -16,7 +16,7 @@ pip install -e .

 ## 快速开始

-运行以下代码可以快速加载 [Lightricks/LTX-2](https://www.modelscope.cn/models/Lightricks/LTX-2) 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 8GB 显存即可运行。
+运行以下代码可以快速加载 [Lightricks/LTX-2.3](https://www.modelscope.cn/models/Lightricks/LTX-2.3) 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 8GB 显存即可运行。

 ```python
 import torch
@@ -24,88 +24,36 @@ from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelCo
 from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2

 vram_config = {
-    "offload_dtype": torch.float8_e5m2,
+    "offload_dtype": torch.bfloat16,
    "offload_device": "cpu",
-    "onload_dtype": torch.float8_e5m2,
-    "onload_device": "cpu",
-    "preparing_dtype": torch.float8_e5m2,
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cuda",
+    "preparing_dtype": torch.bfloat16,
    "preparing_device": "cuda",
    "computation_dtype": torch.bfloat16,
    "computation_device": "cuda",
 }
-"""
-Offical model repo: https://www.modelscope.cn/models/Lightricks/LTX-2
-Repackaged model repo: https://www.modelscope.cn/models/DiffSynth-Studio/LTX-2-Repackage
-For base models of LTX-2, offical checkpoint (with model config ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors"))
-and repackaged checkpoints (with model config ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="*.safetensors")) are both supported.
-We have repackeged the official checkpoints in DiffSynth-Studio/LTX-2-Repackage repo to support separate loading of different submodules,
-and avoid redundant memory usage when users only want to use part of the model.
-"""
-# use the repackaged modelconfig from "DiffSynth-Studio/LTX-2-Repackage" to avoid redundant model loading
 pipe = LTX2AudioVideoPipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config),
-        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="transformer.safetensors", **vram_config),
-        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config),
-        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config),
-        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config),
-        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config),
-        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_encoder.safetensors", **vram_config),
-        ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-spatial-upscaler-x2-1.0.safetensors", **vram_config),
+        ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors", **vram_config),
+        ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-spatial-upscaler-x2-1.0.safetensors", **vram_config),
    ],
    tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"),
-    stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-distilled-lora-384.safetensors"),
-    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+    stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-distilled-lora-384.safetensors"),
 )
-
-# use the following modelconfig if you want to initialize model from offical checkpoints from "Lightricks/LTX-2"
-# pipe = LTX2AudioVideoPipeline.from_pretrained(
-#     torch_dtype=torch.bfloat16,
-#     device="cuda",
-#     model_configs=[
-#         ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config),
-#         ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors", **vram_config),
-#         ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-spatial-upscaler-x2-1.0.safetensors", **vram_config),
-#     ],
-#     tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"),
-#     stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-distilled-lora-384.safetensors"),
-#     vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
-# )
-
-prompt = "A girl is very happy, she is speaking: \"I enjoy working with Diffsynth-Studio, it's a perfect framework.\""
-negative_prompt = (
-    "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, "
-    "grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, "
-    "deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, "
-    "wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of "
-    "field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent "
-    "lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny "
-    "valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, "
-    "mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, "
-    "off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward "
-    "pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, "
-    "inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts."
-)
-height, width, num_frames = 512 * 2, 768 * 2, 121
+prompt = "Two cute orange cats, wearing boxing gloves, stand in a boxing ring and fight each other. They are punching each other fast and yelling: 'I will win!'"
