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run_demo_inference.py

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+#!/usr/bin/env python3
+"""Run ControlNet inference for OSM, Canny, SAM and save outputs to demo_images/."""
+import os
+from pathlib import Path
+
+import torch
+from PIL import Image
+from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
+
+REPO = Path(__file__).resolve().parent
+PROMPT = "Satellite image features a city neighborhood"
+SEED = 42
+
+
+def main():
+    for control_type in ["OSM", "Canny", "SAM"]:
+        subfolder = f"controlnet/GeoSynth-{control_type}"
+        in_dir = REPO / "demo_images" / f"GeoSynth-{control_type}"
+        in_path = in_dir / "input.jpeg"
+        out_path = in_dir / "output.jpeg"
+        if not in_path.exists():
+            print(f"Skipping {control_type}: {in_path} not found")
+            continue
+        print(f"Loading GeoSynth-{control_type}...")
+        controlnet = ControlNetModel.from_pretrained(
+            str(REPO), subfolder=subfolder, torch_dtype=torch.float16
+        )
+        pipe = StableDiffusionControlNetPipeline.from_pretrained(
+            str(REPO), controlnet=controlnet, torch_dtype=torch.float16
+        )
+        pipe = pipe.to("cuda")
+        img = Image.open(in_path).convert("RGB").resize((512, 512))
+        gen = torch.manual_seed(SEED)
+        out = pipe(
+            PROMPT, image=img, generator=gen, num_inference_steps=20
+        ).images[0]
+        out.save(out_path)
+        print(f"Saved {out_path}")
+
+
+if __name__ == "__main__":
+    main()