initial commit

app.py

@@ -53,10 +53,10 @@ def predict_location(image, model_name, cfg=0.0, num_samples=256):
         pipe = PIPES[model_name]
 
         # Get regular predictions
-        predicted_gps = pipe(img, batch_size=num_samples, cfg=cfg, num_steps=32)
+        predicted_gps = pipe(img, batch_size=num_samples, cfg=cfg, num_steps=16)
 
         # Get single high-confidence prediction
-        high_conf_gps = pipe(img, batch_size=1, cfg=2.0, num_steps=32)
+        high_conf_gps = pipe(img, batch_size=1, cfg=2.0, num_steps=16)
         return {
             "lat": predicted_gps[:, 0].astype(float).tolist(),
             "lon": predicted_gps[:, 1].astype(float).tolist(),
@@ -174,7 +174,7 @@ def main():
     # Header with custom styling
     colored_header(
         label="🗺️ Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation",
-        description="Upload an image and our model, PLONK, will predict possible locations! In red we will sample one point with guidance scale 2.0 for the best guess. <br> <br> Project page: https://nicolas-dufour.github.io/plonk",
+        description="Upload an image and our model, PLONK, will predict possible locations! In red we will sample one point with guidance scale 2.0 for the best guess. Project page: https://nicolas-dufour.github.io/plonk",
         color_name="red-70",
     )
 
@@ -207,7 +207,7 @@ def main():
                 "🎲 Number of samples",
                 min_value=1,
                 max_value=5000,
-                value=1000,
+                value=64,
                 step=1,
                 help="Number of location predictions to generate. More samples give better coverage but take longer to compute.",
             )