VictorMylle/Kohya-ss-sd-scripts
1
0
Fork 1
mirror of https://github.com/kohya-ss/sd-scripts.git synced 2026-04-08 06:28:48 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge 24d33083cb into 51435f1718

Browse Source
This commit is contained in:
nickkolok
2026-04-04 21:41:41 +00:00
committed by GitHub
parent 51435f1718 24d33083cb
commit 2080601411
3 changed files with 3 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
train_db.py
View File

@@ -290,7 +290,7 @@ def train(args):
accelerator.print(
f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}"
)
accelerator.print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
accelerator.print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
accelerator.print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")

2
train_textual_inversion.py
View File

@@ -497,7 +497,7 @@ class TextualInversionTrainer:
accelerator.print(
f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}"
)
accelerator.print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
accelerator.print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
accelerator.print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")

2
train_textual_inversion_XTI.py
View File

@@ -388,7 +388,7 @@ def train(args):
logger.info(
f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}"
)
logger.info(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
logger.info(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
logger.info(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")