+negative_prompt = pipe.default_negative_prompt["LTX-2.3"]
 video, audio = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    seed=43,
-    height=height,
-    width=width,
-    num_frames=num_frames,
-    tiled=True,
-    use_two_stage_pipeline=True,
-)
-write_video_audio_ltx2(
-    video=video,
-    audio=audio,
-    output_path='ltx2_twostage.mp4',
-    fps=24,
-    audio_sample_rate=24000,
+    height=1024, width=1536, num_frames=121,
+    tiled=True, use_two_stage_pipeline=True,
 )
+write_video_audio_ltx2(video=video, audio=audio, output_path='video.mp4', fps=24, audio_sample_rate=pipe.audio_vocoder.output_sampling_rate)
 ```

 ## 模型总览
--- a/docs/zh/Pipeline_Usage/Accelerated_Inference.md
+++ b/docs/zh/Pipeline_Usage/Accelerated_Inference.md
@@ -0,0 +1,84 @@
+# 推理加速
+
+扩散模型的去噪过程通常耗时较长。为提升推理速度，可采用多种加速技术，包含多卡并行推理、计算图编译等无损加速方案，以及 Cache、量化等有损加速方案。
+
+当前扩散模型大多基于 Diffusion Transformer 构建，高效注意力机制同样是常用的加速手段。DiffSynth-Studio 目前已支持部分无损加速推理功能。本节重点从多卡并行推理和计算图编译两个维度介绍加速方法。
+
+## 高效注意力机制
+注意力机制的加速细节请参考 [注意力机制实现](../API_Reference/core/attention.md)。
+
+## 多卡并行推理
+DiffSynth-Studio 采用统一序列并行的多卡推理方案。在 DiT 中将 token 序列拆分至多张显卡进行并行处理。底层基于 [xDiT](https://github.com/xdit-project/xDiT) 实现。需要注意，统一序列并行会引入额外通信开销，实际加速比通常低于显卡数量。
+
+目前 DiffSynth-Studio 已支持 [Wan](../Model_Details/Wan.md) 和 [MOVA](../Model_Details/Wan.md) 模型的统一序列并行加速。
+
+首先安装 `xDiT` 依赖。
+```bash
+pip install "xfuser[flash-attn]>=0.4.3"
+```
+
+然后使用 `torchrun` 启动多卡推理。
+```bash
+torchrun --standalone --nproc_per_node=8 examples/wanvideo/acceleration/unified_sequence_parallel.py
+```
+
+构建 pipeline 时配置 `usp=True` 即可实现 USP 并行推理。代码示例如下。
+```python
+import torch
+from PIL import Image
+from diffsynth.utils.data import save_video
+from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
+import torch.distributed as dist
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    use_usp=True,
+    model_configs=[
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"),
+)
+
+# Text-to-video
+video = pipe(
+    prompt="一名宇航员身穿太空服，面朝镜头骑着一匹机械马在火星表面驰骋。红色的荒凉地表延伸至远方，点缀着巨大的陨石坑和奇特的岩石结构。机械马的步伐稳健，扬起微弱的尘埃，展现出未来科技与原始探索的完美结合。宇航员手持操控装置，目光坚定，仿佛正在开辟人类的新疆域。背景是深邃的宇宙和蔚蓝的地球，画面既科幻又充满希望，让人不禁畅想未来的星际生活。",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    seed=0, tiled=True,
+)
+if dist.get_rank() == 0:
+    save_video(video, "video1.mp4", fps=15, quality=5)
+```
+
+## 计算图编译
+PyTorch 2.0 提供了自动计算图编译接口 [torch.compile](https://docs.pytorch.org/tutorials/intermediate/torch_compile_tutorial.html)，能够将 PyTorch 代码即时编译为优化内核，从而提升运行速度。由于扩散模型的推理耗时集中在 DiT 的多步去噪阶段，且 DiT 主要由基础模块堆叠而成，为缩短编译时间，DiffSynth 的 compile 功能采用仅针对基础 Transformer 模块的 [区域编译](https://docs.pytorch.org/tutorials/recipes/regional_compilation.html) 策略。
+
+### Compile 使用示例
+相比常规推理，只需在调用 pipeline 前执行 `pipe.compile_pipeline()` 即可开启编译加速。具体函数定义请参阅[源代码](https://github.com/modelscope/DiffSynth-Studio/blob/166e6d2d38764209f66a74dd0fe468226536ad0f/diffsynth/diffusion/base_pipeline.py#L342)。
+
+`compile_pipeline` 的输入参数主要包含两类。
+
+第一类是编译模型参数 `compile_models`。以 Qwen-Image Pipeline 为例，若仅编译 DiT 模型，保持该参数为空即可。若需额外编译 VAE 等模型，可传入 `compile_models=["vae", "dit"]`。除 DiT 外，其余模型均采用整体编译策略，即把模型的 forward 函数完整编译为计算图。
+
+第二类是编译策略参数。涵盖 `mode`, `dynamic`, `fullgraph` 及其他自定义选项。这些参数会直接传递给 `torch.compile` 接口。若未深入了解这些参数的具体机制，建议保持默认设置。
+
+- `mode` 指定编译模式，包含 `"default"`, `"reduce-overhead"`, `"max-autotune"` 和 `"max-autotune-no-cudagraphs"`。由于 cudagraph 对计算图要求较为严格（例如可能需要配合 `torch.compiler.cudagraph_mark_step_begin()` 使用），`"reduce-overhead"` 和 `"max-autotune"` 模式可能编译失败。
+- `dynamic` 决定是否启用动态形状。对于多数生成模型，修改 prompt、开启 CFG 或调整分辨率都会改变计算图的输入张量形状。设置为 `dynamic=True` 会增加首次运行的编译时长，但支持动态形状，形状改变时无需重编译。设置为 `dynamic=False` 时首次编译较快，但任何改变输入形状的操作都会触发重新编译。对大部分场景，建议设定为 `dynamic=True`。
+- `fullgraph` 设为 `True` 时，底层会尝试将目标模型编译为单一计算图，若失败则报错。设为 `False` 时，底层会在无法连接处设置断点，将模型编译为多个独立计算图。开发者可开启 `True` 来优化编译性能，普通用户建议仅使用 `False`。
+- 其他参数配置请查阅 [api 文档](https://docs.pytorch.org/docs/stable/generated/torch.compile.html)。
+
+### Compile 功能开发者文档
+若需为新接入的 pipeline 提供 compile 支持，应在 pipeline 中配置 `compilable_models` 属性以指定默认编译模型。针对该 pipeline 的 DiT 模型类，还需配置 `_repeated_blocks` 以指定参与区域编译的基础模块类型。
+
+以 Qwen-Image 为例，其 pipeline 配置如下。
+```python
+self.compilable_models = ["dit"]
+```
+
