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- .gitattributes +520 -0
- Wan2.1-T2V-1.3B/.gitattributes +47 -0
- Wan2.1-T2V-1.3B/LICENSE.txt +201 -0
- Wan2.1-T2V-1.3B/README.md +298 -0
- Wan2.1-T2V-1.3B/Wan2.1_VAE.pth +3 -0
- Wan2.1-T2V-1.3B/assets/.DS_Store +0 -0
- Wan2.1-T2V-1.3B/assets/comp_effic.png +3 -0
- Wan2.1-T2V-1.3B/assets/data_for_diff_stage.jpg +3 -0
- Wan2.1-T2V-1.3B/assets/i2v_res.png +3 -0
- Wan2.1-T2V-1.3B/assets/logo.png +0 -0
- Wan2.1-T2V-1.3B/assets/t2v_res.jpg +3 -0
- Wan2.1-T2V-1.3B/assets/vben_1.3b_vs_sota.png +3 -0
- Wan2.1-T2V-1.3B/assets/vben_vs_sota.png +3 -0
- Wan2.1-T2V-1.3B/assets/video_dit_arch.jpg +3 -0
- Wan2.1-T2V-1.3B/assets/video_vae_res.jpg +3 -0
- Wan2.1-T2V-1.3B/config.json +14 -0
- Wan2.1-T2V-1.3B/diffusion_pytorch_model.safetensors +3 -0
- Wan2.1-T2V-1.3B/examples/i2v_input.JPG +3 -0
- Wan2.1-T2V-1.3B/google/umt5-xxl/special_tokens_map.json +308 -0
- Wan2.1-T2V-1.3B/google/umt5-xxl/spiece.model +3 -0
- Wan2.1-T2V-1.3B/google/umt5-xxl/tokenizer.json +3 -0
- Wan2.1-T2V-1.3B/google/umt5-xxl/tokenizer_config.json +2748 -0
- Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth +3 -0
- diffsynth-studio/.github/workflows/logo.gif +3 -0
- diffsynth-studio/.github/workflows/publish.yaml +29 -0
- diffsynth-studio/.gitignore +11 -0
- diffsynth-studio/.vscode/settings.json +5 -0
- diffsynth-studio/LICENSE +201 -0
- diffsynth-studio/README_zh.md +478 -0
- diffsynth-studio/apps/gradio/DiffSynth_Studio.py +252 -0
- diffsynth-studio/apps/gradio/entity_level_control.py +390 -0
- diffsynth-studio/apps/streamlit/DiffSynth_Studio.py +15 -0
- diffsynth-studio/apps/streamlit/pages/1_Image_Creator.py +362 -0
- diffsynth-studio/apps/streamlit/pages/2_Video_Creator.py +197 -0
- diffsynth-studio/diffsynth.egg-info/PKG-INFO +32 -0
- diffsynth-studio/diffsynth.egg-info/SOURCES.txt +252 -0
- diffsynth-studio/diffsynth.egg-info/dependency_links.txt +1 -0
- diffsynth-studio/diffsynth.egg-info/requires.txt +16 -0
- diffsynth-studio/diffsynth.egg-info/top_level.txt +1 -0
- diffsynth-studio/diffsynth/__init__.py +6 -0
- diffsynth-studio/diffsynth/__pycache__/__init__.cpython-310.pyc +0 -0
- diffsynth-studio/diffsynth/__pycache__/__init__.cpython-311.pyc +0 -0
- diffsynth-studio/diffsynth/configs/__init__.py +0 -0
- diffsynth-studio/diffsynth/configs/__pycache__/__init__.cpython-310.pyc +0 -0
- diffsynth-studio/diffsynth/configs/__pycache__/__init__.cpython-311.pyc +0 -0
- diffsynth-studio/diffsynth/configs/__pycache__/model_config.cpython-310.pyc +0 -0
- diffsynth-studio/diffsynth/configs/__pycache__/model_config.cpython-311.pyc +0 -0
- diffsynth-studio/diffsynth/configs/model_config.py +843 -0
- diffsynth-studio/diffsynth/controlnets/__init__.py +2 -0
- diffsynth-studio/diffsynth/controlnets/__pycache__/__init__.cpython-310.pyc +0 -0
.gitattributes
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Wan2.1-T2V-1.3B/.gitattributes
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Wan2.1-T2V-1.3B/LICENSE.txt
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Wan2.1-T2V-1.3B/README.md
ADDED
|
@@ -0,0 +1,298 @@
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|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
- zh
|
| 6 |
+
pipeline_tag: text-to-video
|
| 7 |
+
library_name: diffusers
|
| 8 |
+
tags:
|
| 9 |
+
- video
|
| 10 |
+
- video-generation
|
| 11 |
+
---
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# Wan2.1
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<p align="center">
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<img src="assets/logo.png" width="400"/>
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<p>
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<p align="center">
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💜 <a href=""><b>Wan</b></a>    ｜    🖥️ <a href="https://github.com/Wan-Video/Wan2.1">GitHub</a>    |   🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>   |    📑 <a href="">Paper (Coming soon)</a>    |    📑 <a href="https://wanxai.com">Blog</a>    |   💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>   |    📖 <a href="https://discord.gg/p5XbdQV7">Discord</a>  
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<br>
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-----
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[**Wan: Open and Advanced Large-Scale Video Generative Models**]("#") <be>
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In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features:
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- 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
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- 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.
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- 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
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- 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
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- 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.
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This repository hosts our T2V-1.3B model, a versatile solution for video generation that is compatible with nearly all consumer-grade GPUs. In this way, we hope that **Wan2.1** can serve as an easy-to-use tool for more creative teams in video creation, providing a high-quality foundational model for academic teams with limited computing resources. This will facilitate both the rapid development of the video creation community and the swift advancement of video technology.
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## Video Demos
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<div align="center">
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<video width="80%" controls>
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<source src="https://cloud.video.taobao.com/vod/Jth64Y7wNoPcJki_Bo1ZJTDBvNjsgjlVKsNs05Fqfps.mp4" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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</div>
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## 🔥 Latest News!!
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* Feb 25, 2025: 👋 We've released the inference code and weights of Wan2.1.
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## 📑 Todo List
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- Wan2.1 Text-to-Video
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- [x] Multi-GPU Inference code of the 14B and 1.3B models
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- [x] Checkpoints of the 14B and 1.3B models
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- [x] Gradio demo
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- [ ] Diffusers integration
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- [ ] ComfyUI integration
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- Wan2.1 Image-to-Video
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- [x] Multi-GPU Inference code of the 14B model
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- [x] Checkpoints of the 14B model
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- [x] Gradio demo
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- [ ] Diffusers integration
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- [ ] ComfyUI integration
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## Quickstart
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#### Installation
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Clone the repo:
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```
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git clone https://github.com/Wan-Video/Wan2.1.git
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cd Wan2.1
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```
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Install dependencies:
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```
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# Ensure torch >= 2.4.0
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pip install -r requirements.txt
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```
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#### Model Download
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| Models | Download Link | Notes |
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| --------------|-------------------------------------------------------------------------------|-------------------------------|
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| T2V-14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | Supports both 480P and 720P
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| I2V-14B-720P | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | Supports 720P
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| I2V-14B-480P | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | Supports 480P
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| T2V-1.3B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | Supports 480P
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> 💡Note: The 1.3B model is capable of generating videos at 720P resolution. However, due to limited training at this resolution, the results are generally less stable compared to 480P. For optimal performance, we recommend using 480P resolution.
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Download models using 🤗 huggingface-cli:
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```
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pip install "huggingface_hub[cli]"
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huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B --local-dir ./Wan2.1-T2V-1.3B
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```
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| 101 |
+
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Download models using 🤖 modelscope-cli:
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```
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pip install modelscope
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modelscope download Wan-AI/Wan2.1-T2V-1.3B --local_dir ./Wan2.1-T2V-1.3B
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```
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+
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#### Run Text-to-Video Generation
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This repository supports two Text-to-Video models (1.3B and 14B) and two resolutions (480P and 720P). The parameters and configurations for these models are as follows:
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|
| 112 |
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<table>
|
| 113 |
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<thead>
|
| 114 |
+
<tr>
|
| 115 |
+
<th rowspan="2">Task</th>
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| 116 |
+
<th colspan="2">Resolution</th>
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<th rowspan="2">Model</th>
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+
</tr>
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<tr>
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<th>480P</th>
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<th>720P</th>
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</tr>
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</thead>
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<tbody>
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| 125 |
+
<tr>
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<td>t2v-14B</td>
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| 127 |
+
<td style="color: green;">✔️</td>
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| 128 |
+
<td style="color: green;">✔️</td>
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| 129 |
+
<td>Wan2.1-T2V-14B</td>
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| 130 |
+
</tr>
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| 131 |
+
<tr>
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| 132 |
+
<td>t2v-1.3B</td>
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| 133 |
+
<td style="color: green;">✔️</td>
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| 134 |
+
<td style="color: red;">❌</td>
|
| 135 |
+
<td>Wan2.1-T2V-1.3B</td>
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| 136 |
+
</tr>
|
| 137 |
+
</tbody>
|
| 138 |
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</table>
|
| 139 |
+
|
| 140 |
+
|
| 141 |
+
##### (1) Without Prompt Extention
|
| 142 |
+
|
| 143 |
+
To facilitate implementation, we will start with a basic version of the inference process that skips the [prompt extension](#2-using-prompt-extention) step.
|
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+
|
| 145 |
+
- Single-GPU inference
|
| 146 |
+
|
| 147 |
+
```
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python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
|
| 149 |
+
```
|
| 150 |
+
|
| 151 |
+
If you encounter OOM (Out-of-Memory) issues, you can use the `--offload_model True` and `--t5_cpu` options to reduce GPU memory usage. For example, on an RTX 4090 GPU:
|
| 152 |
+
|
| 153 |
+
```
|
| 154 |
+
python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --offload_model True --t5_cpu --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
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| 155 |
+
```
|
| 156 |
+
|
| 157 |
+
> 💡Note: If you are using the `T2V-1.3B` model, we recommend setting the parameter `--sample_guide_scale 6`. The `--sample_shift parameter` can be adjusted within the range of 8 to 12 based on the performance.
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| 158 |
+
|
| 159 |
+
- Multi-GPU inference using FSDP + xDiT USP
|
| 160 |
+
|
| 161 |
+
```
|
| 162 |
+
pip install "xfuser>=0.4.1"
|
| 163 |
+
torchrun --nproc_per_node=8 generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --dit_fsdp --t5_fsdp --ulysses_size 8 --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
|
| 164 |
+
```
|
| 165 |
+
|
| 166 |
+
|
| 167 |
+
##### (2) Using Prompt Extention
|
| 168 |
+
|
| 169 |
+
Extending the prompts can effectively enrich the details in the generated videos, further enhancing the video quality. Therefore, we recommend enabling prompt extension. We provide the following two methods for prompt extension:
|
| 170 |
+
|
| 171 |
+
- Use the Dashscope API for extension.
|
| 172 |
+
- Apply for a `dashscope.api_key` in advance ([EN](https://www.alibabacloud.com/help/en/model-studio/getting-started/first-api-call-to-qwen) | [CN](https://help.aliyun.com/zh/model-studio/getting-started/first-api-call-to-qwen)).
|
| 173 |
+
- Configure the environment variable `DASH_API_KEY` to specify the Dashscope API key. For users of Alibaba Cloud's international site, you also need to set the environment variable `DASH_API_URL` to 'https://dashscope-intl.aliyuncs.com/api/v1'. For more detailed instructions, please refer to the [dashscope document](https://www.alibabacloud.com/help/en/model-studio/developer-reference/use-qwen-by-calling-api?spm=a2c63.p38356.0.i1).
|
| 174 |
+
- Use the `qwen-plus` model for text-to-video tasks and `qwen-vl-max` for image-to-video tasks.
|
| 175 |
+
- You can modify the model used for extension with the parameter `--prompt_extend_model`. For example:
|
| 176 |
+
```
|
| 177 |
+
DASH_API_KEY=your_key python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'dashscope' --prompt_extend_target_lang 'ch'
|
| 178 |
+
```
|
| 179 |
+
|
| 180 |
+
- Using a local model for extension.
|
| 181 |
+
|
| 182 |
+
- By default, the Qwen model on HuggingFace is used for this extension. Users can choose based on the available GPU memory size.
|
| 183 |
+
- For text-to-video tasks, you can use models like `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-7B-Instruct` and `Qwen/Qwen2.5-3B-Instruct`
|
| 184 |
+
- For image-to-video tasks, you can use models like `Qwen/Qwen2.5-VL-7B-Instruct` and `Qwen/Qwen2.5-VL-3B-Instruct`.
|
| 185 |
+
- Larger models generally provide better extension results but require more GPU memory.
|
| 186 |
+
- You can modify the model used for extension with the parameter `--prompt_extend_model` , allowing you to specify either a local model path or a Hugging Face model. For example:
|
| 187 |
+
|
| 188 |
+
```
|
| 189 |
+
python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'ch'
|
| 190 |
+
```
|
| 191 |
+
|
| 192 |
+
##### (3) Runing local gradio
|
| 193 |
+
|
| 194 |
+
```
|
| 195 |
+
cd gradio
|
| 196 |
+
# if one uses dashscope’s API for prompt extension
|
| 197 |
+
DASH_API_KEY=your_key python t2v_1.3B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir ./Wan2.1-T2V-1.3B
|
| 198 |
+
|
| 199 |
+
# if one uses a local model for prompt extension
|
| 200 |
+
python t2v_1.3B_singleGPU.py --prompt_extend_method 'local_qwen' --ckpt_dir ./Wan2.1-T2V-1.3B
|
| 201 |
+
```
|
| 202 |
+
|
| 203 |
+
|
| 204 |
+
|
| 205 |
+
## Evaluation
|
| 206 |
+
|
| 207 |
+
We employ our **Wan-Bench** framework to evaluate the performance of the T2V-1.3B model, with the results displayed in the table below. The results indicate that our smaller 1.3B model surpasses the overall metrics of larger open-source models, demonstrating the effectiveness of **WanX2.1**'s architecture and the data construction pipeline.
|
| 208 |
+
|
| 209 |
+
<div align="center">
|
| 210 |
+
<img src="assets/vben_1.3b_vs_sota.png" alt="" style="width: 80%;" />
|
| 211 |
+
</div>
|
| 212 |
+
|
| 213 |
+
|
| 214 |
+
|
| 215 |
+
## Computational Efficiency on Different GPUs
|
| 216 |
+
|
| 217 |
+
We test the computational efficiency of different **Wan2.1** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
|
| 218 |
+
|
| 219 |
+
|
| 220 |
+
<div align="center">
|
| 221 |
+
<img src="assets/comp_effic.png" alt="" style="width: 80%;" />
|
| 222 |
+
</div>
|
| 223 |
+
|
| 224 |
+
> The parameter settings for the tests presented in this table are as follows:
|
| 225 |
+
> (1) For the 1.3B model on 8 GPUs, set `--ring_size 8` and `--ulysses_size 1`;
|
| 226 |
+
> (2) For the 14B model on 1 GPU, use `--offload_model True`;
|
| 227 |
+
> (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`;
|
| 228 |
+
> (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled.
|
| 229 |
+
|
| 230 |
+
-------
|
| 231 |
+
|
| 232 |
+
## Introduction of Wan2.1
|
| 233 |
+
|
| 234 |
+
**Wan2.1** is designed on the mainstream diffusion transformer paradigm, achieving significant advancements in generative capabilities through a series of innovations. These include our novel spatio-temporal variational autoencoder (VAE), scalable training strategies, large-scale data construction, and automated evaluation metrics. Collectively, these contributions enhance the model’s performance and versatility.
|
| 235 |
+
|
| 236 |
+
|
| 237 |
+
##### (1) 3D Variational Autoencoders
|
| 238 |
+
We propose a novel 3D causal VAE architecture, termed **Wan-VAE** specifically designed for video generation. By combining multiple strategies, we improve spatio-temporal compression, reduce memory usage, and ensure temporal causality. **Wan-VAE** demonstrates significant advantages in performance efficiency compared to other open-source VAEs. Furthermore, our **Wan-VAE** can encode and decode unlimited-length 1080P videos without losing historical temporal information, making it particularly well-suited for video generation tasks.
|
| 239 |
+
|
| 240 |
+
|
| 241 |
+
<div align="center">
|
| 242 |
+
<img src="assets/video_vae_res.jpg" alt="" style="width: 80%;" />
|
| 243 |
+
</div>
|
| 244 |
+
|
| 245 |
+
|
| 246 |
+
##### (2) Video Diffusion DiT
|
| 247 |
+
|
| 248 |
+
**Wan2.1** is designed using the Flow Matching framework within the paradigm of mainstream Diffusion Transformers. Our model's architecture uses the T5 Encoder to encode multilingual text input, with cross-attention in each transformer block embedding the text into the model structure. Additionally, we employ an MLP with a Linear layer and a SiLU layer to process the input time embeddings and predict six modulation parameters individually. This MLP is shared across all transformer blocks, with each block learning a distinct set of biases. Our experimental findings reveal a significant performance improvement with this approach at the same parameter scale.
|
| 249 |
+
|
| 250 |
+
<div align="center">
|
| 251 |
+
<img src="assets/video_dit_arch.jpg" alt="" style="width: 80%;" />
|
| 252 |
+
</div>
|
| 253 |
+
|
| 254 |
+
|
| 255 |
+
| Model | Dimension | Input Dimension | Output Dimension | Feedforward Dimension | Frequency Dimension | Number of Heads | Number of Layers |
|
| 256 |
+
|--------|-----------|-----------------|------------------|-----------------------|---------------------|-----------------|------------------|
|
| 257 |
+
| 1.3B | 1536 | 16 | 16 | 8960 | 256 | 12 | 30 |
|
| 258 |
+
| 14B | 5120 | 16 | 16 | 13824 | 256 | 40 | 40 |
|
| 259 |
+
|
| 260 |
+
|
| 261 |
+
|
| 262 |
+
##### Data
|
| 263 |
+
|
| 264 |
+
We curated and deduplicated a candidate dataset comprising a vast amount of image and video data. During the data curation process, we designed a four-step data cleaning process, focusing on fundamental dimensions, visual quality and motion quality. Through the robust data processing pipeline, we can easily obtain high-quality, diverse, and large-scale training sets of images and videos.
|
| 265 |
+
|
| 266 |
+
|
| 267 |
+
|
| 268 |
+
|
| 269 |
+
##### Comparisons to SOTA
|
| 270 |
+
We compared **Wan2.1** with leading open-source and closed-source models to evaluate the performace. Using our carefully designed set of 1,035 internal prompts, we tested across 14 major dimensions and 26 sub-dimensions. Then we calculated the total score through a weighted average based on the importance of each dimension. The detailed results are shown in the table below. These results demonstrate our model's superior performance compared to both open-source and closed-source models.
|
| 271 |
+
|
| 272 |
+
|
| 273 |
+
|
| 274 |
+
|
| 275 |
+
## Citation
|
| 276 |
+
If you find our work helpful, please cite us.
|
| 277 |
+
|
| 278 |
+
```
|
| 279 |
+
@article{wan2.1,
|
| 280 |
+
title = {Wan: Open and Advanced Large-Scale Video Generative Models},
|
| 281 |
+
author = {Wan Team},
|
| 282 |
+
journal = {},
|
| 283 |
+
year = {2025}
|
| 284 |
+
}
|
| 285 |
+
```
|
| 286 |
+
|
| 287 |
+
## License Agreement
|
| 288 |
+
The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generate contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
|
| 289 |
+
|
| 290 |
+
|
| 291 |
+
## Acknowledgements
|
| 292 |
+
|
| 293 |
+
We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
|
| 294 |
+
|
| 295 |
+
|
| 296 |
+
|
| 297 |
+
## Contact Us
|
| 298 |
+
If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/p5XbdQV7) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!
