Instructions to use Salesforce/FOFPred with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Salesforce/FOFPred with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Salesforce/FOFPred", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Improve model card: add paper link, project page, and update metadata
#11
by nielsr HF Staff - opened
Hi! I'm Niels from the community science team at Hugging Face.
I've opened this PR to improve the model card for FOFPred. Specifically:
- Linked the model to the paper: Future Optical Flow Prediction Improves Robot Control & Video Generation.
- Added a link to the project page and the GitHub repository.
- Updated the
pipeline_tagtoimage-to-videoas the model generates a sequence of flow frames. - Added a BibTeX citation for the work.
Please review and merge this if it looks good!