Instructions to use Jamichsu/Stream-DiffVSR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jamichsu/Stream-DiffVSR 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("Jamichsu/Stream-DiffVSR", 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
Add model card and metadata
#1
by nielsr HF Staff - opened
Hi, I'm Niels from the community science team at Hugging Face. I'm opening this PR to add a model card to your repository to improve its documentation and discoverability.
This PR includes:
- Metadata for
library_nameandpipeline_tag. - Links to the paper, project page, and official GitHub repository.
- A summary of the model's architecture and capabilities.
- Usage instructions and sample inference commands taken from your GitHub README.
- Citation information for the paper.
Please let me know if you have any questions!
Hi Niels,
Thanks a lot for the PR! Everything looks great to me.
I really appreciate the effort to improve the documentation and discoverability of the project.
Jamichsu changed pull request status to merged