Instructions to use Gaojunyao/CharacterShot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Gaojunyao/CharacterShot 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("Gaojunyao/CharacterShot", 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
Improve model card: add metadata, library name, and paper/code links
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the community science team at Hugging Face.
This PR improves the model card for CharacterShot. Key changes include:
- Adding the
pipeline_tag: image-to-videoandlibrary_name: diffusersmetadata to ensure the model is discoverable and provides the correct code snippets. - Adding links to the paper and the official GitHub repository.
- Providing a concise summary of the framework and a BibTeX citation.
Please let me know if you have any questions!
Gaojunyao changed pull request status to merged