|
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{
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"<extra_id_186>",
|
| 190 |
+
"<extra_id_187>",
|
| 191 |
+
"<extra_id_188>",
|
| 192 |
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"<extra_id_189>",
|
| 193 |
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"<extra_id_190>",
|
| 194 |
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"<extra_id_191>",
|
| 195 |
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|
| 196 |
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|
| 197 |
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|
| 198 |
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|
| 199 |
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|
| 200 |
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|
| 201 |
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|
| 202 |
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"<extra_id_199>",
|
| 203 |
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"<extra_id_200>",
|
| 204 |
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|
| 205 |
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"<extra_id_202>",
|
| 206 |
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"<extra_id_203>",
|
| 207 |
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|
| 208 |
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"<extra_id_205>",
|
| 209 |
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|
| 210 |
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"<extra_id_207>",
|
| 211 |
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"<extra_id_208>",
|
| 212 |
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"<extra_id_209>",
|
| 213 |
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"<extra_id_210>",
|
| 214 |
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"<extra_id_211>",
|
| 215 |
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"<extra_id_212>",
|
| 216 |
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"<extra_id_213>",
|
| 217 |
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"<extra_id_214>",
|
| 218 |
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"<extra_id_215>",
|
| 219 |
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"<extra_id_216>",
|
| 220 |
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"<extra_id_217>",
|
| 221 |
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"<extra_id_218>",
|
| 222 |
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"<extra_id_219>",
|
| 223 |
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|
| 224 |
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"<extra_id_221>",
|
| 225 |
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"<extra_id_222>",
|
| 226 |
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"<extra_id_223>",
|
| 227 |
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|
| 228 |
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|
| 229 |
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|
| 230 |
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|
| 231 |
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|
| 232 |
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|
| 233 |
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|
| 234 |
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|
| 235 |
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|
| 236 |
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|
| 237 |
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"<extra_id_234>",
|
| 238 |
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"<extra_id_235>",
|
| 239 |
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|
| 240 |
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|
| 241 |
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|
| 242 |
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|
| 243 |
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|
| 244 |
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|
| 245 |
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|
| 246 |
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|
| 247 |
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|
| 248 |
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|
| 249 |
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|
| 250 |
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|
| 251 |
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|
| 252 |
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|
| 253 |
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|
| 254 |
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|
| 255 |
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|
| 256 |
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|
| 257 |
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|
| 258 |
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|
| 259 |
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|
| 260 |
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|
| 261 |
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"<extra_id_258>",
|
| 262 |
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"<extra_id_259>",
|
| 263 |
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"<extra_id_260>",
|
| 264 |
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"<extra_id_261>",
|
| 265 |
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"<extra_id_262>",
|
| 266 |
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"<extra_id_263>",
|
| 267 |
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"<extra_id_264>",
|
| 268 |
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"<extra_id_265>",
|
| 269 |
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"<extra_id_266>",
|
| 270 |
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|
| 271 |
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|
| 272 |
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"<extra_id_269>",
|
| 273 |
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"<extra_id_270>",
|
| 274 |
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"<extra_id_271>",
|
| 275 |
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"<extra_id_272>",
|
| 276 |
+
"<extra_id_273>",
|
| 277 |
+
"<extra_id_274>",
|
| 278 |
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"<extra_id_275>",
|
| 279 |
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"<extra_id_276>",
|
| 280 |
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|
| 281 |
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"<extra_id_278>",
|
| 282 |
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"<extra_id_279>",
|
| 283 |
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"<extra_id_280>",
|
| 284 |
+
"<extra_id_281>",
|
| 285 |
+
"<extra_id_282>",
|
| 286 |
+
"<extra_id_283>",
|
| 287 |
+
"<extra_id_284>",
|
| 288 |
+
"<extra_id_285>",
|
| 289 |
+
"<extra_id_286>",
|
| 290 |
+
"<extra_id_287>",
|
| 291 |
+
"<extra_id_288>",
|
| 292 |
+
"<extra_id_289>",
|
| 293 |
+
"<extra_id_290>",
|
| 294 |
+
"<extra_id_291>",
|
| 295 |
+
"<extra_id_292>",
|
| 296 |
+
"<extra_id_293>",
|
| 297 |
+
"<extra_id_294>",
|
| 298 |
+
"<extra_id_295>",
|
| 299 |
+
"<extra_id_296>",
|
| 300 |
+
"<extra_id_297>",
|
| 301 |
+
"<extra_id_298>",
|
| 302 |
+
"<extra_id_299>"
|
| 303 |
+
],
|
| 304 |
+
"bos_token": "<s>",
|
| 305 |
+
"eos_token": "</s>",
|
| 306 |
+
"pad_token": "<pad>",
|
| 307 |
+
"unk_token": "<unk>"
|
| 308 |
+
}
|
Wan2.1-T2V-1.3B/google/umt5-xxl/spiece.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e3909a67b780650b35cf529ac782ad2b6b26e6d1f849d3fbb6a872905f452458
|
| 3 |
+
size 4548313
|
Wan2.1-T2V-1.3B/google/umt5-xxl/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6e197b4d3dbd71da14b4eb255f4fa91c9c1f2068b20a2de2472967ca3d22602b
|
| 3 |
+
size 16837417
|
Wan2.1-T2V-1.3B/google/umt5-xxl/tokenizer_config.json
ADDED
|
@@ -0,0 +1,2748 @@
|
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| 1 |
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| 2 |
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| 18 |
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| 25 |
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|
| 1959 |
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|
| 1960 |
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|
| 1961 |
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|
| 1962 |
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|
| 1963 |
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|
| 1964 |
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|
| 1965 |
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|
| 1966 |
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|
| 1967 |
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|
| 1968 |
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|
| 1969 |
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|
| 1970 |
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|
| 1971 |
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|
| 1972 |
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|
| 1973 |
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|
| 1974 |
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|
| 1975 |
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|
| 1976 |
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|
| 1977 |
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|
| 1978 |
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|
| 1979 |
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|
| 1980 |
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|
| 1981 |
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|
| 1982 |
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|
| 1983 |
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|
| 1984 |
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|
| 1985 |
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|
| 1986 |
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|
| 1987 |
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|
| 1988 |
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|
| 1989 |
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| 1990 |
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| 1991 |
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| 1992 |
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|
| 1993 |
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|
| 1994 |
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| 1995 |
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|
| 1996 |
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| 1997 |
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| 1998 |
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| 1999 |
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| 2000 |
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| 2001 |
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| 2002 |
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| 2003 |
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| 2004 |
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| 2005 |
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| 2006 |
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| 2007 |
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| 2008 |
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| 2009 |
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| 2010 |
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| 2011 |
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| 2012 |
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| 2013 |
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| 2014 |
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| 2015 |
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| 2017 |
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| 2018 |
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| 2019 |
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| 2020 |
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| 2021 |
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| 2022 |
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| 2023 |
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| 2024 |
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| 2025 |
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| 2026 |
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| 2027 |
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| 2028 |
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| 2029 |
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| 2030 |
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| 2031 |
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| 2032 |
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| 2033 |
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| 2034 |
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| 2036 |
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|
| 2379 |
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|
| 2380 |
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|
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|
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|
| 2384 |
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|
| 2385 |
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|
| 2386 |
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| 2387 |
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|
| 2388 |
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|
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|
| 2391 |
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|
| 2392 |
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|
| 2393 |
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|
| 2394 |
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},
|
| 2395 |
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|
| 2396 |
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|
| 2397 |
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|
| 2398 |
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|
| 2399 |
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|
| 2400 |
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|
| 2401 |
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|
| 2402 |
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},
|
| 2403 |
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|
| 2404 |
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|
| 2405 |
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|
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|
| 2408 |
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|
| 2409 |
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|
| 2410 |
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| 2411 |
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|
| 2412 |
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| 2413 |
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|
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|
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| 2417 |
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|
| 2418 |
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|
| 2420 |
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| 2421 |
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|
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|
| 2424 |
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|
| 2425 |
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"special": true
|
| 2426 |
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|
| 2427 |
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"256299": {
|
| 2428 |
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|
| 2429 |
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|
| 2430 |
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"normalized": false,
|
| 2431 |
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|
| 2432 |
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|
| 2433 |
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|
| 2434 |
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|
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|
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"additional_special_tokens": [
|
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| 2570 |
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| 2574 |
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| 2577 |
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| 2578 |
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| 2579 |
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| 2584 |
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| 2585 |
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| 2586 |
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| 2587 |
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| 2588 |
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| 2589 |
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|
| 2590 |
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| 2591 |
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| 2592 |
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| 2593 |
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| 2594 |
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|
| 2595 |
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| 2596 |
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| 2597 |
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| 2598 |
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| 2599 |
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| 2600 |
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| 2601 |
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| 2602 |
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| 2603 |
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| 2605 |
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| 2608 |
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| 2610 |
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| 2612 |
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| 2614 |
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| 2617 |
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| 2618 |
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|
| 2619 |
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| 2620 |
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| 2621 |
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| 2622 |
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|
| 2623 |
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|
| 2624 |
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|
| 2625 |
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|
| 2626 |
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|
| 2627 |
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|
| 2628 |
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|
| 2629 |
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|
| 2630 |
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|
| 2631 |
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|
| 2632 |
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|
| 2633 |
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|
| 2634 |
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|
| 2635 |
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|
| 2636 |
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"<extra_id_199>",
|
| 2637 |
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"<extra_id_200>",
|
| 2638 |
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|
| 2639 |
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|
| 2640 |
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|
| 2641 |
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|
| 2642 |
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|
| 2643 |
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|
| 2644 |
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|
| 2645 |
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"<extra_id_208>",
|
| 2646 |
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|
| 2647 |
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"<extra_id_210>",
|
| 2648 |
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"<extra_id_211>",
|
| 2649 |
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|
| 2650 |
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|
| 2651 |
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"<extra_id_214>",
|
| 2652 |
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"<extra_id_215>",
|
| 2653 |
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"<extra_id_216>",
|
| 2654 |
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"<extra_id_217>",
|
| 2655 |
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|
| 2656 |
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"<extra_id_219>",
|
| 2657 |
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"<extra_id_220>",
|
| 2658 |
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"<extra_id_221>",
|
| 2659 |
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"<extra_id_222>",
|
| 2660 |
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"<extra_id_223>",
|
| 2661 |
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|
| 2662 |
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|
| 2663 |
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|
| 2664 |
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|
| 2665 |
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|
| 2666 |
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|
| 2667 |
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"<extra_id_230>",
|
| 2668 |
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"<extra_id_231>",
|
| 2669 |
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"<extra_id_232>",
|
| 2670 |
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"<extra_id_233>",
|
| 2671 |
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"<extra_id_234>",
|
| 2672 |
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"<extra_id_235>",
|
| 2673 |
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"<extra_id_236>",
|
| 2674 |
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"<extra_id_237>",
|
| 2675 |
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"<extra_id_238>",
|
| 2676 |
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"<extra_id_239>",
|
| 2677 |
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"<extra_id_240>",
|
| 2678 |
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"<extra_id_241>",
|
| 2679 |
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"<extra_id_242>",
|
| 2680 |
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"<extra_id_243>",
|
| 2681 |
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"<extra_id_244>",
|
| 2682 |
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"<extra_id_245>",
|
| 2683 |
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"<extra_id_246>",
|
| 2684 |
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"<extra_id_247>",
|
| 2685 |
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"<extra_id_248>",
|
| 2686 |
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"<extra_id_249>",
|
| 2687 |
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"<extra_id_250>",
|
| 2688 |
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"<extra_id_251>",
|
| 2689 |
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"<extra_id_252>",
|
| 2690 |
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|
| 2691 |
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"<extra_id_254>",
|
| 2692 |
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"<extra_id_255>",
|
| 2693 |
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"<extra_id_256>",
|
| 2694 |
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"<extra_id_257>",
|
| 2695 |
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"<extra_id_258>",
|
| 2696 |
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"<extra_id_259>",
|
| 2697 |
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"<extra_id_260>",
|
| 2698 |
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"<extra_id_261>",
|
| 2699 |
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"<extra_id_262>",
|
| 2700 |
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"<extra_id_263>",
|
| 2701 |
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"<extra_id_264>",
|
| 2702 |
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"<extra_id_265>",
|
| 2703 |
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"<extra_id_266>",
|
| 2704 |
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"<extra_id_267>",
|
| 2705 |
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"<extra_id_268>",
|
| 2706 |
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"<extra_id_269>",
|
| 2707 |
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"<extra_id_270>",
|
| 2708 |
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"<extra_id_271>",
|
| 2709 |
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"<extra_id_272>",
|
| 2710 |
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"<extra_id_273>",
|
| 2711 |
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"<extra_id_274>",
|
| 2712 |
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"<extra_id_275>",
|
| 2713 |
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"<extra_id_276>",
|
| 2714 |
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"<extra_id_277>",
|
| 2715 |
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"<extra_id_278>",
|
| 2716 |
+
"<extra_id_279>",
|
| 2717 |
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"<extra_id_280>",
|
| 2718 |
+
"<extra_id_281>",
|
| 2719 |
+
"<extra_id_282>",
|
| 2720 |
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"<extra_id_283>",
|
| 2721 |
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"<extra_id_284>",
|
| 2722 |
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"<extra_id_285>",
|
| 2723 |
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"<extra_id_286>",
|
| 2724 |
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"<extra_id_287>",
|
| 2725 |
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"<extra_id_288>",
|
| 2726 |
+
"<extra_id_289>",
|
| 2727 |
+
"<extra_id_290>",
|
| 2728 |
+
"<extra_id_291>",
|
| 2729 |
+
"<extra_id_292>",
|
| 2730 |
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"<extra_id_293>",
|
| 2731 |
+
"<extra_id_294>",
|
| 2732 |
+
"<extra_id_295>",
|
| 2733 |
+
"<extra_id_296>",
|
| 2734 |
+
"<extra_id_297>",
|
| 2735 |
+
"<extra_id_298>",
|
| 2736 |
+
"<extra_id_299>"
|
| 2737 |
+
],
|
| 2738 |
+
"bos_token": "<s>",
|
| 2739 |
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"clean_up_tokenization_spaces": true,
|
| 2740 |
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"eos_token": "</s>",
|
| 2741 |
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"extra_ids": 300,
|
| 2742 |
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"model_max_length": 1000000000000000019884624838656,
|
| 2743 |
+
"pad_token": "<pad>",
|
| 2744 |
+
"sp_model_kwargs": {},
|
| 2745 |
+
"spaces_between_special_tokens": false,
|
| 2746 |
+
"tokenizer_class": "T5Tokenizer",
|
| 2747 |
+
"unk_token": "<unk>"
|
| 2748 |
+
}
|
Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7cace0da2b446bbbbc57d031ab6cf163a3d59b366da94e5afe36745b746fd81d
|
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size 11361920418
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diffsynth-studio/.github/workflows/logo.gif
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Git LFS Details
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diffsynth-studio/.github/workflows/publish.yaml
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name: release
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on:
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push:
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tags:
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| 6 |
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- 'v**'
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| 7 |
+
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| 8 |
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concurrency:
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| 9 |
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group: ${{ github.workflow }}-${{ github.ref }}-publish
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| 10 |
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cancel-in-progress: true
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| 11 |
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jobs:
