OneDiff is an out-of-the-box acceleration library for diffusion models, it provides:
- PyTorch Module compilation tools and strong optimized GPU Kernels for diffusion models
- Out-of-the-box acceleration for popular UIs/libs
OneDiff is the abbreviation of "one line of code to accelerate diffusion models". Here is the latest 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
Here is the introduction of OneDiff Community.
- Create an issue
- Chat in Discord:
- Email for Enterprise Edition or other business inquiries: [email protected]
The Full Introduction of OneDiff:
OneDiff interfaces with various front-end sd frameworks upward, and uses a custom virtual machine mixed with PyTorch as the inference engine downward.
- Model stabilityai/stable-diffusion-xl-base-1.0;
- Image size 1024*1024, batch size 1, steps 30;
- NVIDIA A100 80G SXM4;
- Model stabilityai/stable-video-diffusion-img2vid-xt;
- Image size 576*1024, batch size 1, steps 25, decoder chunk size 5;
- NVIDIA A100 80G SXM4;
| Main Function | Details |
|---|---|
| Compiling Time | About 1 minute (SDXL) |
| Deployment Methods | Plug and Play |
| Dynamic Image Size Support | Support with no overhead |
| Model Support | SD1.5~2.1, SDXL, SDXL Turbo, etc. |
| Algorithm Support | SD standard workflow, LoRA, ControlNet, SVD, InstantID, SDXL Lightning, etc. |
| SD Framework Support | ComfyUI, Diffusers, SD-webui |
| Save & Load Accelerated Models | Yes |
| Time of LoRA Switching | Hundreds of milliseconds |
| LoRA Occupancy | Tens of MB to hundreds of MB. |
| Device Support | NVIDIA GPU 3090 RTX/4090 RTX/A100/A800/A10 etc. (Compatibility with Ascend in progress) |
OneDiff supports the acceleration for SOTA models.
- stable: release for public usage, and has long-term support;
- beta: release for professional usage, and has long-term support;
- alpha: early release for expert usage, and is under active development;
| AIGC Type | Models | HF diffusers | ComfyUI | SD web UI | |||
|---|---|---|---|---|---|---|---|
| Community | Enterprise | Community | Enterprise | Community | Enterprise | ||
| Image | SD 1.5 | stable | stable | stable | stable | beta | beta |
| SD 2.1 | stable | stable | stable | stable | beta | beta | |
| SDXL | stable | stable | stable | stable | beta | beta | |
| LoRA | stable | stable | beta | ||||
| ControlNet | stable | stable | |||||
| SDXL Turbo | stable | stable | |||||
| LCM | stable | stable | |||||
| SDXL DeepCache | stable | beta | stable | beta | |||
| InstantID | stable | stable | |||||
| Video | SVD(stable Video Diffusion) | stable | beta | stable | beta | ||
| SVD DeepCache | stable | beta | stable | beta |
Note: Enterprise Edition contains all the functionality in Community Edition.
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
- Compile at one device(such as device 0), then use the compiled result to other device(such as device 1~7).
- This is for special scene and is in the Enterprise Edition.
If you need Enterprise-level Support for your system or business, you can
- subscribe to Enterprise Edition online and get all support after the order: https://siliconflow.com/onediff.html
- or send an email to [email protected] 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 Edition | OneDiff Community Edition | |
|---|---|---|
| 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) for the most used model | Yes | |
| Tools for specific(very large scale) server side deployment | Yes | |
| Technical Support for deployment | High priority support | Community |
| Get the experimental features | Yes |
- Linux
- If you want to use OneDiff on Windows, please use it under WSL.
- NVIDIA GPUs
NOTE: We have updated OneFlow frequently 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 -U --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 -U --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 -U --pre oneflow -f https://oneflow-pro.oss-cn-beijing.aliyuncs.com/branch/community/cu122
Note: You can choose the latest versions you want for diffusers or transformers.
python3 -m pip install "torch" "transformers==4.27.1" "diffusers[torch]==0.19.3"
- 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.
python3 -m pip install huggingface_hub
~/.local/bin/huggingface-cli login
-
run examples to check it works
cd onediff_diffusers_extensions python3 examples/text_to_image.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/*