Fix snitching

app.py

@@ -81,17 +81,11 @@ def make_example(image_path: Path, mask_path: Path) -> EditorValue:
 
 
 def remove_padding(image, original_size):
-    # Get current dimensions
-    padded_width, padded_height = image.size
     original_width, original_height = original_size
-
-    # Calculate cropping box
-    left = (padded_width - original_width) // 2
-    top = (padded_height - original_height) // 2
+    left = max((image.width - original_width) // 2, 0)
+    top = max((image.height - original_height) // 2, 0)
     right = left + original_width
     bottom = top + original_height
-
-    # Crop to original size
     return image.crop((left, top, right, bottom))
 
 
@@ -127,14 +121,10 @@ def infer(
         room_mask.getbbox(alpha_only=False)
     )
     # Add MASK_CONTEXT_PADDING (16 pixels) for the context
-    mask_bbox_x_min -= MASK_CONTEXT_PADDING
-    mask_bbox_x_min = max(mask_bbox_x_min, 0)
-    mask_bbox_y_min -= MASK_CONTEXT_PADDING
-    mask_bbox_y_min = max(mask_bbox_y_min, 0)
-    mask_bbox_x_max += MASK_CONTEXT_PADDING
-    mask_bbox_x_max = min(mask_bbox_x_max, room_mask.width)
-    mask_bbox_y_max += MASK_CONTEXT_PADDING
-    mask_bbox_y_max = min(mask_bbox_y_max, room_mask.height)
+    mask_bbox_x_min = max(mask_bbox_x_min - MASK_CONTEXT_PADDING, 0)
+    mask_bbox_y_min = max(mask_bbox_y_min - MASK_CONTEXT_PADDING, 0)
+    mask_bbox_x_max = min(mask_bbox_x_max + MASK_CONTEXT_PADDING, room_mask.width)
+    mask_bbox_y_max = min(mask_bbox_y_max + MASK_CONTEXT_PADDING, room_mask.height)
 
     bbox_longest_side = max(
         mask_bbox_x_max - mask_bbox_x_min,
@@ -157,7 +147,7 @@ def infer(
     room_image_cropped = ImageOps.fit(
         room_image_cropped,
         (max_dimension, max_dimension),
-        method=Image.Resampling.LANCZOS,
+        method=Image.Resampling.BICUBIC,
         centering=(0.5, 0.5),
     )
 
@@ -167,7 +157,7 @@ def infer(
         mask_bbox_x_max,
         mask_bbox_y_max,
     ))
-    # room_mask_cropped.save("room_mask_croppedv1.png")
+    room_mask_cropped.save("room_mask_croppedv1.png")
     room_mask_cropped = ImageOps.pad(
         room_mask_cropped,
         (max_dimension, max_dimension),
@@ -178,12 +168,12 @@ def infer(
     room_mask_cropped = ImageOps.fit(
         room_mask_cropped,
         (max_dimension, max_dimension),
-        method=Image.Resampling.LANCZOS,
+        method=Image.Resampling.BICUBIC,
         centering=(0.5, 0.5),
     )
 
-    # room_image_cropped.save("room_image_cropped.png")
-    # room_mask_cropped.save("room_mask_cropped.png")
+    room_image_cropped.save("room_image_cropped.png")
+    room_mask_cropped.save("room_mask_cropped.png")
 
     # _room_image = ImageOps.fit(
     #     _room_image,
@@ -239,8 +229,8 @@ def infer(
     prompt = (
         furniture_prompt + ".\n" + SYSTEM_PROMPT if furniture_prompt else SYSTEM_PROMPT
     )
-    # image.save("image.png")
-    # mask.save("mask.png")
+    image.save("image.png")
+    mask.save("mask.png")
     results_images = pipe(
         prompt=prompt,
         image=image,
@@ -264,7 +254,9 @@ def infer(
             max_dimension * 2,
             max_dimension,
         ))
-        image_generated = image_generated.resize((bbox_longest_side, bbox_longest_side))
+        image_generated = image_generated.resize(
+            (bbox_longest_side, bbox_longest_side), Image.Resampling.BICUBIC
+        )
         # Crop back to the bbox (remove the padding)
         image_generated = remove_padding(
             image_generated,