update version

support loading models from state dict
Merge pull request #1233 from modelscope/z-image-release
2026-03-19 14:58:12 +00:00 · 2026-01-30 13:49:27 +08:00 · 2026-01-30 13:47:36 +08:00 · 2026-01-27 18:41:28 +08:00 · 2026-01-27 13:42:48 +08:00 · 2026-01-27 12:16:48 +08:00
164 changed files with 6435 additions and 421 deletions
--- a/.github/workflows/publish.yaml
+++ b/.github/workflows/publish.yaml
@@ -22,7 +22,7 @@ jobs:
      - name: Install wheel
        run: pip install wheel==0.44.0 && pip install -r requirements.txt
      - name: Build DiffSynth
-        run: python setup.py sdist bdist_wheel
+        run: python -m build
      - name: Publish package to PyPI
        run: |
          pip install twine
--- a/README.md
+++ b/README.md
@@ -33,6 +33,16 @@ We believe that a well-developed open-source code framework can lower the thresh

 > Currently, the development personnel of this project are limited, with most of the work handled by [Artiprocher](https://github.com/Artiprocher). 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 27, 2026**: [Z-Image](https://modelscope.cn/models/Tongyi-MAI/Z-Image) is released, and our [Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L) model is released concurrently. You can use it in [ModelScope Studios](https://modelscope.cn/studios/DiffSynth-Studio/Z-Image-i2L). For details, see the [documentation](/docs/zh/Model_Details/Z-Image.md).
+
+- **January 19, 2026**: Added support for [FLUX.2-klein-4B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B) and [FLUX.2-klein-9B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-9B) models, including training and inference capabilities. [Documentation](/docs/en/Model_Details/FLUX2.md) and [example code](/examples/flux2/) are now available.
+
+- **January 12, 2026**: We trained and open-sourced a text-guided image layer separation model ([Model Link](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)). Given an input image and a textual description, the model isolates the image layer corresponding to the described content. For more details, please refer to our blog post ([Chinese version](https://modelscope.cn/learn/4938), [English version](https://huggingface.co/blog/kelseye/qwen-image-layered-control)).
+
+- **December 24, 2025**: Based on Qwen-Image-Edit-2511, we trained an In-Context Editing LoRA model ([Model Link](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA)). This model takes three images as input (Image A, Image B, and Image C), and automatically analyzes the transformation from Image A to Image B, then applies the same transformation to Image C to generate Image D. For more details, please refer to our blog post ([Chinese version](https://mp.weixin.qq.com/s/41aEiN3lXKGCJs1-we4Q2g), [English version](https://huggingface.co/blog/kelseye/qwen-image-edit-2511-icedit-lora)).
+
+- **December 9, 2025** We release a wild model based on DiffSynth-Studio 2.0: [Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L) (Image-to-LoRA). This model takes an image as input and outputs a LoRA. Although this version still has significant room for improvement in terms of generalization, detail preservation, and other aspects, we are open-sourcing these models to inspire more innovative research. For more details, please refer to our [blog](https://huggingface.co/blog/kelseye/qwen-image-i2l).
+
 - **December 4, 2025** DiffSynth-Studio 2.0 released! Many new features online
  - [Documentation](/docs/en/README.md) online: Our documentation is still continuously being optimized and updated
  - [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md) module upgraded, supporting layer-level disk offload, releasing both memory and VRAM simultaneously
@@ -187,21 +197,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```

-<details>
-<summary>Other installation methods</summary>
-
-Install from PyPI (version updates may be delayed; for latest features, install from source)
-
-```
-pip install diffsynth
-```
-
-If you meet problems during installation, they might be caused by upstream dependencies. Please check the docs of these packages:
-
-* [torch](https://pytorch.org/get-started/locally/)
-* [sentencepiece](https://github.com/google/sentencepiece)
-* [cmake](https://cmake.org)
-* [cupy](https://docs.cupy.dev/en/stable/install.html)
+For more installation methods and instructions for non-NVIDIA GPUs, please refer to the [Installation Guide](/docs/en/Pipeline_Usage/Setup.md).

 </details>

@@ -275,9 +271,14 @@ image.save("image.jpg")

 Example code for Z-Image is available at: [/examples/z_image/](/examples/z_image/)

-| Model ID | Inference | Low-VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
+|Model ID|Inference|Low VRAM Inference|Full Training|Validation After Full Training|LoRA Training|Validation After LoRA Training|
 |-|-|-|-|-|-|-|
+|[Tongyi-MAI/Z-Image](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image)|[code](/examples/z_image/model_inference/Z-Image.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image.py)|[code](/examples/z_image/model_training/full/Z-Image.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image.py)|[code](/examples/z_image/model_training/lora/Z-Image.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image.py)|
+|[DiffSynth-Studio/Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L)|[code](/examples/z_image/model_inference/Z-Image-i2L.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-i2L.py)|-|-|-|-|
 |[Tongyi-MAI/Z-Image-Turbo](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo)|[code](/examples/z_image/model_inference/Z-Image-Turbo.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|

 </details>

@@ -327,9 +328,13 @@ image.save("image.jpg")

 Example code for FLUX.2 is available at: [/examples/flux2/](/examples/flux2/)

-| Model ID | Inference | Low-VRAM Inference | LoRA Training | LoRA Training Validation |
-|-|-|-|-|-|
-|[black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev)|[code](/examples/flux2/model_inference/FLUX.2-dev.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-dev.py)|[code](/examples/flux2/model_training/lora/FLUX.2-dev.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-dev.py)|
+| Model ID | Inference | Low-VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
+|-|-|-|-|-|-|-|
+|[black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev)|[code](/examples/flux2/model_inference/FLUX.2-dev.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-dev.py)|-|-|[code](/examples/flux2/model_training/lora/FLUX.2-dev.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-dev.py)|
+|[black-forest-labs/FLUX.2-klein-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B)|[code](/examples/flux2/model_inference/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-4B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-4B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-4B.py)|
+|[black-forest-labs/FLUX.2-klein-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-9B)|[code](/examples/flux2/model_inference/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-9B.py)|
+|[black-forest-labs/FLUX.2-klein-base-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-4B)|[code](/examples/flux2/model_inference/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-4B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-4B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-4B.py)|
+|[black-forest-labs/FLUX.2-klein-base-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-9B)|[code](/examples/flux2/model_inference/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-9B.py)|

 </details>

@@ -408,8 +413,12 @@ Example code for Qwen-Image is available at: [/examples/qwen_image/](/examples/q
 | Model ID | Inference | Low-VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
 |-|-|-|-|-|-|-|
 |[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](/examples/qwen_image/model_inference/Qwen-Image.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image.py)|
+|[Qwen/Qwen-Image-2512](https://www.modelscope.cn/models/Qwen/Qwen-Image-2512)|[code](/examples/qwen_image/model_inference/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-2512.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-2512.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-2512.py)|
 |[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)|
 |[Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py)|
+|[Qwen/Qwen-Image-Edit-2511](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
+|[Qwen/Qwen-Image-Layered](https://www.modelscope.cn/models/Qwen/Qwen-Image-Layered)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py)|
+|[DiffSynth-Studio/Qwen-Image-Layered-Control](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py)|
@@ -420,6 +429,7 @@ Example code for Qwen-Image is available at: [/examples/qwen_image/](/examples/q
 |[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)|[code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|
 |[DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union)|[code](/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py)|
 |[DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-|
+|[DiffSynth-Studio/Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L)|[code](/examples/qwen_image/model_inference/Qwen-Image-i2L.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py)|-|-|-|-|

 </details>

@@ -777,4 +787,3 @@ 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>
-
--- a/README_zh.md
+++ b/README_zh.md
@@ -33,6 +33,16 @@ 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) 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27日** [Z-Image](https://modelscope.cn/models/Tongyi-MAI/Z-Image) 发布，我们的 [Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L) 模型同步发布，在[魔搭创空间](https://modelscope.cn/studios/DiffSynth-Studio/Z-Image-i2L)可直接体验，详见[文档](/docs/zh/Model_Details/Z-Image.md)。
+
+- **2026年1月19日** 新增对 [FLUX.2-klein-4B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B) 和 [FLUX.2-klein-9B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-9B) 模型的支持，包括完整的训练和推理功能。[文档](/docs/zh/Model_Details/FLUX2.md)和[示例代码](/examples/flux2/)现已可用。
+
+- **2026年1月12日** 我们训练并开源了一个文本引导的图层拆分模型（[模型链接](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)），这一模型输入一张图与一段文本描述，模型会将图像中与文本描述相关的图层拆分出来。更多细节请阅读我们的 blog（[中文版](https://modelscope.cn/learn/4938)、[英文版](https://huggingface.co/blog/kelseye/qwen-image-layered-control)）。
+
+- **2025年12月24日** 我们基于 Qwen-Image-Edit-2511 训练了一个 In-Context Editing LoRA 模型（[模型链接](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA)），这个模型可以输入三张图：图A、图B、图C，模型会自行分析图A到图B的变化，并将这样的变化应用到图C，生成图D。更多细节请阅读我们的 blog（[中文版](https://mp.weixin.qq.com/s/41aEiN3lXKGCJs1-we4Q2g)、[英文版](https://huggingface.co/blog/kelseye/qwen-image-edit-2511-icedit-lora)）。
+
+- **2025年12月9日** 我们基于 DiffSynth-Studio 2.0 训练了一个疯狂的模型：[Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L)（Image to LoRA）。这一模型以图像为输入，以 LoRA 为输出。尽管这个版本的模型在泛化能力、细节保持能力等方面还有很大改进空间，我们将这些模型开源，以启发更多创新性的研究工作。更多细节，请参考我们的 [blog](https://huggingface.co/blog/kelseye/qwen-image-i2l)。
+
 - **2025年12月4日** DiffSynth-Studio 2.0 发布！众多新功能上线
  - [文档](/docs/zh/README.md)上线：我们的文档还在持续优化更新中
  - [显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)模块升级，支持 Layer 级别的 Disk Offload，同时释放内存与显存
@@ -187,21 +197,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```

-<details>
-<summary>其他安装方式</summary>
-
-从 pypi 安装（存在版本更新延迟，如需使用最新功能，请从源码安装）
-
-```
-pip install diffsynth
-```
-
-如果在安装过程中遇到问题，可能是由上游依赖包导致的，请参考这些包的文档：
-
-* [torch](https://pytorch.org/get-started/locally/)
-* [sentencepiece](https://github.com/google/sentencepiece)
-* [cmake](https://cmake.org)
-* [cupy](https://docs.cupy.dev/en/stable/install.html)
+更多安装方式，以及非 NVIDIA GPU 的安装，请参考[安装文档](/docs/zh/Pipeline_Usage/Setup.md)。

 </details>

@@ -277,7 +273,12 @@ Z-Image 的示例代码位于：[/examples/z_image/](/examples/z_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|
 |-|-|-|-|-|-|-|
+|[Tongyi-MAI/Z-Image](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image)|[code](/examples/z_image/model_inference/Z-Image.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image.py)|[code](/examples/z_image/model_training/full/Z-Image.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image.py)|[code](/examples/z_image/model_training/lora/Z-Image.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image.py)|
+|[DiffSynth-Studio/Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L)|[code](/examples/z_image/model_inference/Z-Image-i2L.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-i2L.py)|-|-|-|-|
 |[Tongyi-MAI/Z-Image-Turbo](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo)|[code](/examples/z_image/model_inference/Z-Image-Turbo.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|

 </details>

@@ -327,9 +328,13 @@ image.save("image.jpg")

 FLUX.2 的示例代码位于：[/examples/flux2/](/examples/flux2/)

-|模型 ID|推理|低显存推理|LoRA 训练|LoRA 训练后验证|
-|-|-|-|-|-|
-|[black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev)|[code](/examples/flux2/model_inference/FLUX.2-dev.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-dev.py)|[code](/examples/flux2/model_training/lora/FLUX.2-dev.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-dev.py)|
+|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|
+|-|-|-|-|-|-|-|
+|[black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev)|[code](/examples/flux2/model_inference/FLUX.2-dev.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-dev.py)|-|-|[code](/examples/flux2/model_training/lora/FLUX.2-dev.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-dev.py)|
+|[black-forest-labs/FLUX.2-klein-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B)|[code](/examples/flux2/model_inference/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-4B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-4B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-4B.py)|
+|[black-forest-labs/FLUX.2-klein-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-9B)|[code](/examples/flux2/model_inference/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-9B.py)|
+|[black-forest-labs/FLUX.2-klein-base-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-4B)|[code](/examples/flux2/model_inference/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-4B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-4B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-4B.py)|
+|[black-forest-labs/FLUX.2-klein-base-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-9B)|[code](/examples/flux2/model_inference/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-9B.py)|

 </details>

@@ -408,8 +413,12 @@ Qwen-Image 的示例代码位于：[/examples/qwen_image/](/examples/qwen_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|
 |-|-|-|-|-|-|-|
 |[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](/examples/qwen_image/model_inference/Qwen-Image.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image.py)|
+|[Qwen/Qwen-Image-2512](https://www.modelscope.cn/models/Qwen/Qwen-Image-2512)|[code](/examples/qwen_image/model_inference/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-2512.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-2512.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-2512.py)|
 |[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)|
 |[Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py)|
+|[Qwen/Qwen-Image-Edit-2511](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
+|[Qwen/Qwen-Image-Layered](https://www.modelscope.cn/models/Qwen/Qwen-Image-Layered)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py)|
+|[DiffSynth-Studio/Qwen-Image-Layered-Control](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py)|
@@ -420,6 +429,7 @@ Qwen-Image 的示例代码位于：[/examples/qwen_image/](/examples/qwen_image/
 |[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)|[code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|
 |[DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union)|[code](/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py)|
 |[DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-|
+|[DiffSynth-Studio/Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L)|[code](/examples/qwen_image/model_inference/Qwen-Image-i2L.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py)|-|-|-|-|

 </details>

--- a/diffsynth/configs/model_configs.py
+++ b/diffsynth/configs/model_configs.py
@@ -31,6 +31,52 @@ qwen_image_series = [
        "model_class": "diffsynth.models.qwen_image_controlnet.QwenImageBlockWiseControlNet",
        "extra_kwargs": {"additional_in_dim": 4},
    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors")
+        "model_hash": "469c78b61e3e31bc9eec0d0af3d3f2f8",
+        "model_name": "siglip2_image_encoder",
+        "model_class": "diffsynth.models.siglip2_image_encoder.Siglip2ImageEncoder",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors")
+        "model_hash": "5722b5c873720009de96422993b15682",
+        "model_name": "dinov3_image_encoder",
+        "model_class": "diffsynth.models.dinov3_image_encoder.DINOv3ImageEncoder",
+    },
+    {
+        # Example: 
+        "model_hash": "a166c33455cdbd89c0888a3645ca5c0f",
+        "model_name": "qwen_image_image2lora_coarse",
+        "model_class": "diffsynth.models.qwen_image_image2lora.QwenImageImage2LoRAModel",
+    },
+    {
+        # Example: 
+        "model_hash": "a5476e691767a4da6d3a6634a10f7408",
+        "model_name": "qwen_image_image2lora_fine",
+        "model_class": "diffsynth.models.qwen_image_image2lora.QwenImageImage2LoRAModel",
+        "extra_kwargs": {"residual_length": 37*37+7, "residual_mid_dim": 64}
+    },
+    {
+        # Example: 
+        "model_hash": "0aad514690602ecaff932c701cb4b0bb",
+        "model_name": "qwen_image_image2lora_style",
+        "model_class": "diffsynth.models.qwen_image_image2lora.QwenImageImage2LoRAModel",
+        "extra_kwargs": {"compress_dim": 64, "use_residual": False}
+    },
+    {
+        # Example: ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors")
+        "model_hash": "8dc8cda05de16c73afa755e2c1ce2839",
+        "model_name": "qwen_image_dit",
+        "model_class": "diffsynth.models.qwen_image_dit.QwenImageDiT",
+        "extra_kwargs": {"use_layer3d_rope": True, "use_additional_t_cond": True}
+    },
+    {
+        # Example: ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors")
+        "model_hash": "44b39ddc499e027cfb24f7878d7416b9",
+        "model_name": "qwen_image_vae",
+        "model_class": "diffsynth.models.qwen_image_vae.QwenImageVAE",
+        "extra_kwargs": {"image_channels": 4}
+    },
 ]

 wan_series = [
@@ -271,6 +317,13 @@ flux_series = [
        "model_class": "diffsynth.models.flux_dit.FluxDiT",
        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
    },
+    {
+        # Supported due to historical reasons.
+        "model_hash": "605c56eab23e9e2af863ad8f0813a25d",
+        "model_name": "flux_dit",
+        "model_class": "diffsynth.models.flux_dit.FluxDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverterFromDiffusers",
+    },
    {
        # Example: ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors")
        "model_hash": "94eefa3dac9cec93cb1ebaf1747d7b78",
@@ -428,6 +481,13 @@ flux_series = [
        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
        "extra_kwargs": {"disable_guidance_embedder": True},
    },
+    {
+        # Example: ModelConfig(model_id="MAILAND/majicflus_v1", origin_file_pattern="majicflus_v134.safetensors")
+        "model_hash": "3394f306c4cbf04334b712bf5aaed95f",
+        "model_name": "flux_dit",
+        "model_class": "diffsynth.models.flux_dit.FluxDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
+    },
 ]

 flux2_series = [
@@ -450,6 +510,28 @@ flux2_series = [
        "model_name": "flux2_vae",
        "model_class": "diffsynth.models.flux2_vae.Flux2VAE",
    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "3bde7b817fec8143028b6825a63180df",
+        "model_name": "flux2_dit",
+        "model_class": "diffsynth.models.flux2_dit.Flux2DiT",
+        "extra_kwargs": {"guidance_embeds": False, "joint_attention_dim": 7680, "num_attention_heads": 24, "num_layers": 5, "num_single_layers": 20}
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="text_encoder/*.safetensors")
+        "model_hash": "9195f3ea256fcd0ae6d929c203470754",
+        "model_name": "z_image_text_encoder",
+        "model_class": "diffsynth.models.z_image_text_encoder.ZImageTextEncoder",
+        "extra_kwargs": {"model_size": "8B"},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.z_image_text_encoder.ZImageTextEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "39c6fc48f07bebecedbbaa971ff466c8",
+        "model_name": "flux2_dit",
+        "model_class": "diffsynth.models.flux2_dit.Flux2DiT",
+        "extra_kwargs": {"guidance_embeds": False, "joint_attention_dim": 12288, "num_attention_heads": 32, "num_layers": 8, "num_single_layers": 24}
+    },
 ]

 z_image_series = [
@@ -481,6 +563,32 @@ z_image_series = [
        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_vae.FluxVAEDecoderStateDictConverterDiffusers",
        "extra_kwargs": {"use_conv_attention": False},
    },
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Omni-Base", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "aa3563718e5c3ecde3dfbb020ca61180",
+        "model_name": "z_image_dit",
+        "model_class": "diffsynth.models.z_image_dit.ZImageDiT",
+        "extra_kwargs": {"siglip_feat_dim": 1152},
+    },
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Omni-Base", origin_file_pattern="siglip/model.safetensors")
+        "model_hash": "89d48e420f45cff95115a9f3e698d44a",
+        "model_name": "siglip_vision_model_428m",
+        "model_class": "diffsynth.models.siglip2_image_encoder.Siglip2ImageEncoder428M",
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1", origin_file_pattern="Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.safetensors")
+        "model_hash": "1677708d40029ab380a95f6c731a57d7",
+        "model_name": "z_image_controlnet",
+        "model_class": "diffsynth.models.z_image_controlnet.ZImageControlNet",
+    },
+    {
+        # Example: ???
+        "model_hash": "9510cb8cd1dd34ee0e4f111c24905510",
+        "model_name": "z_image_image2lora_style",
+        "model_class": "diffsynth.models.z_image_image2lora.ZImageImage2LoRAModel",
+        "extra_kwargs": {"compress_dim": 128},
+    },
 ]

 MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series
--- a/diffsynth/configs/vram_management_module_maps.py
+++ b/diffsynth/configs/vram_management_module_maps.py
@@ -13,6 +13,7 @@ VRAM_MANAGEMENT_MODULE_MAPS = {
    "diffsynth.models.qwen_image_dit.QwenImageDiT": {
        "diffsynth.models.qwen_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
    },
    "diffsynth.models.qwen_image_text_encoder.QwenImageTextEncoder": {
        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
@@ -32,6 +33,25 @@ VRAM_MANAGEMENT_MODULE_MAPS = {
        "diffsynth.models.qwen_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
    },
+    "diffsynth.models.siglip2_image_encoder.Siglip2ImageEncoder": {
+        "transformers.models.siglip.modeling_siglip.SiglipVisionEmbeddings": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.siglip.modeling_siglip.SiglipMultiheadAttentionPoolingHead": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
+    "diffsynth.models.dinov3_image_encoder.DINOv3ImageEncoder": {
+        "transformers.models.dinov3_vit.modeling_dinov3_vit.DINOv3ViTLayerScale": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.dinov3_vit.modeling_dinov3_vit.DINOv3ViTRopePositionEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.dinov3_vit.modeling_dinov3_vit.DINOv3ViTEmbeddings": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
+    "diffsynth.models.qwen_image_image2lora.QwenImageImage2LoRAModel": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
    "diffsynth.models.wan_video_animate_adapter.WanAnimateAdapter": {
        "diffsynth.models.wan_video_animate_adapter.FaceEncoder": "diffsynth.core.vram.layers.AutoWrappedModule",
        "diffsynth.models.wan_video_animate_adapter.EqualLinear": "diffsynth.core.vram.layers.AutoWrappedModule",
@@ -175,4 +195,19 @@ VRAM_MANAGEMENT_MODULE_MAPS = {
        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
        "diffsynth.models.z_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
    },
+    "diffsynth.models.z_image_controlnet.ZImageControlNet": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "diffsynth.models.z_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.z_image_image2lora.ZImageImage2LoRAModel": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
+    "diffsynth.models.siglip2_image_encoder.Siglip2ImageEncoder428M": {
+        "transformers.models.siglip2.modeling_siglip2.Siglip2VisionEmbeddings": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.siglip2.modeling_siglip2.Siglip2MultiheadAttentionPoolingHead": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
 }
--- a/diffsynth/core/init.py
+++ b/diffsynth/core/init.py
@@ -3,3 +3,4 @@ from .data import *
 from .gradient import *
 from .loader import *
 from .vram import *
+from .device import *
--- a/diffsynth/core/data/operators.py
+++ b/diffsynth/core/data/operators.py
@@ -53,12 +53,14 @@ class ToStr(DataProcessingOperator):


 class LoadImage(DataProcessingOperator):
-    def __init__(self, convert_RGB=True):
+    def __init__(self, convert_RGB=True, convert_RGBA=False):
        self.convert_RGB = convert_RGB
+        self.convert_RGBA = convert_RGBA
    
    def __call__(self, data: str):
        image = Image.open(data)
        if self.convert_RGB: image = image.convert("RGB")
+        if self.convert_RGBA: image = image.convert("RGBA")
        return image


--- a/diffsynth/core/data/unified_dataset.py
+++ b/diffsynth/core/data/unified_dataset.py
@@ -10,6 +10,7 @@ class UnifiedDataset(torch.utils.data.Dataset):
        data_file_keys=tuple(),
        main_data_operator=lambda x: x,
        special_operator_map=None,
+        max_data_items=None,
    ):
        self.base_path = base_path
        self.metadata_path = metadata_path
@@ -18,6 +19,7 @@ class UnifiedDataset(torch.utils.data.Dataset):
        self.main_data_operator = main_data_operator
        self.cached_data_operator = LoadTorchPickle()
        self.special_operator_map = {} if special_operator_map is None else special_operator_map
+        self.max_data_items = max_data_items
        self.data = []
        self.cached_data = []
        self.load_from_cache = metadata_path is None
@@ -97,7 +99,9 @@ class UnifiedDataset(torch.utils.data.Dataset):
        return data

    def __len__(self):
-        if self.load_from_cache:
+        if self.max_data_items is not None:
+            return self.max_data_items
+        elif self.load_from_cache:
            return len(self.cached_data) * self.repeat
        else:
            return len(self.data) * self.repeat
--- a/diffsynth/core/device/init.py
+++ b/diffsynth/core/device/init.py
@@ -0,0 +1,2 @@
+from .npu_compatible_device import parse_device_type, parse_nccl_backend, get_available_device_type, get_device_name
+from .npu_compatible_device import IS_NPU_AVAILABLE, IS_CUDA_AVAILABLE
--- a/diffsynth/core/device/npu_compatible_device.py
+++ b/diffsynth/core/device/npu_compatible_device.py
@@ -0,0 +1,107 @@
+import importlib
+import torch
+from typing import Any
+
+
+def is_torch_npu_available():
+    return importlib.util.find_spec("torch_npu") is not None
+
+
+IS_CUDA_AVAILABLE = torch.cuda.is_available()
+IS_NPU_AVAILABLE = is_torch_npu_available() and torch.npu.is_available()
+
+if IS_NPU_AVAILABLE:
+    import torch_npu
+
+    torch.npu.config.allow_internal_format = False
+
+
+def get_device_type() -> str:
+    """Get device type based on current machine, currently only support CPU, CUDA, NPU."""
+    if IS_CUDA_AVAILABLE:
+        device = "cuda"
+    elif IS_NPU_AVAILABLE:
+        device = "npu"
+    else:
+        device = "cpu"
+
+    return device
+
+
+def get_torch_device() -> Any:
+    """Get torch attribute based on device type, e.g. torch.cuda or torch.npu"""
+    device_name = get_device_type()
+
+    try:
+        return getattr(torch, device_name)
+    except AttributeError:
+        print(f"Device namespace '{device_name}' not found in torch, try to load 'torch.cuda'.")
+        return torch.cuda
+
+
+def get_device_id() -> int:
+    """Get current device id based on device type."""
+    return get_torch_device().current_device()
+
+
+def get_device_name() -> str:
+    """Get current device name based on device type."""
+    return f"{get_device_type()}:{get_device_id()}"
+
+
+def synchronize() -> None:
+    """Execute torch synchronize operation."""
+    get_torch_device().synchronize()
+
+
+def empty_cache() -> None:
+    """Execute torch empty cache operation."""
+    get_torch_device().empty_cache()
+
+
+def get_nccl_backend() -> str:
+    """Return distributed communication backend type based on device type."""
+    if IS_CUDA_AVAILABLE:
+        return "nccl"
+    elif IS_NPU_AVAILABLE:
+        return "hccl"
+    else:
+        raise RuntimeError(f"No available distributed communication backend found on device type {get_device_type()}.")
+
+
+def enable_high_precision_for_bf16():
+    """
+    Set high accumulation dtype for matmul and reduction.
+    """
+    if IS_CUDA_AVAILABLE:
+        torch.backends.cuda.matmul.allow_tf32 = False
+        torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
+
+    if IS_NPU_AVAILABLE:
+        torch.npu.matmul.allow_tf32 = False
+        torch.npu.matmul.allow_bf16_reduced_precision_reduction = False
+
+
+def parse_device_type(device):
+    if isinstance(device, str):
+        if device.startswith("cuda"):
+            return "cuda"
+        elif device.startswith("npu"):
+            return "npu"
+        else:
+            return "cpu"
+    elif isinstance(device, torch.device):
+        return device.type
+
+
+def parse_nccl_backend(device_type):
+    if device_type == "cuda":
+        return "nccl"
+    elif device_type == "npu":
+        return "hccl"
+    else:
+        raise RuntimeError(f"No available distributed communication backend found on device type {device_type}.")
+
+
+def get_available_device_type():
+    return get_device_type()
--- a/diffsynth/core/loader/config.py
+++ b/diffsynth/core/loader/config.py
@@ -1,5 +1,5 @@
 import torch, glob, os
-from typing import Optional, Union
+from typing import Optional, Union, Dict
 from dataclasses import dataclass
 from modelscope import snapshot_download
 from huggingface_hub import snapshot_download as hf_snapshot_download
@@ -23,6 +23,7 @@ class ModelConfig:
    computation_device: Optional[Union[str, torch.device]] = None
    computation_dtype: Optional[torch.dtype] = None
    clear_parameters: bool = False
+    state_dict: Dict[str, torch.Tensor] = None
    
    def check_input(self):
        if self.path is None and self.model_id is None:
@@ -97,6 +98,7 @@ class ModelConfig:
        self.reset_local_model_path()
        if self.require_downloading():
            self.download()
+        if self.path is None:
            if self.origin_file_pattern is None or self.origin_file_pattern == "":
                self.path = os.path.join(self.local_model_path, self.model_id)
            else:
--- a/diffsynth/core/loader/file.py
+++ b/diffsynth/core/loader/file.py
@@ -2,16 +2,25 @@ from safetensors import safe_open
 import torch, hashlib


-def load_state_dict(file_path, torch_dtype=None, device="cpu"):
+def load_state_dict(file_path, torch_dtype=None, device="cpu", pin_memory=False, verbose=0):
    if isinstance(file_path, list):
        state_dict = {}
        for file_path_ in file_path:
-            state_dict.update(load_state_dict(file_path_, torch_dtype, device))
-        return state_dict
-    if file_path.endswith(".safetensors"):
-        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
+            state_dict.update(load_state_dict(file_path_, torch_dtype, device, pin_memory=pin_memory, verbose=verbose))
    else:
-        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device)
+        if verbose >= 1:
+            print(f"Loading file [started]: {file_path}")
+        if file_path.endswith(".safetensors"):
+            state_dict = load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
+        else:
+            state_dict = load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device)
+        # If load state dict in CPU memory, `pin_memory=True` will make `model.to("cuda")` faster.
+        if pin_memory:
+            for i in state_dict:
+                state_dict[i] = state_dict[i].pin_memory()
+        if verbose >= 1:
+            print(f"Loading file [done]: {file_path}")
+    return state_dict


 def load_state_dict_from_safetensors(file_path, torch_dtype=None, device="cpu"):
--- a/diffsynth/core/loader/model.py
+++ b/diffsynth/core/loader/model.py
@@ -5,7 +5,7 @@ from .file import load_state_dict
 import torch


-def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, use_disk_map=False, module_map=None, vram_config=None, vram_limit=None):
+def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, use_disk_map=False, module_map=None, vram_config=None, vram_limit=None, state_dict=None):
    config = {} if config is None else config
    # Why do we use `skip_model_initialization`?
    # It skips the random initialization of model parameters,
@@ -20,7 +20,7 @@ def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, devic
        dtypes = [vram_config["offload_dtype"], vram_config["onload_dtype"], vram_config["preparing_dtype"], vram_config["computation_dtype"]]
        dtype = [d for d in dtypes if d != "disk"][0]
        if vram_config["offload_device"] != "disk":
-            state_dict = DiskMap(path, device, torch_dtype=dtype)
+            if state_dict is None: state_dict = DiskMap(path, device, torch_dtype=dtype)
            if state_dict_converter is not None:
                state_dict = state_dict_converter(state_dict)
            else:
@@ -35,7 +35,9 @@ def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, devic
        # Sometimes a model file contains multiple models,
        # and DiskMap can load only the parameters of a single model,
        # avoiding the need to load all parameters in the file.
-        if use_disk_map:
+        if state_dict is not None:
+            pass
+        elif use_disk_map:
            state_dict = DiskMap(path, device, torch_dtype=torch_dtype)
        else:
            state_dict = load_state_dict(path, torch_dtype, device)
--- a/diffsynth/core/vram/layers.py
+++ b/diffsynth/core/vram/layers.py
@@ -2,6 +2,7 @@ import torch, copy
 from typing import Union
 from .initialization import skip_model_initialization
 from .disk_map import DiskMap
+from ..device import parse_device_type, get_device_name, IS_NPU_AVAILABLE


 class AutoTorchModule(torch.nn.Module):
@@ -32,6 +33,7 @@ class AutoTorchModule(torch.nn.Module):
        )
        self.state = 0
        self.name = ""
+        self.computation_device_type = parse_device_type(self.computation_device)

    def set_dtype_and_device(
        self,
@@ -61,7 +63,8 @@ class AutoTorchModule(torch.nn.Module):
        return r

    def check_free_vram(self):
-        gpu_mem_state = torch.cuda.mem_get_info(self.computation_device)
+        device = self.computation_device if not IS_NPU_AVAILABLE else get_device_name()
+        gpu_mem_state = getattr(torch, self.computation_device_type).mem_get_info(device)
        used_memory = (gpu_mem_state[1] - gpu_mem_state[0]) / (1024**3)
        return used_memory < self.vram_limit

@@ -307,6 +310,7 @@ class AutoWrappedLinear(torch.nn.Linear, AutoTorchModule):
        self.lora_B_weights = []
        self.lora_merger = None
        self.enable_fp8 = computation_dtype in [torch.float8_e4m3fn, torch.float8_e4m3fnuz]
+        self.computation_device_type = parse_device_type(self.computation_device)
        
        if offload_dtype == "disk":
            self.disk_map = disk_map
--- a/diffsynth/diffusion/base_pipeline.py
+++ b/diffsynth/diffusion/base_pipeline.py
@@ -3,10 +3,12 @@ import torch
 import numpy as np
 from einops import repeat, reduce
 from typing import Union
-from ..core import AutoTorchModule, AutoWrappedLinear, load_state_dict, ModelConfig
+from ..core import AutoTorchModule, AutoWrappedLinear, load_state_dict, ModelConfig, parse_device_type
+from ..core.device.npu_compatible_device import get_device_type
 from ..utils.lora import GeneralLoRALoader
 from ..models.model_loader import ModelPool
 from ..utils.controlnet import ControlNetInput
+from ..core.device import get_device_name, IS_NPU_AVAILABLE


 class PipelineUnit:
@@ -60,7 +62,7 @@ class BasePipeline(torch.nn.Module):

    def __init__(
        self,
-        device="cuda", torch_dtype=torch.float16,
+        device=get_device_type(), torch_dtype=torch.float16,
        height_division_factor=64, width_division_factor=64,
        time_division_factor=None, time_division_remainder=None,
    ):
@@ -68,6 +70,7 @@ class BasePipeline(torch.nn.Module):
        # The device and torch_dtype is used for the storage of intermediate variables, not models.
        self.device = device
        self.torch_dtype = torch_dtype
+        self.device_type = parse_device_type(device)
        # The following parameters are used for shape check.
        self.height_division_factor = height_division_factor
        self.width_division_factor = width_division_factor
@@ -154,7 +157,7 @@ class BasePipeline(torch.nn.Module):
                            for module in model.modules():
                                if hasattr(module, "offload"):
                                    module.offload()
-            torch.cuda.empty_cache()
+            getattr(torch, self.device_type).empty_cache()
            # onload models
            for name, model in self.named_children():
                if name in model_names:
@@ -176,7 +179,8 @@ class BasePipeline(torch.nn.Module):

        
    def get_vram(self):
-        return torch.cuda.mem_get_info(self.device)[1] / (1024 ** 3)
+        device = self.device if not IS_NPU_AVAILABLE else get_device_name()
+        return getattr(torch, self.device_type).mem_get_info(device)[1] / (1024 ** 3)
    
    def get_module(self, model, name):
        if "." in name:
@@ -233,6 +237,7 @@ class BasePipeline(torch.nn.Module):
        alpha=1,
        hotload=None,
        state_dict=None,
+        verbose=1,
    ):
        if state_dict is None:
            if isinstance(lora_config, str):
@@ -259,12 +264,13 @@ class BasePipeline(torch.nn.Module):
                        updated_num += 1
                        module.lora_A_weights.append(lora[lora_a_name] * alpha)
                        module.lora_B_weights.append(lora[lora_b_name])
-            print(f"{updated_num} tensors are patched by LoRA. You can use `pipe.clear_lora()` to clear all LoRA layers.")
+            if verbose >= 1:
+                print(f"{updated_num} tensors are patched by LoRA. You can use `pipe.clear_lora()` to clear all LoRA layers.")
        else:
            lora_loader.fuse_lora_to_base_model(module, lora, alpha=alpha)
            
            
-    def clear_lora(self):
+    def clear_lora(self, verbose=1):
        cleared_num = 0
        for name, module in self.named_modules():
            if isinstance(module, AutoWrappedLinear):
@@ -274,7 +280,8 @@ class BasePipeline(torch.nn.Module):
                    module.lora_A_weights.clear()
                if hasattr(module, "lora_B_weights"):
                    module.lora_B_weights.clear()
-        print(f"{cleared_num} LoRA layers are cleared.")
+        if verbose >= 1:
+            print(f"{cleared_num} LoRA layers are cleared.")
        
    
    def download_and_load_models(self, model_configs: list[ModelConfig] = [], vram_limit: float = None):
@@ -289,6 +296,7 @@ class BasePipeline(torch.nn.Module):
                vram_config=vram_config,
                vram_limit=vram_limit,
                clear_parameters=model_config.clear_parameters,
+                state_dict=model_config.state_dict,
            )
        return model_pool
    
@@ -302,8 +310,13 @@ class BasePipeline(torch.nn.Module):
    
    
    def cfg_guided_model_fn(self, model_fn, cfg_scale, inputs_shared, inputs_posi, inputs_nega, **inputs_others):
+        if inputs_shared.get("positive_only_lora", None) is not None:
+            self.clear_lora(verbose=0)
+            self.load_lora(self.dit, state_dict=inputs_shared["positive_only_lora"], verbose=0)
        noise_pred_posi = model_fn(**inputs_posi, **inputs_shared, **inputs_others)
        if cfg_scale != 1.0:
+            if inputs_shared.get("positive_only_lora", None) is not None:
+                self.clear_lora(verbose=0)
            noise_pred_nega = model_fn(**inputs_nega, **inputs_shared, **inputs_others)
            noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
        else:
--- a/diffsynth/diffusion/flow_match.py
+++ b/diffsynth/diffusion/flow_match.py
@@ -89,13 +89,18 @@ class FlowMatchScheduler():
        return float(mu)
    