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build-n-publish:
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| 14 |
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runs-on: ubuntu-20.04
|
| 15 |
+
#if: startsWith(github.event.ref, 'refs/tags')
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| 16 |
+
steps:
|
| 17 |
+
- uses: actions/checkout@v2
|
| 18 |
+
- name: Set up Python 3.10
|
| 19 |
+
uses: actions/setup-python@v2
|
| 20 |
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with:
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| 21 |
+
python-version: '3.10'
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| 22 |
+
- name: Install wheel
|
| 23 |
+
run: pip install wheel==0.44.0 && pip install -r requirements.txt
|
| 24 |
+
- name: Build DiffSynth
|
| 25 |
+
run: python setup.py sdist bdist_wheel
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| 26 |
+
- name: Publish package to PyPI
|
| 27 |
+
run: |
|
| 28 |
+
pip install twine
|
| 29 |
+
twine upload dist/* --skip-existing -u __token__ -p ${{ secrets.PYPI_API_TOKEN }}
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diffsynth-studio/.gitignore
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@@ -0,0 +1,11 @@
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| 1 |
+
*.pyc
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| 2 |
+
*.log
|
| 3 |
+
diffsynth.egg-info/
|
| 4 |
+
checkpoint/
|
| 5 |
+
experiments/
|
| 6 |
+
test_outputs/
|
| 7 |
+
ckpt/
|
| 8 |
+
onnx/
|
| 9 |
+
models/Qwen/Qwen-Image
|
| 10 |
+
models/HunyuanVideo
|
| 11 |
+
models/Wan-AI
|
diffsynth-studio/.vscode/settings.json
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@@ -0,0 +1,5 @@
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+
{
|
| 2 |
+
"python-envs.defaultEnvManager": "ms-python.python:conda",
|
| 3 |
+
"python-envs.defaultPackageManager": "ms-python.python:conda",
|
| 4 |
+
"python-envs.pythonProjects": []
|
| 5 |
+
}
|
diffsynth-studio/LICENSE
ADDED
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@@ -0,0 +1,201 @@
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|
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Apache License
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diffsynth-studio/README_zh.md
ADDED
|
@@ -0,0 +1,478 @@
|
|
|
|
|
|
|
|
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|
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|
| 1 |
+
# DiffSynth-Studio
|
| 2 |
+
|
| 3 |
+
<a href="https://github.com/modelscope/DiffSynth-Studio"><img src=".github/workflows/logo.gif" title="Logo" style="max-width:100%;" width="55" /></a> <a href="https://trendshift.io/repositories/10946" target="_blank"><img src="https://trendshift.io/api/badge/repositories/10946" alt="modelscope%2FDiffSynth-Studio | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a></p>
|
| 4 |
+
|
| 5 |
+
[](https://pypi.org/project/DiffSynth/)
|
| 6 |
+
[](https://github.com/modelscope/DiffSynth-Studio/blob/master/LICENSE)
|
| 7 |
+
[](https://github.com/modelscope/DiffSynth-Studio/issues)
|
| 8 |
+
[](https://GitHub.com/modelscope/DiffSynth-Studio/pull/)
|
| 9 |
+
[](https://GitHub.com/modelscope/DiffSynth-Studio/commit/)
|
| 10 |
+
|
| 11 |
+
[Switch to English](./README.md)
|
| 12 |
+
|
| 13 |
+
## 简介
|
| 14 |
+
|
| 15 |
+
欢迎来到 Diffusion 模型的魔法世界！DiffSynth-Studio 是由[魔搭社区](https://www.modelscope.cn/)团队开发和维护的开源 Diffusion 模型引擎。我们期望以框架建设孵化技术创新，凝聚开源社区的力量，探索生成式模型技术的边界！
|
| 16 |
+
|
| 17 |
+
DiffSynth 目前包括两个开源项目：
|
| 18 |
+
* [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio): 聚焦于激进的技术探索，面向学术界，提供更前沿的模型能力支持。
|
| 19 |
+
* [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine): 聚焦于稳定的模型部署，面向工业界，提供更高的计算性能与更稳定的功能。
|
| 20 |
+
|
| 21 |
+
[DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) 与 [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine) 作为魔搭社区 [AIGC 专区](https://modelscope.cn/aigc/home) 的核心技术支撑，提供了强大的AI生成内容能力。欢迎体验我们精心打造的产品化功能，开启您的AI创作之旅！
|
| 22 |
+
|
| 23 |
+
## 安装
|
| 24 |
+
|
| 25 |
+
从源码安装（推荐）：
|
| 26 |
+
|
| 27 |
+
```
|
| 28 |
+
git clone https://github.com/modelscope/DiffSynth-Studio.git
|
| 29 |
+
cd DiffSynth-Studio
|
| 30 |
+
pip install -e .
|
| 31 |
+
```
|
| 32 |
+
|
| 33 |
+
<details>
|
| 34 |
+
<summary>其他安装方式</summary>
|
| 35 |
+
|
| 36 |
+
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）
|
| 37 |
+
|
| 38 |
+
```
|
| 39 |
+
pip install diffsynth
|
| 40 |
+
```
|
| 41 |
+
|
| 42 |
+
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：
|
| 43 |
+
|
| 44 |
+
* [torch](https://pytorch.org/get-started/locally/)
|
| 45 |
+
* [sentencepiece](https://github.com/google/sentencepiece)
|
| 46 |
+
* [cmake](https://cmake.org)
|
| 47 |
+
* [cupy](https://docs.cupy.dev/en/stable/install.html)
|
| 48 |
+
|
| 49 |
+
</details>
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
## 基础框架
|
| 54 |
+
|
| 55 |
+
DiffSynth-Studio 为主流 Diffusion 模型（包括 FLUX、Wan 等）重新设计了推理和训练流水线，能够实现高效的显存管理、灵活的模型训练。
|
| 56 |
+
|
| 57 |
+
### Qwen-Image 系列 (🔥新模型)
|
| 58 |
+
|
| 59 |
+
详细页面：[./examples/qwen_image/](./examples/qwen_image/)
|
| 60 |
+
|
| 61 |
+
|
| 62 |
+
|
| 63 |
+
<details>
|
| 64 |
+
|
| 65 |
+
<summary>快速开始</summary>
|
| 66 |
+
|
| 67 |
+
```python
|
| 68 |
+
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
| 69 |
+
import torch
|
| 70 |
+
|
| 71 |
+
pipe = QwenImagePipeline.from_pretrained(
|
| 72 |
+
torch_dtype=torch.bfloat16,
|
| 73 |
+
device="cuda",
|
| 74 |
+
model_configs=[
|
| 75 |
+
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
|
| 76 |
+
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
|
| 77 |
+
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
| 78 |
+
],
|
| 79 |
+
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
|
| 80 |
+
)
|
| 81 |
+
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。"
|
| 82 |
+
image = pipe(prompt, seed=0, num_inference_steps=40)
|
| 83 |
+
image.save("image.jpg")
|
| 84 |
+
```
|
| 85 |
+
|
| 86 |
+
</details>
|
| 87 |
+
|
| 88 |
+
<details>
|
| 89 |
+
|
| 90 |
+
<summary>模型总览</summary>
|
| 91 |
+
|
| 92 |
+
|模型 ID|推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
|
| 93 |
+
|-|-|-|-|-|-|
|
| 94 |
+
|[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_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)|
|
| 95 |
+
|
| 96 |
+
</details>
|
| 97 |
+
|
| 98 |
+
### FLUX 系列
|
| 99 |
+
|
| 100 |
+
详细页面：[./examples/flux/](./examples/flux/)
|
| 101 |
+
|
| 102 |
+
|
| 103 |
+
|
| 104 |
+
<details>
|
| 105 |
+
|
| 106 |
+
<summary>快速开始</summary>
|
| 107 |
+
|
| 108 |
+
```python
|
| 109 |
+
import torch
|
| 110 |
+
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
| 111 |
+
|
| 112 |
+
pipe = FluxImagePipeline.from_pretrained(
|
| 113 |
+
torch_dtype=torch.bfloat16,
|
| 114 |
+
device="cuda",
|
| 115 |
+
model_configs=[
|
| 116 |
+
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
| 117 |
+
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
| 118 |
+
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
| 119 |
+
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
| 120 |
+
],
|
| 121 |
+
)
|
| 122 |
+
|
| 123 |
+
image = pipe(prompt="a cat", seed=0)
|
| 124 |
+
image.save("image.jpg")
|
| 125 |
+
```
|
| 126 |
+
|
| 127 |
+
</details>
|
| 128 |
+
|
| 129 |
+
<details>
|
| 130 |
+
|
| 131 |
+
<summary>模型总览</summary>
|
| 132 |
+
|
| 133 |
+
|模型 ID|额外参数|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|
|
| 134 |
+
|-|-|-|-|-|-|-|-|
|
| 135 |
+
|[FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev.py)|
|
| 136 |
+
|[FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev)||[code](./examples/flux/model_inference/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py)|
|
| 137 |
+
|[FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev)|`kontext_images`|[code](./examples/flux/model_inference/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py)|
|
| 138 |
+
|[FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|
|
| 139 |
+
|[FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py)|
|
| 140 |
+
|[FLUX.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py)|
|
| 141 |
+
|[FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter)|`ipadapter_images`, `ipadapter_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py)|
|
| 142 |
+
|[FLUX.1-dev-InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou)|`infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py)|
|
| 143 |
+
|[FLUX.1-dev-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen)|`eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint`|[code](./examples/flux/model_inference/FLUX.1-dev-EliGen.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py)|-|-|[code](./examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py)|
|
| 144 |
+
|[FLUX.1-dev-LoRA-Encoder](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev)|`lora_encoder_inputs`, `lora_encoder_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py)|-|-|
|
| 145 |
+
|[FLUX.1-dev-LoRA-Fusion-Preview](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py)|-|-|-|-|-|
|
| 146 |
+
|[Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit)|`step1x_reference_image`|[code](./examples/flux/model_inference/Step1X-Edit.py)|[code](./examples/flux/model_inference_low_vram/Step1X-Edit.py)|[code](./examples/flux/model_training/full/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_full/Step1X-Edit.py)|[code](./examples/flux/model_training/lora/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_lora/Step1X-Edit.py)|
|
| 147 |
+
|[FLEX.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview)|`flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop`|[code](./examples/flux/model_inference/FLEX.2-preview.py)|[code](./examples/flux/model_inference_low_vram/FLEX.2-preview.py)|[code](./examples/flux/model_training/full/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_full/FLEX.2-preview.py)|[code](./examples/flux/model_training/lora/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_lora/FLEX.2-preview.py)|
|
| 148 |
+
|[Nexus-Gen](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2)|`nexus_gen_reference_image`|[code](./examples/flux/model_inference/Nexus-Gen-Editing.py)|[code](./examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py)|[code](./examples/flux/model_training/full/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_full/Nexus-Gen.py)|[code](./examples/flux/model_training/lora/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_lora/Nexus-Gen.py)|
|
| 149 |
+
|
| 150 |
+
</details>
|
| 151 |
+
|
| 152 |
+
### Wan 系列
|
| 153 |
+
|
| 154 |
+
详细页面：[./examples/wanvideo/](./examples/wanvideo/)
|
| 155 |
+
|
| 156 |
+
https://github.com/user-attachments/assets/1d66ae74-3b02-40a9-acc3-ea95fc039314
|
| 157 |
+
|
| 158 |
+
<details>
|
| 159 |
+
|
| 160 |
+
<summary>快速开始</summary>
|
| 161 |
+
|
| 162 |
+
```python
|
| 163 |
+
import torch
|
| 164 |
+
from diffsynth import save_video
|
| 165 |
+
from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
|
| 166 |
+
|
| 167 |
+
pipe = WanVideoPipeline.from_pretrained(
|
| 168 |
+
torch_dtype=torch.bfloat16,
|
| 169 |
+
device="cuda",
|
| 170 |
+
model_configs=[
|
| 171 |
+
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
| 172 |
+
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
| 173 |
+
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
| 174 |
+
],
|
| 175 |
+
)
|
| 176 |
+
pipe.enable_vram_management()
|
| 177 |
+
|
| 178 |
+
video = pipe(
|
| 179 |
+
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。",
|
| 180 |
+
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",
|
| 181 |
+
seed=0, tiled=True,
|
| 182 |
+
)
|
| 183 |
+
save_video(video, "video1.mp4", fps=15, quality=5)
|
| 184 |
+
```
|
| 185 |
+
|
| 186 |
+
</details>
|
| 187 |
+
|
| 188 |
+
<details>
|
| 189 |
+
|
| 190 |
+
<summary>模型总览</summary>
|
| 191 |
+
|
| 192 |
+
|模型 ID|额外参数|推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
|
| 193 |
+
|-|-|-|-|-|-|-|
|
| 194 |
+
|[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py)|
|
| 195 |
+
|[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py)|
|
| 196 |
+
|[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py)|
|
| 197 |
+
|[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py)|
|
| 198 |
+
|[Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py)|
|
| 199 |
+
|[Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py)|
|
| 200 |
+
|[Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py)|
|
| 201 |
+
|[Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py)|
|
| 202 |
+
|[PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py)|
|
| 203 |
+
|[PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py)|
|
| 204 |
+
|[PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py)|
|
| 205 |
+
|[PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py)|
|
| 206 |
+
|[PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py)|
|
| 207 |
+
|[PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/examples/wanmodel_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py)|
|
| 208 |
+
|[PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py)|
|
| 209 |
+
|[PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py)|
|
| 210 |
+
|[PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|
|
| 211 |
+
|[PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|
|
| 212 |
+
|[iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py)|
|
| 213 |
+
|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py)|
|
| 214 |
+
|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py)|
|
| 215 |
+
|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|`motion_bucket_id`|[code](./examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)|
|
| 216 |
+
|
| 217 |
+
</details>
|
| 218 |
+
|
| 219 |
+
|
| 220 |
+
|
| 221 |
+
### 更多模型
|
| 222 |
+
|
| 223 |
+
|
| 224 |
+
|
| 225 |
+
<details>
|
| 226 |
+
<summary>图像生成模型</summary>
|
| 227 |
+
|
| 228 |
+
详细页面：[./examples/image_synthesis/](./examples/image_synthesis/)
|
| 229 |
+
|
| 230 |
+
|FLUX|Stable Diffusion 3|
|
| 231 |
+
|-|-|
|
| 232 |
+
|||
|
| 233 |
+
|
| 234 |
+
|Kolors|Hunyuan-DiT|
|
| 235 |
+
|-|-|
|
| 236 |
+
|||
|
| 237 |
+
|
| 238 |
+
|Stable Diffusion|Stable Diffusion XL|
|
| 239 |
+
|-|-|
|
| 240 |
+
|||
|
| 241 |
+
|
| 242 |
+
</details>
|
| 243 |
+
|
| 244 |
+
|
| 245 |
+
|
| 246 |
+
<details>
|
| 247 |
+
<summary>视频生成模型</summary>
|
| 248 |
+
|
| 249 |
+
- HunyuanVideo：[./examples/HunyuanVideo/](./examples/HunyuanVideo/)
|
| 250 |
+
|
| 251 |
+
https://github.com/user-attachments/assets/48dd24bb-0cc6-40d2-88c3-10feed3267e9
|
| 252 |
+
|
| 253 |
+
- StepVideo：[./examples/stepvideo/](./examples/stepvideo/)
|
| 254 |
+
|
| 255 |
+
https://github.com/user-attachments/assets/5954fdaa-a3cf-45a3-bd35-886e3cc4581b
|
| 256 |
+
|
| 257 |
+
- CogVideoX：[./examples/CogVideoX/](./examples/CogVideoX/)
|
| 258 |
+
|
| 259 |
+
https://github.com/user-attachments/assets/26b044c1-4a60-44a4-842f-627ff289d006
|
| 260 |
+
|
| 261 |
+
</details>
|
| 262 |
+
|
| 263 |
+
|
| 264 |
+
|
| 265 |
+
<details>
|
| 266 |
+
<summary>图像质量评估模型</summary>
|
| 267 |
+
|
| 268 |
+
我们集成了一系列图像质量评估模型，这些模型可以用于图像生成模型的评测、对齐训练等场景中。
|
| 269 |
+
|
| 270 |
+
详细页面：[./examples/image_quality_metric/](./examples/image_quality_metric/)
|
| 271 |
+
|
| 272 |
+
* [ImageReward](https://github.com/THUDM/ImageReward)
|
| 273 |
+
* [Aesthetic](https://github.com/christophschuhmann/improved-aesthetic-predictor)
|
| 274 |
+
* [PickScore](https://github.com/yuvalkirstain/pickscore)
|
| 275 |
+
* [CLIP](https://github.com/openai/CLIP)
|
| 276 |
+
* [HPSv2](https://github.com/tgxs002/HPSv2)
|
| 277 |
+
* [HPSv2.1](https://github.com/tgxs002/HPSv2)
|
| 278 |
+
* [MPS](https://github.com/Kwai-Kolors/MPS)
|
| 279 |
+
|
| 280 |
+
</details>
|
| 281 |
+
|
| 282 |
+
|
| 283 |
+
|
| 284 |
+
## 创新成果
|
| 285 |
+
|
| 286 |
+
DiffSynth-Studio 不仅仅是一个工程化的模型框架，更是创新成果的孵化器。
|
| 287 |
+
|
| 288 |
+
<details>
|
| 289 |
+
<summary>Nexus-Gen: 统一架构的图像理解、生成、编辑</summary>
|
| 290 |
+
|
| 291 |
+
- 详细页面：https://github.com/modelscope/Nexus-Gen
|
| 292 |
+
- 论文：[Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356)
|
| 293 |
+
- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2)
|
| 294 |
+
- 数据集：[ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset)
|
| 295 |
+
- 在线体验：[ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen)
|
| 296 |
+
|
| 297 |
+
|
| 298 |
+
|
| 299 |
+
</details>
|
| 300 |
+
|
| 301 |
+
|
| 302 |
+
|
| 303 |
+
<details>
|
| 304 |
+
<summary>ArtAug: 图像生成模型的美学提升</summary>
|
| 305 |
+
|
| 306 |
+
- 详细页面：[./examples/ArtAug/](./examples/ArtAug/)
|
| 307 |
+
- 论文：[ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction](https://arxiv.org/abs/2412.12888)
|
| 308 |
+
- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1)
|
| 309 |
+
- 在线体验：[ModelScope AIGC Tab](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0)
|
| 310 |
+
|
| 311 |
+
|FLUX.1-dev|FLUX.1-dev + ArtAug LoRA|
|
| 312 |
+
|-|-|
|
| 313 |
+
|||
|
| 314 |
+
|
| 315 |
+
</details>
|
| 316 |
+
|
| 317 |
+
|
| 318 |
+
|
| 319 |
+
<details>
|
| 320 |
+
|
| 321 |
+
<summary>EliGen: 精准的图像分区控制</summary>
|
| 322 |
+
|
| 323 |
+
- 详细页面：[./examples/EntityControl/](./examples/EntityControl/)
|
| 324 |
+
- 论文：[EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097)
|
| 325 |
+
- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen)
|
| 326 |
+
- 在线体验：[ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen)
|
| 327 |
+
- 数据集：[EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)
|
| 328 |
+
|
| 329 |
+
|实体控制区域|生成图像|
|
| 330 |
+
|-|-|
|
| 331 |
+
|||
|
| 332 |
+
|
| 333 |
+
</details>
|
| 334 |
+
|
| 335 |
+
|
| 336 |
+
|
| 337 |
+
<details>
|
| 338 |
+
|
| 339 |
+
<summary>ExVideo: 视频生成模型的扩展训练</summary>
|
| 340 |
+
|
| 341 |
+
- 项目页面：[Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/)
|
| 342 |
+
- 论文：[ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning](https://arxiv.org/abs/2406.14130)
|
| 343 |
+
- 代码样例：[./examples/ExVideo/](./examples/ExVideo/)
|
| 344 |
+
- 模型：[ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1)
|
| 345 |
+
|
| 346 |
+
https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc
|
| 347 |
+
|
| 348 |
+
</details>
|
| 349 |
+
|
| 350 |
+
|
| 351 |
+
|
| 352 |
+
<details>
|
| 353 |
+
|
| 354 |
+
<summary>Diffutoon: 高分辨率动漫风格视频渲染</summary>
|
| 355 |
+
|
| 356 |
+
- 项目页面：[Project Page](https://ecnu-cilab.github.io/DiffutoonProjectPage/)
|
| 357 |
+
- 论文：[Diffutoon: High-Resolution Editable Toon Shading via Diffusion Models](https://arxiv.org/abs/2401.16224)
|
| 358 |
+
- 代码样例：[./examples/Diffutoon/](./examples/Diffutoon/)
|
| 359 |
+
|
| 360 |
+
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd
|
| 361 |
+
|
| 362 |
+
</details>
|
| 363 |
+
|
| 364 |
+
|
| 365 |
+
|
| 366 |
+
<details>
|
| 367 |
+
|
| 368 |
+
<summary>DiffSynth: 本项目的初代版本</summary>
|
| 369 |
+
|
| 370 |
+
- 项目页面：[Project Page](https://ecnu-cilab.github.io/DiffSynth.github.io/)
|
| 371 |
+
- 论文：[DiffSynth: Latent In-Iteration Deflickering for Realistic Video Synthesis](https://arxiv.org/abs/2308.03463)
|
| 372 |
+
- 代码样例：[./examples/diffsynth/](./examples/diffsynth/)
|
| 373 |
+
|
| 374 |
+
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea
|
| 375 |
+
|
| 376 |
+
</details>
|
| 377 |
+
|
| 378 |
+
|
| 379 |
+
|
| 380 |
+
## 更新历史
|
| 381 |
+
- **2025年8月1日** [FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) 开源，这是一个专注于美学摄影的文生图模型。我们第一时间提供了全方位支持，包括低显存逐层 offload、LoRA 训练、全量训练。详细信息请参考 [./examples/flux/](./examples/flux/)。
|
| 382 |
+
|
| 383 |
+
- **2025年8月4日** 🔥 Qwen-Image 开源，欢迎图像生成模型家族新成员！
|
| 384 |
+
|
| 385 |
+
- **2025年7月28日** Wan 2.2 开源，我们第一时间提供了全方位支持，包括低显存逐层 offload、FP8 量化、序列并行、LoRA 训练、全量训练。详细信息请参考 [./examples/wanvideo/](./examples/wanvideo/)。
|
| 386 |
+
|
| 387 |
+
- **2025年7月11日** 我们提出 Nexus-Gen，一个将大语言模型（LLM）的语言推理能力与扩散模型的图像生成能力相结合的统一框架。该框架支持无缝的图像理解、生成和编辑任务。
|
| 388 |
+
- 论文: [Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356)
|
| 389 |
+
- Github 仓库: https://github.com/modelscope/Nexus-Gen
|
| 390 |
+
- 模型: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2)
|
| 391 |
+
- 训练数据集: [ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset)
|
| 392 |
+
- 在线体验: [ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen)
|
| 393 |
+
|
| 394 |
+
- **2025年6月15日** ModelScope 官方评测框架 [EvalScope](https://github.com/modelscope/evalscope) 现已支持文生图生成评测。请参考[最佳实践](https://evalscope.readthedocs.io/zh-cn/latest/best_practice/t2i_eval.html)指南进行尝试。
|
| 395 |
+
|
| 396 |
+
- **2025年3月25日** 我们的新开源项目 [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine) 现已开源！专注于稳定的模型部署，面向工业界，提供更好的工程支持、更高的计算性能和更稳定的功能。
|
| 397 |
+
|
| 398 |
+
<details>
|
| 399 |
+
<summary>更多</summary>
|
| 400 |
+
|
| 401 |
+
- **2025年3月31日** 我们支持 InfiniteYou，一种用于 FLUX 的人脸特征保留方法。更多细节请参考 [./examples/InfiniteYou/](./examples/InfiniteYou/)。
|
| 402 |
+
|
| 403 |
+
- **2025年3月13日** 我们支持 HunyuanVideo-I2V，即腾讯开源的 HunyuanVideo 的图像到视频生成版本。更多细节请参考 [./examples/HunyuanVideo/](./examples/HunyuanVideo/)。
|
| 404 |
+
|
| 405 |
+
- **2025年2月25日** 我们支持 Wan-Video，这是阿里巴巴开源的一系列最先进的视频合成模型。详见 [./examples/wanvideo/](./examples/wanvideo/)。
|
| 406 |
+
|
| 407 |
+
- **2025年2月17日** 我们支持 [StepVideo](https://modelscope.cn/models/stepfun-ai/stepvideo-t2v/summary)！先进的视频合成模型！详见 [./examples/stepvideo](./examples/stepvideo/)。
|
| 408 |
+
|
| 409 |
+
- **2024年12月31日** 我们提出 EliGen，一种用于精确实体级别控制的文本到图像生成的新框架，并辅以修复融合管道，将其能力扩展到图像修复任务。EliGen 可以无缝集成现有的社区模型，如 IP-Adapter 和 In-Context LoRA，提升其通用性。更多详情，请见 [./examples/EntityControl](./examples/EntityControl/)。
|
| 410 |
+
- 论文: [EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097)
|
| 411 |
+
- 模型: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen)
|
| 412 |
+
- 在线体验: [ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen)
|
| 413 |
+
- 训练数据集: [EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)
|
| 414 |
+
|
| 415 |
+
- **2024年12月19日** 我们为 HunyuanVideo 实现了高级显存管理，使得在 24GB 显存下可以生成分辨率为 129x720x1280 的视频，或在仅 6GB 显存下生成分辨率为 129x512x384 的视频。更多细节请参考 [./examples/HunyuanVideo/](./examples/HunyuanVideo/)。
|
| 416 |
+
|
| 417 |
+
- **2024年12月18日** 我们提出 ArtAug，一种通过合成-理解交互来改进文生图模型的方法。我们以 LoRA 格式为 FLUX.1-dev 训练了一个 ArtAug 增强模块。该模型将 Qwen2-VL-72B 的美学理解融入 FLUX.1-dev，从而提升了生成图像的质量。
|
| 418 |
+
- 论文: https://arxiv.org/abs/2412.12888
|
| 419 |
+
- 示例: https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/ArtAug
|
| 420 |
+
- 模型: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1)
|
| 421 |
+
- 演示: [ModelScope](https://modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0), HuggingFace (即将上线)
|
| 422 |
+
|
| 423 |
+
- **2024年10月25日** 我们提供了广泛的 FLUX ControlNet 支持。该项目支持许多不同的 ControlNet 模型，并且可以自由组合，即使它们的结构不同。此外，ControlNet 模型兼容高分辨率优化和分区控制技术，能够实现非常强大的可控图像生成。详见 [`./examples/ControlNet/`](./examples/ControlNet/)。
|
| 424 |
+
|
| 425 |
+
- **2024年10月8日** 我们发布了基于 CogVideoX-5B 和 ExVideo 的扩展 LoRA。您可以从 [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) 或 [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) 下载此模型。
|
| 426 |
+
|
| 427 |
+
- **2024年8月22日** 本项目现已支持 CogVideoX-5B。详见 [此处](/examples/video_synthesis/)。我们为这个文生视频模型提供了几个有趣的功能，包括：
|
| 428 |
+
- 文本到视频
|
| 429 |
+
- 视频编辑
|
| 430 |
+
- 自我超分
|
| 431 |
+
- 视频插帧
|
| 432 |
+
|
| 433 |
+
- **2024年8月22日** 我们实现了一个有趣的画笔功能，支持所有文生图模型。现在，您可以在 AI 的辅助下使用画笔创作惊艳的图像了！
|
| 434 |
+
- 在我们的 [WebUI](#usage-in-webui) 中使用它。
|
| 435 |
+
|
| 436 |
+
- **2024年8月21日** DiffSynth-Studio 现已支持 FLUX。
|
| 437 |
+
- 启用 CFG 和高分辨率修复以提升视觉质量。详见 [此处](/examples/image_synthesis/README.md)
|
| 438 |
+
- LoRA、ControlNet 和其他附加模型将很快推出。
|
| 439 |
+
|
| 440 |
+
- **2024年6月21日** 我们提出 ExVideo，一种旨在增强视频生成模型能力的后训练微调技术。我们将 Stable Video Diffusion 进行了扩展，实现了长达 128 帧的长视频生成。
|
| 441 |
+
- [项目页面](https://ecnu-cilab.github.io/ExVideoProjectPage/)
|
| 442 |
+
- 源代码已在此仓库中发布。详见 [`examples/ExVideo`](./examples/ExVideo/)。
|
| 443 |
+
- 模型已发布于 [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) 和 [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1)。
|
| 444 |
+
- 技术报告已发布于 [arXiv](https://arxiv.org/abs/2406.14130)。
|
| 445 |
+
- 您可以在此 [演示](https://huggingface.co/spaces/modelscope/ExVideo-SVD-128f-v1) 中试用 ExVideo！
|
| 446 |
+
|
| 447 |
+
- **2024年6月13日** DiffSynth Studio 已迁移至 ModelScope。开发团队也从“我”转变为“我们”。当然，我仍会参与后续的开发和维护工作。
|
| 448 |
+
|
| 449 |
+
- **2024年1月29日** 我们提出 Diffutoon，这是一个出色的卡通着色解决方案。
|
| 450 |
+
- [项目页面](https://ecnu-cilab.github.io/DiffutoonProjectPage/)
|
| 451 |
+
- 源代码已在此项目中发布。
|
| 452 |
+
- 技术报告（IJCAI 2024）已发布于 [arXiv](https://arxiv.org/abs/2401.16224)。
|
| 453 |
+
|
| 454 |
+
- **2023年12月8日** 我们决定启动一个新项目，旨在释放扩散模型的潜力，尤其是在视频合成方面。该项目的开发工作正式开始。
|
| 455 |
+
|
| 456 |
+
- **2023年11月15日** 我们提出 FastBlend，一种强大的视频去闪烁算法。
|
| 457 |
+
- sd-webui 扩展已发布于 [GitHub](https://github.com/Artiprocher/sd-webui-fastblend)。
|
| 458 |
+
- 演示视频已在 Bilibili 上展示，包含三个任务：
|
| 459 |
+
- [视频去闪烁](https://www.bilibili.com/video/BV1d94y1W7PE)
|
| 460 |
+
- [视频插帧](https://www.bilibili.com/video/BV1Lw411m71p)
|
| 461 |
+
- [图像驱动的视频渲染](https://www.bilibili.com/video/BV1RB4y1Z7LF)
|
| 462 |
+
- 技术报告已发布于 [arXiv](https://arxiv.org/abs/2311.09265)。
|
| 463 |
+
- 其他用户开发的非官方 ComfyUI 扩展已发布于 [GitHub](https://github.com/AInseven/ComfyUI-fastblend)。
|
| 464 |
+
|
| 465 |
+
- **2023年10月1日** 我们发布了该项目的早期版本，名为 FastSDXL。这是构建一个扩散引擎的初步尝试。
|
| 466 |
+
- 源代码已发布于 [GitHub](https://github.com/Artiprocher/FastSDXL)。
|
| 467 |
+
- FastSDXL 包含一个可训练的 OLSS 调度器，以提高效率。
|
| 468 |
+
- OLSS 的原始仓库位于 [此处](https://github.com/alibaba/EasyNLP/tree/master/diffusion/olss_scheduler)。
|
| 469 |
+
- 技术报告（CIKM 2023）已发布于 [arXiv](https://arxiv.org/abs/2305.14677)。
|
| 470 |
+
- 演示视频已发布于 [Bilibili](https://www.bilibili.com/video/BV1w8411y7uj)。
|
| 471 |
+
- 由于 OLSS 需要额外训练，我们未在本项目中实现它。
|
| 472 |
+
|
| 473 |
+
- **2023年8月29日** 我们提出 DiffSynth，一个视频合成框架。
|
| 474 |
+
- [项目页面](https://ecnu-cilab.github.io/DiffSynth.github.io/)。
|
| 475 |
+
- 源代码已发布在 [EasyNLP](https://github.com/alibaba/EasyNLP/tree/master/diffusion/DiffSynth)。
|
| 476 |
+
- 技术报告（ECML PKDD 2024）已发布于 [arXiv](https://arxiv.org/abs/2308.03463)。
|
| 477 |
+
|
| 478 |
+
</details>
|
diffsynth-studio/apps/gradio/DiffSynth_Studio.py
ADDED
|
@@ -0,0 +1,252 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import gradio as gr
|
| 2 |
+
from diffsynth import ModelManager, SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline, FluxImagePipeline
|
| 3 |
+
import os, torch
|
| 4 |
+
from PIL import Image
|
| 5 |
+
import numpy as np
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
config = {
|
| 9 |
+
"model_config": {
|
| 10 |
+
"Stable Diffusion": {
|
| 11 |
+
"model_folder": "models/stable_diffusion",
|
| 12 |
+
"pipeline_class": SDImagePipeline,
|
| 13 |
+
"default_parameters": {
|
| 14 |
+
"cfg_scale": 7.0,
|
| 15 |
+
"height": 512,
|
| 16 |
+
"width": 512,
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"Stable Diffusion XL": {
|
| 20 |
+
"model_folder": "models/stable_diffusion_xl",
|
| 21 |
+
"pipeline_class": SDXLImagePipeline,
|
| 22 |
+
"default_parameters": {
|
| 23 |
+
"cfg_scale": 7.0,
|
| 24 |
+
}
|
| 25 |
+
},
|
| 26 |
+
"Stable Diffusion 3": {
|
| 27 |
+
"model_folder": "models/stable_diffusion_3",
|
| 28 |
+
"pipeline_class": SD3ImagePipeline,
|
| 29 |
+
"default_parameters": {
|
| 30 |
+
"cfg_scale": 7.0,
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"Stable Diffusion XL Turbo": {
|
| 34 |
+
"model_folder": "models/stable_diffusion_xl_turbo",
|
| 35 |
+
"pipeline_class": SDXLImagePipeline,
|
| 36 |
+
"default_parameters": {
|
| 37 |
+
"negative_prompt": "",
|
| 38 |
+
"cfg_scale": 1.0,
|
| 39 |
+
"num_inference_steps": 1,
|
| 40 |
+
"height": 512,
|
| 41 |
+
"width": 512,
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
"Kolors": {
|
| 45 |
+
"model_folder": "models/kolors",
|
| 46 |
+
"pipeline_class": SDXLImagePipeline,
|
| 47 |
+
"default_parameters": {
|
| 48 |
+
"cfg_scale": 7.0,
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"HunyuanDiT": {
|
| 52 |
+
"model_folder": "models/HunyuanDiT",
|
| 53 |
+
"pipeline_class": HunyuanDiTImagePipeline,
|
| 54 |
+
"default_parameters": {
|
| 55 |
+
"cfg_scale": 7.0,
|
| 56 |
+
}
|
| 57 |
+
},
|
| 58 |
+
"FLUX": {
|
| 59 |
+
"model_folder": "models/FLUX",
|
| 60 |
+
"pipeline_class": FluxImagePipeline,
|
| 61 |
+
"default_parameters": {
|
| 62 |
+
"cfg_scale": 1.0,
|
| 63 |
+
}
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"max_num_painter_layers": 8,
|
| 67 |
+
"max_num_model_cache": 1,
|
| 68 |
+
}
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
def load_model_list(model_type):
|
| 72 |
+
if model_type is None:
|
| 73 |
+
return []
|
| 74 |
+
folder = config["model_config"][model_type]["model_folder"]
|
| 75 |
+
file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")]
|
| 76 |
+
if model_type in ["HunyuanDiT", "Kolors", "FLUX"]:
|
| 77 |
+
file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))]
|
| 78 |
+
file_list = sorted(file_list)
|
| 79 |
+
return file_list
|
| 80 |
+
|
| 81 |
+
|
| 82 |
+
def load_model(model_type, model_path):
|
| 83 |
