Instructions to use flashlizard/Sketch3DVE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use flashlizard/Sketch3DVE 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("flashlizard/Sketch3DVE", 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
Commit History
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Add model card with metadata, links, and sample usage
Verified signatureThis PR significantly enhances the model card for the Sketch3DVE model by adding crucial information for better discoverability and usability. Key additions include: - The `pipeline_tag: image-to-video`, which correctly categorizes the model's functionality for video editing from image inputs. - The `library_name: diffusers`, indicating compatibility with the Hugging Face Diffusers library, confirmed by the model's `CogVideoControlNetModel` class. - Links to the original research paper and the project homepage, which also serves as the code repository. - The full paper abstract to provide comprehensive context about the model's capabilities and methodology. - A conceptual `Sample Usage` code snippet, demonstrating how this ControlNet model can be loaded and integrated within a `diffusers` pipeline, aiding developers in its programmatic use. These improvements will make the model more accessible and useful to the community.9563d42
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