Update

1 files changed, 32 insertions, 9 deletions

diff --git a/dreambooth.py b/dreambooth.py
index 1ba8dc0..9e2645b 100644
--- a/dreambooth.py
+++ b/dreambooth.py
@@ -15,7 +15,7 @@ from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import LoggerType, set_seed
 from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
+from diffusers.optimization import get_scheduler, get_cosine_with_hard_restarts_schedule_with_warmup
 from diffusers.training_utils import EMAModel
 from PIL import Image
 from tqdm.auto import tqdm
@@ -150,10 +150,16 @@ def parse_args():
     parser.add_argument(
         "--lr_warmup_steps",
         type=int,
-        default=500,
+        default=300,
         help="Number of steps for the warmup in the lr scheduler."
     )
     parser.add_argument(
+        "--lr_cycles",
+        type=int,
+        default=2,
+        help="Number of restart cycles in the lr scheduler."
+    )
+    parser.add_argument(
         "--use_ema",
         action="store_true",
         default=True,
@@ -167,7 +173,7 @@ def parse_args():
     parser.add_argument(
         "--ema_power",
         type=float,
-        default=6 / 7
+        default=9 / 10
     )
     parser.add_argument(
         "--ema_max_decay",
@@ -296,6 +302,13 @@ def parse_args():
     return args
 
 
+def save_args(basepath: Path, args, extra={}):
+    info = {"args": vars(args)}
+    info["args"].update(extra)
+    with open(basepath.joinpath("args.json"), "w") as f:
+        json.dump(info, f, indent=4)
+
+
 def freeze_params(params):
     for param in params:
         param.requires_grad = False
@@ -455,6 +468,8 @@ def main():
 
     logging.basicConfig(filename=basepath.joinpath("log.txt"), level=logging.DEBUG)
 
+    save_args(basepath, args)
+
     # If passed along, set the training seed now.
     if args.seed is not None:
         set_seed(args.seed)
@@ -614,12 +629,20 @@ def main():
         args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
         overrode_max_train_steps = True
 
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
-        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
-    )
+    if args.lr_scheduler == "cosine_with_restarts":
+        lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
+            optimizer=optimizer,
+            num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
+            num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+            num_cycles=args.lr_cycles,
+        )
+    else:
+        lr_scheduler = get_scheduler(
+            args.lr_scheduler,
+            optimizer=optimizer,
+            num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
+            num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+        )
 
     unet, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
         unet, optimizer, train_dataloader, val_dataloader, lr_scheduler