+
global model_dict
|
| 84 |
+
model_key = f"{model_type}:{model_path}"
|
| 85 |
+
if model_key in model_dict:
|
| 86 |
+
return model_dict[model_key]
|
| 87 |
+
model_path = os.path.join(config["model_config"][model_type]["model_folder"], model_path)
|
| 88 |
+
model_manager = ModelManager()
|
| 89 |
+
if model_type == "HunyuanDiT":
|
| 90 |
+
model_manager.load_models([
|
| 91 |
+
os.path.join(model_path, "clip_text_encoder/pytorch_model.bin"),
|
| 92 |
+
os.path.join(model_path, "mt5/pytorch_model.bin"),
|
| 93 |
+
os.path.join(model_path, "model/pytorch_model_ema.pt"),
|
| 94 |
+
os.path.join(model_path, "sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"),
|
| 95 |
+
])
|
| 96 |
+
elif model_type == "Kolors":
|
| 97 |
+
model_manager.load_models([
|
| 98 |
+
os.path.join(model_path, "text_encoder"),
|
| 99 |
+
os.path.join(model_path, "unet/diffusion_pytorch_model.safetensors"),
|
| 100 |
+
os.path.join(model_path, "vae/diffusion_pytorch_model.safetensors"),
|
| 101 |
+
])
|
| 102 |
+
elif model_type == "FLUX":
|
| 103 |
+
model_manager.torch_dtype = torch.bfloat16
|
| 104 |
+
file_list = [
|
| 105 |
+
os.path.join(model_path, "text_encoder/model.safetensors"),
|
| 106 |
+
os.path.join(model_path, "text_encoder_2"),
|
| 107 |
+
]
|
| 108 |
+
for file_name in os.listdir(model_path):
|
| 109 |
+
if file_name.endswith(".safetensors"):
|
| 110 |
+
file_list.append(os.path.join(model_path, file_name))
|
| 111 |
+
model_manager.load_models(file_list)
|
| 112 |
+
else:
|
| 113 |
+
model_manager.load_model(model_path)
|
| 114 |
+
pipe = config["model_config"][model_type]["pipeline_class"].from_model_manager(model_manager)
|
| 115 |
+
while len(model_dict) + 1 > config["max_num_model_cache"]:
|
| 116 |
+
key = next(iter(model_dict.keys()))
|
| 117 |
+
model_manager_to_release, _ = model_dict[key]
|
| 118 |
+
model_manager_to_release.to("cpu")
|
| 119 |
+
del model_dict[key]
|
| 120 |
+
torch.cuda.empty_cache()
|
| 121 |
+
model_dict[model_key] = model_manager, pipe
|
| 122 |
+
return model_manager, pipe
|
| 123 |
+
|
| 124 |
+
|
| 125 |
+
model_dict = {}
|
| 126 |
+
|
| 127 |
+
with gr.Blocks() as app:
|
| 128 |
+
gr.Markdown("# DiffSynth-Studio Painter")
|
| 129 |
+
with gr.Row():
|
| 130 |
+
with gr.Column(scale=382, min_width=100):
|
| 131 |
+
|
| 132 |
+
with gr.Accordion(label="Model"):
|
| 133 |
+
model_type = gr.Dropdown(choices=[i for i in config["model_config"]], label="Model type")
|
| 134 |
+
model_path = gr.Dropdown(choices=[], interactive=True, label="Model path")
|
| 135 |
+
|
| 136 |
+
@gr.on(inputs=model_type, outputs=model_path, triggers=model_type.change)
|
| 137 |
+
def model_type_to_model_path(model_type):
|
| 138 |
+
return gr.Dropdown(choices=load_model_list(model_type))
|
| 139 |
+
|
| 140 |
+
with gr.Accordion(label="Prompt"):
|
| 141 |
+
prompt = gr.Textbox(label="Prompt", lines=3)
|
| 142 |
+
negative_prompt = gr.Textbox(label="Negative prompt", lines=1)
|
| 143 |
+
cfg_scale = gr.Slider(minimum=1.0, maximum=10.0, value=7.0, step=0.1, interactive=True, label="Classifier-free guidance scale")
|
| 144 |
+
embedded_guidance = gr.Slider(minimum=0.0, maximum=10.0, value=0.0, step=0.1, interactive=True, label="Embedded guidance scale (only for FLUX)")
|
| 145 |
+
|
| 146 |
+
with gr.Accordion(label="Image"):
|
| 147 |
+
num_inference_steps = gr.Slider(minimum=1, maximum=100, value=20, step=1, interactive=True, label="Inference steps")
|
| 148 |
+
height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height")
|
| 149 |
+
width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width")
|
| 150 |
+
with gr.Column():
|
| 151 |
+
use_fixed_seed = gr.Checkbox(value=True, interactive=False, label="Use fixed seed")
|
| 152 |
+
seed = gr.Number(minimum=0, maximum=10**9, value=0, interactive=True, label="Random seed", show_label=False)
|
| 153 |
+
|
| 154 |
+
@gr.on(
|
| 155 |
+
inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width],
|
| 156 |
+
outputs=[prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width],
|
| 157 |
+
triggers=model_path.change
|
| 158 |
+
)
|
| 159 |
+
def model_path_to_default_params(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width):
|
| 160 |
+
load_model(model_type, model_path)
|
| 161 |
+
cfg_scale = config["model_config"][model_type]["default_parameters"].get("cfg_scale", cfg_scale)
|
| 162 |
+
embedded_guidance = config["model_config"][model_type]["default_parameters"].get("embedded_guidance", embedded_guidance)
|
| 163 |
+
num_inference_steps = config["model_config"][model_type]["default_parameters"].get("num_inference_steps", num_inference_steps)
|
| 164 |
+
height = config["model_config"][model_type]["default_parameters"].get("height", height)
|
| 165 |
+
width = config["model_config"][model_type]["default_parameters"].get("width", width)
|
| 166 |
+
return prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width
|
| 167 |
+
|
| 168 |
+
|
| 169 |
+
with gr.Column(scale=618, min_width=100):
|
| 170 |
+
with gr.Accordion(label="Painter"):
|
| 171 |
+
enable_local_prompt_list = []
|
| 172 |
+
local_prompt_list = []
|
| 173 |
+
mask_scale_list = []
|
| 174 |
+
canvas_list = []
|
| 175 |
+
for painter_layer_id in range(config["max_num_painter_layers"]):
|
| 176 |
+
with gr.Tab(label=f"Layer {painter_layer_id}"):
|
| 177 |
+
enable_local_prompt = gr.Checkbox(label="Enable", value=False, key=f"enable_local_prompt_{painter_layer_id}")
|
| 178 |
+
local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}")
|
| 179 |
+
mask_scale = gr.Slider(minimum=0.0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Mask scale", key=f"mask_scale_{painter_layer_id}")
|
| 180 |
+
canvas = gr.ImageEditor(canvas_size=(512, 1), sources=None, layers=False, interactive=True, image_mode="RGBA",
|
| 181 |
+
brush=gr.Brush(default_size=100, default_color="#000000", colors=["#000000"]),
|
| 182 |
+
label="Painter", key=f"canvas_{painter_layer_id}")
|
| 183 |
+
@gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear, enable_local_prompt.change], show_progress="hidden")
|
| 184 |
+
def resize_canvas(height, width, canvas):
|
| 185 |
+
h, w = canvas["background"].shape[:2]
|
| 186 |
+
if h != height or width != w:
|
| 187 |
+
return np.ones((height, width, 3), dtype=np.uint8) * 255
|
| 188 |
+
else:
|
| 189 |
+
return canvas
|
| 190 |
+
|
| 191 |
+
enable_local_prompt_list.append(enable_local_prompt)
|
| 192 |
+
local_prompt_list.append(local_prompt)
|
| 193 |
+
mask_scale_list.append(mask_scale)
|
| 194 |
+
canvas_list.append(canvas)
|
| 195 |
+
with gr.Accordion(label="Results"):
|
| 196 |
+
run_button = gr.Button(value="Generate", variant="primary")
|
| 197 |
+
output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil")
|
| 198 |
+
with gr.Row():
|
| 199 |
+
with gr.Column():
|
| 200 |
+
output_to_painter_button = gr.Button(value="Set as painter's background")
|
| 201 |
+
with gr.Column():
|
| 202 |
+
output_to_input_button = gr.Button(value="Set as input image")
|
| 203 |
+
painter_background = gr.State(None)
|
| 204 |
+
input_background = gr.State(None)
|
| 205 |
+
@gr.on(
|
| 206 |
+
inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, seed] + enable_local_prompt_list + local_prompt_list + mask_scale_list + canvas_list,
|
| 207 |
+
outputs=[output_image],
|
| 208 |
+
triggers=run_button.click
|
| 209 |
+
)
|
| 210 |
+
def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, seed, *args, progress=gr.Progress()):
|
| 211 |
+
_, pipe = load_model(model_type, model_path)
|
| 212 |
+
input_params = {
|
| 213 |
+
"prompt": prompt,
|
| 214 |
+
"negative_prompt": negative_prompt,
|
| 215 |
+
"cfg_scale": cfg_scale,
|
| 216 |
+
"num_inference_steps": num_inference_steps,
|
| 217 |
+
"height": height,
|
| 218 |
+
"width": width,
|
| 219 |
+
"progress_bar_cmd": progress.tqdm,
|
| 220 |
+
}
|
| 221 |
+
if isinstance(pipe, FluxImagePipeline):
|
| 222 |
+
input_params["embedded_guidance"] = embedded_guidance
|
| 223 |
+
enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list = (
|
| 224 |
+
args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]],
|
| 225 |
+
args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]],
|
| 226 |
+
args[2 * config["max_num_painter_layers"]: 3 * config["max_num_painter_layers"]],
|
| 227 |
+
args[3 * config["max_num_painter_layers"]: 4 * config["max_num_painter_layers"]]
|
| 228 |
+
)
|
| 229 |
+
local_prompts, masks, mask_scales = [], [], []
|
| 230 |
+
for enable_local_prompt, local_prompt, mask_scale, canvas in zip(
|
| 231 |
+
enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list
|
| 232 |
+
):
|
| 233 |
+
if enable_local_prompt:
|
| 234 |
+
local_prompts.append(local_prompt)
|
| 235 |
+
masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB"))
|
| 236 |
+
mask_scales.append(mask_scale)
|
| 237 |
+
input_params.update({
|
| 238 |
+
"local_prompts": local_prompts,
|
| 239 |
+
"masks": masks,
|
| 240 |
+
"mask_scales": mask_scales,
|
| 241 |
+
})
|
| 242 |
+
torch.manual_seed(seed)
|
| 243 |
+
image = pipe(**input_params)
|
| 244 |
+
return image
|
| 245 |
+
|
| 246 |
+
@gr.on(inputs=[output_image] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click)
|
| 247 |
+
def send_output_to_painter_background(output_image, *canvas_list):
|
| 248 |
+
for canvas in canvas_list:
|
| 249 |
+
h, w = canvas["background"].shape[:2]
|
| 250 |
+
canvas["background"] = output_image.resize((w, h))
|
| 251 |
+
return tuple(canvas_list)
|
| 252 |
+
app.launch()
|
diffsynth-studio/apps/gradio/entity_level_control.py
ADDED
|
@@ -0,0 +1,390 @@
|
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|
|
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|
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|
|
|
|
|
|
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|
|
|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import torch
|
| 3 |
+
import numpy as np
|
| 4 |
+
from PIL import Image, ImageDraw, ImageFont
|
| 5 |
+
import random
|
| 6 |
+
import json
|
| 7 |
+
import gradio as gr
|
| 8 |
+
from diffsynth import ModelManager, FluxImagePipeline, download_customized_models
|
| 9 |
+
from modelscope import dataset_snapshot_download
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/entity_control/*")
|
| 13 |
+
example_json = 'data/examples/eligen/entity_control/ui_examples.json'
|
| 14 |
+
with open(example_json, 'r') as f:
|
| 15 |
+
examples = json.load(f)['examples']
|
| 16 |
+
|
| 17 |
+
for idx in range(len(examples)):
|
| 18 |
+
example_id = examples[idx]['example_id']
|
| 19 |
+
entity_prompts = examples[idx]['local_prompt_list']
|
| 20 |
+
examples[idx]['mask_lists'] = [Image.open(f"data/examples/eligen/entity_control/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))]
|
| 21 |
+
|
| 22 |
+
def create_canvas_data(background, masks):
|
| 23 |
+
if background.shape[-1] == 3:
|
| 24 |
+
background = np.dstack([background, np.full(background.shape[:2], 255, dtype=np.uint8)])
|
| 25 |
+
layers = []
|
| 26 |
+
for mask in masks:
|
| 27 |
+
if mask is not None:
|
| 28 |
+
mask_single_channel = mask if mask.ndim == 2 else mask[..., 0]
|
| 29 |
+
layer = np.zeros((mask_single_channel.shape[0], mask_single_channel.shape[1], 4), dtype=np.uint8)
|
| 30 |
+
layer[..., -1] = mask_single_channel
|
| 31 |
+
layers.append(layer)
|
| 32 |
+
else:
|
| 33 |
+
layers.append(np.zeros_like(background))
|
| 34 |
+
|
| 35 |
+
composite = background.copy()
|
| 36 |
+
for layer in layers:
|
| 37 |
+
if layer.size > 0:
|
| 38 |
+
composite = np.where(layer[..., -1:] > 0, layer, composite)
|
| 39 |
+
return {
|
| 40 |
+
"background": background,
|
| 41 |
+
"layers": layers,
|
| 42 |
+
"composite": composite,
|
| 43 |
+
}
|
| 44 |
+
|
| 45 |
+
def load_example(load_example_button):
|
| 46 |
+
example_idx = int(load_example_button.split()[-1]) - 1
|
| 47 |
+
example = examples[example_idx]
|
| 48 |
+
result = [
|
| 49 |
+
50,
|
| 50 |
+
example["global_prompt"],
|
| 51 |
+
example["negative_prompt"],
|
| 52 |
+
example["seed"],
|
| 53 |
+
*example["local_prompt_list"],
|
| 54 |
+
]
|
| 55 |
+
num_entities = len(example["local_prompt_list"])
|
| 56 |
+
result += [""] * (config["max_num_painter_layers"] - num_entities)
|
| 57 |
+
masks = []
|
| 58 |
+
for mask in example["mask_lists"]:
|
| 59 |
+
mask_single_channel = np.array(mask.convert("L"))
|
| 60 |
+
masks.append(mask_single_channel)
|
| 61 |
+
for _ in range(config["max_num_painter_layers"] - len(masks)):
|
| 62 |
+
blank_mask = np.zeros_like(masks[0]) if masks else np.zeros((512, 512), dtype=np.uint8)
|
| 63 |
+
masks.append(blank_mask)
|
| 64 |
+
background = np.ones((masks[0].shape[0], masks[0].shape[1], 4), dtype=np.uint8) * 255
|
| 65 |
+
canvas_data_list = []
|
| 66 |
+
for mask in masks:
|
| 67 |
+
canvas_data = create_canvas_data(background, [mask])
|
| 68 |
+
canvas_data_list.append(canvas_data)
|
| 69 |
+
result.extend(canvas_data_list)
|
| 70 |
+
return result
|
| 71 |
+
|
| 72 |
+
def save_mask_prompts(masks, mask_prompts, global_prompt, seed=0, random_dir='0000000'):
|
| 73 |
+
save_dir = os.path.join('workdirs/tmp_mask', random_dir)
|
| 74 |
+
print(f'save to {save_dir}')
|
| 75 |
+
os.makedirs(save_dir, exist_ok=True)
|
| 76 |
+
for i, mask in enumerate(masks):
|
| 77 |
+
save_path = os.path.join(save_dir, f'{i}.png')
|
| 78 |
+
mask.save(save_path)
|
| 79 |
+
sample = {
|
| 80 |
+
"global_prompt": global_prompt,
|
| 81 |
+
"mask_prompts": mask_prompts,
|
| 82 |
+
"seed": seed,
|
| 83 |
+
}
|
| 84 |
+
with open(os.path.join(save_dir, f"prompts.json"), 'w') as f:
|
| 85 |
+
json.dump(sample, f, indent=4)
|
| 86 |
+
|
| 87 |
+
def visualize_masks(image, masks, mask_prompts, font_size=35, use_random_colors=False):
|
| 88 |
+
# Create a blank image for overlays
|
| 89 |
+
overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
|
| 90 |
+
colors = [
|
| 91 |
+
(165, 238, 173, 80),
|
| 92 |
+
(76, 102, 221, 80),
|
| 93 |
+
(221, 160, 77, 80),
|
| 94 |
+
(204, 93, 71, 80),
|
| 95 |
+
(145, 187, 149, 80),
|
| 96 |
+
(134, 141, 172, 80),
|
| 97 |
+
(157, 137, 109, 80),
|
| 98 |
+
(153, 104, 95, 80),
|
| 99 |
+
(165, 238, 173, 80),
|
| 100 |
+
(76, 102, 221, 80),
|
| 101 |
+
(221, 160, 77, 80),
|
| 102 |
+
(204, 93, 71, 80),
|
| 103 |
+
(145, 187, 149, 80),
|
| 104 |
+
(134, 141, 172, 80),
|
| 105 |
+
(157, 137, 109, 80),
|
| 106 |
+
(153, 104, 95, 80),
|
| 107 |
+
]
|
| 108 |
+
# Generate random colors for each mask
|
| 109 |
+
if use_random_colors:
|
| 110 |
+
colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
|
| 111 |
+
# Font settings
|
| 112 |
+
try:
|
| 113 |
+
font = ImageFont.truetype("arial", font_size) # Adjust as needed
|
| 114 |
+
except IOError:
|
| 115 |
+
font = ImageFont.load_default(font_size)
|
| 116 |
+
# Overlay each mask onto the overlay image
|
| 117 |
+
for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
|
| 118 |
+
if mask is None:
|
| 119 |
+
continue
|
| 120 |
+
# Convert mask to RGBA mode
|
| 121 |
+
mask_rgba = mask.convert('RGBA')
|
| 122 |
+
mask_data = mask_rgba.getdata()
|
| 123 |
+
new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
|
| 124 |
+
mask_rgba.putdata(new_data)
|
| 125 |
+
# Draw the mask prompt text on the mask
|
| 126 |
+
draw = ImageDraw.Draw(mask_rgba)
|
| 127 |
+
mask_bbox = mask.getbbox() # Get the bounding box of the mask
|
| 128 |
+
if mask_bbox is None:
|
| 129 |
+
continue
|
| 130 |
+
text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position
|
| 131 |
+
draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
|
| 132 |
+
# Alpha composite the overlay with this mask
|
| 133 |
+
overlay = Image.alpha_composite(overlay, mask_rgba)
|
| 134 |
+
# Composite the overlay onto the original image
|
| 135 |
+
result = Image.alpha_composite(image.convert('RGBA'), overlay)
|
| 136 |
+
return result
|
| 137 |
+
|
| 138 |
+
config = {
|
| 139 |
+
"model_config": {
|
| 140 |
+
"FLUX": {
|
| 141 |
+
"model_folder": "models/FLUX",
|
| 142 |
+
"pipeline_class": FluxImagePipeline,
|
| 143 |
+
"default_parameters": {
|
| 144 |
+
"cfg_scale": 3.0,
|
| 145 |
+
"embedded_guidance": 3.5,
|
| 146 |
+
"num_inference_steps": 30,
|
| 147 |
+
}
|
| 148 |
+
},
|
| 149 |
+
},
|
| 150 |
+
"max_num_painter_layers": 8,
|
| 151 |
+
"max_num_model_cache": 1,
|
| 152 |
+
}
|
| 153 |
+
|
| 154 |
+
model_dict = {}
|
| 155 |
+
|
| 156 |
+
def load_model(model_type='FLUX', model_path='FLUX.1-dev'):
|
| 157 |
+
global model_dict
|
| 158 |
+
model_key = f"{model_type}:{model_path}"
|
| 159 |
+
if model_key in model_dict:
|
| 160 |
+
return model_dict[model_key]
|
| 161 |
+
model_path = os.path.join(config["model_config"][model_type]["model_folder"], model_path)
|
| 162 |
+
model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"])
|
| 163 |
+
model_manager.load_lora(
|
| 164 |
+
download_customized_models(
|
| 165 |
+
model_id="DiffSynth-Studio/Eligen",
|
| 166 |
+
origin_file_path="model_bf16.safetensors",
|
| 167 |
+
local_dir="models/lora/entity_control",
|
| 168 |
+
),
|
| 169 |
+
lora_alpha=1,
|
| 170 |
+
)
|
| 171 |
+
pipe = config["model_config"][model_type]["pipeline_class"].from_model_manager(model_manager)
|
| 172 |
+
model_dict[model_key] = model_manager, pipe
|
| 173 |
+
return model_manager, pipe
|
| 174 |
+
|
| 175 |
+
|
| 176 |
+
with gr.Blocks() as app:
|
| 177 |
+
gr.Markdown(
|
| 178 |
+
"""## EliGen: Entity-Level Controllable Text-to-Image Model
|
| 179 |
+
1. On the left, input the **global prompt** for the overall image, such as "a person stands by the river."
|
| 180 |
+
2. On the right, input the **local prompt** for each entity, such as "person," and draw the corresponding mask in the **Entity Mask Painter**. Generally, solid rectangular masks yield better results.
|
| 181 |
+
3. Click the **Generate** button to create the image. By selecting different **random seeds**, you can generate diverse images.
|
| 182 |
+
4. **You can directly click the "Load Example" button on any sample at the bottom to load example inputs.**
|
| 183 |
+
"""
|
| 184 |
+
)
|
| 185 |
+
|
| 186 |
+
loading_status = gr.Textbox(label="Loading Model...", value="Loading model... Please wait...", visible=True)
|
| 187 |
+
main_interface = gr.Column(visible=False)
|
| 188 |
+
|
| 189 |
+
def initialize_model():
|
| 190 |
+
try:
|
| 191 |
+
load_model()
|
| 192 |
+
return {
|
| 193 |
+
loading_status: gr.update(value="Model loaded successfully!", visible=False),
|
| 194 |
+
main_interface: gr.update(visible=True),
|
| 195 |
+
}
|
| 196 |
+
except Exception as e:
|
| 197 |
+
print(f'Failed to load model with error: {e}')
|
| 198 |
+
return {
|
| 199 |
+
loading_status: gr.update(value=f"Failed to load model: {str(e)}", visible=True),
|
| 200 |
+
main_interface: gr.update(visible=True),
|
| 201 |
+
}
|
| 202 |
+
|
| 203 |
+
app.load(initialize_model, inputs=None, outputs=[loading_status, main_interface])
|
| 204 |
+
|
| 205 |
+
with main_interface:
|
| 206 |
+
with gr.Row():
|
| 207 |
+
local_prompt_list = []
|
| 208 |
+
canvas_list = []
|
| 209 |
+
random_mask_dir = gr.State(f'{random.randint(0, 1000000):08d}')
|
| 210 |
+
with gr.Column(scale=382, min_width=100):
|
| 211 |
+
model_type = gr.State('FLUX')
|
| 212 |
+
model_path = gr.State('FLUX.1-dev')
|
| 213 |
+
with gr.Accordion(label="Global prompt"):
|
| 214 |
+
prompt = gr.Textbox(label="Global Prompt", lines=3)
|
| 215 |
+
negative_prompt = gr.Textbox(label="Negative prompt", value="worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw, blur,", lines=3)
|
| 216 |
+
with gr.Accordion(label="Inference Options", open=True):
|
| 217 |
+
seed = gr.Number(minimum=0, maximum=10**9, value=42, interactive=True, label="Random seed", show_label=True)
|
| 218 |
+
num_inference_steps = gr.Slider(minimum=1, maximum=100, value=30, step=1, interactive=True, label="Inference steps")
|
| 219 |
+
cfg_scale = gr.Slider(minimum=2.0, maximum=10.0, value=3.0, step=0.1, interactive=True, label="Classifier-free guidance scale")
|
| 220 |
+
embedded_guidance = gr.Slider(minimum=0.0, maximum=10.0, value=3.5, step=0.1, interactive=True, label="Embedded guidance scale")
|
| 221 |
+
height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height")
|
| 222 |
+
width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width")
|
| 223 |
+
with gr.Accordion(label="Inpaint Input Image", open=False):
|
| 224 |
+
input_image = gr.Image(sources=None, show_label=False, interactive=True, type="pil")
|
| 225 |
+
background_weight = gr.Slider(minimum=0.0, maximum=1000., value=0., step=1, interactive=False, label="background_weight", visible=False)
|
| 226 |
+
|
| 227 |
+
with gr.Column():
|
| 228 |
+
reset_input_button = gr.Button(value="Reset Inpaint Input")
|
| 229 |
+
send_input_to_painter = gr.Button(value="Set as painter's background")
|
| 230 |
+
@gr.on(inputs=[input_image], outputs=[input_image], triggers=reset_input_button.click)
|
| 231 |
+
def reset_input_image(input_image):
|
| 232 |
+
return None
|
| 233 |
+
|
| 234 |
+
with gr.Column(scale=618, min_width=100):
|
| 235 |
+
with gr.Accordion(label="Entity Painter"):
|
| 236 |
+
for painter_layer_id in range(config["max_num_painter_layers"]):
|
| 237 |
+
with gr.Tab(label=f"Entity {painter_layer_id}"):
|
| 238 |
+
local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}")
|
| 239 |
+
canvas = gr.ImageEditor(
|
| 240 |
+
canvas_size=(512, 512),
|
| 241 |
+
sources=None,
|
| 242 |
+
layers=False,
|
| 243 |
+
interactive=True,
|
| 244 |
+
image_mode="RGBA",
|
| 245 |
+
brush=gr.Brush(
|
| 246 |
+
default_size=50,
|
| 247 |
+
default_color="#000000",
|
| 248 |
+
colors=["#000000"],
|
| 249 |
+
),
|
| 250 |
+
label="Entity Mask Painter",
|
| 251 |
+
key=f"canvas_{painter_layer_id}",
|
| 252 |
+
width=width,
|
| 253 |
+
height=height,
|
| 254 |
+
)
|
| 255 |
+
@gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear], show_progress="hidden")
|
| 256 |
+
def resize_canvas(height, width, canvas):
|
| 257 |
+
h, w = canvas["background"].shape[:2]
|
| 258 |
+
if h != height or width != w:
|
| 259 |
+
return np.ones((height, width, 3), dtype=np.uint8) * 255
|
| 260 |
+
else:
|
| 261 |
+
return canvas
|
| 262 |
+
local_prompt_list.append(local_prompt)
|
| 263 |
+
canvas_list.append(canvas)
|
| 264 |
+
with gr.Accordion(label="Results"):
|
| 265 |
+
run_button = gr.Button(value="Generate", variant="primary")
|
| 266 |
+
output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil")
|
| 267 |
+
with gr.Row():
|
| 268 |
+
with gr.Column():
|
| 269 |
+
output_to_painter_button = gr.Button(value="Set as painter's background")
|
| 270 |
+
with gr.Column():
|
| 271 |
+
return_with_mask = gr.Checkbox(value=False, interactive=True, label="show result with mask painting")
|
| 272 |
+
output_to_input_button = gr.Button(value="Set as input image", visible=False, interactive=False)
|
| 273 |
+
real_output = gr.State(None)
|
| 274 |
+
mask_out = gr.State(None)
|
| 275 |
+
|
| 276 |
+
@gr.on(
|
| 277 |
+
inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir] + local_prompt_list + canvas_list,
|
| 278 |
+
outputs=[output_image, real_output, mask_out],
|
| 279 |
+
triggers=run_button.click
|
| 280 |
+
)
|
| 281 |
+
def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir, *args, progress=gr.Progress()):
|
| 282 |
+
_, pipe = load_model(model_type, model_path)
|
| 283 |
+
input_params = {
|
| 284 |
+
"prompt": prompt,
|
| 285 |
+
"negative_prompt": negative_prompt,
|
| 286 |
+
"cfg_scale": cfg_scale,
|
| 287 |
+
"num_inference_steps": num_inference_steps,
|
| 288 |
+
"height": height,
|
| 289 |
+
"width": width,
|
| 290 |
+
"progress_bar_cmd": progress.tqdm,
|
| 291 |
+
}
|
| 292 |
+
if isinstance(pipe, FluxImagePipeline):
|
| 293 |
+
input_params["embedded_guidance"] = embedded_guidance
|
| 294 |
+
if input_image is not None:
|
| 295 |
+
input_params["input_image"] = input_image.resize((width, height)).convert("RGB")
|
| 296 |
+
input_params["enable_eligen_inpaint"] = True
|
| 297 |
+
|
| 298 |
+
local_prompt_list, canvas_list = (
|
| 299 |
+
args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]],
|
| 300 |
+
args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]],
|
| 301 |
+
)
|
| 302 |
+
local_prompts, masks = [], []
|
| 303 |
+
for local_prompt, canvas in zip(local_prompt_list, canvas_list):
|
| 304 |
+
if isinstance(local_prompt, str) and len(local_prompt) > 0:
|
| 305 |
+
local_prompts.append(local_prompt)
|
| 306 |
+
masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB"))
|
| 307 |
+
entity_masks = None if len(masks) == 0 else masks
|
| 308 |
+
entity_prompts = None if len(local_prompts) == 0 else local_prompts
|
| 309 |
+
input_params.update({
|
| 310 |
+
"eligen_entity_prompts": entity_prompts,
|
| 311 |
+
"eligen_entity_masks": entity_masks,
|
| 312 |
+
})
|
| 313 |
+
torch.manual_seed(seed)
|
| 314 |
+
# save_mask_prompts(masks, local_prompts, prompt, seed, random_mask_dir)
|
| 315 |
+
image = pipe(**input_params)
|
| 316 |
+
masks = [mask.resize(image.size) for mask in masks]
|
| 317 |
+
image_with_mask = visualize_masks(image, masks, local_prompts)
|
| 318 |
+
|
| 319 |
+
real_output = gr.State(image)
|
| 320 |
+
mask_out = gr.State(image_with_mask)
|
| 321 |
+
|
| 322 |
+
if return_with_mask:
|
| 323 |
+
return image_with_mask, real_output, mask_out
|
| 324 |
+
return image, real_output, mask_out
|
| 325 |
+
|
| 326 |
+
@gr.on(inputs=[input_image] + canvas_list, outputs=canvas_list, triggers=send_input_to_painter.click)
|
| 327 |
+
def send_input_to_painter_background(input_image, *canvas_list):
|
| 328 |
+
if input_image is None:
|
| 329 |
+
return tuple(canvas_list)
|
| 330 |
+
for canvas in canvas_list:
|
| 331 |
+
h, w = canvas["background"].shape[:2]
|
| 332 |
+
canvas["background"] = input_image.resize((w, h))
|
| 333 |
+
return tuple(canvas_list)
|
| 334 |
+
@gr.on(inputs=[real_output] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click)
|
| 335 |
+
def send_output_to_painter_background(real_output, *canvas_list):
|
| 336 |
+
if real_output is None:
|
| 337 |
+
return tuple(canvas_list)
|
| 338 |
+
for canvas in canvas_list:
|
| 339 |
+
h, w = canvas["background"].shape[:2]
|
| 340 |
+
canvas["background"] = real_output.value.resize((w, h))
|
| 341 |
+
return tuple(canvas_list)
|
| 342 |
+
@gr.on(inputs=[return_with_mask, real_output, mask_out], outputs=[output_image], triggers=[return_with_mask.change], show_progress="hidden")
|
| 343 |
+
def show_output(return_with_mask, real_output, mask_out):
|
| 344 |
+
if return_with_mask:
|
| 345 |
+
return mask_out.value
|
| 346 |
+
else:
|
| 347 |
+
return real_output.value
|
| 348 |
+
@gr.on(inputs=[real_output], outputs=[input_image], triggers=output_to_input_button.click)
|
| 349 |
+
def send_output_to_pipe_input(real_output):
|
| 350 |
+
return real_output.value
|
| 351 |
+
|
| 352 |
+
with gr.Column():
|
| 353 |
+
gr.Markdown("## Examples")
|
| 354 |
+
for i in range(0, len(examples), 2):
|
| 355 |
+
with gr.Row():
|
| 356 |
+
if i < len(examples):
|
| 357 |
+
example = examples[i]
|
| 358 |
+
with gr.Column():
|
| 359 |
+
example_image = gr.Image(
|
| 360 |
+
value=f"data/examples/eligen/entity_control/example_{example['example_id']}/example_image.png",
|
| 361 |
+
label=example["description"],
|
| 362 |
+
interactive=False,
|
| 363 |
+
width=1024,
|
| 364 |
+
height=512
|
| 365 |
+
)
|
| 366 |
+
load_example_button = gr.Button(value=f"Load Example {example['example_id']}")
|
| 367 |
+
load_example_button.click(
|
| 368 |
+
load_example,
|
| 369 |
+
inputs=[load_example_button],
|
| 370 |
+
outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list
|
| 371 |
+
)
|
| 372 |
+
|
| 373 |
+
if i + 1 < len(examples):
|
| 374 |
+
example = examples[i + 1]
|
| 375 |
+
with gr.Column():
|
| 376 |
+
example_image = gr.Image(
|
| 377 |
+
value=f"data/examples/eligen/entity_control/example_{example['example_id']}/example_image.png",
|
| 378 |
+
label=example["description"],
|
| 379 |
+
interactive=False,
|
| 380 |
+
width=1024,
|
| 381 |
+
height=512
|
| 382 |
+
)
|
| 383 |
+
load_example_button = gr.Button(value=f"Load Example {example['example_id']}")
|
| 384 |
+
load_example_button.click(
|
| 385 |
+
load_example,
|
| 386 |
+
inputs=[load_example_button],
|
| 387 |
+
outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list
|
| 388 |
+
)
|
| 389 |
+
app.config["show_progress"] = "hidden"
|
| 390 |
+
app.launch()
|
diffsynth-studio/apps/streamlit/DiffSynth_Studio.py
ADDED
|
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Set web page format
|
| 2 |
+
import streamlit as st
|
| 3 |
+
st.set_page_config(layout="wide")
|
| 4 |
+
# Disable virtual VRAM on windows system
|
| 5 |
+
import torch
|
| 6 |
+
torch.cuda.set_per_process_memory_fraction(0.999, 0)
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
st.markdown("""
|
| 10 |
+
# DiffSynth Studio
|
| 11 |
+
|
| 12 |
+
[Source Code](https://github.com/Artiprocher/DiffSynth-Studio)
|
| 13 |
+
|
| 14 |
+
Welcome to DiffSynth Studio.
