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https://github.com/rapidrabbit76/stable-diffusion-API
Unofficial Fastapi implementation of Stable-Diffusion API
https://github.com/rapidrabbit76/stable-diffusion-API
fastapi generative-model image2image python pytorch stable-diffusion text2image unofficial-api
Last synced: 19 days ago
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Unofficial Fastapi implementation of Stable-Diffusion API
- Host: GitHub
- URL: https://github.com/rapidrabbit76/stable-diffusion-API
- Owner: rapidrabbit76
- Created: 2022-09-13T16:56:51.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-12-09T11:50:59.000Z (about 2 years ago)
- Last Synced: 2025-01-03T18:43:24.697Z (about 1 month ago)
- Topics: fastapi, generative-model, image2image, python, pytorch, stable-diffusion, text2image, unofficial-api
- Language: Python
- Homepage:
- Size: 5.87 MB
- Stars: 80
- Watchers: 4
- Forks: 17
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Unofficial Fastapi implementation Stable-Diffusion API
UNOFFICIAL, [Stable-Diffusion](https://github.com/CompVis/stable-diffusion) api using FastAPI
# Samples
| Text2Image-01 | Text2Image-02 |
| :--------------------------------: | :--------------------------------: |
|  |  |
| Image2Image-01 | Image2Image-02 |
|  |  |
| Inpaint-01 | Inpaint-02 |
|  |  |
|  |  |
# Features
- [x] long-prompt-weighting support
- [x] text2image
- [x] image2image
- [x] inpaints
- [x] negative-prompt
- [x] celery async task (check celery_task [branch](https://github.com/rapidrabbit76/stable-diffusion-API/tree/celery_task))
- [x] original ```ckpt``` format support
- [ ] object storage support
- [ ] stable-diffusion 2.0 support
- [ ] token size checker
- [ ] JAX/Flax pipeline
# Requirements
## API
```txt
fastapi[all]==0.80.0
fastapi-restful==0.4.3
fastapi-health==0.4.0
service-streamer==0.1.2
pydantic==1.9.2
diffusers==0.3.0
transformers==4.19.2
scipy
ftfy
```
## Frontend
```txt
streamlit==1.12.2
requests==2.27.1
requests-toolbelt==0.9.1
pydantic==1.8.2
streamlit-drawable-canvas==0.9.2
```
# API
## /text2image
create image from input prompt
inputs:
- prompt(str): text prompt
- num_images(int): number of images
- guidance_scale(float): guidance scale for stable-diffusion
- height(int): image height
- width(int): image width
- seed(int): generator seed
outputs:
- prompt(str): input text prompt
- task_id(str): uuid4 hex string
- image_urls(str): generated images url
## /image2image
create image from input image
inputs:
- prompt(str): text prompt
- init_image(imagefile): init image for i2i task
- num_images(int): number of images
- guidance_scale(float): guidance scale for stable-diffusion
- seed(int): generator seed
outputs:
- prompt(str): input text prompt
- task_id(str): uuid4 hex string
- image_urls(str): generated images url
# Environment variable
```bash
# env setting is in
>> ./core/settings/settings.py
```
| Name | Default | Desc |
| ------------------------ | ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| MODEL_ID | CompVis/stable-diffusion-v1-4 | huggingface repo id or model path |
| ENABLE_ATTENTION_SLICING | True | [Enable sliced attention computation.](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline.enable_attention_slicing) |
| CUDA_DEVICE | "cuda" | target cuda device |
| CUDA_DEVICES | [0] | visible cuda device |
| MB_BATCH_SIZE | 1 | Micro Batch: MAX Batch size |
| MB_TIMEOUT | 120 | Micro Batch: timeout sec |
| HUGGINGFACE_TOKEN | None | huggingface access token |
| IMAGESERVER_URL | None | result image base url |
| SAVE_DIR | static | result image save dir |
| CORS_ALLOW_ORIGINS | [*] | cross origin resource sharing setting for FastAPI |
# RUN from code (API)
## 1. install python Requirements
```bash
pip install -r requirements.txt
```
## 2. downlaod and caching huggingface model
```bash
python huggingface_model_download.py
# check stable-diffusion model in huggingface cache dir
[[ -d ~/.cache/huggingface/diffusers/models--CompVis--stable-diffusion-v1-4 ]] && echo "exist"
>> exist
```
## 3. update settings.py in ./core/settings/settings.py
```python
# example
class ModelSetting(BaseSettings):
MODEL_ID: str = "CompVis/stable-diffusion-v1-4" # huggingface repo id
ENABLE_ATTENTION_SLICING: bool = True
...
class Settings(
...
):
HUGGINGFACE_TOKEN: str = "YOUR HUGGINGFACE ACCESS TOKEN"
IMAGESERVER_URL: str = "http://localhost:3000/images"
SAVE_DIR: str = 'static'
...
```
## 4. RUN API from code
```bash
bash docker/api/start.sh
```
# RUN from code (frontend)
## 1. install python Requirements
```bash
pip install \
streamlit==1.12.2 \
requests==2.27.1 \
requests-toolbelt==0.9.1 \
pydantic==1.8.2 \
streamlit-drawable-canvas==0.9.2
```
## 2. RUN streamlit frontend
```bash
streamlit run inpaint.py
```
# RUN using Docker (docker-compose)
## 1. Image Build
```bash
docker-compose build
```
## 3. update docker-compose.yaml file in repo root
```yaml
version: "3.7"
services:
api:
...
volumes:
# mount huggingface model cache dir path to container root user home dir
- /model:/model # if you load pretraind model
- ...
environment:
...
MODEL_ID: "CompVis/stable-diffusion-v1-4"
HUGGINGFACE_TOKEN: {YOUR HUGGINGFACE ACCESS TOKEN}
...
deploy:
...
frontend:
...
```
## 4. Container RUN
```bash
docker-compose up -d
# or API only
docker-compsoe up -d api
# or frontend only
docker-compsoe up -d frontend
```
## References
- [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion)
- [huggingface, stable-diffusion](https://huggingface.co/CompVis)
- [teamhide/fastapi-boilerplate](https://github.com/teamhide/fastapi-boilerplate)