Image-to-Image
Diffusers
Safetensors
DDPMPipeline
computed-tomography
ct-reconstruction
diffusion-model
inverse-problems
dm4ct
sparse-view-ct
Instructions to use jiayangshi/lodoind_pixel_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jiayangshi/lodoind_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/lodoind_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, part of the community science team at Hugging Face. I've updated the model card to include the pipeline_tag: image-to-image and library_name: diffusers metadata, which will help improve the model's visibility on the Hub. I also cleaned up the model card formatting to include direct links to the paper and project page, and fixed a syntax error in the usage snippet to provide a working example using the diffusers library.
Thanks for the update!
jiayangshi changed pull request status to merged