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
| license: apache-2.0 | |
| datasets: | |
| - R-J/SPI-2M | |
| library_name: diffusers | |
| tags: | |
| - medical | |
| # StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images | |
| 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) | |
|  | |
| ### Organisation | |
| - checkpoint: StainFuser trained weights trained at 512x512 resolution with mixed magnification | |
| - training: contains SD per-trained weights for backbone initialistaion in training | |
| ### Citation | |
| ``` | |
| @misc{jewsbury2024stainfuser, | |
| title={StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images}, | |
| author={Robert Jewsbury and Ruoyu Wang and Abhir Bhalerao and Nasir Rajpoot and Quoc Dang Vu}, | |
| year={2024}, | |
| eprint={2403.09302}, | |
| archivePrefix={arXiv}, | |
| primaryClass={eess.IV} | |
| } | |
| ``` |