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
Commit History
-
Add pipeline tag and improve model card (#1)
- Add pipeline tag and improve model card (0cbc2c3bf236822963c51ea47bbbe62b5b318d6b) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>0f4a0dc
-
Update README.md
Verified signature894ad96 -
Update README.md
Verified signature2ff743f -
Upload DDPMPipeline
Verified signatureb1f14a6 -
initial commit
Verified signaturee4cf5c5