+其 DiT 配置如下。
+```python
+class QwenImageDiT(torch.nn.Module):
+    _repeated_blocks = ["QwenImageTransformerBlock"]
+```
--- a/docs/zh/index.rst
+++ b/docs/zh/index.rst
@@ -13,6 +13,7 @@

   Pipeline_Usage/Setup
   Pipeline_Usage/Model_Inference
+   Pipeline_Usage/Accelerated_Inference
   Pipeline_Usage/VRAM_management
   Pipeline_Usage/Model_Training
   Pipeline_Usage/Environment_Variables
@@ -29,6 +30,7 @@
   Model_Details/Z-Image
   Model_Details/Anima
   Model_Details/LTX-2
+   Model_Details/ERNIE-Image

 .. toctree::
   :maxdepth: 2
--- a/examples/ernie_image/model_inference/ERNIE-Image-Turbo.py
+++ b/examples/ernie_image/model_inference/ERNIE-Image-Turbo.py
@@ -0,0 +1,25 @@
+from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
+import torch
+
+pipe = ErnieImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device='cuda',
+    model_configs=[
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image-Turbo", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors"),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="tokenizer/"),
+)
+
+image = pipe(
+    prompt="一只黑白相间的中华田园犬",
+    negative_prompt="",
+    height=1024,
+    width=1024,
+    seed=42,
+    num_inference_steps=8,
+    cfg_scale=1.0,
+    sigma_shift=4.0,
+)
+image.save("output_turbo.jpg")
--- a/examples/ernie_image/model_inference/ERNIE-Image.py
+++ b/examples/ernie_image/model_inference/ERNIE-Image.py
@@ -0,0 +1,24 @@
+from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
+import torch
+
+pipe = ErnieImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device='cuda',
+    model_configs=[
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors"),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="tokenizer/"),
+)
+
+image = pipe(
+    prompt="一只黑白相间的中华田园犬",
+    negative_prompt="",
+    height=1024,
+    width=1024,
+    seed=42,
+    num_inference_steps=50,
+    cfg_scale=4.0,
+)
+image.save("output.jpg")
--- a/examples/ernie_image/model_inference_low_vram/ERNIE-Image-Turbo.py
+++ b/examples/ernie_image/model_inference_low_vram/ERNIE-Image-Turbo.py
@@ -0,0 +1,37 @@
+from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": torch.bfloat16,
+    "offload_device": "cpu",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+pipe = ErnieImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device='cuda',
+    model_configs=[
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image-Turbo", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors", **vram_config),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="tokenizer/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+image = pipe(
+    prompt="一只黑白相间的中华田园犬",
+    negative_prompt="",
+    height=1024,
+    width=1024,
+    seed=42,
+    num_inference_steps=8,
+    cfg_scale=1.0,
+    sigma_shift=4.0,
+)
+image.save("output_turbo.jpg")
--- a/examples/ernie_image/model_inference_low_vram/ERNIE-Image.py
+++ b/examples/ernie_image/model_inference_low_vram/ERNIE-Image.py
@@ -0,0 +1,36 @@
+from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": torch.bfloat16,
+    "offload_device": "cpu",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+pipe = ErnieImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device='cuda',
+    model_configs=[
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors", **vram_config),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="tokenizer/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+image = pipe(
+    prompt="一只黑白相间的中华田园犬",
+    negative_prompt="",
+    height=1024,
+    width=1024,
+    seed=42,
+    num_inference_steps=50,
+    cfg_scale=4.0,
+)
+image.save("output.jpg")
--- a/examples/ernie_image/model_training/full/ERNIE-Image.sh
+++ b/examples/ernie_image/model_training/full/ERNIE-Image.sh
@@ -0,0 +1,17 @@
+# Dataset: data/diffsynth_example_dataset/ernie_image/Ernie-Image-T2I/
+
+accelerate launch --config_file examples/ernie_image/model_training/full/accelerate_config_zero3.yaml \
+  examples/ernie_image/model_training/train.py \
+  --dataset_base_path data/diffsynth_example_dataset/ernie_image/Ernie-Image-T2I \
