Instructions to use R-J/StainFuser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use R-J/StainFuser with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("R-J/StainFuser", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
WIP: initial readme
Browse files
README.md
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license: apache-2.0
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license: apache-2.0
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datasets:
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- R-J/SPI-2M
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library_name: diffusers
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tags:
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- medical
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# StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images
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Weights from paper (coming soon)
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### Organisation
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- checkpoint: StainFuser trained weights trained at 512x512 resolution with mixed magnification
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- training: contains SD per-trained weights for backbone initialistaion in training
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### Citation
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TBD
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