    @staticmethod
-    def set_timesteps_flux2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=1024//16*1024//16):
+    def set_timesteps_flux2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=None):
        sigma_min = 1 / num_inference_steps
        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)
-        mu = FlowMatchScheduler.compute_empirical_mu(dynamic_shift_len, num_inference_steps)
+        if dynamic_shift_len is None:
+            # If you ask me why I set mu=0.8,
+            # I can only say that it yields better training results.
+            mu = 0.8
+        else:
+            mu = FlowMatchScheduler.compute_empirical_mu(dynamic_shift_len, num_inference_steps)
        sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
        timesteps = sigmas * num_train_timesteps
        return sigmas, timesteps
--- a/diffsynth/diffusion/logger.py
+++ b/diffsynth/diffusion/logger.py
@@ -10,7 +10,7 @@ class ModelLogger:
        self.num_steps = 0


-    def on_step_end(self, accelerator: Accelerator, model: torch.nn.Module, save_steps=None):
+    def on_step_end(self, accelerator: Accelerator, model: torch.nn.Module, save_steps=None, **kwargs):
        self.num_steps += 1
        if save_steps is not None and self.num_steps % save_steps == 0:
            self.save_model(accelerator, model, f"step-{self.num_steps}.safetensors")
--- a/diffsynth/diffusion/runner.py
+++ b/diffsynth/diffusion/runner.py
@@ -40,7 +40,7 @@ def launch_training_task(
                    loss = model(data)
                accelerator.backward(loss)
                optimizer.step()
-                model_logger.on_step_end(accelerator, model, save_steps)
+                model_logger.on_step_end(accelerator, model, save_steps, loss=loss)
                scheduler.step()
        if save_steps is None:
            model_logger.on_epoch_end(accelerator, model, epoch_id)
--- a/diffsynth/diffusion/training_module.py
+++ b/diffsynth/diffusion/training_module.py
@@ -1,4 +1,4 @@
-import torch, json
+import torch, json, os
 from ..core import ModelConfig, load_state_dict
 from ..utils.controlnet import ControlNetInput
 from peft import LoraConfig, inject_adapter_in_model
@@ -127,16 +127,67 @@ class DiffusionTrainingModule(torch.nn.Module):
        if model_id_with_origin_paths is not None:
            model_id_with_origin_paths = model_id_with_origin_paths.split(",")
            for model_id_with_origin_path in model_id_with_origin_paths:
-                model_id, origin_file_pattern = model_id_with_origin_path.split(":")
                vram_config = self.parse_vram_config(
                    fp8=model_id_with_origin_path in fp8_models,
                    offload=model_id_with_origin_path in offload_models,
                    device=device
                )
-                model_configs.append(ModelConfig(model_id=model_id, origin_file_pattern=origin_file_pattern, **vram_config))
+                config = self.parse_path_or_model_id(model_id_with_origin_path)
+                model_configs.append(ModelConfig(model_id=config.model_id, origin_file_pattern=config.origin_file_pattern, **vram_config))
        return model_configs
    
+
+    def parse_path_or_model_id(self, model_id_with_origin_path, default_value=None):
+        if model_id_with_origin_path is None:
+            return default_value
+        elif os.path.exists(model_id_with_origin_path):
+            return ModelConfig(path=model_id_with_origin_path)
+        else:
+            if ":" not in model_id_with_origin_path:
+                raise ValueError(f"Failed to parse model config: {model_id_with_origin_path}. This is neither a valid path nor in the format of `model_id/origin_file_pattern`.")
+            split_id = model_id_with_origin_path.rfind(":")
+            model_id = model_id_with_origin_path[:split_id]
+            origin_file_pattern = model_id_with_origin_path[split_id + 1:]
+            return ModelConfig(model_id=model_id, origin_file_pattern=origin_file_pattern)
+
+
+    def auto_detect_lora_target_modules(
+        self,
+        model: torch.nn.Module,
+        search_for_linear=False,
+        linear_detector=lambda x: min(x.weight.shape) >= 512,
+        block_list_detector=lambda x: isinstance(x, torch.nn.ModuleList) and len(x) > 1,
+        name_prefix="",
+    ):
+        lora_target_modules = []
+        if search_for_linear:
+            for name, module in model.named_modules():
+                module_name = name_prefix + ["", "."][name_prefix != ""] + name
+                if isinstance(module, torch.nn.Linear) and linear_detector(module):
+                    lora_target_modules.append(module_name)
+        else:
+            for name, module in model.named_children():
+                module_name = name_prefix + ["", "."][name_prefix != ""] + name
+                lora_target_modules += self.auto_detect_lora_target_modules(
+                    module,
+                    search_for_linear=block_list_detector(module),
+                    linear_detector=linear_detector,
+                    block_list_detector=block_list_detector,
+                    name_prefix=module_name,
+                )
+        return lora_target_modules
    
+
+    def parse_lora_target_modules(self, model, lora_target_modules):
+        if lora_target_modules == "":
+            print("No LoRA target modules specified. The framework will automatically search for them.")
+            lora_target_modules = self.auto_detect_lora_target_modules(model)
+            print(f"LoRA will be patched at {lora_target_modules}.")
+        else:
+            lora_target_modules = lora_target_modules.split(",")
+        return lora_target_modules
+
+
    def switch_pipe_to_training_mode(
        self,
        pipe,
@@ -166,7 +217,7 @@ class DiffusionTrainingModule(torch.nn.Module):
                return
            model = self.add_lora_to_model(
                getattr(pipe, lora_base_model),
-                target_modules=lora_target_modules.split(","),
+                target_modules=self.parse_lora_target_modules(getattr(pipe, lora_base_model), lora_target_modules),
                lora_rank=lora_rank,
                upcast_dtype=pipe.torch_dtype,
            )
--- a/diffsynth/models/dinov3_image_encoder.py
+++ b/diffsynth/models/dinov3_image_encoder.py
@@ -0,0 +1,96 @@
+from transformers import DINOv3ViTModel, DINOv3ViTImageProcessorFast
+from transformers.models.dinov3_vit.modeling_dinov3_vit import DINOv3ViTConfig
+import torch
+
+from ..core.device.npu_compatible_device import get_device_type
+
+
+class DINOv3ImageEncoder(DINOv3ViTModel):
+    def __init__(self):
+        config = DINOv3ViTConfig(
+            architectures = [
+                "DINOv3ViTModel"
+            ],
+            attention_dropout = 0.0,
+            drop_path_rate = 0.0,
+            dtype = "float32",
+            hidden_act = "silu",
+            hidden_size = 4096,
+            image_size = 224,
+            initializer_range = 0.02,
+            intermediate_size = 8192,
+            key_bias = False,
+            layer_norm_eps = 1e-05,
+            layerscale_value = 1.0,
+            mlp_bias = True,
+            model_type = "dinov3_vit",
+            num_attention_heads = 32,
+            num_channels = 3,
+            num_hidden_layers = 40,
+            num_register_tokens = 4,
+            patch_size = 16,
+            pos_embed_jitter = None,
+            pos_embed_rescale = 2.0,
+            pos_embed_shift = None,
+            proj_bias = True,
+            query_bias = False,
+            rope_theta = 100.0,
+            transformers_version = "4.56.1",
+            use_gated_mlp = True,
+            value_bias = False
+        )
+        super().__init__(config)
+        self.processor = DINOv3ViTImageProcessorFast(
+            crop_size = None,
+            data_format = "channels_first",
+            default_to_square = True,
+            device = None,
+            disable_grouping = None,
+            do_center_crop = None,
+            do_convert_rgb = None,
+            do_normalize = True,
+            do_rescale = True,
+            do_resize = True,
+            image_mean = [
+                0.485,
+                0.456,
+                0.406
+            ],
+            image_processor_type = "DINOv3ViTImageProcessorFast",
+            image_std = [
+                0.229,
+                0.224,
+                0.225
+            ],
+            input_data_format = None,
+            resample = 2,
+            rescale_factor = 0.00392156862745098,
+            return_tensors = None,
+            size = {
+                "height": 224,
+                "width": 224
+            }
+        )
+        
+    def forward(self, image, torch_dtype=torch.bfloat16, device=get_device_type()):
+        inputs = self.processor(images=image, return_tensors="pt")
+        pixel_values = inputs["pixel_values"].to(dtype=torch_dtype, device=device)
+        bool_masked_pos = None
+        head_mask = None
+        
+        pixel_values = pixel_values.to(torch_dtype)
+        hidden_states = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
+        position_embeddings = self.rope_embeddings(pixel_values)
+
+        for i, layer_module in enumerate(self.layer):
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            hidden_states = layer_module(
+                hidden_states,
+                attention_mask=layer_head_mask,
+                position_embeddings=position_embeddings,
+            )
+
+        sequence_output = self.norm(hidden_states)
+        pooled_output = sequence_output[:, 0, :]
+
+        return pooled_output
--- a/diffsynth/models/flux2_dit.py
+++ b/diffsynth/models/flux2_dit.py
@@ -823,7 +823,13 @@ class Flux2PosEmbed(nn.Module):


 class Flux2TimestepGuidanceEmbeddings(nn.Module):
-    def __init__(self, in_channels: int = 256, embedding_dim: int = 6144, bias: bool = False):
+    def __init__(
+        self,
+        in_channels: int = 256,
+        embedding_dim: int = 6144,
+        bias: bool = False,
+        guidance_embeds: bool = True,
+    ):
        super().__init__()

        self.time_proj = Timesteps(num_channels=in_channels, flip_sin_to_cos=True, downscale_freq_shift=0)
@@ -831,20 +837,24 @@ class Flux2TimestepGuidanceEmbeddings(nn.Module):
            in_channels=in_channels, time_embed_dim=embedding_dim, sample_proj_bias=bias
        )

-        self.guidance_embedder = TimestepEmbedding(
-            in_channels=in_channels, time_embed_dim=embedding_dim, sample_proj_bias=bias
-        )
+        if guidance_embeds:
+            self.guidance_embedder = TimestepEmbedding(
+                in_channels=in_channels, time_embed_dim=embedding_dim, sample_proj_bias=bias
+            )
+        else:
+            self.guidance_embedder = None

    def forward(self, timestep: torch.Tensor, guidance: torch.Tensor) -> torch.Tensor:
        timesteps_proj = self.time_proj(timestep)
        timesteps_emb = self.timestep_embedder(timesteps_proj.to(timestep.dtype))  # (N, D)

-        guidance_proj = self.time_proj(guidance)
-        guidance_emb = self.guidance_embedder(guidance_proj.to(guidance.dtype))  # (N, D)
-
-        time_guidance_emb = timesteps_emb + guidance_emb
-
-        return time_guidance_emb
+        if guidance is not None and self.guidance_embedder is not None:
+            guidance_proj = self.time_proj(guidance)
+            guidance_emb = self.guidance_embedder(guidance_proj.to(guidance.dtype))  # (N, D)
+            time_guidance_emb = timesteps_emb + guidance_emb
+            return time_guidance_emb
+        else:
+            return timesteps_emb


 class Flux2Modulation(nn.Module):
@@ -882,6 +892,7 @@ class Flux2DiT(torch.nn.Module):
        axes_dims_rope: Tuple[int, ...] = (32, 32, 32, 32),
        rope_theta: int = 2000,
        eps: float = 1e-6,
+        guidance_embeds: bool = True,
    ):
        super().__init__()
        self.out_channels = out_channels or in_channels
@@ -892,7 +903,10 @@ class Flux2DiT(torch.nn.Module):

        # 2. Combined timestep + guidance embedding
        self.time_guidance_embed = Flux2TimestepGuidanceEmbeddings(
-            in_channels=timestep_guidance_channels, embedding_dim=self.inner_dim, bias=False
+            in_channels=timestep_guidance_channels,
+            embedding_dim=self.inner_dim,
+            bias=False,
+            guidance_embeds=guidance_embeds,
        )

        # 3. Modulation (double stream and single stream blocks share modulation parameters, resp.)
@@ -953,34 +967,9 @@ class Flux2DiT(torch.nn.Module):
        txt_ids: torch.Tensor = None,
        guidance: torch.Tensor = None,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
        use_gradient_checkpointing=False,
        use_gradient_checkpointing_offload=False,
-    ) -> Union[torch.Tensor]:
-        """
-        The [`FluxTransformer2DModel`] forward method.
-
-        Args:
-            hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`):
-                Input `hidden_states`.
-            encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`):
-                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
-            timestep ( `torch.LongTensor`):
-                Used to indicate denoising step.
-            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
-                A list of tensors that if specified are added to the residuals of transformer blocks.
-            joint_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
-            `tuple` where the first element is the sample tensor.
-        """
+    ):
        # 0. Handle input arguments
        if joint_attention_kwargs is not None:
            joint_attention_kwargs = joint_attention_kwargs.copy()
@@ -992,7 +981,9 @@ class Flux2DiT(torch.nn.Module):

        # 1. Calculate timestep embedding and modulation parameters
        timestep = timestep.to(hidden_states.dtype) * 1000
-        guidance = guidance.to(hidden_states.dtype) * 1000
+
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype) * 1000

        temb = self.time_guidance_embed(timestep, guidance)

--- a/diffsynth/models/general_modules.py
+++ b/diffsynth/models/general_modules.py
@@ -19,7 +19,7 @@ def get_timestep_embedding(
    )
    exponent = exponent / (half_dim - downscale_freq_shift)

-    emb = torch.exp(exponent).to(timesteps.device)
+    emb = torch.exp(exponent)
    if align_dtype_to_timestep:
        emb = emb.to(timesteps.dtype)
    emb = timesteps[:, None].float() * emb[None, :]
@@ -78,7 +78,7 @@ class DiffusersCompatibleTimestepProj(torch.nn.Module):


 class TimestepEmbeddings(torch.nn.Module):
-    def __init__(self, dim_in, dim_out, computation_device=None, diffusers_compatible_format=False, scale=1, align_dtype_to_timestep=False):
+    def __init__(self, dim_in, dim_out, computation_device=None, diffusers_compatible_format=False, scale=1, align_dtype_to_timestep=False, use_additional_t_cond=False):
        super().__init__()
        self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device, scale=scale, align_dtype_to_timestep=align_dtype_to_timestep)
        if diffusers_compatible_format:
@@ -87,10 +87,17 @@ class TimestepEmbeddings(torch.nn.Module):
            self.timestep_embedder = torch.nn.Sequential(
                torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
            )
+        self.use_additional_t_cond = use_additional_t_cond
+        if use_additional_t_cond:
+            self.addition_t_embedding = torch.nn.Embedding(2, dim_out)

-    def forward(self, timestep, dtype):
+    def forward(self, timestep, dtype, addition_t_cond=None):
        time_emb = self.time_proj(timestep).to(dtype)
        time_emb = self.timestep_embedder(time_emb)
+        if addition_t_cond is not None:
+            addition_t_emb = self.addition_t_embedding(addition_t_cond)
+            addition_t_emb = addition_t_emb.to(dtype=dtype)
+            time_emb = time_emb + addition_t_emb
        return time_emb


--- a/diffsynth/models/longcat_video_dit.py
+++ b/diffsynth/models/longcat_video_dit.py
@@ -9,6 +9,7 @@ import numpy as np
 import torch.nn.functional as F
 from einops import rearrange, repeat
 from .wan_video_dit import flash_attention
+from ..core.device.npu_compatible_device import get_device_type
 from ..core.gradient import gradient_checkpoint_forward


@@ -373,7 +374,7 @@ class FinalLayer_FP32(nn.Module):
        B, N, C = x.shape
        T, _, _ = latent_shape

-        with amp.autocast('cuda', dtype=torch.float32):
+        with amp.autocast(get_device_type(), dtype=torch.float32):
            shift, scale = self.adaLN_modulation(t).unsqueeze(2).chunk(2, dim=-1) # [B, T, 1, C]
            x = modulate_fp32(self.norm_final, x.view(B, T, -1, C), shift, scale).view(B, N, C)
            x = self.linear(x)
@@ -583,7 +584,7 @@ class LongCatSingleStreamBlock(nn.Module):
        T, _, _ = latent_shape # S != T*H*W in case of CP split on H*W.

        # compute modulation params in fp32
-        with amp.autocast(device_type='cuda', dtype=torch.float32):
+        with amp.autocast(device_type=get_device_type(), dtype=torch.float32):
            shift_msa, scale_msa, gate_msa, \
            shift_mlp, scale_mlp, gate_mlp = \
                self.adaLN_modulation(t).unsqueeze(2).chunk(6, dim=-1) # [B, T, 1, C]
@@ -602,7 +603,7 @@ class LongCatSingleStreamBlock(nn.Module):
        else:
            x_s = attn_outputs

-        with amp.autocast(device_type='cuda', dtype=torch.float32):
+        with amp.autocast(device_type=get_device_type(), dtype=torch.float32):
            x = x + (gate_msa * x_s.view(B, -1, N//T, C)).view(B, -1, C) # [B, N, C]
        x = x.to(x_dtype)

@@ -615,7 +616,7 @@ class LongCatSingleStreamBlock(nn.Module):
        # ffn with modulation
        x_m = modulate_fp32(self.mod_norm_ffn, x.view(B, -1, N//T, C), shift_mlp, scale_mlp).view(B, -1, C)
        x_s = self.ffn(x_m)
-        with amp.autocast(device_type='cuda', dtype=torch.float32):
+        with amp.autocast(device_type=get_device_type(), dtype=torch.float32):
            x = x + (gate_mlp * x_s.view(B, -1, N//T, C)).view(B, -1, C) # [B, N, C]
        x = x.to(x_dtype)

@@ -797,7 +798,7 @@ class LongCatVideoTransformer3DModel(torch.nn.Module):

        hidden_states = self.x_embedder(hidden_states)  # [B, N, C]

-        with amp.autocast(device_type='cuda', dtype=torch.float32):
+        with amp.autocast(device_type=get_device_type(), dtype=torch.float32):
            t = self.t_embedder(timestep.float().flatten(), dtype=torch.float32).reshape(B, N_t, -1)  # [B, T, C_t]

        encoder_hidden_states = self.y_embedder(encoder_hidden_states)  # [B, 1, N_token, C]
--- a/diffsynth/models/model_loader.py
+++ b/diffsynth/models/model_loader.py
@@ -29,7 +29,7 @@ class ModelPool:
            module_map = None
        return module_map
    
-    def load_model_file(self, config, path, vram_config, vram_limit=None):
+    def load_model_file(self, config, path, vram_config, vram_limit=None, state_dict=None):
        model_class = self.import_model_class(config["model_class"])
        model_config = config.get("extra_kwargs", {})
        if "state_dict_converter" in config:
@@ -43,6 +43,7 @@ class ModelPool:
            state_dict_converter,
            use_disk_map=True,
            vram_config=vram_config, module_map=module_map, vram_limit=vram_limit,
+            state_dict=state_dict,
        )
        return model
    
@@ -59,7 +60,7 @@ class ModelPool:
        }
        return vram_config
    
-    def auto_load_model(self, path, vram_config=None, vram_limit=None, clear_parameters=False):
+    def auto_load_model(self, path, vram_config=None, vram_limit=None, clear_parameters=False, state_dict=None):
        print(f"Loading models from: {json.dumps(path, indent=4)}")
        if vram_config is None:
            vram_config = self.default_vram_config()
@@ -67,7 +68,7 @@ class ModelPool:
        loaded = False
        for config in MODEL_CONFIGS:
            if config["model_hash"] == model_hash:
-                model = self.load_model_file(config, path, vram_config, vram_limit=vram_limit)
+                model = self.load_model_file(config, path, vram_config, vram_limit=vram_limit, state_dict=state_dict)
                if clear_parameters: self.clear_parameters(model)
                self.model.append(model)
                model_name = config["model_name"]
--- a/diffsynth/models/nexus_gen_ar_model.py
+++ b/diffsynth/models/nexus_gen_ar_model.py
@@ -583,7 +583,7 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMi
            is_compileable = model_kwargs["past_key_values"].is_compileable and self._supports_static_cache
            is_compileable = is_compileable and not self.generation_config.disable_compile
            if is_compileable and (
-                self.device.type == "cuda" or generation_config.compile_config._compile_all_devices
+                self.device.type in ["cuda", "npu"] or generation_config.compile_config._compile_all_devices
            ):
                os.environ["TOKENIZERS_PARALLELISM"] = "0"
                model_forward = self.get_compiled_call(generation_config.compile_config)
--- a/diffsynth/models/qwen_image_dit.py
+++ b/diffsynth/models/qwen_image_dit.py
@@ -1,4 +1,4 @@
-import torch, math
+import torch, math, functools
 import torch.nn as nn
 from typing import Tuple, Optional, Union, List
 from einops import rearrange
@@ -225,6 +225,121 @@ class QwenEmbedRope(nn.Module):
        return vid_freqs, txt_freqs


+class QwenEmbedLayer3DRope(nn.Module):
+    def __init__(self, theta: int, axes_dim: List[int], scale_rope=False):
+        super().__init__()
+        self.theta = theta
+        self.axes_dim = axes_dim
+        pos_index = torch.arange(4096)
+        neg_index = torch.arange(4096).flip(0) * -1 - 1
+        self.pos_freqs = torch.cat(
+            [
+                self.rope_params(pos_index, self.axes_dim[0], self.theta),
+                self.rope_params(pos_index, self.axes_dim[1], self.theta),
+                self.rope_params(pos_index, self.axes_dim[2], self.theta),
+            ],
+            dim=1,
+        )
+        self.neg_freqs = torch.cat(
+            [
+                self.rope_params(neg_index, self.axes_dim[0], self.theta),
+                self.rope_params(neg_index, self.axes_dim[1], self.theta),
+                self.rope_params(neg_index, self.axes_dim[2], self.theta),
+            ],
+            dim=1,
+        )
+
+        self.scale_rope = scale_rope
+
+    def rope_params(self, index, dim, theta=10000):
+        """
+        Args:
+            index: [0, 1, 2, 3] 1D Tensor representing the position index of the token
+        """
+        assert dim % 2 == 0
+        freqs = torch.outer(index, 1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float32).div(dim)))
+        freqs = torch.polar(torch.ones_like(freqs), freqs)
+        return freqs
+
+    def forward(self, video_fhw, txt_seq_lens, device):
+        """
+        Args: video_fhw: [frame, height, width] a list of 3 integers representing the shape of the video Args:
+        txt_length: [bs] a list of 1 integers representing the length of the text
+        """
+        if self.pos_freqs.device != device:
+            self.pos_freqs = self.pos_freqs.to(device)
+            self.neg_freqs = self.neg_freqs.to(device)
+
+        video_fhw = [video_fhw]
+        if isinstance(video_fhw, list):
+            video_fhw = video_fhw[0]
+        if not isinstance(video_fhw, list):
+            video_fhw = [video_fhw]
+
+        vid_freqs = []
+        max_vid_index = 0
+        layer_num = len(video_fhw) - 1
+        for idx, fhw in enumerate(video_fhw):
+            frame, height, width = fhw
+            if idx != layer_num:
+                video_freq = self._compute_video_freqs(frame, height, width, idx)
+            else:
+                ### For the condition image, we set the layer index to -1
+                video_freq = self._compute_condition_freqs(frame, height, width)
+            video_freq = video_freq.to(device)
+            vid_freqs.append(video_freq)
+
+            if self.scale_rope:
+                max_vid_index = max(height // 2, width // 2, max_vid_index)
+            else:
+                max_vid_index = max(height, width, max_vid_index)
+
+        max_vid_index = max(max_vid_index, layer_num)
+        max_len = max(txt_seq_lens)
+        txt_freqs = self.pos_freqs[max_vid_index : max_vid_index + max_len, ...]
+        vid_freqs = torch.cat(vid_freqs, dim=0)
+
+        return vid_freqs, txt_freqs
+
+    @functools.lru_cache(maxsize=None)
+    def _compute_video_freqs(self, frame, height, width, idx=0):
+        seq_lens = frame * height * width
+        freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+        freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+
+        freqs_frame = freqs_pos[0][idx : idx + frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
+        if self.scale_rope:
+            freqs_height = torch.cat([freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], dim=0)
+            freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)
+            freqs_width = torch.cat([freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]], dim=0)
+            freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1)
+        else:
+            freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1)
+            freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1)
+
+        freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1)
+        return freqs.clone().contiguous()
+
+    @functools.lru_cache(maxsize=None)
+    def _compute_condition_freqs(self, frame, height, width):
+        seq_lens = frame * height * width
+        freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+        freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+
+        freqs_frame = freqs_neg[0][-1:].view(frame, 1, 1, -1).expand(frame, height, width, -1)
+        if self.scale_rope:
+            freqs_height = torch.cat([freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], dim=0)
+            freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)
+            freqs_width = torch.cat([freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]], dim=0)
+            freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1)
+        else:
+            freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1)
+            freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1)
+
+        freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1)
+        return freqs.clone().contiguous()
+
+
 class QwenFeedForward(nn.Module):
    def __init__(
        self,
@@ -352,9 +467,38 @@ class QwenImageTransformerBlock(nn.Module):
        self.txt_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
        self.txt_mlp = QwenFeedForward(dim=dim, dim_out=dim)
    
-    def _modulate(self, x, mod_params):
+    def _modulate(self, x, mod_params, index=None):
        shift, scale, gate = mod_params.chunk(3, dim=-1)
-        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1), gate.unsqueeze(1)    
+        if index is not None:
+            # Assuming mod_params batch dim is 2*actual_batch (chunked into 2 parts)
+            # So shift, scale, gate have shape [2*actual_batch, d]
+            actual_batch = shift.size(0) // 2
+            shift_0, shift_1 = shift[:actual_batch], shift[actual_batch:]  # each: [actual_batch, d]
+            scale_0, scale_1 = scale[:actual_batch], scale[actual_batch:]
+            gate_0, gate_1 = gate[:actual_batch], gate[actual_batch:]
+
+            # index: [b, l] where b is actual batch size
+            # Expand to [b, l, 1] to match feature dimension
+            index_expanded = index.unsqueeze(-1)  # [b, l, 1]
+
+            # Expand chunks to [b, 1, d] then broadcast to [b, l, d]
+            shift_0_exp = shift_0.unsqueeze(1)  # [b, 1, d]
+            shift_1_exp = shift_1.unsqueeze(1)  # [b, 1, d]
+            scale_0_exp = scale_0.unsqueeze(1)
+            scale_1_exp = scale_1.unsqueeze(1)
+            gate_0_exp = gate_0.unsqueeze(1)
+            gate_1_exp = gate_1.unsqueeze(1)
+
+            # Use torch.where to select based on index
+            shift_result = torch.where(index_expanded == 0, shift_0_exp, shift_1_exp)
+            scale_result = torch.where(index_expanded == 0, scale_0_exp, scale_1_exp)
+            gate_result = torch.where(index_expanded == 0, gate_0_exp, gate_1_exp)
+        else:
+            shift_result = shift.unsqueeze(1)
+            scale_result = scale.unsqueeze(1)
+            gate_result = gate.unsqueeze(1)
+
+        return x * (1 + scale_result) + shift_result, gate_result

    def forward(
        self,
@@ -364,13 +508,16 @@ class QwenImageTransformerBlock(nn.Module):
        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        enable_fp8_attention = False,
+        modulate_index: Optional[List[int]] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:

        img_mod_attn, img_mod_mlp = self.img_mod(temb).chunk(2, dim=-1)  # [B, 3*dim] each
+        if modulate_index is not None:
+            temb = torch.chunk(temb, 2, dim=0)[0]
        txt_mod_attn, txt_mod_mlp = self.txt_mod(temb).chunk(2, dim=-1)  # [B, 3*dim] each

        img_normed = self.img_norm1(image)
-        img_modulated, img_gate = self._modulate(img_normed, img_mod_attn)
+        img_modulated, img_gate = self._modulate(img_normed, img_mod_attn, index=modulate_index)

        txt_normed = self.txt_norm1(text)
        txt_modulated, txt_gate = self._modulate(txt_normed, txt_mod_attn)
@@ -387,7 +534,7 @@ class QwenImageTransformerBlock(nn.Module):
        text = text + txt_gate * txt_attn_out

        img_normed_2 = self.img_norm2(image)
-        img_modulated_2, img_gate_2 = self._modulate(img_normed_2, img_mod_mlp)
+        img_modulated_2, img_gate_2 = self._modulate(img_normed_2, img_mod_mlp, index=modulate_index)

        txt_normed_2 = self.txt_norm2(text)
        txt_modulated_2, txt_gate_2 = self._modulate(txt_normed_2, txt_mod_mlp)
@@ -405,12 +552,17 @@ class QwenImageDiT(torch.nn.Module):
    def __init__(
        self,
        num_layers: int = 60,
+        use_layer3d_rope: bool = False,
+        use_additional_t_cond: bool = False,
    ):
        super().__init__()

-        self.pos_embed = QwenEmbedRope(theta=10000, axes_dim=[16,56,56], scale_rope=True) 
+        if not use_layer3d_rope:
+            self.pos_embed = QwenEmbedRope(theta=10000, axes_dim=[16,56,56], scale_rope=True)
+        else:
+            self.pos_embed = QwenEmbedLayer3DRope(theta=10000, axes_dim=[16,56,56], scale_rope=True)

-        self.time_text_embed = TimestepEmbeddings(256, 3072, diffusers_compatible_format=True, scale=1000, align_dtype_to_timestep=True)
+        self.time_text_embed = TimestepEmbeddings(256, 3072, diffusers_compatible_format=True, scale=1000, align_dtype_to_timestep=False, use_additional_t_cond=use_additional_t_cond)
        self.txt_norm = RMSNorm(3584, eps=1e-6)

        self.img_in = nn.Linear(64, 3072)
--- a/diffsynth/models/qwen_image_image2lora.py
+++ b/diffsynth/models/qwen_image_image2lora.py
@@ -0,0 +1,128 @@
+import torch
+
+
+class CompressedMLP(torch.nn.Module):
+    def __init__(self, in_dim, mid_dim, out_dim, bias=False):
+        super().__init__()
+        self.proj_in = torch.nn.Linear(in_dim, mid_dim, bias=bias)
+        self.proj_out = torch.nn.Linear(mid_dim, out_dim, bias=bias)
+        
+    def forward(self, x, residual=None):
+        x = self.proj_in(x)
+        if residual is not None: x = x + residual
+        x = self.proj_out(x)
+        return x
+
+
+class ImageEmbeddingToLoraMatrix(torch.nn.Module):
+    def __init__(self, in_dim, compress_dim, lora_a_dim, lora_b_dim, rank):
+        super().__init__()
+        self.proj_a = CompressedMLP(in_dim, compress_dim, lora_a_dim * rank)
+        self.proj_b = CompressedMLP(in_dim, compress_dim, lora_b_dim * rank)
+        self.lora_a_dim = lora_a_dim
+        self.lora_b_dim = lora_b_dim
+        self.rank = rank
+        
+    def forward(self, x, residual=None):
+        lora_a = self.proj_a(x, residual).view(self.rank, self.lora_a_dim)
+        lora_b = self.proj_b(x, residual).view(self.lora_b_dim, self.rank)
+        return lora_a, lora_b
+
+
+class SequencialMLP(torch.nn.Module):
+    def __init__(self, length, in_dim, mid_dim, out_dim, bias=False):
+        super().__init__()
+        self.proj_in = torch.nn.Linear(in_dim, mid_dim, bias=bias)
+        self.proj_out = torch.nn.Linear(length * mid_dim, out_dim, bias=bias)
+        self.length = length
+        self.in_dim = in_dim
+        self.mid_dim = mid_dim
+        
+    def forward(self, x):
+        x = x.view(self.length, self.in_dim)
+        x = self.proj_in(x)
+        x = x.view(1, self.length * self.mid_dim)
+        x = self.proj_out(x)
+        return x
+
+
+class LoRATrainerBlock(torch.nn.Module):
+    def __init__(self, lora_patterns, in_dim=1536+4096, compress_dim=128, rank=4, block_id=0, use_residual=True, residual_length=64+7, residual_dim=3584, residual_mid_dim=1024):
+        super().__init__()
+        self.lora_patterns = lora_patterns
+        self.block_id = block_id
+        self.layers = []
+        for name, lora_a_dim, lora_b_dim in self.lora_patterns:
+            self.layers.append(ImageEmbeddingToLoraMatrix(in_dim, compress_dim, lora_a_dim, lora_b_dim, rank))
+        self.layers = torch.nn.ModuleList(self.layers)
+        if use_residual:
+            self.proj_residual = SequencialMLP(residual_length, residual_dim, residual_mid_dim, compress_dim)
+        else:
+            self.proj_residual = None
+    
+    def forward(self, x, residual=None):
+        lora = {}
+        if self.proj_residual is not None: residual = self.proj_residual(residual)
+        for lora_pattern, layer in zip(self.lora_patterns, self.layers):
+            name = lora_pattern[0]
+            lora_a, lora_b = layer(x, residual=residual)
+            lora[f"transformer_blocks.{self.block_id}.{name}.lora_A.default.weight"] = lora_a
+            lora[f"transformer_blocks.{self.block_id}.{name}.lora_B.default.weight"] = lora_b
+        return lora
+    
+
+class QwenImageImage2LoRAModel(torch.nn.Module):
+    def __init__(self, num_blocks=60, use_residual=True, compress_dim=128, rank=4, residual_length=64+7, residual_mid_dim=1024):
+        super().__init__()
+        self.lora_patterns = [
+            [
+                ("attn.to_q", 3072, 3072),
+                ("attn.to_k", 3072, 3072),
+                ("attn.to_v", 3072, 3072),
+                ("attn.to_out.0", 3072, 3072),
+            ],
+            [
+                ("img_mlp.net.2", 3072*4, 3072),
+                ("img_mod.1", 3072, 3072*6),
+            ],
+            [
+                ("attn.add_q_proj", 3072, 3072),
+                ("attn.add_k_proj", 3072, 3072),
+                ("attn.add_v_proj", 3072, 3072),
+                ("attn.to_add_out", 3072, 3072),
+            ],
+            [
+                ("txt_mlp.net.2", 3072*4, 3072),
+                ("txt_mod.1", 3072, 3072*6),
+            ],
+        ]
+        self.num_blocks = num_blocks
+        self.blocks = []
+        for lora_patterns in self.lora_patterns:
+            for block_id in range(self.num_blocks):
+                self.blocks.append(LoRATrainerBlock(lora_patterns, block_id=block_id, use_residual=use_residual, compress_dim=compress_dim, rank=rank, residual_length=residual_length, residual_mid_dim=residual_mid_dim))
+        self.blocks = torch.nn.ModuleList(self.blocks)
+        self.residual_scale = 0.05
+        self.use_residual = use_residual
+        
+    def forward(self, x, residual=None):
+        if residual is not None:
+            if self.use_residual:
+                residual = residual * self.residual_scale
+            else:
+                residual = None
+        lora = {}
+        for block in self.blocks:
+            lora.update(block(x, residual))
+        return lora
+    
+    def initialize_weights(self):
+        state_dict = self.state_dict()
+        for name in state_dict:
+            if ".proj_a." in name:
+                state_dict[name] = state_dict[name] * 0.3
+            elif ".proj_b.proj_out." in name:
+                state_dict[name] = state_dict[name] * 0
+            elif ".proj_residual.proj_out." in name:
+                state_dict[name] = state_dict[name] * 0.3
+        self.load_state_dict(state_dict)
--- a/diffsynth/models/qwen_image_vae.py
+++ b/diffsynth/models/qwen_image_vae.py
@@ -366,6 +366,7 @@ class QwenImageEncoder3d(nn.Module):
        temperal_downsample=[True, True, False],
        dropout=0.0,
        non_linearity: str = "silu",
+        image_channels=3
    ):
        super().__init__()
        self.dim = dim
@@ -381,7 +382,7 @@ class QwenImageEncoder3d(nn.Module):
        scale = 1.0

        # init block
-        self.conv_in = QwenImageCausalConv3d(3, dims[0], 3, padding=1)
+        self.conv_in = QwenImageCausalConv3d(image_channels, dims[0], 3, padding=1)

        # downsample blocks
        self.down_blocks = torch.nn.ModuleList([])
@@ -544,6 +545,7 @@ class QwenImageDecoder3d(nn.Module):
        temperal_upsample=[False, True, True],
        dropout=0.0,
        non_linearity: str = "silu",
+        image_channels=3,
    ):
        super().__init__()
        self.dim = dim
@@ -594,7 +596,7 @@ class QwenImageDecoder3d(nn.Module):

        # output blocks
        self.norm_out = QwenImageRMS_norm(out_dim, images=False)
-        self.conv_out = QwenImageCausalConv3d(out_dim, 3, 3, padding=1)
+        self.conv_out = QwenImageCausalConv3d(out_dim, image_channels, 3, padding=1)

        self.gradient_checkpointing = False

@@ -647,6 +649,7 @@ class QwenImageVAE(torch.nn.Module):
        attn_scales: List[float] = [],
        temperal_downsample: List[bool] = [False, True, True],
        dropout: float = 0.0,
+        image_channels: int = 3,
    ) -> None:
        super().__init__()

