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[**中文说明**](https://github.com/LowinLi/stable-diffusion-streamlit/blob/main/README_CN.md) | [**English**](https://github.com/LowinLi/stable-diffusion-streamlit/blob/main/README.md)
# stable-diffusion-streamlit
- [1.Introduction](#1-introduction)
- [2.Getting Started](#2-getting-started)
- [3.Quantization Performance](#3-quantization-performance)
- [4.Streamlit Progress Bar](#4-streamlit-progress-bar)
- [5.To Do](#5-to-do)
- [6.Get Help](#6-get-help)
- [7.Acknowledgements](#7-acknowledgements)
## 1. Introduction
+ Create beautiful apps using [Streamlit](https://github.com/streamlit/streamlit) to test [CompVis/stable-diffusion-v1-4](https://huggingface.co./CompVis/stable-diffusion-v1-4) model quantized by [OnnxRuntime](https://github.com/microsoft/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
```yaml
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](https://mpost.io/best-100-stable-diffusion-prompts-the-most-beautiful-ai-text-to-image-prompts/) or [50-text-to-image-prompts-for-ai](https://decentralizedcreator.com/50-text-to-image-prompts-for-ai-art-generator-stable-diffusion-a-visual-treat-inside/)
+ 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](https://github.com/LowinLi/stable-diffusion-streamlit/blob/main/docker/dockerfile)
+ [building progress in Github Action](https://github.com/LowinLi/stable-diffusion-streamlit/actions/runs/3202674839/jobs/5231895605)
+ 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](https://huggingface.co./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](https://huggingface.co./docs/diffusers/using-diffusers/img2img)
- [ ] Add the Text-Guided Image-Inpainting Pipeline in [Huggingface/Diffusers](https://huggingface.co./docs/diffusers/using-diffusers/inpaint)
## 6. Get Help
+ Contact me at [email protected]
+ If appropriate, open an issue on GitHub
## 7. Acknowledgements
+ [Huggingface/Diffusers](https://github.com/huggingface/diffusers)
+ [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion)
+ [Streamlit](https://github.com/streamlit/streamlit)
+ [OnnxRuntime](https://github.com/microsoft/onnxruntime)
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