VictorMylle/Kohya-ss-sd-scripts
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fix print "saving" and "epoch" in newline

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Fair
2023-05-07 02:51:01 +08:00
parent e6ad3cbc66
commit b08154dc36
11 changed files with 21 additions and 21 deletions
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2
fine_tune.py
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@@ -266,7 +266,7 @@ def train(args):
accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name) accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name)
for epoch in range(num_train_epochs): for epoch in range(num_train_epochs):
print(f"epoch {epoch+1}/{num_train_epochs}") print(f"\nepoch {epoch+1}/{num_train_epochs}")
current_epoch.value = epoch + 1 current_epoch.value = epoch + 1
for m in training_models: for m in training_models:

12
library/train_util.py
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@@ -1424,7 +1424,7 @@ def debug_dataset(train_dataset, show_input_ids=False):
epoch = 1 epoch = 1
while True: while True:
print(f"epoch: {epoch}") print(f"\nepoch: {epoch}")
steps = (epoch - 1) * len(train_dataset) + 1 steps = (epoch - 1) * len(train_dataset) + 1
indices = list(range(len(train_dataset))) indices = list(range(len(train_dataset)))
@@ -3083,7 +3083,7 @@ def save_sd_model_on_epoch_end_or_stepwise(
ckpt_name = get_step_ckpt_name(args, ext, global_step) ckpt_name = get_step_ckpt_name(args, ext, global_step)
ckpt_file = os.path.join(args.output_dir, ckpt_name) ckpt_file = os.path.join(args.output_dir, ckpt_name)
print(f"saving checkpoint: {ckpt_file}") print(f"\nsaving checkpoint: {ckpt_file}")
model_util.save_stable_diffusion_checkpoint( model_util.save_stable_diffusion_checkpoint(
args.v2, ckpt_file, text_encoder, unet, src_path, epoch_no, global_step, save_dtype, vae args.v2, ckpt_file, text_encoder, unet, src_path, epoch_no, global_step, save_dtype, vae
) )
@@ -3109,7 +3109,7 @@ def save_sd_model_on_epoch_end_or_stepwise(
else: else:
out_dir = os.path.join(args.output_dir, STEP_DIFFUSERS_DIR_NAME.format(model_name, global_step)) out_dir = os.path.join(args.output_dir, STEP_DIFFUSERS_DIR_NAME.format(model_name, global_step))
print(f"saving model: {out_dir}") print(f"\nsaving model: {out_dir}")
model_util.save_diffusers_checkpoint( model_util.save_diffusers_checkpoint(
args.v2, out_dir, text_encoder, unet, src_path, vae=vae, use_safetensors=use_safetensors args.v2, out_dir, text_encoder, unet, src_path, vae=vae, use_safetensors=use_safetensors
) )
@@ -3136,7 +3136,7 @@ def save_sd_model_on_epoch_end_or_stepwise(
def save_and_remove_state_on_epoch_end(args: argparse.Namespace, accelerator, epoch_no): def save_and_remove_state_on_epoch_end(args: argparse.Namespace, accelerator, epoch_no):
model_name = default_if_none(args.output_name, DEFAULT_EPOCH_NAME) model_name = default_if_none(args.output_name, DEFAULT_EPOCH_NAME)
print(f"saving state at epoch {epoch_no}") print(f"\nsaving state at epoch {epoch_no}")
os.makedirs(args.output_dir, exist_ok=True) os.makedirs(args.output_dir, exist_ok=True)
state_dir = os.path.join(args.output_dir, EPOCH_STATE_NAME.format(model_name, epoch_no)) state_dir = os.path.join(args.output_dir, EPOCH_STATE_NAME.format(model_name, epoch_no))
@@ -3157,7 +3157,7 @@ def save_and_remove_state_on_epoch_end(args: argparse.Namespace, accelerator, ep
def save_and_remove_state_stepwise(args: argparse.Namespace, accelerator, step_no): def save_and_remove_state_stepwise(args: argparse.Namespace, accelerator, step_no):
model_name = default_if_none(args.output_name, DEFAULT_STEP_NAME) model_name = default_if_none(args.output_name, DEFAULT_STEP_NAME)
print(f"saving state at step {step_no}") print(f"\nsaving state at step {step_no}")
os.makedirs(args.output_dir, exist_ok=True) os.makedirs(args.output_dir, exist_ok=True)
state_dir = os.path.join(args.output_dir, STEP_STATE_NAME.format(model_name, step_no)) state_dir = os.path.join(args.output_dir, STEP_STATE_NAME.format(model_name, step_no))
@@ -3182,7 +3182,7 @@ def save_and_remove_state_stepwise(args: argparse.Namespace, accelerator, step_n
def save_state_on_train_end(args: argparse.Namespace, accelerator): def save_state_on_train_end(args: argparse.Namespace, accelerator):
model_name = default_if_none(args.output_name, DEFAULT_LAST_OUTPUT_NAME) model_name = default_if_none(args.output_name, DEFAULT_LAST_OUTPUT_NAME)
print("saving last state.") print("\nsaving last state.")
os.makedirs(args.output_dir, exist_ok=True) os.makedirs(args.output_dir, exist_ok=True)
state_dir = os.path.join(args.output_dir, LAST_STATE_NAME.format(model_name)) state_dir = os.path.join(args.output_dir, LAST_STATE_NAME.format(model_name))

