Diffusers
Safetensors
English
histopathology
he-staining
breast-cancer
diffusion
counterfactuals
explainability
film
conditional-image-generation
Instructions to use a12donhf/CPathOGen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use a12donhf/CPathOGen with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("a12donhf/CPathOGen", 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
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Download examples/paper_tile/README.md from a12donhf/CPathOGen: direct link, hf CLI and curl.
- Browser
- Download file 846 Bytes
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https://huggingface.co/a12donhf/CPathOGen/resolve/main/examples/paper_tile/README.md
- Command line
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hf download hf://a12donhf/CPathOGen/examples/paper_tile/README.md
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curl -L -o README.md https://huggingface.co/a12donhf/CPathOGen/resolve/main/examples/paper_tile/README.md
846 Bytes
Single paper-tile example
Tile: TCGA-E2-A15D_x46080_y24576_TR.
map.npz: the original five-channel spatial condition, using themapkey and shape(512, 512, 5).morphology.json: 16 already standardized morphology/appearance values, in the checkpoint's feature order.input_map.png: the paper visualization; use the NPZ, not this RGB preview, for inference.reference_generated.png: the previously generated paper-package baseline, not a new inference result.metadata.json: seed, sampling settings, channel order, and condition provenance.
Use seed 1872879198, 30 steps, and spatial strength 2. Different hardware or precision can yield different pixels. This example needs neither the full dataset nor a nucleus analyzer. Follow the Colab cell in the model card to generate a new image from these conditions.