This release is a pre-release and may not be stable for production use.
OneDiff
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
For example:
OneDiff is the abbreviation of "one line of code to accelerate diffusion models".
News
The latest news:
- :rocket:OneDiff 1.0 is out! (Acceleration of SD & SVD with one line of code)
- :rocket:Accelerating Stable Video Diffusion 3x faster with OneDiff DeepCache + Int8
- :rocket:Accelerating SDXL 3x faster with DeepCache and OneDiff
Community and Support
Here is the introduction of OneDiff Community.
- Create an issue
- Chat in Discord:
- Email for Enterprise Edition or other business inquiries: contact@siliconflow.com
OS and GPU Compatibility
-
Linux
- If you want to use OneDiff on Windows, please use it under WSL.
- The guide to install OneDiff in WSL2.
-
NVIDIA GPUs
The Full Introduction of OneDiff:
About OneDiff
Architecture
OneDiff interfaces with various front-end sd frameworks upward, and uses a custom virtual machine mixed with PyTorch as the inference engine downward.
State-of-the-art performance
SDXL E2E time
- Model stabilityai/stable-diffusion-xl-base-1.0;
- Image size 1024*1024, batch size 1, steps 30;
- NVIDIA A100 80G SXM4;
SVD E2E time
- Model stabilityai/stable-video-diffusion-img2vid-xt;
- Image size 576*1024, batch size 1, steps 25, decoder chunk size 5;
- NVIDIA A100 80G SXM4;
Note that we haven't got the way to run SVD with TensorRT on Feb 29 2024.
Features
| Functionality | 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) |
Acceleration for State-of-the-art models
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 should be careful to use;
| AIGC Type | Models | HF diffusers | ComfyUI | SD web UI | |||
|---|---|---|---|---|---|---|---|
| Community | Enterprise | Community | Enterprise | Community | Enterprise | ||
| Image | SD 1.5 | stable | stable | stable | stable | stable | stable |
| SD 2.1 | stable | stable | stable | stable | stable | stable | |
| SDXL | stable | stable | stable | stable | stable | stable | |
| LoRA | stable | stable | stable | ||||
| ControlNet | stable | stable | |||||
| SDXL Turbo | stable | stable | |||||
| LCM | stable | stable | |||||
| SDXL DeepCache | alpha | alpha | alpha | alpha | |||
| InstantID | beta | beta | |||||
| Video | SVD(stable Video Diffusion) | stable | stable | stable | stable | ||
| SVD DeepCache | alpha | alpha | alpha | alpha |
Note: Enterprise Edition contains all the functionality in Community Edition.
Acceleration for production environment
PyTorch Module compilation
Avoid compilation time for new input shape
Avoid compilation time for online serving
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).
OneDiff Quality Evaluation
We also maintain a repository for benchmarking the quality of generation after acceleration using OneDiff: OneDiffGenMetrics
OneDiff Enterprise Edition
If you need Enterprise-level Support for your system or business, you can
- Subscribe to the OneDiff Enterprise Edition directly through our website. Upon purchase, you'll gain immediate access to comprehensive support: https://siliconflow.com/onediff.html
- For a more personalized approach, please email us at contact@siliconflow.com. Include details about your use case, deployment size, and any specific needs you might have.
The OneDiff Enterprise Edition is available for a monthly subscription and is designed to be cost-effective, even for systems utilizing a single GPU.
| 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 |
Installation
OneDiff Installation
1. Install OneFlow
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
2. Install torch and diffusers
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"
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
huggingface-cli login
3 maintainers
Metadata
3 maintainers
Release files for onediff 1.1.0.dev202405220128
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-1.1.0.dev202405220128.tar.gz | 77.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| onediff-1.1.0.dev202405220128-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 167.5 kB
Release files / onediff-1.1.0.dev202405220128.tar.gz
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