|
| 15 |
+
""")
|
diffsynth-studio/apps/streamlit/pages/1_Image_Creator.py
ADDED
|
@@ -0,0 +1,362 @@
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|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch, os, io, json, time
|
| 2 |
+
import numpy as np
|
| 3 |
+
from PIL import Image
|
| 4 |
+
import streamlit as st
|
| 5 |
+
st.set_page_config(layout="wide")
|
| 6 |
+
from streamlit_drawable_canvas import st_canvas
|
| 7 |
+
from diffsynth.models import ModelManager
|
| 8 |
+
from diffsynth.pipelines import SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline, FluxImagePipeline
|
| 9 |
+
from diffsynth.data.video import crop_and_resize
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
config = {
|
| 13 |
+
"Stable Diffusion": {
|
| 14 |
+
"model_folder": "models/stable_diffusion",
|
| 15 |
+
"pipeline_class": SDImagePipeline,
|
| 16 |
+
"fixed_parameters": {}
|
| 17 |
+
},
|
| 18 |
+
"Stable Diffusion XL": {
|
| 19 |
+
"model_folder": "models/stable_diffusion_xl",
|
| 20 |
+
"pipeline_class": SDXLImagePipeline,
|
| 21 |
+
"fixed_parameters": {}
|
| 22 |
+
},
|
| 23 |
+
"Stable Diffusion 3": {
|
| 24 |
+
"model_folder": "models/stable_diffusion_3",
|
| 25 |
+
"pipeline_class": SD3ImagePipeline,
|
| 26 |
+
"fixed_parameters": {}
|
| 27 |
+
},
|
| 28 |
+
"Stable Diffusion XL Turbo": {
|
| 29 |
+
"model_folder": "models/stable_diffusion_xl_turbo",
|
| 30 |
+
"pipeline_class": SDXLImagePipeline,
|
| 31 |
+
"fixed_parameters": {
|
| 32 |
+
"negative_prompt": "",
|
| 33 |
+
"cfg_scale": 1.0,
|
| 34 |
+
"num_inference_steps": 1,
|
| 35 |
+
"height": 512,
|
| 36 |
+
"width": 512,
|
| 37 |
+
}
|
| 38 |
+
},
|
| 39 |
+
"Kolors": {
|
| 40 |
+
"model_folder": "models/kolors",
|
| 41 |
+
"pipeline_class": SDXLImagePipeline,
|
| 42 |
+
"fixed_parameters": {}
|
| 43 |
+
},
|
| 44 |
+
"HunyuanDiT": {
|
| 45 |
+
"model_folder": "models/HunyuanDiT",
|
| 46 |
+
"pipeline_class": HunyuanDiTImagePipeline,
|
| 47 |
+
"fixed_parameters": {
|
| 48 |
+
"height": 1024,
|
| 49 |
+
"width": 1024,
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"FLUX": {
|
| 53 |
+
"model_folder": "models/FLUX",
|
| 54 |
+
"pipeline_class": FluxImagePipeline,
|
| 55 |
+
"fixed_parameters": {
|
| 56 |
+
"cfg_scale": 1.0,
|
| 57 |
+
}
|
| 58 |
+
}
|
| 59 |
+
}
|
| 60 |
+
|
| 61 |
+
|
| 62 |
+
def load_model_list(model_type):
|
| 63 |
+
folder = config[model_type]["model_folder"]
|
| 64 |
+
file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")]
|
| 65 |
+
if model_type in ["HunyuanDiT", "Kolors", "FLUX"]:
|
| 66 |
+
file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))]
|
| 67 |
+
file_list = sorted(file_list)
|
| 68 |
+
return file_list
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
def release_model():
|
| 72 |
+
if "model_manager" in st.session_state:
|
| 73 |
+
st.session_state["model_manager"].to("cpu")
|
| 74 |
+
del st.session_state["loaded_model_path"]
|
| 75 |
+
del st.session_state["model_manager"]
|
| 76 |
+
del st.session_state["pipeline"]
|
| 77 |
+
torch.cuda.empty_cache()
|
| 78 |
+
|
| 79 |
+
|
| 80 |
+
def load_model(model_type, model_path):
|
| 81 |
+
model_manager = ModelManager()
|
| 82 |
+
if model_type == "HunyuanDiT":
|
| 83 |
+
model_manager.load_models([
|
| 84 |
+
os.path.join(model_path, "clip_text_encoder/pytorch_model.bin"),
|
| 85 |
+
os.path.join(model_path, "mt5/pytorch_model.bin"),
|
| 86 |
+
os.path.join(model_path, "model/pytorch_model_ema.pt"),
|
| 87 |
+
os.path.join(model_path, "sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"),
|
| 88 |
+
])
|
| 89 |
+
elif model_type == "Kolors":
|
| 90 |
+
model_manager.load_models([
|
| 91 |
+
os.path.join(model_path, "text_encoder"),
|
| 92 |
+
os.path.join(model_path, "unet/diffusion_pytorch_model.safetensors"),
|
| 93 |
+
os.path.join(model_path, "vae/diffusion_pytorch_model.safetensors"),
|
| 94 |
+
])
|
| 95 |
+
elif model_type == "FLUX":
|
| 96 |
+
model_manager.torch_dtype = torch.bfloat16
|
| 97 |
+
file_list = [
|
| 98 |
+
os.path.join(model_path, "text_encoder/model.safetensors"),
|
| 99 |
+
os.path.join(model_path, "text_encoder_2"),
|
| 100 |
+
]
|
| 101 |
+
for file_name in os.listdir(model_path):
|
| 102 |
+
if file_name.endswith(".safetensors"):
|
| 103 |
+
file_list.append(os.path.join(model_path, file_name))
|
| 104 |
+
model_manager.load_models(file_list)
|
| 105 |
+
else:
|
| 106 |
+
model_manager.load_model(model_path)
|
| 107 |
+
pipeline = config[model_type]["pipeline_class"].from_model_manager(model_manager)
|
| 108 |
+
st.session_state.loaded_model_path = model_path
|
| 109 |
+
st.session_state.model_manager = model_manager
|
| 110 |
+
st.session_state.pipeline = pipeline
|
| 111 |
+
return model_manager, pipeline
|
| 112 |
+
|
| 113 |
+
|
| 114 |
+
def use_output_image_as_input(update=True):
|
| 115 |
+
# Search for input image
|
| 116 |
+
output_image_id = 0
|
| 117 |
+
selected_output_image = None
|
| 118 |
+
while True:
|
| 119 |
+
if f"use_output_as_input_{output_image_id}" not in st.session_state:
|
| 120 |
+
break
|
| 121 |
+
if st.session_state[f"use_output_as_input_{output_image_id}"]:
|
| 122 |
+
selected_output_image = st.session_state["output_images"][output_image_id]
|
| 123 |
+
break
|
| 124 |
+
output_image_id += 1
|
| 125 |
+
if update and selected_output_image is not None:
|
| 126 |
+
st.session_state["input_image"] = selected_output_image
|
| 127 |
+
return selected_output_image is not None
|
| 128 |
+
|
| 129 |
+
|
| 130 |
+
def apply_stroke_to_image(stroke_image, image):
|
| 131 |
+
image = np.array(image.convert("RGB")).astype(np.float32)
|
| 132 |
+
height, width, _ = image.shape
|
| 133 |
+
|
| 134 |
+
stroke_image = np.array(Image.fromarray(stroke_image).resize((width, height))).astype(np.float32)
|
| 135 |
+
weight = stroke_image[:, :, -1:] / 255
|
| 136 |
+
stroke_image = stroke_image[:, :, :-1]
|
| 137 |
+
|
| 138 |
+
image = stroke_image * weight + image * (1 - weight)
|
| 139 |
+
image = np.clip(image, 0, 255).astype(np.uint8)
|
| 140 |
+
image = Image.fromarray(image)
|
| 141 |
+
return image
|
| 142 |
+
|
| 143 |
+
|
| 144 |
+
@st.cache_data
|
| 145 |
+
def image2bits(image):
|
| 146 |
+
image_byte = io.BytesIO()
|
| 147 |
+
image.save(image_byte, format="PNG")
|
| 148 |
+
image_byte = image_byte.getvalue()
|
| 149 |
+
return image_byte
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
def show_output_image(image):
|
| 153 |
+
st.image(image, use_column_width="always")
|
| 154 |
+
st.button("Use it as input image", key=f"use_output_as_input_{image_id}")
|
| 155 |
+
st.download_button("Download", data=image2bits(image), file_name="image.png", mime="image/png", key=f"download_output_{image_id}")
|
| 156 |
+
|
| 157 |
+
|
| 158 |
+
column_input, column_output = st.columns(2)
|
| 159 |
+
with st.sidebar:
|
| 160 |
+
# Select a model
|
| 161 |
+
with st.expander("Model", expanded=True):
|
| 162 |
+
model_type = st.selectbox("Model type", [model_type_ for model_type_ in config])
|
| 163 |
+
fixed_parameters = config[model_type]["fixed_parameters"]
|
| 164 |
+
model_path_list = ["None"] + load_model_list(model_type)
|
| 165 |
+
model_path = st.selectbox("Model path", model_path_list)
|
| 166 |
+
|
| 167 |
+
# Load the model
|
| 168 |
+
if model_path == "None":
|
| 169 |
+
# No models are selected. Release VRAM.
|
| 170 |
+
st.markdown("No models are selected.")
|
| 171 |
+
release_model()
|
| 172 |
+
else:
|
| 173 |
+
# A model is selected.
|
| 174 |
+
model_path = os.path.join(config[model_type]["model_folder"], model_path)
|
| 175 |
+
if st.session_state.get("loaded_model_path", "") != model_path:
|
| 176 |
+
# The loaded model is not the selected model. Reload it.
|
| 177 |
+
st.markdown(f"Loading model at {model_path}.")
|
| 178 |
+
st.markdown("Please wait a moment...")
|
| 179 |
+
release_model()
|
| 180 |
+
model_manager, pipeline = load_model(model_type, model_path)
|
| 181 |
+
st.markdown("Done.")
|
| 182 |
+
else:
|
| 183 |
+
# The loaded model is not the selected model. Fetch it from `st.session_state`.
|
| 184 |
+
st.markdown(f"Loading model at {model_path}.")
|
| 185 |
+
st.markdown("Please wait a moment...")
|
| 186 |
+
model_manager, pipeline = st.session_state.model_manager, st.session_state.pipeline
|
| 187 |
+
st.markdown("Done.")
|
| 188 |
+
|
| 189 |
+
# Show parameters
|
| 190 |
+
with st.expander("Prompt", expanded=True):
|
| 191 |
+
prompt = st.text_area("Positive prompt")
|
| 192 |
+
if "negative_prompt" in fixed_parameters:
|
| 193 |
+
negative_prompt = fixed_parameters["negative_prompt"]
|
| 194 |
+
else:
|
| 195 |
+
negative_prompt = st.text_area("Negative prompt")
|
| 196 |
+
if "cfg_scale" in fixed_parameters:
|
| 197 |
+
cfg_scale = fixed_parameters["cfg_scale"]
|
| 198 |
+
else:
|
| 199 |
+
cfg_scale = st.slider("Classifier-free guidance scale", min_value=1.0, max_value=10.0, value=7.5)
|
| 200 |
+
with st.expander("Image", expanded=True):
|
| 201 |
+
if "num_inference_steps" in fixed_parameters:
|
| 202 |
+
num_inference_steps = fixed_parameters["num_inference_steps"]
|
| 203 |
+
else:
|
| 204 |
+
num_inference_steps = st.slider("Inference steps", min_value=1, max_value=100, value=20)
|
| 205 |
+
if "height" in fixed_parameters:
|
| 206 |
+
height = fixed_parameters["height"]
|
| 207 |
+
else:
|
| 208 |
+
height = st.select_slider("Height", options=[256, 512, 768, 1024, 2048], value=512)
|
| 209 |
+
if "width" in fixed_parameters:
|
| 210 |
+
width = fixed_parameters["width"]
|
| 211 |
+
else:
|
| 212 |
+
width = st.select_slider("Width", options=[256, 512, 768, 1024, 2048], value=512)
|
| 213 |
+
num_images = st.number_input("Number of images", value=2)
|
| 214 |
+
use_fixed_seed = st.checkbox("Use fixed seed", value=False)
|
| 215 |
+
if use_fixed_seed:
|
| 216 |
+
seed = st.number_input("Random seed", min_value=0, max_value=10**9, step=1, value=0)
|
| 217 |
+
|
| 218 |
+
# Other fixed parameters
|
| 219 |
+
denoising_strength = 1.0
|
| 220 |
+
repetition = 1
|
| 221 |
+
|
| 222 |
+
|
| 223 |
+
# Show input image
|
| 224 |
+
with column_input:
|
| 225 |
+
with st.expander("Input image (Optional)", expanded=True):
|
| 226 |
+
with st.container(border=True):
|
| 227 |
+
column_white_board, column_upload_image = st.columns([1, 2])
|
| 228 |
+
with column_white_board:
|
| 229 |
+
create_white_board = st.button("Create white board")
|
| 230 |
+
delete_input_image = st.button("Delete input image")
|
| 231 |
+
with column_upload_image:
|
| 232 |
+
upload_image = st.file_uploader("Upload image", type=["png", "jpg"], key="upload_image")
|
| 233 |
+
|
| 234 |
+
if upload_image is not None:
|
| 235 |
+
st.session_state["input_image"] = crop_and_resize(Image.open(upload_image), height, width)
|
| 236 |
+
elif create_white_board:
|
| 237 |
+
st.session_state["input_image"] = Image.fromarray(np.ones((height, width, 3), dtype=np.uint8) * 255)
|
| 238 |
+
else:
|
| 239 |
+
use_output_image_as_input()
|
| 240 |
+
|
| 241 |
+
if delete_input_image and "input_image" in st.session_state:
|
| 242 |
+
del st.session_state.input_image
|
| 243 |
+
if delete_input_image and "upload_image" in st.session_state:
|
| 244 |
+
del st.session_state.upload_image
|
| 245 |
+
|
| 246 |
+
input_image = st.session_state.get("input_image", None)
|
| 247 |
+
if input_image is not None:
|
| 248 |
+
with st.container(border=True):
|
| 249 |
+
column_drawing_mode, column_color_1, column_color_2 = st.columns([4, 1, 1])
|
| 250 |
+
with column_drawing_mode:
|
| 251 |
+
drawing_mode = st.radio("Drawing tool", ["transform", "freedraw", "line", "rect"], horizontal=True, index=1)
|
| 252 |
+
with column_color_1:
|
| 253 |
+
stroke_color = st.color_picker("Stroke color")
|
| 254 |
+
with column_color_2:
|
| 255 |
+
fill_color = st.color_picker("Fill color")
|
| 256 |
+
stroke_width = st.slider("Stroke width", min_value=1, max_value=50, value=10)
|
| 257 |
+
with st.container(border=True):
|
| 258 |
+
denoising_strength = st.slider("Denoising strength", min_value=0.0, max_value=1.0, value=0.7)
|
| 259 |
+
repetition = st.slider("Repetition", min_value=1, max_value=8, value=1)
|
| 260 |
+
with st.container(border=True):
|
| 261 |
+
input_width, input_height = input_image.size
|
| 262 |
+
canvas_result = st_canvas(
|
| 263 |
+
fill_color=fill_color,
|
| 264 |
+
stroke_width=stroke_width,
|
| 265 |
+
stroke_color=stroke_color,
|
| 266 |
+
background_color="rgba(255, 255, 255, 0)",
|
| 267 |
+
background_image=input_image,
|
| 268 |
+
update_streamlit=True,
|
| 269 |
+
height=int(512 / input_width * input_height),
|
| 270 |
+
width=512,
|
| 271 |
+
drawing_mode=drawing_mode,
|
| 272 |
+
key="canvas"
|
| 273 |
+
)
|
| 274 |
+
|
| 275 |
+
num_painter_layer = st.number_input("Number of painter layers", min_value=0, max_value=10, step=1, value=0)
|
| 276 |
+
local_prompts, masks, mask_scales = [], [], []
|
| 277 |
+
white_board = Image.fromarray(np.ones((512, 512, 3), dtype=np.uint8) * 255)
|
| 278 |
+
painter_layers_json_data = []
|
| 279 |
+
for painter_tab_id in range(num_painter_layer):
|
| 280 |
+
with st.expander(f"Painter layer {painter_tab_id}", expanded=True):
|
| 281 |
+
enable_local_prompt = st.checkbox(f"Enable prompt {painter_tab_id}", value=True)
|
| 282 |
+
local_prompt = st.text_area(f"Prompt {painter_tab_id}")
|
| 283 |
+
mask_scale = st.slider(f"Mask scale {painter_tab_id}", min_value=0.0, max_value=3.0, value=1.0)
|
| 284 |
+
stroke_width = st.slider(f"Stroke width {painter_tab_id}", min_value=1, max_value=300, value=100)
|
| 285 |
+
canvas_result_local = st_canvas(
|
| 286 |
+
fill_color="#000000",
|
| 287 |
+
stroke_width=stroke_width,
|
| 288 |
+
stroke_color="#000000",
|
| 289 |
+
background_color="rgba(255, 255, 255, 0)",
|
| 290 |
+
background_image=white_board,
|
| 291 |
+
update_streamlit=True,
|
| 292 |
+
height=512,
|
| 293 |
+
width=512,
|
| 294 |
+
drawing_mode="freedraw",
|
| 295 |
+
key=f"canvas_{painter_tab_id}"
|
| 296 |
+
)
|
| 297 |
+
if canvas_result_local.json_data is not None:
|
| 298 |
+
painter_layers_json_data.append(canvas_result_local.json_data.copy())
|
| 299 |
+
painter_layers_json_data[-1]["prompt"] = local_prompt
|
| 300 |
+
if enable_local_prompt:
|
| 301 |
+
local_prompts.append(local_prompt)
|
| 302 |
+
if canvas_result_local.image_data is not None:
|
| 303 |
+
mask = apply_stroke_to_image(canvas_result_local.image_data, white_board)
|
| 304 |
+
else:
|
| 305 |
+
mask = white_board
|
| 306 |
+
mask = Image.fromarray(255 - np.array(mask))
|
| 307 |
+
masks.append(mask)
|
| 308 |
+
mask_scales.append(mask_scale)
|
| 309 |
+
save_painter_layers = st.button("Save painter layers")
|
| 310 |
+
if save_painter_layers:
|
| 311 |
+
os.makedirs("data/painter_layers", exist_ok=True)
|
| 312 |
+
json_file_path = f"data/painter_layers/{time.time_ns()}.json"
|
| 313 |
+
with open(json_file_path, "w") as f:
|
| 314 |
+
json.dump(painter_layers_json_data, f, indent=4)
|
| 315 |
+
st.markdown(f"Painter layers are saved in {json_file_path}.")
|
| 316 |
+
|
| 317 |
+
|
| 318 |
+
with column_output:
|
| 319 |
+
run_button = st.button("Generate image", type="primary")
|
| 320 |
+
auto_update = st.checkbox("Auto update", value=False)
|
| 321 |
+
num_image_columns = st.slider("Columns", min_value=1, max_value=8, value=2)
|
| 322 |
+
image_columns = st.columns(num_image_columns)
|
| 323 |
+
|
| 324 |
+
# Run
|
| 325 |
+
if (run_button or auto_update) and model_path != "None":
|
| 326 |
+
|
| 327 |
+
if input_image is not None:
|
| 328 |
+
input_image = input_image.resize((width, height))
|
| 329 |
+
if canvas_result.image_data is not None:
|
| 330 |
+
input_image = apply_stroke_to_image(canvas_result.image_data, input_image)
|
| 331 |
+
|
| 332 |
+
output_images = []
|
| 333 |
+
for image_id in range(num_images * repetition):
|
| 334 |
+
if use_fixed_seed:
|
| 335 |
+
torch.manual_seed(seed + image_id)
|
| 336 |
+
else:
|
| 337 |
+
torch.manual_seed(np.random.randint(0, 10**9))
|
| 338 |
+
if image_id >= num_images:
|
| 339 |
+
input_image = output_images[image_id - num_images]
|
| 340 |
+
with image_columns[image_id % num_image_columns]:
|
| 341 |
+
progress_bar_st = st.progress(0.0)
|
| 342 |
+
image = pipeline(
|
| 343 |
+
prompt, negative_prompt=negative_prompt,
|
| 344 |
+
local_prompts=local_prompts, masks=masks, mask_scales=mask_scales,
|
| 345 |
+
cfg_scale=cfg_scale, num_inference_steps=num_inference_steps,
|
| 346 |
+
height=height, width=width,
|
| 347 |
+
input_image=input_image, denoising_strength=denoising_strength,
|
| 348 |
+
progress_bar_st=progress_bar_st
|
| 349 |
+
)
|
| 350 |
+
output_images.append(image)
|
| 351 |
+
progress_bar_st.progress(1.0)
|
| 352 |
+
show_output_image(image)
|
| 353 |
+
st.session_state["output_images"] = output_images
|
| 354 |
+
|
| 355 |
+
elif "output_images" in st.session_state:
|
| 356 |
+
for image_id in range(len(st.session_state.output_images)):
|
| 357 |
+
with image_columns[image_id % num_image_columns]:
|
| 358 |
+
image = st.session_state.output_images[image_id]
|
| 359 |
+
progress_bar = st.progress(1.0)
|
| 360 |
+
show_output_image(image)
|
| 361 |
+
if "upload_image" in st.session_state and use_output_image_as_input(update=False):
|
| 362 |
+
st.markdown("If you want to use an output image as input image, please delete the uploaded image manually.")
|
diffsynth-studio/apps/streamlit/pages/2_Video_Creator.py
ADDED
|
@@ -0,0 +1,197 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import streamlit as st
|
| 2 |
+
st.set_page_config(layout="wide")
|
| 3 |
+
from diffsynth import SDVideoPipelineRunner
|
| 4 |
+
import os
|
| 5 |
+
import numpy as np
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
def load_model_list(folder):
|
| 9 |
+
file_list = os.listdir(folder)
|
| 10 |
+
file_list = [i for i in file_list if i.endswith(".safetensors") or i.endswith(".pth") or i.endswith(".ckpt")]
|
| 11 |
+
file_list = sorted(file_list)
|
| 12 |
+
return file_list
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
def match_processor_id(model_name, supported_processor_id_list):
|
| 16 |
+
sorted_processor_id = [i[1] for i in sorted([(-len(i), i) for i in supported_processor_id_list])]
|
| 17 |
+
for processor_id in sorted_processor_id:
|
| 18 |
+
if processor_id in model_name:
|
| 19 |
+
return supported_processor_id_list.index(processor_id) + 1
|
| 20 |
+
return 0
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
config = {
|
| 24 |
+
"models": {
|
| 25 |
+
"model_list": [],
|
| 26 |
+
"textual_inversion_folder": "models/textual_inversion",
|
| 27 |
+
"device": "cuda",
|
| 28 |
+
"lora_alphas": [],
|
| 29 |
+
"controlnet_units": []
|
| 30 |
+
},
|
| 31 |
+
"data": {
|
| 32 |
+
"input_frames": None,
|
| 33 |
+
"controlnet_frames": [],
|
| 34 |
+
"output_folder": "output",
|
| 35 |
+
"fps": 60
|
| 36 |
+
},
|
| 37 |
+
"pipeline": {
|
| 38 |
+
"seed": 0,
|
| 39 |
+
"pipeline_inputs": {}
|
| 40 |
+
}
|
| 41 |
+
}
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
with st.expander("Model", expanded=True):
|
| 45 |
+
stable_diffusion_ckpt = st.selectbox("Stable Diffusion", ["None"] + load_model_list("models/stable_diffusion"))
|
| 46 |
+
if stable_diffusion_ckpt != "None":
|
| 47 |
+
config["models"]["model_list"].append(os.path.join("models/stable_diffusion", stable_diffusion_ckpt))
|
| 48 |
+
animatediff_ckpt = st.selectbox("AnimateDiff", ["None"] + load_model_list("models/AnimateDiff"))
|
| 49 |
+
if animatediff_ckpt != "None":
|
| 50 |
+
config["models"]["model_list"].append(os.path.join("models/AnimateDiff", animatediff_ckpt))
|
| 51 |
+
column_lora, column_lora_alpha = st.columns([2, 1])
|
| 52 |
+
with column_lora:
|
| 53 |
+
sd_lora_ckpt = st.selectbox("LoRA", ["None"] + load_model_list("models/lora"))
|
| 54 |
+
with column_lora_alpha:
|
| 55 |
+
lora_alpha = st.slider("LoRA Alpha", min_value=-4.0, max_value=4.0, value=1.0, step=0.1)
|
| 56 |
+
if sd_lora_ckpt != "None":
|
| 57 |
+
config["models"]["model_list"].append(os.path.join("models/lora", sd_lora_ckpt))
|
| 58 |
+
config["models"]["lora_alphas"].append(lora_alpha)
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
with st.expander("Data", expanded=True):
|
| 62 |
+
with st.container(border=True):
|
| 63 |
+
input_video = st.text_input("Input Video File Path (e.g., data/your_video.mp4)", value="")
|
| 64 |
+
column_height, column_width, column_start_frame_index, column_end_frame_index = st.columns([2, 2, 1, 1])
|
| 65 |
+
with column_height:
|
| 66 |
+
height = st.select_slider("Height", options=[256, 512, 768, 1024, 1536, 2048], value=1024)
|
| 67 |
+
with column_width:
|
| 68 |
+
width = st.select_slider("Width", options=[256, 512, 768, 1024, 1536, 2048], value=1024)
|
| 69 |
+
with column_start_frame_index:
|
| 70 |
+
start_frame_id = st.number_input("Start Frame id", value=0)
|
| 71 |
+
with column_end_frame_index:
|
| 72 |
+
end_frame_id = st.number_input("End Frame id", value=16)
|
| 73 |
+
if input_video != "":
|
| 74 |
+
config["data"]["input_frames"] = {
|
| 75 |
+
"video_file": input_video,
|
| 76 |
+
"image_folder": None,
|
| 77 |
+
"height": height,
|
| 78 |
+
"width": width,
|
| 79 |
+
"start_frame_id": start_frame_id,
|
| 80 |
+
"end_frame_id": end_frame_id
|
| 81 |
+
}
|
| 82 |
+
with st.container(border=True):
|
| 83 |
+
output_video = st.text_input("Output Video File Path (e.g., data/a_folder_to_save_something)", value="output")
|
| 84 |
+
fps = st.number_input("FPS", value=60)
|
| 85 |
+
config["data"]["output_folder"] = output_video
|
| 86 |
+
config["data"]["fps"] = fps
|
| 87 |
+
|
| 88 |
+
|
| 89 |
+
with st.expander("ControlNet Units", expanded=True):
|
| 90 |
+
supported_processor_id_list = ["canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "tile"]
|
| 91 |
+
controlnet_units = st.tabs(["ControlNet Unit 0", "ControlNet Unit 1", "ControlNet Unit 2"])
|
| 92 |
+
for controlnet_id in range(len(controlnet_units)):
|
| 93 |
+
with controlnet_units[controlnet_id]:
|
| 94 |
+
controlnet_ckpt = st.selectbox("ControlNet", ["None"] + load_model_list("models/ControlNet"),
|
| 95 |
+
key=f"controlnet_ckpt_{controlnet_id}")
|
| 96 |
+
processor_id = st.selectbox("Processor", ["None"] + supported_processor_id_list,
|
| 97 |
+
index=match_processor_id(controlnet_ckpt, supported_processor_id_list),
|
| 98 |
+
disabled=controlnet_ckpt == "None", key=f"processor_id_{controlnet_id}")
|
| 99 |
+
controlnet_scale = st.slider("Scale", min_value=0.0, max_value=1.0, step=0.01, value=0.5,
|
| 100 |
+
disabled=controlnet_ckpt == "None", key=f"controlnet_scale_{controlnet_id}")
|
| 101 |
+
use_input_video_as_controlnet_input = st.checkbox("Use input video as ControlNet input", value=True,
|
| 102 |
+
disabled=controlnet_ckpt == "None",
|
| 103 |
+
key=f"use_input_video_as_controlnet_input_{controlnet_id}")
|
| 104 |
+
if not use_input_video_as_controlnet_input:
|
| 105 |
+
controlnet_input_video = st.text_input("ControlNet Input Video File Path", value="",
|
| 106 |
+
disabled=controlnet_ckpt == "None", key=f"controlnet_input_video_{controlnet_id}")
|
| 107 |
+
column_height, column_width, column_start_frame_index, column_end_frame_index = st.columns([2, 2, 1, 1])
|
| 108 |
+
with column_height:
|
| 109 |
+
height = st.select_slider("Height", options=[256, 512, 768, 1024, 1536, 2048], value=1024,
|
| 110 |
+
disabled=controlnet_ckpt == "None", key=f"controlnet_height_{controlnet_id}")
|
| 111 |
+
with column_width:
|
| 112 |
+
width = st.select_slider("Width", options=[256, 512, 768, 1024, 1536, 2048], value=1024,
|
| 113 |
+
disabled=controlnet_ckpt == "None", key=f"controlnet_width_{controlnet_id}")
|
| 114 |
+
with column_start_frame_index:
|
| 115 |
+
start_frame_id = st.number_input("Start Frame id", value=0,
|
| 116 |
+
disabled=controlnet_ckpt == "None", key=f"controlnet_start_frame_id_{controlnet_id}")
|
| 117 |
+
with column_end_frame_index:
|
| 118 |
+
end_frame_id = st.number_input("End Frame id", value=16,
|
| 119 |
+
disabled=controlnet_ckpt == "None", key=f"controlnet_end_frame_id_{controlnet_id}")
|
| 120 |
+
if input_video != "":
|
| 121 |
+
config["data"]["input_video"] = {
|
| 122 |
+
"video_file": input_video,
|
| 123 |
+
"image_folder": None,
|
| 124 |
+
"height": height,
|
| 125 |
+
"width": width,
|
| 126 |
+
"start_frame_id": start_frame_id,
|
| 127 |
+
"end_frame_id": end_frame_id
|
| 128 |
+
}
|
| 129 |
+
if controlnet_ckpt != "None":
|
| 130 |
+
config["models"]["model_list"].append(os.path.join("models/ControlNet", controlnet_ckpt))
|
| 131 |
+
config["models"]["controlnet_units"].append({
|
| 132 |
+
"processor_id": processor_id,
|
| 133 |
+
"model_path": os.path.join("models/ControlNet", controlnet_ckpt),
|
| 134 |
+
"scale": controlnet_scale,
|
| 135 |
+
})
|
| 136 |
+
if use_input_video_as_controlnet_input:
|
| 137 |
+
config["data"]["controlnet_frames"].append(config["data"]["input_frames"])
|
| 138 |
+
else:
|
| 139 |
+
config["data"]["controlnet_frames"].append({
|
| 140 |
+
"video_file": input_video,
|
| 141 |
+
"image_folder": None,
|
| 142 |
+
"height": height,
|
| 143 |
+
"width": width,
|
| 144 |
+
"start_frame_id": start_frame_id,
|
| 145 |
+
"end_frame_id": end_frame_id
|
| 146 |
+
})
|
| 147 |
+
|
| 148 |
+
|
| 149 |
+
with st.container(border=True):
|
| 150 |
+
with st.expander("Seed", expanded=True):
|
| 151 |
+
use_fixed_seed = st.checkbox("Use fixed seed", value=False)
|
| 152 |
+
if use_fixed_seed:
|
| 153 |
+
seed = st.number_input("Random seed", min_value=0, max_value=10**9, step=1, value=0)
|
| 154 |
+
else:
|
| 155 |
+
seed = np.random.randint(0, 10**9)
|
| 156 |
+
with st.expander("Textual Guidance", expanded=True):
|
| 157 |
+
prompt = st.text_area("Positive prompt")
|
| 158 |
+
negative_prompt = st.text_area("Negative prompt")
|
| 159 |
+
column_cfg_scale, column_clip_skip = st.columns(2)
|
| 160 |
+
with column_cfg_scale:
|
| 161 |
+
cfg_scale = st.slider("Classifier-free guidance scale", min_value=1.0, max_value=10.0, value=7.0)
|
| 162 |
+
with column_clip_skip:
|
| 163 |
+
clip_skip = st.slider("Clip Skip", min_value=1, max_value=4, value=1)
|
| 164 |
+
with st.expander("Denoising", expanded=True):
|
| 165 |
+
column_num_inference_steps, column_denoising_strength = st.columns(2)
|
| 166 |
+
with column_num_inference_steps:
|
| 167 |
+
num_inference_steps = st.slider("Inference steps", min_value=1, max_value=100, value=10)
|
| 168 |
+
with column_denoising_strength:
|
| 169 |
+
denoising_strength = st.slider("Denoising strength", min_value=0.0, max_value=1.0, value=1.0)
|
| 170 |
+
with st.expander("Efficiency", expanded=False):
|
| 171 |
+
animatediff_batch_size = st.slider("Animatediff batch size (sliding window size)", min_value=1, max_value=32, value=16, step=1)
|
| 172 |
+
animatediff_stride = st.slider("Animatediff stride",
|
| 173 |
+
min_value=1,
|
| 174 |
+
max_value=max(2, animatediff_batch_size),
|
| 175 |
+
value=max(1, animatediff_batch_size // 2),
|
| 176 |
+
step=1)
|
| 177 |
+
unet_batch_size = st.slider("UNet batch size", min_value=1, max_value=32, value=1, step=1)
|
| 178 |
+
controlnet_batch_size = st.slider("ControlNet batch size", min_value=1, max_value=32, value=1, step=1)
|
| 179 |
+
cross_frame_attention = st.checkbox("Enable Cross-Frame Attention", value=False)
|
| 180 |
+
config["pipeline"]["seed"] = seed
|
| 181 |
+
config["pipeline"]["pipeline_inputs"] = {
|
| 182 |
+
"prompt": prompt,
|
| 183 |
+
"negative_prompt": negative_prompt,
|
| 184 |
+
"cfg_scale": cfg_scale,
|
| 185 |
+
"clip_skip": clip_skip,
|
| 186 |
+
"denoising_strength": denoising_strength,
|
| 187 |
+
"num_inference_steps": num_inference_steps,
|
| 188 |
+
"animatediff_batch_size": animatediff_batch_size,
|
| 189 |
+
"animatediff_stride": animatediff_stride,
|
| 190 |
+
"unet_batch_size": unet_batch_size,
|
| 191 |
+
"controlnet_batch_size": controlnet_batch_size,
|
| 192 |
+
"cross_frame_attention": cross_frame_attention,
|
| 193 |
+
}
|
| 194 |
+
|
| 195 |
+
run_button = st.button("☢️Run☢️", type="primary")
|
| 196 |
+
if run_button:
|
| 197 |
+
SDVideoPipelineRunner(in_streamlit=True).run(config)
|
diffsynth-studio/diffsynth.egg-info/PKG-INFO
ADDED
|
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Metadata-Version: 2.4
|
| 2 |
+
Name: diffsynth
|
| 3 |
+
Version: 1.1.7
|
| 4 |
+
Summary: Enjoy the magic of Diffusion models!
