Image-to-Image
Diffusers
Safetensors
DDPMPipeline
computed-tomography
ct-reconstruction
diffusion-model
inverse-problems
dm4ct
sparse-view-ct
Instructions to use jiayangshi/synchrotron_pixel_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jiayangshi/synchrotron_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/synchrotron_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
Improve model card metadata and fix usage snippet
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the community team at Hugging Face.
I've noticed this model is part of the DM4CT benchmark for CT reconstruction. This PR improves the model card by:
- Adding the
pipeline_tag: image-to-imageto improve discoverability. - Fixing a syntax error in the Python usage snippet.
- Linking the model to the associated paper page on Hugging Face.
jiayangshi changed pull request status to merged