WSI Generation with DDPM
A Diffusion Model for Generating WSI Patches
How to use the model?
from diffusers import DiffusionPipeline
wsi_generator = DiffusionPipeline.from_pretrained("kaveh/wsi_generator")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
wsi_generator.to(device)
generated_image = wsi_generator().images[0]
generated_image.save("wsi_generated.png")
there is also a docker image available for this model in the following link: https://hub.docker.com/r/kaveh8/wsi-ddpm
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This model is not currently available via any of the supported third-party Inference Providers, and
the HF Inference API does not support diffusers models with pipeline type unconditional-image-generation