Add files using upload-large-folder tool

README.md

@@ -9,6 +9,11 @@ tags:
   - histopathology
   - brca
   - custom-pipeline
+widget:
+  - src: demo_images/input.jpeg
+    prompt: Sample BRCA conditioning embedding (magnification class 0)
+    output:
+      url: demo_images/output.jpeg
 ---
 
 # BiliSakura/ZoomLDM-CDM-brca
@@ -50,7 +55,7 @@ from diffusers import DiffusionPipeline
 
 pipe = DiffusionPipeline.from_pretrained(
     "BiliSakura/ZoomLDM-CDM-brca",
-    custom_pipeline="pipelin.py",
+    custom_pipeline="pipeline.py",
     trust_remote_code=True,
 ).to("cuda")
 

run_demo_inference.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+"""Run ZoomLDM-CDM-brca demo inference and save visualization to demo_images/."""
+from pathlib import Path
+
+import numpy as np
+import torch
+from diffusers import DiffusionPipeline
+from PIL import Image, ImageDraw
+
+
+def main() -> None:
+    repo = Path(__file__).resolve().parent
+    demo_dir = repo / "demo_images"
+    demo_dir.mkdir(exist_ok=True)
+
+    pipe = DiffusionPipeline.from_pretrained(
+        str(repo),
+        custom_pipeline=str(repo / "pipeline.py"),
+        trust_remote_code=True,
+        local_files_only=True,
+    ).to("cuda")
+
+    magnification = torch.tensor([0], device="cuda")
+    out = pipe(
+        batch_size=1,
+        magnification=magnification,
+        num_inference_steps=50,
+        guidance_scale=1.0,
+    )
+
+    # out.samples: (1, 512, 65) -> visualize first sample as a heatmap-like image
+    sample = out.samples[0].detach().float().cpu().numpy()
+    sample = (sample - sample.min()) / (sample.max() - sample.min() + 1e-8)
+    sample_u8 = (sample * 255).astype(np.uint8)  # (512, 65)
+    img = Image.fromarray(sample_u8, mode="L").resize((520, 512), Image.Resampling.NEAREST).convert("RGB")
+    img.save(demo_dir / "output.jpeg")
+
+    # Simple input card to make widget context explicit
+    card = Image.new("RGB", (520, 512), color=(245, 245, 245))
+    draw = ImageDraw.Draw(card)
+    draw.text((24, 24), "ZoomLDM-CDM-brca", fill=(20, 20, 20))
+    draw.text((24, 64), "Input: magnification class = 0", fill=(40, 40, 40))
+    draw.text((24, 100), "Output: sampled conditioning tensor visualization", fill=(40, 40, 40))
+    card.save(demo_dir / "input.jpeg")
+
+    print(f"Saved {demo_dir / 'input.jpeg'}")
+    print(f"Saved {demo_dir / 'output.jpeg'}")
+
+
+if __name__ == "__main__":
+    main()