Image-to-Image
Diffusers
Safetensors
LDMPipeline
computed-tomography
ct-reconstruction
diffusion-model
latent-diffusion
inverse-problems
dm4ct
sparse-view-ct
Instructions to use jiayangshi/synchrotron_latent_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jiayangshi/synchrotron_latent_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_latent_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 and metadata
#1
by nielsr HF Staff - opened
Hi, I'm Niels from the Hugging Face community science team.
This PR improves the model card for the DM4CT latent diffusion model. The following changes were made:
- Added the
pipeline_tag: image-to-imageto the YAML metadata to improve model discoverability. - Added the official project page link.
- Filled in technical placeholders (like latent resolution) using information from the configuration files.
- Fixed the sample usage code snippet to follow standard
diffuserslogic. - Linked the paper to its Hugging Face paper page.
jiayangshi changed pull request status to merged