@@ -655,13 +658,13 @@ class QwenImageVAE(torch.nn.Module):
        self.temperal_upsample = temperal_downsample[::-1]

        self.encoder = QwenImageEncoder3d(
-            base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout
+            base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout, image_channels=image_channels,
        )
        self.quant_conv = QwenImageCausalConv3d(z_dim * 2, z_dim * 2, 1)
        self.post_quant_conv = QwenImageCausalConv3d(z_dim, z_dim, 1)

        self.decoder = QwenImageDecoder3d(
-            base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout
+            base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout, image_channels=image_channels,
        )

        mean = [
--- a/diffsynth/models/siglip2_image_encoder.py
+++ b/diffsynth/models/siglip2_image_encoder.py
@@ -0,0 +1,134 @@
+from transformers.models.siglip.modeling_siglip import SiglipVisionTransformer, SiglipVisionConfig
+from transformers import SiglipImageProcessor, Siglip2VisionModel, Siglip2VisionConfig, Siglip2ImageProcessorFast
+import torch
+
+from diffsynth.core.device.npu_compatible_device import get_device_type
+
+
+class Siglip2ImageEncoder(SiglipVisionTransformer):
+    def __init__(self):
+        config = SiglipVisionConfig(
+            attention_dropout = 0.0,
+            dtype = "float32",
+            hidden_act = "gelu_pytorch_tanh",
+            hidden_size = 1536,
+            image_size = 384,
+            intermediate_size = 6144,
+            layer_norm_eps = 1e-06,
+            model_type = "siglip_vision_model",
+            num_attention_heads = 16,
+            num_channels = 3,
+            num_hidden_layers = 40,
+            patch_size = 16,
+            transformers_version = "4.56.1",
+            _attn_implementation = "sdpa"
+        )
+        super().__init__(config)
+        self.processor = SiglipImageProcessor(
+            do_convert_rgb = None,
+            do_normalize = True,
+            do_rescale = True,
+            do_resize = True,
+            image_mean = [
+                0.5,
+                0.5,
+                0.5
+            ],
+            image_processor_type = "SiglipImageProcessor",
+            image_std = [
+                0.5,
+                0.5,
+                0.5
+            ],
+            processor_class = "SiglipProcessor",
+            resample = 2,
+            rescale_factor = 0.00392156862745098,
+            size = {
+                "height": 384,
+                "width": 384
+            }
+        )
+        
+    def forward(self, image, torch_dtype=torch.bfloat16, device=get_device_type()):
+        pixel_values = self.processor(images=[image], return_tensors="pt")["pixel_values"]
+        pixel_values = pixel_values.to(device=device, dtype=torch_dtype)
+        output_attentions = False
+        output_hidden_states = False
+        interpolate_pos_encoding = False
+
+        hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
+
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        last_hidden_state = encoder_outputs.last_hidden_state
+        last_hidden_state = self.post_layernorm(last_hidden_state)
+
+        pooler_output = self.head(last_hidden_state) if self.use_head else None
+
+        return pooler_output
+
+
+class Siglip2ImageEncoder428M(Siglip2VisionModel):
+    def __init__(self):
+        config = Siglip2VisionConfig(
+            attention_dropout = 0.0,
+            dtype = "bfloat16",
+            hidden_act = "gelu_pytorch_tanh",
+            hidden_size = 1152,
+            intermediate_size = 4304,
+            layer_norm_eps = 1e-06,
+            model_type = "siglip2_vision_model",
+            num_attention_heads = 16,
+            num_channels = 3,
+            num_hidden_layers = 27,
+            num_patches = 256,
+            patch_size = 16,
+            transformers_version = "4.57.1"
+        )
+        super().__init__(config)
+        self.processor = Siglip2ImageProcessorFast(
+            **{
+                "data_format": "channels_first",
+                "default_to_square": True,
+                "device": None,
+                "disable_grouping": None,
+                "do_convert_rgb": None,
+                "do_normalize": True,
+                "do_pad": None,
+                "do_rescale": True,
+                "do_resize": True,
+                "image_mean": [
+                    0.5,
+                    0.5,
+                    0.5
+                ],
+                "image_processor_type": "Siglip2ImageProcessorFast",
+                "image_std": [
+                    0.5,
+                    0.5,
+                    0.5
+                ],
+                "input_data_format": None,
+                "max_num_patches": 256,
+                "pad_size": None,
+                "patch_size": 16,
+                "processor_class": "Siglip2Processor",
+                "resample": 2,
+                "rescale_factor": 0.00392156862745098,
+                "return_tensors": None,
+            }
+        )
+        
+    def forward(self, image, torch_dtype=torch.bfloat16, device="cuda"):
+        siglip_inputs = self.processor(images=[image], return_tensors="pt").to(device)
+        shape = siglip_inputs.spatial_shapes[0]
+        hidden_state = super().forward(**siglip_inputs).last_hidden_state
+        B, N, C = hidden_state.shape
+        hidden_state = hidden_state[:, : shape[0] * shape[1]]
+        hidden_state = hidden_state.view(shape[0], shape[1], C)
+        hidden_state = hidden_state.to(torch_dtype)
+        return hidden_state
--- a/diffsynth/models/step1x_text_encoder.py
+++ b/diffsynth/models/step1x_text_encoder.py
@@ -1,10 +1,11 @@
 import torch
 from typing import Optional, Union
 from .qwen_image_text_encoder import QwenImageTextEncoder
+from ..core.device.npu_compatible_device import get_device_type, get_torch_device


 class Step1xEditEmbedder(torch.nn.Module):
-    def __init__(self, model: QwenImageTextEncoder, processor, max_length=640, dtype=torch.bfloat16, device="cuda"):
+    def __init__(self, model: QwenImageTextEncoder, processor, max_length=640, dtype=torch.bfloat16, device=get_device_type()):
        super().__init__()
        self.max_length = max_length
        self.dtype = dtype
@@ -77,13 +78,13 @@ User Prompt:'''
            self.max_length,
            self.model.config.hidden_size,
            dtype=torch.bfloat16,
-            device=torch.cuda.current_device(),
+            device=get_torch_device().current_device(),
        )
        masks = torch.zeros(
            len(text_list),
            self.max_length,
            dtype=torch.long,
-            device=torch.cuda.current_device(),
+            device=get_torch_device().current_device(),
        )

        def split_string(s):
@@ -158,7 +159,7 @@ User Prompt:'''
                else:
                    token_list.append(token_each)

-            new_txt_ids = torch.cat(token_list, dim=1).to("cuda")
+            new_txt_ids = torch.cat(token_list, dim=1).to(get_device_type())

            new_txt_ids = new_txt_ids.to(old_inputs_ids.device)

@@ -167,15 +168,15 @@ User Prompt:'''
            inputs.input_ids = (
                torch.cat([old_inputs_ids[0, :idx1], new_txt_ids[0, idx2:]], dim=0)
                .unsqueeze(0)
-                .to("cuda")
+                .to(get_device_type())
            )
-            inputs.attention_mask = (inputs.input_ids > 0).long().to("cuda")
+            inputs.attention_mask = (inputs.input_ids > 0).long().to(get_device_type())
            outputs = self.model_forward(
                self.model,
                input_ids=inputs.input_ids,
                attention_mask=inputs.attention_mask,
-                pixel_values=inputs.pixel_values.to("cuda"),
-                image_grid_thw=inputs.image_grid_thw.to("cuda"),
+                pixel_values=inputs.pixel_values.to(get_device_type()),
+                image_grid_thw=inputs.image_grid_thw.to(get_device_type()),
                output_hidden_states=True,
            )

@@ -188,7 +189,7 @@ User Prompt:'''
            masks[idx, : min(self.max_length, emb.shape[1] - 217)] = torch.ones(
                (min(self.max_length, emb.shape[1] - 217)),
                dtype=torch.long,
-                device=torch.cuda.current_device(),
+                device=get_torch_device().current_device(),
            )

        return embs, masks
--- a/diffsynth/models/wan_video_dit.py
+++ b/diffsynth/models/wan_video_dit.py
@@ -5,6 +5,7 @@ import math
 from typing import Tuple, Optional
 from einops import rearrange
 from .wan_video_camera_controller import SimpleAdapter
+
 try:
    import flash_attn_interface
    FLASH_ATTN_3_AVAILABLE = True
@@ -92,6 +93,7 @@ def rope_apply(x, freqs, num_heads):
    x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
    x_out = torch.view_as_complex(x.to(torch.float64).reshape(
        x.shape[0], x.shape[1], x.shape[2], -1, 2))
+    freqs = freqs.to(torch.complex64) if freqs.device == "npu" else freqs
    x_out = torch.view_as_real(x_out * freqs).flatten(2)
    return x_out.to(x.dtype)

--- a/diffsynth/models/z_image_controlnet.py
+++ b/diffsynth/models/z_image_controlnet.py
@@ -0,0 +1,154 @@
+from .z_image_dit import ZImageTransformerBlock
+from ..core.gradient import gradient_checkpoint_forward
+from torch.nn.utils.rnn import pad_sequence
+import torch
+from torch import nn
+
+
+class ZImageControlTransformerBlock(ZImageTransformerBlock):
+    def __init__(
+        self, 
+        layer_id: int = 1000,
+        dim: int = 3840,
+        n_heads: int = 30,
+        n_kv_heads: int = 30,
+        norm_eps: float = 1e-5,
+        qk_norm: bool = True,
+        modulation = True,
+        block_id = 0
+    ):
+        super().__init__(layer_id, dim, n_heads, n_kv_heads, norm_eps, qk_norm, modulation)
+        self.block_id = block_id
+        if block_id == 0:
+            self.before_proj = nn.Linear(self.dim, self.dim)
+        self.after_proj = nn.Linear(self.dim, self.dim)
+
+    def forward(self, c, x, **kwargs):
+        if self.block_id == 0:
+            c = self.before_proj(c) + x
+            all_c = []
+        else:
+            all_c = list(torch.unbind(c))
+            c = all_c.pop(-1)
+
+        c = super().forward(c, **kwargs)
+        c_skip = self.after_proj(c)
+        all_c += [c_skip, c]
+        c = torch.stack(all_c)
+        return c
+
+
+class ZImageControlNet(torch.nn.Module):
+    def __init__(
+        self,
+        control_layers_places=(0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28),
+        control_in_dim=33,
+        dim=3840,
+        n_refiner_layers=2,
+    ):
+        super().__init__()
+        self.control_layers = nn.ModuleList([ZImageControlTransformerBlock(layer_id=i, block_id=i) for i in control_layers_places])
+        self.control_all_x_embedder = nn.ModuleDict({"2-1": nn.Linear(1 * 2 * 2 * control_in_dim, dim, bias=True)})
+        self.control_noise_refiner = nn.ModuleList([ZImageControlTransformerBlock(block_id=layer_id) for layer_id in range(n_refiner_layers)])
+        self.control_layers_mapping = {0: 0, 2: 1, 4: 2, 6: 3, 8: 4, 10: 5, 12: 6, 14: 7, 16: 8, 18: 9, 20: 10, 22: 11, 24: 12, 26: 13, 28: 14}
+
+    def forward_layers(
+        self,
+        x,
+        cap_feats,
+        control_context,
+        control_context_item_seqlens,
+        kwargs,
+        use_gradient_checkpointing=False,
+        use_gradient_checkpointing_offload=False,
+    ):
+        bsz = len(control_context)
+        # unified
+        cap_item_seqlens = [len(_) for _ in cap_feats]
+        control_context_unified = []
+        for i in range(bsz):
+            control_context_len = control_context_item_seqlens[i]
+            cap_len = cap_item_seqlens[i]
+            control_context_unified.append(torch.cat([control_context[i][:control_context_len], cap_feats[i][:cap_len]]))
+        c = pad_sequence(control_context_unified, batch_first=True, padding_value=0.0)
+
+        # arguments
+        new_kwargs = dict(x=x)
+        new_kwargs.update(kwargs)
+        
+        for layer in self.control_layers:
+            c = gradient_checkpoint_forward(
+                layer,
+                use_gradient_checkpointing=use_gradient_checkpointing,
+                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                c=c, **new_kwargs
+            )
+ 
+        hints = torch.unbind(c)[:-1]
+        return hints
+    
+    def forward_refiner(
+        self,
+        dit,
+        x,
+        cap_feats,
+        control_context,
+        kwargs,
+        t=None,
+        patch_size=2,
+        f_patch_size=1,
+        use_gradient_checkpointing=False,
+        use_gradient_checkpointing_offload=False,
+    ):
+        # embeddings
+        bsz = len(control_context)
+        device = control_context[0].device
+        (
+            control_context,
+            control_context_size,
+            control_context_pos_ids,
+            control_context_inner_pad_mask,
+        ) = dit.patchify_controlnet(control_context, patch_size, f_patch_size, cap_feats[0].size(0))
+
+        # control_context embed & refine
+        control_context_item_seqlens = [len(_) for _ in control_context]
+        assert all(_ % 2 == 0 for _ in control_context_item_seqlens)
+        control_context_max_item_seqlen = max(control_context_item_seqlens)
+
+        control_context = torch.cat(control_context, dim=0)
+        control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context)
+
+        # Match t_embedder output dtype to control_context for layerwise casting compatibility
+        adaln_input = t.type_as(control_context)
+        control_context[torch.cat(control_context_inner_pad_mask)] = dit.x_pad_token.to(dtype=control_context.dtype, device=control_context.device)
+        control_context = list(control_context.split(control_context_item_seqlens, dim=0))
+        control_context_freqs_cis = list(dit.rope_embedder(torch.cat(control_context_pos_ids, dim=0)).split(control_context_item_seqlens, dim=0))
+
+        control_context = pad_sequence(control_context, batch_first=True, padding_value=0.0)
+        control_context_freqs_cis = pad_sequence(control_context_freqs_cis, batch_first=True, padding_value=0.0)
+        control_context_attn_mask = torch.zeros((bsz, control_context_max_item_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(control_context_item_seqlens):
+            control_context_attn_mask[i, :seq_len] = 1
+        c = control_context
+
+        # arguments
+        new_kwargs = dict(
+            x=x, 
+            attn_mask=control_context_attn_mask,
+            freqs_cis=control_context_freqs_cis, 
+            adaln_input=adaln_input,
+        )
+        new_kwargs.update(kwargs)
+        
+        for layer in self.control_noise_refiner:
+            c = gradient_checkpoint_forward(
+                layer,
+                use_gradient_checkpointing=use_gradient_checkpointing,
+                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                c=c, **new_kwargs
+            )
+ 
+        hints = torch.unbind(c)[:-1]
+        control_context = torch.unbind(c)[-1]
+
+        return hints, control_context, control_context_item_seqlens
--- a/diffsynth/models/z_image_dit.py
+++ b/diffsynth/models/z_image_dit.py
@@ -6,13 +6,15 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch.nn.utils.rnn import pad_sequence

-from torch.nn import RMSNorm
+from .general_modules import RMSNorm
 from ..core.attention import attention_forward
+from ..core.device.npu_compatible_device import IS_NPU_AVAILABLE, get_device_type
 from ..core.gradient import gradient_checkpoint_forward


 ADALN_EMBED_DIM = 256
 SEQ_MULTI_OF = 32
+X_PAD_DIM = 64


 class TimestepEmbedder(nn.Module):
@@ -38,7 +40,7 @@ class TimestepEmbedder(nn.Module):

    @staticmethod
    def timestep_embedding(t, dim, max_period=10000):
-        with torch.amp.autocast("cuda", enabled=False):
+        with torch.amp.autocast(get_device_type(), enabled=False):
            half = dim // 2
            freqs = torch.exp(
                -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half
@@ -86,7 +88,7 @@ class Attention(torch.nn.Module):
        self.norm_q = RMSNorm(head_dim, eps=1e-5)
        self.norm_k = RMSNorm(head_dim, eps=1e-5)
    
-    def forward(self, hidden_states, freqs_cis):
+    def forward(self, hidden_states, freqs_cis, attention_mask):
        query = self.to_q(hidden_states)
        key = self.to_k(hidden_states)
        value = self.to_v(hidden_states)
@@ -103,7 +105,7 @@ class Attention(torch.nn.Module):

        # Apply RoPE
        def apply_rotary_emb(x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
-            with torch.amp.autocast("cuda", enabled=False):
+            with torch.amp.autocast(get_device_type(), enabled=False):
                x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2))
                freqs_cis = freqs_cis.unsqueeze(2)
                x_out = torch.view_as_real(x * freqs_cis).flatten(3)
@@ -123,6 +125,7 @@ class Attention(torch.nn.Module):
            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,
        )

        # Reshape back
@@ -136,6 +139,20 @@ class Attention(torch.nn.Module):
        return output


+def select_per_token(
+    value_noisy: torch.Tensor,
+    value_clean: torch.Tensor,
+    noise_mask: torch.Tensor,
+    seq_len: int,
+) -> torch.Tensor:
+    noise_mask_expanded = noise_mask.unsqueeze(-1)  # (batch, seq_len, 1)
+    return torch.where(
+        noise_mask_expanded == 1,
+        value_noisy.unsqueeze(1).expand(-1, seq_len, -1),
+        value_clean.unsqueeze(1).expand(-1, seq_len, -1),
+    )
+
+
 class ZImageTransformerBlock(nn.Module):
    def __init__(
        self,
@@ -180,40 +197,53 @@ class ZImageTransformerBlock(nn.Module):
        attn_mask: torch.Tensor,
        freqs_cis: torch.Tensor,
        adaln_input: Optional[torch.Tensor] = None,
+        noise_mask: Optional[torch.Tensor] = None,
+        adaln_noisy: Optional[torch.Tensor] = None,
+        adaln_clean: Optional[torch.Tensor] = None,
    ):
        if self.modulation:
-            assert adaln_input is not None
-            scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).unsqueeze(1).chunk(4, dim=2)
-            gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
-            scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
+            seq_len = x.shape[1]
+
+            if noise_mask is not None:
+                # Per-token modulation: different modulation for noisy/clean tokens
+                mod_noisy = self.adaLN_modulation(adaln_noisy)
+                mod_clean = self.adaLN_modulation(adaln_clean)
+
+                scale_msa_noisy, gate_msa_noisy, scale_mlp_noisy, gate_mlp_noisy = mod_noisy.chunk(4, dim=1)
+                scale_msa_clean, gate_msa_clean, scale_mlp_clean, gate_mlp_clean = mod_clean.chunk(4, dim=1)
+
+                gate_msa_noisy, gate_mlp_noisy = gate_msa_noisy.tanh(), gate_mlp_noisy.tanh()
+                gate_msa_clean, gate_mlp_clean = gate_msa_clean.tanh(), gate_mlp_clean.tanh()
+
+                scale_msa_noisy, scale_mlp_noisy = 1.0 + scale_msa_noisy, 1.0 + scale_mlp_noisy
+                scale_msa_clean, scale_mlp_clean = 1.0 + scale_msa_clean, 1.0 + scale_mlp_clean
+
+                scale_msa = select_per_token(scale_msa_noisy, scale_msa_clean, noise_mask, seq_len)
+                scale_mlp = select_per_token(scale_mlp_noisy, scale_mlp_clean, noise_mask, seq_len)
+                gate_msa = select_per_token(gate_msa_noisy, gate_msa_clean, noise_mask, seq_len)
+                gate_mlp = select_per_token(gate_mlp_noisy, gate_mlp_clean, noise_mask, seq_len)
+            else:
+                # Global modulation: same modulation for all tokens (avoid double select)
+                mod = self.adaLN_modulation(adaln_input)
+                scale_msa, gate_msa, scale_mlp, gate_mlp = mod.unsqueeze(1).chunk(4, dim=2)
+                gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
+                scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp

            # Attention block
            attn_out = self.attention(
-                self.attention_norm1(x) * scale_msa,
-                freqs_cis=freqs_cis,
+                self.attention_norm1(x) * scale_msa, attention_mask=attn_mask, freqs_cis=freqs_cis
            )
            x = x + gate_msa * self.attention_norm2(attn_out)

            # FFN block
-            x = x + gate_mlp * self.ffn_norm2(
-                self.feed_forward(
-                    self.ffn_norm1(x) * scale_mlp,
-                )
-            )
+            x = x + gate_mlp * self.ffn_norm2(self.feed_forward(self.ffn_norm1(x) * scale_mlp))
        else:
            # Attention block
-            attn_out = self.attention(
-                self.attention_norm1(x),
-                freqs_cis=freqs_cis,
-            )
+            attn_out = self.attention(self.attention_norm1(x), attention_mask=attn_mask, freqs_cis=freqs_cis)
            x = x + self.attention_norm2(attn_out)

            # FFN block
-            x = x + self.ffn_norm2(
-                self.feed_forward(
-                    self.ffn_norm1(x),
-                )
-            )
+            x = x + self.ffn_norm2(self.feed_forward(self.ffn_norm1(x)))

        return x

@@ -229,9 +259,21 @@ class FinalLayer(nn.Module):
            nn.Linear(min(hidden_size, ADALN_EMBED_DIM), hidden_size, bias=True),
        )

-    def forward(self, x, c):
-        scale = 1.0 + self.adaLN_modulation(c)
-        x = self.norm_final(x) * scale.unsqueeze(1)
+    def forward(self, x, c=None, noise_mask=None, c_noisy=None, c_clean=None):
+        seq_len = x.shape[1]
+
+        if noise_mask is not None:
+            # Per-token modulation
+            scale_noisy = 1.0 + self.adaLN_modulation(c_noisy)
+            scale_clean = 1.0 + self.adaLN_modulation(c_clean)
+            scale = select_per_token(scale_noisy, scale_clean, noise_mask, seq_len)
+        else:
+            # Original global modulation
+            assert c is not None, "Either c or (c_noisy, c_clean) must be provided"
+            scale = 1.0 + self.adaLN_modulation(c)
+            scale = scale.unsqueeze(1)
+
+        x = self.norm_final(x) * scale
        x = self.linear(x)
        return x

@@ -274,7 +316,10 @@ class RopeEmbedder:
        result = []
        for i in range(len(self.axes_dims)):
            index = ids[:, i]
-            result.append(self.freqs_cis[i][index])
+            if IS_NPU_AVAILABLE:
+                result.append(torch.index_select(self.freqs_cis[i], 0, index))
+            else:
+                result.append(self.freqs_cis[i][index])
        return torch.cat(result, dim=-1)


@@ -299,6 +344,7 @@ class ZImageDiT(nn.Module):
        t_scale=1000.0,
        axes_dims=[32, 48, 48],
        axes_lens=[1024, 512, 512],
+        siglip_feat_dim=None,
    ) -> None:
        super().__init__()
        self.in_channels = in_channels
@@ -359,6 +405,32 @@ class ZImageDiT(nn.Module):
            nn.Linear(cap_feat_dim, dim, bias=True),
        )

+        # Optional SigLIP components (for Omni variant)
+        self.siglip_feat_dim = siglip_feat_dim
+        if siglip_feat_dim is not None:
+            self.siglip_embedder = nn.Sequential(
+                RMSNorm(siglip_feat_dim, eps=norm_eps), nn.Linear(siglip_feat_dim, dim, bias=True)
+            )
+            self.siglip_refiner = nn.ModuleList(
+                [
+                    ZImageTransformerBlock(
+                        2000 + layer_id,
+                        dim,
+                        n_heads,
+                        n_kv_heads,
+                        norm_eps,
+                        qk_norm,
+                        modulation=False,
+                    )
+                    for layer_id in range(n_refiner_layers)
+                ]
+            )
+            self.siglip_pad_token = nn.Parameter(torch.empty((1, dim)))
+        else:
+            self.siglip_embedder = None
+            self.siglip_refiner = None
+            self.siglip_pad_token = None
+
        self.x_pad_token = nn.Parameter(torch.empty((1, dim)))
        self.cap_pad_token = nn.Parameter(torch.empty((1, dim)))

@@ -375,22 +447,57 @@ class ZImageDiT(nn.Module):

        self.rope_embedder = RopeEmbedder(theta=rope_theta, axes_dims=axes_dims, axes_lens=axes_lens)

-    def unpatchify(self, x: List[torch.Tensor], size: List[Tuple], patch_size, f_patch_size) -> List[torch.Tensor]:
+    def unpatchify(
+        self,
+        x: List[torch.Tensor],
+        size: List[Tuple],
+        patch_size = 2,
+        f_patch_size = 1,
+        x_pos_offsets: Optional[List[Tuple[int, int]]] = None,
+    ) -> List[torch.Tensor]:
        pH = pW = patch_size
        pF = f_patch_size
        bsz = len(x)
        assert len(size) == bsz
-        for i in range(bsz):
-            F, H, W = size[i]
-            ori_len = (F // pF) * (H // pH) * (W // pW)
-            # "f h w pf ph pw c -> c (f pf) (h ph) (w pw)"
-            x[i] = (
-                x[i][:ori_len]
-                .view(F // pF, H // pH, W // pW, pF, pH, pW, self.out_channels)
-                .permute(6, 0, 3, 1, 4, 2, 5)
-                .reshape(self.out_channels, F, H, W)
-            )
-        return x
+
+        if x_pos_offsets is not None:
+            # Omni: extract target image from unified sequence (cond_images + target)
+            result = []
+            for i in range(bsz):
+                unified_x = x[i][x_pos_offsets[i][0] : x_pos_offsets[i][1]]
+                cu_len = 0
+                x_item = None
+                for j in range(len(size[i])):
+                    if size[i][j] is None:
+                        ori_len = 0
+                        pad_len = SEQ_MULTI_OF
+                        cu_len += pad_len + ori_len
+                    else:
+                        F, H, W = size[i][j]
+                        ori_len = (F // pF) * (H // pH) * (W // pW)
+                        pad_len = (-ori_len) % SEQ_MULTI_OF
+                        x_item = (
+                            unified_x[cu_len : cu_len + ori_len]
+                            .view(F // pF, H // pH, W // pW, pF, pH, pW, self.out_channels)
+                            .permute(6, 0, 3, 1, 4, 2, 5)
+                            .reshape(self.out_channels, F, H, W)
+                        )
+                        cu_len += ori_len + pad_len
+                result.append(x_item)  # Return only the last (target) image
+            return result
+        else:
+            # Original mode: simple unpatchify
+            for i in range(bsz):
+                F, H, W = size[i]
+                ori_len = (F // pF) * (H // pH) * (W // pW)
+                # "f h w pf ph pw c -> c (f pf) (h ph) (w pw)"
+                x[i] = (
+                    x[i][:ori_len]
+                    .view(F // pF, H // pH, W // pW, pF, pH, pW, self.out_channels)
+                    .permute(6, 0, 3, 1, 4, 2, 5)
+                    .reshape(self.out_channels, F, H, W)
+                )
+            return x

    @staticmethod
    def create_coordinate_grid(size, start=None, device=None):
@@ -405,8 +512,8 @@ class ZImageDiT(nn.Module):
        self,
        all_image: List[torch.Tensor],
        all_cap_feats: List[torch.Tensor],
-        patch_size: int,
-        f_patch_size: int,
+        patch_size: int = 2,
+        f_patch_size: int = 1,
    ):
        pH = pW = patch_size
        pF = f_patch_size
@@ -490,90 +597,487 @@ class ZImageDiT(nn.Module):
            image_padded_feat = torch.cat([image, image[-1:].repeat(image_padding_len, 1)], dim=0)
            all_image_out.append(image_padded_feat)

+        return all_image_out, all_cap_feats_out, {
+            "x_size": all_image_size,
+            "x_pos_ids": all_image_pos_ids,
+            "cap_pos_ids": all_cap_pos_ids,
+            "x_pad_mask": all_image_pad_mask,
+            "cap_pad_mask": all_cap_pad_mask
+        }
+    # (
+    #         all_img_out,
+    #         all_cap_out,
+    #         all_img_size,
+    #         all_img_pos_ids,
+    #         all_cap_pos_ids,
+    #         all_img_pad_mask,
+    #         all_cap_pad_mask,
+    #     )
+
+    def patchify_controlnet(
+        self,
+        all_image: List[torch.Tensor],
+        patch_size: int = 2,
+        f_patch_size: int = 1,
+        cap_padding_len: int = None,
+    ):
+        pH = pW = patch_size
+        pF = f_patch_size
+        device = all_image[0].device
+
+        all_image_out = []
+        all_image_size = []
+        all_image_pos_ids = []
+        all_image_pad_mask = []
+
+        for i, image in enumerate(all_image):
+            ### Process Image
+            C, F, H, W = image.size()
+            all_image_size.append((F, H, W))
+            F_tokens, H_tokens, W_tokens = F // pF, H // pH, W // pW
+
+            image = image.view(C, F_tokens, pF, H_tokens, pH, W_tokens, pW)
+            # "c f pf h ph w pw -> (f h w) (pf ph pw c)"
+            image = image.permute(1, 3, 5, 2, 4, 6, 0).reshape(F_tokens * H_tokens * W_tokens, pF * pH * pW * C)
+
+            image_ori_len = len(image)
+            image_padding_len = (-image_ori_len) % SEQ_MULTI_OF
+
+            image_ori_pos_ids = self.create_coordinate_grid(
+                size=(F_tokens, H_tokens, W_tokens),
+                start=(cap_padding_len + 1, 0, 0),
+                device=device,
+            ).flatten(0, 2)
+            image_padding_pos_ids = (
+                self.create_coordinate_grid(
+                    size=(1, 1, 1),
+                    start=(0, 0, 0),
+                    device=device,
+                )
+                .flatten(0, 2)
+                .repeat(image_padding_len, 1)
+            )
+            image_padded_pos_ids = torch.cat([image_ori_pos_ids, image_padding_pos_ids], dim=0)
+            all_image_pos_ids.append(image_padded_pos_ids)
+            # pad mask
+            all_image_pad_mask.append(
+                torch.cat(
+                    [
+                        torch.zeros((image_ori_len,), dtype=torch.bool, device=device),
+                        torch.ones((image_padding_len,), dtype=torch.bool, device=device),
+                    ],
+                    dim=0,
+                )
+            )
+            # padded feature
+            image_padded_feat = torch.cat([image, image[-1:].repeat(image_padding_len, 1)], dim=0)
+            all_image_out.append(image_padded_feat)
+
        return (
            all_image_out,
-            all_cap_feats_out,
            all_image_size,
            all_image_pos_ids,
-            all_cap_pos_ids,
            all_image_pad_mask,
-            all_cap_pad_mask,
        )
+    
+    def _prepare_sequence(
+        self,
+        feats: List[torch.Tensor],
+        pos_ids: List[torch.Tensor],
+        inner_pad_mask: List[torch.Tensor],
+        pad_token: torch.nn.Parameter,
+        noise_mask: Optional[List[List[int]]] = None,
+        device: torch.device = None,
+    ):
+        """Prepare sequence: apply pad token, RoPE embed, pad to batch, create attention mask."""
+        item_seqlens = [len(f) for f in feats]
+        max_seqlen = max(item_seqlens)
+        bsz = len(feats)
+
+        # Pad token
+        feats_cat = torch.cat(feats, dim=0)
+        feats_cat[torch.cat(inner_pad_mask)] = pad_token.to(dtype=feats_cat.dtype, device=feats_cat.device)
+        feats = list(feats_cat.split(item_seqlens, dim=0))
+
+        # RoPE
+        freqs_cis = list(self.rope_embedder(torch.cat(pos_ids, dim=0)).split([len(p) for p in pos_ids], dim=0))
+
+        # Pad to batch
+        feats = pad_sequence(feats, batch_first=True, padding_value=0.0)
+        freqs_cis = pad_sequence(freqs_cis, batch_first=True, padding_value=0.0)[:, : feats.shape[1]]
+
+        # Attention mask
+        attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(item_seqlens):
+            attn_mask[i, :seq_len] = 1
+
+        # Noise mask
+        noise_mask_tensor = None
+        if noise_mask is not None:
+            noise_mask_tensor = pad_sequence(
+                [torch.tensor(m, dtype=torch.long, device=device) for m in noise_mask],
+                batch_first=True,
+                padding_value=0,
+            )[:, : feats.shape[1]]
+
+        return feats, freqs_cis, attn_mask, item_seqlens, noise_mask_tensor
+    
+    def _build_unified_sequence(
+        self,
+        x: torch.Tensor,
+        x_freqs: torch.Tensor,
+        x_seqlens: List[int],
+        x_noise_mask: Optional[List[List[int]]],
+        cap: torch.Tensor,
+        cap_freqs: torch.Tensor,
+        cap_seqlens: List[int],
+        cap_noise_mask: Optional[List[List[int]]],
+        siglip: Optional[torch.Tensor],
+        siglip_freqs: Optional[torch.Tensor],
+        siglip_seqlens: Optional[List[int]],
+        siglip_noise_mask: Optional[List[List[int]]],
+        omni_mode: bool,
+        device: torch.device,
+    ):
+        """Build unified sequence: x, cap, and optionally siglip.
+        Basic mode order: [x, cap]; Omni mode order: [cap, x, siglip]
+        """
+        bsz = len(x_seqlens)
+        unified = []
+        unified_freqs = []
+        unified_noise_mask = []
+
+        for i in range(bsz):
+            x_len, cap_len = x_seqlens[i], cap_seqlens[i]
+
+            if omni_mode:
+                # Omni: [cap, x, siglip]
+                if siglip is not None and siglip_seqlens is not None:
+                    sig_len = siglip_seqlens[i]
+                    unified.append(torch.cat([cap[i][:cap_len], x[i][:x_len], siglip[i][:sig_len]]))
+                    unified_freqs.append(
+                        torch.cat([cap_freqs[i][:cap_len], x_freqs[i][:x_len], siglip_freqs[i][:sig_len]])
+                    )
+                    unified_noise_mask.append(
+                        torch.tensor(
+                            cap_noise_mask[i] + x_noise_mask[i] + siglip_noise_mask[i], dtype=torch.long, device=device
+                        )
+                    )
+                else:
+                    unified.append(torch.cat([cap[i][:cap_len], x[i][:x_len]]))
+                    unified_freqs.append(torch.cat([cap_freqs[i][:cap_len], x_freqs[i][:x_len]]))
+                    unified_noise_mask.append(
+                        torch.tensor(cap_noise_mask[i] + x_noise_mask[i], dtype=torch.long, device=device)
+                    )
+            else:
+                # Basic: [x, cap]
+                unified.append(torch.cat([x[i][:x_len], cap[i][:cap_len]]))
+                unified_freqs.append(torch.cat([x_freqs[i][:x_len], cap_freqs[i][:cap_len]]))
+
+        # Compute unified seqlens
+        if omni_mode:
+            if siglip is not None and siglip_seqlens is not None:
+                unified_seqlens = [a + b + c for a, b, c in zip(cap_seqlens, x_seqlens, siglip_seqlens)]
+            else:
+                unified_seqlens = [a + b for a, b in zip(cap_seqlens, x_seqlens)]
+        else:
+            unified_seqlens = [a + b for a, b in zip(x_seqlens, cap_seqlens)]
+
+        max_seqlen = max(unified_seqlens)
+
+        # Pad to batch
+        unified = pad_sequence(unified, batch_first=True, padding_value=0.0)
+        unified_freqs = pad_sequence(unified_freqs, batch_first=True, padding_value=0.0)
+
+        # Attention mask
+        attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(unified_seqlens):
+            attn_mask[i, :seq_len] = 1
+
+        # Noise mask
+        noise_mask_tensor = None
+        if omni_mode:
+            noise_mask_tensor = pad_sequence(unified_noise_mask, batch_first=True, padding_value=0)[
+                :, : unified.shape[1]
+            ]
+
+        return unified, unified_freqs, attn_mask, noise_mask_tensor
+    
+    def _pad_with_ids(
+        self,
+        feat: torch.Tensor,
+        pos_grid_size: Tuple,
+        pos_start: Tuple,
+        device: torch.device,
+        noise_mask_val: Optional[int] = None,
+    ):
+        """Pad feature to SEQ_MULTI_OF, create position IDs and pad mask."""
+        ori_len = len(feat)
+        pad_len = (-ori_len) % SEQ_MULTI_OF
+        total_len = ori_len + pad_len
+
+        # Pos IDs
+        ori_pos_ids = self.create_coordinate_grid(size=pos_grid_size, start=pos_start, device=device).flatten(0, 2)
+        if pad_len > 0:
+            pad_pos_ids = (
+                self.create_coordinate_grid(size=(1, 1, 1), start=(0, 0, 0), device=device)
+                .flatten(0, 2)
+                .repeat(pad_len, 1)
+            )
+            pos_ids = torch.cat([ori_pos_ids, pad_pos_ids], dim=0)
+            padded_feat = torch.cat([feat, feat[-1:].repeat(pad_len, 1)], dim=0)
+            pad_mask = torch.cat(
+                [
+                    torch.zeros(ori_len, dtype=torch.bool, device=device),
+                    torch.ones(pad_len, dtype=torch.bool, device=device),
+                ]
+            )
+        else:
+            pos_ids = ori_pos_ids
+            padded_feat = feat
+            pad_mask = torch.zeros(ori_len, dtype=torch.bool, device=device)
+
+        noise_mask = [noise_mask_val] * total_len if noise_mask_val is not None else None  # token level
+        return padded_feat, pos_ids, pad_mask, total_len, noise_mask
+    
+    def _patchify_image(self, image: torch.Tensor, patch_size: int, f_patch_size: int):
+        """Patchify a single image tensor: (C, F, H, W) -> (num_patches, patch_dim)."""
+        pH, pW, pF = patch_size, patch_size, f_patch_size
+        C, F, H, W = image.size()
+        F_tokens, H_tokens, W_tokens = F // pF, H // pH, W // pW
+        image = image.view(C, F_tokens, pF, H_tokens, pH, W_tokens, pW)
+        image = image.permute(1, 3, 5, 2, 4, 6, 0).reshape(F_tokens * H_tokens * W_tokens, pF * pH * pW * C)
+        return image, (F, H, W), (F_tokens, H_tokens, W_tokens)
+    
+    def patchify_and_embed_omni(
+        self,
+        all_x: List[List[torch.Tensor]],
+        all_cap_feats: List[List[torch.Tensor]],
+        all_siglip_feats: List[List[torch.Tensor]],
+        patch_size: int = 2,
+        f_patch_size: int = 1,
+        images_noise_mask: List[List[int]] = None,
+    ):
+        """Patchify for omni mode: multiple images per batch item with noise masks."""
+        bsz = len(all_x)
+        device = all_x[0][-1].device
+        dtype = all_x[0][-1].dtype
+
+        all_x_out, all_x_size, all_x_pos_ids, all_x_pad_mask, all_x_len, all_x_noise_mask = [], [], [], [], [], []
+        all_cap_out, all_cap_pos_ids, all_cap_pad_mask, all_cap_len, all_cap_noise_mask = [], [], [], [], []
+        all_sig_out, all_sig_pos_ids, all_sig_pad_mask, all_sig_len, all_sig_noise_mask = [], [], [], [], []
+
+        for i in range(bsz):
+            num_images = len(all_x[i])
+            cap_feats_list, cap_pos_list, cap_mask_list, cap_lens, cap_noise = [], [], [], [], []
+            cap_end_pos = []
+            cap_cu_len = 1
+
+            # Process captions
+            for j, cap_item in enumerate(all_cap_feats[i]):
+                noise_val = images_noise_mask[i][j] if j < len(images_noise_mask[i]) else 1
+                cap_out, cap_pos, cap_mask, cap_len, cap_nm = self._pad_with_ids(
+                    cap_item,
+                    (len(cap_item) + (-len(cap_item)) % SEQ_MULTI_OF, 1, 1),
+                    (cap_cu_len, 0, 0),
+                    device,
+                    noise_val,
+                )
+                cap_feats_list.append(cap_out)
+                cap_pos_list.append(cap_pos)
+                cap_mask_list.append(cap_mask)
+                cap_lens.append(cap_len)
+                cap_noise.extend(cap_nm)
+                cap_cu_len += len(cap_item)
+                cap_end_pos.append(cap_cu_len)
+                cap_cu_len += 2  # for image vae and siglip tokens
+
+            all_cap_out.append(torch.cat(cap_feats_list, dim=0))
+            all_cap_pos_ids.append(torch.cat(cap_pos_list, dim=0))
+            all_cap_pad_mask.append(torch.cat(cap_mask_list, dim=0))
+            all_cap_len.append(cap_lens)
+            all_cap_noise_mask.append(cap_noise)
+
+            # Process images
+            x_feats_list, x_pos_list, x_mask_list, x_lens, x_size, x_noise = [], [], [], [], [], []
+            for j, x_item in enumerate(all_x[i]):
+                noise_val = images_noise_mask[i][j]
+                if x_item is not None:
+                    x_patches, size, (F_t, H_t, W_t) = self._patchify_image(x_item, patch_size, f_patch_size)
+                    x_out, x_pos, x_mask, x_len, x_nm = self._pad_with_ids(
+                        x_patches, (F_t, H_t, W_t), (cap_end_pos[j], 0, 0), device, noise_val
+                    )
+                    x_size.append(size)
+                else:
+                    x_len = SEQ_MULTI_OF
+                    x_out = torch.zeros((x_len, X_PAD_DIM), dtype=dtype, device=device)
+                    x_pos = self.create_coordinate_grid((1, 1, 1), (0, 0, 0), device).flatten(0, 2).repeat(x_len, 1)
+                    x_mask = torch.ones(x_len, dtype=torch.bool, device=device)
+                    x_nm = [noise_val] * x_len
+                    x_size.append(None)
+                x_feats_list.append(x_out)
+                x_pos_list.append(x_pos)
+                x_mask_list.append(x_mask)
+                x_lens.append(x_len)
+                x_noise.extend(x_nm)
+
+            all_x_out.append(torch.cat(x_feats_list, dim=0))
+            all_x_pos_ids.append(torch.cat(x_pos_list, dim=0))
+            all_x_pad_mask.append(torch.cat(x_mask_list, dim=0))
+            all_x_size.append(x_size)
+            all_x_len.append(x_lens)
+            all_x_noise_mask.append(x_noise)
+
+            # Process siglip
+            if all_siglip_feats[i] is None:
+                all_sig_len.append([0] * num_images)
+                all_sig_out.append(None)
+            else:
+                sig_feats_list, sig_pos_list, sig_mask_list, sig_lens, sig_noise = [], [], [], [], []
+                for j, sig_item in enumerate(all_siglip_feats[i]):
+                    noise_val = images_noise_mask[i][j]
+                    if sig_item is not None:
+                        sig_H, sig_W, sig_C = sig_item.size()
+                        sig_flat = sig_item.permute(2, 0, 1).reshape(sig_H * sig_W, sig_C)
+                        sig_out, sig_pos, sig_mask, sig_len, sig_nm = self._pad_with_ids(
+                            sig_flat, (1, sig_H, sig_W), (cap_end_pos[j] + 1, 0, 0), device, noise_val
+                        )
+                        # Scale position IDs to match x resolution
+                        if x_size[j] is not None:
+                            sig_pos = sig_pos.float()
+                            sig_pos[..., 1] = sig_pos[..., 1] / max(sig_H - 1, 1) * (x_size[j][1] - 1)
+                            sig_pos[..., 2] = sig_pos[..., 2] / max(sig_W - 1, 1) * (x_size[j][2] - 1)
+                            sig_pos = sig_pos.to(torch.int32)
+                    else:
+                        sig_len = SEQ_MULTI_OF
+                        sig_out = torch.zeros((sig_len, self.siglip_feat_dim), dtype=dtype, device=device)
+                        sig_pos = (
+                            self.create_coordinate_grid((1, 1, 1), (0, 0, 0), device).flatten(0, 2).repeat(sig_len, 1)
+                        )
+                        sig_mask = torch.ones(sig_len, dtype=torch.bool, device=device)
+                        sig_nm = [noise_val] * sig_len
+                    sig_feats_list.append(sig_out)
+                    sig_pos_list.append(sig_pos)
+                    sig_mask_list.append(sig_mask)
+                    sig_lens.append(sig_len)
+                    sig_noise.extend(sig_nm)
+
+                all_sig_out.append(torch.cat(sig_feats_list, dim=0))
+                all_sig_pos_ids.append(torch.cat(sig_pos_list, dim=0))
+                all_sig_pad_mask.append(torch.cat(sig_mask_list, dim=0))
+                all_sig_len.append(sig_lens)
+                all_sig_noise_mask.append(sig_noise)
+
+        # Compute x position offsets
+        all_x_pos_offsets = [(sum(all_cap_len[i]), sum(all_cap_len[i]) + sum(all_x_len[i])) for i in range(bsz)]
+
+        return (
+            all_x_out,
+            all_cap_out,
+            all_sig_out,
+            all_x_size,
+            all_x_pos_ids,
+            all_cap_pos_ids,
+            all_sig_pos_ids,
+            all_x_pad_mask,
+            all_cap_pad_mask,
+            all_sig_pad_mask,
+            all_x_pos_offsets,
+            all_x_noise_mask,
+            all_cap_noise_mask,
+            all_sig_noise_mask,
+        )
+        return all_x_out, all_cap_out, all_sig_out, {
+            "x_size": x_size,
+            "x_pos_ids": all_x_pos_ids,
+            "cap_pos_ids": all_cap_pos_ids,
+            "sig_pos_ids": all_sig_pos_ids,
+            "x_pad_mask": all_x_pad_mask,
+            "cap_pad_mask": all_cap_pad_mask,
+            "sig_pad_mask": all_sig_pad_mask,
+            "x_pos_offsets": all_x_pos_offsets,
+            "x_noise_mask": all_x_noise_mask,
+            "cap_noise_mask": all_cap_noise_mask,
+            "sig_noise_mask": all_sig_noise_mask,
+        }

