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Fix issue where dadaptation code was pushed by mistake

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This commit is contained in:
bmaltais 2023-02-20 08:26:45 -05:00
parent 1807c548b5
commit 34ab8448fb
2 changed files with 37 additions and 86 deletions
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31
fine_tune.py
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@ -14,9 +14,6 @@ from diffusers import DDPMScheduler
import library.train_util as train_util import library.train_util as train_util
import torch.optim as optim
import dadaptation
def collate_fn(examples): def collate_fn(examples):
return examples[0] return examples[0]
@ -172,9 +169,7 @@ def train(args):
optimizer_class = torch.optim.AdamW optimizer_class = torch.optim.AdamW
# betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略 # betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略
# optimizer = optimizer_class(params_to_optimize, lr=args.learning_rate) optimizer = optimizer_class(params_to_optimize, lr=args.learning_rate)
print('enable dadatation.')
optimizer = dadaptation.DAdaptAdam(params_to_optimize, lr=1.0, decouple=True, weight_decay=0)
# dataloaderを準備する # dataloaderを準備する
# DataLoaderのプロセス数：0はメインプロセスになる # DataLoaderのプロセス数：0はメインプロセスになる
@ -188,20 +183,8 @@ def train(args):
print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}") print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
# lr schedulerを用意する # lr schedulerを用意する
# lr_scheduler = diffusers.optimization.get_scheduler( lr_scheduler = diffusers.optimization.get_scheduler(
# args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps) args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps)
# For Adam
# lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer=optimizer,
# lr_lambda=[lambda epoch: 1],
# last_epoch=-1,
# verbose=False)
# For SGD optim
lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer=optimizer,
lr_lambda=[lambda epoch: 1],
last_epoch=-1,
verbose=True)
# 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
if args.full_fp16: if args.full_fp16:
@ -320,16 +303,12 @@ def train(args):
current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず
if args.logging_dir is not None: if args.logging_dir is not None:
# logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]} logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
# accelerator.log(logs, step=global_step)
logs = {"loss": current_loss, "dlr": optimizer.param_groups[0]['d']*optimizer.param_groups[0]['lr']}
accelerator.log(logs, step=global_step) accelerator.log(logs, step=global_step)
loss_total += current_loss loss_total += current_loss
avr_loss = loss_total / (step+1) avr_loss = loss_total / (step+1)
# logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]} logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
# progress_bar.set_postfix(**logs)
logs = {"avg_loss": avr_loss, "dlr": optimizer.param_groups[0]['d']*optimizer.param_groups[0]['lr']} # , "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs) progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps: if global_step >= args.max_train_steps:

92
train_textual_inversion.py
View File

@ -13,41 +13,34 @@ from diffusers import DDPMScheduler
import library.train_util as train_util import library.train_util as train_util
from library.train_util import DreamBoothDataset, FineTuningDataset from library.train_util import DreamBoothDataset, FineTuningDataset
import torch.optim as optim
import dadaptation
# imagenet_templates_small = [
# "a photo of a {}",
# "a rendering of a {}",
# "a cropped photo of the {}",
# "the photo of a {}",
# "a photo of a clean {}",
# "a photo of a dirty {}",
# "a dark photo of the {}",
# "a photo of my {}",
# "a photo of the cool {}",
# "a close-up photo of a {}",
# "a bright photo of the {}",
# "a cropped photo of a {}",
# "a photo of the {}",
# "a good photo of the {}",
# "a photo of one {}",
# "a close-up photo of the {}",
# "a rendition of the {}",
# "a photo of the clean {}",
# "a rendition of a {}",
# "a photo of a nice {}",
# "a good photo of a {}",
# "a photo of the nice {}",
# "a photo of the small {}",
# "a photo of the weird {}",
# "a photo of the large {}",
# "a photo of a cool {}",
# "a photo of a small {}",
# ]
imagenet_templates_small = [ imagenet_templates_small = [
"{}", "a photo of a {}",
"a rendering of a {}",
"a cropped photo of the {}",
"the photo of a {}",
"a photo of a clean {}",
"a photo of a dirty {}",
"a dark photo of the {}",
"a photo of my {}",
"a photo of the cool {}",
"a close-up photo of a {}",
"a bright photo of the {}",
"a cropped photo of a {}",
"a photo of the {}",
"a good photo of the {}",
"a photo of one {}",
"a close-up photo of the {}",
"a rendition of the {}",
"a photo of the clean {}",
"a rendition of a {}",
"a photo of a nice {}",
"a good photo of a {}",
"a photo of the nice {}",
"a photo of the small {}",
"a photo of the weird {}",
"a photo of the large {}",
"a photo of a cool {}",
"a photo of a small {}",
] ]
imagenet_style_templates_small = [ imagenet_style_templates_small = [
@ -227,12 +220,7 @@ def train(args):
trainable_params = text_encoder.get_input_embeddings().parameters() trainable_params = text_encoder.get_input_embeddings().parameters()
# betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略 # betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略
# optimizer = optimizer_class(trainable_params, lr=args.learning_rate) optimizer = optimizer_class(trainable_params, lr=args.learning_rate)
print('enable dadapation.')
optimizer = dadaptation.DAdaptAdam(trainable_params, lr=1.0, decouple=True, weight_decay=0)
# optimizer = dadaptation.DAdaptSGD(trainable_params, lr=1.0, weight_decay=0, d0=1e-6)
# optimizer = dadaptation.DAdaptAdaGrad(trainable_params, lr=1.0, weight_decay=0, d0=1e-6)
# dataloaderを準備する # dataloaderを準備する
# DataLoaderのプロセス数：0はメインプロセスになる # DataLoaderのプロセス数：0はメインプロセスになる
@ -246,20 +234,8 @@ def train(args):
print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}") print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
# lr schedulerを用意する # lr schedulerを用意する
# lr_scheduler = diffusers.optimization.get_scheduler( lr_scheduler = diffusers.optimization.get_scheduler(
# args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps) args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps)
# For Adam
lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer=optimizer,
lr_lambda=[lambda epoch: 1],
last_epoch=-1,
verbose=False)
# For SGD optim
# lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer=optimizer,
# lr_lambda=[lambda epoch: 1],
# last_epoch=-1,
# verbose=False)
# acceleratorがなんかよろしくやってくれるらしい # acceleratorがなんかよろしくやってくれるらしい
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
@ -400,16 +376,12 @@ def train(args):
current_loss = loss.detach().item() current_loss = loss.detach().item()
if args.logging_dir is not None: if args.logging_dir is not None:
#logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]} logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
avr_loss = loss_total / (step+1)
logs = {"loss": avr_loss, "dlr0": optimizer.param_groups[0]['d']*optimizer.param_groups[0]['lr']}
accelerator.log(logs, step=global_step) accelerator.log(logs, step=global_step)
loss_total += current_loss loss_total += current_loss
avr_loss = loss_total / (step+1) avr_loss = loss_total / (step+1)
# logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]} logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
logs = {"loss": avr_loss, "dlr0": optimizer.param_groups[0]['d']*optimizer.param_groups[0]['lr']}
progress_bar.set_postfix(**logs) progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps: if global_step >= args.max_train_steps: