crop based on hard coded resolution: 1080 (h) x 1920 (w) effectively resulting in no crop

train_dreambooth_lora_sdxl_advanced.py

@@ -1092,17 +1092,11 @@ class DreamBoothDataset(Dataset):
             if not single_image and args.random_flip and random.random() < 0.5:
                 # flip
                 image = train_flip(image)
-            if args.center_crop or single_image:
-                pass
-                #y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
-                #x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
-                #image = train_crop(image)
-            else:
-                pass
-                #y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
-                #image = crop(image, y1, x1, h, w)
-            #crop_top_left = (y1, x1)
-            #self.crop_top_lefts.append(crop_top_left)
+            y1 = max(0, int(round((image.height - 1080) / 2.0)))
+            x1 = max(0, int(round((image.width - 1920) / 2.0)))
+            image = train_crop(image)
+            crop_top_left = (y1, x1)
+            self.crop_top_lefts.append(crop_top_left)
             image = train_transforms(image)
             self.pixel_values.append(image)
 
@@ -1127,17 +1121,11 @@ class DreamBoothDataset(Dataset):
                 if args.random_flip and random.random() < 0.5:
                     # flip
                     image = train_flip(image)
-                if args.center_crop:
-                    pass
-                    #y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
-                    #x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
-                    #image = train_crop(image)
-                else:
-                    pass
-                    #y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
-                    #image = crop(image, y1, x1, h, w)
-                #crop_top_left = (y1, x1)
-                #self.crop_top_lefts_class_imgs.append(crop_top_left)
+                y1 = max(0, int(round((image.height - 1080) / 2.0)))
+                x1 = max(0, int(round((image.width - 1920) / 2.0)))
+                image = train_crop(image)
+                crop_top_left = (y1, x1)
+                self.crop_top_lefts_class_imgs.append(crop_top_left)
                 image = train_transforms(image)
                 self.pixel_values_class_imgs.append(image)