    def forward(
        self,
        x: List[torch.Tensor],
        t,
        cap_feats: List[torch.Tensor],
+        siglip_feats = None,
+        image_noise_mask = None,
        patch_size=2,
        f_patch_size=1,
        use_gradient_checkpointing=False,
        use_gradient_checkpointing_offload=False,
    ):
-        assert patch_size in self.all_patch_size
-        assert f_patch_size in self.all_f_patch_size
+        assert patch_size in self.all_patch_size and f_patch_size in self.all_f_patch_size
+        omni_mode = isinstance(x[0], list)
+        device = x[0][-1].device if omni_mode else x[0].device

-        bsz = len(x)
-        device = x[0].device
-        t = t * self.t_scale
-        t = self.t_embedder(t)
+        if omni_mode:
+            # Dual embeddings: noisy (t) and clean (t=1)
+            t_noisy = self.t_embedder(t * self.t_scale).type_as(x[0][-1])
+            t_clean = self.t_embedder(torch.ones_like(t) * self.t_scale).type_as(x[0][-1])
+            adaln_input = None
+        else:
+            # Single embedding for all tokens
+            adaln_input = self.t_embedder(t * self.t_scale).type_as(x[0])
+            t_noisy = t_clean = None

-        adaln_input = t
-
-        (
-            x,
-            cap_feats,
-            x_size,
-            x_pos_ids,
-            cap_pos_ids,
-            x_inner_pad_mask,
-            cap_inner_pad_mask,
-        ) = self.patchify_and_embed(x, cap_feats, patch_size, f_patch_size)
+        # Patchify
+        if omni_mode:
+            (
+                x,
+                cap_feats,
+                siglip_feats,
+                x_size,
+                x_pos_ids,
+                cap_pos_ids,
+                siglip_pos_ids,
+                x_pad_mask,
+                cap_pad_mask,
+                siglip_pad_mask,
+                x_pos_offsets,
+                x_noise_mask,
+                cap_noise_mask,
+                siglip_noise_mask,
+            ) = self.patchify_and_embed_omni(x, cap_feats, siglip_feats, patch_size, f_patch_size, image_noise_mask)
+        else:
+            (
+                x,
+                cap_feats,
+                x_size,
+                x_pos_ids,
+                cap_pos_ids,
+                x_pad_mask,
+                cap_pad_mask,
+            ) = self.patchify_and_embed(x, cap_feats, patch_size, f_patch_size)
+            x_pos_offsets = x_noise_mask = cap_noise_mask = siglip_noise_mask = None

        # x embed & refine
-        x_item_seqlens = [len(_) for _ in x]
-        assert all(_ % SEQ_MULTI_OF == 0 for _ in x_item_seqlens)
-        x_max_item_seqlen = max(x_item_seqlens)
-
-        x = torch.cat(x, dim=0)
-        x = self.all_x_embedder[f"{patch_size}-{f_patch_size}"](x)
-        x[torch.cat(x_inner_pad_mask)] = self.x_pad_token.to(dtype=x.dtype, device=x.device)
-        x = list(x.split(x_item_seqlens, dim=0))
-        x_freqs_cis = list(self.rope_embedder(torch.cat(x_pos_ids, dim=0)).split(x_item_seqlens, dim=0))
-
-        x = pad_sequence(x, batch_first=True, padding_value=0.0)
-        x_freqs_cis = pad_sequence(x_freqs_cis, batch_first=True, padding_value=0.0)
-        x_attn_mask = torch.zeros((bsz, x_max_item_seqlen), dtype=torch.bool, device=device)
-        for i, seq_len in enumerate(x_item_seqlens):
-            x_attn_mask[i, :seq_len] = 1
+        x_seqlens = [len(xi) for xi in x]
+        x = self.all_x_embedder[f"{patch_size}-{f_patch_size}"](torch.cat(x, dim=0))  # embed
+        x, x_freqs, x_mask, _, x_noise_tensor = self._prepare_sequence(
+            list(x.split(x_seqlens, dim=0)), x_pos_ids, x_pad_mask, self.x_pad_token, x_noise_mask, device
+        )

        for layer in self.noise_refiner:
            x = gradient_checkpoint_forward(
                layer,
                use_gradient_checkpointing=use_gradient_checkpointing,
                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
-                x=x,
-                attn_mask=x_attn_mask,
-                freqs_cis=x_freqs_cis,
-                adaln_input=adaln_input,
+                x=x, attn_mask=x_mask, freqs_cis=x_freqs, adaln_input=adaln_input, noise_mask=x_noise_tensor, adaln_noisy=t_noisy, adaln_clean=t_clean,
            )

-        # cap embed & refine
-        cap_item_seqlens = [len(_) for _ in cap_feats]
-        assert all(_ % SEQ_MULTI_OF == 0 for _ in cap_item_seqlens)
-        cap_max_item_seqlen = max(cap_item_seqlens)
-
-        cap_feats = torch.cat(cap_feats, dim=0)
-        cap_feats = self.cap_embedder(cap_feats)
-        cap_feats[torch.cat(cap_inner_pad_mask)] = self.cap_pad_token.to(dtype=x.dtype, device=x.device)
-        cap_feats = list(cap_feats.split(cap_item_seqlens, dim=0))
-        cap_freqs_cis = list(self.rope_embedder(torch.cat(cap_pos_ids, dim=0)).split(cap_item_seqlens, dim=0))
-
-        cap_feats = pad_sequence(cap_feats, batch_first=True, padding_value=0.0)
-        cap_freqs_cis = pad_sequence(cap_freqs_cis, batch_first=True, padding_value=0.0)
-        cap_attn_mask = torch.zeros((bsz, cap_max_item_seqlen), dtype=torch.bool, device=device)
-        for i, seq_len in enumerate(cap_item_seqlens):
-            cap_attn_mask[i, :seq_len] = 1
+        # Cap embed & refine
+        cap_seqlens = [len(ci) for ci in cap_feats]
+        cap_feats = self.cap_embedder(torch.cat(cap_feats, dim=0))  # embed
+        cap_feats, cap_freqs, cap_mask, _, _ = self._prepare_sequence(
+            list(cap_feats.split(cap_seqlens, dim=0)), cap_pos_ids, cap_pad_mask, self.cap_pad_token, None, device
+        )

        for layer in self.context_refiner:
            cap_feats = gradient_checkpoint_forward(
@@ -581,41 +1085,68 @@ class ZImageDiT(nn.Module):
                use_gradient_checkpointing=use_gradient_checkpointing,
                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
                x=cap_feats,
-                attn_mask=cap_attn_mask,
-                freqs_cis=cap_freqs_cis,
+                attn_mask=cap_mask,
+                freqs_cis=cap_freqs,
            )

-        # unified
-        unified = []
-        unified_freqs_cis = []
-        for i in range(bsz):
-            x_len = x_item_seqlens[i]
-            cap_len = cap_item_seqlens[i]
-            unified.append(torch.cat([x[i][:x_len], cap_feats[i][:cap_len]]))
-            unified_freqs_cis.append(torch.cat([x_freqs_cis[i][:x_len], cap_freqs_cis[i][:cap_len]]))
-        unified_item_seqlens = [a + b for a, b in zip(cap_item_seqlens, x_item_seqlens)]
-        assert unified_item_seqlens == [len(_) for _ in unified]
-        unified_max_item_seqlen = max(unified_item_seqlens)
+        # Siglip embed & refine
+        siglip_seqlens = siglip_freqs = None
+        if omni_mode and siglip_feats[0] is not None and self.siglip_embedder is not None:
+            siglip_seqlens = [len(si) for si in siglip_feats]
+            siglip_feats = self.siglip_embedder(torch.cat(siglip_feats, dim=0))  # embed
+            siglip_feats, siglip_freqs, siglip_mask, _, _ = self._prepare_sequence(
+                list(siglip_feats.split(siglip_seqlens, dim=0)),
+                siglip_pos_ids,
+                siglip_pad_mask,
+                self.siglip_pad_token,
+                None,
+                device,
+            )

-        unified = pad_sequence(unified, batch_first=True, padding_value=0.0)
-        unified_freqs_cis = pad_sequence(unified_freqs_cis, batch_first=True, padding_value=0.0)
-        unified_attn_mask = torch.zeros((bsz, unified_max_item_seqlen), dtype=torch.bool, device=device)
-        for i, seq_len in enumerate(unified_item_seqlens):
-            unified_attn_mask[i, :seq_len] = 1
+            for layer in self.siglip_refiner:
+                siglip_feats = gradient_checkpoint_forward(
+                    layer,
+                    use_gradient_checkpointing=use_gradient_checkpointing,
+                    use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                    x=siglip_feats, attn_mask=siglip_mask, freqs_cis=siglip_freqs,
+                )

-        for layer in self.layers:
+        # Unified sequence
+        unified, unified_freqs, unified_mask, unified_noise_tensor = self._build_unified_sequence(
+            x,
+            x_freqs,
+            x_seqlens,
+            x_noise_mask,
+            cap_feats,
+            cap_freqs,
+            cap_seqlens,
+            cap_noise_mask,
+            siglip_feats,
+            siglip_freqs,
+            siglip_seqlens,
+            siglip_noise_mask,
+            omni_mode,
+            device,
+        )
+
+        # Main transformer layers
+        for layer_idx, layer in enumerate(self.layers):
            unified = gradient_checkpoint_forward(
                layer,
                use_gradient_checkpointing=use_gradient_checkpointing,
                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
-                x=unified,
-                attn_mask=unified_attn_mask,
-                freqs_cis=unified_freqs_cis,
-                adaln_input=adaln_input,
+                x=unified, attn_mask=unified_mask, freqs_cis=unified_freqs, adaln_input=adaln_input, noise_mask=unified_noise_tensor, adaln_noisy=t_noisy, adaln_clean=t_clean
            )

-        unified = self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, adaln_input)
-        unified = list(unified.unbind(dim=0))
-        x = self.unpatchify(unified, x_size, patch_size, f_patch_size)
+        unified = (
+            self.all_final_layer[f"{patch_size}-{f_patch_size}"](
+                unified, noise_mask=unified_noise_tensor, c_noisy=t_noisy, c_clean=t_clean
+            )
+            if omni_mode
+            else self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, c=adaln_input)
+        )

-        return x, {}
+        # Unpatchify
+        x = self.unpatchify(list(unified.unbind(dim=0)), x_size, patch_size, f_patch_size, x_pos_offsets)
+
+        return x
--- a/diffsynth/models/z_image_image2lora.py
+++ b/diffsynth/models/z_image_image2lora.py
@@ -0,0 +1,189 @@
+import torch
+from .qwen_image_image2lora import ImageEmbeddingToLoraMatrix, SequencialMLP
+
+
+class LoRATrainerBlock(torch.nn.Module):
+    def __init__(self, lora_patterns, in_dim=1536+4096, compress_dim=128, rank=4, block_id=0, use_residual=True, residual_length=64+7, residual_dim=3584, residual_mid_dim=1024, prefix="transformer_blocks"):
+        super().__init__()
+        self.prefix = prefix
+        self.lora_patterns = lora_patterns
+        self.block_id = block_id
+        self.layers = []
+        for name, lora_a_dim, lora_b_dim in self.lora_patterns:
+            self.layers.append(ImageEmbeddingToLoraMatrix(in_dim, compress_dim, lora_a_dim, lora_b_dim, rank))
+        self.layers = torch.nn.ModuleList(self.layers)
+        if use_residual:
+            self.proj_residual = SequencialMLP(residual_length, residual_dim, residual_mid_dim, compress_dim)
+        else:
+            self.proj_residual = None
+    
+    def forward(self, x, residual=None):
+        lora = {}
+        if self.proj_residual is not None: residual = self.proj_residual(residual)
+        for lora_pattern, layer in zip(self.lora_patterns, self.layers):
+            name = lora_pattern[0]
+            lora_a, lora_b = layer(x, residual=residual)
+            lora[f"{self.prefix}.{self.block_id}.{name}.lora_A.default.weight"] = lora_a
+            lora[f"{self.prefix}.{self.block_id}.{name}.lora_B.default.weight"] = lora_b
+        return lora
+
+
+class ZImageImage2LoRAComponent(torch.nn.Module):
+    def __init__(self, lora_patterns, prefix, num_blocks=60, use_residual=True, compress_dim=128, rank=4, residual_length=64+7, residual_mid_dim=1024):
+        super().__init__()
+        self.lora_patterns = lora_patterns
+        self.num_blocks = num_blocks
+        self.blocks = []
+        for lora_patterns in self.lora_patterns:
+            for block_id in range(self.num_blocks):
+                self.blocks.append(LoRATrainerBlock(lora_patterns, block_id=block_id, use_residual=use_residual, compress_dim=compress_dim, rank=rank, residual_length=residual_length, residual_mid_dim=residual_mid_dim, prefix=prefix))
+        self.blocks = torch.nn.ModuleList(self.blocks)
+        self.residual_scale = 0.05
+        self.use_residual = use_residual
+        
+    def forward(self, x, residual=None):
+        if residual is not None:
+            if self.use_residual:
+                residual = residual * self.residual_scale
+            else:
+                residual = None
+        lora = {}
+        for block in self.blocks:
+            lora.update(block(x, residual))
+        return lora
+
+
+class ZImageImage2LoRAModel(torch.nn.Module):
+    def __init__(self, use_residual=False, compress_dim=64, rank=4, residual_length=64+7, residual_mid_dim=1024):
+        super().__init__()
+        lora_patterns = [
+            [
+                ("attention.to_q", 3840, 3840),
+                ("attention.to_k", 3840, 3840),
+                ("attention.to_v", 3840, 3840),
+                ("attention.to_out.0", 3840, 3840),
+            ],
+            [
+                ("feed_forward.w1", 3840, 10240),
+                ("feed_forward.w2", 10240, 3840),
+                ("feed_forward.w3", 3840, 10240),
+            ],
+        ]
+        config = {
+            "lora_patterns": lora_patterns,
+            "use_residual": use_residual,
+            "compress_dim": compress_dim,
+            "rank": rank,
+            "residual_length": residual_length,
+            "residual_mid_dim": residual_mid_dim,
+        }
+        self.layers_lora = ZImageImage2LoRAComponent(
+            prefix="layers",
+            num_blocks=30,
+            **config,
+        )
+        self.context_refiner_lora = ZImageImage2LoRAComponent(
+            prefix="context_refiner",
+            num_blocks=2,
+            **config,
+        )
+        self.noise_refiner_lora = ZImageImage2LoRAComponent(
+            prefix="noise_refiner",
+            num_blocks=2,
+            **config,
+        )
+        
+    def forward(self, x, residual=None):
+        lora = {}
+        lora.update(self.layers_lora(x, residual=residual))
+        lora.update(self.context_refiner_lora(x, residual=residual))
+        lora.update(self.noise_refiner_lora(x, residual=residual))
+        return lora
+
+    def initialize_weights(self):
+        state_dict = self.state_dict()
+        for name in state_dict:
+            if ".proj_a." in name:
+                state_dict[name] = state_dict[name] * 0.3
+            elif ".proj_b.proj_out." in name:
+                state_dict[name] = state_dict[name] * 0
+            elif ".proj_residual.proj_out." in name:
+                state_dict[name] = state_dict[name] * 0.3
+        self.load_state_dict(state_dict)
+
+
+class ImageEmb2LoRAWeightCompressed(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, emb_dim, rank):
+        super().__init__()
+        self.lora_a = torch.nn.Parameter(torch.randn((rank, in_dim)))
+        self.lora_b = torch.nn.Parameter(torch.randn((out_dim, rank)))
+        self.proj = torch.nn.Linear(emb_dim, rank * rank, bias=True)
+        self.rank = rank
+    
+    def forward(self, x):
+        x = self.proj(x).view(self.rank, self.rank)
+        lora_a = x @ self.lora_a
+        lora_b = self.lora_b
+        return lora_a, lora_b
+
+
+class ZImageImage2LoRAModelCompressed(torch.nn.Module):
+    def __init__(self, emb_dim=1536+4096, rank=32):
+        super().__init__()
+        target_layers = [
+            ("attention.to_q", 3840, 3840),
+            ("attention.to_k", 3840, 3840),
+            ("attention.to_v", 3840, 3840),
+            ("attention.to_out.0", 3840, 3840),
+            ("feed_forward.w1", 3840, 10240),
+            ("feed_forward.w2", 10240, 3840),
+            ("feed_forward.w3", 3840, 10240),
+        ]
+        self.lora_patterns = [
+            {
+                "prefix": "layers",
+                "num_layers": 30,
+                "target_layers": target_layers,
+            },
+            {
+                "prefix": "context_refiner",
+                "num_layers": 2,
+                "target_layers": target_layers,
+            },
+            {
+                "prefix": "noise_refiner",
+                "num_layers": 2,
+                "target_layers": target_layers,
+            },
+        ]
+        module_dict = {}
+        for lora_pattern in self.lora_patterns:
+            prefix, num_layers, target_layers = lora_pattern["prefix"], lora_pattern["num_layers"], lora_pattern["target_layers"]
+            for layer_id in range(num_layers):
+                for layer_name, in_dim, out_dim in target_layers:
+                    name = f"{prefix}.{layer_id}.{layer_name}".replace(".", "___")
+                    model = ImageEmb2LoRAWeightCompressed(in_dim, out_dim, emb_dim, rank)
+                    module_dict[name] = model
+        self.module_dict = torch.nn.ModuleDict(module_dict)
+
+    def forward(self, x, residual=None):
+        lora = {}
+        for name, module in self.module_dict.items():
+            name = name.replace("___", ".")
+            name_a, name_b = f"{name}.lora_A.default.weight", f"{name}.lora_B.default.weight"
+            lora_a, lora_b = module(x)
+            lora[name_a] = lora_a
+            lora[name_b] = lora_b
+        return lora
+
+    def initialize_weights(self):
+        state_dict = self.state_dict()
+        for name in state_dict:
+            if "lora_b" in name:
+                state_dict[name] = state_dict[name] * 0
+            elif "lora_a" in name:
+                state_dict[name] = state_dict[name] * 0.2
+            elif "proj.weight" in name:
+                print(name)
+                state_dict[name] = state_dict[name] * 0.2
+        self.load_state_dict(state_dict)
--- a/diffsynth/models/z_image_text_encoder.py
+++ b/diffsynth/models/z_image_text_encoder.py
@@ -3,38 +3,71 @@ import torch


 class ZImageTextEncoder(torch.nn.Module):
-    def __init__(self):
+    def __init__(self, model_size="4B"):
        super().__init__()
-        config = Qwen3Config(**{
-            "architectures": [
-                "Qwen3ForCausalLM"
-            ],
-            "attention_bias": False,
-            "attention_dropout": 0.0,
-            "bos_token_id": 151643,
-            "eos_token_id": 151645,
-            "head_dim": 128,
-            "hidden_act": "silu",
-            "hidden_size": 2560,
-            "initializer_range": 0.02,
-            "intermediate_size": 9728,
-            "max_position_embeddings": 40960,
-            "max_window_layers": 36,
-            "model_type": "qwen3",
-            "num_attention_heads": 32,
-            "num_hidden_layers": 36,
-            "num_key_value_heads": 8,
-            "rms_norm_eps": 1e-06,
-            "rope_scaling": None,
-            "rope_theta": 1000000,
-            "sliding_window": None,
-            "tie_word_embeddings": True,
-            "torch_dtype": "bfloat16",
-            "transformers_version": "4.51.0",
-            "use_cache": True,
-            "use_sliding_window": False,
-            "vocab_size": 151936
-        })
+        config_dict = {
+            "4B": Qwen3Config(**{
+                "architectures": [
+                    "Qwen3ForCausalLM"
+                ],
+                "attention_bias": False,
+                "attention_dropout": 0.0,
+                "bos_token_id": 151643,
+                "eos_token_id": 151645,
+                "head_dim": 128,
+                "hidden_act": "silu",
+                "hidden_size": 2560,
+                "initializer_range": 0.02,
+                "intermediate_size": 9728,
+                "max_position_embeddings": 40960,
+                "max_window_layers": 36,
+                "model_type": "qwen3",
+                "num_attention_heads": 32,
+                "num_hidden_layers": 36,
+                "num_key_value_heads": 8,
+                "rms_norm_eps": 1e-06,
+                "rope_scaling": None,
+                "rope_theta": 1000000,
+                "sliding_window": None,
+                "tie_word_embeddings": True,
+                "torch_dtype": "bfloat16",
+                "transformers_version": "4.51.0",
+                "use_cache": True,
+                "use_sliding_window": False,
+                "vocab_size": 151936
+            }),
+            "8B": Qwen3Config(**{
+                "architectures": [
+                    "Qwen3ForCausalLM"
+                ],
+                "attention_bias": False,
+                "attention_dropout": 0.0,
+                "bos_token_id": 151643,
+                "dtype": "bfloat16",
+                "eos_token_id": 151645,
+                "head_dim": 128,
+                "hidden_act": "silu",
+                "hidden_size": 4096,
+                "initializer_range": 0.02,
+                "intermediate_size": 12288,
+                "max_position_embeddings": 40960,
+                "max_window_layers": 36,
+                "model_type": "qwen3",
+                "num_attention_heads": 32,
+                "num_hidden_layers": 36,
+                "num_key_value_heads": 8,
+                "rms_norm_eps": 1e-06,
+                "rope_scaling": None,
+                "rope_theta": 1000000,
+                "sliding_window": None,
+                "tie_word_embeddings": False,
+                "transformers_version": "4.56.1",
+                "use_cache": True,
+                "use_sliding_window": False,
+                "vocab_size": 151936
+            })
+        }
+        config = config_dict[model_size]
        self.model = Qwen3Model(config)
    
    def forward(self, *args, **kwargs):
--- a/diffsynth/pipelines/flux2_image.py
+++ b/diffsynth/pipelines/flux2_image.py
@@ -1,4 +1,4 @@
-import torch, math
+import torch, math, torchvision
 from PIL import Image
 from typing import Union
 from tqdm import tqdm
@@ -6,25 +6,28 @@ from einops import rearrange
 import numpy as np
 from typing import Union, List, Optional, Tuple

+from ..core.device.npu_compatible_device import get_device_type
 from ..diffusion import FlowMatchScheduler
 from ..core import ModelConfig, gradient_checkpoint_forward
 from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput

-from transformers import AutoProcessor
+from transformers import AutoProcessor, AutoTokenizer
 from ..models.flux2_text_encoder import Flux2TextEncoder
 from ..models.flux2_dit import Flux2DiT
 from ..models.flux2_vae import Flux2VAE
+from ..models.z_image_text_encoder import ZImageTextEncoder


 class Flux2ImagePipeline(BasePipeline):

-    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+    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("FLUX.2")
        self.text_encoder: Flux2TextEncoder = None
+        self.text_encoder_qwen3: ZImageTextEncoder = None
        self.dit: Flux2DiT = None
        self.vae: Flux2VAE = None
        self.tokenizer: AutoProcessor = None
@@ -32,8 +35,10 @@ class Flux2ImagePipeline(BasePipeline):
        self.units = [
            Flux2Unit_ShapeChecker(),
            Flux2Unit_PromptEmbedder(),
+            Flux2Unit_Qwen3PromptEmbedder(),
            Flux2Unit_NoiseInitializer(),
            Flux2Unit_InputImageEmbedder(),
+            Flux2Unit_EditImageEmbedder(),
            Flux2Unit_ImageIDs(),
        ]
        self.model_fn = model_fn_flux2
@@ -42,7 +47,7 @@ class Flux2ImagePipeline(BasePipeline):
    @staticmethod
    def from_pretrained(
        torch_dtype: torch.dtype = torch.bfloat16,
-        device: Union[str, torch.device] = "cuda",
+        device: Union[str, torch.device] = get_device_type(),
        model_configs: list[ModelConfig] = [],
        tokenizer_config: ModelConfig = ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="tokenizer/"),
        vram_limit: float = None,
@@ -53,11 +58,12 @@ class Flux2ImagePipeline(BasePipeline):
        
        # Fetch models
        pipe.text_encoder = model_pool.fetch_model("flux2_text_encoder")
+        pipe.text_encoder_qwen3 = model_pool.fetch_model("z_image_text_encoder")
        pipe.dit = model_pool.fetch_model("flux2_dit")
        pipe.vae = model_pool.fetch_model("flux2_vae")
        if tokenizer_config is not None:
            tokenizer_config.download_if_necessary()
-            pipe.tokenizer = AutoProcessor.from_pretrained(tokenizer_config.path)
+            pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
        
        # VRAM Management
        pipe.vram_management_enabled = pipe.check_vram_management_state()
@@ -75,6 +81,9 @@ class Flux2ImagePipeline(BasePipeline):
        # Image
        input_image: Image.Image = None,
        denoising_strength: float = 1.0,
+        # Edit
+        edit_image: Union[Image.Image, List[Image.Image]] = None,
+        edit_image_auto_resize: bool = True,
        # Shape
        height: int = 1024,
        width: int = 1024,
@@ -98,6 +107,7 @@ class Flux2ImagePipeline(BasePipeline):
        inputs_shared = {
            "cfg_scale": cfg_scale, "embedded_guidance": embedded_guidance,
            "input_image": input_image, "denoising_strength": denoising_strength,
+            "edit_image": edit_image, "edit_image_auto_resize": edit_image_auto_resize,
            "height": height, "width": width,
            "seed": seed, "rand_device": rand_device,
            "num_inference_steps": num_inference_steps,
@@ -275,6 +285,10 @@ class Flux2Unit_PromptEmbedder(PipelineUnit):
        return prompt_embeds, text_ids

    def process(self, pipe: Flux2ImagePipeline, prompt):
+        # Skip if Qwen3 text encoder is available (handled by Qwen3PromptEmbedder)
+        if pipe.text_encoder_qwen3 is not None:
+            return {}
+        
        pipe.load_models_to_device(self.onload_model_names)
        prompt_embeds, text_ids = self.encode_prompt(
            pipe.text_encoder, pipe.tokenizer, prompt,
@@ -283,6 +297,136 @@ class Flux2Unit_PromptEmbedder(PipelineUnit):
        return {"prompt_embeds": prompt_embeds, "text_ids": text_ids}


+class Flux2Unit_Qwen3PromptEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt": "prompt"},
+            input_params_nega={"prompt": "negative_prompt"},
+            output_params=("prompt_emb", "prompt_emb_mask"),
+            onload_model_names=("text_encoder_qwen3",)
+        )
+        self.hidden_states_layers = (9, 18, 27)  # Qwen3 layers
+
+    def get_qwen3_prompt_embeds(
+        self,
+        text_encoder: ZImageTextEncoder,
+        tokenizer: AutoTokenizer,
+        prompt: Union[str, List[str]],
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        max_sequence_length: int = 512,
+    ):
+        dtype = text_encoder.dtype if dtype is None else dtype
+        device = text_encoder.device if device is None else device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        all_input_ids = []
+        all_attention_masks = []
+
+        for single_prompt in prompt:
+            messages = [{"role": "user", "content": single_prompt}]
+            text = tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True,
+                enable_thinking=False,
+            )
+            inputs = tokenizer(
+                text,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=max_sequence_length,
+            )
+
+            all_input_ids.append(inputs["input_ids"])
+            all_attention_masks.append(inputs["attention_mask"])
+
+        input_ids = torch.cat(all_input_ids, dim=0).to(device)
+        attention_mask = torch.cat(all_attention_masks, dim=0).to(device)
+
+        # Forward pass through the model
+        with torch.inference_mode():
+            output = text_encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                output_hidden_states=True,
+                use_cache=False,
+            )
+
+        # Only use outputs from intermediate layers and stack them
+        out = torch.stack([output.hidden_states[k] for k in self.hidden_states_layers], dim=1)
+        out = out.to(dtype=dtype, device=device)
+
+        batch_size, num_channels, seq_len, hidden_dim = out.shape
+        prompt_embeds = out.permute(0, 2, 1, 3).reshape(batch_size, seq_len, num_channels * hidden_dim)
+        return prompt_embeds
+
+    def prepare_text_ids(
+        self,
+        x: torch.Tensor,  # (B, L, D) or (L, D)
+        t_coord: Optional[torch.Tensor] = None,
+    ):
+        B, L, _ = x.shape
+        out_ids = []
+
+        for i in range(B):
+            t = torch.arange(1) if t_coord is None else t_coord[i]
+            h = torch.arange(1)
+            w = torch.arange(1)
+            l = torch.arange(L)
+
+            coords = torch.cartesian_prod(t, h, w, l)
+            out_ids.append(coords)
+
+        return torch.stack(out_ids)
+
+    def encode_prompt(
+        self,
+        text_encoder: ZImageTextEncoder,
+        tokenizer: AutoTokenizer,
+        prompt: Union[str, List[str]],
+        dtype = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 512,
+    ):
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt_embeds is None:
+            prompt_embeds = self.get_qwen3_prompt_embeds(
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                prompt=prompt,
+                dtype=dtype,
+                device=device,
+                max_sequence_length=max_sequence_length,
+            )
+
+        batch_size, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        text_ids = self.prepare_text_ids(prompt_embeds)
+        text_ids = text_ids.to(device)
+        return prompt_embeds, text_ids
+
+    def process(self, pipe: Flux2ImagePipeline, prompt):
+        # Check if Qwen3 text encoder is available
+        if pipe.text_encoder_qwen3 is None:
+            return {}
+        
+        pipe.load_models_to_device(self.onload_model_names)
+        prompt_embeds, text_ids = self.encode_prompt(
+            pipe.text_encoder_qwen3, pipe.tokenizer, prompt,
+            dtype=pipe.torch_dtype, device=pipe.device,
+        )
+        return {"prompt_embeds": prompt_embeds, "text_ids": text_ids}
+
+
 class Flux2Unit_NoiseInitializer(PipelineUnit):
    def __init__(self):
        super().__init__(
@@ -318,6 +462,75 @@ class Flux2Unit_InputImageEmbedder(PipelineUnit):
            return {"latents": latents, "input_latents": input_latents}


