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
UPD: adding link to github
Browse files
README.md
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# StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images
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Repo containing model weights from the [paper](https://arxiv.org/abs/2403.09302)
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### Organisation
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- checkpoint: StainFuser trained weights trained at 512x512 resolution with mixed magnification
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# StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images
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Repo containing model weights from the [paper](https://arxiv.org/abs/2403.09302), Github code can be found [here](https://github.com/R-J96/stainFuser)
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### Organisation
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- checkpoint: StainFuser trained weights trained at 512x512 resolution with mixed magnification
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