+  --dataset_metadata_path data/diffsynth_example_dataset/ernie_image/Ernie-Image-T2I/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "PaddlePaddle/ERNIE-Image:transformer/diffusion_pytorch_model*.safetensors,PaddlePaddle/ERNIE-Image:text_encoder/model.safetensors,PaddlePaddle/ERNIE-Image:vae/diffusion_pytorch_model.safetensors" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Ernie-Image-T2I_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters
--- a/examples/ernie_image/model_training/full/accelerate_config_zero3.yaml
+++ b/examples/ernie_image/model_training/full/accelerate_config_zero3.yaml
@@ -0,0 +1,23 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  gradient_accumulation_steps: 1
+  offload_optimizer_device: none
+  offload_param_device: none
+  zero3_init_flag: true
+  zero3_save_16bit_model: true
+  zero_stage: 3
+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: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
--- a/examples/ernie_image/model_training/lora/ERNIE-Image.sh
+++ b/examples/ernie_image/model_training/lora/ERNIE-Image.sh
@@ -0,0 +1,19 @@
+# Dataset: data/diffsynth_example_dataset/ernie_image/Ernie-Image-T2I/
+# Download: modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "ernie_image/Ernie-Image-T2I/*" --local_dir ./data/diffsynth_example_dataset
+
+accelerate launch examples/ernie_image/model_training/train.py \
+  --dataset_base_path data/diffsynth_example_dataset/ernie_image/Ernie-Image-T2I \
+  --dataset_metadata_path data/diffsynth_example_dataset/ernie_image/Ernie-Image-T2I/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "PaddlePaddle/ERNIE-Image:transformer/diffusion_pytorch_model*.safetensors,PaddlePaddle/ERNIE-Image:text_encoder/model.safetensors,PaddlePaddle/ERNIE-Image:vae/diffusion_pytorch_model.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Ernie-Image-T2I_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,to_out.0" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters
--- a/examples/ernie_image/model_training/train.py
+++ b/examples/ernie_image/model_training/train.py
@@ -0,0 +1,129 @@
+import torch, os, argparse, accelerate
+from diffsynth.core import UnifiedDataset
+from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
+from diffsynth.diffusion import *
+from diffsynth.core.data.operators import *
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+class ErnieImageTrainingModule(DiffusionTrainingModule):
+    def __init__(
+        self,
+        model_paths=None, model_id_with_origin_paths=None,
+        tokenizer_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,
+        device="cpu",
+        task="sft",
+    ):
+        super().__init__()
+        # Load models
+        model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device)
+        tokenizer_config = ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="tokenizer/") if tokenizer_path is None else ModelConfig(tokenizer_path)
+        self.pipe = ErnieImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config)
+        self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
+
+        # Training mode
+        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,
+        )
+
+        # 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.task = task
+        self.task_to_loss = {
+            "sft:data_process": lambda pipe, inputs_shared, inputs_posi, inputs_nega: (inputs_shared, inputs_posi, inputs_nega),
+            "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),
+        }
+
+    def get_pipeline_inputs(self, data):
+        inputs_posi = {"prompt": data["prompt"]}
+        inputs_nega = {"negative_prompt": ""}
+        inputs_shared = {
+            "input_image": data["image"],
+            "height": data["image"].size[1],
+            "width": data["image"].size[0],
+            "cfg_scale": 1,
+            "rand_device": self.pipe.device,
+            "use_gradient_checkpointing": self.use_gradient_checkpointing,
+            "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
+        }
+        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 ernie_image_parser():
+    parser = argparse.ArgumentParser(description="ERNIE-Image training.")
