DiffSynth-Engine

DiffSynth-Engine is a high-performance engine geared towards buidling efficient inference pipelines for diffusion models.

Key Features:

• Thoughtfully-Designed Implementation: We carefully re-implemented key components in Diffusion pipelines, such as sampler and scheduler, without introducing external dependencies on libraries like k-diffusion, ldm, or sgm.

• Extensive Model Support: Compatible with popular formats (e.g., CivitAI) of base models and LoRA models , catering to diverse use cases.

• Versatile Resource Management: Comprehensive support for varous model quantization (e.g., FP8, INT8) and offloading strategies, enabling loading of larger diffusion models (e.g., Flux.1 Dev) on limited hardware budget of GPU memory.

• Optimized Performance: Carefully-crafted inference pipeline to achieve fast generation across various hardware environments.

• Cross-Platform Support: Runnable on Windows, macOS (Apple Silicon), and Linux, ensuring a smooth experience across different operating systems.

Quick Start

Requirements

• Python 3.10+
• NVIDIA GPU with compute capability 8.6+ (e.g., RTX 50 Series, RTX 40 Series, RTX 30 Series. Please see here for more details about your GPUs.) or Apple Silicon M-series.

Installation

Install released version (from PyPI):

pip3 install diffsynth-engine

Install from source:

git clone https://github.com/modelscope/diffsynth-engine.git && cd diffsynth-engine
pip3 install -e .

Usage

Text to image

from diffsynth_engine import fetch_model, FluxImagePipeline

model_path = fetch_model("muse/flux-with-vae", path="flux1-dev-with-vae.safetensors")
pipe = FluxImagePipeline.from_pretrained(model_path, device='cuda:0')
image = pipe(prompt="a cat")
image.save("image.png")

Text to image with LoRA

from diffsynth_engine import fetch_model, FluxImagePipeline

model_path = fetch_model("muse/flux-with-vae", path="flux1-dev-with-vae.safetensors")
lora_path = fetch_model("DonRat/MAJICFLUS_SuperChinesestyleheongsam", path="麦橘超国风旗袍.safetensors")

pipe = FluxImagePipeline.from_pretrained(model_path, device='cuda:0')
pipe.load_lora(path=lora_path, scale=1.0)
image = pipe(prompt="a girl, qipao")
image.save("image.png")

For more details, please refer to our tutorials (English, 中文).

Showcase

Contact

If you have any questions or feedback, please scan the QR code below, or send email to [email protected].

Contributing

We welcome contributions to DiffSynth-Engine. After Install from source, we recommand developers install this project using following command to setup the development environment.

pip install -e '.[dev]'

TODO: Please refer to CONTRIBUTING.md for more details.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Citation

If you use this codebase, or otherwise found our work helpful, please cite:

@misc{diffsynth-engine2025,
      title={DiffSynth-Engine: a high-performance diffusion inference engine},
      author={Zhipeng Di, Guoxuan Zhu, Zhongjie Duan, Zihao Chu, Yingda Chen, Weiyi Lu},
      year={2025},
      publisher = {GitHub},
      howpublished = {\url{https://github.com/modelscope/diffsynth-engine}},
}