Merge branch 'main' into staging-migration

trainer/diffusers_trainer.py

@@ -48,9 +48,7 @@ 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.
+bool_t = lambda x: x.lower() in ['true', 'yes', '1']
 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.')
@@ -62,10 +60,10 @@ 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', action='store_true', help='Use EMA for finetuning')
+parser.add_argument('--use_ema', type=bool_t, default='False', 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', 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('--gradient_checkpointing', dest='gradient_checkpointing', type=bool_t, default='False', help='Enable gradient checkpointing')
+parser.add_argument('--use_8bit_adam', dest='use_8bit_adam', type=bool_t, default='False', 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')
@@ -76,22 +74,18 @@ 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', action='store_true', help='Shuffle dataset')
+parser.add_argument('--shuffle', dest='shuffle', type=bool_t, default='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', action='store_true', help='Train in mixed precision')
+parser.add_argument('--fp16', dest='fp16', type=bool_t, default='False', 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', 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='Do not perform migration of dataset while the `--resize` flag is active. Migration creates an adjacent folder to the dataset with <dataset_dirname>_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.')
-
+parser.add_argument('--clip_penultimate', type=bool_t, default='False', help='Use penultimate CLIP layer for text embedding')
+parser.add_argument('--output_bucket_info', type=bool_t, default='False', help='Outputs bucket information and exits')
+parser.add_argument('--resize', type=bool_t, default='False', help="Resizes dataset's images to the appropriate bucket dimensions.")
+parser.add_argument('--use_xformers', type=bool_t, default='False', help='Use memory efficient attention')
 args = parser.parse_args()
 
 def setup():