Instructions to use ziheng1234/ImageCritic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ziheng1234/ImageCritic 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("ziheng1234/ImageCritic", 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: add metadata, paper, project page, code, and usage details
#1
by nielsr HF Staff - opened
This PR significantly enhances the model card by:
- Adding
license: cc-by-nc-4.0,pipeline_tag: image-to-image, andlibrary_name: diffusersto the metadata for improved discoverability and user experience. - Updating the paper link to the Hugging Face paper page, and adding links to the project page, GitHub repository, Hugging Face Space, and Hugging Face Dataset.
- Populating the main content with a concise model description, visual results (teaser and comparison images), detailed installation and quick inference instructions, model and dataset download options, an online demo link, a BibTeX citation, contact information, and license details, all extracted from the paper and GitHub README.
Please review and merge if everything looks good.
ziheng1234 changed pull request status to merged