|
| 5 |
+
Author: Artiprocher
|
| 6 |
+
Classifier: Programming Language :: Python :: 3
|
| 7 |
+
Classifier: License :: OSI Approved :: Apache Software License
|
| 8 |
+
Classifier: Operating System :: OS Independent
|
| 9 |
+
Requires-Python: >=3.6
|
| 10 |
+
License-File: LICENSE
|
| 11 |
+
Requires-Dist: torch>=2.0.0
|
| 12 |
+
Requires-Dist: torchvision
|
| 13 |
+
Requires-Dist: cupy-cuda12x
|
| 14 |
+
Requires-Dist: transformers==4.56.0
|
| 15 |
+
Requires-Dist: controlnet-aux==0.0.7
|
| 16 |
+
Requires-Dist: imageio
|
| 17 |
+
Requires-Dist: imageio[ffmpeg]
|
| 18 |
+
Requires-Dist: safetensors
|
| 19 |
+
Requires-Dist: einops
|
| 20 |
+
Requires-Dist: sentencepiece
|
| 21 |
+
Requires-Dist: protobuf
|
| 22 |
+
Requires-Dist: modelscope
|
| 23 |
+
Requires-Dist: ftfy
|
| 24 |
+
Requires-Dist: pynvml
|
| 25 |
+
Requires-Dist: pandas
|
| 26 |
+
Requires-Dist: accelerate
|
| 27 |
+
Dynamic: author
|
| 28 |
+
Dynamic: classifier
|
| 29 |
+
Dynamic: license-file
|
| 30 |
+
Dynamic: requires-dist
|
| 31 |
+
Dynamic: requires-python
|
| 32 |
+
Dynamic: summary
|
diffsynth-studio/diffsynth.egg-info/SOURCES.txt
ADDED
|
@@ -0,0 +1,252 @@
|
|
|
|
|
|
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|
|
|
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|
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|
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|
|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
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|
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|
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|
|
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|
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|
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|
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|
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|
|
|
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|
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|
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|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
|
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|
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|
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|
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|
|
|
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|
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|
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|
|
|
|
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|
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|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
|
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|
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|
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|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
LICENSE
|
| 2 |
+
setup.py
|
| 3 |
+
diffsynth/__init__.py
|
| 4 |
+
diffsynth.egg-info/PKG-INFO
|
| 5 |
+
diffsynth.egg-info/SOURCES.txt
|
| 6 |
+
diffsynth.egg-info/dependency_links.txt
|
| 7 |
+
diffsynth.egg-info/requires.txt
|
| 8 |
+
diffsynth.egg-info/top_level.txt
|
| 9 |
+
diffsynth/configs/__init__.py
|
| 10 |
+
diffsynth/configs/model_config.py
|
| 11 |
+
diffsynth/controlnets/__init__.py
|
| 12 |
+
diffsynth/controlnets/controlnet_unit.py
|
| 13 |
+
diffsynth/controlnets/processors.py
|
| 14 |
+
diffsynth/data/__init__.py
|
| 15 |
+
diffsynth/data/simple_text_image.py
|
| 16 |
+
diffsynth/data/video.py
|
| 17 |
+
diffsynth/distributed/__init__.py
|
| 18 |
+
diffsynth/distributed/xdit_context_parallel.py
|
| 19 |
+
diffsynth/extensions/__init__.py
|
| 20 |
+
diffsynth/extensions/ESRGAN/__init__.py
|
| 21 |
+
diffsynth/extensions/FastBlend/__init__.py
|
| 22 |
+
diffsynth/extensions/FastBlend/api.py
|
| 23 |
+
diffsynth/extensions/FastBlend/cupy_kernels.py
|
| 24 |
+
diffsynth/extensions/FastBlend/data.py
|
| 25 |
+
diffsynth/extensions/FastBlend/patch_match.py
|
| 26 |
+
diffsynth/extensions/FastBlend/runners/__init__.py
|
| 27 |
+
diffsynth/extensions/FastBlend/runners/accurate.py
|
| 28 |
+
diffsynth/extensions/FastBlend/runners/balanced.py
|
| 29 |
+
diffsynth/extensions/FastBlend/runners/fast.py
|
| 30 |
+
diffsynth/extensions/FastBlend/runners/interpolation.py
|
| 31 |
+
diffsynth/extensions/ImageQualityMetric/__init__.py
|
| 32 |
+
diffsynth/extensions/ImageQualityMetric/aesthetic.py
|
| 33 |
+
diffsynth/extensions/ImageQualityMetric/clip.py
|
| 34 |
+
diffsynth/extensions/ImageQualityMetric/config.py
|
| 35 |
+
diffsynth/extensions/ImageQualityMetric/hps.py
|
| 36 |
+
diffsynth/extensions/ImageQualityMetric/imagereward.py
|
| 37 |
+
diffsynth/extensions/ImageQualityMetric/mps.py
|
| 38 |
+
diffsynth/extensions/ImageQualityMetric/pickscore.py
|
| 39 |
+
diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py
|
| 40 |
+
diffsynth/extensions/ImageQualityMetric/BLIP/blip.py
|
| 41 |
+
diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py
|
| 42 |
+
diffsynth/extensions/ImageQualityMetric/BLIP/med.py
|
| 43 |
+
diffsynth/extensions/ImageQualityMetric/BLIP/vit.py
|
| 44 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py
|
| 45 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py
|
| 46 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/constants.py
|
| 47 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/factory.py
|
| 48 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/generation_utils.py
|
| 49 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py
|
| 50 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py
|
| 51 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/loss.py
|
| 52 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/model.py
|
| 53 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py
|
| 54 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/openai.py
|
| 55 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py
|
| 56 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py
|
| 57 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py
|
| 58 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py
|
| 59 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/transform.py
|
| 60 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py
|
| 61 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/utils.py
|
| 62 |
+
diffsynth/extensions/ImageQualityMetric/open_clip/version.py
|
| 63 |
+
diffsynth/extensions/ImageQualityMetric/trainer/__init__.py
|
| 64 |
+
diffsynth/extensions/ImageQualityMetric/trainer/models/__init__.py
|
| 65 |
+
diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py
|
| 66 |
+
diffsynth/extensions/ImageQualityMetric/trainer/models/clip_model.py
|
| 67 |
+
diffsynth/extensions/ImageQualityMetric/trainer/models/cross_modeling.py
|
| 68 |
+
diffsynth/extensions/RIFE/__init__.py
|
| 69 |
+
diffsynth/extensions/pixartsigma/__init__.py
|
| 70 |
+
diffsynth/extensions/pixartsigma/diffusion_utils.py
|
| 71 |
+
diffsynth/extensions/pixartsigma/dpm_solver.py
|
| 72 |
+
diffsynth/extensions/pixartsigma/gaussian_diffusion.py
|
| 73 |
+
diffsynth/extensions/pixartsigma/utils.py
|
| 74 |
+
diffsynth/extensions/pixartsigma/dpm/__init__.py
|
| 75 |
+
diffsynth/extensions/pixartsigma/dpm/dpm_solver.py
|
| 76 |
+
diffsynth/extensions/pixartsigma/nets/PixArt.py
|
| 77 |
+
diffsynth/extensions/pixartsigma/nets/PixArtMS.py
|
| 78 |
+
diffsynth/extensions/pixartsigma/nets/PixArt_blocks.py
|
| 79 |
+
diffsynth/extensions/pixartsigma/nets/__init__.py
|
| 80 |
+
diffsynth/lora/__init__.py
|
| 81 |
+
diffsynth/lora/flux_lora.py
|
| 82 |
+
diffsynth/models/__init__.py
|
| 83 |
+
diffsynth/models/attention.py
|
| 84 |
+
diffsynth/models/cog_dit.py
|
| 85 |
+
diffsynth/models/cog_vae.py
|
| 86 |
+
diffsynth/models/downloader.py
|
| 87 |
+
diffsynth/models/flux_controlnet.py
|
| 88 |
+
diffsynth/models/flux_dit.py
|
| 89 |
+
diffsynth/models/flux_infiniteyou.py
|
| 90 |
+
diffsynth/models/flux_ipadapter.py
|
| 91 |
+
diffsynth/models/flux_lora_encoder.py
|
| 92 |
+
diffsynth/models/flux_text_encoder.py
|
| 93 |
+
diffsynth/models/flux_vae.py
|
| 94 |
+
diffsynth/models/flux_value_control.py
|
| 95 |
+
diffsynth/models/hunyuan_dit.py
|
| 96 |
+
diffsynth/models/hunyuan_dit_text_encoder.py
|
| 97 |
+
diffsynth/models/hunyuan_video_dit.py
|
| 98 |
+
diffsynth/models/hunyuan_video_text_encoder.py
|
| 99 |
+
diffsynth/models/hunyuan_video_vae_decoder.py
|
| 100 |
+
diffsynth/models/hunyuan_video_vae_encoder.py
|
| 101 |
+
diffsynth/models/kolors_text_encoder.py
|
| 102 |
+
diffsynth/models/lora.py
|
| 103 |
+
diffsynth/models/model_manager.py
|
| 104 |
+
diffsynth/models/nexus_gen.py
|
| 105 |
+
diffsynth/models/nexus_gen_ar_model.py
|
| 106 |
+
diffsynth/models/nexus_gen_projector.py
|
| 107 |
+
diffsynth/models/omnigen.py
|
| 108 |
+
diffsynth/models/qwen_image_dit.py
|
| 109 |
+
diffsynth/models/qwen_image_text_encoder.py
|
| 110 |
+
diffsynth/models/qwen_image_vae.py
|
| 111 |
+
diffsynth/models/qwenvl.py
|
| 112 |
+
diffsynth/models/sd3_dit.py
|
| 113 |
+
diffsynth/models/sd3_temp_dit.py
|
| 114 |
+
diffsynth/models/sd3_text_encoder.py
|
| 115 |
+
diffsynth/models/sd3_vae_decoder.py
|
| 116 |
+
diffsynth/models/sd3_vae_encoder.py
|
| 117 |
+
diffsynth/models/sd_controlnet.py
|
| 118 |
+
diffsynth/models/sd_ipadapter.py
|
| 119 |
+
diffsynth/models/sd_motion.py
|
| 120 |
+
diffsynth/models/sd_text_encoder.py
|
| 121 |
+
diffsynth/models/sd_unet.py
|
| 122 |
+
diffsynth/models/sd_vae_decoder.py
|
| 123 |
+
diffsynth/models/sd_vae_encoder.py
|
| 124 |
+
diffsynth/models/sdxl_controlnet.py
|
| 125 |
+
diffsynth/models/sdxl_ipadapter.py
|
| 126 |
+
diffsynth/models/sdxl_motion.py
|
| 127 |
+
diffsynth/models/sdxl_text_encoder.py
|
| 128 |
+
diffsynth/models/sdxl_unet.py
|
| 129 |
+
diffsynth/models/sdxl_vae_decoder.py
|
| 130 |
+
diffsynth/models/sdxl_vae_encoder.py
|
| 131 |
+
diffsynth/models/step1x_connector.py
|
| 132 |
+
diffsynth/models/stepvideo_dit.py
|
| 133 |
+
diffsynth/models/stepvideo_text_encoder.py
|
| 134 |
+
diffsynth/models/stepvideo_vae.py
|
| 135 |
+
diffsynth/models/svd_image_encoder.py
|
| 136 |
+
diffsynth/models/svd_unet.py
|
| 137 |
+
diffsynth/models/svd_vae_decoder.py
|
| 138 |
+
diffsynth/models/svd_vae_encoder.py
|
| 139 |
+
diffsynth/models/tiler.py
|
| 140 |
+
diffsynth/models/utils.py
|
| 141 |
+
diffsynth/models/wan_video_camera_controller.py
|
| 142 |
+
diffsynth/models/wan_video_dit.py
|
| 143 |
+
diffsynth/models/wan_video_dit_dino.py
|
| 144 |
+
diffsynth/models/wan_video_image_encoder.py
|
| 145 |
+
diffsynth/models/wan_video_motion_controller.py
|
| 146 |
+
diffsynth/models/wan_video_text_encoder.py
|
| 147 |
+
diffsynth/models/wan_video_vace.py
|
| 148 |
+
diffsynth/models/wan_video_vae.py
|
| 149 |
+
diffsynth/pipelines/__init__.py
|
| 150 |
+
diffsynth/pipelines/base.py
|
| 151 |
+
diffsynth/pipelines/cog_video.py
|
| 152 |
+
diffsynth/pipelines/dancer.py
|
| 153 |
+
diffsynth/pipelines/flux_image.py
|
| 154 |
+
diffsynth/pipelines/flux_image_new.py
|
| 155 |
+
diffsynth/pipelines/hunyuan_image.py
|
| 156 |
+
diffsynth/pipelines/hunyuan_video.py
|
| 157 |
+
diffsynth/pipelines/omnigen_image.py
|
| 158 |
+
diffsynth/pipelines/pipeline_runner.py
|
| 159 |
+
diffsynth/pipelines/qwen_image.py
|
| 160 |
+
diffsynth/pipelines/sd3_image.py
|
| 161 |
+
diffsynth/pipelines/sd_image.py
|
| 162 |
+
diffsynth/pipelines/sd_video.py
|
| 163 |
+
diffsynth/pipelines/sdxl_image.py
|
| 164 |
+
diffsynth/pipelines/sdxl_video.py
|
| 165 |
+
diffsynth/pipelines/step_video.py
|
| 166 |
+
diffsynth/pipelines/svd_video.py
|
| 167 |
+
diffsynth/pipelines/wan_video.py
|
| 168 |
+
diffsynth/pipelines/wan_video_new.py
|
| 169 |
+
diffsynth/processors/FastBlend.py
|
| 170 |
+
diffsynth/processors/PILEditor.py
|
| 171 |
+
diffsynth/processors/RIFE.py
|
| 172 |
+
diffsynth/processors/__init__.py
|
| 173 |
+
diffsynth/processors/base.py
|
| 174 |
+
diffsynth/processors/sequencial_processor.py
|
| 175 |
+
diffsynth/prompters/__init__.py
|
| 176 |
+
diffsynth/prompters/base_prompter.py
|
| 177 |
+
diffsynth/prompters/cog_prompter.py
|
| 178 |
+
diffsynth/prompters/flux_prompter.py
|
| 179 |
+
diffsynth/prompters/hunyuan_dit_prompter.py
|
| 180 |
+
diffsynth/prompters/hunyuan_video_prompter.py
|
| 181 |
+
diffsynth/prompters/kolors_prompter.py
|
| 182 |
+
diffsynth/prompters/omnigen_prompter.py
|
| 183 |
+
diffsynth/prompters/omost.py
|
| 184 |
+
diffsynth/prompters/prompt_refiners.py
|
| 185 |
+
diffsynth/prompters/sd3_prompter.py
|
| 186 |
+
diffsynth/prompters/sd_prompter.py
|
| 187 |
+
diffsynth/prompters/sdxl_prompter.py
|
| 188 |
+
diffsynth/prompters/stepvideo_prompter.py
|
| 189 |
+
diffsynth/prompters/wan_prompter.py
|
| 190 |
+
diffsynth/schedulers/__init__.py
|
| 191 |
+
diffsynth/schedulers/continuous_ode.py
|
| 192 |
+
diffsynth/schedulers/ddim.py
|
| 193 |
+
diffsynth/schedulers/flow_match.py
|
| 194 |
+
diffsynth/tokenizer_configs/__init__.py
|
| 195 |
+
diffsynth/tokenizer_configs/cog/tokenizer/added_tokens.json
|
| 196 |
+
diffsynth/tokenizer_configs/cog/tokenizer/special_tokens_map.json
|
| 197 |
+
diffsynth/tokenizer_configs/cog/tokenizer/spiece.model
|
| 198 |
+
diffsynth/tokenizer_configs/cog/tokenizer/tokenizer_config.json
|
| 199 |
+
diffsynth/tokenizer_configs/flux/tokenizer_1/merges.txt
|
| 200 |
+
diffsynth/tokenizer_configs/flux/tokenizer_1/special_tokens_map.json
|
| 201 |
+
diffsynth/tokenizer_configs/flux/tokenizer_1/tokenizer_config.json
|
| 202 |
+
diffsynth/tokenizer_configs/flux/tokenizer_1/vocab.json
|
| 203 |
+
diffsynth/tokenizer_configs/flux/tokenizer_2/special_tokens_map.json
|
| 204 |
+
diffsynth/tokenizer_configs/flux/tokenizer_2/spiece.model
|
| 205 |
+
diffsynth/tokenizer_configs/flux/tokenizer_2/tokenizer.json
|
| 206 |
+
diffsynth/tokenizer_configs/flux/tokenizer_2/tokenizer_config.json
|
| 207 |
+
diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/special_tokens_map.json
|
| 208 |
+
diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/tokenizer_config.json
|
| 209 |
+
diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/vocab.txt
|
| 210 |
+
diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/vocab_org.txt
|
| 211 |
+
diffsynth/tokenizer_configs/hunyuan_dit/tokenizer_t5/config.json
|
| 212 |
+
diffsynth/tokenizer_configs/hunyuan_dit/tokenizer_t5/special_tokens_map.json
|
| 213 |
+
diffsynth/tokenizer_configs/hunyuan_dit/tokenizer_t5/spiece.model
|
| 214 |
+
diffsynth/tokenizer_configs/hunyuan_dit/tokenizer_t5/tokenizer_config.json
|
| 215 |
+
diffsynth/tokenizer_configs/hunyuan_video/tokenizer_1/merges.txt
|
| 216 |
+
diffsynth/tokenizer_configs/hunyuan_video/tokenizer_1/special_tokens_map.json
|
| 217 |
+
diffsynth/tokenizer_configs/hunyuan_video/tokenizer_1/tokenizer_config.json
|
| 218 |
+
diffsynth/tokenizer_configs/hunyuan_video/tokenizer_1/vocab.json
|
| 219 |
+
diffsynth/tokenizer_configs/hunyuan_video/tokenizer_2/preprocessor_config.json
|
| 220 |
+
diffsynth/tokenizer_configs/hunyuan_video/tokenizer_2/special_tokens_map.json
|
| 221 |
+
diffsynth/tokenizer_configs/hunyuan_video/tokenizer_2/tokenizer.json
|
| 222 |
+
diffsynth/tokenizer_configs/hunyuan_video/tokenizer_2/tokenizer_config.json
|
| 223 |
+
diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer.model
|
| 224 |
+
diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer_config.json
|
| 225 |
+
diffsynth/tokenizer_configs/kolors/tokenizer/vocab.txt
|
| 226 |
+
diffsynth/tokenizer_configs/stable_diffusion/tokenizer/merges.txt
|
| 227 |
+
diffsynth/tokenizer_configs/stable_diffusion/tokenizer/special_tokens_map.json
|
| 228 |
+
diffsynth/tokenizer_configs/stable_diffusion/tokenizer/tokenizer_config.json
|
| 229 |
+
diffsynth/tokenizer_configs/stable_diffusion/tokenizer/vocab.json
|
| 230 |
+
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_1/merges.txt
|
| 231 |
+
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_1/special_tokens_map.json
|
| 232 |
+
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_1/tokenizer_config.json
|
| 233 |
+
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_1/vocab.json
|
| 234 |
+
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_2/merges.txt
|
| 235 |
+
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_2/special_tokens_map.json
|
| 236 |
+
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_2/tokenizer_config.json
|
| 237 |
+
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_2/vocab.json
|
| 238 |
+
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_3/special_tokens_map.json
|
| 239 |
+
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_3/spiece.model
|
| 240 |
+
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_3/tokenizer.json
|
| 241 |
+
diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_3/tokenizer_config.json
|
| 242 |
+
diffsynth/tokenizer_configs/stable_diffusion_xl/tokenizer_2/merges.txt
|
| 243 |
+
diffsynth/tokenizer_configs/stable_diffusion_xl/tokenizer_2/special_tokens_map.json
|
| 244 |
+
diffsynth/tokenizer_configs/stable_diffusion_xl/tokenizer_2/tokenizer_config.json
|
| 245 |
+
diffsynth/tokenizer_configs/stable_diffusion_xl/tokenizer_2/vocab.json
|
| 246 |
+
diffsynth/trainers/__init__.py
|
| 247 |
+
diffsynth/trainers/text_to_image.py
|
| 248 |
+
diffsynth/trainers/utils.py
|
| 249 |
+
diffsynth/utils/__init__.py
|
| 250 |
+
diffsynth/vram_management/__init__.py
|
| 251 |
+
diffsynth/vram_management/gradient_checkpointing.py
|
| 252 |
+
diffsynth/vram_management/layers.py
|
diffsynth-studio/diffsynth.egg-info/dependency_links.txt
ADDED
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diffsynth-studio/diffsynth.egg-info/requires.txt
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| 1 |
+
torch>=2.0.0
|
| 2 |
+
torchvision
|
| 3 |
+
cupy-cuda12x
|
| 4 |
+
transformers==4.56.0
|
| 5 |
+
controlnet-aux==0.0.7
|
| 6 |
+
imageio
|
| 7 |
+
imageio[ffmpeg]
|
| 8 |
+
safetensors
|
| 9 |
+
einops
|
| 10 |
+
sentencepiece
|
| 11 |
+
protobuf
|
| 12 |
+
modelscope
|
| 13 |
+
ftfy
|
| 14 |
+
pynvml
|
| 15 |
+
pandas
|
| 16 |
+
accelerate
|
diffsynth-studio/diffsynth.egg-info/top_level.txt
ADDED
|
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diffsynth
|
diffsynth-studio/diffsynth/__init__.py
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+
from .data import *
|
| 2 |
+
from .models import *
|
| 3 |
+
from .prompters import *
|
| 4 |
+
from .schedulers import *
|
| 5 |
+
from .pipelines import *
|
| 6 |
+
from .controlnets import *
|
diffsynth-studio/diffsynth/__pycache__/__init__.cpython-310.pyc
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diffsynth-studio/diffsynth/configs/__init__.py
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diffsynth-studio/diffsynth/configs/__pycache__/__init__.cpython-311.pyc
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|
| 1 |
+
from typing_extensions import Literal, TypeAlias
|
| 2 |
+
|
| 3 |
+
from ..models.sd_text_encoder import SDTextEncoder
|
| 4 |
+
from ..models.sd_unet import SDUNet
|
| 5 |
+
from ..models.sd_vae_encoder import SDVAEEncoder
|
| 6 |
+
from ..models.sd_vae_decoder import SDVAEDecoder
|
| 7 |
+
|
| 8 |
+
from ..models.sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
|
| 9 |
+
from ..models.sdxl_unet import SDXLUNet
|
| 10 |
+
from ..models.sdxl_vae_decoder import SDXLVAEDecoder
|
| 11 |
+
from ..models.sdxl_vae_encoder import SDXLVAEEncoder
|
| 12 |
+
|
| 13 |
+
from ..models.sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3
|
| 14 |
+
from ..models.sd3_dit import SD3DiT
|
| 15 |
+
from ..models.sd3_vae_decoder import SD3VAEDecoder
|
| 16 |
+
from ..models.sd3_vae_encoder import SD3VAEEncoder
|
| 17 |
+
|
| 18 |
+
from ..models.sd_controlnet import SDControlNet
|
| 19 |
+
from ..models.sdxl_controlnet import SDXLControlNetUnion
|
| 20 |
+
|
| 21 |
+
from ..models.sd_motion import SDMotionModel
|
| 22 |
+
from ..models.sdxl_motion import SDXLMotionModel
|
| 23 |
+
|
| 24 |
+
from ..models.svd_image_encoder import SVDImageEncoder
|
| 25 |
+
from ..models.svd_unet import SVDUNet
|
| 26 |
+
from ..models.svd_vae_decoder import SVDVAEDecoder
|
| 27 |
+
from ..models.svd_vae_encoder import SVDVAEEncoder
|
| 28 |
+
|
| 29 |
+
from ..models.sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder
|
| 30 |
+
from ..models.sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder
|
| 31 |
+
|
| 32 |
+
from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder
|
| 33 |
+
from ..models.hunyuan_dit import HunyuanDiT
|
| 34 |
+
|
| 35 |
+
from ..models.flux_dit import FluxDiT
|
| 36 |
+
from ..models.flux_text_encoder import FluxTextEncoder2
|
| 37 |
+
from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder
|
| 38 |
+
from ..models.flux_controlnet import FluxControlNet
|
| 39 |
+
from ..models.flux_ipadapter import FluxIpAdapter
|
| 40 |
+
from ..models.flux_infiniteyou import InfiniteYouImageProjector
|
| 41 |
+
|
| 42 |
+
from ..models.cog_vae import CogVAEEncoder, CogVAEDecoder
|
| 43 |
+
from ..models.cog_dit import CogDiT
|
| 44 |
+
|
| 45 |
+
from ..models.omnigen import OmniGenTransformer
|
| 46 |
+
|
| 47 |
+
from ..models.hunyuan_video_vae_decoder import HunyuanVideoVAEDecoder
|
| 48 |
+
from ..models.hunyuan_video_vae_encoder import HunyuanVideoVAEEncoder
|
| 49 |
+
|
| 50 |
+
from ..extensions.RIFE import IFNet
|
| 51 |
+
from ..extensions.ESRGAN import RRDBNet
|
| 52 |
+
|
| 53 |
+
from ..models.hunyuan_video_dit import HunyuanVideoDiT
|
| 54 |
+
|
| 55 |
+
from ..models.stepvideo_vae import StepVideoVAE
|
| 56 |
+
from ..models.stepvideo_dit import StepVideoModel
|
| 57 |
+
|
| 58 |
+
from ..models.wan_video_dit import WanModel
|
| 59 |
+
from ..models.wan_video_text_encoder import WanTextEncoder
|
| 60 |
+
from ..models.wan_video_image_encoder import WanImageEncoder
|
| 61 |
+
from ..models.wan_video_vae import WanVideoVAE, WanVideoVAE38
|
| 62 |
+
from ..models.wan_video_motion_controller import WanMotionControllerModel
|
| 63 |
+
from ..models.wan_video_vace import VaceWanModel
|
| 64 |
+
|
| 65 |
+
from ..models.step1x_connector import Qwen2Connector
|
| 66 |
+
|
| 67 |
+
from ..models.flux_value_control import SingleValueEncoder
|
| 68 |
+
|
| 69 |
+
from ..lora.flux_lora import FluxLoraPatcher
|
| 70 |
+
from ..models.flux_lora_encoder import FluxLoRAEncoder
|
| 71 |
+
|
| 72 |
+
from ..models.nexus_gen_projector import NexusGenAdapter, NexusGenImageEmbeddingMerger