+class Flux2Unit_EditImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("edit_image", "edit_image_auto_resize"),
+            output_params=("edit_latents", "edit_image_ids"),
+            onload_model_names=("vae",)
+        )
+
+    def calculate_dimensions(self, target_area, ratio):
+        import math
+        width = math.sqrt(target_area * ratio)
+        height = width / ratio
+        width = round(width / 32) * 32
+        height = round(height / 32) * 32
+        return width, height
+    
+    def crop_and_resize(self, image, target_height, target_width):
+        width, height = image.size
+        scale = max(target_width / width, target_height / height)
+        image = torchvision.transforms.functional.resize(
+            image,
+            (round(height*scale), round(width*scale)),
+            interpolation=torchvision.transforms.InterpolationMode.BILINEAR
+        )
+        image = torchvision.transforms.functional.center_crop(image, (target_height, target_width))
+        return image
+
+    def edit_image_auto_resize(self, edit_image):
+        calculated_width, calculated_height = self.calculate_dimensions(1024 * 1024, edit_image.size[0] / edit_image.size[1])
+        return self.crop_and_resize(edit_image, calculated_height, calculated_width)
+    
+    def process_image_ids(self, image_latents, scale=10):
+        t_coords = [scale + scale * t for t in torch.arange(0, len(image_latents))]
+        t_coords = [t.view(-1) for t in t_coords]
+
+        image_latent_ids = []
+        for x, t in zip(image_latents, t_coords):
+            x = x.squeeze(0)
+            _, height, width = x.shape
+
+            x_ids = torch.cartesian_prod(t, torch.arange(height), torch.arange(width), torch.arange(1))
+            image_latent_ids.append(x_ids)
+
+        image_latent_ids = torch.cat(image_latent_ids, dim=0)
+        image_latent_ids = image_latent_ids.unsqueeze(0)
+
+        return image_latent_ids
+
+    def process(self, pipe: Flux2ImagePipeline, edit_image, edit_image_auto_resize):
+        if edit_image is None:
+            return {}
+        pipe.load_models_to_device(self.onload_model_names)
+        if isinstance(edit_image, Image.Image):
+            edit_image = [edit_image]
+        resized_edit_image, edit_latents = [], []
+        for image in edit_image:
+            # Preprocess
+            if edit_image_auto_resize is None or edit_image_auto_resize:
+                image = self.edit_image_auto_resize(image)
+            resized_edit_image.append(image)
+            # Encode
+            image = pipe.preprocess_image(image)
+            latents = pipe.vae.encode(image)
+            edit_latents.append(latents)
+        edit_image_ids = self.process_image_ids(edit_latents).to(pipe.device)
+        edit_latents = torch.concat([rearrange(latents, "B C H W -> B (H W) C") for latents in edit_latents], dim=1)
+        return {"edit_latents": edit_latents, "edit_image_ids": edit_image_ids}
+
+
 class Flux2Unit_ImageIDs(PipelineUnit):
    def __init__(self):
        super().__init__(
@@ -352,10 +565,17 @@ def model_fn_flux2(
    prompt_embeds=None,
    text_ids=None,
    image_ids=None,
+    edit_latents=None,
+    edit_image_ids=None,
    use_gradient_checkpointing=False,
    use_gradient_checkpointing_offload=False,
    **kwargs,
 ):
+    image_seq_len = latents.shape[1]
+    if edit_latents is not None:
+        image_seq_len = latents.shape[1]
+        latents = torch.concat([latents, edit_latents], dim=1)
+        image_ids = torch.concat([image_ids, edit_image_ids], dim=1)
    embedded_guidance = torch.tensor([embedded_guidance], device=latents.device)
    model_output = dit(
        hidden_states=latents,
@@ -367,4 +587,5 @@ def model_fn_flux2(
        use_gradient_checkpointing=use_gradient_checkpointing,
        use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
    )
+    model_output = model_output[:, :image_seq_len]
    return model_output
--- a/diffsynth/pipelines/flux_image.py
+++ b/diffsynth/pipelines/flux_image.py
@@ -6,6 +6,7 @@ from einops import rearrange, repeat
 import numpy as np
 from transformers import CLIPTokenizer, T5TokenizerFast

+from ..core.device.npu_compatible_device import get_device_type
 from ..diffusion import FlowMatchScheduler
 from ..core import ModelConfig, gradient_checkpoint_forward, load_state_dict
 from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
@@ -55,7 +56,7 @@ class MultiControlNet(torch.nn.Module):

 class FluxImagePipeline(BasePipeline):

-    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+    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,
@@ -117,7 +118,7 @@ class FluxImagePipeline(BasePipeline):
    @staticmethod
    def from_pretrained(
        torch_dtype: torch.dtype = torch.bfloat16,
-        device: Union[str, torch.device] = "cuda",
+        device: Union[str, torch.device] = get_device_type(),
        model_configs: list[ModelConfig] = [],
        tokenizer_1_config: ModelConfig = ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="tokenizer/"),
        tokenizer_2_config: ModelConfig = ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="tokenizer_2/"),
@@ -377,7 +378,7 @@ class FluxImageUnit_PromptEmbedder(PipelineUnit):
        text_encoder_2,
        prompt,
        positive=True,
-        device="cuda",
+        device=get_device_type(),
        t5_sequence_length=512,
    ):
        pooled_prompt_emb = self.encode_prompt_using_clip(prompt, text_encoder_1, tokenizer_1, 77, device)
@@ -558,7 +559,7 @@ class FluxImageUnit_EntityControl(PipelineUnit):
        text_encoder_2,
        prompt,
        positive=True,
-        device="cuda",
+        device=get_device_type(),
        t5_sequence_length=512,
    ):
        pooled_prompt_emb = self.encode_prompt_using_clip(prompt, text_encoder_1, tokenizer_1, 77, device)
@@ -793,7 +794,7 @@ class FluxImageUnit_ValueControl(PipelineUnit):


 class InfinitYou(torch.nn.Module):
-    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+    def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
        super().__init__()
        from facexlib.recognition import init_recognition_model
        from insightface.app import FaceAnalysis
--- a/diffsynth/pipelines/qwen_image.py
+++ b/diffsynth/pipelines/qwen_image.py
@@ -4,20 +4,26 @@ from typing import Union
 from tqdm import tqdm
 from einops import rearrange
 import numpy as np
+from math import prod

+from ..core.device.npu_compatible_device import get_device_type
 from ..diffusion import FlowMatchScheduler
 from ..core import ModelConfig, gradient_checkpoint_forward
 from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
+from ..utils.lora.merge import merge_lora

 from ..models.qwen_image_dit import QwenImageDiT
 from ..models.qwen_image_text_encoder import QwenImageTextEncoder
 from ..models.qwen_image_vae import QwenImageVAE
 from ..models.qwen_image_controlnet import QwenImageBlockWiseControlNet
+from ..models.siglip2_image_encoder import Siglip2ImageEncoder
+from ..models.dinov3_image_encoder import DINOv3ImageEncoder
+from ..models.qwen_image_image2lora import QwenImageImage2LoRAModel


 class QwenImagePipeline(BasePipeline):

-    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+    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,
@@ -30,6 +36,11 @@ class QwenImagePipeline(BasePipeline):
        self.vae: QwenImageVAE = None
        self.blockwise_controlnet: QwenImageBlockwiseMultiControlNet = None
        self.tokenizer: Qwen2Tokenizer = None
+        self.siglip2_image_encoder: Siglip2ImageEncoder = None
+        self.dinov3_image_encoder: DINOv3ImageEncoder = None
+        self.image2lora_style: QwenImageImage2LoRAModel = None
+        self.image2lora_coarse: QwenImageImage2LoRAModel = None
+        self.image2lora_fine: QwenImageImage2LoRAModel = None
        self.processor: Qwen2VLProcessor = None
        self.in_iteration_models = ("dit", "blockwise_controlnet")
        self.units = [
@@ -38,6 +49,7 @@ class QwenImagePipeline(BasePipeline):
            QwenImageUnit_InputImageEmbedder(),
            QwenImageUnit_Inpaint(),
            QwenImageUnit_EditImageEmbedder(),
+            QwenImageUnit_LayerInputImageEmbedder(),
            QwenImageUnit_ContextImageEmbedder(),
            QwenImageUnit_PromptEmbedder(),
            QwenImageUnit_EntityControl(),
@@ -49,7 +61,7 @@ class QwenImagePipeline(BasePipeline):
    @staticmethod
    def from_pretrained(
        torch_dtype: torch.dtype = torch.bfloat16,
-        device: Union[str, torch.device] = "cuda",
+        device: Union[str, torch.device] = get_device_type(),
        model_configs: list[ModelConfig] = [],
        tokenizer_config: ModelConfig = ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
        processor_config: ModelConfig = None,
@@ -72,6 +84,11 @@ class QwenImagePipeline(BasePipeline):
            processor_config.download_if_necessary()
            from transformers import Qwen2VLProcessor
            pipe.processor = Qwen2VLProcessor.from_pretrained(processor_config.path)
+        pipe.siglip2_image_encoder = model_pool.fetch_model("siglip2_image_encoder")
+        pipe.dinov3_image_encoder = model_pool.fetch_model("dinov3_image_encoder")
+        pipe.image2lora_style = model_pool.fetch_model("qwen_image_image2lora_style")
+        pipe.image2lora_coarse = model_pool.fetch_model("qwen_image_image2lora_coarse")
+        pipe.image2lora_fine = model_pool.fetch_model("qwen_image_image2lora_fine")
        
        # VRAM Management
        pipe.vram_management_enabled = pipe.check_vram_management_state()
@@ -111,6 +128,11 @@ class QwenImagePipeline(BasePipeline):
        edit_image: Image.Image = None,
        edit_image_auto_resize: bool = True,
        edit_rope_interpolation: bool = False,
+        # Qwen-Image-Edit-2511
+        zero_cond_t: bool = False,
+        # Qwen-Image-Layered
+        layer_input_image: Image.Image = None,
+        layer_num: int = None,
        # In-context control
        context_image: Image.Image = None,
        # Tile
@@ -142,6 +164,9 @@ class QwenImagePipeline(BasePipeline):
            "eligen_entity_prompts": eligen_entity_prompts, "eligen_entity_masks": eligen_entity_masks, "eligen_enable_on_negative": eligen_enable_on_negative,
            "edit_image": edit_image, "edit_image_auto_resize": edit_image_auto_resize, "edit_rope_interpolation": edit_rope_interpolation, 
            "context_image": context_image,
+            "zero_cond_t": zero_cond_t,
+            "layer_input_image": layer_input_image,
+            "layer_num": layer_num,
        }
        for unit in self.units:
            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
@@ -161,7 +186,10 @@ class QwenImagePipeline(BasePipeline):
        # Decode
        self.load_models_to_device(['vae'])
        image = self.vae.decode(inputs_shared["latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
-        image = self.vae_output_to_image(image)
+        if layer_num is None:
+            image = self.vae_output_to_image(image)
+        else:
+            image = [self.vae_output_to_image(i, pattern="C H W") for i in image]
        self.load_models_to_device([])

        return image
@@ -212,12 +240,15 @@ class QwenImageUnit_ShapeChecker(PipelineUnit):
 class QwenImageUnit_NoiseInitializer(PipelineUnit):
    def __init__(self):
        super().__init__(
-            input_params=("height", "width", "seed", "rand_device"),
+            input_params=("height", "width", "seed", "rand_device", "layer_num"),
            output_params=("noise",),
        )

-    def process(self, pipe: QwenImagePipeline, height, width, seed, rand_device):
-        noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
+    def process(self, pipe: QwenImagePipeline, height, width, seed, rand_device, layer_num):
+        if layer_num is None:
+            noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
+        else:
+            noise = pipe.generate_noise((layer_num + 1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
        return {"noise": noise}


@@ -234,8 +265,15 @@ class QwenImageUnit_InputImageEmbedder(PipelineUnit):
        if input_image is None:
            return {"latents": noise, "input_latents": None}
        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, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+        if isinstance(input_image, list):
+            input_latents = []
+            for image in input_image:
+                image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype)
+                input_latents.append(pipe.vae.encode(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride))
+            input_latents = torch.concat(input_latents, dim=0)
+        else:
+            image = pipe.preprocess_image(input_image).to(device=pipe.device, dtype=pipe.torch_dtype)
+            input_latents = pipe.vae.encode(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
        if pipe.scheduler.training:
            return {"latents": noise, "input_latents": input_latents}
        else:
@@ -243,6 +281,22 @@ class QwenImageUnit_InputImageEmbedder(PipelineUnit):
            return {"latents": latents, "input_latents": input_latents}


+class QwenImageUnit_LayerInputImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("layer_input_image", "tiled", "tile_size", "tile_stride"),
+            output_params=("layer_input_latents",),
+            onload_model_names=("vae",)
+        )
+
+    def process(self, pipe: QwenImagePipeline, layer_input_image, tiled, tile_size, tile_stride):
+        if layer_input_image is None:
+            return {}
+        pipe.load_models_to_device(['vae'])
+        image = pipe.preprocess_image(layer_input_image).to(device=pipe.device, dtype=pipe.torch_dtype)
+        latents = pipe.vae.encode(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+        return {"layer_input_latents": latents}
+

 class QwenImageUnit_Inpaint(PipelineUnit):
    def __init__(self):
@@ -515,6 +569,116 @@ class QwenImageUnit_EditImageEmbedder(PipelineUnit):
        return {"edit_latents": edit_latents, "edit_image": resized_edit_image}


+class QwenImageUnit_Image2LoRAEncode(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("image2lora_images",),
+            output_params=("image2lora_x", "image2lora_residual", "image2lora_residual_highres"),
+            onload_model_names=("siglip2_image_encoder", "dinov3_image_encoder", "text_encoder"),
+        )
+        from ..core.data.operators import ImageCropAndResize
+        self.processor_lowres = ImageCropAndResize(height=28*8, width=28*8)
+        self.processor_highres = ImageCropAndResize(height=1024, width=1024)
+
+    def extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor):
+        bool_mask = mask.bool()
+        valid_lengths = bool_mask.sum(dim=1)
+        selected = hidden_states[bool_mask]
+        split_result = torch.split(selected, valid_lengths.tolist(), dim=0)
+        return split_result
+
+    def encode_prompt_edit(self, pipe: QwenImagePipeline, prompt, edit_image):
+        prompt = [prompt]
+        template =  "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
+        drop_idx = 64
+        txt = [template.format(e) for e in prompt]
+        model_inputs = pipe.processor(text=txt, images=edit_image, padding=True, return_tensors="pt").to(pipe.device)
+        hidden_states = pipe.text_encoder(input_ids=model_inputs.input_ids, attention_mask=model_inputs.attention_mask, pixel_values=model_inputs.pixel_values, image_grid_thw=model_inputs.image_grid_thw, output_hidden_states=True,)[-1]
+        split_hidden_states = self.extract_masked_hidden(hidden_states, model_inputs.attention_mask)
+        split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
+        max_seq_len = max([e.size(0) for e in split_hidden_states])
+        prompt_embeds = torch.stack([torch.cat([u, u.new_zeros(max_seq_len - u.size(0), u.size(1))]) for u in split_hidden_states])
+        prompt_embeds = prompt_embeds.to(dtype=pipe.torch_dtype, device=pipe.device)
+        return prompt_embeds.view(1, -1)
+    
+    def encode_images_using_siglip2(self, pipe: QwenImagePipeline, images: list[Image.Image]):
+        pipe.load_models_to_device(["siglip2_image_encoder"])
+        embs = []
+        for image in images:
+            image = self.processor_highres(image)
+            embs.append(pipe.siglip2_image_encoder(image).to(pipe.torch_dtype))
+        embs = torch.stack(embs)
+        return embs
+    
+    def encode_images_using_dinov3(self, pipe: QwenImagePipeline, images: list[Image.Image]):
+        pipe.load_models_to_device(["dinov3_image_encoder"])
+        embs = []
+        for image in images:
+            image = self.processor_highres(image)
+            embs.append(pipe.dinov3_image_encoder(image).to(pipe.torch_dtype))
+        embs = torch.stack(embs)
+        return embs
+    
+    def encode_images_using_qwenvl(self, pipe: QwenImagePipeline, images: list[Image.Image], highres=False):
+        pipe.load_models_to_device(["text_encoder"])
+        embs = []
+        for image in images:
+            image = self.processor_highres(image) if highres else self.processor_lowres(image)
+            embs.append(self.encode_prompt_edit(pipe, prompt="", edit_image=image))
+        embs = torch.stack(embs)
+        return embs
+
+    def encode_images(self, pipe: QwenImagePipeline, images: list[Image.Image]):
+        if images is None:
+            return {}
+        if not isinstance(images, list):
+            images = [images]
+        embs_siglip2 = self.encode_images_using_siglip2(pipe, images)
+        embs_dinov3 = self.encode_images_using_dinov3(pipe, images)
+        x = torch.concat([embs_siglip2, embs_dinov3], dim=-1)
+        residual = None
+        residual_highres = None
+        if pipe.image2lora_coarse is not None:
+            residual = self.encode_images_using_qwenvl(pipe, images, highres=False)
+        if pipe.image2lora_fine is not None:
+            residual_highres = self.encode_images_using_qwenvl(pipe, images, highres=True)
+        return x, residual, residual_highres
+
+    def process(self, pipe: QwenImagePipeline, image2lora_images):
+        if image2lora_images is None:
+            return {}
+        x, residual, residual_highres = self.encode_images(pipe, image2lora_images)
+        return {"image2lora_x": x, "image2lora_residual": residual, "image2lora_residual_highres": residual_highres}
+
+
+class QwenImageUnit_Image2LoRADecode(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("image2lora_x", "image2lora_residual", "image2lora_residual_highres"),
+            output_params=("lora",),
+            onload_model_names=("image2lora_coarse", "image2lora_fine", "image2lora_style"),
+        )
+    
+    def process(self, pipe: QwenImagePipeline, image2lora_x, image2lora_residual, image2lora_residual_highres):
+        if image2lora_x is None:
+            return {}
+        loras = []
+        if pipe.image2lora_style is not None:
+            pipe.load_models_to_device(["image2lora_style"])
+            for x in image2lora_x:
+                loras.append(pipe.image2lora_style(x=x, residual=None))
+        if pipe.image2lora_coarse is not None:
+            pipe.load_models_to_device(["image2lora_coarse"])
+            for x, residual in zip(image2lora_x, image2lora_residual):
+                loras.append(pipe.image2lora_coarse(x=x, residual=residual))
+        if pipe.image2lora_fine is not None:
+            pipe.load_models_to_device(["image2lora_fine"])
+            for x, residual in zip(image2lora_x, image2lora_residual_highres):
+                loras.append(pipe.image2lora_fine(x=x, residual=residual))
+        lora = merge_lora(loras, alpha=1 / len(image2lora_x))
+        return {"lora": lora}
+
+
 class QwenImageUnit_ContextImageEmbedder(PipelineUnit):
    def __init__(self):
        super().__init__(
@@ -549,18 +713,26 @@ def model_fn_qwen_image(
    entity_prompt_emb_mask=None,
    entity_masks=None,
    edit_latents=None,
+    layer_input_latents=None,
+    layer_num=None,
    context_latents=None,
    enable_fp8_attention=False,
    use_gradient_checkpointing=False,
    use_gradient_checkpointing_offload=False,
    edit_rope_interpolation=False,
+    zero_cond_t=False,
    **kwargs
 ):
-    img_shapes = [(latents.shape[0], latents.shape[2]//2, latents.shape[3]//2)]
+    if layer_num is None:
+        layer_num = 1
+        img_shapes = [(1, latents.shape[2]//2, latents.shape[3]//2)]
+    else:
+        layer_num = layer_num + 1
+        img_shapes = [(1, latents.shape[2]//2, latents.shape[3]//2)] * layer_num
    txt_seq_lens = prompt_emb_mask.sum(dim=1).tolist()
    timestep = timestep / 1000
    
-    image = rearrange(latents, "B C (H P) (W Q) -> B (H W) (C P Q)", H=height//16, W=width//16, P=2, Q=2)
+    image = rearrange(latents, "(B N) C (H P) (W Q) -> B (N H W) (C P Q)", H=height//16, W=width//16, P=2, Q=2, N=layer_num)
    image_seq_len = image.shape[1]

    if context_latents is not None:
@@ -572,9 +744,27 @@ def model_fn_qwen_image(
        img_shapes += [(e.shape[0], e.shape[2]//2, e.shape[3]//2) for e in edit_latents_list]
        edit_image = [rearrange(e, "B C (H P) (W Q) -> B (H W) (C P Q)", H=e.shape[2]//2, W=e.shape[3]//2, P=2, Q=2) for e in edit_latents_list]
        image = torch.cat([image] + edit_image, dim=1)
+    if layer_input_latents is not None:
+        layer_num = layer_num + 1
+        img_shapes += [(layer_input_latents.shape[0], layer_input_latents.shape[2]//2, layer_input_latents.shape[3]//2)]
+        layer_input_latents = rearrange(layer_input_latents, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
+        image = torch.cat([image, layer_input_latents], dim=1)

    image = dit.img_in(image)
-    conditioning = dit.time_text_embed(timestep, image.dtype)
+    if zero_cond_t:
+        timestep = torch.cat([timestep, timestep * 0], dim=0)
+        modulate_index = torch.tensor(
+            [[0] * prod(sample[0]) + [1] * sum([prod(s) for s in sample[1:]]) for sample in [img_shapes]],
+            device=timestep.device,
+            dtype=torch.int,
+        )
+    else:
+        modulate_index = None
+    conditioning = dit.time_text_embed(
+        timestep,
+        image.dtype,
+        addition_t_cond=None if not dit.time_text_embed.use_additional_t_cond else torch.tensor([0]).to(device=image.device, dtype=torch.long)
+    )

    if entity_prompt_emb is not None:
        text, image_rotary_emb, attention_mask = dit.process_entity_masks(
@@ -604,6 +794,7 @@ def model_fn_qwen_image(
            image_rotary_emb=image_rotary_emb,
            attention_mask=attention_mask,
            enable_fp8_attention=enable_fp8_attention,
+            modulate_index=modulate_index,
        )
        if blockwise_controlnet_conditioning is not None:
            image_slice = image[:, :image_seq_len].clone()
@@ -614,9 +805,11 @@ def model_fn_qwen_image(
            )
            image[:, :image_seq_len] = image_slice + controlnet_output
    
+    if zero_cond_t:
+        conditioning = conditioning.chunk(2, dim=0)[0]
    image = dit.norm_out(image, conditioning)
    image = dit.proj_out(image)
    image = image[:, :image_seq_len]
    
-    latents = rearrange(image, "B (H W) (C P Q) -> B C (H P) (W Q)", H=height//16, W=width//16, P=2, Q=2)
+    latents = rearrange(image, "B (N H W) (C P Q) -> (B N) C (H P) (W Q)", H=height//16, W=width//16, P=2, Q=2, B=1)
    return latents
--- a/diffsynth/pipelines/wan_video.py
+++ b/diffsynth/pipelines/wan_video.py
@@ -11,6 +11,7 @@ from typing import Optional
 from typing_extensions import Literal
 from transformers import Wav2Vec2Processor

+from ..core.device.npu_compatible_device import get_device_type
 from ..diffusion import FlowMatchScheduler
 from ..core import ModelConfig, gradient_checkpoint_forward
 from ..diffusion.base_pipeline import BasePipeline, PipelineUnit
@@ -30,7 +31,7 @@ from ..models.longcat_video_dit import LongCatVideoTransformer3DModel

 class WanVideoPipeline(BasePipeline):

-    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+    def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
        super().__init__(
            device=device, torch_dtype=torch_dtype,
            height_division_factor=16, width_division_factor=16, time_division_factor=4, time_division_remainder=1
@@ -98,7 +99,7 @@ class WanVideoPipeline(BasePipeline):
    @staticmethod
    def from_pretrained(
        torch_dtype: torch.dtype = torch.bfloat16,
-        device: Union[str, torch.device] = "cuda",
+        device: Union[str, torch.device] = get_device_type(),
        model_configs: list[ModelConfig] = [],
        tokenizer_config: ModelConfig = ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"),
        audio_processor_config: ModelConfig = None,
@@ -122,11 +123,15 @@ class WanVideoPipeline(BasePipeline):
                    model_config.model_id = redirect_dict[model_config.origin_file_pattern][0]
                    model_config.origin_file_pattern = redirect_dict[model_config.origin_file_pattern][1]
        
-        # Initialize pipeline
-        pipe = WanVideoPipeline(device=device, torch_dtype=torch_dtype)
        if use_usp:
            from ..utils.xfuser import initialize_usp
-            initialize_usp()
+            initialize_usp(device)
+            import torch.distributed as dist
+            from ..core.device.npu_compatible_device import get_device_name
+            if dist.is_available() and dist.is_initialized():
+                device = get_device_name()
+        # Initialize pipeline
+        pipe = WanVideoPipeline(device=device, torch_dtype=torch_dtype)
        model_pool = pipe.download_and_load_models(model_configs, vram_limit)
        
        # Fetch models
@@ -241,6 +246,7 @@ class WanVideoPipeline(BasePipeline):
        tea_cache_model_id: Optional[str] = "",
        # progress_bar
        progress_bar_cmd=tqdm,
+        output_type: Optional[Literal["quantized", "floatpoint"]] = "quantized",
    ):
        # Scheduler
        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
@@ -320,9 +326,11 @@ class WanVideoPipeline(BasePipeline):
        # Decode
        self.load_models_to_device(['vae'])
        video = self.vae.decode(inputs_shared["latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
-        video = self.vae_output_to_video(video)
+        if output_type == "quantized":
+            video = self.vae_output_to_video(video)
+        elif output_type == "floatpoint":
+            pass
        self.load_models_to_device([])
-
        return video


@@ -823,9 +831,9 @@ class WanVideoUnit_S2V(PipelineUnit):
        pipe.load_models_to_device(["vae"])
        motion_frames = 73
        kwargs = {}
-        if motion_video is not None and len(motion_video) > 0:
-            assert len(motion_video) == motion_frames, f"motion video must have {motion_frames} frames, but got {len(motion_video)}"
-            motion_latents = pipe.preprocess_video(motion_video)
+        if motion_video is not None:
+            assert motion_video.shape[2] == motion_frames, f"motion video must have {motion_frames} frames, but got {motion_video.shape[2]}"
+            motion_latents = motion_video
            kwargs["drop_motion_frames"] = False
        else:
            motion_latents = torch.zeros([1, 3, motion_frames, height, width], dtype=pipe.torch_dtype, device=pipe.device)
@@ -957,7 +965,7 @@ class WanVideoUnit_AnimateInpaint(PipelineUnit):
            onload_model_names=("vae",)
        )
        
-    def get_i2v_mask(self, lat_t, lat_h, lat_w, mask_len=1, mask_pixel_values=None, device="cuda"):
+    def get_i2v_mask(self, lat_t, lat_h, lat_w, mask_len=1, mask_pixel_values=None, device=get_device_type()):
        if mask_pixel_values is None:
            msk = torch.zeros(1, (lat_t-1) * 4 + 1, lat_h, lat_w, device=device)
        else:
--- a/diffsynth/pipelines/z_image.py
+++ b/diffsynth/pipelines/z_image.py
@@ -4,21 +4,29 @@ from typing import Union
 from tqdm import tqdm
 from einops import rearrange
 import numpy as np
-from typing import Union, List, Optional, Tuple
+from typing import Union, List, Optional, Tuple, Iterable, Dict

+from ..core.device.npu_compatible_device import get_device_type
 from ..diffusion import FlowMatchScheduler
 from ..core import ModelConfig, gradient_checkpoint_forward
+from ..core.data.operators import ImageCropAndResize
 from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
+from ..utils.lora import merge_lora

 from transformers import AutoTokenizer
 from ..models.z_image_text_encoder import ZImageTextEncoder
 from ..models.z_image_dit import ZImageDiT
 from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder
+from ..models.siglip2_image_encoder import Siglip2ImageEncoder428M
+from ..models.z_image_controlnet import ZImageControlNet
+from ..models.siglip2_image_encoder import Siglip2ImageEncoder
+from ..models.dinov3_image_encoder import DINOv3ImageEncoder
+from ..models.z_image_image2lora import ZImageImage2LoRAModel


 class ZImagePipeline(BasePipeline):

-    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+    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,
@@ -28,13 +36,22 @@ class ZImagePipeline(BasePipeline):
        self.dit: ZImageDiT = None
        self.vae_encoder: FluxVAEEncoder = None
        self.vae_decoder: FluxVAEDecoder = None
+        self.image_encoder: Siglip2ImageEncoder428M = None
+        self.controlnet: ZImageControlNet = None
+        self.siglip2_image_encoder: Siglip2ImageEncoder = None
+        self.dinov3_image_encoder: DINOv3ImageEncoder = None
+        self.image2lora_style: ZImageImage2LoRAModel = None
        self.tokenizer: AutoTokenizer = None
-        self.in_iteration_models = ("dit",)
+        self.in_iteration_models = ("dit", "controlnet")
        self.units = [
            ZImageUnit_ShapeChecker(),
            ZImageUnit_PromptEmbedder(),
            ZImageUnit_NoiseInitializer(),
            ZImageUnit_InputImageEmbedder(),
+            ZImageUnit_EditImageAutoResize(),
+            ZImageUnit_EditImageEmbedderVAE(),
+            ZImageUnit_EditImageEmbedderSiglip(),
+            ZImageUnit_PAIControlNet(),
        ]
        self.model_fn = model_fn_z_image
    
@@ -42,7 +59,7 @@ class ZImagePipeline(BasePipeline):
    @staticmethod
    def from_pretrained(
        torch_dtype: torch.dtype = torch.bfloat16,
-        device: Union[str, torch.device] = "cuda",
+        device: Union[str, torch.device] = get_device_type(),
        model_configs: list[ModelConfig] = [],
        tokenizer_config: ModelConfig = ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
        vram_limit: float = None,
@@ -56,6 +73,11 @@ class ZImagePipeline(BasePipeline):
        pipe.dit = model_pool.fetch_model("z_image_dit")
        pipe.vae_encoder = model_pool.fetch_model("flux_vae_encoder")
        pipe.vae_decoder = model_pool.fetch_model("flux_vae_decoder")
+        pipe.image_encoder = model_pool.fetch_model("siglip_vision_model_428m")
+        pipe.controlnet = model_pool.fetch_model("z_image_controlnet")
+        pipe.siglip2_image_encoder = model_pool.fetch_model("siglip2_image_encoder")
+        pipe.dinov3_image_encoder = model_pool.fetch_model("dinov3_image_encoder")
+        pipe.image2lora_style = model_pool.fetch_model("z_image_image2lora_style")
        if tokenizer_config is not None:
            tokenizer_config.download_if_necessary()
            pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
@@ -75,6 +97,9 @@ class ZImagePipeline(BasePipeline):
        # Image
        input_image: Image.Image = None,
        denoising_strength: float = 1.0,
+        # Edit
+        edit_image: Image.Image = None,
+        edit_image_auto_resize: bool = True,
        # Shape
        height: int = 1024,
        width: int = 1024,
@@ -83,11 +108,17 @@ class ZImagePipeline(BasePipeline):
        rand_device: str = "cpu",
        # Steps
        num_inference_steps: int = 8,
+        sigma_shift: float = None,
+        # ControlNet
+        controlnet_inputs: List[ControlNetInput] = None,
+        # Image to LoRA
+        image2lora_images: List[Image.Image] = None,
+        positive_only_lora: Dict[str, torch.Tensor] = None,
        # Progress bar
        progress_bar_cmd = tqdm,
    ):
        # Scheduler
-        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength)
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
        
        # Parameters
        inputs_posi = {
@@ -102,6 +133,9 @@ class ZImagePipeline(BasePipeline):
            "height": height, "width": width,
            "seed": seed, "rand_device": rand_device,
            "num_inference_steps": num_inference_steps,
+            "edit_image": edit_image, "edit_image_auto_resize": edit_image_auto_resize,
+            "controlnet_inputs": controlnet_inputs,
+            "image2lora_images": image2lora_images, "positive_only_lora": positive_only_lora,
        }
        for unit in self.units:
            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
@@ -143,12 +177,13 @@ class ZImageUnit_PromptEmbedder(PipelineUnit):
    def __init__(self):
        super().__init__(
            seperate_cfg=True,
+            input_params=("edit_image",),
            input_params_posi={"prompt": "prompt"},
            input_params_nega={"prompt": "negative_prompt"},
            output_params=("prompt_embeds",),
            onload_model_names=("text_encoder",)
        )
-    
+
    def encode_prompt(
        self,
        pipe,
@@ -194,10 +229,81 @@ class ZImageUnit_PromptEmbedder(PipelineUnit):
            embeddings_list.append(prompt_embeds[i][prompt_masks[i]])

        return embeddings_list
+    
+    def encode_prompt_omni(
+        self,
+        pipe,
+        prompt: Union[str, List[str]],
+        edit_image=None,
+        device: Optional[torch.device] = None,
+        max_sequence_length: int = 512,
+    ) -> List[torch.FloatTensor]:
+        if isinstance(prompt, str):
+            prompt = [prompt]

-    def process(self, pipe: ZImagePipeline, prompt):
+        if edit_image is None:
+            num_condition_images = 0
+        elif isinstance(edit_image, list):
+            num_condition_images = len(edit_image)
+        else:
+            num_condition_images = 1
+
+        for i, prompt_item in enumerate(prompt):
+            if num_condition_images == 0:
+                prompt[i] = ["<|im_start|>user\n" + prompt_item + "<|im_end|>\n<|im_start|>assistant\n"]
+            elif num_condition_images > 0:
+                prompt_list = ["<|im_start|>user\n<|vision_start|>"]
+                prompt_list += ["<|vision_end|><|vision_start|>"] * (num_condition_images - 1)
+                prompt_list += ["<|vision_end|>" + prompt_item + "<|im_end|>\n<|im_start|>assistant\n<|vision_start|>"]
+                prompt_list += ["<|vision_end|><|im_end|>"]
+                prompt[i] = prompt_list
+
+        flattened_prompt = []
+        prompt_list_lengths = []
+
+        for i in range(len(prompt)):
+            prompt_list_lengths.append(len(prompt[i]))
+            flattened_prompt.extend(prompt[i])
+
+        text_inputs = pipe.tokenizer(
+            flattened_prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids.to(device)
+        prompt_masks = text_inputs.attention_mask.to(device).bool()
+
+        prompt_embeds = pipe.text_encoder(
+            input_ids=text_input_ids,
+            attention_mask=prompt_masks,
+            output_hidden_states=True,
+        ).hidden_states[-2]
+
+        embeddings_list = []
+        start_idx = 0
+        for i in range(len(prompt_list_lengths)):
+            batch_embeddings = []
+            end_idx = start_idx + prompt_list_lengths[i]
+            for j in range(start_idx, end_idx):
+                batch_embeddings.append(prompt_embeds[j][prompt_masks[j]])
+            embeddings_list.append(batch_embeddings)
+            start_idx = end_idx
+
+        return embeddings_list
+
+    def process(self, pipe: ZImagePipeline, prompt, edit_image):
        pipe.load_models_to_device(self.onload_model_names)
-        prompt_embeds = self.encode_prompt(pipe, prompt, pipe.device)
+        if hasattr(pipe, "dit") and pipe.dit.siglip_embedder is not None:
+            # Z-Image-Turbo and Z-Image-Omni-Base use different prompt encoding methods.
+            # We determine which encoding method to use based on the model architecture.
+            # If you are using two-stage split training,
+            # please use `--offload_models` instead of skipping the DiT model loading.
+            prompt_embeds = self.encode_prompt_omni(pipe, prompt, edit_image, pipe.device)
+        else:
+            prompt_embeds = self.encode_prompt(pipe, prompt, pipe.device)
        return {"prompt_embeds": prompt_embeds}


@@ -234,24 +340,330 @@ class ZImageUnit_InputImageEmbedder(PipelineUnit):
            return {"latents": latents, "input_latents": input_latents}