2
networks/extract_lora_from_dylora.py
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@@ -94,7 +94,7 @@ def split(args):
filename, ext = os.path.splitext(args.save_to) filename, ext = os.path.splitext(args.save_to)
model_file_name = filename + f"-{new_rank:04d}{ext}" model_file_name = filename + f"-{new_rank:04d}{ext}"
print(f"saving model to: {model_file_name}") print(f"\nsaving model to: {model_file_name}")
save_to_file(model_file_name, state_dict, new_metadata) save_to_file(model_file_name, state_dict, new_metadata)

4
networks/merge_lora.py
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@@ -193,12 +193,12 @@ def merge(args):
merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype) merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
print(f"saving SD model to: {args.save_to}") print(f"\nsaving SD model to: {args.save_to}")
model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet, args.sd_model, 0, 0, save_dtype, vae) model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet, args.sd_model, 0, 0, save_dtype, vae)
else: else:
state_dict = merge_lora_models(args.models, args.ratios, merge_dtype) state_dict = merge_lora_models(args.models, args.ratios, merge_dtype)
print(f"saving model to: {args.save_to}") print(f"\nsaving model to: {args.save_to}")
save_to_file(args.save_to, state_dict, state_dict, save_dtype) save_to_file(args.save_to, state_dict, state_dict, save_dtype)

4
networks/merge_lora_old.py
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@@ -148,13 +148,13 @@ def merge(args):
merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype) merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
print(f"saving SD model to: {args.save_to}") print(f"\nsaving SD model to: {args.save_to}")
model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet, model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet,
args.sd_model, 0, 0, save_dtype, vae) args.sd_model, 0, 0, save_dtype, vae)
else: else:
state_dict, _, _ = merge_lora_models(args.models, args.ratios, merge_dtype) state_dict, _, _ = merge_lora_models(args.models, args.ratios, merge_dtype)
print(f"saving model to: {args.save_to}") print(f"\nsaving model to: {args.save_to}")
save_to_file(args.save_to, state_dict, state_dict, save_dtype) save_to_file(args.save_to, state_dict, state_dict, save_dtype)

2
networks/resize_lora.py
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@@ -326,7 +326,7 @@ def resize(args):
metadata["sshs_model_hash"] = model_hash metadata["sshs_model_hash"] = model_hash
metadata["sshs_legacy_hash"] = legacy_hash metadata["sshs_legacy_hash"] = legacy_hash
print(f"saving model to: {args.save_to}") print(f"\nsaving model to: {args.save_to}")
save_to_file(args.save_to, state_dict, state_dict, save_dtype, metadata) save_to_file(args.save_to, state_dict, state_dict, save_dtype, metadata)

2
networks/svd_merge_lora.py
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@@ -160,7 +160,7 @@ def merge(args):
new_conv_rank = args.new_conv_rank if args.new_conv_rank is not None else args.new_rank new_conv_rank = args.new_conv_rank if args.new_conv_rank is not None else args.new_rank
state_dict = merge_lora_models(args.models, args.ratios, args.new_rank, new_conv_rank, args.device, merge_dtype) state_dict = merge_lora_models(args.models, args.ratios, args.new_rank, new_conv_rank, args.device, merge_dtype)
print(f"saving model to: {args.save_to}") print(f"\nsaving model to: {args.save_to}")
save_to_file(args.save_to, state_dict, save_dtype) save_to_file(args.save_to, state_dict, save_dtype)

