README.md

中文说明 | English

stable-diffusion-streamlit

• 1.Introduction
• 2.Getting Started
• 3.Quantization Performance
• 4.Streamlit Progress Bar
• 5.To Do
• 6.Get Help
• 7.Acknowledgements

1. Introduction

• Create beautiful apps using Streamlit to test CompVis/stable-diffusion-v1-4 model quantized by OnnxRuntime cutting down memory 75%.
  • Streamlit:
    • an open-source app framework for Machine Learning and Data Science teams. Create beautiful web apps in minutes.
  • CompVis/stable-diffusion-v1-4:
    • a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
  • OnnxRuntime:
    • a cross-platform, faster inference and lower costs accelerator for machine learning models.

2. Getting Started

2.1. Deployment

• docker-compose up -d

version: "2.3"
services:
  stable-diffusion-streamlit-onnxquantized:
    container_name: stable-diffusion-streamlit-onnxquantized
    image: lowinli98/stable-diffusion-streamlit-onnxquantized:v0.2
    expose:
      - 8501
    ports:
      - "8501:8501"
    environment:
      - APP_TITLE=Stable Diffusion Streamlit
    restart: always
    volumes:
      - /etc/localtime:/etc/localtime
      - ./volume:/app/pages/model/result

2.2. Usage

• 2.2.1. Copy an awesome prompt from Blogs like best-100-stable-diffusion-prompts or 50-text-to-image-prompts-for-ai

• 2.2.2. Open http://localhost:8501 and click "文本转图片" on the left sidebar.

• 2.2.3. Fix the runtime parameters, paste your prompt into the text area and click the "开始生成" button.

  

• 2.2.4. Wait for a while until the progress bar goes to the end, then you will get a generated image.

  

• 2.2.5. Click "画廊" on the left sidebar to see all the images you had generated.

  

3. Quantization Performance

• The model in the docker container has been quantized by OnnxRuntime in the building of the docker image.

  • dockerfile
  • building progress in Github Action

• The quantized model will be smaller and cut down the inference time a little(UINT8), while the performance of the image generated is almost the same as the original model.

• This is an amazing feature because CompVis/stable-diffusion-v1-4 can be deployed on most home computers. The following table shows the comparison of the quantized model and the original model.

---

model	memory used	inference 49 steps waste time
pytorch	5.2GB	6m56s
onnx	5.0GB	4m34s
onnx-quantized(UINT8)	1.3GB	4m29s

• CPU:

  • Intel(R) Xeon(R) CPU E5-2650 v3 @ 2.30GHz
  • 10 core

• image generated by PyTorch model

  

• image generated by Onnx model

  

• image generated by Onnx-Quantized(UINT8) model

  

4. Streamlit Progress Bar

To generate an awesome image, the model needs to be inferences with many steps. So it will take a long time to finish the whole pipeline. To make the user experience better, a progress bar is added to show the pipeline progress. With another thread in Python, the progress bar can be updated by the pipeline scheduler counter.

5. To Do

• Add the Text-Guided Image-to-Image Pipeline in Huggingface/Diffusers
• Add the Text-Guided Image-Inpainting Pipeline in Huggingface/Diffusers

6. Get Help

• Contact me at [email protected]
• If appropriate, open an issue on GitHub

7. Acknowledgements

• Huggingface/Diffusers
• CompVis/stable-diffusion
• Streamlit
• OnnxRuntime