|
| 73 |
+
from ..models.nexus_gen import NexusGenAutoregressiveModel
|
| 74 |
+
|
| 75 |
+
from ..models.qwen_image_dit import QwenImageDiT
|
| 76 |
+
from ..models.qwen_image_text_encoder import QwenImageTextEncoder
|
| 77 |
+
from ..models.qwen_image_vae import QwenImageVAE
|
| 78 |
+
|
| 79 |
+
model_loader_configs = [
|
| 80 |
+
# These configs are provided for detecting model type automatically.
|
| 81 |
+
# The format is (state_dict_keys_hash, state_dict_keys_hash_with_shape, model_names, model_classes, model_resource)
|
| 82 |
+
(None, "091b0e30e77c76626b3ba62acdf95343", ["sd_controlnet"], [SDControlNet], "civitai"),
|
| 83 |
+
(None, "4a6c8306a27d916dea81263c8c88f450", ["hunyuan_dit_clip_text_encoder"], [HunyuanDiTCLIPTextEncoder], "civitai"),
|
| 84 |
+
(None, "f4aec400fe394297961218c768004521", ["hunyuan_dit"], [HunyuanDiT], "civitai"),
|
| 85 |
+
(None, "9e6e58043a5a2e332803ed42f6ee7181", ["hunyuan_dit_t5_text_encoder"], [HunyuanDiTT5TextEncoder], "civitai"),
|
| 86 |
+
(None, "13115dd45a6e1c39860f91ab073b8a78", ["sdxl_vae_encoder", "sdxl_vae_decoder"], [SDXLVAEEncoder, SDXLVAEDecoder], "diffusers"),
|
| 87 |
+
(None, "d78aa6797382a6d455362358a3295ea9", ["sd_ipadapter_clip_image_encoder"], [IpAdapterCLIPImageEmbedder], "diffusers"),
|
| 88 |
+
(None, "e291636cc15e803186b47404262ef812", ["sd_ipadapter"], [SDIpAdapter], "civitai"),
|
| 89 |
+
(None, "399c81f2f8de8d1843d0127a00f3c224", ["sdxl_ipadapter_clip_image_encoder"], [IpAdapterXLCLIPImageEmbedder], "diffusers"),
|
| 90 |
+
(None, "a64eac9aa0db4b9602213bc0131281c7", ["sdxl_ipadapter"], [SDXLIpAdapter], "civitai"),
|
| 91 |
+
(None, "52817e4fdd89df154f02749ca6f692ac", ["sdxl_unet"], [SDXLUNet], "diffusers"),
|
| 92 |
+
(None, "03343c606f16d834d6411d0902b53636", ["sd_text_encoder", "sd_unet", "sd_vae_decoder", "sd_vae_encoder"], [SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder], "civitai"),
|
| 93 |
+
(None, "d4ba77a7ece070679b4a987f58f201e9", ["sd_text_encoder"], [SDTextEncoder], "civitai"),
|
| 94 |
+
(None, "d0c89e55c5a57cf3981def0cb1c9e65a", ["sd_vae_decoder", "sd_vae_encoder"], [SDVAEDecoder, SDVAEEncoder], "civitai"),
|
| 95 |
+
(None, "3926bf373b39a67eeafd7901478a47a7", ["sd_unet"], [SDUNet], "civitai"),
|
| 96 |
+
(None, "1e0c39ec176b9007c05f76d52b554a4d", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
|
| 97 |
+
(None, "d9e0290829ba8d98e28e1a2b1407db4a", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_text_encoder_3", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
|
| 98 |
+
(None, "5072d0b24e406b49507abe861cf97691", ["sd3_text_encoder_3"], [SD3TextEncoder3], "civitai"),
|
| 99 |
+
(None, "4cf64a799d04260df438c6f33c9a047e", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"),
|
| 100 |
+
(None, "d9b008a867c498ab12ad24042eff8e3f", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"), # SDXL-Turbo
|
| 101 |
+
(None, "025bb7452e531a3853d951d77c63f032", ["sdxl_text_encoder", "sdxl_text_encoder_2"], [SDXLTextEncoder, SDXLTextEncoder2], "civitai"),
|
| 102 |
+
(None, "298997b403a4245c04102c9f36aac348", ["sdxl_unet"], [SDXLUNet], "civitai"),
|
| 103 |
+
(None, "2a07abce74b4bdc696b76254ab474da6", ["svd_image_encoder", "svd_unet", "svd_vae_decoder", "svd_vae_encoder"], [SVDImageEncoder, SVDUNet, SVDVAEDecoder, SVDVAEEncoder], "civitai"),
|
| 104 |
+
(None, "c96a285a6888465f87de22a984d049fb", ["sd_motion_modules"], [SDMotionModel], "civitai"),
|
| 105 |
+
(None, "72907b92caed19bdb2adb89aa4063fe2", ["sdxl_motion_modules"], [SDXLMotionModel], "civitai"),
|
| 106 |
+
(None, "31d2d9614fba60511fc9bf2604aa01f7", ["sdxl_controlnet"], [SDXLControlNetUnion], "diffusers"),
|
| 107 |
+
(None, "94eefa3dac9cec93cb1ebaf1747d7b78", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"),
|
| 108 |
+
(None, "1aafa3cc91716fb6b300cc1cd51b85a3", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "diffusers"),
|
| 109 |
+
(None, "21ea55f476dfc4fd135587abb59dfe5d", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "civitai"),
|
| 110 |
+
(None, "a29710fea6dddb0314663ee823598e50", ["flux_dit"], [FluxDiT], "civitai"),
|
| 111 |
+
(None, "57b02550baab820169365b3ee3afa2c9", ["flux_dit"], [FluxDiT], "civitai"),
|
| 112 |
+
(None, "3394f306c4cbf04334b712bf5aaed95f", ["flux_dit"], [FluxDiT], "civitai"),
|
| 113 |
+
(None, "023f054d918a84ccf503481fd1e3379e", ["flux_dit"], [FluxDiT], "civitai"),
|
| 114 |
+
(None, "d02f41c13549fa5093d3521f62a5570a", ["flux_dit"], [FluxDiT], "civitai"),
|
| 115 |
+
(None, "605c56eab23e9e2af863ad8f0813a25d", ["flux_dit"], [FluxDiT], "diffusers"),
|
| 116 |
+
(None, "0629116fce1472503a66992f96f3eb1a", ["flux_value_controller"], [SingleValueEncoder], "civitai"),
|
| 117 |
+
(None, "280189ee084bca10f70907bf6ce1649d", ["cog_vae_encoder", "cog_vae_decoder"], [CogVAEEncoder, CogVAEDecoder], "diffusers"),
|
| 118 |
+
(None, "9b9313d104ac4df27991352fec013fd4", ["rife"], [IFNet], "civitai"),
|
| 119 |
+
(None, "6b7116078c4170bfbeaedc8fe71f6649", ["esrgan"], [RRDBNet], "civitai"),
|
| 120 |
+
(None, "61cbcbc7ac11f169c5949223efa960d1", ["omnigen_transformer"], [OmniGenTransformer], "diffusers"),
|
| 121 |
+
(None, "78d18b9101345ff695f312e7e62538c0", ["flux_controlnet"], [FluxControlNet], "diffusers"),
|
| 122 |
+
(None, "b001c89139b5f053c715fe772362dd2a", ["flux_controlnet"], [FluxControlNet], "diffusers"),
|
| 123 |
+
(None, "52357cb26250681367488a8954c271e8", ["flux_controlnet"], [FluxControlNet], "diffusers"),
|
| 124 |
+
(None, "0cfd1740758423a2a854d67c136d1e8c", ["flux_controlnet"], [FluxControlNet], "diffusers"),
|
| 125 |
+
(None, "7f9583eb8ba86642abb9a21a4b2c9e16", ["flux_controlnet"], [FluxControlNet], "diffusers"),
|
| 126 |
+
(None, "43ad5aaa27dd4ee01b832ed16773fa52", ["flux_controlnet"], [FluxControlNet], "diffusers"),
|
| 127 |
+
(None, "c07c0f04f5ff55e86b4e937c7a40d481", ["infiniteyou_image_projector"], [InfiniteYouImageProjector], "diffusers"),
|
| 128 |
+
(None, "4daaa66cc656a8fe369908693dad0a35", ["flux_ipadapter"], [FluxIpAdapter], "diffusers"),
|
| 129 |
+
(None, "51aed3d27d482fceb5e0739b03060e8f", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
|
| 130 |
+
(None, "98cc34ccc5b54ae0e56bdea8688dcd5a", ["sd3_text_encoder_2"], [SD3TextEncoder2], "civitai"),
|
| 131 |
+
(None, "77ff18050dbc23f50382e45d51a779fe", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
|
| 132 |
+
(None, "5da81baee73198a7c19e6d2fe8b5148e", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"),
|
| 133 |
+
(None, "aeb82dce778a03dcb4d726cb03f3c43f", ["hunyuan_video_vae_decoder", "hunyuan_video_vae_encoder"], [HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder], "diffusers"),
|
| 134 |
+
(None, "b9588f02e78f5ccafc9d7c0294e46308", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"),
|
| 135 |
+
(None, "84ef4bd4757f60e906b54aa6a7815dc6", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"),
|
| 136 |
+
(None, "68beaf8429b7c11aa8ca05b1bd0058bd", ["stepvideo_vae"], [StepVideoVAE], "civitai"),
|
| 137 |
+
(None, "5c0216a2132b082c10cb7a0e0377e681", ["stepvideo_dit"], [StepVideoModel], "civitai"),
|
| 138 |
+
(None, "9269f8db9040a9d860eaca435be61814", ["wan_video_dit"], [WanModel], "civitai"),
|
| 139 |
+
(None, "aafcfd9672c3a2456dc46e1cb6e52c70", ["wan_video_dit"], [WanModel], "civitai"),
|
| 140 |
+
(None, "6bfcfb3b342cb286ce886889d519a77e", ["wan_video_dit"], [WanModel], "civitai"),
|
| 141 |
+
(None, "6d6ccde6845b95ad9114ab993d917893", ["wan_video_dit"], [WanModel], "civitai"),
|
| 142 |
+
(None, "6bfcfb3b342cb286ce886889d519a77e", ["wan_video_dit"], [WanModel], "civitai"),
|
| 143 |
+
(None, "349723183fc063b2bfc10bb2835cf677", ["wan_video_dit"], [WanModel], "civitai"),
|
| 144 |
+
(None, "efa44cddf936c70abd0ea28b6cbe946c", ["wan_video_dit"], [WanModel], "civitai"),
|
| 145 |
+
(None, "3ef3b1f8e1dab83d5b71fd7b617f859f", ["wan_video_dit"], [WanModel], "civitai"),
|
| 146 |
+
(None, "70ddad9d3a133785da5ea371aae09504", ["wan_video_dit"], [WanModel], "civitai"),
|
| 147 |
+
(None, "26bde73488a92e64cc20b0a7485b9e5b", ["wan_video_dit"], [WanModel], "civitai"),
|
| 148 |
+
(None, "ac6a5aa74f4a0aab6f64eb9a72f19901", ["wan_video_dit"], [WanModel], "civitai"),
|
| 149 |
+
(None, "b61c605c2adbd23124d152ed28e049ae", ["wan_video_dit"], [WanModel], "civitai"),
|
| 150 |
+
(None, "1f5ab7703c6fc803fdded85ff040c316", ["wan_video_dit"], [WanModel], "civitai"),
|
| 151 |
+
(None, "5b013604280dd715f8457c6ed6d6a626", ["wan_video_dit"], [WanModel], "civitai"),
|
| 152 |
+
(None, "a61453409b67cd3246cf0c3bebad47ba", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
|
| 153 |
+
(None, "7a513e1f257a861512b1afd387a8ecd9", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
|
| 154 |
+
(None, "cb104773c6c2cb6df4f9529ad5c60d0b", ["wan_video_dit"], [WanModel], "diffusers"),
|
| 155 |
+
(None, "9c8818c2cbea55eca56c7b447df170da", ["wan_video_text_encoder"], [WanTextEncoder], "civitai"),
|
| 156 |
+
(None, "5941c53e207d62f20f9025686193c40b", ["wan_video_image_encoder"], [WanImageEncoder], "civitai"),
|
| 157 |
+
(None, "1378ea763357eea97acdef78e65d6d96", ["wan_video_vae"], [WanVideoVAE], "civitai"),
|
| 158 |
+
(None, "ccc42284ea13e1ad04693284c7a09be6", ["wan_video_vae"], [WanVideoVAE], "civitai"),
|
| 159 |
+
(None, "e1de6c02cdac79f8b739f4d3698cd216", ["wan_video_vae"], [WanVideoVAE38], "civitai"),
|
| 160 |
+
(None, "dbd5ec76bbf977983f972c151d545389", ["wan_video_motion_controller"], [WanMotionControllerModel], "civitai"),
|
| 161 |
+
(None, "d30fb9e02b1dbf4e509142f05cf7dd50", ["flux_dit", "step1x_connector"], [FluxDiT, Qwen2Connector], "civitai"),
|
| 162 |
+
(None, "30143afb2dea73d1ac580e0787628f8c", ["flux_lora_patcher"], [FluxLoraPatcher], "civitai"),
|
| 163 |
+
(None, "77c2e4dd2440269eb33bfaa0d004f6ab", ["flux_lora_encoder"], [FluxLoRAEncoder], "civitai"),
|
| 164 |
+
(None, "3e6c61b0f9471135fc9c6d6a98e98b6d", ["flux_dit", "nexus_gen_generation_adapter"], [FluxDiT, NexusGenAdapter], "civitai"),
|
| 165 |
+
(None, "63c969fd37cce769a90aa781fbff5f81", ["flux_dit", "nexus_gen_editing_adapter"], [FluxDiT, NexusGenImageEmbeddingMerger], "civitai"),
|
| 166 |
+
(None, "2bd19e845116e4f875a0a048e27fc219", ["nexus_gen_llm"], [NexusGenAutoregressiveModel], "civitai"),
|
| 167 |
+
(None, "0319a1cb19835fb510907dd3367c95ff", ["qwen_image_dit"], [QwenImageDiT], "civitai"),
|
| 168 |
+
(None, "8004730443f55db63092006dd9f7110e", ["qwen_image_text_encoder"], [QwenImageTextEncoder], "diffusers"),
|
| 169 |
+
(None, "ed4ea5824d55ec3107b09815e318123a", ["qwen_image_vae"], [QwenImageVAE], "diffusers"),
|
| 170 |
+
]
|
| 171 |
+
huggingface_model_loader_configs = [
|
| 172 |
+
# These configs are provided for detecting model type automatically.
|
| 173 |
+
# The format is (architecture_in_huggingface_config, huggingface_lib, model_name, redirected_architecture)
|
| 174 |
+
("ChatGLMModel", "diffsynth.models.kolors_text_encoder", "kolors_text_encoder", None),
|
| 175 |
+
("MarianMTModel", "transformers.models.marian.modeling_marian", "translator", None),
|
| 176 |
+
("BloomForCausalLM", "transformers.models.bloom.modeling_bloom", "beautiful_prompt", None),
|
| 177 |
+
("Qwen2ForCausalLM", "transformers.models.qwen2.modeling_qwen2", "qwen_prompt", None),
|
| 178 |
+
# ("LlamaForCausalLM", "transformers.models.llama.modeling_llama", "omost_prompt", None),
|
| 179 |
+
("T5EncoderModel", "diffsynth.models.flux_text_encoder", "flux_text_encoder_2", "FluxTextEncoder2"),
|
| 180 |
+
("CogVideoXTransformer3DModel", "diffsynth.models.cog_dit", "cog_dit", "CogDiT"),
|
| 181 |
+
("SiglipModel", "transformers.models.siglip.modeling_siglip", "siglip_vision_model", "SiglipVisionModel"),
|
| 182 |
+
("LlamaForCausalLM", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoLLMEncoder"),
|
| 183 |
+
("LlavaForConditionalGeneration", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoMLLMEncoder"),
|
| 184 |
+
("Step1Model", "diffsynth.models.stepvideo_text_encoder", "stepvideo_text_encoder_2", "STEP1TextEncoder"),
|
| 185 |
+
("Qwen2_5_VLForConditionalGeneration", "diffsynth.models.qwenvl", "qwenvl", "Qwen25VL_7b_Embedder"),
|
| 186 |
+
]
