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7
README.md
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@@ -33,6 +33,8 @@ We believe that a well-developed open-source code framework can lower the thresh
> Currently, the development personnel of this project are limited, with most of the work handled by [Artiprocher](https://github.com/Artiprocher). Therefore, the progress of new feature development will be relatively slow, and the speed of responding to and resolving issues is limited. We apologize for this and ask developers to understand.
- **January 19, 2026**: Added support for [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)).
@@ -321,7 +323,9 @@ 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)|
|[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)|
</details>
@@ -774,4 +778,3 @@ 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>

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README_zh.md
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@@ -33,6 +33,8 @@ DiffSynth 目前包括两个开源项目：
> 目前本项目的开发人员有限，大部分工作由 [Artiprocher](https://github.com/Artiprocher) 负责，因此新功能的开发进展会比较缓慢，issue 的回复和解决速度有限，我们对此感到非常抱歉，请各位开发者理解。
- **2026年1月19日** 新增对 [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)）。
@@ -321,7 +323,9 @@ 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)|
|[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)|
</details>

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docs/en/Model_Details/FLUX2.md
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@@ -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 +59,18 @@ 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)|
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 +146,4 @@ 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/).

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docs/zh/Model_Details/FLUX2.md
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@@ -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 +59,18 @@ 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)|
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 +146,4 @@ 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/)。