Instructions to use ainativestudio/hunyuanvideo-endpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ainativestudio/hunyuanvideo-endpoint with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ainativestudio/hunyuanvideo-endpoint", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 1,191 Bytes
ed795b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ---
tags:
- endpoints-template
- video-generation
- diffusers
inference: false
---
# hunyuanvideo-endpoint
HunyuanVideo with custom handler for Inference Endpoints
## Usage
This repository contains a custom inference handler for HuggingFace Inference Endpoints.
### Deploy
1. Go to [HuggingFace Inference Endpoints](https://ui.endpoints.huggingface.co/)
2. Click "New Endpoint"
3. Select this repository: `ainativestudio/hunyuanvideo-endpoint`
4. Choose GPU instance (NVIDIA L4 or A100)
5. Deploy
### API Usage
```python
import requests
import base64
response = requests.post(
"YOUR_ENDPOINT_URL",
headers={"Authorization": "Bearer YOUR_TOKEN"},
json={
"inputs": "A beautiful sunset over the ocean",
"num_inference_steps": 50,
"guidance_scale": 6.0
}
)
video_base64 = response.json()["video"]
video_bytes = base64.b64decode(video_base64)
with open("output.mp4", "wb") as f:
f.write(video_bytes)
```
## Custom Handler
The `handler.py` file implements the `EndpointHandler` class required for custom inference.
See the [HuggingFace documentation](https://huggingface.co/docs/inference-endpoints/guides/custom_handler) for more details.
|