Revert "[Examples] Uniform notations in train_flux_lora (#10011)"

This reverts commit 173e1b1.

Author: yiyixuxu

examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py

@@ -971,7 +971,6 @@ class DreamBoothDataset(Dataset):
 
     def __init__(
         self,
-        args,
         instance_data_root,
         instance_prompt,
         class_prompt,
@@ -981,8 +980,10 @@ def __init__(
         class_num=None,
         size=1024,
         repeats=1,
+        center_crop=False,
     ):
         self.size = size
+        self.center_crop = center_crop
 
         self.instance_prompt = instance_prompt
         self.custom_instance_prompts = None
@@ -1074,11 +1075,11 @@ def __init__(
                 # flip
                 image = train_flip(image)
             if args.center_crop:
-                y1 = max(0, int(round((image.height - self.size) / 2.0)))
-                x1 = max(0, int(round((image.width - self.size) / 2.0)))
+                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, (self.size, self.size))
+                y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
                 image = crop(image, y1, x1, h, w)
             image = train_transforms(image)
             self.pixel_values.append(image)
@@ -1826,7 +1827,6 @@ def load_model_hook(models, input_dir):
 
     # Dataset and DataLoaders creation:
     train_dataset = DreamBoothDataset(
-        args=args,
         instance_data_root=args.instance_data_dir,
         instance_prompt=args.instance_prompt,
         train_text_encoder_ti=args.train_text_encoder_ti,
@@ -1836,6 +1836,7 @@ def load_model_hook(models, input_dir):
         class_num=args.num_class_images,
         size=args.resolution,
         repeats=args.repeats,
+        center_crop=args.center_crop,
     )
 
     train_dataloader = torch.utils.data.DataLoader(