Image-to-Video
Diffusers
ONNX
video-generation
video diffusion transformer
audio-driven avatar animation
Instructions to use FrancisRing/FlashPortrait with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FrancisRing/FlashPortrait with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FrancisRing/FlashPortrait", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0e1b86884d6f931daf6749da3972864c3202c9a1b8365fe3290dad2a2da7b87
- Size of remote file:
- 57.1 GB
- SHA256:
- 15039ea32d10d091007d20ab0a10e75674f38d732ec9c437a41a23acabbef180
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.