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
Commit History
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Improve model card metadata and fix usage snippet
Verified signatureHi! 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-image` to improve discoverability. - Fixing a syntax error in the Python usage snippet. - Linking the model to the associated paper page on Hugging Face.c21c960
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Update README.md
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