+class ZImageUnit_EditImageAutoResize(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("edit_image", "edit_image_auto_resize"),
+            output_params=("edit_image",),
+        )
+
+    def process(self, pipe: ZImagePipeline, edit_image, edit_image_auto_resize):
+        if edit_image is None:
+            return {}
+        if edit_image_auto_resize is None or not edit_image_auto_resize:
+            return {}
+        operator = ImageCropAndResize(max_pixels=1024*1024, height_division_factor=16, width_division_factor=16)
+        if not isinstance(edit_image, list):
+            edit_image = [edit_image]
+        edit_image = [operator(i) for i in edit_image]
+        return {"edit_image": edit_image}
+
+
+class ZImageUnit_EditImageEmbedderSiglip(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("edit_image",),
+            output_params=("image_embeds",),
+            onload_model_names=("image_encoder",)
+        )
+
+    def process(self, pipe: ZImagePipeline, edit_image):
+        if edit_image is None:
+            return {}
+        pipe.load_models_to_device(self.onload_model_names)
+        if not isinstance(edit_image, list):
+            edit_image = [edit_image]
+        image_emb = []
+        for image_ in edit_image:
+            image_emb.append(pipe.image_encoder(image_, device=pipe.device))
+        return {"image_embeds": image_emb}
+
+
+class ZImageUnit_EditImageEmbedderVAE(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("edit_image",),
+            output_params=("image_latents",),
+            onload_model_names=("vae_encoder",)
+        )
+
+    def process(self, pipe: ZImagePipeline, edit_image):
+        if edit_image is None:
+            return {}
+        pipe.load_models_to_device(self.onload_model_names)
+        if not isinstance(edit_image, list):
+            edit_image = [edit_image]
+        image_latents = []
+        for image_ in edit_image:
+            image_ = pipe.preprocess_image(image_)
+            image_latents.append(pipe.vae_encoder(image_))
+        return {"image_latents": image_latents}
+
+
+class ZImageUnit_PAIControlNet(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("controlnet_inputs", "height", "width"),
+            output_params=("control_context", "control_scale"),
+            onload_model_names=("vae_encoder",)
+        )
+
+    def process(self, pipe: ZImagePipeline, controlnet_inputs: List[ControlNetInput], height, width):
+        if controlnet_inputs is None:
+            return {}
+        if len(controlnet_inputs) != 1:
+            print("Z-Image ControlNet doesn't support multi-ControlNet. Only one image will be used.")
+        controlnet_input = controlnet_inputs[0]
+        pipe.load_models_to_device(self.onload_model_names)
+
+        control_image = controlnet_input.image
+        if control_image is not None:
+            control_image = pipe.preprocess_image(control_image)
+            control_latents = pipe.vae_encoder(control_image)
+        else:
+            control_latents = torch.ones((1, 16, height // 8, width // 8), dtype=pipe.torch_dtype, device=pipe.device) * -1
+        
+        inpaint_mask = controlnet_input.inpaint_mask
+        if inpaint_mask is not None:
+            inpaint_mask = pipe.preprocess_image(inpaint_mask, min_value=0, max_value=1)
+            inpaint_image = controlnet_input.inpaint_image
+            inpaint_image = pipe.preprocess_image(inpaint_image)
+            inpaint_image = inpaint_image * (inpaint_mask < 0.5)
+            inpaint_mask = torch.nn.functional.interpolate(1 - inpaint_mask, (height // 8, width // 8), mode='nearest')[:, :1]
+        else:
+            inpaint_mask = torch.zeros((1, 1, height // 8, width // 8), dtype=pipe.torch_dtype, device=pipe.device)
+            inpaint_image = torch.zeros((1, 3, height, width), dtype=pipe.torch_dtype, device=pipe.device)
+        inpaint_latent = pipe.vae_encoder(inpaint_image)
+
+        control_context = torch.concat([control_latents, inpaint_mask, inpaint_latent], dim=1)
+        control_context = rearrange(control_context, "B C H W -> B C 1 H W")
+        return {"control_context": control_context, "control_scale": controlnet_input.scale}
+
+
 def model_fn_z_image(
    dit: ZImageDiT,
+    controlnet: ZImageControlNet = None,
    latents=None,
    timestep=None,
    prompt_embeds=None,
+    image_embeds=None,
+    image_latents=None,
    use_gradient_checkpointing=False,
    use_gradient_checkpointing_offload=False,
    **kwargs,
 ):
+    # Due to the complex and verbose codebase of Z-Image,
+    # we are temporarily using this inelegant structure.
+    # We will refactor this part in the future (if time permits).
+    if dit.siglip_embedder is None:
+        return model_fn_z_image_turbo(
+            dit,
+            controlnet=controlnet,
+            latents=latents,
+            timestep=timestep,
+            prompt_embeds=prompt_embeds,
+            image_embeds=image_embeds,
+            image_latents=image_latents,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+            **kwargs,
+        )
    latents = [rearrange(latents, "B C H W -> C B H W")]
+    if dit.siglip_embedder is not None:
+        if image_latents is not None:
+            image_latents = [rearrange(image_latent, "B C H W -> C B H W") for image_latent in image_latents]
+            latents = [image_latents + latents]
+            image_noise_mask = [[0] * len(image_latents) + [1]]
+        else:
+            latents = [latents]
+            image_noise_mask = [[1]]
+        image_embeds = [image_embeds]
+    else:
+        image_noise_mask = None
    timestep = (1000 - timestep) / 1000
    model_output = dit(
        latents,
        timestep,
        prompt_embeds,
+        siglip_feats=image_embeds,
+        image_noise_mask=image_noise_mask,
        use_gradient_checkpointing=use_gradient_checkpointing,
        use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
-    )[0][0]
+    )[0]
    model_output = -model_output
    model_output = rearrange(model_output, "C B H W -> B C H W")
    return model_output
+
+
+class ZImageUnit_Image2LoRAEncode(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("image2lora_images",),
+            output_params=("image2lora_x",),
+            onload_model_names=("siglip2_image_encoder", "dinov3_image_encoder",),
+        )
+        from ..core.data.operators import ImageCropAndResize
+        self.processor_highres = ImageCropAndResize(height=1024, width=1024)
+    
+    def encode_images_using_siglip2(self, pipe: ZImagePipeline, images: list[Image.Image]):
+        pipe.load_models_to_device(["siglip2_image_encoder"])
+        embs = []
+        for image in images:
+            image = self.processor_highres(image)
+            embs.append(pipe.siglip2_image_encoder(image).to(pipe.torch_dtype))
+        embs = torch.stack(embs)
+        return embs
+    
+    def encode_images_using_dinov3(self, pipe: ZImagePipeline, images: list[Image.Image]):
+        pipe.load_models_to_device(["dinov3_image_encoder"])
+        embs = []
+        for image in images:
+            image = self.processor_highres(image)
+            embs.append(pipe.dinov3_image_encoder(image).to(pipe.torch_dtype))
+        embs = torch.stack(embs)
+        return embs
+
+    def encode_images(self, pipe: ZImagePipeline, images: list[Image.Image]):
+        if images is None:
+            return {}
+        if not isinstance(images, list):
+            images = [images]
+        embs_siglip2 = self.encode_images_using_siglip2(pipe, images)
+        embs_dinov3 = self.encode_images_using_dinov3(pipe, images)
+        x = torch.concat([embs_siglip2, embs_dinov3], dim=-1)
+        return x
+
+    def process(self, pipe: ZImagePipeline, image2lora_images):
+        if image2lora_images is None:
+            return {}
+        x = self.encode_images(pipe, image2lora_images)
+        return {"image2lora_x": x}
+
+
+class ZImageUnit_Image2LoRADecode(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("image2lora_x",),
+            output_params=("lora",),
+            onload_model_names=("image2lora_style",),
+        )
+    
+    def process(self, pipe: ZImagePipeline, image2lora_x):
+        if image2lora_x is None:
+            return {}
+        loras = []
+        if pipe.image2lora_style is not None:
+            pipe.load_models_to_device(["image2lora_style"])
+            for x in image2lora_x:
+                loras.append(pipe.image2lora_style(x=x, residual=None))
+        lora = merge_lora(loras, alpha=1 / len(image2lora_x))
+        return {"lora": lora}
+
+
+def model_fn_z_image_turbo(
+    dit: ZImageDiT,
+    controlnet: ZImageControlNet = None,
+    latents=None,
+    timestep=None,
+    prompt_embeds=None,
+    image_embeds=None,
+    image_latents=None,
+    control_context=None,
+    control_scale=None,
+    use_gradient_checkpointing=False,
+    use_gradient_checkpointing_offload=False,
+    **kwargs,
+):
+    while isinstance(prompt_embeds, list):
+        prompt_embeds = prompt_embeds[0]
+    while isinstance(latents, list):
+        latents = latents[0]
+    while isinstance(image_embeds, list):
+        image_embeds = image_embeds[0]
+
+    # Timestep
+    timestep = 1000 - timestep
+    t_noisy = dit.t_embedder(timestep)
+    t_clean = dit.t_embedder(torch.ones_like(timestep) * 1000)
+
+    # Patchify
+    latents = rearrange(latents, "B C H W -> C B H W")
+    x, cap_feats, patch_metadata = dit.patchify_and_embed([latents], [prompt_embeds])
+    x = x[0]
+    cap_feats = cap_feats[0]
+
+    # Noise refine
+    x = dit.all_x_embedder["2-1"](x)
+    x[torch.cat(patch_metadata.get("x_pad_mask"))] = dit.x_pad_token.to(dtype=x.dtype, device=x.device)
+    x_freqs_cis = dit.rope_embedder(torch.cat(patch_metadata.get("x_pos_ids"), dim=0))
+    x = rearrange(x, "L C -> 1 L C")
+    x_freqs_cis = rearrange(x_freqs_cis, "L C -> 1 L C")
+
+    if control_context is not None:
+        kwargs = dict(attn_mask=None, freqs_cis=x_freqs_cis, adaln_input=t_noisy)
+        refiner_hints, control_context, control_context_item_seqlens = controlnet.forward_refiner(
+            dit, x, [cap_feats], control_context, kwargs, t=t_noisy, patch_size=2, f_patch_size=1,
+            use_gradient_checkpointing=use_gradient_checkpointing, use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+        )
+    
+    for layer_id, layer in enumerate(dit.noise_refiner):
+        x = gradient_checkpoint_forward(
+            layer,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+            x=x,
+            attn_mask=None,
+            freqs_cis=x_freqs_cis,
+            adaln_input=t_noisy,
+        )
+        if control_context is not None:
+            x = x + refiner_hints[layer_id] * control_scale
+
+    # Prompt refine
+    cap_feats = dit.cap_embedder(cap_feats)
+    cap_feats[torch.cat(patch_metadata.get("cap_pad_mask"))] = dit.cap_pad_token.to(dtype=x.dtype, device=x.device)
+    cap_freqs_cis = dit.rope_embedder(torch.cat(patch_metadata.get("cap_pos_ids"), dim=0))
+    cap_feats = rearrange(cap_feats, "L C -> 1 L C")
+    cap_freqs_cis = rearrange(cap_freqs_cis, "L C -> 1 L C")
+    
+    for layer in dit.context_refiner:
+        cap_feats = gradient_checkpoint_forward(
+            layer,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+            x=cap_feats,
+            attn_mask=None,
+            freqs_cis=cap_freqs_cis,
+        )
+
+    # Unified
+    unified = torch.cat([x, cap_feats], dim=1)
+    unified_freqs_cis = torch.cat([x_freqs_cis, cap_freqs_cis], dim=1)
+
+    if control_context is not None:
+        kwargs = dict(attn_mask=None, freqs_cis=unified_freqs_cis, adaln_input=t_noisy)
+        hints = controlnet.forward_layers(
+            unified, cap_feats, control_context, control_context_item_seqlens, kwargs,
+            use_gradient_checkpointing=use_gradient_checkpointing, use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+        )
+
+    for layer_id, layer in enumerate(dit.layers):
+        unified = gradient_checkpoint_forward(
+            layer,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+            x=unified,
+            attn_mask=None,
+            freqs_cis=unified_freqs_cis,
+            adaln_input=t_noisy,
+        )
+        if control_context is not None:
+            if layer_id in controlnet.control_layers_mapping:
+                unified = unified + hints[controlnet.control_layers_mapping[layer_id]] * control_scale
+    
+    # Output
+    unified = dit.all_final_layer["2-1"](unified, t_noisy)
+    x = dit.unpatchify([unified[0]], patch_metadata.get("x_size"))[0]
+    x = rearrange(x, "C B H W -> B C H W")
+    x = -x
+    return x
--- a/diffsynth/utils/controlnet/annotator.py
+++ b/diffsynth/utils/controlnet/annotator.py
@@ -1,12 +1,13 @@
 from typing_extensions import Literal, TypeAlias

+from diffsynth.core.device.npu_compatible_device import get_device_type

 Processor_id: TypeAlias = Literal[
    "canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "normal", "tile", "none", "inpaint"
 ]

 class Annotator:
-    def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device='cuda', skip_processor=False):
+    def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device=get_device_type(), skip_processor=False):
        if not skip_processor:
            if processor_id == "canny":
                from controlnet_aux.processor import CannyDetector
--- a/diffsynth/utils/controlnet/controlnet_input.py
+++ b/diffsynth/utils/controlnet/controlnet_input.py
@@ -9,5 +9,6 @@ class ControlNetInput:
    start: float = 1.0
    end: float = 0.0
    image: Image.Image = None
+    inpaint_image: Image.Image = None
    inpaint_mask: Image.Image = None
    processor_id: str = None
--- a/diffsynth/utils/lora/init.py
+++ b/diffsynth/utils/lora/init.py
@@ -1 +1,3 @@
 from .general import GeneralLoRALoader
+from .merge import merge_lora
+from .reset_rank import reset_lora_rank
--- a/diffsynth/utils/lora/flux.py
+++ b/diffsynth/utils/lora/flux.py
@@ -149,6 +149,8 @@ class FluxLoRALoader(GeneralLoRALoader):
                                        dtype=state_dict_[name].dtype)
                    else:
                        state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn."))
+
+                    mlp = mlp.to(device=state_dict_[name].device)
                    if 'lora_A' in name:
                        param = torch.concat([
                            state_dict_.pop(name),
@@ -202,3 +204,99 @@ class FluxLoRALoader(GeneralLoRALoader):
                        state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
                        state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))  
        return state_dict_
+
+
+class FluxLoRAConverter:
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def align_to_opensource_format(state_dict, alpha=None):
+        prefix_rename_dict = {
+            "single_blocks": "lora_unet_single_blocks",
+            "blocks": "lora_unet_double_blocks",
+        }
+        middle_rename_dict = {
+            "norm.linear": "modulation_lin",
+            "to_qkv_mlp": "linear1",
+            "proj_out": "linear2",
+
+            "norm1_a.linear": "img_mod_lin",
+            "norm1_b.linear": "txt_mod_lin",
+            "attn.a_to_qkv": "img_attn_qkv",
+            "attn.b_to_qkv": "txt_attn_qkv",
+            "attn.a_to_out": "img_attn_proj",
+            "attn.b_to_out": "txt_attn_proj",
+            "ff_a.0": "img_mlp_0",
+            "ff_a.2": "img_mlp_2",
+            "ff_b.0": "txt_mlp_0",
+            "ff_b.2": "txt_mlp_2",
+        }
+        suffix_rename_dict = {
+            "lora_B.weight": "lora_up.weight",
+            "lora_A.weight": "lora_down.weight",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            names = name.split(".")
+            if names[-2] != "lora_A" and names[-2] != "lora_B":
+                names.pop(-2)
+            prefix = names[0]
+            middle = ".".join(names[2:-2])
+            suffix = ".".join(names[-2:])
+            block_id = names[1]
+            if middle not in middle_rename_dict:
+                continue
+            rename = prefix_rename_dict[prefix] + "_" + block_id + "_" + middle_rename_dict[middle] + "." + suffix_rename_dict[suffix]
+            state_dict_[rename] = param
+            if rename.endswith("lora_up.weight"):
+                lora_alpha = alpha if alpha is not None else param.shape[-1]
+                state_dict_[rename.replace("lora_up.weight", "alpha")] = torch.tensor((lora_alpha,))[0]
+        return state_dict_
+    
+    @staticmethod
+    def align_to_diffsynth_format(state_dict):
+        rename_dict = {
+            "lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.default.weight",
+            "lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.default.weight",
+            "lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.default.weight",
+            "lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.default.weight",
+            "lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.default.weight",
+            "lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.default.weight",
+            "lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.default.weight",
+        }
+        def guess_block_id(name):
+            names = name.split("_")
+            for i in names:
+                if i.isdigit():
+                    return i, name.replace(f"_{i}_", "_blockid_")
+            return None, None
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            block_id, source_name = guess_block_id(name)
+            if source_name in rename_dict:
+                target_name = rename_dict[source_name]
+                target_name = target_name.replace(".blockid.", f".{block_id}.")
+                state_dict_[target_name] = param
+            else:
+                state_dict_[name] = param
+        return state_dict_
--- a/diffsynth/utils/lora/reset_rank.py
+++ b/diffsynth/utils/lora/reset_rank.py
@@ -0,0 +1,20 @@
+import torch
+
+def decomposite(tensor_A, tensor_B, rank):
+    dtype, device = tensor_A.dtype, tensor_A.device
+    weight = tensor_B @ tensor_A
+    U, S, V = torch.pca_lowrank(weight.float(), q=rank)
+    tensor_A = (V.T).to(dtype=dtype, device=device).contiguous()
+    tensor_B = (U @ torch.diag(S)).to(dtype=dtype, device=device).contiguous()
+    return tensor_A, tensor_B
+
+def reset_lora_rank(lora, rank):
+    lora_merged = {}
+    keys = [i for i in lora.keys() if ".lora_A." in i]
+    for key in keys:
+        tensor_A = lora[key]
+        tensor_B = lora[key.replace(".lora_A.", ".lora_B.")]
+        tensor_A, tensor_B = decomposite(tensor_A, tensor_B, rank)
+        lora_merged[key] = tensor_A
+        lora_merged[key.replace(".lora_A.", ".lora_B.")] = tensor_B
+    return lora_merged
--- a/diffsynth/utils/state_dict_converters/flux_dit.py
+++ b/diffsynth/utils/state_dict_converters/flux_dit.py
@@ -89,4 +89,109 @@ def FluxDiTStateDictConverter(state_dict):
                state_dict_[rename] = state_dict[original_name]
        else:
            pass
+    return state_dict_
+
+
+def FluxDiTStateDictConverterFromDiffusers(state_dict):
+    global_rename_dict = {
+        "context_embedder": "context_embedder",
+        "x_embedder": "x_embedder",
+        "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
+        "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
+        "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0",
+        "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2",
+        "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
+        "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
+        "norm_out.linear": "final_norm_out.linear",
+        "proj_out": "final_proj_out",
+    }
+    rename_dict = {
+        "proj_out": "proj_out",
+        "norm1.linear": "norm1_a.linear",
+        "norm1_context.linear": "norm1_b.linear",
+        "attn.to_q": "attn.a_to_q",
+        "attn.to_k": "attn.a_to_k",
+        "attn.to_v": "attn.a_to_v",
+        "attn.to_out.0": "attn.a_to_out",
+        "attn.add_q_proj": "attn.b_to_q",
+        "attn.add_k_proj": "attn.b_to_k",
+        "attn.add_v_proj": "attn.b_to_v",
+        "attn.to_add_out": "attn.b_to_out",
+        "ff.net.0.proj": "ff_a.0",
+        "ff.net.2": "ff_a.2",
+        "ff_context.net.0.proj": "ff_b.0",
+        "ff_context.net.2": "ff_b.2",
+        "attn.norm_q": "attn.norm_q_a",
+        "attn.norm_k": "attn.norm_k_a",
+        "attn.norm_added_q": "attn.norm_q_b",
+        "attn.norm_added_k": "attn.norm_k_b",
+    }
+    rename_dict_single = {
+        "attn.to_q": "a_to_q",
+        "attn.to_k": "a_to_k",
+        "attn.to_v": "a_to_v",
+        "attn.norm_q": "norm_q_a",
+        "attn.norm_k": "norm_k_a",
+        "norm.linear": "norm.linear",
+        "proj_mlp": "proj_in_besides_attn",
+        "proj_out": "proj_out",
+    }
+    state_dict_ = {}
+    for name in state_dict:
+        param = state_dict[name]
+        if name.endswith(".weight") or name.endswith(".bias"):
+            suffix = ".weight" if name.endswith(".weight") else ".bias"
+            prefix = name[:-len(suffix)]
+            if prefix in global_rename_dict:
+                if global_rename_dict[prefix] == "final_norm_out.linear":
+                    param = torch.concat([param[3072:], param[:3072]], dim=0)
+                state_dict_[global_rename_dict[prefix] + suffix] = param
+            elif prefix.startswith("transformer_blocks."):
+                names = prefix.split(".")
+                names[0] = "blocks"
+                middle = ".".join(names[2:])
+                if middle in rename_dict:
+                    name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
+                    state_dict_[name_] = param
+            elif prefix.startswith("single_transformer_blocks."):
+                names = prefix.split(".")
+                names[0] = "single_blocks"
+                middle = ".".join(names[2:])
+                if middle in rename_dict_single:
+                    name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]])
+                    state_dict_[name_] = param
+                else:
+                    pass
+            else:
+                pass
+    for name in list(state_dict_.keys()):
+        if "single_blocks." in name and ".a_to_q." in name:
+            mlp = state_dict_.get(name.replace(".a_to_q.", ".proj_in_besides_attn."), None)
+            if mlp is None:
+                mlp = torch.zeros(4 * state_dict_[name].shape[0],
+                                    *state_dict_[name].shape[1:],
+                                    dtype=state_dict_[name].dtype)
+            else:
+                state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn."))
+            param = torch.concat([
+                state_dict_.pop(name),
+                state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
+                state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
+                mlp,
+            ], dim=0)
+            name_ = name.replace(".a_to_q.", ".to_qkv_mlp.")
+            state_dict_[name_] = param
+    for name in list(state_dict_.keys()):
+        for component in ["a", "b"]:
+            if f".{component}_to_q." in name:
+                name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.")
+                param = torch.concat([
+                    state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
+                    state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
+                    state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
+                ], dim=0)
+                state_dict_[name_] = param
+                state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q."))
+                state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
+                state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))
    return state_dict_
--- a/diffsynth/utils/state_dict_converters/z_image_text_encoder.py
+++ b/diffsynth/utils/state_dict_converters/z_image_text_encoder.py
@@ -0,0 +1,6 @@
+def ZImageTextEncoderStateDictConverter(state_dict):
+    state_dict_ = {}
+    for name in state_dict:
+        if name != "lm_head.weight":
+            state_dict_[name] = state_dict[name]
+    return state_dict_
--- a/diffsynth/utils/xfuser/xdit_context_parallel.py
+++ b/diffsynth/utils/xfuser/xdit_context_parallel.py
@@ -5,19 +5,20 @@ from xfuser.core.distributed import (get_sequence_parallel_rank,
                                     get_sequence_parallel_world_size,
                                     get_sp_group)
 from xfuser.core.long_ctx_attention import xFuserLongContextAttention
+from ...core.device import parse_nccl_backend, parse_device_type


-def initialize_usp():
+def initialize_usp(device_type):
    import torch.distributed as dist
    from xfuser.core.distributed import initialize_model_parallel, init_distributed_environment
-    dist.init_process_group(backend="nccl", init_method="env://")
+    dist.init_process_group(backend=parse_nccl_backend(device_type), init_method="env://")
    init_distributed_environment(rank=dist.get_rank(), world_size=dist.get_world_size())
    initialize_model_parallel(
        sequence_parallel_degree=dist.get_world_size(),
        ring_degree=1,
        ulysses_degree=dist.get_world_size(),
    )
-    torch.cuda.set_device(dist.get_rank())
+    getattr(torch, device_type).set_device(dist.get_rank())


 def sinusoidal_embedding_1d(dim, position):
@@ -49,7 +50,7 @@ def rope_apply(x, freqs, num_heads):
    sp_rank = get_sequence_parallel_rank()
    freqs = pad_freqs(freqs, s_per_rank * sp_size)
    freqs_rank = freqs[(sp_rank * s_per_rank):((sp_rank + 1) * s_per_rank), :, :]
-
+    freqs_rank = freqs_rank.to(torch.complex64) if freqs_rank.device == "npu" else freqs_rank
    x_out = torch.view_as_real(x_out * freqs_rank).flatten(2)
    return x_out.to(x.dtype)

@@ -141,5 +142,5 @@ def usp_attn_forward(self, x, freqs):
    x = x.flatten(2)

    del q, k, v
-    torch.cuda.empty_cache()
+    getattr(torch, parse_device_type(x.device)).empty_cache()
    return self.o(x)
--- a/docs/en/Model_Details/FLUX2.md
+++ b/docs/en/Model_Details/FLUX2.md
@@ -2,6 +2,15 @@

 FLUX.2 is an image generation model trained and open-sourced by Black Forest Labs.

+## Model Lineage
+
+```mermaid
+graph LR;
+    FLUX.2-Series-->black-forest-labs/FLUX.2-dev;
+    FLUX.2-Series-->black-forest-labs/FLUX.2-klein-4B;
+    FLUX.2-Series-->black-forest-labs/FLUX.2-klein-9B;
+```
+
 ## Installation

 Before using this project for model inference and training, please install DiffSynth-Studio first.
@@ -50,16 +59,20 @@ image.save("image.jpg")

 ## Model Overview

-| Model ID | Inference | Low VRAM Inference | LoRA Training | Validation After LoRA Training |
-| - | - | - | - | - |
-| [black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev) | [code](/examples/flux2/model_inference/FLUX.2-dev.py) | [code](/examples/flux2/model_inference_low_vram/FLUX.2-dev.py) | [code](/examples/flux2/model_training/lora/FLUX.2-dev.sh) | [code](/examples/flux2/model_training/validate_lora/FLUX.2-dev.py) |
+| Model ID | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
+| - | - | - | - | - | - | - |
+|[black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev)|[code](/examples/flux2/model_inference/FLUX.2-dev.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-dev.py)|-|-|[code](/examples/flux2/model_training/lora/FLUX.2-dev.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-dev.py)|
+|[black-forest-labs/FLUX.2-klein-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B)|[code](/examples/flux2/model_inference/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-4B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-4B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-4B.py)|
+|[black-forest-labs/FLUX.2-klein-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-9B)|[code](/examples/flux2/model_inference/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-9B.py)|
+|[black-forest-labs/FLUX.2-klein-base-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-4B)|[code](/examples/flux2/model_inference/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-4B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-4B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-4B.py)|
+|[black-forest-labs/FLUX.2-klein-base-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-9B)|[code](/examples/flux2/model_inference/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-9B.py)|

 Special Training Scripts:

-* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md), [code](/examples/flux/model_training/special/differential_training/)
-* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/flux/model_training/special/fp8_training/)
-* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/flux/model_training/special/split_training/)
-* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/flux/model_training/lora/FLUX.1-dev-Distill-LoRA.sh)
+* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md)
+* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md)
+* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md)
+* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md)

 ## Model Inference

@@ -135,4 +148,4 @@ We have built a sample image dataset for your testing. You can download this dat
 modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
 ```

-We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
+We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
--- a/docs/en/Model_Details/Qwen-Image.md
+++ b/docs/en/Model_Details/Qwen-Image.md
@@ -81,8 +81,12 @@ graph LR;
 | Model ID | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
 | - | - | - | - | - | - | - |
 | [Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) | [code](/examples/qwen_image/model_inference/Qwen-Image.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image.py) |
+|[Qwen/Qwen-Image-2512](https://www.modelscope.cn/models/Qwen/Qwen-Image-2512)|[code](/examples/qwen_image/model_inference/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-2512.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-2512.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-2512.py)|
 | [Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py) |
 | [Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py) |
+|[Qwen/Qwen-Image-Edit-2511](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
+|[Qwen/Qwen-Image-Layered](https://www.modelscope.cn/models/Qwen/Qwen-Image-Layered)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py)|
+|[DiffSynth-Studio/Qwen-Image-Layered-Control](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py)|
 | [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
 | [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
 | [DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py) |
@@ -93,6 +97,7 @@ graph LR;
 | [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py) |
 | [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) | [code](/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py) |
 | [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py) | - | - | - | - |
+|[DiffSynth-Studio/Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L)|[code](/examples/qwen_image/model_inference/Qwen-Image-i2L.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py)|-|-|-|-|

 Special Training Scripts:

--- a/docs/en/Model_Details/Z-Image.md
+++ b/docs/en/Model_Details/Z-Image.md
@@ -50,9 +50,14 @@ image.save("image.jpg")

 ## Model Overview

-| Model ID | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
-| - | - | - | - | - | - | - |
-| [Tongyi-MAI/Z-Image-Turbo](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo) | [code](/examples/z_image/model_inference/Z-Image-Turbo.py) | [code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo.py) | [code](/examples/z_image/model_training/full/Z-Image-Turbo.sh) | [code](/examples/z_image/model_training/validate_full/Z-Image-Turbo.py) | [code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh) | [code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo.py) |
+|Model ID|Inference|Low VRAM Inference|Full Training|Validation After Full Training|LoRA Training|Validation After LoRA Training|
+|-|-|-|-|-|-|-|
+|[Tongyi-MAI/Z-Image](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image)|[code](/examples/z_image/model_inference/Z-Image.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image.py)|[code](/examples/z_image/model_training/full/Z-Image.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image.py)|[code](/examples/z_image/model_training/lora/Z-Image.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image.py)|
+|[DiffSynth-Studio/Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L)|[code](/examples/z_image/model_inference/Z-Image-i2L.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-i2L.py)|-|-|-|-|
+|[Tongyi-MAI/Z-Image-Turbo](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo)|[code](/examples/z_image/model_inference/Z-Image-Turbo.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|

 Special Training Scripts:

@@ -75,6 +80,9 @@ Input parameters for `ZImagePipeline` inference include:
 * `seed`: Random seed. Default is `None`, meaning completely random.
 * `rand_device`: Computing device for generating random Gaussian noise matrix, default is `"cpu"`. When set to `cuda`, different GPUs will produce different generation results.
 * `num_inference_steps`: Number of inference steps, default value is 8.
+* `controlnet_inputs`: Inputs for ControlNet models.
+* `edit_image`: Edit images for image editing models, supporting multiple images.
+* `positive_only_lora`: LoRA weights used only in positive prompts.

 If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.

@@ -138,4 +146,4 @@ Training Tips:
    * Differential LoRA Training ([code](/examples/z_image/model_training/special/differential_training/)) + Acceleration Configuration Inference
        * An additional LoRA needs to be loaded in differential LoRA training, e.g., [ostris/zimage_turbo_training_adapter](https://www.modelscope.cn/models/ostris/zimage_turbo_training_adapter)
    * Standard SFT Training ([code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + Trajectory Imitation Distillation Training ([code](/examples/z_image/model_training/special/trajectory_imitation/)) + Acceleration Configuration Inference
-    * Standard SFT Training ([code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + Load Distillation Acceleration LoRA During Inference ([model](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-Turbo-DistillFix)) + Acceleration Configuration Inference
+    * Standard SFT Training ([code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + Load Distillation Acceleration LoRA During Inference ([model](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-Turbo-DistillPatch)) + Acceleration Configuration Inference
--- a/docs/en/Pipeline_Usage/GPU_support.md
+++ b/docs/en/Pipeline_Usage/GPU_support.md
@@ -0,0 +1,84 @@
+# GPU/NPU Support
+
+`DiffSynth-Studio` supports various GPUs and NPUs. This document explains how to run model inference and training on these devices.
+
+Before you begin, please follow the [Installation Guide](/docs/en/Pipeline_Usage/Setup.md) to install the required GPU/NPU dependencies.
+
+## NVIDIA GPU
+
+All sample code provided by this project supports NVIDIA GPUs by default, requiring no additional modifications.
+
+## AMD GPU
+
+AMD provides PyTorch packages based on ROCm, so most models can run without code changes. A small number of models may not be compatible due to their reliance on CUDA-specific instructions.
+
+## Ascend NPU
+### Inference
+When using Ascend NPU, you need to replace `"cuda"` with `"npu"` in your code.
+
+For example, here is the inference code for **Wan2.1-T2V-1.3B**, modified for Ascend NPU:
+
+```diff
+import torch
+from diffsynth.utils.data import save_video, VideoData
+from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
+from diffsynth.core.device.npu_compatible_device import get_device_name
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.bfloat16,
+-   "preparing_device": "cuda",
+   "preparing_device": "npu",
+    "computation_dtype": torch.bfloat16,
+-   "computation_device": "cuda",
+   "computation_device": "npu",
+}
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+-   device="cuda",
+   device="npu",
+    model_configs=[
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"),
+-   vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
+   vram_limit=torch.npu.mem_get_info(get_device_name())[1] / (1024 ** 3) - 2,
+)
+
+video = pipe(
+    prompt="Documentary-style photography: a lively puppy running swiftly across lush green grass. The puppy has brownish-yellow fur, upright ears, and an alert, joyful expression. Sunlight bathes its body, making the fur appear exceptionally soft and shiny. The background is an open field with occasional wildflowers, and faint blue sky with scattered white clouds in the distance. Strong perspective captures the motion of the running puppy and the vitality of the surrounding grass. Mid-shot, side-moving viewpoint.",
+    negative_prompt="Overly vibrant colors, overexposed, static, blurry details, subtitles, artistic style, painting, still image, overall grayish tone, worst quality, low quality, JPEG artifacts, ugly, distorted, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, fused fingers, motionless scene, cluttered background, three legs, many people in background, walking backward",
+    seed=0, tiled=True,
+)
+save_video(video, "video.mp4", fps=15, quality=5)
+```
+
+### Training
+NPU startup script samples have been added for each type of model,the scripts are stored in the `examples/xxx/special/npu_training`, for example `examples/wanvideo/model_training/special/npu_training/Wan2.2-T2V-A14B-NPU.sh`.
+
+In the NPU training scripts, NPU specific environment variables that can optimize performance have been added, and relevant parameters have been enabled for specific models.
+
+#### Environment variables
+```shell
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+```
+`expandable_segments:<value>`: Enable the memory pool expansion segment function, which is the virtual memory feature.
+
+```shell
+export CPU_AFFINITY_CONF=1
+```
+Set 0 or not set: indicates not enabling the binding function
+
+1: Indicates enabling coarse-grained kernel binding
+
+2: Indicates enabling fine-grained kernel binding
+
+#### Parameters for specific models
+| Model          | Parameter                 | Note              |
+|----------------|---------------------------|-------------------|
+| Wan 14B series | --initialize_model_on_cpu | The 14B model needs to be initialized on the CPU |
--- a/docs/en/Pipeline_Usage/Setup.md
+++ b/docs/en/Pipeline_Usage/Setup.md
@@ -14,8 +14,40 @@ Install from PyPI (there may be delays in version updates; for latest features,
 pip install diffsynth
 ```

