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Only add warning for deprecated scaling vpred loss function

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catboxanon
2024-10-21 08:12:53 -04:00
parent 8fc30f8205
commit e1b63c2249
9 changed files with 14 additions and 13 deletions
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2
fine_tune.py
View File

@@ -383,7 +383,7 @@ def train(args):
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
if args.scale_v_pred_loss_like_noise_pred:
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
if args.debiased_estimation_loss:
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)

11
library/train_util.py
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@@ -3727,15 +3727,16 @@ def verify_training_args(args: argparse.Namespace):
if args.adaptive_noise_scale is not None and args.noise_offset is None:
raise ValueError("adaptive_noise_scale requires noise_offset / adaptive_noise_scaleを使用するにはnoise_offsetが必要です")
if args.scale_v_pred_loss_like_noise_pred:
logger.warning(
f"scale_v_pred_loss_like_noise_pred is deprecated. it is suggested to use min_snr_gamma or debiased_estimation_loss"
+ " / scale_v_pred_loss_like_noise_pred は非推奨です。min_snr_gammaまたはdebiased_estimation_lossを使用することをお勧めします"
)
if args.scale_v_pred_loss_like_noise_pred and not args.v_parameterization:
raise ValueError(
"scale_v_pred_loss_like_noise_pred can be enabled only with v_parameterization / scale_v_pred_loss_like_noise_predはv_parameterizationが有効なときのみ有効にできます"
)
if args.scale_v_pred_loss_like_noise_pred and args.zero_terminal_snr:
raise ValueError(
"zero_terminal_snr enabled. scale_v_pred_loss_like_noise_pred will not be used / zero_terminal_snrが有効です。scale_v_pred_loss_like_noise_predは使用されません"
)
if args.v_pred_like_loss and args.v_parameterization:
raise ValueError(

2
sdxl_train.py
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@@ -725,7 +725,7 @@ def train(args):
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
if args.scale_v_pred_loss_like_noise_pred:
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
if args.v_pred_like_loss:
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)

2
sdxl_train_control_net_lllite.py
View File

@@ -474,7 +474,7 @@ def train(args):
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
if args.scale_v_pred_loss_like_noise_pred:
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
if args.v_pred_like_loss:
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)

2
sdxl_train_control_net_lllite_old.py
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@@ -434,7 +434,7 @@ def train(args):
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
if args.scale_v_pred_loss_like_noise_pred:
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
if args.v_pred_like_loss:
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)

2
train_db.py
View File

@@ -370,7 +370,7 @@ def train(args):
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
if args.scale_v_pred_loss_like_noise_pred:
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
if args.debiased_estimation_loss:
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)

2
train_network.py
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@@ -993,7 +993,7 @@ class NetworkTrainer:
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
if args.scale_v_pred_loss_like_noise_pred:
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
if args.v_pred_like_loss:
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)

2
train_textual_inversion.py
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@@ -598,7 +598,7 @@ class TextualInversionTrainer:
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
if args.scale_v_pred_loss_like_noise_pred:
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
if args.v_pred_like_loss:
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)

2
train_textual_inversion_XTI.py
View File

@@ -483,7 +483,7 @@ def train(args):
loss = loss * loss_weights
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
if args.scale_v_pred_loss_like_noise_pred:
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
if args.debiased_estimation_loss:
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)