|
| 187 |
+
patch_model_loader_configs = [
|
| 188 |
+
# These configs are provided for detecting model type automatically.
|
| 189 |
+
# The format is (state_dict_keys_hash_with_shape, model_name, model_class, extra_kwargs)
|
| 190 |
+
("9a4ab6869ac9b7d6e31f9854e397c867", ["svd_unet"], [SVDUNet], {"add_positional_conv": 128}),
|
| 191 |
+
]
|
| 192 |
+
|
| 193 |
+
preset_models_on_huggingface = {
|
| 194 |
+
"HunyuanDiT": [
|
| 195 |
+
("Tencent-Hunyuan/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"),
|
| 196 |
+
("Tencent-Hunyuan/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"),
|
| 197 |
+
("Tencent-Hunyuan/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"),
|
| 198 |
+
("Tencent-Hunyuan/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"),
|
| 199 |
+
],
|
| 200 |
+
"stable-video-diffusion-img2vid-xt": [
|
| 201 |
+
("stabilityai/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"),
|
| 202 |
+
],
|
| 203 |
+
"ExVideo-SVD-128f-v1": [
|
| 204 |
+
("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"),
|
| 205 |
+
],
|
| 206 |
+
# Stable Diffusion
|
| 207 |
+
"StableDiffusion_v15": [
|
| 208 |
+
("benjamin-paine/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"),
|
| 209 |
+
],
|
| 210 |
+
"DreamShaper_8": [
|
| 211 |
+
("Yntec/Dreamshaper8", "dreamshaper_8.safetensors", "models/stable_diffusion"),
|
| 212 |
+
],
|
| 213 |
+
# Textual Inversion
|
| 214 |
+
"TextualInversion_VeryBadImageNegative_v1.3": [
|
| 215 |
+
("gemasai/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"),
|
| 216 |
+
],
|
| 217 |
+
# Stable Diffusion XL
|
| 218 |
+
"StableDiffusionXL_v1": [
|
| 219 |
+
("stabilityai/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"),
|
| 220 |
+
],
|
| 221 |
+
"BluePencilXL_v200": [
|
| 222 |
+
("frankjoshua/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"),
|
| 223 |
+
],
|
| 224 |
+
"StableDiffusionXL_Turbo": [
|
| 225 |
+
("stabilityai/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"),
|
| 226 |
+
],
|
| 227 |
+
# Stable Diffusion 3
|
| 228 |
+
"StableDiffusion3": [
|
| 229 |
+
("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"),
|
| 230 |
+
],
|
| 231 |
+
"StableDiffusion3_without_T5": [
|
| 232 |
+
("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"),
|
| 233 |
+
],
|
| 234 |
+
# ControlNet
|
| 235 |
+
"ControlNet_v11f1p_sd15_depth": [
|
| 236 |
+
("lllyasviel/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"),
|
| 237 |
+
("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
|
| 238 |
+
],
|
| 239 |
+
"ControlNet_v11p_sd15_softedge": [
|
| 240 |
+
("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"),
|
| 241 |
+
("lllyasviel/Annotators", "ControlNetHED.pth", "models/Annotators")
|
| 242 |
+
],
|
| 243 |
+
"ControlNet_v11f1e_sd15_tile": [
|
| 244 |
+
("lllyasviel/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet")
|
| 245 |
+
],
|
| 246 |
+
"ControlNet_v11p_sd15_lineart": [
|
| 247 |
+
("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"),
|
| 248 |
+
("lllyasviel/Annotators", "sk_model.pth", "models/Annotators"),
|
| 249 |
+
("lllyasviel/Annotators", "sk_model2.pth", "models/Annotators")
|
| 250 |
+
],
|
| 251 |
+
"ControlNet_union_sdxl_promax": [
|
| 252 |
+
("xinsir/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"),
|
| 253 |
+
("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
|
| 254 |
+
],
|
| 255 |
+
# AnimateDiff
|
| 256 |
+
"AnimateDiff_v2": [
|
| 257 |
+
("guoyww/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"),
|
| 258 |
+
],
|
| 259 |
+
"AnimateDiff_xl_beta": [
|
| 260 |
+
("guoyww/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"),
|
| 261 |
+
],
|
| 262 |
+
|
| 263 |
+
# Qwen Prompt
|
| 264 |
+
"QwenPrompt": [
|
| 265 |
+
("Qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 266 |
+
("Qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 267 |
+
("Qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 268 |
+
("Qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 269 |
+
("Qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 270 |
+
("Qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 271 |
+
("Qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 272 |
+
("Qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 273 |
+
],
|
| 274 |
+
# Beautiful Prompt
|
| 275 |
+
"BeautifulPrompt": [
|
| 276 |
+
("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
| 277 |
+
("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
| 278 |
+
("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
| 279 |
+
("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
| 280 |
+
("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
| 281 |
+
("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
| 282 |
+
],
|
| 283 |
+
# Omost prompt
|
| 284 |
+
"OmostPrompt":[
|
| 285 |
+
("lllyasviel/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 286 |
+
("lllyasviel/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 287 |
+
("lllyasviel/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 288 |
+
("lllyasviel/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 289 |
+
("lllyasviel/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 290 |
+
("lllyasviel/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 291 |
+
("lllyasviel/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 292 |
+
("lllyasviel/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 293 |
+
],
|
| 294 |
+
# Translator
|
| 295 |
+
"opus-mt-zh-en": [
|
| 296 |
+
("Helsinki-NLP/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"),
|
| 297 |
+
("Helsinki-NLP/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"),
|
| 298 |
+
("Helsinki-NLP/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"),
|
| 299 |
+
("Helsinki-NLP/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"),
|
| 300 |
+
("Helsinki-NLP/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"),
|
| 301 |
+
("Helsinki-NLP/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"),
|
| 302 |
+
("Helsinki-NLP/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"),
|
| 303 |
+
("Helsinki-NLP/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"),
|
| 304 |
+
],
|
| 305 |
+
# IP-Adapter
|
| 306 |
+
"IP-Adapter-SD": [
|
| 307 |
+
("h94/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"),
|
| 308 |
+
("h94/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"),
|
| 309 |
+
],
|
| 310 |
+
"IP-Adapter-SDXL": [
|
| 311 |
+
("h94/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"),
|
| 312 |
+
("h94/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"),
|
| 313 |
+
],
|
| 314 |
+
"SDXL-vae-fp16-fix": [
|
| 315 |
+
("madebyollin/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix")
|
| 316 |
+
],
|
| 317 |
+
# Kolors
|
| 318 |
+
"Kolors": [
|
| 319 |
+
("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"),
|
| 320 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"),
|
| 321 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 322 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 323 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 324 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 325 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 326 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 327 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 328 |
+
("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"),
|
| 329 |
+
("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"),
|
| 330 |
+
],
|
| 331 |
+
# FLUX
|
| 332 |
+
"FLUX.1-dev": [
|
| 333 |
+
("black-forest-labs/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
|
| 334 |
+
("black-forest-labs/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
| 335 |
+
("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
| 336 |
+
("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
| 337 |
+
("black-forest-labs/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
| 338 |
+
("black-forest-labs/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
|
| 339 |
+
("black-forest-labs/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"),
|
| 340 |
+
],
|
| 341 |
+
"InstantX/FLUX.1-dev-IP-Adapter": {
|
| 342 |
+
"file_list": [
|
| 343 |
+
("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"),
|
| 344 |
+
("google/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
|
| 345 |
+
("google/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
|
| 346 |
+
],
|
| 347 |
+
"load_path": [
|
| 348 |
+
"models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin",
|
| 349 |
+
"models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder",
|
| 350 |
+
],
|
| 351 |
+
},
|
| 352 |
+
# RIFE
|
| 353 |
+
"RIFE": [
|
| 354 |
+
("AlexWortega/RIFE", "flownet.pkl", "models/RIFE"),
|
| 355 |
+
],
|
| 356 |
+
# CogVideo
|
| 357 |
+
"CogVideoX-5B": [
|
| 358 |
+
("THUDM/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
| 359 |
+
("THUDM/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
| 360 |
+
("THUDM/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
| 361 |
+
("THUDM/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
| 362 |
+
("THUDM/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"),
|
| 363 |
+
("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"),
|
| 364 |
+
("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
|
| 365 |
+
("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
|
| 366 |
+
("THUDM/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"),
|
| 367 |
+
],
|
| 368 |
+
# Stable Diffusion 3.5
|
| 369 |
+
"StableDiffusion3.5-large": [
|
| 370 |
+
("stabilityai/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"),
|
| 371 |
+
("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
|
| 372 |
+
("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
|
| 373 |
+
("stabilityai/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
|
| 374 |
+
],
|
| 375 |
+
}
|
| 376 |
+
preset_models_on_modelscope = {
|
| 377 |
+
# Hunyuan DiT
|
| 378 |
+
"HunyuanDiT": [
|
| 379 |
+
("modelscope/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"),
|
| 380 |
+
("modelscope/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"),
|
| 381 |
+
("modelscope/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"),
|
| 382 |
+
("modelscope/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"),
|
| 383 |
+
],
|
| 384 |
+
# Stable Video Diffusion
|
| 385 |
+
"stable-video-diffusion-img2vid-xt": [
|
| 386 |
+
("AI-ModelScope/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"),
|
| 387 |
+
],
|
| 388 |
+
# ExVideo
|
| 389 |
+
"ExVideo-SVD-128f-v1": [
|
| 390 |
+
("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"),
|
| 391 |
+
],
|
| 392 |
+
"ExVideo-CogVideoX-LoRA-129f-v1": [
|
| 393 |
+
("ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1", "ExVideo-CogVideoX-LoRA-129f-v1.safetensors", "models/lora"),
|
| 394 |
+
],
|
| 395 |
+
# Stable Diffusion
|
| 396 |
+
"StableDiffusion_v15": [
|
| 397 |
+
("AI-ModelScope/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"),
|
| 398 |
+
],
|
| 399 |
+
"DreamShaper_8": [
|
| 400 |
+
("sd_lora/dreamshaper_8", "dreamshaper_8.safetensors", "models/stable_diffusion"),
|
| 401 |
+
],
|
| 402 |
+
"AingDiffusion_v12": [
|
| 403 |
+
("sd_lora/aingdiffusion_v12", "aingdiffusion_v12.safetensors", "models/stable_diffusion"),
|
| 404 |
+
],
|
| 405 |
+
"Flat2DAnimerge_v45Sharp": [
|
| 406 |
+
("sd_lora/Flat-2D-Animerge", "flat2DAnimerge_v45Sharp.safetensors", "models/stable_diffusion"),
|
| 407 |
+
],
|
| 408 |
+
# Textual Inversion
|
| 409 |
+
"TextualInversion_VeryBadImageNegative_v1.3": [
|
| 410 |
+
("sd_lora/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"),
|
| 411 |
+
],
|
| 412 |
+
# Stable Diffusion XL
|
| 413 |
+
"StableDiffusionXL_v1": [
|
| 414 |
+
("AI-ModelScope/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"),
|
| 415 |
+
],
|
| 416 |
+
"BluePencilXL_v200": [
|
| 417 |
+
("sd_lora/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"),
|
| 418 |
+
],
|
| 419 |
+
"StableDiffusionXL_Turbo": [
|
| 420 |
+
("AI-ModelScope/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"),
|
| 421 |
+
],
|
| 422 |
+
"SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0": [
|
| 423 |
+
("sd_lora/zyd232_ChineseInkStyle_SDXL_v1_0", "zyd232_ChineseInkStyle_SDXL_v1_0.safetensors", "models/lora"),
|
| 424 |
+
],
|
| 425 |
+
# Stable Diffusion 3
|
| 426 |
+
"StableDiffusion3": [
|
| 427 |
+
("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"),
|
| 428 |
+
],
|
| 429 |
+
"StableDiffusion3_without_T5": [
|
| 430 |
+
("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"),
|
| 431 |
+
],
|
| 432 |
+
# ControlNet
|
| 433 |
+
"ControlNet_v11f1p_sd15_depth": [
|
| 434 |
+
("AI-ModelScope/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"),
|
| 435 |
+
("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
|
| 436 |
+
],
|
| 437 |
+
"ControlNet_v11p_sd15_softedge": [
|
| 438 |
+
("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"),
|
| 439 |
+
("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators")
|
| 440 |
+
],
|
| 441 |
+
"ControlNet_v11f1e_sd15_tile": [
|
| 442 |
+
("AI-ModelScope/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet")
|
| 443 |
+
],
|
| 444 |
+
"ControlNet_v11p_sd15_lineart": [
|
| 445 |
+
("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"),
|
| 446 |
+
("sd_lora/Annotators", "sk_model.pth", "models/Annotators"),
|
| 447 |
+
("sd_lora/Annotators", "sk_model2.pth", "models/Annotators")
|
| 448 |
+
],
|
| 449 |
+
"ControlNet_union_sdxl_promax": [
|
| 450 |
+
("AI-ModelScope/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"),
|
| 451 |
+
("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
|
| 452 |
+
],
|
| 453 |
+
"Annotators:Depth": [
|
| 454 |
+
("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators"),
|
| 455 |
+
],
|
| 456 |
+
"Annotators:Softedge": [
|
| 457 |
+
("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators"),
|
| 458 |
+
],
|
| 459 |
+
"Annotators:Lineart": [
|
| 460 |
+
("sd_lora/Annotators", "sk_model.pth", "models/Annotators"),
|
| 461 |
+
("sd_lora/Annotators", "sk_model2.pth", "models/Annotators"),
|
| 462 |
+
],
|
| 463 |
+
"Annotators:Normal": [
|
| 464 |
+
("sd_lora/Annotators", "scannet.pt", "models/Annotators"),
|
| 465 |
+
],
|
| 466 |
+
"Annotators:Openpose": [
|
| 467 |
+
("sd_lora/Annotators", "body_pose_model.pth", "models/Annotators"),
|
| 468 |
+
("sd_lora/Annotators", "facenet.pth", "models/Annotators"),
|
| 469 |
+
("sd_lora/Annotators", "hand_pose_model.pth", "models/Annotators"),
|
| 470 |
+
],
|
| 471 |
+
# AnimateDiff
|
| 472 |
+
"AnimateDiff_v2": [
|
| 473 |
+
("Shanghai_AI_Laboratory/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"),
|
| 474 |
+
],
|
| 475 |
+
"AnimateDiff_xl_beta": [
|
| 476 |
+
("Shanghai_AI_Laboratory/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"),
|
| 477 |
+
],
|
| 478 |
+
# RIFE
|
| 479 |
+
"RIFE": [
|
| 480 |
+
("Damo_XR_Lab/cv_rife_video-frame-interpolation", "flownet.pkl", "models/RIFE"),
|
| 481 |
+
],
|
| 482 |
+
# Qwen Prompt
|
| 483 |
+
"QwenPrompt": {
|
| 484 |
+
"file_list": [
|
| 485 |
+
("qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 486 |
+
("qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 487 |
+
("qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 488 |
+
("qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 489 |
+
("qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 490 |
+
("qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 491 |
+
("qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 492 |
+
("qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
| 493 |
+
],
|
| 494 |
+
"load_path": [
|
| 495 |
+
"models/QwenPrompt/qwen2-1.5b-instruct",
|
| 496 |
+
],
|
| 497 |
+
},
|
| 498 |
+
# Beautiful Prompt
|
| 499 |
+
"BeautifulPrompt": {
|
| 500 |
+
"file_list": [
|
| 501 |
+
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
| 502 |
+
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
| 503 |
+
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
| 504 |
+
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
| 505 |
+
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
| 506 |
+
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
| 507 |
+
],
|
| 508 |
+
"load_path": [
|
| 509 |
+
"models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd",
|
| 510 |
+
],
|
| 511 |
+
},
|
| 512 |
+
# Omost prompt
|
| 513 |
+
"OmostPrompt": {
|
| 514 |
+
"file_list": [
|
| 515 |
+
("Omost/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 516 |
+
("Omost/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 517 |
+
("Omost/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 518 |
+
("Omost/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 519 |
+
("Omost/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 520 |
+
("Omost/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 521 |
+
("Omost/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 522 |
+
("Omost/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
| 523 |
+
],
|
| 524 |
+
"load_path": [
|
| 525 |
+
"models/OmostPrompt/omost-llama-3-8b-4bits",
|
| 526 |
+
],
|
| 527 |
+
},
|
| 528 |
+
# Translator
|
| 529 |
+
"opus-mt-zh-en": {
|
| 530 |
+
"file_list": [
|
| 531 |
+
("moxying/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"),
|
| 532 |
+
("moxying/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"),
|
| 533 |
+
("moxying/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"),
|
| 534 |
+
("moxying/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"),
|
| 535 |
+
("moxying/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"),
|
| 536 |
+
("moxying/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"),
|
| 537 |
+
("moxying/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"),
|
| 538 |
+
("moxying/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"),
|
| 539 |
+
],
|
| 540 |
+
"load_path": [
|
| 541 |
+
"models/translator/opus-mt-zh-en",
|
| 542 |
+
],
|
| 543 |
+
},
|
| 544 |
+
# IP-Adapter
|
| 545 |
+
"IP-Adapter-SD": [
|
| 546 |
+
("AI-ModelScope/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"),
|
| 547 |
+
("AI-ModelScope/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"),
|
| 548 |
+
],
|
| 549 |
+
"IP-Adapter-SDXL": [
|
| 550 |
+
("AI-ModelScope/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"),
|
| 551 |
+
("AI-ModelScope/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"),
|
| 552 |
+
],
|
| 553 |
+
# Kolors
|
| 554 |
+
"Kolors": {
|
| 555 |
+
"file_list": [
|
| 556 |
+
("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"),
|
| 557 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"),
|
| 558 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 559 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 560 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 561 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 562 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 563 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 564 |
+
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
| 565 |
+
("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"),
|
| 566 |
+
("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"),
|
| 567 |
+
],
|
| 568 |
+
"load_path": [
|
| 569 |
+
"models/kolors/Kolors/text_encoder",
|
| 570 |
+
"models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors",
|
| 571 |
+
"models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors",
|
| 572 |
+
],
|
| 573 |
+
},
|
| 574 |
+
"SDXL-vae-fp16-fix": [
|
| 575 |
+
("AI-ModelScope/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix")
|
| 576 |
+
],
|
| 577 |
+
# FLUX
|
| 578 |
+
"FLUX.1-dev": {
|
| 579 |
+
"file_list": [
|
| 580 |
+
("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
|
| 581 |
+
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
| 582 |
+
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
| 583 |
+
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
| 584 |
+
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
| 585 |
+
("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
|
| 586 |
+
("AI-ModelScope/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"),
|
| 587 |
+
],
|
| 588 |
+
"load_path": [
|
| 589 |
+
"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
|
| 590 |
+
"models/FLUX/FLUX.1-dev/text_encoder_2",
|
| 591 |
+
"models/FLUX/FLUX.1-dev/ae.safetensors",
|
| 592 |
+
"models/FLUX/FLUX.1-dev/flux1-dev.safetensors"
|
| 593 |
+
],
|
| 594 |
+
},
|
| 595 |
+
"FLUX.1-schnell": {
|
| 596 |
+
"file_list": [
|
| 597 |
+
("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
|
| 598 |
+
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
| 599 |
+
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
| 600 |
+
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
| 601 |
+
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
| 602 |
+
("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
|
| 603 |
+
("AI-ModelScope/FLUX.1-schnell", "flux1-schnell.safetensors", "models/FLUX/FLUX.1-schnell"),
|
| 604 |
+
],
|
| 605 |
+
"load_path": [
|
| 606 |
+
"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
|
| 607 |
+
"models/FLUX/FLUX.1-dev/text_encoder_2",
|
| 608 |
+
"models/FLUX/FLUX.1-dev/ae.safetensors",
|
| 609 |
+
"models/FLUX/FLUX.1-schnell/flux1-schnell.safetensors"
|
| 610 |
+
],
|
| 611 |
+
},
|
| 612 |
+
"InstantX/FLUX.1-dev-Controlnet-Union-alpha": [
|
| 613 |
+
("InstantX/FLUX.1-dev-Controlnet-Union-alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha"),
|
| 614 |
+
],
|
| 615 |
+
"jasperai/Flux.1-dev-Controlnet-Depth": [
|
| 616 |
+
("jasperai/Flux.1-dev-Controlnet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Depth"),
|
| 617 |
+
],
|
| 618 |
+
"jasperai/Flux.1-dev-Controlnet-Surface-Normals": [
|
| 619 |
+
("jasperai/Flux.1-dev-Controlnet-Surface-Normals", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Surface-Normals"),
|
| 620 |
+
],
|
| 621 |
+
"jasperai/Flux.1-dev-Controlnet-Upscaler": [
|
| 622 |
+
("jasperai/Flux.1-dev-Controlnet-Upscaler", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler"),
|
| 623 |
+
],
|
| 624 |
+
"alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha": [
|
| 625 |
+
("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha"),
|
| 626 |
+
],
|
| 627 |
+
"alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta": [
|
| 628 |
+
("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"),
|
| 629 |
+
],
|
| 630 |
+
"Shakker-Labs/FLUX.1-dev-ControlNet-Depth": [
|
| 631 |
+
("Shakker-Labs/FLUX.1-dev-ControlNet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Depth"),
|
| 632 |
+
],
|
| 633 |
+
"Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro": [
|
| 634 |
+
("Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro"),
|
| 635 |
+
],
|
| 636 |
+
"InstantX/FLUX.1-dev-IP-Adapter": {
|
| 637 |
+
"file_list": [
|
| 638 |
+
("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"),
|
| 639 |
+
("AI-ModelScope/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
|
| 640 |
+
("AI-ModelScope/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
|
| 641 |
+
],
|
| 642 |
+
"load_path": [
|
| 643 |
+
"models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin",
|
| 644 |
+
"models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder",
|
| 645 |
+
],
|
| 646 |
+
},
|
| 647 |
+
"InfiniteYou":{
|
| 648 |
+
"file_list":[
|
| 649 |
+
("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"),
|
| 650 |
+
("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"),
|
| 651 |
+
("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/image_proj_model.bin", "models/InfiniteYou"),
|
| 652 |
+
("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/1k3d68.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
|
| 653 |
+
("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/2d106det.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
|
| 654 |
+
("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/genderage.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
|
| 655 |
+
("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/glintr100.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
|
| 656 |
+
("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/scrfd_10g_bnkps.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
|
| 657 |
+
],
|
| 658 |
+
"load_path":[
|
| 659 |
+
[
|
| 660 |
+
"models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors",
|
| 661 |
+
"models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors"
|
| 662 |
+
],
|
| 663 |
+
"models/InfiniteYou/image_proj_model.bin",
|
| 664 |
+
],
|
| 665 |
+
},
|
| 666 |
+
# ESRGAN
|
| 667 |
+
"ESRGAN_x4": [
|
| 668 |
+
("AI-ModelScope/Real-ESRGAN", "RealESRGAN_x4.pth", "models/ESRGAN"),
|
| 669 |
+
],
|
| 670 |
+
# RIFE
|
| 671 |
+
"RIFE": [
|
| 672 |
+
("AI-ModelScope/RIFE", "flownet.pkl", "models/RIFE"),
|
| 673 |
+
],
|
| 674 |
+
# Omnigen
|
| 675 |
+
"OmniGen-v1": {
|
| 676 |
+
"file_list": [
|
| 677 |
+
("BAAI/OmniGen-v1", "vae/diffusion_pytorch_model.safetensors", "models/OmniGen/OmniGen-v1/vae"),
|
| 678 |
+
("BAAI/OmniGen-v1", "model.safetensors", "models/OmniGen/OmniGen-v1"),
|
| 679 |
+
("BAAI/OmniGen-v1", "config.json", "models/OmniGen/OmniGen-v1"),
|
| 680 |
+
("BAAI/OmniGen-v1", "special_tokens_map.json", "models/OmniGen/OmniGen-v1"),
|
| 681 |
+
("BAAI/OmniGen-v1", "tokenizer_config.json", "models/OmniGen/OmniGen-v1"),
|
| 682 |
+
("BAAI/OmniGen-v1", "tokenizer.json", "models/OmniGen/OmniGen-v1"),
|
| 683 |
+
],
|
| 684 |
+
"load_path": [
|
| 685 |
+
"models/OmniGen/OmniGen-v1/vae/diffusion_pytorch_model.safetensors",
|
| 686 |
+
"models/OmniGen/OmniGen-v1/model.safetensors",
|
| 687 |
+
]
|
| 688 |
+
},
|
| 689 |
+
# CogVideo
|
| 690 |
+
"CogVideoX-5B": {
|
| 691 |
+
"file_list": [
|
| 692 |
+
("ZhipuAI/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
| 693 |
+
("ZhipuAI/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
| 694 |
+
("ZhipuAI/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
| 695 |
+
("ZhipuAI/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
| 696 |
+
("ZhipuAI/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"),
|
| 697 |
+
("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"),
|
| 698 |
+
("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
|
| 699 |
+
("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
|
| 700 |
+
("ZhipuAI/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"),
|
| 701 |
+
],
|
| 702 |
+
"load_path": [
|
| 703 |
+
"models/CogVideo/CogVideoX-5b/text_encoder",
|
| 704 |
+
"models/CogVideo/CogVideoX-5b/transformer",
|
| 705 |
+
"models/CogVideo/CogVideoX-5b/vae/diffusion_pytorch_model.safetensors",
|
| 706 |
+
],
|
| 707 |
+
},
|
| 708 |
+
# Stable Diffusion 3.5
|
| 709 |
+
"StableDiffusion3.5-large": [
|
| 710 |
+
("AI-ModelScope/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"),
|
| 711 |
+
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
|
| 712 |
+
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
|
| 713 |
+
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
|
| 714 |
+
],
|
| 715 |
+
"StableDiffusion3.5-medium": [
|
| 716 |
+
("AI-ModelScope/stable-diffusion-3.5-medium", "sd3.5_medium.safetensors", "models/stable_diffusion_3"),
|
| 717 |
+
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
|
| 718 |
+
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
|
| 719 |
+
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
|
| 720 |
+
],
|
| 721 |
+
"StableDiffusion3.5-large-turbo": [
|
| 722 |
+
("AI-ModelScope/stable-diffusion-3.5-large-turbo", "sd3.5_large_turbo.safetensors", "models/stable_diffusion_3"),
|
| 723 |
+
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
|
| 724 |
+
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
|
| 725 |
+
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
|
| 726 |
+
],
|
| 727 |
+
"HunyuanVideo":{
|
| 728 |
+
"file_list": [
|
| 729 |
+
("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"),
|
| 730 |
+
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
| 731 |
+
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
| 732 |
+
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
| 733 |
+
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
| 734 |
+
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"),
|
| 735 |
+
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"),
|
| 736 |
+
("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"),
|
| 737 |
+
("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideo/transformers")
|
| 738 |
+
],
|
| 739 |
+
"load_path": [
|
| 740 |
+
"models/HunyuanVideo/text_encoder/model.safetensors",
|
| 741 |
+
"models/HunyuanVideo/text_encoder_2",
|
| 742 |
+
"models/HunyuanVideo/vae/pytorch_model.pt",
|
| 743 |
+
"models/HunyuanVideo/transformers/mp_rank_00_model_states.pt"
|
| 744 |
+
],
|
| 745 |
+
},
|
| 746 |
+
"HunyuanVideoI2V":{
|
| 747 |
+
"file_list": [
|
| 748 |
+
("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideoI2V/text_encoder"),
|
| 749 |
+
("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00001-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
|
| 750 |
+
("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00002-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
|
| 751 |
+
("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00003-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
|
| 752 |
+
("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00004-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
|
| 753 |
+
("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "config.json", "models/HunyuanVideoI2V/text_encoder_2"),
|
| 754 |
+
("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model.safetensors.index.json", "models/HunyuanVideoI2V/text_encoder_2"),
|
| 755 |
+
("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/vae/pytorch_model.pt", "models/HunyuanVideoI2V/vae"),
|
| 756 |
+
("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideoI2V/transformers")
|
| 757 |
+
],
|
| 758 |
+
"load_path": [
|
| 759 |
+
"models/HunyuanVideoI2V/text_encoder/model.safetensors",
|
| 760 |
+
"models/HunyuanVideoI2V/text_encoder_2",
|
| 761 |
+
"models/HunyuanVideoI2V/vae/pytorch_model.pt",
|
| 762 |
+
"models/HunyuanVideoI2V/transformers/mp_rank_00_model_states.pt"
|
| 763 |
+
],
|
| 764 |
+
},
|
| 765 |
+
"HunyuanVideo-fp8":{
|
| 766 |
+
"file_list": [
|
| 767 |
+
("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"),
|
| 768 |
+
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
| 769 |
+
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
| 770 |
+
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
| 771 |
+
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
| 772 |
+
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"),
|
| 773 |
+
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"),
|
| 774 |
+
("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"),
|
| 775 |
+
("DiffSynth-Studio/HunyuanVideo-safetensors", "model.fp8.safetensors", "models/HunyuanVideo/transformers")
|
| 776 |
+
],
|
| 777 |
+
"load_path": [
|
| 778 |
+
"models/HunyuanVideo/text_encoder/model.safetensors",
|
| 779 |
+
"models/HunyuanVideo/text_encoder_2",
|
| 780 |
+
"models/HunyuanVideo/vae/pytorch_model.pt",
|
| 781 |
+
"models/HunyuanVideo/transformers/model.fp8.safetensors"
|
| 782 |
+
],
|
| 783 |
+
},
|
| 784 |
+
}
|
| 785 |
+
Preset_model_id: TypeAlias = Literal[
|
| 786 |
+
"HunyuanDiT",
|
| 787 |
+
"stable-video-diffusion-img2vid-xt",
|
| 788 |
+
"ExVideo-SVD-128f-v1",
|
| 789 |
+
"ExVideo-CogVideoX-LoRA-129f-v1",
|
| 790 |
+
"StableDiffusion_v15",
|
| 791 |
+
"DreamShaper_8",
|
| 792 |
+
"AingDiffusion_v12",
|
| 793 |
+
"Flat2DAnimerge_v45Sharp",
|
| 794 |
+
"TextualInversion_VeryBadImageNegative_v1.3",
|
| 795 |
+
"StableDiffusionXL_v1",
|
| 796 |
+
"BluePencilXL_v200",
|
| 797 |
+
"StableDiffusionXL_Turbo",
|
| 798 |
+
"ControlNet_v11f1p_sd15_depth",
|
| 799 |
+
"ControlNet_v11p_sd15_softedge",
|
| 800 |
+
"ControlNet_v11f1e_sd15_tile",
|
| 801 |
+
"ControlNet_v11p_sd15_lineart",
|
| 802 |
+
"AnimateDiff_v2",
|
| 803 |
+
"AnimateDiff_xl_beta",
|
| 804 |
+
"RIFE",
|
| 805 |
+
"BeautifulPrompt",
|
| 806 |
+
"opus-mt-zh-en",
|
| 807 |
+
"IP-Adapter-SD",
|
| 808 |
+
"IP-Adapter-SDXL",
|
| 809 |
+
"StableDiffusion3",
|
| 810 |
+
"StableDiffusion3_without_T5",
|
| 811 |
+
"Kolors",
|
| 812 |
+
"SDXL-vae-fp16-fix",
|
| 813 |
+
"ControlNet_union_sdxl_promax",
|
| 814 |
+
"FLUX.1-dev",
|
| 815 |
+
"FLUX.1-schnell",
|
| 816 |
+
"InstantX/FLUX.1-dev-Controlnet-Union-alpha",
|
| 817 |
+
"jasperai/Flux.1-dev-Controlnet-Depth",
|
| 818 |
+
"jasperai/Flux.1-dev-Controlnet-Surface-Normals",
|
| 819 |
+
"jasperai/Flux.1-dev-Controlnet-Upscaler",
|
| 820 |
+
"alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha",
|
| 821 |
+
"alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta",
|
| 822 |
+
"Shakker-Labs/FLUX.1-dev-ControlNet-Depth",
|
| 823 |
+
"Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro",
|
| 824 |
+
"InstantX/FLUX.1-dev-IP-Adapter",
|
| 825 |
+
"InfiniteYou",
|
| 826 |
+
"SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0",
|
| 827 |
+
"QwenPrompt",
|
| 828 |
+
"OmostPrompt",
|
| 829 |
+
"ESRGAN_x4",
|
| 830 |
+
"RIFE",
|
| 831 |
+
"OmniGen-v1",
|
| 832 |
+
"CogVideoX-5B",
|
| 833 |
+
"Annotators:Depth",
|
| 834 |
+
"Annotators:Softedge",
|
| 835 |
+
"Annotators:Lineart",
|
| 836 |
+
"Annotators:Normal",
|
| 837 |
+
"Annotators:Openpose",
|
| 838 |
+
"StableDiffusion3.5-large",
|
| 839 |
+
"StableDiffusion3.5-medium",
|
| 840 |
+
"HunyuanVideo",
|
| 841 |
+
"HunyuanVideo-fp8",
|
| 842 |
+
"HunyuanVideoI2V",
|
| 843 |
+
]
|
diffsynth-studio/diffsynth/controlnets/__init__.py
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager, FluxMultiControlNetManager
|
| 2 |
+
from .processors import Annotator
|
diffsynth-studio/diffsynth/controlnets/__pycache__/__init__.cpython-310.pyc
ADDED
|
Binary file (417 Bytes). View file
|
|
|