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Stable Diffusion FastAPI Server

This project is a web server that provides a FastAPI-based interface for generating images using Stable Diffusion models. It supports both text-to-image (/txt2img) and image-to-image (/img2img) generation endpoints. The server is designed to only run on 1 gpu, if multiple are needed, use ai-maestro-router in conjunction with multiple stablediffusion-fastapi-server instances

Prerequisites

  • Docker
  • NVIDIA GPU with driver installed
  • NVIDIA Container Toolkit

Usage

Docker Run

The container accepts an environment variable MODEL_NAME which will default to stabilityai/sdxl-turbo. You can swap out this with any other model that works with the Diffusion pipelines AutoPipelineForText2Image and AutoPipelineForImage2Image.

In order to make /img2img and /upload work, redis must be setup. Pass in REDIS_HOST and REDIS_PORT (defaults are localhost and 6379).

Ports and gpus are configured with standard docker flags.

To use the most recent stable image, pull the latest tag:

docker run -e MODEL_NAME=stabilityai/sdxl-turbo  -p 8000:8000 ghcr.io/jemeyer/stablediffusion-fastapi-server:latest

This will start the server and make it accessible at http://localhost:8000.

GPU Configuration

If you have an NVIDIA GPU and want to use it with the UI, you can pass the --gpus flag to docker run:

  • To use all available GPUs (only 1 - if multiple are needed, use ai-maestro-router):
docker run --gpus all -e MODEL_NAME=stabilityai/sdxl-turbo -p 8000:8000 ghcr.io/jemeyer/stablediffusion-fastapi-server:latest
  • To use a specific number of GPUs:
docker run --gpus 1 -e MODEL_NAME=stabilityai/sdxl-turbo -p 8000:8000 ghcr.io/jemeyer/stablediffusion-fastapi-server:latest
  • To use a specific GPU by its device ID (e.g., GPU 2), and define a different model_name:
docker run --gpus -e MODEL_NAME=stabilityai/sd-turbo device=2 -p 8000:8000 ghcr.io/jemeyer/stablediffusion-fastapi-server:latest

Note that you need to have the NVIDIA Container Toolkit installed on your host for GPU passthrough to work.

Docker Compose

You can also use Stable Diffusion WebUI with Docker Compose. Here's an example docker-compose.yml file:

services:
  stablediffusion-fastapi-server:
    image: ghcr.io/jemeyer/stablediffusion-fastapi-server:latest
    ports:
      - 8000:8000
    environment:
      - MODEL_NAME=stabilityai/sdxl-turbo
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

This configuration will start a container using the latest image and make it accessible at http://localhost:8000. It also configures the container to use 1 GPU.

To use a specific GPU, you can use the device_ids property instead of count:

reservations:
  devices:
    - driver: nvidia
      device_ids: ["2"]
      capabilities: [gpu]

Start the container with:

docker-compose up -d

Endpoints

  • /txt2img: Generate an image from a text prompt
    • curl -H "Content-Type: application/json" -d '{"prompt": "PROMPT"}' http://IP_ADDR:8000/txt2img --output t2i_generated_image.png
    • Returns a fastAPI StreamingResponse of type image/png
  • /upload: Upload an image (to then be refereneced by img2img)
    • curl -X POST -F "file=@./t2i_generated_image.jpg" http://IP_ADDR:8000/upload
    • Returns {"file_id":"FILE_ID"}
  • /img2img: Generate an image based on an input image id and a text prompt
    • curl -H "Content-Type: application/json" -d '{"file_id":"FILE_ID", "prompt": "PROMPT"}' http://IP_ADDR:8000/img2img --output i2igenerated_image.png
    • Returns a fastAPI StreamingResponse of type image/png

This server uses -turbo SD models, and as such no negPrompt is used.

Hardware

Below you will find a comparison table that outlines the specifications of various GPUs, the model used, the amount of VRAM required for operation, and the observed range for image generation speed. It's important to note that these values are not hard limits but rather observations based on running these models under certain conditions. As such, your results may vary.

GPU Model Used VRAM Used Generation Speed Range
NVIDIA GeForce RTX 3090 SDXL-Turbo 10.8 GiB .65 - .70 seconds
NVIDIA GeForce RTX 3090 SD-Turbo 4.25 GiB .42 - .50 seconds
NVIDIA Tesla P100 SDXL-Turbo 10 GiB 1.8 - 2 seconds
NVIDIA Tesla P100 SD-Turbo 4.7 GiB 1.3 - 1.5 seconds

Development

  1. Clone the repository:

    git clone https://github.com/jemeyer/stable-diffusion-fastapi-server.git
cd stable-diffusion-fastapi-server

  2. Set up a virtual environment using pyenv:

    pyenv virtualenv 3.10.13 stable-diffusion-fastapi-server
pyenv local stable-diffusion-fastapi-server

  3. Install the required dependencies:

    pip install torch==2.1.0+cu118 --index-url https://download.pytorch.org/
pip install .

    Note: The torch package is explicitly installed from the PyTorch download URL to ensure compatibility with the CUDA version.

Dockerization

The server can be dockerized for easy deployment and scalability. A Dockerfile is provided to build the Docker image.

To build the Docker image, run the following command:

docker build -t jemeyer/stablediffusion-fastapi-server .

To run the Docker container, use the following command:

docker run -p 8000:8000 jemeyer/stablediffusion-fastapi-server

The server will be accessible at http://localhost:8000.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

License

This project is licensed under Apache 2.0 - see the LICENSE file for details.

Acknowledgements