Instructions to use acceptee/DiT4SR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use acceptee/DiT4SR 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("acceptee/DiT4SR", 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 for DiT4SR
#1
by nielsr HF Staff - opened
This PR improves the model card for DiT4SR by:
- Adding the
pipeline_tag: image-to-imageandlibrary_name: diffusersto the metadata, enhancing discoverability and integration on the Hub. - Including a link to the official project page for more details and usage instructions.
- Incorporating the paper's full abstract to provide comprehensive context.
acceptee changed pull request status to merged