https://github.com/runpod-workers/worker-fasthunyuan
WIP
https://github.com/runpod-workers/worker-fasthunyuan
Last synced: 12 months ago
JSON representation
WIP
- Host: GitHub
- URL: https://github.com/runpod-workers/worker-fasthunyuan
- Owner: runpod-workers
- Created: 2025-01-03T13:38:37.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-03T13:39:31.000Z (over 1 year ago)
- Last Synced: 2025-06-23T13:56:51.523Z (12 months ago)
- Language: Python
- Homepage:
- Size: 4.88 KB
- Stars: 1
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# FastHunyuan RunPod Worker
This is a RunPod worker implementation for FastHunyuan, a fast inference version of the Hunyuan text-to-video model.
## Development Guide
### Prerequisites
- RunPod account with API access
- NVIDIA GPU with CUDA support
- (Optional) VSCode for remote development
### Development Setup
1. Create a development pod on RunPod:
- Go to [RunPod Console](https://www.runpod.io/console/pods)
- Click "Deploy"
- Select base image: `runpod/pytorch:2.1.2-py3.10-cuda12.1.0` (includes SSH and JupyterLab)
- Choose your GPU type
- Name your pod (e.g., "fasthunyuan-dev")
- Deploy
2. Connect to your pod (choose one method):
a. Using RunPod Web Terminal:
- Click "Connect" on your pod in RunPod console
- Select "SSH Terminal" or use the web terminal
b. Using VSCode (Recommended for development):
- Follow the [official RunPod guide for VSCode setup](https://docs.runpod.io/tutorials/pods/connect-to-vscode)
- This allows you to develop directly on the pod with full IDE support
3. Install dependencies:
```bash
cd /workspace
chmod +x builder/install.sh
./builder/install.sh
```
4. Test the handler:
```bash
python src/handler.py
```
### Development Tips
- The handler is located in `src/handler.py`
- Dependencies are managed in `builder/requirements.txt`
- System dependencies and installation steps are in `builder/install.sh`
- Using VSCode remote development provides a better development experience with:
- Integrated terminal
- Code completion
- Debugging capabilities
- Git integration
## Production Guide
### Building the Docker Image
1. Build the image:
```bash
docker build -t your-registry/fasthunyuan-worker:version .
```
Note: For production, we use a minimal base image (`nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04`) without development tools.
2. Push to your registry:
```bash
docker push your-registry/fasthunyuan-worker:version
```
### Deploying on RunPod
1. Create a template on RunPod:
- Base image: your-registry/fasthunyuan-worker:version
- Container disk: at least 10GB
- HTTP endpoints: enabled
- Environment variables:
```
HUGGING_FACE_HUB_TOKEN=your_token
```
2. Deploy serverless endpoints using the template
### API Usage
Example request:
```json
{
"input": {
"prompt": "A cinematic video of a beautiful landscape",
"height": 720,
"width": 1280,
"num_frames": 45,
"num_inference_steps": 6,
"seed": 1024,
"fps": 24
}
}
```
Example response:
```json
{
"video_path": "/tmp/output_1024.mp4"
}
```
## Project Structure
```
.
├── builder/
│ ├── install.sh # Development installation script
│ └── requirements.txt # Python dependencies
├── src/
│ └── handler.py # RunPod handler implementation
├── Dockerfile # Production container definition
└── README.md # This file
```
## License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.