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add dadapation to other trainers

This commit is contained in:
bmaltais 2023-02-16 19:33:46 -05:00
parent 655f885cf4
commit f9863e3950
4 changed files with 56 additions and 14 deletions
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31
fine_tune.py
View File

@ -14,6 +14,9 @@ from diffusers import DDPMScheduler
import library.train_util as train_util
import torch.optim as optim
import dadaptation
def collate_fn(examples):
return examples[0]
@ -162,7 +165,9 @@ def train(args):
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
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のプロセス数：0はメインプロセスになる
@ -176,8 +181,20 @@ 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}")
# lr schedulerを用意する
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)
# 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)
# 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
if args.full_fp16:
@ -293,12 +310,16 @@ def train(args):
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:
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)
loss_total += current_loss
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)
if global_step >= args.max_train_steps:

2
tools/crop_images_to_n_buckets.py
View File

@ -20,7 +20,7 @@ def sort_images_by_aspect_ratio(path):
"""Sort all images in a folder by aspect ratio"""
images = []
for filename in os.listdir(path):
if filename.endswith(".jpg") or filename.endswith(".jpeg") or filename.endswith(".png"):
if filename.endswith(".jpg") or filename.endswith(".jpeg") or filename.endswith(".png") or filename.endswith(".webp"):
img_path = os.path.join(path, filename)
images.append((img_path, aspect_ratio(img_path)))
# sort the list of tuples based on the aspect ratio

23
train_db.py
View File

@ -17,6 +17,8 @@ from diffusers import DDPMScheduler
import library.train_util as train_util
from library.train_util import DreamBoothDataset
import torch.optim as optim
import dadaptation
def collate_fn(examples):
return examples[0]
@ -133,13 +135,16 @@ def train(args):
trainable_params = unet.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
optimizer = optimizer_class(trainable_params, lr=args.learning_rate)
# optimizer = optimizer_class(trainable_params, lr=args.learning_rate)
# 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
n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers, persistent_workers=args.persistent_data_loader_workers)
print('enable dadatation.')
optimizer = dadaptation.DAdaptAdam(trainable_params, lr=1.0, decouple=True, weight_decay=0, d0=0.00000001)
# 学習ステップ数を計算する
if args.max_train_epochs is not None:
@ -150,8 +155,14 @@ def train(args):
args.stop_text_encoder_training = args.max_train_steps + 1 # do not stop until end
# lr schedulerを用意する
lr_scheduler = diffusers.optimization.get_scheduler(
args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps)
# lr_scheduler = diffusers.optimization.get_scheduler(
# args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps)
# For Adam
lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer=optimizer,
lr_lambda=[lambda epoch: 1],
last_epoch=-1,
verbose=False)
# 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:
@ -288,12 +299,14 @@ def train(args):
current_loss = loss.detach().item()
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]}
logs = {"loss": current_loss, "dlr": optimizer.param_groups[0]['d']*optimizer.param_groups[0]['lr']}
accelerator.log(logs, step=global_step)
loss_total += current_loss
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 = {"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)
if global_step >= args.max_train_steps:

14
train_network.py
View File

@ -222,7 +222,7 @@ def train(args):
print('enable dadatation.')
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-8,)
# optimizer = dadaptation.DAdaptAdaGrad(trainable_params, lr=1.0, weight_decay=0, d0=1e-6)
# 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
@ -242,10 +242,18 @@ def train(args):
# num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
# num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
# override lr_scheduler.
# For Adam
lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer=optimizer,
lr_lambda=[lambda epoch: 0.5, lambda epoch: 1],
lr_lambda=[lambda epoch: 0.25, lambda epoch: 1],
last_epoch=-1,
verbose=False)
# For SGD optim
# lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer=optimizer,
# lr_lambda=[lambda epoch: 1, lambda epoch: 0.5],
# last_epoch=-1,
# verbose=False)
# 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:
@ -462,7 +470,7 @@ def train(args):
avr_loss = loss_total / (step+1)
# logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
# progress_bar.set_postfix(**logs)
logs_str = f"loss: {avr_loss:.3f}, dlr: {optimizer.param_groups[0]['d']*optimizer.param_groups[0]['lr']:.2e}"
logs_str = f"loss: {avr_loss:.3f}, dlr0: {optimizer.param_groups[0]['d']*optimizer.param_groups[0]['lr']:.2e}, dlr1: {optimizer.param_groups[1]['d']*optimizer.param_groups[1]['lr']:.2e}"
progress_bar.set_postfix_str(logs_str)
if args.logging_dir is not None: