OneDiffusion is an open-source one-stop shop for facilitating the deployment of any diffusion models in production. It caters specifically to the needs of diffusion models, supporting both pretrained and fine-tuned diffusion models with LoRA adapters.
Key features include:
- 🌐 Broad compatibility: Support both pretrained and LoRA-adapted diffusion models, providing flexibility in choosing and deploying the appropriate model for various image generation tasks.
- 💪 Optimized performance and scalability: Automatically select the best optimizations like half-precision weights or xFormers to achieve best inference speed out of the box.
- ⌛️ Dynamic LoRA adapter loading: Dynamically load and unload LoRA adapters on every request, providing greater adaptability and ensuring the models remain responsive to changing inputs and conditions.
- 🍱 First-class support for BentoML: Seamless integration with the BentoML ecosystem, allowing you to build Bentos and push them to BentoCloud.
OneDiffusion is designed for AI application developers who require a robust and flexible platform for deploying diffusion models in production. The platform offers tools and features to fine-tune, serve, deploy, and monitor these models effectively, streamlining the end-to-end workflow for diffusion model deployment.
Currently, OneDiffusion supports the following models:
- Stable Diffusion v1.4, v1.5 and v2.0
- Stable Diffusion XL v1.0
- Stable Diffusion XL Turbo
More models (for example, ControlNet and DeepFloyd IF) will be added soon.
Note
If you want to deploy Stable Video Diffusion, see the project BentoSVD.
To quickly get started with OneDiffusion, follow the instructions below or try this tutorial in Google Colab: Serving Stable Diffusion with OneDiffusion.
You have installed Python 3.8 (or later) and pip.
Install OneDiffusion by using pip as follows:
pip install onediffusionTo verify the installation, run:
$ onediffusion -h
Usage: onediffusion [OPTIONS] COMMAND [ARGS]...
██████╗ ███╗ ██╗███████╗██████╗ ██╗███████╗███████╗██╗ ██╗███████╗██╗ ██████╗ ███╗ ██╗
██╔═══██╗████╗ ██║██╔════╝██╔══██╗██║██╔════╝██╔════╝██║ ██║██╔════╝██║██╔═══██╗████╗ ██║
██║ ██║██╔██╗ ██║█████╗ ██║ ██║██║█████╗ █████╗ ██║ ██║███████╗██║██║ ██║██╔██╗ ██║
██║ ██║██║╚██╗██║██╔══╝ ██║ ██║██║██╔══╝ ██╔══╝ ██║ ██║╚════██║██║██║ ██║██║╚██╗██║
╚██████╔╝██║ ╚████║███████╗██████╔╝██║██║ ██║ ╚██████╔╝███████║██║╚██████╔╝██║ ╚████║
╚═════╝ ╚═╝ ╚═══╝╚══════╝╚═════╝ ╚═╝╚═╝ ╚═╝ ╚═════╝ ╚══════╝╚═╝ ╚═════╝ ╚═╝ ╚═══╝
An open platform for operating diffusion models in production.
Fine-tune, serve, deploy, and monitor any diffusion models with ease.
Options:
-v, --version Show the version and exit.
-h, --help Show this message and exit.
Commands:
build Package a given model into a Bento.
download Setup diffusion models interactively.
start Start any diffusion models as a REST server.OneDiffusion allows you to quickly spin up any diffusion models. To start a server, run:
onediffusion start stable-diffusionThis starts a server at http://0.0.0.0:3000/. You can interact with it by visiting the web UI or send a request via curl.
curl -X 'POST' \
'http://0.0.0.0:3000/text2img' \
-H 'accept: image/jpeg' \
-H 'Content-Type: application/json' \
--output output.jpg \
-d '{
"prompt": "a bento box",
"negative_prompt": null,
"height": 768,
"width": 768,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"eta": 0
}'By default, OneDiffusion uses stabilityai/stable-diffusion-2 to start the server. To use a specific model version, add the --model-id option as below:
onediffusion start stable-diffusion --model-id runwayml/stable-diffusion-v1-5To specify another pipeline, use the --pipeline option as below. The img2img pipeline allows you to modify images based on a given prompt and image.
onediffusion start stable-diffusion --pipeline "img2img"OneDiffusion downloads the models to the BentoML local Model Store if they have not been registered before. To view your models, install BentoML first with pip install bentoml and then run:
$ bentoml models list
Tag Module Size Creation Time
pt-sd-stabilityai--stable-diffusion-2:1e128c8891e52218b74cde8f26dbfc701cb99d79 bentoml.diffusers 4.81 GiB 2023-08-16 17:52:33
pt-sdxl-stabilityai--stable-diffusion-xl-base-1.0:bf714989e22c57ddc1c453bf74dab4521acb81d8 bentoml.diffusers 13.24 GiB 2023-08-16 16:09:01OneDiffusion also supports running Stable Diffusion XL 1.0, the most advanced development in the Stable Diffusion text-to-image suite of models launched by Stability AI. To start an XL server, simply run:
onediffusion start stable-diffusion-xlIt downloads the model automatically if it does not exist locally. Options such as --model-id are also supported. For more information, run onediffusion start stable-diffusion-xl --help.
