This project provides a research-focused platform for exploring cutting-edge diffusion models, particularly for video synthesis and image generation. It targets academic researchers and developers seeking to integrate and experiment with a wide array of state-of-the-art diffusion models, offering novel inference capabilities and a flexible framework for innovation.

How It Works

DiffSynth Studio integrates numerous open-source diffusion models, including FLUX, Wan-Video, CogVideoX, and Stable Diffusion variants. It supports advanced techniques like ControlNet for fine-grained control, LoRA for efficient fine-tuning, and specialized pipelines for tasks such as video editing, stylization, and toon shading. The project emphasizes aggressive technological exploration, enabling users to combine different models and techniques for novel applications.

Quick Start & Requirements

• Install: pip install -e . (recommended from source) or pip install diffsynth (PyPI, may lag).
• Prerequisites: torch, sentencepiece, cmake, cupy.
• Models: Download pre-set models via diffsynth.download_models or custom models from ModelScope/HuggingFace.
• Docs: https://diffsynth-studio.readthedocs.io/zh-cn/latest/index.html

Highlighted Details

• Supports a broad range of video synthesis models (e.g., Wan-Video, HunyuanVideo, CogVideoX, ExVideo) and image models (e.g., FLUX, Kolors, Stable Diffusion 3).
• Features advanced capabilities like ControlNet compatibility for complex image generation, extended video generation (up to 128 frames), and toon shading.
• Includes research contributions like EliGen for entity-level control and ArtAug for aesthetic enhancement.
• Offers both Python API and WebUI (Gradio/Streamlit) for usage.

Maintenance & Community

The project has transitioned to ModelScope and is actively maintained. It has released numerous updates and research papers, indicating ongoing development. Links to demos and model repositories are provided.

Licensing & Compatibility

The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project is primarily targeted at academic exploration and may require significant technical expertise to set up and utilize effectively. The PyPI installation may not always reflect the latest features.