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
- Xet hash:
- bd0be160f9e280719d3a1d034a6a7d30f4943108ee7cb2a5142c7890af747651
- Size of remote file:
- 497 MB
- SHA256:
- c0118b4f5fca971a261633f041c1a922166d4d817cd19bea282ed453e50fa8e6
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