onediff: A Python repository from ErwannMillon

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".

News

Community & Support

Create an issue
Chat in Discord:
Email for business inquiry: contact@siliconflow.com
OneDiff Development Roadmap

State-of-the-art performance

Easy to use

Out-of-the-box acceleration for popular UIs/libs
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
- InstantID
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

OneDiff Online Playground

Online Playground

OneDiff Enterprise Edition

If you need Enterprise-level Support for your system or business, you can

subscribe Enterprise Edition online and get all support after the order: https://siliconflow.com/onediff.html
or send an email to contact@siliconflow.com 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.

	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

OS and GPU support

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

OneDiff Installation

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

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/*
```

ErwannMillon/onediff