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Typos and lingering is_train

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This commit is contained in:
rockerBOO
2025-01-03 01:18:15 -05:00
parent 7470173044
commit 534059dea5
3 changed files with 4 additions and 8 deletions
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2
library/config_util.py
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@@ -535,7 +535,7 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
shuffle_caption: {subset.shuffle_caption}
keep_tokens: {subset.keep_tokens}
caption_dropout_rate: {subset.caption_dropout_rate}
caption_dropout_every_n_epoches: {subset.caption_dropout_every_n_epochs}
caption_dropout_every_n_epochs: {subset.caption_dropout_every_n_epochs}
caption_tag_dropout_rate: {subset.caption_tag_dropout_rate}
caption_prefix: {subset.caption_prefix}
caption_suffix: {subset.caption_suffix}

4
library/train_util.py
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@@ -2092,7 +2092,6 @@ class FineTuningDataset(BaseDataset):
bucket_reso_steps: int,
bucket_no_upscale: bool,
debug_dataset: bool,
is_train: bool,
validation_seed: int,
validation_split: float,
) -> None:
@@ -2312,7 +2311,6 @@ class ControlNetDataset(BaseDataset):
def __init__(
self,
subsets: Sequence[ControlNetSubset],
is_train: bool,
batch_size: int,
resolution,
network_multiplier: float,
@@ -2362,7 +2360,6 @@ class ControlNetDataset(BaseDataset):
self.dreambooth_dataset_delegate = DreamBoothDataset(
db_subsets,
is_train,
batch_size,
resolution,
network_multiplier,
@@ -2382,7 +2379,6 @@ class ControlNetDataset(BaseDataset):
self.batch_size = batch_size
self.num_train_images = self.dreambooth_dataset_delegate.num_train_images
self.num_reg_images = self.dreambooth_dataset_delegate.num_reg_images
self.is_train = is_train
self.validation_split = validation_split
self.validation_seed = validation_seed

6
train_network.py
View File

@@ -380,11 +380,11 @@ class NetworkTrainer:
else:
return typing.cast(torch.IntTensor, torch.tensor(timesteps_list).unsqueeze(1).repeat(1, batch_size).to(latents.device))
choosen_timesteps_list = pick_timesteps_list()
chosen_timesteps_list = pick_timesteps_list()
total_loss = torch.zeros((batch_size, 1)).to(latents.device)
# Use input timesteps_list or use described timesteps above
for fixed_timestep in choosen_timesteps_list:
for fixed_timestep in chosen_timesteps_list:
fixed_timestep = typing.cast(torch.IntTensor, fixed_timestep)
# Predict the noise residual
@@ -447,7 +447,7 @@ class NetworkTrainer:
total_loss += loss
return total_loss / len(choosen_timesteps_list)
return total_loss / len(chosen_timesteps_list)
def train(self, args):
session_id = random.randint(0, 2**32)