https://github.com/satyajitghana/sd3-ui
Modern web interface for Stable Diffusion 3 text-to-image generation. Features real-time progress tracking, local storage, and a sleek dark mode UI. Built with Next.js 15, FastAPI, and TorchServe.
https://github.com/satyajitghana/sd3-ui
deeplearning fastapi gen-ai image-generation nextjs nextjs15 python stable-diffusion stable-diffusion-3 text-to-image torchserve typescript
Last synced: 3 months ago
JSON representation
Modern web interface for Stable Diffusion 3 text-to-image generation. Features real-time progress tracking, local storage, and a sleek dark mode UI. Built with Next.js 15, FastAPI, and TorchServe.
- Host: GitHub
- URL: https://github.com/satyajitghana/sd3-ui
- Owner: satyajitghana
- Created: 2024-12-06T16:09:50.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-06T16:19:09.000Z (over 1 year ago)
- Last Synced: 2025-02-04T13:43:35.610Z (over 1 year ago)
- Topics: deeplearning, fastapi, gen-ai, image-generation, nextjs, nextjs15, python, stable-diffusion, stable-diffusion-3, text-to-image, torchserve, typescript
- Language: TypeScript
- Homepage:
- Size: 8.13 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# TSAI Stable Diffusion 3 UI
A modern web interface for Stable Diffusion 3 text-to-image generation, built with Next.js and FastAPI.
## Project Structure
```
.
├── . # Next.js frontend
├── model-server/ # FastAPI and TorchServe backend
│ ├── server.py # FastAPI server
│ ├── config.properties
│ └── sd3_handler.py # TorchServe handler
└── README.md
```
## Quick Start
### 1. Setting up the Frontend (sd3-ui)
```bash
cd sd3-ui
npm install
npm run dev
```
The UI will be available at http://localhost:3000
### 2. Setting up the Backend
#### 2.1 Preparing the Model
First, download the Stable Diffusion 3 model:
```bash
cd model-server
```
```python
#!/usr/bin/env python3
from diffusers import StableDiffusion3Pipeline
import torch
pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers",
torch_dtype=torch.bfloat16
)
pipe.save_pretrained("./sd3-model")
```
Then zip the model artifacts:
```bash
cd sd3-model
zip -0 -r ../sd3-model.zip *
```
#### 2.2 Creating the Model Archive (.mar)
Start a TorchServe container:
```bash
docker run -it --rm --shm-size=1g \
--ulimit memlock=-1 \
--ulimit stack=67108864 \
--gpus all \
-v `pwd`:/opt/src \
pytorch/torchserve:0.12.0-gpu bash
```
Create the model archive:
```bash
cd /opt/src
torch-model-archiver --model-name sd3 \
--version 1.0 \
--handler sd3_handler.py \
--extra-files sd3-model.zip \
-r requirements.txt \
--archive-format zip-store
```
#### 2.3 Starting TorchServe
```bash
docker run --rm --shm-size=1g \
--ulimit memlock=-1 \
--ulimit stack=67108864 \
-p8080:8080 \
-p8081:8081 \
-p8082:8082 \
-p7070:7070 \
-p7071:7071 \
--gpus all \
-v /path/to/config.properties:/home/model-server/config.properties \
--mount type=bind,source=/path/to/models,target=/tmp/models \
pytorch/torchserve:0.12.0-gpu \
torchserve --model-store=/tmp/models
```
#### 2.4 Starting the FastAPI Server
Install requirements:
```bash
pip install -r requirements-server.txt
```
Start the server:
```bash
cd model-server
python server.py
```
The FastAPI server will be available at http://localhost:9080
## Features
- 🎨 Modern, responsive UI with dark mode
- 🖼️ Real-time generation progress
- 💾 Local storage for generated images
- ⬇️ Download generated images
- 🗑️ Delete unwanted generations
- ⏱️ Generation time tracking
## Environment Variables
Create a `.env.local` file in the sd3-ui directory:
```env
BACKEND_URL=http://localhost:9080
```
## API Endpoints
- `POST /text-to-image`: Generate image from text prompt
- `GET /results/{job_id}`: Get generation results
- `GET /health`: Health check endpoint
## Requirements
### Frontend
- Node.js 22
- npm or yarn
### Backend
- Python 3.11+
- CUDA-compatible GPU
- Docker
- See `requirements-server.txt` for Python packages
## Development
The frontend uses Next.js 15 with App Router and is styled using Tailwind CSS and shadcn/ui components. The backend uses FastAPI for the API server and TorchServe for model serving.
## License
MIT
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.