Image-to-Video
Diffusers
Safetensors
ti2v
Text-to-Video
Image-to-Video
Diffusion Video Model
World Model
Instructions to use stdstu123/Yume-5B-720P with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stdstu123/Yume-5B-720P 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("stdstu123/Yume-5B-720P", 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 metadata and add paper links
#1
by nielsr HF Staff - opened
This PR improves the model card for Yume-1.5 by:
- Adding the
image-to-videopipeline tag. - Removing the invalid
<World Model>library name. - Linking the research papers (Yume and Yume-1.5) to their Hugging Face paper pages or Arxiv links.
- Adding a sample usage section based on the GitHub repository's inference scripts.
Thanks for improving the model card !
Thanks, feel free to merge/
nielsr changed pull request status to closed