This release is a pre-release and may not be stable for production use.
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
- 🚀Accelerating Stable Video Diffusion 3x faster with OneDiff DeepCache + Int8
- 🚀Accelerating SDXL 3x faster with DeepCache and OneDiff
- 🚀InstantID can run 1.8x Faster with OneDiff
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
OneDiff 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 .
NOTE: If you intend to utilize plugins for ComfyUI/StableDiffusion-WebUI, we highly recommend installing OneDiff from the source rather than PyPI. This is necessary as you'll need to manually copy (or create a soft link) for the relevant code into the extension folder of these UIs/Libs.
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/*
3 maintainers
Metadata
3 maintainers
Release files for onediff 0.12.1.dev202402220123
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| onediff-0.12.1.dev202402220123.tar.gz | 73.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| onediff-0.12.1.dev202402220123-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 156.8 kB
Release files / onediff-0.12.1.dev202402220123.tar.gz
| Download URL | onediff-0.12.1.dev202402220123.tar.gz |
|---|---|
| Size | 73.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.9.18
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Release files / onediff-0.12.1.dev202402220123-py3-none-any.whl
| Download URL | onediff-0.12.1.dev202402220123-py3-none-any.whl |
|---|---|
| Size | 83.2 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.9.18
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