#1010th of 24 in Image and video
diffsynth-studio
Python engine for running and training diffusion image, video and audio models
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- Stars
- 13.3k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Nov 2025
- Language
- Python
12,011 on 31 Mar 202613,282 on 20 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
DiffSynth-Studio is a Python framework from the ModelScope community for inference and training of diffusion models covering image, video and audio generation, plus image quality metrics. It schedules weights across disk, RAM and VRAM and supports NF4 and INT8 quantization so large models run on small GPUs. Most models that support inference also support LoRA or full training.
Who it is for: Developers running or fine-tuning diffusion models on limited-VRAM GPUs
Ranked among open-source alternatives to Midjourney (#10 of 10).
Strengths
- Inference and training for most supported models, including LoRA and adapter models
- VRAM offloading and quantization (bitsandbytes, torchao, EntroPack) for low-VRAM GPUs
- Broad model coverage: Wan, LTX-2, Qwen-Image, FLUX, ACE-Step, Stable Diffusion
- Separate ComfyUI node package and LoRA training WebUI built on it
Weaknesses
- Code-first library; the README describes no built-in web UI
- Only two main maintainers; README warns of slow issue responses
- Major version update dropped some older features
- Last release was 2025-11-18, while new models land on the main branch
What it needs
- GPU optional
- Needs PyTorch
- Models: Wan, LTX-2, Qwen-Image, FLUX.2, Z-Image
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