-If you encounter issues during installation, they may be caused by upstream dependency packages. Please refer to the documentation for these packages:
+## GPU/NPU Support
+
+* **NVIDIA GPU**
+
+Install as described above.
+
+* **AMD GPU**
+
+You need to install the `torch` package with ROCm support. Taking ROCm 6.4 (as of the article update date: December 15, 2025) on Linux as an example, run the following command:
+
+```shell
+pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6.4
+```
+
+* **Ascend NPU**
+
+1. Install [CANN](https://www.hiascend.com/document/detail/zh/canncommercial/83RC1/softwareinst/instg/instg_quick.html?Mode=PmIns&InstallType=local&OS=openEuler&Software=cannToolKit) through official documentation.
+
+2. Install from source
+   ```shell
+   git clone https://github.com/modelscope/DiffSynth-Studio.git
+   cd DiffSynth-Studio
+   # aarch64/ARM
+   pip install -e .[npu_aarch64] --extra-index-url "https://download.pytorch.org/whl/cpu"
+   # x86
+   pip install -e .[npu]
+
+When using Ascend NPU, please replace `"cuda"` with `"npu"` in your Python code. For details, see [NPU Support](/docs/en/Pipeline_Usage/GPU_support.md#ascend-npu).
+
+## Other Installation Issues
+
+If you encounter issues during installation, they may be caused by upstream dependencies. Please refer to the documentation for these packages:

 * [torch](https://pytorch.org/get-started/locally/)
+* [Ascend/pytorch](https://github.com/Ascend/pytorch)
 * [sentencepiece](https://github.com/google/sentencepiece)
-* [cmake](https://cmake.org)
+* [cmake](https://cmake.org)
--- a/docs/en/README.md
+++ b/docs/en/README.md
@@ -31,6 +31,7 @@ This section introduces the basic usage of `DiffSynth-Studio`, including how to
 * [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md)
 * [Model Training](/docs/en/Pipeline_Usage/Model_Training.md)
 * [Environment Variables](/docs/en/Pipeline_Usage/Environment_Variables.md)
+* [GPU/NPU Support](/docs/en/Pipeline_Usage/GPU_support.md)

 ## Section 2: Model Details

--- a/docs/zh/Model_Details/FLUX2.md
+++ b/docs/zh/Model_Details/FLUX2.md
@@ -2,6 +2,15 @@

 FLUX.2 是由 Black Forest Labs 训练并开源的图像生成模型。

+## 模型血缘
+
+```mermaid
+graph LR;
+    FLUX.2-Series-->black-forest-labs/FLUX.2-dev;
+    FLUX.2-Series-->black-forest-labs/FLUX.2-klein-4B;
+    FLUX.2-Series-->black-forest-labs/FLUX.2-klein-9B;
+```
+
 ## 安装

 在使用本项目进行模型推理和训练前，请先安装 DiffSynth-Studio。
@@ -50,16 +59,20 @@ image.save("image.jpg")

 ## 模型总览

-|模型 ID|推理|低显存推理|LoRA 训练|LoRA 训练后验证|
-|-|-|-|-|-|
-|[black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev)|[code](/examples/flux2/model_inference/FLUX.2-dev.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-dev.py)|[code](/examples/flux2/model_training/lora/FLUX.2-dev.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-dev.py)|
+|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|
+|-|-|-|-|-|-|-|
+|[black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev)|[code](/examples/flux2/model_inference/FLUX.2-dev.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-dev.py)|-|-|[code](/examples/flux2/model_training/lora/FLUX.2-dev.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-dev.py)|
+|[black-forest-labs/FLUX.2-klein-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B)|[code](/examples/flux2/model_inference/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-4B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-4B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-4B.py)|
+|[black-forest-labs/FLUX.2-klein-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-9B)|[code](/examples/flux2/model_inference/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-9B.py)|
+|[black-forest-labs/FLUX.2-klein-base-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-4B)|[code](/examples/flux2/model_inference/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-4B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-4B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-4B.py)|
+|[black-forest-labs/FLUX.2-klein-base-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-9B)|[code](/examples/flux2/model_inference/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-9B.py)|

 特殊训练脚本：

-* 差分 LoRA 训练：[doc](/docs/zh/Training/Differential_LoRA.md)、[code](/examples/flux/model_training/special/differential_training/)
-* FP8 精度训练：[doc](/docs/zh/Training/FP8_Precision.md)、[code](/examples/flux/model_training/special/fp8_training/)
-* 两阶段拆分训练：[doc](/docs/zh/Training/Split_Training.md)、[code](/examples/flux/model_training/special/split_training/)
-* 端到端直接蒸馏：[doc](/docs/zh/Training/Direct_Distill.md)、[code](/examples/flux/model_training/lora/FLUX.1-dev-Distill-LoRA.sh)
+* 差分 LoRA 训练：[doc](/docs/zh/Training/Differential_LoRA.md)
+* FP8 精度训练：[doc](/docs/zh/Training/FP8_Precision.md)
+* 两阶段拆分训练：[doc](/docs/zh/Training/Split_Training.md)
+* 端到端直接蒸馏：[doc](/docs/zh/Training/Direct_Distill.md)

 ## 模型推理

@@ -135,4 +148,4 @@ FLUX.2 系列模型统一通过 [`examples/flux2/model_training/train.py`](/exam
 modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_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](/docs/zh/Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](/docs/Training/)。
+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](/docs/zh/Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](/docs/Training/)。
--- a/docs/zh/Model_Details/Qwen-Image.md
+++ b/docs/zh/Model_Details/Qwen-Image.md
@@ -81,8 +81,12 @@ graph LR;
 |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|
 |-|-|-|-|-|-|-|
 |[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](/examples/qwen_image/model_inference/Qwen-Image.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image.py)|
+|[Qwen/Qwen-Image-2512](https://www.modelscope.cn/models/Qwen/Qwen-Image-2512)|[code](/examples/qwen_image/model_inference/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-2512.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-2512.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-2512.py)|
 |[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)|
 |[Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py)|
+|[Qwen/Qwen-Image-Edit-2511](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
+|[Qwen/Qwen-Image-Layered](https://www.modelscope.cn/models/Qwen/Qwen-Image-Layered)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py)|
+|[DiffSynth-Studio/Qwen-Image-Layered-Control](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py)|
@@ -93,6 +97,7 @@ graph LR;
 |[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)|[code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|
 |[DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union)|[code](/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py)|
 |[DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-|
+|[DiffSynth-Studio/Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L)|[code](/examples/qwen_image/model_inference/Qwen-Image-i2L.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py)|-|-|-|-|

 特殊训练脚本：

--- a/docs/zh/Model_Details/Z-Image.md
+++ b/docs/zh/Model_Details/Z-Image.md
@@ -52,7 +52,12 @@ image.save("image.jpg")

 |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|
 |-|-|-|-|-|-|-|
+|[Tongyi-MAI/Z-Image](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image)|[code](/examples/z_image/model_inference/Z-Image.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image.py)|[code](/examples/z_image/model_training/full/Z-Image.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image.py)|[code](/examples/z_image/model_training/lora/Z-Image.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image.py)|
+|[DiffSynth-Studio/Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L)|[code](/examples/z_image/model_inference/Z-Image-i2L.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-i2L.py)|-|-|-|-|
 |[Tongyi-MAI/Z-Image-Turbo](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo)|[code](/examples/z_image/model_inference/Z-Image-Turbo.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|

 特殊训练脚本：

@@ -75,6 +80,9 @@ image.save("image.jpg")
 * `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 `"cpu"`。当设置为 `cuda` 时，在不同 GPU 上会导致不同的生成结果。
 * `num_inference_steps`: 推理次数，默认值为 8。
+* `controlnet_inputs`: ControlNet 模型的输入。
+* `edit_image`: 编辑模型的待编辑图像，支持多张图像。
+* `positive_only_lora`: 仅在正向提示词中使用的 LoRA 权重。

 如果显存不足，请开启[显存管理](/docs/zh/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。

@@ -138,4 +146,4 @@ modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir
    * 差分 LoRA 训练（[code](/examples/z_image/model_training/special/differential_training/)） + 加速配置推理
        * 差分 LoRA 训练中需加载一个额外的 LoRA，例如 [ostris/zimage_turbo_training_adapter](https://www.modelscope.cn/models/ostris/zimage_turbo_training_adapter)
    * 标准 SFT 训练（[code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)）+ 轨迹模仿蒸馏训练（[code](/examples/z_image/model_training/special/trajectory_imitation/)）+ 加速配置推理
-    * 标准 SFT 训练（[code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)）+ 推理时加载蒸馏加速 LoRA（[model](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-Turbo-DistillFix)） + 加速配置推理
+    * 标准 SFT 训练（[code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)）+ 推理时加载蒸馏加速 LoRA（[model](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-Turbo-DistillPatch)） + 加速配置推理
--- a/docs/zh/Pipeline_Usage/GPU_support.md
+++ b/docs/zh/Pipeline_Usage/GPU_support.md
@@ -0,0 +1,84 @@
+# GPU/NPU 支持
+
+`DiffSynth-Studio` 支持多种 GPU/NPU，本文介绍如何在这些设备上运行模型推理和训练。
+
+在开始前，请参考[安装依赖](/docs/zh/Pipeline_Usage/Setup.md)安装好 GPU/NPU 相关的依赖包。
+
+## NVIDIA GPU
+
+本项目提供的所有样例代码默认支持 NVIDIA GPU，无需额外修改。
+
+## AMD GPU
+
+AMD 提供了基于 ROCm 的 torch 包，所以大多数模型无需修改代码即可运行，少数模型由于依赖特定的 cuda 指令无法运行。
+
+## Ascend NPU
+### 推理
+使用 Ascend NPU 时，需把代码中的 `"cuda"` 改为 `"npu"`。
+
+例如，Wan2.1-T2V-1.3B 的推理代码：
+
+```diff
+import torch
+from diffsynth.utils.data import save_video, VideoData
+from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
+from diffsynth.core.device.npu_compatible_device import get_device_name
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.bfloat16,
+-   "preparing_device": "cuda",
+   "preparing_device": "npu",
+    "computation_dtype": torch.bfloat16,
+-   "computation_device": "cuda",
+   "computation_device": "npu",
+}
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+-   device="cuda",
+   device="npu",
+    model_configs=[
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"),
+-   vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
+   vram_limit=torch.npu.mem_get_info(get_device_name())[1] / (1024 ** 3) - 2,
+)
+
+video = pipe(
+    prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。",
+    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,
+)
+save_video(video, "video.mp4", fps=15, quality=5)
+```
+
+### 训练
+当前已为每类模型添加NPU的启动脚本样例，脚本存放在`examples/xxx/special/npu_training`目录下，例如 `examples/wanvideo/model_training/special/npu_training/Wan2.2-T2V-A14B-NPU.sh`。
+
+在NPU训练脚本中，添加了可以优化性能的NPU特有环境变量，并针对特定模型开启了相关参数。
+
+#### 环境变量
+```shell
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+```
+`expandable_segments:<value>`: 使能内存池扩展段功能，即虚拟内存特征。
+
+```shell
+export CPU_AFFINITY_CONF=1
+```
+设置0或未设置: 表示不启用绑核功能
+
+1: 表示开启粗粒度绑核
+
+2: 表示开启细粒度绑核
+
+#### 特定模型需要开启的参数
+| 模型        | 参数 | 备注                |
+|-----------|------|-------------------|
+| Wan 14B系列 | --initialize_model_on_cpu | 14B模型需要在cpu上进行初始化 |
--- a/docs/zh/Pipeline_Usage/Setup.md
+++ b/docs/zh/Pipeline_Usage/Setup.md
@@ -14,8 +14,40 @@ pip install -e .
 pip install diffsynth
 ```

