onediff 0.12.1.dev202401230352

an out-of-the-box acceleration library for diffusion models

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OneDiff is an out-of-the-box acceleration library for diffusion models (especially for ComfyUI, HF diffusers, and Stable Diffusion web UI).

OneDiff is the abbreviation of "one line of code to accelerate diffusion models".

Need help or communicate

• Create an issue
• Chat in Discord:
• Email for business inquiry: [e-mail]
• OneDiff Development Roadmap

Easy to use

• Out-of-the-box acceleration for popular UIs/libs
  • ComfyUI
  • HF diffusers 🤗
  • Stable Diffusion web UI
  • Acceleration with oneflow_compile
• Acceleration for state-of-the-art Models
  • SDXL
  • SDXL Turbo
  • SD 1.5/2.1
  • LoRA (and dynamic switching LoRA)
  • ControlNet
  • LCM and LCM LoRA
  • Stable Video Diffusion
  • DeepCache
• Ready for production
  • Support Multi-resolution input
  • Compile and save the compiled result offline, then load it online for serving
    • Save and Load the compiled graph
    • Change device of the compiled graph to do multi-process serving

State-of-the-art performance

OS and GPU support

• Linux
  • If you want to use OneDiff on Windows, please use it under WSL.
• NVIDIA GPUs

OneDiff Online Playground

Online Playground

OneDiff Enterprise Edition

If you need Enterprise-level Support for your system or business, please send an email to [e-mail] and tell us about your user case, deployment scale, and requirements.

OneDiff Enterprise Edition can be subscripted for one month and one GPU and the cost is low: https://siliconflow.com/onediff.html

	OneDiff Enterprise	OneDiff Community
SD/SDXL series model Optimization	Yes	Yes
UNet/VAE/ControlNet Optimization	Yes	Yes
LoRA(and dynamic switching LoRA)	Yes	Yes
SDXL Turbo/LCM	Yes	Yes
Stable Video Diffusion	Yes	Yes
HF diffusers	Yes	Yes
ComfyUI	Yes	Yes
Stable Diffusion web UI	Yes	Yes
Multiple Resolutions	Yes(No time cost for most of the cases)	Yes(No time cost for most of the cases)
More Extreme and Dedicated optimization(usually another 20~100% performance gain)	Yes
Technical Support for deployment	High priority support	Community
Get the latest technology/feature	Yes

Install from source or Using in Docker

Install from source

1. Install OneFlow

NOTE: We have updated OneFlow a lot for OneDiff, so please install OneFlow by the links below.

• CUDA 11.8

  # For NA/EU users
  python3 -m pip install -U --pre oneflow -f https://github.com/siliconflow/oneflow_releases/releases/expanded_assets/community_cu118
  

  # For CN users
  python3 -m pip install --pre oneflow -f https://oneflow-pro.oss-cn-beijing.aliyuncs.com/branch/community/cu118
  

Click to get OneFlow packages for other CUDA versions.

• CUDA 12.1

  # For NA/EU users
  python3 -m pip install -U --pre oneflow -f https://github.com/siliconflow/oneflow_releases/releases/expanded_assets/community_cu121
  

  # For CN users
  python3 -m pip install --pre oneflow -f https://oneflow-pro.oss-cn-beijing.aliyuncs.com/branch/community/cu121
  

• CUDA 12.2

  # For NA/EU users
  python3 -m pip install -U --pre oneflow -f https://github.com/siliconflow/oneflow_releases/releases/expanded_assets/community_cu122
  

  # For CN users
  python3 -m pip install --pre oneflow -f https://oneflow-pro.oss-cn-beijing.aliyuncs.com/branch/community/cu122
  

2. Install torch and diffusers

python3 -m pip install "torch" "transformers==4.27.1" "diffusers[torch]==0.19.3"

3. Install OneDiff

• From PyPI

python3 -m pip install --pre onediff

• From source

git clone https://github.com/siliconflow/onediff.git
cd onediff && python3 -m pip install -e .

4. (Optional)Login huggingface-cli

python3 -m pip install huggingface_hub
 ~/.local/bin/huggingface-cli login

Docker

docker pull oneflowinc/onediff:20231106

Release

• run examples to check it works

  python3 examples/text_to_image.py
  python3 examples/text_to_image_dpmsolver.py
  

• bump version in these files:

  .github/workflows/pub.yml
  src/onediff/__init__.py
  

• install build package

  python3 -m pip install build
  

• build wheel

  rm -rf dist
  python3 -m build
  

• upload to pypi

  twine upload dist/*