Image-to-Video
Diffusers
Safetensors
ti2v
Text-to-Video
Image-to-Video
Diffusion Video Model
World Model
Eval Results
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
- Xet hash:
- 56c8f47c2cc694558c2e53e9b10eb461ca602750e639eec83e3a4a68a3028bdd
- Size of remote file:
- 140 MB
- SHA256:
- 966ec45d7aa676c9d9f3a27c812683cbc1e5077b84e39e64ddb9b78db3753b5a
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