+## GPU/NPU 支持
+
+* NVIDIA GPU
+
+按照以上方式安装即可。
+
+* AMD GPU
+
+需安装支持 ROCm 的 `torch` 包，以 ROCm 6.4（本文更新于 2025 年 12 月 15 日）、Linux 系统为例，请运行以下命令
+
+```shell
+pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6.4
+```
+
+* Ascend NPU
+
+1. 通过官方文档安装[CANN](https://www.hiascend.com/document/detail/zh/canncommercial/83RC1/softwareinst/instg/instg_quick.html?Mode=PmIns&InstallType=local&OS=openEuler&Software=cannToolKit)
+
+2. 从源码安装
+   ```shell
+   git clone https://github.com/modelscope/DiffSynth-Studio.git
+   cd DiffSynth-Studio
+   # aarch64/ARM
+   pip install -e .[npu_aarch64] --extra-index-url "https://download.pytorch.org/whl/cpu"
+   # x86
+   pip install -e .[npu]
+
+使用 Ascend NPU 时，请将 Python 代码中的 `"cuda"` 改为 `"npu"`，详见[NPU 支持](/docs/zh/Pipeline_Usage/GPU_support.md#ascend-npu)。
+
+## 其他安装问题
+
 如果在安装过程中遇到问题，可能是由上游依赖包导致的，请参考这些包的文档：

 * [torch](https://pytorch.org/get-started/locally/)
+* [Ascend/pytorch](https://github.com/Ascend/pytorch)
 * [sentencepiece](https://github.com/google/sentencepiece)
 * [cmake](https://cmake.org)
--- a/docs/zh/README.md
+++ b/docs/zh/README.md
@@ -31,6 +31,7 @@ graph LR;
 * [显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)
 * [模型训练](/docs/zh/Pipeline_Usage/Model_Training.md)
 * [环境变量](/docs/zh/Pipeline_Usage/Environment_Variables.md)
+* [GPU/NPU 支持](/docs/zh/Pipeline_Usage/GPU_support.md)

 ## Section 2: 模型详解

--- a/examples/dev_tools/unit_test.py
+++ b/examples/dev_tools/unit_test.py
@@ -108,7 +108,14 @@ def test_flux():
    run_inference("examples/flux/model_training/validate_lora")


+def test_z_image():
+    run_inference("examples/z_image/model_inference")
+    run_inference("examples/z_image/model_inference_low_vram")
+    run_train_multi_GPU("examples/z_image/model_training/full")
+    run_inference("examples/z_image/model_training/validate_full")
+    run_train_single_GPU("examples/z_image/model_training/lora")
+    run_inference("examples/z_image/model_training/validate_lora")
+
+
 if __name__ == "__main__":
-    test_qwen_image()
-    test_flux()
-    test_wan()
+    test_z_image()
--- a/examples/flux/model_training/special/npu_training/FLUX.1-Kontext-dev-NPU.sh
+++ b/examples/flux/model_training/special/npu_training/FLUX.1-Kontext-dev-NPU.sh
@@ -0,0 +1,17 @@
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export CPU_AFFINITY_CONF=1
+
+accelerate launch --config_file examples/flux/model_training/full/accelerate_config_zero2offload.yaml examples/flux/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_kontext.csv \
+  --data_file_keys "image,kontext_images" \
+  --max_pixels 1048576 \
+  --dataset_repeat 400 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/*.safetensors,black-forest-labs/FLUX.1-dev:ae.safetensors" \
+  --learning_rate 1e-5 \
+  --num_epochs 1 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.1-Kontext-dev_full" \
+  --trainable_models "dit" \
+  --extra_inputs "kontext_images" \
+  --use_gradient_checkpointing
--- a/examples/flux/model_training/special/npu_training/FLUX.1-dev-NPU.sh
+++ b/examples/flux/model_training/special/npu_training/FLUX.1-dev-NPU.sh
@@ -0,0 +1,15 @@
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export CPU_AFFINITY_CONF=1
+
+accelerate launch --config_file examples/flux/model_training/full/accelerate_config_zero2offload.yaml examples/flux/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 400 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/*.safetensors,black-forest-labs/FLUX.1-dev:ae.safetensors" \
+  --learning_rate 1e-5 \
+  --num_epochs 1 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.1-dev_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing
--- a/examples/flux2/model_inference/FLUX.2-klein-4B.py
+++ b/examples/flux2/model_inference/FLUX.2-klein-4B.py
@@ -0,0 +1,21 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
+)
+prompt = "Masterpiece, best quality. Anime-style portrait of a woman in a blue dress, underwater, surrounded by colorful bubbles."
+image = pipe(prompt, seed=0, rand_device="cuda", num_inference_steps=4)
+image.save("image_FLUX.2-klein-4B.jpg")
+
+prompt = "change the color of the clothes to red"
+image = pipe(prompt, edit_image=[image], seed=1, rand_device="cuda", num_inference_steps=4)
+image.save("image_edit_FLUX.2-klein-4B.jpg")
--- a/examples/flux2/model_inference/FLUX.2-klein-9B.py
+++ b/examples/flux2/model_inference/FLUX.2-klein-9B.py
@@ -0,0 +1,21 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="tokenizer/"),
+)
+prompt = "Masterpiece, best quality. Anime-style portrait of a woman in a blue dress, underwater, surrounded by colorful bubbles."
+image = pipe(prompt, seed=0, rand_device="cuda", num_inference_steps=4)
+image.save("image_FLUX.2-klein-9B.jpg")
+
+prompt = "change the color of the clothes to red"
+image = pipe(prompt, edit_image=[image], seed=1, rand_device="cuda", num_inference_steps=4)
+image.save("image_edit_FLUX.2-klein-9B.jpg")
--- a/examples/flux2/model_inference/FLUX.2-klein-base-4B.py
+++ b/examples/flux2/model_inference/FLUX.2-klein-base-4B.py
@@ -0,0 +1,21 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-4B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
+)
+prompt = "Masterpiece, best quality. Anime-style portrait of a woman in a blue dress, underwater, surrounded by colorful bubbles."
+image = pipe(prompt, seed=0, rand_device="cuda", num_inference_steps=50, cfg_scale=4)
+image.save("image_FLUX.2-klein-base-4B.jpg")
+
+prompt = "change the color of the clothes to red"
+image = pipe(prompt, edit_image=[image], seed=1, rand_device="cuda", num_inference_steps=50, cfg_scale=4)
+image.save("image_edit_FLUX.2-klein-base-4B.jpg")
--- a/examples/flux2/model_inference/FLUX.2-klein-base-9B.py
+++ b/examples/flux2/model_inference/FLUX.2-klein-base-9B.py
@@ -0,0 +1,21 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-9B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="tokenizer/"),
+)
+prompt = "Masterpiece, best quality. Anime-style portrait of a woman in a blue dress, underwater, surrounded by colorful bubbles."
+image = pipe(prompt, seed=0, rand_device="cuda", num_inference_steps=50, cfg_scale=4)
+image.save("image_FLUX.2-klein-base-9B.jpg")
+
+prompt = "change the color of the clothes to red"
+image = pipe(prompt, edit_image=[image], seed=1, rand_device="cuda", num_inference_steps=50, cfg_scale=4)
+image.save("image_edit_FLUX.2-klein-base-9B.jpg")
--- a/examples/flux2/model_inference_low_vram/FLUX.2-klein-4B.py
+++ b/examples/flux2/model_inference_low_vram/FLUX.2-klein-4B.py
@@ -0,0 +1,31 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors", **vram_config),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="transformer/*.safetensors", **vram_config),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
+)
+prompt = "Masterpiece, best quality. Anime-style portrait of a woman in a blue dress, underwater, surrounded by colorful bubbles."
+image = pipe(prompt, seed=0, rand_device="cuda", num_inference_steps=4)
+image.save("image_FLUX.2-klein-4B.jpg")
+
+prompt = "change the color of the clothes to red"
+image = pipe(prompt, edit_image=[image], seed=1, rand_device="cuda", num_inference_steps=4)
+image.save("image_edit_FLUX.2-klein-4B.jpg")
--- a/examples/flux2/model_inference_low_vram/FLUX.2-klein-9B.py
+++ b/examples/flux2/model_inference_low_vram/FLUX.2-klein-9B.py
@@ -0,0 +1,31 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="text_encoder/*.safetensors", **vram_config),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="transformer/*.safetensors", **vram_config),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
+)
+prompt = "Masterpiece, best quality. Anime-style portrait of a woman in a blue dress, underwater, surrounded by colorful bubbles."
+image = pipe(prompt, seed=0, rand_device="cuda", num_inference_steps=4)
+image.save("image_FLUX.2-klein-9B.jpg")
+
+prompt = "change the color of the clothes to red"
+image = pipe(prompt, edit_image=[image], seed=1, rand_device="cuda", num_inference_steps=4)
+image.save("image_edit_FLUX.2-klein-9B.jpg")
--- a/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-4B.py
+++ b/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-4B.py
@@ -0,0 +1,31 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors", **vram_config),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-4B", origin_file_pattern="transformer/*.safetensors", **vram_config),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
+)
+prompt = "Masterpiece, best quality. Anime-style portrait of a woman in a blue dress, underwater, surrounded by colorful bubbles."
+image = pipe(prompt, seed=0, rand_device="cuda", num_inference_steps=50, cfg_scale=4)
+image.save("image_FLUX.2-klein-base-4B.jpg")
+
+prompt = "change the color of the clothes to red"
+image = pipe(prompt, edit_image=[image], seed=1, rand_device="cuda", num_inference_steps=50, cfg_scale=4)
+image.save("image_edit_FLUX.2-klein-base-4B.jpg")
--- a/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-9B.py
+++ b/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-9B.py
@@ -0,0 +1,31 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="text_encoder/*.safetensors", **vram_config),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-9B", origin_file_pattern="transformer/*.safetensors", **vram_config),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="tokenizer/"),
+)
+prompt = "Masterpiece, best quality. Anime-style portrait of a woman in a blue dress, underwater, surrounded by colorful bubbles."
+image = pipe(prompt, seed=0, rand_device="cuda", num_inference_steps=50, cfg_scale=4)
+image.save("image_FLUX.2-klein-base-9B.jpg")
+
+prompt = "change the color of the clothes to red"
+image = pipe(prompt, edit_image=[image], seed=1, rand_device="cuda", num_inference_steps=50, cfg_scale=4)
+image.save("image_edit_FLUX.2-klein-base-9B.jpg")
--- a/examples/flux2/model_training/full/FLUX.2-klein-4B.sh
+++ b/examples/flux2/model_training/full/FLUX.2-klein-4B.sh
@@ -0,0 +1,30 @@
+accelerate launch examples/flux2/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-4B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-4B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-4B:vae/diffusion_pytorch_model.safetensors" \
+  --tokenizer_path "black-forest-labs/FLUX.2-klein-4B:tokenizer/" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.2-klein-4B_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing
+
+# Edit
+# accelerate launch examples/flux2/model_training/train.py \
+#   --dataset_base_path data/example_image_dataset \
+#   --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \
+#   --data_file_keys "image,edit_image" \
+#   --extra_inputs "edit_image" \
+#   --max_pixels 1048576 \
+#   --dataset_repeat 50 \
+#   --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-4B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-4B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-4B:vae/diffusion_pytorch_model.safetensors" \
+#   --tokenizer_path "black-forest-labs/FLUX.2-klein-4B:tokenizer/" \
+#   --learning_rate 1e-5 \
+#   --num_epochs 2 \
+#   --remove_prefix_in_ckpt "pipe.dit." \
+#   --output_path "./models/train/FLUX.2-klein-4B_full" \
+#   --trainable_models "dit" \
+#   --use_gradient_checkpointing
--- a/examples/flux2/model_training/full/FLUX.2-klein-9B.sh
+++ b/examples/flux2/model_training/full/FLUX.2-klein-9B.sh
@@ -0,0 +1,31 @@
+# This script is tested on 8*A100
+accelerate launch --config_file examples/flux2/model_training/full/accelerate_config.yaml examples/flux2/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-9B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-9B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-9B:vae/diffusion_pytorch_model.safetensors" \
+  --tokenizer_path "black-forest-labs/FLUX.2-klein-9B:tokenizer/" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.2-klein-9B_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing
+
+# Edit
+# accelerate launch --config_file examples/flux2/model_training/full/accelerate_config.yaml examples/flux2/model_training/train.py \
+#   --dataset_base_path data/example_image_dataset \
+#   --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \
+#   --data_file_keys "image,edit_image" \
+#   --extra_inputs "edit_image" \
+#   --max_pixels 1048576 \
+#   --dataset_repeat 50 \
+#   --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-9B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-9B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-9B:vae/diffusion_pytorch_model.safetensors" \
+#   --tokenizer_path "black-forest-labs/FLUX.2-klein-9B:tokenizer/" \
+#   --learning_rate 1e-5 \
+#   --num_epochs 2 \
+#   --remove_prefix_in_ckpt "pipe.dit." \
+#   --output_path "./models/train/FLUX.2-klein-9B_full" \
+#   --trainable_models "dit" \
+#   --use_gradient_checkpointing
--- a/examples/flux2/model_training/full/FLUX.2-klein-base-4B.sh
+++ b/examples/flux2/model_training/full/FLUX.2-klein-base-4B.sh
@@ -0,0 +1,30 @@
+accelerate launch examples/flux2/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-4B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-base-4B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-4B:vae/diffusion_pytorch_model.safetensors" \
+  --tokenizer_path "black-forest-labs/FLUX.2-klein-4B:tokenizer/" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.2-klein-base-4B_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing
+
+# Edit
+# accelerate launch examples/flux2/model_training/train.py \
+#   --dataset_base_path data/example_image_dataset \
+#   --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \
+#   --data_file_keys "image,edit_image" \
+#   --extra_inputs "edit_image" \
+#   --max_pixels 1048576 \
+#   --dataset_repeat 50 \
+#   --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-4B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-base-4B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-4B:vae/diffusion_pytorch_model.safetensors" \
+#   --tokenizer_path "black-forest-labs/FLUX.2-klein-4B:tokenizer/" \
+#   --learning_rate 1e-5 \
+#   --num_epochs 2 \
+#   --remove_prefix_in_ckpt "pipe.dit." \
+#   --output_path "./models/train/FLUX.2-klein-base-4B_full" \
+#   --trainable_models "dit" \
+#   --use_gradient_checkpointing
--- a/examples/flux2/model_training/full/FLUX.2-klein-base-9B.sh
+++ b/examples/flux2/model_training/full/FLUX.2-klein-base-9B.sh
@@ -0,0 +1,31 @@
+# This script is tested on 8*A100
+accelerate launch --config_file examples/flux2/model_training/full/accelerate_config.yaml examples/flux2/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-9B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-base-9B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-9B:vae/diffusion_pytorch_model.safetensors" \
+  --tokenizer_path "black-forest-labs/FLUX.2-klein-9B:tokenizer/" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.2-klein-base-9B_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing
+
+# Edit
+# accelerate launch --config_file examples/flux2/model_training/full/accelerate_config.yaml examples/flux2/model_training/train.py \
+#   --dataset_base_path data/example_image_dataset \
+#   --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \
+#   --data_file_keys "image,edit_image" \
+#   --extra_inputs "edit_image" \
+#   --max_pixels 1048576 \
+#   --dataset_repeat 50 \
+#   --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-9B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-base-9B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-9B:vae/diffusion_pytorch_model.safetensors" \
+#   --tokenizer_path "black-forest-labs/FLUX.2-klein-9B:tokenizer/" \
+#   --learning_rate 1e-5 \
+#   --num_epochs 2 \
+#   --remove_prefix_in_ckpt "pipe.dit." \
+#   --output_path "./models/train/FLUX.2-klein-base-9B_full" \
+#   --trainable_models "dit" \
+#   --use_gradient_checkpointing
--- a/examples/flux2/model_training/full/accelerate_config.yaml
+++ b/examples/flux2/model_training/full/accelerate_config.yaml
@@ -0,0 +1,22 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  gradient_accumulation_steps: 1
+  offload_optimizer_device: none
+  offload_param_device: none
+  zero3_init_flag: false
+  zero_stage: 2
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
--- a/examples/flux2/model_training/lora/FLUX.2-klein-4B.sh
+++ b/examples/flux2/model_training/lora/FLUX.2-klein-4B.sh
@@ -0,0 +1,34 @@
+accelerate launch examples/flux2/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-4B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-4B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-4B:vae/diffusion_pytorch_model.safetensors" \
+  --tokenizer_path "black-forest-labs/FLUX.2-klein-4B:tokenizer/" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.2-klein-4B_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,to_out.0,add_q_proj,add_k_proj,add_v_proj,to_add_out,linear_in,linear_out,to_qkv_mlp_proj,single_transformer_blocks.0.attn.to_out,single_transformer_blocks.1.attn.to_out,single_transformer_blocks.2.attn.to_out,single_transformer_blocks.3.attn.to_out,single_transformer_blocks.4.attn.to_out,single_transformer_blocks.5.attn.to_out,single_transformer_blocks.6.attn.to_out,single_transformer_blocks.7.attn.to_out,single_transformer_blocks.8.attn.to_out,single_transformer_blocks.9.attn.to_out,single_transformer_blocks.10.attn.to_out,single_transformer_blocks.11.attn.to_out,single_transformer_blocks.12.attn.to_out,single_transformer_blocks.13.attn.to_out,single_transformer_blocks.14.attn.to_out,single_transformer_blocks.15.attn.to_out,single_transformer_blocks.16.attn.to_out,single_transformer_blocks.17.attn.to_out,single_transformer_blocks.18.attn.to_out,single_transformer_blocks.19.attn.to_out" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing
+
+# Edit
+# accelerate launch examples/flux2/model_training/train.py \
+#   --dataset_base_path data/example_image_dataset \
+#   --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \
+#   --data_file_keys "image,edit_image" \
+#   --extra_inputs "edit_image" \
+#   --max_pixels 1048576 \
+#   --dataset_repeat 50 \
+#   --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-4B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-4B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-4B:vae/diffusion_pytorch_model.safetensors" \
+#   --tokenizer_path "black-forest-labs/FLUX.2-klein-4B:tokenizer/" \
+#   --learning_rate 1e-4 \
+#   --num_epochs 5 \
+#   --remove_prefix_in_ckpt "pipe.dit." \
+#   --output_path "./models/train/FLUX.2-klein-4B_lora" \
+#   --lora_base_model "dit" \
+#   --lora_target_modules "to_q,to_k,to_v,to_out.0,add_q_proj,add_k_proj,add_v_proj,to_add_out,linear_in,linear_out,to_qkv_mlp_proj,single_transformer_blocks.0.attn.to_out,single_transformer_blocks.1.attn.to_out,single_transformer_blocks.2.attn.to_out,single_transformer_blocks.3.attn.to_out,single_transformer_blocks.4.attn.to_out,single_transformer_blocks.5.attn.to_out,single_transformer_blocks.6.attn.to_out,single_transformer_blocks.7.attn.to_out,single_transformer_blocks.8.attn.to_out,single_transformer_blocks.9.attn.to_out,single_transformer_blocks.10.attn.to_out,single_transformer_blocks.11.attn.to_out,single_transformer_blocks.12.attn.to_out,single_transformer_blocks.13.attn.to_out,single_transformer_blocks.14.attn.to_out,single_transformer_blocks.15.attn.to_out,single_transformer_blocks.16.attn.to_out,single_transformer_blocks.17.attn.to_out,single_transformer_blocks.18.attn.to_out,single_transformer_blocks.19.attn.to_out" \
+#   --lora_rank 32 \
+#   --use_gradient_checkpointing
--- a/examples/flux2/model_training/lora/FLUX.2-klein-9B.sh
+++ b/examples/flux2/model_training/lora/FLUX.2-klein-9B.sh
@@ -0,0 +1,34 @@
+accelerate launch examples/flux2/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-9B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-9B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-9B:vae/diffusion_pytorch_model.safetensors" \
+  --tokenizer_path "black-forest-labs/FLUX.2-klein-9B:tokenizer/" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.2-klein-9B_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,to_out.0,add_q_proj,add_k_proj,add_v_proj,to_add_out,linear_in,linear_out,to_qkv_mlp_proj,single_transformer_blocks.0.attn.to_out,single_transformer_blocks.1.attn.to_out,single_transformer_blocks.2.attn.to_out,single_transformer_blocks.3.attn.to_out,single_transformer_blocks.4.attn.to_out,single_transformer_blocks.5.attn.to_out,single_transformer_blocks.6.attn.to_out,single_transformer_blocks.7.attn.to_out,single_transformer_blocks.8.attn.to_out,single_transformer_blocks.9.attn.to_out,single_transformer_blocks.10.attn.to_out,single_transformer_blocks.11.attn.to_out,single_transformer_blocks.12.attn.to_out,single_transformer_blocks.13.attn.to_out,single_transformer_blocks.14.attn.to_out,single_transformer_blocks.15.attn.to_out,single_transformer_blocks.16.attn.to_out,single_transformer_blocks.17.attn.to_out,single_transformer_blocks.18.attn.to_out,single_transformer_blocks.19.attn.to_out,single_transformer_blocks.20.attn.to_out,single_transformer_blocks.21.attn.to_out,single_transformer_blocks.22.attn.to_out,single_transformer_blocks.23.attn.to_out" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing
+
+# Edit
+# accelerate launch examples/flux2/model_training/train.py \
+#   --dataset_base_path data/example_image_dataset \
+#   --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \
+#   --data_file_keys "image,edit_image" \
+#   --extra_inputs "edit_image" \
+#   --max_pixels 1048576 \
+#   --dataset_repeat 50 \
+#   --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-9B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-9B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-9B:vae/diffusion_pytorch_model.safetensors" \
+#   --tokenizer_path "black-forest-labs/FLUX.2-klein-9B:tokenizer/" \
+#   --learning_rate 1e-4 \
+#   --num_epochs 5 \
+#   --remove_prefix_in_ckpt "pipe.dit." \
+#   --output_path "./models/train/FLUX.2-klein-9B_lora" \
+#   --lora_base_model "dit" \
+#   --lora_target_modules "to_q,to_k,to_v,to_out.0,add_q_proj,add_k_proj,add_v_proj,to_add_out,linear_in,linear_out,to_qkv_mlp_proj,single_transformer_blocks.0.attn.to_out,single_transformer_blocks.1.attn.to_out,single_transformer_blocks.2.attn.to_out,single_transformer_blocks.3.attn.to_out,single_transformer_blocks.4.attn.to_out,single_transformer_blocks.5.attn.to_out,single_transformer_blocks.6.attn.to_out,single_transformer_blocks.7.attn.to_out,single_transformer_blocks.8.attn.to_out,single_transformer_blocks.9.attn.to_out,single_transformer_blocks.10.attn.to_out,single_transformer_blocks.11.attn.to_out,single_transformer_blocks.12.attn.to_out,single_transformer_blocks.13.attn.to_out,single_transformer_blocks.14.attn.to_out,single_transformer_blocks.15.attn.to_out,single_transformer_blocks.16.attn.to_out,single_transformer_blocks.17.attn.to_out,single_transformer_blocks.18.attn.to_out,single_transformer_blocks.19.attn.to_out,single_transformer_blocks.20.attn.to_out,single_transformer_blocks.21.attn.to_out,single_transformer_blocks.22.attn.to_out,single_transformer_blocks.23.attn.to_out" \
+#   --lora_rank 32 \
+#   --use_gradient_checkpointing
--- a/examples/flux2/model_training/lora/FLUX.2-klein-base-4B.sh
+++ b/examples/flux2/model_training/lora/FLUX.2-klein-base-4B.sh
@@ -0,0 +1,34 @@
+accelerate launch examples/flux2/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-4B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-base-4B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-4B:vae/diffusion_pytorch_model.safetensors" \
+  --tokenizer_path "black-forest-labs/FLUX.2-klein-4B:tokenizer/" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.2-klein-base-4B_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,to_out.0,add_q_proj,add_k_proj,add_v_proj,to_add_out,linear_in,linear_out,to_qkv_mlp_proj,single_transformer_blocks.0.attn.to_out,single_transformer_blocks.1.attn.to_out,single_transformer_blocks.2.attn.to_out,single_transformer_blocks.3.attn.to_out,single_transformer_blocks.4.attn.to_out,single_transformer_blocks.5.attn.to_out,single_transformer_blocks.6.attn.to_out,single_transformer_blocks.7.attn.to_out,single_transformer_blocks.8.attn.to_out,single_transformer_blocks.9.attn.to_out,single_transformer_blocks.10.attn.to_out,single_transformer_blocks.11.attn.to_out,single_transformer_blocks.12.attn.to_out,single_transformer_blocks.13.attn.to_out,single_transformer_blocks.14.attn.to_out,single_transformer_blocks.15.attn.to_out,single_transformer_blocks.16.attn.to_out,single_transformer_blocks.17.attn.to_out,single_transformer_blocks.18.attn.to_out,single_transformer_blocks.19.attn.to_out" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing
+
+# Edit
+# accelerate launch examples/flux2/model_training/train.py \
+#   --dataset_base_path data/example_image_dataset \
+#   --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \
+#   --data_file_keys "image,edit_image" \
+#   --extra_inputs "edit_image" \
+#   --max_pixels 1048576 \
+#   --dataset_repeat 50 \
+#   --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-4B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-base-4B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-4B:vae/diffusion_pytorch_model.safetensors" \
+#   --tokenizer_path "black-forest-labs/FLUX.2-klein-4B:tokenizer/" \
+#   --learning_rate 1e-4 \
+#   --num_epochs 5 \
+#   --remove_prefix_in_ckpt "pipe.dit." \
+#   --output_path "./models/train/FLUX.2-klein-base-4B_lora" \
+#   --lora_base_model "dit" \
+#   --lora_target_modules "to_q,to_k,to_v,to_out.0,add_q_proj,add_k_proj,add_v_proj,to_add_out,linear_in,linear_out,to_qkv_mlp_proj,single_transformer_blocks.0.attn.to_out,single_transformer_blocks.1.attn.to_out,single_transformer_blocks.2.attn.to_out,single_transformer_blocks.3.attn.to_out,single_transformer_blocks.4.attn.to_out,single_transformer_blocks.5.attn.to_out,single_transformer_blocks.6.attn.to_out,single_transformer_blocks.7.attn.to_out,single_transformer_blocks.8.attn.to_out,single_transformer_blocks.9.attn.to_out,single_transformer_blocks.10.attn.to_out,single_transformer_blocks.11.attn.to_out,single_transformer_blocks.12.attn.to_out,single_transformer_blocks.13.attn.to_out,single_transformer_blocks.14.attn.to_out,single_transformer_blocks.15.attn.to_out,single_transformer_blocks.16.attn.to_out,single_transformer_blocks.17.attn.to_out,single_transformer_blocks.18.attn.to_out,single_transformer_blocks.19.attn.to_out" \
+#   --lora_rank 32 \
+#   --use_gradient_checkpointing
--- a/examples/flux2/model_training/lora/FLUX.2-klein-base-9B.sh
+++ b/examples/flux2/model_training/lora/FLUX.2-klein-base-9B.sh
@@ -0,0 +1,34 @@
+accelerate launch examples/flux2/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-9B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-base-9B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-9B:vae/diffusion_pytorch_model.safetensors" \
+  --tokenizer_path "black-forest-labs/FLUX.2-klein-9B:tokenizer/" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/FLUX.2-klein-base-9B_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,to_out.0,add_q_proj,add_k_proj,add_v_proj,to_add_out,linear_in,linear_out,to_qkv_mlp_proj,single_transformer_blocks.0.attn.to_out,single_transformer_blocks.1.attn.to_out,single_transformer_blocks.2.attn.to_out,single_transformer_blocks.3.attn.to_out,single_transformer_blocks.4.attn.to_out,single_transformer_blocks.5.attn.to_out,single_transformer_blocks.6.attn.to_out,single_transformer_blocks.7.attn.to_out,single_transformer_blocks.8.attn.to_out,single_transformer_blocks.9.attn.to_out,single_transformer_blocks.10.attn.to_out,single_transformer_blocks.11.attn.to_out,single_transformer_blocks.12.attn.to_out,single_transformer_blocks.13.attn.to_out,single_transformer_blocks.14.attn.to_out,single_transformer_blocks.15.attn.to_out,single_transformer_blocks.16.attn.to_out,single_transformer_blocks.17.attn.to_out,single_transformer_blocks.18.attn.to_out,single_transformer_blocks.19.attn.to_out,single_transformer_blocks.20.attn.to_out,single_transformer_blocks.21.attn.to_out,single_transformer_blocks.22.attn.to_out,single_transformer_blocks.23.attn.to_out" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing
+
+# Edit
+# accelerate launch examples/flux2/model_training/train.py \
+#   --dataset_base_path data/example_image_dataset \
+#   --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \
+#   --data_file_keys "image,edit_image" \
+#   --extra_inputs "edit_image" \
+#   --max_pixels 1048576 \
+#   --dataset_repeat 50 \
+#   --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-9B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-base-9B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-9B:vae/diffusion_pytorch_model.safetensors" \
+#   --tokenizer_path "black-forest-labs/FLUX.2-klein-9B:tokenizer/" \
+#   --learning_rate 1e-4 \
+#   --num_epochs 5 \
+#   --remove_prefix_in_ckpt "pipe.dit." \
+#   --output_path "./models/train/FLUX.2-klein-base-9B_lora" \
+#   --lora_base_model "dit" \
+#   --lora_target_modules "to_q,to_k,to_v,to_out.0,add_q_proj,add_k_proj,add_v_proj,to_add_out,linear_in,linear_out,to_qkv_mlp_proj,single_transformer_blocks.0.attn.to_out,single_transformer_blocks.1.attn.to_out,single_transformer_blocks.2.attn.to_out,single_transformer_blocks.3.attn.to_out,single_transformer_blocks.4.attn.to_out,single_transformer_blocks.5.attn.to_out,single_transformer_blocks.6.attn.to_out,single_transformer_blocks.7.attn.to_out,single_transformer_blocks.8.attn.to_out,single_transformer_blocks.9.attn.to_out,single_transformer_blocks.10.attn.to_out,single_transformer_blocks.11.attn.to_out,single_transformer_blocks.12.attn.to_out,single_transformer_blocks.13.attn.to_out,single_transformer_blocks.14.attn.to_out,single_transformer_blocks.15.attn.to_out,single_transformer_blocks.16.attn.to_out,single_transformer_blocks.17.attn.to_out,single_transformer_blocks.18.attn.to_out,single_transformer_blocks.19.attn.to_out,single_transformer_blocks.20.attn.to_out,single_transformer_blocks.21.attn.to_out,single_transformer_blocks.22.attn.to_out,single_transformer_blocks.23.attn.to_out" \
+#   --lora_rank 32 \
+#   --use_gradient_checkpointing
--- a/examples/flux2/model_training/train.py
+++ b/examples/flux2/model_training/train.py
@@ -24,7 +24,7 @@ class Flux2ImageTrainingModule(DiffusionTrainingModule):
        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="black-forest-labs/FLUX.2-dev", origin_file_pattern="tokenizer/") if tokenizer_path is None else ModelConfig(tokenizer_path)
+        tokenizer_config = self.parse_path_or_model_id(tokenizer_path, default_value=ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="tokenizer/"))
        self.pipe = Flux2ImagePipeline.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)

--- a/examples/flux2/model_training/validate_full/FLUX.2-klein-4B.py
+++ b/examples/flux2/model_training/validate_full/FLUX.2-klein-4B.py
@@ -0,0 +1,20 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+from diffsynth.core import load_state_dict
+import torch
+
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
+)
+state_dict = load_state_dict("./models/train/FLUX.2-klein-4B_full/epoch-1.safetensors", torch_dtype=torch.bfloat16)
+pipe.dit.load_state_dict(state_dict)
+prompt = "a dog"
+image = pipe(prompt=prompt, seed=0, num_inference_steps=40, cfg_scale=4, height=768, width=768)
+image.save("image.jpg")
--- a/examples/flux2/model_training/validate_full/FLUX.2-klein-9B.py
+++ b/examples/flux2/model_training/validate_full/FLUX.2-klein-9B.py
@@ -0,0 +1,20 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+from diffsynth.core import load_state_dict
+import torch
+
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="tokenizer/"),
+)
+state_dict = load_state_dict("./models/train/FLUX.2-klein-9B_full/epoch-1.safetensors", torch_dtype=torch.bfloat16)
+pipe.dit.load_state_dict(state_dict)
+prompt = "a dog"
+image = pipe(prompt=prompt, seed=0, num_inference_steps=40, cfg_scale=4, height=768, width=768)
+image.save("image.jpg")
--- a/examples/flux2/model_training/validate_full/FLUX.2-klein-base-4B.py
+++ b/examples/flux2/model_training/validate_full/FLUX.2-klein-base-4B.py
@@ -0,0 +1,20 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+from diffsynth.core import load_state_dict
+import torch
+
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-4B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
+)
+state_dict = load_state_dict("./models/train/FLUX.2-klein-base-4B_full/epoch-1.safetensors", torch_dtype=torch.bfloat16)
+pipe.dit.load_state_dict(state_dict)
+prompt = "a dog"
+image = pipe(prompt=prompt, seed=0, num_inference_steps=40, cfg_scale=4, height=768, width=768)
+image.save("image.jpg")
--- a/examples/flux2/model_training/validate_full/FLUX.2-klein-base-9B.py
+++ b/examples/flux2/model_training/validate_full/FLUX.2-klein-base-9B.py
@@ -0,0 +1,20 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+from diffsynth.core import load_state_dict
+import torch
+
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-9B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="tokenizer/"),
+)
+state_dict = load_state_dict("./models/train/FLUX.2-klein-base-9B_full/epoch-1.safetensors", torch_dtype=torch.bfloat16)
+pipe.dit.load_state_dict(state_dict)
+prompt = "a dog"
+image = pipe(prompt=prompt, seed=0, num_inference_steps=40, cfg_scale=4, height=768, width=768)
+image.save("image.jpg")
--- a/examples/flux2/model_training/validate_lora/FLUX.2-klein-4B.py
+++ b/examples/flux2/model_training/validate_lora/FLUX.2-klein-4B.py
@@ -0,0 +1,18 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
+)
+pipe.load_lora(pipe.dit, "./models/train/FLUX.2-klein-4B_lora/epoch-4.safetensors")
+prompt = "a dog"
+image = pipe(prompt=prompt, seed=0, num_inference_steps=40, cfg_scale=4, height=768, width=768)
+image.save("image.jpg")
--- a/examples/flux2/model_training/validate_lora/FLUX.2-klein-9B.py
+++ b/examples/flux2/model_training/validate_lora/FLUX.2-klein-9B.py
@@ -0,0 +1,18 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="tokenizer/"),
+)
+pipe.load_lora(pipe.dit, "./models/train/FLUX.2-klein-9B_lora/epoch-4.safetensors")
+prompt = "a dog"
+image = pipe(prompt=prompt, seed=0, num_inference_steps=40, cfg_scale=4, height=768, width=768)
+image.save("image.jpg")
--- a/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-4B.py
+++ b/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-4B.py
@@ -0,0 +1,18 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-4B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-4B", origin_file_pattern="tokenizer/"),
+)
+pipe.load_lora(pipe.dit, "./models/train/FLUX.2-klein-base-4B_lora/epoch-4.safetensors")
+prompt = "a dog"
+image = pipe(prompt=prompt, seed=0, num_inference_steps=40, cfg_scale=4, height=768, width=768)
+image.save("image.jpg")
--- a/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-9B.py
+++ b/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-9B.py
@@ -0,0 +1,18 @@
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+import torch
+
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-9B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="tokenizer/"),
+)
+pipe.load_lora(pipe.dit, "./models/train/FLUX.2-klein-base-9B_lora/epoch-4.safetensors")
+prompt = "a dog"
+image = pipe(prompt=prompt, seed=0, num_inference_steps=40, cfg_scale=4, height=768, width=768)
+image.save("image.jpg")
--- a/examples/qwen_image/model_inference/Qwen-Image-2512.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-2512.py
@@ -0,0 +1,17 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+import torch
+
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-2512", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
+image = pipe(prompt, seed=0, num_inference_steps=40)
+image.save("image.jpg")
--- a/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py
@@ -0,0 +1,44 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+from PIL import Image
+import torch
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+
+dataset_snapshot_download(
+    "DiffSynth-Studio/example_image_dataset",
+    allow_file_pattern="qwen_image_edit/*",
+    local_dir="data/example_image_dataset",
+)
+
+prompt = "生成这两个人的合影"
+edit_image = [
+    Image.open("data/example_image_dataset/qwen_image_edit/image1.jpg"),
+    Image.open("data/example_image_dataset/qwen_image_edit/image2.jpg"),
+]
+image = pipe(
+    prompt,
+    edit_image=edit_image,
+    seed=1,
+    num_inference_steps=40,
+    height=1152,
+    width=896,
+    edit_image_auto_resize=True,
+    zero_cond_t=True, # This is a special parameter introduced by Qwen-Image-Edit-2511
+)
+image.save("image.jpg")
+
+# Qwen-Image-Edit-2511 is a multi-image editing model.
+# Please use a list to input `edit_image`, even if the input contains only one image.
+# edit_image = [Image.open("image.jpg")]
+# Please do not input the image directly.
+# edit_image = Image.open("image.jpg")
--- a/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py
@@ -0,0 +1,34 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from modelscope import snapshot_download
+from PIL import Image
+import torch
+
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+
+snapshot_download(
+    model_id="DiffSynth-Studio/Qwen-Image-Layered-Control",
+    allow_file_pattern="assets/image_1_input.png",
+    local_dir="data/layered_input"
+)
+
+prompt = "A cartoon skeleton character wearing a purple hat and holding a gift box"
+input_image = Image.open("data/layered_input/assets/image_1_input.png").convert("RGBA").resize((1024, 1024))
+images = pipe(
+    prompt,
+    seed=0,
+    num_inference_steps=30, cfg_scale=4,
+    height=1024, width=1024,
+    layer_input_image=input_image,
+    layer_num=0,
+)
+images[0].save("image.png")
--- a/examples/qwen_image/model_inference/Qwen-Image-Layered.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-Layered.py
@@ -0,0 +1,36 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+from PIL import Image
+import torch
+
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+
+dataset_snapshot_download(
+    "DiffSynth-Studio/example_image_dataset",
+    allow_patterns="layer/image.png",
+    local_dir="data/example_image_dataset"
+)
+
+# Prompt should be provided to the pipeline. Our pipeline will not generate the prompt.
+prompt = 'A cheerful child with brown hair is waving enthusiastically under a bright blue sky filled with colorful confetti and balloons. The word "HELLO!" is prominently displayed in bold red letters above the child, while "Have a Great Day!" appears in elegant cursive at the bottom right corner. The scene is vibrant and festive, with a mix of pastel colors and dynamic shapes creating a joyful atmosphere.'
+# Height and width should be consistent with input_image and be divided evenly by 16
+input_image = Image.open("data/example_image_dataset/layer/image.png").convert("RGBA").resize((864, 480))
+images = pipe(
+    prompt,
+    seed=1, num_inference_steps=50,
+    height=480, width=864,
+    layer_input_image=input_image, layer_num=3,
+)
+for i, image in enumerate(images):
+    if i == 0: continue # The first image is the input image.
+    image.save(f"image_{i}.png")
--- a/examples/qwen_image/model_inference/Qwen-Image-i2L.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-i2L.py
@@ -0,0 +1,110 @@
+from diffsynth.pipelines.qwen_image import (
+    QwenImagePipeline, ModelConfig,
+    QwenImageUnit_Image2LoRAEncode, QwenImageUnit_Image2LoRADecode
+)
+from diffsynth.utils.lora import merge_lora
+from diffsynth import load_state_dict
+from modelscope import snapshot_download
+from safetensors.torch import save_file
+import torch
+from PIL import Image
+
+
+def demo_style():
+    # Load models
+    pipe = QwenImagePipeline.from_pretrained(
+        torch_dtype=torch.bfloat16,
+        device="cuda",
+        model_configs=[
+            ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors"),
+            ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors"),
+            ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Style.safetensors"),
+        ],
+        processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+    )
+
+    # Load images
+    snapshot_download(
+        model_id="DiffSynth-Studio/Qwen-Image-i2L",
+        allow_file_pattern="assets/style/1/*",
+        local_dir="data/examples"
+    )
+    images = [
+        Image.open("data/examples/assets/style/1/0.jpg"),
+        Image.open("data/examples/assets/style/1/1.jpg"),
+        Image.open("data/examples/assets/style/1/2.jpg"),
+        Image.open("data/examples/assets/style/1/3.jpg"),
+        Image.open("data/examples/assets/style/1/4.jpg"),
+    ]
+
+    # Model inference
+    with torch.no_grad():
+        embs = QwenImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=images)
+        lora = QwenImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"]
+    save_file(lora, "model_style.safetensors")
+
+
+def demo_coarse_fine_bias():
+    # Load models
+    pipe = QwenImagePipeline.from_pretrained(
+        torch_dtype=torch.bfloat16,
+        device="cuda",
+        model_configs=[
+            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+            ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors"),
+            ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors"),
+            ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Coarse.safetensors"),
+            ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Fine.safetensors"),
+        ],
+        processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+    )
+
+    # Load images
+    snapshot_download(
+        model_id="DiffSynth-Studio/Qwen-Image-i2L",
+        allow_file_pattern="assets/lora/3/*",
+        local_dir="data/examples"
+    )
+    images = [
+        Image.open("data/examples/assets/lora/3/0.jpg"),
+        Image.open("data/examples/assets/lora/3/1.jpg"),
+        Image.open("data/examples/assets/lora/3/2.jpg"),
+        Image.open("data/examples/assets/lora/3/3.jpg"),
+        Image.open("data/examples/assets/lora/3/4.jpg"),
+        Image.open("data/examples/assets/lora/3/5.jpg"),
+    ]
+
+    # Model inference
+    with torch.no_grad():
+        embs = QwenImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=images)
+        lora = QwenImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"]
+        lora_bias = ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Bias.safetensors")
+        lora_bias.download_if_necessary()
+        lora_bias = load_state_dict(lora_bias.path, torch_dtype=torch.bfloat16, device="cuda")
+        lora = merge_lora([lora, lora_bias])
+    save_file(lora, "model_coarse_fine_bias.safetensors")
+
+
+def generate_image(lora_path, prompt, seed):
+    pipe = QwenImagePipeline.from_pretrained(
+        torch_dtype=torch.bfloat16,
+        device="cuda",
+        model_configs=[
+            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+        ],
+        tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+    )
+    pipe.load_lora(pipe.dit, lora_path)
+    image = pipe(prompt, seed=seed, height=1024, width=1024, num_inference_steps=50)
+    return image
+
+
+demo_style()
+image = generate_image("model_style.safetensors", "a cat", 0)
+image.save("image_1.jpg")
+
+demo_coarse_fine_bias()
+image = generate_image("model_coarse_fine_bias.safetensors", "bowl", 1)
+image.save("image_2.jpg")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py
@@ -0,0 +1,28 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+import torch
+
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-2512", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
+image = pipe(prompt, seed=0, num_inference_steps=40)
+image.save("image.jpg")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py
@@ -0,0 +1,54 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+from PIL import Image
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+
+dataset_snapshot_download(
+    "DiffSynth-Studio/example_image_dataset",
+    allow_file_pattern="qwen_image_edit/*",
+    local_dir="data/example_image_dataset",
+)
+
+prompt = "生成这两个人的合影"
+edit_image = [
+    Image.open("data/example_image_dataset/qwen_image_edit/image1.jpg"),
+    Image.open("data/example_image_dataset/qwen_image_edit/image2.jpg"),
+]
+image = pipe(
+    prompt,
+    edit_image=edit_image,
+    seed=1,
+    num_inference_steps=40,
+    height=1152,
+    width=896,
+    edit_image_auto_resize=True,
+    zero_cond_t=True, # This is a special parameter introduced by Qwen-Image-Edit-2511
+)
+image.save("image.jpg")
+
+# Qwen-Image-Edit-2511 is a multi-image editing model.
+# Please use a list to input `edit_image`, even if the input contains only one image.
+# edit_image = [Image.open("image.jpg")]
+# Please do not input the image directly.
+# edit_image = Image.open("image.jpg")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py
@@ -0,0 +1,44 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from modelscope import snapshot_download
+from PIL import Image
+import torch
+
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+
+snapshot_download(
+    model_id="DiffSynth-Studio/Qwen-Image-Layered-Control",
+    allow_file_pattern="assets/image_1_input.png",
+    local_dir="data/layered_input"
+)
+
+prompt = "A cartoon skeleton character wearing a purple hat and holding a gift box"
+input_image = Image.open("data/layered_input/assets/image_1_input.png").convert("RGBA").resize((1024, 1024))
+images = pipe(
+    prompt,
+    seed=0,
+    num_inference_steps=30, cfg_scale=4,
+    height=1024, width=1024,
+    layer_input_image=input_image,
+    layer_num=0,
+)
+images[0].save("image.png")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py
@@ -0,0 +1,46 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+from PIL import Image
+import torch
+
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.float8_e4m3fn,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.float8_e4m3fn,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+
+dataset_snapshot_download(
+    "DiffSynth-Studio/example_image_dataset",
+    allow_patterns="layer/image.png",
+    local_dir="data/example_image_dataset"
+)
+
+# Prompt should be provided to the pipeline. Our pipeline will not generate the prompt.
+prompt = 'A cheerful child with brown hair is waving enthusiastically under a bright blue sky filled with colorful confetti and balloons. The word "HELLO!" is prominently displayed in bold red letters above the child, while "Have a Great Day!" appears in elegant cursive at the bottom right corner. The scene is vibrant and festive, with a mix of pastel colors and dynamic shapes creating a joyful atmosphere.'
+# Height and width should be consistent with input_image and be divided evenly by 16
+input_image = Image.open("data/example_image_dataset/layer/image.png").convert("RGBA").resize((864, 480))
+images = pipe(
+    prompt,
+    seed=1, num_inference_steps=50,
+    height=480, width=864,
+    layer_input_image=input_image, layer_num=3,
+)
+for i, image in enumerate(images):
+    if i == 0: continue # The first image is the input image.
+    image.save(f"image_{i}.png")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py
@@ -0,0 +1,134 @@
+from diffsynth.pipelines.qwen_image import (
+    QwenImagePipeline, ModelConfig,
+    QwenImageUnit_Image2LoRAEncode, QwenImageUnit_Image2LoRADecode
+)
+from diffsynth.utils.lora import merge_lora
+from diffsynth import load_state_dict
+from modelscope import snapshot_download
+from safetensors.torch import save_file
+import torch
+from PIL import Image
+
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+vram_config_disk_offload = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+def demo_style():
+    # Load models
+    pipe = QwenImagePipeline.from_pretrained(
+        torch_dtype=torch.bfloat16,
+        device="cuda",
+        model_configs=[
+            ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors", **vram_config_disk_offload),
+            ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors", **vram_config_disk_offload),
+            ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Style.safetensors", **vram_config_disk_offload),
+        ],
+        processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+        vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+    )
+
+    # Load images
+    snapshot_download(
+        model_id="DiffSynth-Studio/Qwen-Image-i2L",
+        allow_file_pattern="assets/style/1/*",
+        local_dir="data/examples"
+    )
+    images = [
+        Image.open("data/examples/assets/style/1/0.jpg"),
+        Image.open("data/examples/assets/style/1/1.jpg"),
+        Image.open("data/examples/assets/style/1/2.jpg"),
+        Image.open("data/examples/assets/style/1/3.jpg"),
+        Image.open("data/examples/assets/style/1/4.jpg"),
+    ]
+
+    # Model inference
+    with torch.no_grad():
+        embs = QwenImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=images)
+        lora = QwenImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"]
+    save_file(lora, "model_style.safetensors")
+
+
+def demo_coarse_fine_bias():
+    # Load models
+    pipe = QwenImagePipeline.from_pretrained(
+        torch_dtype=torch.bfloat16,
+        device="cuda",
+        model_configs=[
+            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config_disk_offload),
+            ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors", **vram_config_disk_offload),
+            ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors", **vram_config_disk_offload),
+            ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Coarse.safetensors", **vram_config_disk_offload),
+            ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Fine.safetensors", **vram_config_disk_offload),
+        ],
+        processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+        vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+    )
+
+    # Load images
+    snapshot_download(
+        model_id="DiffSynth-Studio/Qwen-Image-i2L",
+        allow_file_pattern="assets/lora/3/*",
+        local_dir="data/examples"
+    )
+    images = [
+        Image.open("data/examples/assets/lora/3/0.jpg"),
+        Image.open("data/examples/assets/lora/3/1.jpg"),
+        Image.open("data/examples/assets/lora/3/2.jpg"),
+        Image.open("data/examples/assets/lora/3/3.jpg"),
+        Image.open("data/examples/assets/lora/3/4.jpg"),
+        Image.open("data/examples/assets/lora/3/5.jpg"),
+    ]
+
+    # Model inference
+    with torch.no_grad():
+        embs = QwenImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=images)
+        lora = QwenImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"]
+        lora_bias = ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-i2L", origin_file_pattern="Qwen-Image-i2L-Bias.safetensors")
+        lora_bias.download_if_necessary()
+        lora_bias = load_state_dict(lora_bias.path, torch_dtype=torch.bfloat16, device="cuda")
+        lora = merge_lora([lora, lora_bias])
+    save_file(lora, "model_coarse_fine_bias.safetensors")
+
+
+def generate_image(lora_path, prompt, seed):
+    pipe = QwenImagePipeline.from_pretrained(
+        torch_dtype=torch.bfloat16,
+        device="cuda",
+        model_configs=[
+            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
+            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
+        ],
+        tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+        vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+    )
+    pipe.load_lora(pipe.dit, lora_path)
+    image = pipe(prompt, seed=seed, height=1024, width=1024, num_inference_steps=50)
+    return image
+
+
+demo_style()
+image = generate_image("model_style.safetensors", "a cat", 0)
+image.save("image_1.jpg")
+
+demo_coarse_fine_bias()
+image = generate_image("model_coarse_fine_bias.safetensors", "bowl", 1)
+image.save("image_2.jpg")
--- a/examples/qwen_image/model_training/full/Qwen-Image-2512.sh
+++ b/examples/qwen_image/model_training/full/Qwen-Image-2512.sh
@@ -0,0 +1,13 @@
+accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image-2512:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-2512_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing \
+  --find_unused_parameters
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Artiprocher	53fe42af1b	update version	2026-01-30 13:49:27 +08:00
Artiprocher	ee9a3b4405	support loading models from state dict	2026-01-30 13:47:36 +08:00
Zhongjie Duan	22695e9be0	Merge pull request #1233 from modelscope/z-image-release Z-Image and Z-Image-i2L	2026-01-27 18:41:28 +08:00
Artiprocher	98290190ec	update z-image-i2L demo	2026-01-27 13:42:48 +08:00
Artiprocher	3f4de2cc7f	update z-image-i2L examples	2026-01-27 12:16:48 +08:00
Artiprocher	d12bf71bcc	support z-image and z-image-i2L	2026-01-27 10:56:15 +08:00
Zhongjie Duan	ffb7a138f7	Merge pull request #1228 from modelscope/klein-bugfix change klein image resize to crop	2026-01-22 10:34:17 +08:00
Artiprocher	548304667f	change klein image resize to crop	2026-01-22 10:33:29 +08:00
Zhongjie Duan	273143136c	Merge pull request #1227 from modelscope/modelscope-service-patch update to 2.0.3	2026-01-21 20:23:13 +08:00
Artiprocher	030ebe649a	update to 2.0.3	2026-01-21 20:22:43 +08:00
Zhongjie Duan	90921d2293	Merge pull request #1226 from modelscope/klein-train-fix improve flux2 training performance	2026-01-21 15:44:52 +08:00
Artiprocher	b61131c693	improve flux2 training performance	2026-01-21 15:44:15 +08:00
Zhongjie Duan	37fbb3248a	Merge pull request #1222 from modelscope/trainer-update support auto detact lora target modules	2026-01-21 11:06:19 +08:00
Artiprocher	d13f533f42	support auto detact lora target modules	2026-01-21 11:05:05 +08:00
Zhongjie Duan	3743b1307c	Merge pull request #1219 from modelscope/klein-edit support klein edit	2026-01-20 12:59:12 +08:00
Artiprocher	a835df984c	support klein edit	2026-01-20 12:58:18 +08:00
Zhongjie Duan	3e4b47e424	Merge pull request #1207 from Feng0w0/cuda_replace [NPU]:Replace 'cuda' in the project with abstract interfaces	2026-01-20 10:13:04 +08:00
Zhongjie Duan	dd8d902624	Merge branch 'main' into cuda_replace	2026-01-20 10:12:31 +08:00
Zhongjie Duan	a8b340c098	Merge pull request #1191 from Feng0w0/wan_rope [model][NPU]:Wan model rope use torch.complex64 in NPU	2026-01-20 10:05:22 +08:00
Zhongjie Duan	88497b5c13	Merge pull request #1217 from modelscope/klein-update support klein base models	2026-01-19 21:14:47 +08:00
Artiprocher	1e90c72d94	support klein base models	2026-01-19 21:11:58 +08:00
Zhongjie Duan	3dd82a738e	Merge pull request #1215 from lzws/main updata learning rate in wan-vace training scripts	2026-01-19 17:48:42 +08:00
Artiprocher	8ad2d9884b	update lr in wan-vace training scripts	2026-01-19 17:43:07 +08:00
Artiprocher	70f531b724	update wan-vace training scripts	2026-01-19 17:37:30 +08:00
Zhongjie Duan	37c2868b61	Merge pull request #1214 from modelscope/klein Support FLUX.2-klein	2026-01-19 17:36:39 +08:00
Artiprocher	a18e6233b5	updata wan-vace training scripts	2026-01-19 17:35:08 +08:00
Artiprocher	2336d5f6b3	update doc	2026-01-19 17:27:32 +08:00
Artiprocher	b6ccb362b9	support flux.2 klein	2026-01-19 16:56:14 +08:00
Artiprocher	ae52d93694	support klein 4b models	2026-01-16 13:09:41 +08:00
feng0w0	ad91d41601	[NPU]:Replace 'cuda' in the project with abstract interfaces	2026-01-16 10:28:24 +08:00
feng0w0	dce77ec4d1	[NPU]:Replace 'cuda' in the project with abstract interfaces	2026-01-15 20:35:41 +08:00
feng0w0	5c0b07d939	[NPU]:Replace 'cuda' in the project with abstract interfaces	2026-01-15 20:34:52 +08:00
feng0w0	19e429d889	Merge remote-tracking branch 'origin/cuda_replace' into cuda_replace	2026-01-15 20:33:21 +08:00
feng0w0	209a350c0f	[NPU]:Replace 'cuda' in the project with abstract interfaces	2026-01-15 20:33:01 +08:00
feng0w0	a3c2744a43	[NPU]:Replace 'cuda' in the project with abstract interfaces	2026-01-15 20:04:54 +08:00
Zhongjie Duan	55e8346da3	Blog link (#1202 ) * update README	2026-01-15 12:31:55 +08:00
Zhongjie Duan	b7979b2633	Merge pull request #1200 from modelscope/flux-compatibility-fix fix flux compatibility issues	2026-01-14 20:50:18 +08:00
Artiprocher	c90aaa2798	fix flux compatibility issues	2026-01-14 20:49:36 +08:00
Zhongjie Duan	0c617d5d9e	Merge pull request #1194 from lzws/main wan usp bug fix	2026-01-14 16:34:06 +08:00
lzws	fd87b72754	wan usp bug fix	2026-01-14 16:33:02 +08:00
Zhongjie Duan	db75508ba0	Merge pull request #1199 from modelscope/z-image-bugfix fix RMSNorm precision	2026-01-14 16:32:33 +08:00
Artiprocher	acba342a63	fix RMSNorm precision	2026-01-14 16:29:43 +08:00
feng0w0	d16877e695	[model][NPU]:Wan model rope use torch.complex64 in NPU	2026-01-13 11:17:51 +08:00
lzws	e99cdcf3b8	wan usp bug fix	2026-01-12 22:08:48 +08:00
Zhongjie Duan	a236a17f17	Merge pull request #1193 from modelscope/qwen-image-layered-control support qwen-image-layered-control	2026-01-12 17:24:06 +08:00
Artiprocher	03e530dc39	support qwen-image-layered-control	2026-01-12 17:20:01 +08:00
feng0w0	6be244233a	[model][NPU]:Wan model rope use torch.complex64 in NPU	2026-01-12 11:34:41 +08:00
feng0w0	544c391936	[model][NPU]:Wan model rope use torch.complex64 in NPU	2026-01-12 11:24:11 +08:00
Feng	f4d06ce3fc	Merge branch 'modelscope:main' into wan_rope	2026-01-12 11:21:09 +08:00
Zhongjie Duan	ffedb9eb52	Merge pull request #1187 from jiaqixuac/patch-1 Update package inclusion pattern in pyproject.toml	2026-01-12 10:12:20 +08:00
Zhongjie Duan	381067515c	Merge pull request #1176 from Feng0w0/z-image-rope [model][NPU]: Z-image model support NPU	2026-01-12 10:11:22 +08:00
Zhongjie Duan	00f2d1aa5d	Merge pull request #1169 from Feng0w0/sample_add Docs:Supplement NPU training script samples and documentation instruction	2026-01-12 10:08:38 +08:00
Zhongjie Duan	8cc3bece6d	Merge pull request #1167 from Feng0w0/install_env Docs:Supplement NPU environment installation document	2026-01-12 10:07:30 +08:00
Jiaqi Xu	f4bf592064	Update pyproject.toml Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-01-10 09:32:35 +08:00
Jiaqi Xu	3235393fb5	Update package inclusion pattern in pyproject.toml Update to install all the sub-packages inside diffsynth. Otherwise, the installed packages only contain __init__.py	2026-01-10 09:28:45 +08:00
feng0w0	3b662da31e	[model][NPU]:Wan model rope use torch.complex64 in NPU	2026-01-09 18:11:40 +08:00
feng0w0	19ce3048c1	[model][NPU]:Wan model rope use torch.complex64 in NPU	2026-01-09 18:06:41 +08:00
Zhongjie Duan	de0aa946f7	Merge pull request #1184 from modelscope/z-image-omni-base-dev update package version	2026-01-08 17:27:33 +08:00
Artiprocher	f376202a49	update package version	2026-01-08 17:26:29 +08:00
Zhongjie Duan	a13ecfc46b	Merge pull request #1183 from modelscope/z-image-omni-base-dev fix unused parameters in z-image-omni-base	2026-01-08 17:03:20 +08:00
Artiprocher	10a1853eda	fix unused parameters in z-image-omni-base	2026-01-08 17:02:41 +08:00
Zhongjie Duan	0efab85674	Support Z-Image-Omni-Base and its related models Support Z-Image-Omni-Base and its related models.	2026-01-08 13:43:59 +08:00
Artiprocher	f45a0ffd02	support z-image-omni-base vram management	2026-01-08 13:41:00 +08:00
Artiprocher	8ba528a8f6	bugfix	2026-01-08 13:21:33 +08:00
Artiprocher	dd479e5bff	support z-image-omni-base-i2L	2026-01-07 20:36:53 +08:00
Artiprocher	bac39b1cd2	support z-image controlnet	2026-01-07 15:56:53 +08:00
feng0w0	c1c9a4853b	[model][NPU]:Z-image model support NPU	2026-01-07 11:42:19 +08:00
feng0w0	3ee5f53a36	[model][NPU]:Z-image model support NPU	2026-01-07 11:31:22 +08:00
Artiprocher	32449a6aa0	support z-image-omni-base training	2026-01-05 20:04:00 +08:00
Zhongjie Duan	a6884f6b3a	Merge pull request #1171 from YZBPXX/main Fix issue where LoRa loads on a device different from Dit	2026-01-05 16:39:02 +08:00
Zhongjie Duan	b078666640	Merge pull request #1173 from modelscope/flux-compatibility-patch flux compatibility patch	2026-01-05 16:20:25 +08:00
Artiprocher	7604ca1e52	flux compatibility patch	2026-01-05 16:04:20 +08:00
feng0w0	62c3d406d9	Docs:Supplement NPU training script samples and documentation instruction	2026-01-05 15:42:55 +08:00
Artiprocher	5745c9f200	support z-image-omni-base	2026-01-05 14:45:01 +08:00
feng0w0	86829120c2	Docs:Supplement NPU training script samples and documentation instruction	2026-01-05 09:59:11 +08:00
yaozhengbing	60ac96525b	Fix issue where LoRa loads on a device different from Dit	2025-12-31 21:31:01 +08:00
feng0w0	07b1f5702f	Docs:Supplement NPU training script samples and documentation instruction	2025-12-31 10:01:21 +08:00
feng0w0	507e7e5d36	Docs:Supplement NPU training script samples and documentation instruction	2025-12-30 19:58:47 +08:00
Zhongjie Duan	ab8580f77e	Merge pull request #1166 from modelscope/qwen-image-2512 support qwen-image-2512	2025-12-30 16:47:07 +08:00
Artiprocher	6454259853	support qwen-image-2512	2025-12-30 16:43:41 +08:00
feng0w0	9cc1697d4d	Docs:Supplement NPU environment installation document	2025-12-30 15:57:13 +08:00
Zhongjie Duan	8f1d10fb43	Merge pull request #1150 from modelscope/qwen-image-layered support qwen-image-layered	2025-12-20 14:05:38 +08:00
Artiprocher	20e1aaf908	bugfix	2025-12-20 14:00:22 +08:00
Artiprocher	c6722b3f56	support qwen-image-layered	2025-12-19 19:06:37 +08:00
Zhongjie Duan	11315d7a40	Merge pull request #1147 from modelscope/qwen-image-edit-2511 Qwen image edit 2511	2025-12-18 19:23:44 +08:00
Artiprocher	68d97a9844	update doc	2025-12-18 19:22:22 +08:00
Artiprocher	4629d4cf9e	support qwen-image-edit-2511	2025-12-18 19:16:52 +08:00
Zhongjie Duan	3cb5cec906	Merge pull request #1143 from modelscope/readme-update update README	2025-12-17 16:32:29 +08:00
Artiprocher	b7e16b9034	update README	2025-12-17 16:30:41 +08:00
Zhongjie Duan	83d1e7361f	Merge pull request #1136 from modelscope/bugfix-device bugfix	2025-12-16 16:12:05 +08:00
Artiprocher	1547c3f786	bugfix	2025-12-16 16:09:29 +08:00
Zhongjie Duan	bfaaf12bf4	Merge pull request #1129 from modelscope/ascend Support Ascend NPU	2025-12-15 19:13:40 +08:00
Zhongjie Duan	47545e1aab	Merge pull request #1126 from Leoooo333/main Fixed: Wan S2V Long video severe quality downgrade	2025-12-15 19:09:39 +08:00
Artiprocher	7c6905a432	support ascend npu	2025-12-15 15:50:12 +08:00
Artiprocher	2883bc1b76	support ascend npu	2025-12-15 15:48:42 +08:00
Zhongjie Duan	78d8842ddf	Merge pull request #1128 from modelscope/amd_install update installation instructions for AMD	2025-12-15 14:35:50 +08:00
Artiprocher	5821a664a0	update AMD GPU support	2025-12-15 14:30:13 +08:00
Zhongjie Duan	ab9aa1a087	Merge pull request #1124 from lzws/main add wan usp example	2025-12-15 12:57:58 +08:00
Junming Chen	a4d34d9f3d	Append: set video compress quality as original version.	2025-12-14 20:53:26 +00:00
Junming Chen	127cc9007a	Fixed: S2V Long video severe quality downgrade	2025-12-14 20:30:34 +00:00
lzws	e1f5db5f5c	add wan usp example	2025-12-12 20:24:27 +08:00
Zhongjie Duan	e316fb717f	Merge pull request #1122 from modelscope/flux-lora-revert revert FluxLoRAConverter due to dependency issues	2025-12-12 17:19:48 +08:00
Artiprocher	64c5139502	revert FluxLoRAConverter due to dependency issues	2025-12-12 17:19:13 +08:00
Mahdi-CV	5da9611a74	Update README.md Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2025-12-11 09:57:15 -08:00
Mahdi-CV	733750d01b	Update README.md Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2025-12-11 09:57:06 -08:00
Mahdi-CV	edc95359d0	Update README_zh.md Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2025-12-11 09:56:48 -08:00
lzws	f2d0241e26	Update Z-Image.md	2025-12-11 16:43:38 +08:00
lzws	7b5d7f4af5	Update Z-Image.md	2025-12-11 16:41:46 +08:00
Mahdi Ghodsi	1fa9a6c60c	updated README both Eng and Ch to reflect the AMD installation	2025-12-10 16:14:56 -08:00
Mahdi Ghodsi	51efa128d3	adding amd requirements file	2025-12-10 14:40:38 -08:00
Zhongjie Duan	421c6a5fce	Merge pull request #1109 from modelscope/bugfix1 fix typo	2025-12-09 23:30:15 +09:00
Artiprocher	864080d8f2	fix typo	2025-12-09 22:29:50 +08:00
Zhongjie Duan	ba372dd295	Merge pull request #1108 from modelscope/i2L Qwen-Image-i2L (Image to LoRA)	2025-12-09 23:10:02 +09:00
Artiprocher	1ceb02f673	update README	2025-12-09 22:08:47 +08:00
Artiprocher	30f93161fb	support i2L	2025-12-09 22:07:35 +08:00
Zhongjie Duan	3ee3cc3104	Merge pull request #1093 from modelscope/diffsynth-2.0-patch DiffSynth-Studio 2.0 major update	2025-12-04 16:38:31 +08:00
root	c2218f5c73	DiffSynth-Studio 2.0 major update	2025-12-04 16:34:24 +08:00
root	72af7122b3	DiffSynth-Studio 2.0 major update	2025-12-04 16:33:07 +08:00
Zhongjie Duan	afd101f345	Merge pull request #1058 from modelscope/download support downloading resource	2025-11-18 10:30:16 +08:00
Artiprocher	1313f4dd63	support downloading resource	2025-11-18 10:29:07 +08:00
Zhongjie Duan	8332ecebb7	Merge pull request #1034 from modelscope/video_as_prompt Video as prompt	2025-11-04 17:32:50 +08:00
Zhongjie Duan	401d7d74a5	Merge pull request #1025 from krahets/patch-1 Fix sinusoidal_embedding calculation for bf16 precision.	2025-11-04 15:08:11 +08:00
Yudong Jin	b8d7d55568	Fix dtype issue in time embedding calculation	2025-11-01 03:11:03 +08:00