Instructions to use stdstu123/Yume-I2V-540P with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stdstu123/Yume-I2V-540P 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-I2V-540P", 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
Enhance model card with metadata and links
#1
by nielsr HF Staff - opened
This PR significantly improves the model card for the Yume model. It adds the image-to-video pipeline tag, ensuring the model is discoverable under relevant tasks on the Hugging Face Hub. It also specifies diffusers as the library_name as the project leverages the Diffusers library. This PR also includes links to the paper and project page.
Thanks for improving the model card !