Allow adapter image to be cropped to match bucket cropping

extensions_built_in/sd_trainer/SDTrainer.py

@@ -68,9 +68,21 @@ class SDTrainer(BaseSDTrainProcess):
         adapter_tensors = []
         # load images with torch transforms
         for idx, adapter_image in enumerate(adapter_images):
-            img = Image.open(adapter_image)
-            # resize to match batch shape
-            img = img.resize((width, height))
+            # we need to centrally crop the largest dimension of the image to match the batch shape after scaling
+            # to the smallest dimension
+            img: Image.Image = Image.open(adapter_image)
+            if img.width > img.height:
+                # scale down so height is the same as batch
+                new_height = height
+                new_width = int(img.width * (height / img.height))
+            else:
+                new_width = width
+                new_height = int(img.height * (width / img.width))
+
+            img = img.resize((new_width, new_height))
+            crop_fn = transforms.CenterCrop((height, width))
+            # crop the center to match batch
+            img = crop_fn(img)
             img = adapter_transforms(img)
             adapter_tensors.append(img)