Image-to-Image
Diffusers
Safetensors
DDPMPipeline
computed-tomography
ct-reconstruction
diffusion-model
inverse-problems
dm4ct
sparse-view-ct
Instructions to use jiayangshi/lodochallenge_pixel_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jiayangshi/lodochallenge_pixel_diffusion 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("jiayangshi/lodochallenge_pixel_diffusion", 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 pipeline tag and improve model card
#1
by nielsr HF Staff - opened
Hi, I'm Niels from the Hugging Face community team.
This PR improves the model card for the LoDoChallenge pixel diffusion model:
- Adds the
image-to-imagepipeline tag to the metadata. - Fixes the usage snippet to correctly showcase how to use the model with the
diffuserslibrary. - Adds links to the official project page, codebase, and research paper.
- Adds a BibTeX citation for the DM4CT paper.
jiayangshi changed pull request status to merged