VictorMylle/Kohya-ss-sd-scripts
1
0
Fork 1
mirror of https://github.com/kohya-ss/sd-scripts.git synced 2026-04-14 08:21:46 +00:00
Code Issues Packages Projects Releases Wiki Activity

Update train_network.py for DAdaptAdaGrad

Browse Source
This commit is contained in:
青龍聖者@bdsqlsz
2023-04-27 00:23:32 +08:00
committed by GitHub
parent eccc07537b
commit 725cce77e4
1 changed files with 2 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
train_network.py
View File

@@ -44,7 +44,7 @@ def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_sche
logs["lr/textencoder"] = float(lrs[0])
logs["lr/unet"] = float(lrs[-1]) # may be same to textencoder
if args.optimizer_type.lower() == "DAdaptation".lower() or args.optimizer_type.lower() == "DAdaptAdam".lower() or args.optimizer_type.lower() == "DAdaptAdan".lower() or args.optimizer_type.lower() == "DAdaptSGD".lower(): # tracking d*lr value of unet.
if args.optimizer_type.lower() == "DAdaptation".lower() or args.optimizer_type.lower() == "DAdaptAdam".lower() or args.optimizer_type.lower() == "DAdaptAdaGrad".lower() or args.optimizer_type.lower() == "DAdaptAdan".lower() or args.optimizer_type.lower() == "DAdaptSGD".lower(): # tracking d*lr value of unet.
logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
else:
idx = 0
@@ -54,7 +54,7 @@ def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_sche
for i in range(idx, len(lrs)):
logs[f"lr/group{i}"] = float(lrs[i])
if args.optimizer_type.lower() == "DAdaptation".lower() or args.optimizer_type.lower() == "DAdaptAdam".lower() or args.optimizer_type.lower() == "DAdaptAdan".lower() or args.optimizer_type.lower() == "DAdaptSGD".lower():
if args.optimizer_type.lower() == "DAdaptation".lower() or args.optimizer_type.lower() == "DAdaptAdam".lower() or args.optimizer_type.lower() == "DAdaptAdaGrad".lower() or args.optimizer_type.lower() == "DAdaptAdan".lower() or args.optimizer_type.lower() == "DAdaptSGD".lower():
logs[f"lr/d*lr/group{i}"] = (
lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
)