591 lines
25 KiB
Python
591 lines
25 KiB
Python
import importlib
|
||
import argparse
|
||
import gc
|
||
import math
|
||
import os
|
||
import toml
|
||
from multiprocessing import Value
|
||
|
||
from tqdm import tqdm
|
||
import torch
|
||
from accelerate.utils import set_seed
|
||
import diffusers
|
||
from diffusers import DDPMScheduler
|
||
|
||
import library.train_util as train_util
|
||
import library.config_util as config_util
|
||
from library.config_util import (
|
||
ConfigSanitizer,
|
||
BlueprintGenerator,
|
||
)
|
||
import library.custom_train_functions as custom_train_functions
|
||
from library.custom_train_functions import apply_snr_weight
|
||
|
||
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 = [
|
||
"a painting in the style of {}",
|
||
"a rendering in the style of {}",
|
||
"a cropped painting in the style of {}",
|
||
"the painting in the style of {}",
|
||
"a clean painting in the style of {}",
|
||
"a dirty painting in the style of {}",
|
||
"a dark painting in the style of {}",
|
||
"a picture in the style of {}",
|
||
"a cool painting in the style of {}",
|
||
"a close-up painting in the style of {}",
|
||
"a bright painting in the style of {}",
|
||
"a cropped painting in the style of {}",
|
||
"a good painting in the style of {}",
|
||
"a close-up painting in the style of {}",
|
||
"a rendition in the style of {}",
|
||
"a nice painting in the style of {}",
|
||
"a small painting in the style of {}",
|
||
"a weird painting in the style of {}",
|
||
"a large painting in the style of {}",
|
||
]
|
||
|
||
|
||
def train(args):
|
||
if args.output_name is None:
|
||
args.output_name = args.token_string
|
||
use_template = args.use_object_template or args.use_style_template
|
||
|
||
train_util.verify_training_args(args)
|
||
train_util.prepare_dataset_args(args, True)
|
||
|
||
cache_latents = args.cache_latents
|
||
|
||
if args.seed is not None:
|
||
set_seed(args.seed)
|
||
|
||
tokenizer = train_util.load_tokenizer(args)
|
||
|
||
# acceleratorを準備する
|
||
print("prepare accelerator")
|
||
accelerator, unwrap_model = train_util.prepare_accelerator(args)
|
||
|
||
# mixed precisionに対応した型を用意しておき適宜castする
|
||
weight_dtype, save_dtype = train_util.prepare_dtype(args)
|
||
|
||
# モデルを読み込む
|
||
text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype)
|
||
|
||
# Convert the init_word to token_id
|
||
if args.init_word is not None:
|
||
init_token_ids = tokenizer.encode(args.init_word, add_special_tokens=False)
|
||
if len(init_token_ids) > 1 and len(init_token_ids) != args.num_vectors_per_token:
|
||
print(
|
||
f"token length for init words is not same to num_vectors_per_token, init words is repeated or truncated / 初期化単語のトークン長がnum_vectors_per_tokenと合わないため、繰り返しまたは切り捨てが発生します: length {len(init_token_ids)}"
|
||
)
|
||
else:
|
||
init_token_ids = None
|
||
|
||
# add new word to tokenizer, count is num_vectors_per_token
|
||
token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)]
|
||
num_added_tokens = tokenizer.add_tokens(token_strings)
|
||
assert (
|
||
num_added_tokens == args.num_vectors_per_token
|
||
), f"tokenizer has same word to token string. please use another one / 指定したargs.token_stringは既に存在します。別の単語を使ってください: {args.token_string}"
|
||
|
||
token_ids = tokenizer.convert_tokens_to_ids(token_strings)
|
||
print(f"tokens are added: {token_ids}")
|
||
assert min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1, f"token ids is not ordered"
|
||
assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"
|
||
|
||
# Resize the token embeddings as we are adding new special tokens to the tokenizer
|
||
text_encoder.resize_token_embeddings(len(tokenizer))
|
||
|
||
# Initialise the newly added placeholder token with the embeddings of the initializer token
|
||
token_embeds = text_encoder.get_input_embeddings().weight.data
|
||
if init_token_ids is not None:
|
||
for i, token_id in enumerate(token_ids):
|
||
token_embeds[token_id] = token_embeds[init_token_ids[i % len(init_token_ids)]]
|
||
# print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
|
||
|
||
# load weights
|
||
if args.weights is not None:
|
||
embeddings = load_weights(args.weights)
|
||
assert len(token_ids) == len(
|
||
embeddings
|
||
), f"num_vectors_per_token is mismatch for weights / 指定した重みとnum_vectors_per_tokenの値が異なります: {len(embeddings)}"
|
||
# print(token_ids, embeddings.size())
|
||
for token_id, embedding in zip(token_ids, embeddings):
|
||
token_embeds[token_id] = embedding
|
||
# print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
|
||
print(f"weighs loaded")
|
||
|
||
print(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}")
|
||
|
||
# データセットを準備する
|
||
blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False))
|
||
if args.dataset_config is not None:
|
||
print(f"Load dataset config from {args.dataset_config}")
|
||
user_config = config_util.load_user_config(args.dataset_config)
|
||
ignored = ["train_data_dir", "reg_data_dir", "in_json"]
|
||
if any(getattr(args, attr) is not None for attr in ignored):
|
||
print(
|
||
"ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
|
||
", ".join(ignored)
|
||
)
|
||
)
|
||
else:
|
||
use_dreambooth_method = args.in_json is None
|
||
if use_dreambooth_method:
|
||
print("Use DreamBooth method.")
