Here, let's fix this while we're at it.

trainer/diffusers_trainer.py

@@ -48,6 +48,9 @@ torch.backends.cuda.matmul.allow_tf32 = True
 
 # defaults should be good for everyone
 # TODO: add custom VAE support. should be simple with diffusers
+# use action='store_bool' when looking for boolean values so the arguments are treated like flags (as expected)
+# just keep in mind it's logically flipped from 'default', 
+# ('--foo', action='store_false') returns false when the flag exists, and true if it does not.
 parser = argparse.ArgumentParser(description='Stable Diffusion Finetuner')
 parser.add_argument('--model', type=str, default=None, required=True, help='The name of the model to use for finetuning. Could be HuggingFace ID or a directory')
 parser.add_argument('--resume', type=str, default=None, help='The path to the checkpoint to resume from. If not specified, will create a new run.')
@@ -59,10 +62,10 @@ parser.add_argument('--bucket_side_max', type=int, default=768, help='The maximu
 parser.add_argument('--lr', type=float, default=5e-6, help='Learning rate')
 parser.add_argument('--epochs', type=int, default=10, help='Number of epochs to train for')
 parser.add_argument('--batch_size', type=int, default=1, help='Batch size')
-parser.add_argument('--use_ema', type=str, default='False', help='Use EMA for finetuning')
+parser.add_argument('--use_ema', action='store_true', help='Use EMA for finetuning')
 parser.add_argument('--ucg', type=float, default=0.1, help='Percentage chance of dropping out the text condition per batch. Ranges from 0.0 to 1.0 where 1.0 means 100% text condition dropout.') # 10% dropout probability
-parser.add_argument('--gradient_checkpointing', dest='gradient_checkpointing', type=str, default='False', help='Enable gradient checkpointing')
-parser.add_argument('--use_8bit_adam', dest='use_8bit_adam', type=str, default='False', help='Use 8-bit Adam optimizer')
+parser.add_argument('--gradient_checkpointing', dest='gradient_checkpointing', action='store_true', help='Enable gradient checkpointing')
+parser.add_argument('--use_8bit_adam', dest='use_8bit_adam', action='store_true', help='Use 8-bit Adam optimizer')
 parser.add_argument('--adam_beta1', type=float, default=0.9, help='Adam beta1')
 parser.add_argument('--adam_beta2', type=float, default=0.999, help='Adam beta2')
 parser.add_argument('--adam_weight_decay', type=float, default=1e-2, help='Adam weight decay')
@@ -73,31 +76,24 @@ parser.add_argument('--seed', type=int, default=42, help='Seed for random number
 parser.add_argument('--output_path', type=str, default='./output', help='Root path for all outputs.')
 parser.add_argument('--save_steps', type=int, default=500, help='Number of steps to save checkpoints at.')
 parser.add_argument('--resolution', type=int, default=512, help='Image resolution to train against. Lower res images will be scaled up to this resolution and higher res images will be scaled down.')
-parser.add_argument('--shuffle', dest='shuffle', type=str, default='True', help='Shuffle dataset')
+parser.add_argument('--shuffle', dest='shuffle', action='store_true', help='Shuffle dataset')
 parser.add_argument('--hf_token', type=str, default=None, required=False, help='A HuggingFace token is needed to download private models for training.')
 parser.add_argument('--project_id', type=str, default='diffusers', help='Project ID for reporting to WandB')
-parser.add_argument('--fp16', dest='fp16', type=str, default='False', help='Train in mixed precision')
+parser.add_argument('--fp16', dest='fp16', action='store_true', help='Train in mixed precision')
 parser.add_argument('--image_log_steps', type=int, default=100, help='Number of steps to log images at.')
 parser.add_argument('--image_log_amount', type=int, default=4, help='Number of images to log every image_log_steps')
 parser.add_argument('--image_log_inference_steps', type=int, default=50, help='Number of inference steps to use to log images.')
 parser.add_argument('--image_log_scheduler', type=str, default="PNDMScheduler", help='Number of inference steps to use to log images.')
-parser.add_argument('--clip_penultimate', type=str, default='False', help='Use penultimate CLIP layer for text embedding')
-parser.add_argument('--output_bucket_info', type=str, default='False', help='Outputs bucket information and exits')
-parser.add_argument('--resize', type=str, default='False', help="Resizes dataset's images to the appropriate bucket dimensions.")
-parser.add_argument('--use_xformers', type=str, default='False', help='Use memory efficient attention')
-parser.add_argument('--extended_validation', type=str, default='False', help='Perform extended validation of images to catch truncated or corrupt images.')
-parser.add_argument('--data_migration', type=str, default='True', help='Perform migration of resized images into a directory relative to the dataset path. Saves into `<dataset_directory_name>_cropped`.')
-parser.add_argument('--skip_validation', type=str, default='False', help='Skip validation of images, useful for speeding up loading of very large datasets that have already been validated.')
+parser.add_argument('--clip_penultimate', action='store_true', help='Use penultimate CLIP layer for text embedding')
+parser.add_argument('--output_bucket_info', action='store_true', help='Outputs bucket information and exits')
+parser.add_argument('--resize', action='store_true', help="Resizes dataset's images to the appropriate bucket dimensions.")
+parser.add_argument('--use_xformers', action='store_true', help='Use memory efficient attention')
+parser.add_argument('--extended_validation', action='store_true', help='Perform extended validation of images to catch truncated or corrupt images.')
+parser.add_argument('--no_migration', action='store_true', help='Perform migration of resized images into a directory relative to the dataset path. Saves into `<dataset_directory_name>_cropped`.')
+parser.add_argument('--skip_validation', action='store_true', help='Skip validation of images, useful for speeding up loading of very large datasets that have already been validated.')
 
 args = parser.parse_args()
 
-for arg in vars(args):
-    if type(getattr(args, arg)) == str:
-        if getattr(args, arg).lower() == 'true':
-            setattr(args, arg, True)
-        elif getattr(args, arg).lower() == 'false':
-            setattr(args, arg, False)
-
 def setup():
     torch.distributed.init_process_group("nccl", init_method="env://")
 
@@ -194,12 +190,12 @@ class Validation():
         return True
 
 class Resize():
-    def __init__(self, is_resizing: bool, is_migrating: bool) -> None:
+    def __init__(self, is_resizing: bool, is_not_migrating: bool) -> None:
         if not is_resizing:
             self.resize = self.__no_op
             return
 
-        if is_migrating:
+        if not is_not_migrating:
             self.resize = self.__migration
             dataset_path = os.path.split(args.dataset)
             self.__directory = os.path.join(
@@ -267,7 +263,7 @@ class ImageStore:
             args.extended_validation
         ).validate
 
-        self.resizer = Resize(args.resize, args.data_migration).resize
+        self.resizer = Resize(args.resize, args.no_migration).resize
 
         self.image_files = [x for x in self.image_files if self.validator(x)]
 
@@ -718,7 +714,7 @@ def main():
     )
     
     # Migrate dataset
-    if args.resize and args.data_migration:
+    if args.resize and not args.no_migration:
         for _, batch in enumerate(train_dataloader):
             continue
         print(f"Completed resize and migration to '{args.dataset}_cropped' please relaunch the trainer without the --resize argument and train on the migrated dataset.")