Similarly, visit http://0.0.0.0:3000/ or send a request via curl to interact with the XL server. Example prompt:
{
"prompt": "the scene is a picturesque environment with beautiful flowers and trees. In the center, there is a small cat. The cat is shown with its chin being scratched. It is crouched down peacefully. The cat's eyes are filled with excitement and satisfaction as it uses its small paws to hold onto the food, emitting a content purring sound.",
"negative_prompt": null,
"height": 1024,
"width": 1024,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"eta": 0
}
Example output:
SDXL Turbo is a distilled version of SDXL 1.0 and is capable of creating images in a single step, with improved real-time text-to-image output quality and sampling fidelity.
To serve SDXL Turbo locally, run:
onediffusion start stable-diffusion-xl --model-id stabilityai/sdxl-turboVisit http://0.0.0.0:3000/ or send a request via curl to interact with the server. Example prompt:
{
"prompt": "Create a serene landscape at sunset, with a tranquil lake reflecting the vibrant colors of the sky. Surrounding the lake are lush, green forests and distant mountains.",
"height": 512,
"width": 512,
"num_inference_steps": 1,
"guidance_scale": 0.0
}
Note
SDXL Turbo can run inference with only one step, so you can set num_inference_steps to 1 and this is enough to generate high quality images. However, increasing the number of steps to 2, 3 or 4 should improve image quality. In addition, make sure you set guidance_scale to 0.0 to disable it as the model was trained without it. See the official release notes to learn more.
Example output:
Low-Rank Adaptation (LoRA) is a training method to fine-tune models without the need to retrain all parameters. You can add LoRA weights to your diffusion models for specific data needs.
Add the --lora-weights option as below:
onediffusion start stable-diffusion-xl --lora-weights "/path/to/lora-weights.safetensors"Alternatively, dynamically load LoRA weights by adding the lora_weights field:
{
"prompt": "the scene is a picturesque environment with beautiful flowers and trees. In the center, there is a small cat. The cat is shown with its chin being scratched. It is crouched down peacefully. The cat's eyes are filled with excitement and satisfaction as it uses its small paws to hold onto the food, emitting a content purring sound.",
"negative_prompt": null,
"height": 1024,
"width": 1024,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"eta": 0,
"lora_weights": "/path/to/lora-weights.safetensors"
}
By specifying the path of LoRA weights at runtime, you can influence model outputs dynamically. Even with identical prompts, the application of different LoRA weights can yield vastly different results. Example output (oil painting vs. pixel):
If you want to download a diffusion model without starting a server, use the onediffusion download command. For example:
onediffusion download stable-diffusion --model-id "CompVis/stable-diffusion-v1-4"
You can create a BentoML Runner with diffusers_simple.stable_diffusion.create_runner(), which downloads the model specified automatically if it does not exist locally.
import bentoml
# Create a Runner for a Stable Diffusion model
runner = bentoml.diffusers_simple.stable_diffusion.create_runner("CompVis/stable-diffusion-v1-4")
# Create a Runner for a Stable Diffusion XL model
runner_xl = bentoml.diffusers_simple.stable_diffusion_xl.create_runner("stabilityai/stable-diffusion-xl-base-1.0")You can then wrap the Runner into a BentoML Service. See the BentoML documentation for more details.
A Bento in BentoML is a deployable artifact with all the source code, models, data files, and dependency configurations. You can build a Bento for a supported diffusion model directly by running onediffusion build.
# Build a Bento with a Stable Diffusion model
onediffusion build stable-diffusion
# Build a Bento with a Stable Diffusion XL model
onediffusion build stable-diffusion-xlTo specify the model to be packaged into the Bento, use --model-id. Otherwise, OneDiffusion packages the default model into the Bento. If the model does not exist locally, OneDiffusion downloads the model automatically. In addition, the pipeline to use can also be specified through --pipeline. By default, OneDiffusion uses the text2image pipeline.
To package LoRA weights into the Bento, use the --lora-dir option to specify the directory where LoRA files are stored. These files can be dynamically loaded to the model when deployed with Docker or BentoCloud to create task-specific images.
onediffusion build stable-diffusion-xl --lora-dir "/path/to/lorafiles/dir/"If you only have a single LoRA file to use, run the following instead:
onediffusion build stable-diffusion-xl --lora-weights "/path/to/lorafile"Each Bento has a BENTO_TAG containing both the Bento name and the version. To customize it, specify --name and --version options.
onediffusion build stable-diffusion-xl --name sdxl --version v1Once your Bento is ready, log in to BentoCloud and run the following command to push the Bento.
bentoml push BENTO_TAGAlternatively, create a Docker image by containerizing the Bento with the following command. You can retrieve the BENTO_TAG by running bentoml list.
bentoml containerize BENTO_TAGYou can then deploy the image to any Docker-compatible environments.
We are working to improve OneDiffusion in the following ways and invite anyone who is interested in the project to participate 🤝.
- Support more models, such as ControlNet and DeepFloyd IF
- Support more pipelines, such as inpainting
- Add a Python API client to interact with diffusion models
- Implement advanced optimization like AITemplate
- Offer a unified fine-tuning training API
We weclome contributions of all kinds to the OneDiffusion project! Check out the following resources to start your OneDiffusion journey and stay tuned for more announcements about OneDiffusion and BentoML.
- Submit a pull request or create an issue in the OneDiffusion GitHub repository.
- Join the BentoML community on Slack.
- Follow us on Twitter and Linkedin.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent