[advanced sdxl lora script] - fix #6967 bug when using prior preservation loss (#6968)

* fix bug in micro-conditioning of class images

* fix bug in micro-conditioning of class images

* style

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -939,6 +939,32 @@ class DreamBoothDataset(Dataset):
             self.class_data_root = Path(class_data_root)
             self.class_data_root.mkdir(parents=True, exist_ok=True)
             self.class_images_path = list(self.class_data_root.iterdir())
+
+            self.original_sizes_class_imgs = []
+            self.crop_top_lefts_class_imgs = []
+            self.pixel_values_class_imgs = []
+            self.class_images = [Image.open(path) for path in self.class_images_path]
+            for image in self.class_images:
+                image = exif_transpose(image)
+                if not image.mode == "RGB":
+                    image = image.convert("RGB")
+                self.original_sizes_class_imgs.append((image.height, image.width))
+                image = train_resize(image)
+                if args.random_flip and random.random() < 0.5:
+                    # flip
+                    image = train_flip(image)
+                if args.center_crop:
+                    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:
+                    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)
+                image = train_transforms(image)
+                self.pixel_values_class_imgs.append(image)
+
             if class_num is not None:
                 self.num_class_images = min(len(self.class_images_path), class_num)
             else:
@@ -961,12 +987,9 @@ class DreamBoothDataset(Dataset):
 
     def __getitem__(self, index):
         example = {}
-        instance_image = self.pixel_values[index % self.num_instance_images]
-        original_size = self.original_sizes[index % self.num_instance_images]
-        crop_top_left = self.crop_top_lefts[index % self.num_instance_images]
-        example["instance_images"] = instance_image
-        example["original_size"] = original_size
-        example["crop_top_left"] = crop_top_left
+        example["instance_images"] = self.pixel_values[index % self.num_instance_images]
+        example["original_size"] = self.original_sizes[index % self.num_instance_images]
+        example["crop_top_left"] = self.crop_top_lefts[index % self.num_instance_images]
 
         if self.custom_instance_prompts:
             caption = self.custom_instance_prompts[index % self.num_instance_images]
@@ -983,13 +1006,10 @@ class DreamBoothDataset(Dataset):
             example["instance_prompt"] = self.instance_prompt
 
         if self.class_data_root:
-            class_image = Image.open(self.class_images_path[index % self.num_class_images])
-            class_image = exif_transpose(class_image)
-
-            if not class_image.mode == "RGB":
-                class_image = class_image.convert("RGB")
-            example["class_images"] = self.image_transforms(class_image)
             example["class_prompt"] = self.class_prompt
+            example["class_images"] = self.pixel_values_class_imgs[index % self.num_class_images]
+            example["class_original_size"] = self.original_sizes_class_imgs[index % self.num_class_images]
+            example["class_crop_top_left"] = self.crop_top_lefts_class_imgs[index % self.num_class_images]
 
         return example
 
@@ -1005,6 +1025,8 @@ def collate_fn(examples, with_prior_preservation=False):
     if with_prior_preservation:
         pixel_values += [example["class_images"] for example in examples]
         prompts += [example["class_prompt"] for example in examples]
+        original_sizes += [example["class_original_size"] for example in examples]
+        crop_top_lefts += [example["class_crop_top_left"] for example in examples]
 
     pixel_values = torch.stack(pixel_values)
     pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()