Instructions to use MochunniaN1/One-to-All-1.3b_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MochunniaN1/One-to-All-1.3b_2 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("MochunniaN1/One-to-All-1.3b_2", 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 with metadata, links, and usage example
#1
by nielsr HF Staff - opened
This PR significantly improves the model card for "One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer" by:
- Integrating the
pipeline_tag: image-to-videoto correctly categorize the model for better discoverability on the Hugging Face Hub. - Adding
library_name: diffusers, which enables the automatic "how to use" widget on the model page due to its compatibility with the Diffusers library. - Including direct links to the paper (https://huggingface.co/papers/2511.22940), project page (https://ssj9596.github.io/one-to-all-animation-project/), and the GitHub repository (https://github.com/ssj9596/One-to-All-Animation).
- Adding a "Quick Inference" section with a code snippet directly sourced from the official GitHub README to provide immediate usability instructions.
- Incorporating "Highlights", "Showcase" with visual examples, and "Dependencies and Installation" sections for comprehensive information.
Please review and merge this PR if everything looks good.
MochunniaN1 changed pull request status to merged