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simplify and update alpha mask to work with various cases

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Kohya S
2024-05-19 21:26:18 +09:00
parent f2dd43e198
commit da6fea3d97
10 changed files with 140 additions and 105 deletions
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10
train_network.py
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@@ -774,7 +774,9 @@ class NetworkTrainer:
if args.log_tracker_config is not None:
init_kwargs = toml.load(args.log_tracker_config)
accelerator.init_trackers(
"network_train" if args.log_tracker_name is None else args.log_tracker_name, config=train_util.get_sanitized_config_or_none(args), init_kwargs=init_kwargs
"network_train" if args.log_tracker_name is None else args.log_tracker_name,
config=train_util.get_sanitized_config_or_none(args),
init_kwargs=init_kwargs,
)
loss_recorder = train_util.LossRecorder()
@@ -902,10 +904,8 @@ class NetworkTrainer:
loss = train_util.conditional_loss(
noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
)
if args.masked_loss:
loss = apply_masked_loss(loss, batch["conditioning_images"][:, 0].unsqueeze(1))
if "alpha_mask" in batch and batch["alpha_mask"] is not None:
loss = apply_masked_loss(loss, batch["alpha_mask"])
if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None):
loss = apply_masked_loss(loss, batch)
loss = loss.mean([1, 2, 3])
loss_weights = batch["loss_weights"] # 各sampleごとのweight