+    parser = add_general_config(parser)
+    parser = add_image_size_config(parser)
+    parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.")
+    return parser
+
+
+if __name__ == "__main__":
+    parser = ernie_image_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=lambda x: x,
+        special_operator_map={
+            "image": ToAbsolutePath(args.dataset_base_path) >> LoadImage() >> ImageCropAndResize(args.height, args.width, args.max_pixels, 16, 16),
+        },
+    )
+    model = ErnieImageTrainingModule(
+        model_paths=args.model_paths,
+        model_id_with_origin_paths=args.model_id_with_origin_paths,
+        tokenizer_path=args.tokenizer_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,
+        task=args.task,
+        device=accelerator.device,
+    )
+    model_logger = ModelLogger(
+        args.output_path,
+        remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
+    )
+    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)
--- a/examples/ernie_image/model_training/validate_full/ERNIE-Image.py
+++ b/examples/ernie_image/model_training/validate_full/ERNIE-Image.py
@@ -0,0 +1,25 @@
+import torch
+from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
+from diffsynth.core import load_state_dict
+
+pipe = ErnieImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors"),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+)
+
+state_dict = load_state_dict("./models/train/Ernie-Image-T2I_full/epoch-1.safetensors")
+pipe.dit.load_state_dict(state_dict)
+
+image = pipe(
+    prompt="a professional photo of a cute dog",
+    seed=0,
+    num_inference_steps=50,
+    cfg_scale=4.0,
+)
+image.save("image_full.jpg")
+print("Full validation image saved to image_full.jpg")
--- a/examples/ernie_image/model_training/validate_lora/ERNIE-Image.py
+++ b/examples/ernie_image/model_training/validate_lora/ERNIE-Image.py
@@ -0,0 +1,25 @@
+import torch
+from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
+from diffsynth.core.loader.file import load_state_dict
+
+pipe = ErnieImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors"),
+        ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+)
+
+lora_state_dict = load_state_dict("./models/train/Ernie-Image-T2I_lora/epoch-4.safetensors", torch_dtype=torch.bfloat16, device="cuda")
+pipe.load_lora(pipe.dit, state_dict=lora_state_dict, alpha=1.0)
+
+image = pipe(
+    prompt="a professional photo of a cute dog",
+    seed=0,
+    num_inference_steps=50,
+    cfg_scale=4.0,
+)
+image.save("image_lora.jpg")
+print("LoRA validation image saved to image_lora.jpg")
--- a/examples/flux2/model_inference/FLUX.2-dev.py
+++ b/examples/flux2/model_inference/FLUX.2-dev.py
@@ -1,6 +1,6 @@
 from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
 import torch
-
+from PIL import Image

 vram_config = {
    "offload_dtype": torch.bfloat16,
@@ -25,3 +25,8 @@ pipe = Flux2ImagePipeline.from_pretrained(
 prompt = "Realistic macro photograph of a hermit crab using a soda can as its shell, partially emerging from the can, captured with sharp detail and natural colors, on a sunlit beach with soft shadows and a shallow depth of field, with blurred ocean waves in the background. The can has the text `BFL Diffusers` on it and it has a color gradient that start with #FF5733 at the top and transitions to #33FF57 at the bottom."
