README.md

metadata

license: apache-2.0
library_name: diffusers
pipeline_tag: image-to-image
tags:
  - zoomldm
  - histopathology
  - brca
  - latent-diffusion
  - custom-pipeline
  - arxiv:2411.16969
widget:
  - src: demo_images/input.jpeg
    prompt: BRCA sample conditioned on demo SSL feature (mag=0)
    output:
      url: demo_images/output.jpeg

we do not have a full checkpoint conversion validation, if you encounter pipeline loading failure and unsidered output, please contact me via bili_sakura@zju.edu.cn

BiliSakura/ZoomLDM-brca

Diffusers-format BRCA variant of ZoomLDM with a bundled custom pipeline and local ldm modules.

Model Description

• Architecture: ZoomLDM latent diffusion pipeline (UNet + VAE + conditioning encoder)
• Domain: Histopathology (BRCA)
• Conditioning: UNI-style SSL feature maps + magnification level (0..4)
• Format: Self-contained local folder for DiffusionPipeline.from_pretrained(...)

Intended Use

Use this model for conditional multi-scale BRCA patch generation when you have compatible pre-extracted SSL features.

Out-of-Scope Use

• Not intended for diagnosis, treatment planning, or other clinical decisions.
• Not a general-purpose text-to-image model.
• Not validated for data outside the expected acquisition/distribution range.

Files

• unet/, vae/, conditioning_encoder/, scheduler/
• model_index.json
• pipeline_zoomldm.py
• ldm/ (bundled dependency modules)

Usage

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "BiliSakura/ZoomLDM-brca",
    custom_pipeline="pipeline_zoomldm.py",
    trust_remote_code=True,
).to("cuda")

out = pipe(
    ssl_features=ssl_feat_tensor.to("cuda"),      # BRCA UNI-style SSL embeddings
    magnification=torch.tensor([0]).to("cuda"),   # 0..4
    num_inference_steps=50,
    guidance_scale=2.0,
)
images = out.images

Demo Generation (dataset-backed)

This repo includes run_demo_inference.py, which uses local repo assets only:

• image: demo_images/input.jpeg
• SSL feature: demo_data/0_ssl_feat.npy
• magnification label: 0

Run:

python run_demo_inference.py

Limitations

• Requires correctly precomputed BRCA conditioning features.
• Magnification conditioning must match expected integer codes.
• Generated content may reflect biases and artifacts from training data.

Citation

@InProceedings{Yellapragada_2025_CVPR,
  author = {Yellapragada, Srikar and Graikos, Alexandros and Triaridis, Kostas and Prasanna, Prateek and Gupta, Rajarsi and Saltz, Joel and Samaras, Dimitris},
  title = {ZoomLDM: Latent Diffusion Model for Multi-scale Image Generation},
  booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
  month = {June},
  year = {2025},
  pages = {23453-23463}
}