Image-to-Image
Diffusers
Safetensors
LDMPipeline
computed-tomography
ct-reconstruction
diffusion-model
latent-diffusion
inverse-problems
dm4ct
sparse-view-ct
Instructions to use jiayangshi/lodoind_latent_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jiayangshi/lodoind_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/lodoind_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: add pipeline tag, paper link, and fix usage snippet
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the community science team at Hugging Face.
I've opened this PR to improve the model card for this artifact:
- Added
pipeline_tag: image-to-imageto the YAML metadata to improve discoverability. - Added a link to the Hugging Face paper page.
- Fixed the
diffusersusage snippet to be syntactically correct and use theLDMPipelineas specified in yourmodel_index.json.
This helps researchers find and cite your work more easily!
Thanks for the updates!
jiayangshi changed pull request status to merged