2
train_db.py
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@@ -240,7 +240,7 @@ def train(args):
loss_list = [] loss_list = []
loss_total = 0.0 loss_total = 0.0
for epoch in range(num_train_epochs): for epoch in range(num_train_epochs):
print(f"epoch {epoch+1}/{num_train_epochs}") print(f"\nepoch {epoch+1}/{num_train_epochs}")
current_epoch.value = epoch + 1 current_epoch.value = epoch + 1
# 指定したステップ数までText Encoderを学習する：epoch最初の状態 # 指定したステップ数までText Encoderを学習する：epoch最初の状態

4
train_network.py
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@@ -532,7 +532,7 @@ def train(args):
os.makedirs(args.output_dir, exist_ok=True) os.makedirs(args.output_dir, exist_ok=True)
ckpt_file = os.path.join(args.output_dir, ckpt_name) ckpt_file = os.path.join(args.output_dir, ckpt_name)
print(f"saving checkpoint: {ckpt_file}") print(f"\nsaving checkpoint: {ckpt_file}")
metadata["ss_training_finished_at"] = str(time.time()) metadata["ss_training_finished_at"] = str(time.time())
metadata["ss_steps"] = str(steps) metadata["ss_steps"] = str(steps)
metadata["ss_epoch"] = str(epoch_no) metadata["ss_epoch"] = str(epoch_no)
@@ -550,7 +550,7 @@ def train(args):
# training loop # training loop
for epoch in range(num_train_epochs): for epoch in range(num_train_epochs):
if is_main_process: if is_main_process:
print(f"epoch {epoch+1}/{num_train_epochs}") print(f"\nepoch {epoch+1}/{num_train_epochs}")
current_epoch.value = epoch + 1 current_epoch.value = epoch + 1
metadata["ss_epoch"] = str(epoch + 1) metadata["ss_epoch"] = str(epoch + 1)

4
train_textual_inversion.py
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@@ -347,7 +347,7 @@ def train(args):
os.makedirs(args.output_dir, exist_ok=True) os.makedirs(args.output_dir, exist_ok=True)
ckpt_file = os.path.join(args.output_dir, ckpt_name) ckpt_file = os.path.join(args.output_dir, ckpt_name)
print(f"saving checkpoint: {ckpt_file}") print(f"\nsaving checkpoint: {ckpt_file}")
save_weights(ckpt_file, embs, save_dtype) save_weights(ckpt_file, embs, save_dtype)
if args.huggingface_repo_id is not None: if args.huggingface_repo_id is not None:
huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload) huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload)
@@ -360,7 +360,7 @@ def train(args):
# training loop # training loop
for epoch in range(num_train_epochs): for epoch in range(num_train_epochs):
print(f"epoch {epoch+1}/{num_train_epochs}") print(f"\nepoch {epoch+1}/{num_train_epochs}")
current_epoch.value = epoch + 1 current_epoch.value = epoch + 1
text_encoder.train() text_encoder.train()

4
train_textual_inversion_XTI.py
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@@ -381,7 +381,7 @@ def train(args):
os.makedirs(args.output_dir, exist_ok=True) os.makedirs(args.output_dir, exist_ok=True)
ckpt_file = os.path.join(args.output_dir, ckpt_name) ckpt_file = os.path.join(args.output_dir, ckpt_name)
print(f"saving checkpoint: {ckpt_file}") print(f"\nsaving checkpoint: {ckpt_file}")
save_weights(ckpt_file, embs, save_dtype) save_weights(ckpt_file, embs, save_dtype)
if args.huggingface_repo_id is not None: if args.huggingface_repo_id is not None:
huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload) huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload)
@@ -394,7 +394,7 @@ def train(args):
# training loop # training loop
for epoch in range(num_train_epochs): for epoch in range(num_train_epochs):
print(f"epoch {epoch+1}/{num_train_epochs}") print(f"\nepoch {epoch+1}/{num_train_epochs}")
current_epoch.value = epoch + 1 current_epoch.value = epoch + 1
text_encoder.train() text_encoder.train()