Image-to-Image
Diffusers
Safetensors
English
virtual-try-on
diffusion
stable-diffusion
multi-reference
computer-vision
comfyui
gradio
aigc
Instructions to use zhengchong/FastFit-MR-1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zhengchong/FastFit-MR-1024 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("zhengchong/FastFit-MR-1024", 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 metadata and add HF paper link
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding the
pipeline_tag: image-to-imageto enable better discoverability on the Hugging Face Hub (https://huggingface.co/models?pipeline_tag=image-to-image) and removing it from the generaltagslist. - Explicitly adding
library_name: diffusersin the metadata, as indicated by the acknowledgements and configuration files. This enables an automated "How to use" widget on the model page. - Adding a link to the Hugging Face paper page: https://huggingface.co/papers/2508.20586, in addition to the existing arXiv link.
- Correcting an incorrect GitHub link in the "Acknowledgement" section to ensure accuracy.
zhengchong changed pull request status to merged