 image = pipe(prompt, seed=42, rand_device="cuda", num_inference_steps=50)
 image.save("image_FLUX.2-dev.jpg")
+
+prompt  = "Transform the image into Japanese anime style"
+edit_image = [Image.open("image_FLUX.2-dev.jpg")]
+image = pipe(prompt, seed=42, rand_device="cuda", edit_image=edit_image, num_inference_steps=50, embedded_guidance=2.5)
+image.save("image_FLUX.2-dev_edit.jpg")
--- a/examples/flux2/model_inference_low_vram/FLUX.2-dev.py
+++ b/examples/flux2/model_inference_low_vram/FLUX.2-dev.py
@@ -1,5 +1,6 @@
 from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
 import torch
+from PIL import Image

 vram_config = {
    "offload_dtype": "disk",
@@ -24,4 +25,9 @@ pipe = Flux2ImagePipeline.from_pretrained(
 )
 prompt = "High resolution. A dreamy underwater portrait of a serene young woman in a flowing blue dress. Her hair floats softly around her face, strands delicately suspended in the water. Clear, shimmering light filters through, casting gentle highlights, while tiny bubbles rise around her. Her expression is calm, her features finely detailed—creating a tranquil, ethereal scene."
 image = pipe(prompt, seed=42, rand_device="cuda", num_inference_steps=50)
-image.save("image.jpg")
+image.save("image.jpg")
+
+prompt  = "Transform the image into Japanese anime style"
+edit_image = [Image.open("image.jpg")]
+image = pipe(prompt, seed=42, rand_device="cuda", edit_image=edit_image, num_inference_steps=50, embedded_guidance=2.5)
+image.save("image_edit.jpg")
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "diffsynth"
-version = "2.0.6"
+version = "2.0.8"
 description = "Enjoy the magic of Diffusion models!"
 authors = [{name = "ModelScope Team"}]
 license = {text = "Apache-2.0"}
Author	SHA1	Message	Date
Artiprocher	622469feea	update to version 2.0.8	2026-04-14 16:57:44 +08:00
Hong Zhang	c80fec2a56	add acceleration inference docs (#1371 ) * add acceleration inference docs * minor fix	2026-04-14 13:29:29 +08:00
Hong Zhang	b5d04ceb30	support ernie-image-turbo (#1391 ) * support ernie-image-turbo * pr review fix * fix modelname	2026-04-14 11:35:43 +08:00
Hong Zhang	960d8c62c0	Support ERNIE-Image (#1389 ) * ernie-image pipeline * ernie-image inference and training * style fix * ernie docs * lowvram * final style fix * pr-review * pr-fix round2 * set uniform training weight * fix * update lowvram docs	2026-04-13 14:57:10 +08:00
Zhongjie Duan	f77b6357c5	add Discord link in README (#1390 ) * add discord link * add discord link	2026-04-13 14:50:48 +08:00
Zhongjie Duan	166e6d2d38	update version to 2.0.7 (#1370 )	2026-03-24 17:37:54 +08:00
lzws	5e7e3db0af	update flux.2-dev editing examples (#1369 ) * add FireRed-Image-Edit-1.1 * flux.2-dev-edit * flux.2-dev-edit * flux.2-dev-edit	2026-03-24 17:32:45 +08:00
Hong Zhang	ae8cb139e8	Support Torch Compile (#1368 ) * support simple compile * add support for compile * minor fix * minor fix * minor fix	2026-03-24 11:19:43 +08:00
Zhongjie Duan	e2a3a987da	update LTX-2.3 doc (#1365 )	2026-03-23 17:14:50 +08:00
Zhongjie Duan	f7b9ae7d57	update LTX-2.3 doc (#1364 )	2026-03-23 17:10:53 +08:00
Cao Yuan	5d198287f0	[feat] add VACE sequence parallel (#1345 ) * add VACE sequence parallel * resolve conflict --------- Co-authored-by: yuan <yuan@yuandeMacBook-Pro.local> Co-authored-by: Hong Zhang <41229682+mi804@users.noreply.github.com>	2026-03-23 15:46:27 +08:00
Zhongjie Duan	5bccd60c80	compatibility patch (#1363 )	2026-03-23 11:24:49 +08:00