add grad ckpt

examples/dreambooth/train_dreambooth.py

@@ -120,6 +120,11 @@ def parse_args():
         default=1,
         help="Number of updates steps to accumulate before performing a backward/update pass.",
     )
+    parser.add_argument(
+        "--gradient_checkpointing",
+        action="store_true",
+        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+    )
     parser.add_argument(
         "--learning_rate",
         type=float,
@@ -388,10 +393,14 @@ def main():
         args.pretrained_model_name_or_path, subfolder="unet", use_auth_token=args.use_auth_token
     )
 
+    if args.gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+
     if args.scale_lr:
         args.learning_rate = (
             args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
         )
+
     optimizer = torch.optim.AdamW(
         unet.parameters(),  # only optimize unet
         lr=args.learning_rate,