|
||
user_config = {
|
||
"datasets": [
|
||
{"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
|
||
]
|
||
}
|
||
else:
|
||
print("Train with captions.")
|
||
user_config = {
|
||
"datasets": [
|
||
{
|
||
"subsets": [
|
||
{
|
||
"image_dir": args.train_data_dir,
|
||
"metadata_file": args.in_json,
|
||
}
|
||
]
|
||
}
|
||
]
|
||
}
|
||
|
||
blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
|
||
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
|
||
|
||
current_epoch = Value('i',0)
|
||
current_step = Value('i',0)
|
||
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
|
||
collater = train_util.collater_class(current_epoch,current_step, ds_for_collater)
|
||
|
||
# make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
|
||
if use_template:
|
||
print("use template for training captions. is object: {args.use_object_template}")
|
||
templates = imagenet_templates_small if args.use_object_template else imagenet_style_templates_small
|
||
replace_to = " ".join(token_strings)
|
||
captions = []
|
||
for tmpl in templates:
|
||
captions.append(tmpl.format(replace_to))
|
||
train_dataset_group.add_replacement("", captions)
|
||
|
||
if args.num_vectors_per_token > 1:
|
||
prompt_replacement = (args.token_string, replace_to)
|
||
else:
|
||
prompt_replacement = None
|
||
else:
|
||
if args.num_vectors_per_token > 1:
|
||
replace_to = " ".join(token_strings)
|
||
train_dataset_group.add_replacement(args.token_string, replace_to)
|
||
prompt_replacement = (args.token_string, replace_to)
|
||
else:
|
||
prompt_replacement = None
|
||
|
||
if args.debug_dataset:
|
||
train_util.debug_dataset(train_dataset_group, show_input_ids=True)
|
||
return
|
||
if len(train_dataset_group) == 0:
|
||
print("No data found. Please verify arguments / 画像がありません。引数指定を確認してください")
|
||
return
|
||
|
||
if cache_latents:
|
||
assert (
|
||
train_dataset_group.is_latent_cacheable()
|
||
), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
|
||
|
||
# モデルに xformers とか memory efficient attention を組み込む
|
||
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
|
||
|
||
# 学習を準備する
|
||
if cache_latents:
|
||
vae.to(accelerator.device, dtype=weight_dtype)
|
||
vae.requires_grad_(False)
|
||
vae.eval()
|
||
with torch.no_grad():
|
||
train_dataset_group.cache_latents(vae, args.vae_batch_size)
|
||
vae.to("cpu")
|
||
if torch.cuda.is_available():
|
||
torch.cuda.empty_cache()
|
||
gc.collect()
|
||
|
||
if args.gradient_checkpointing:
|
||
unet.enable_gradient_checkpointing()
|
||
text_encoder.gradient_checkpointing_enable()
|
||
|
||
# 学習に必要なクラスを準備する
|
||
print("prepare optimizer, data loader etc.")
|
||
trainable_params = text_encoder.get_input_embeddings().parameters()
|
||
_, _, optimizer = train_util.get_optimizer(args, trainable_params)
|
||
|
||
# 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_group,
|
||
batch_size=1,
|
||
shuffle=True,
|
||
collate_fn=collater,
|
||
num_workers=n_workers,
|
||
persistent_workers=args.persistent_data_loader_workers,
|
||
)
|
||
|
||
# 学習ステップ数を計算する
|
||
if args.max_train_epochs is not None:
|
||
args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_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}")
|
||
|
||
# データセット側にも学習ステップを送信
|
||
train_dataset_group.set_max_train_steps(args.max_train_steps)
|
||
|
||
# lr schedulerを用意する
|
||
lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
|
||
|
||
# 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
|
||
)
|
||
|
||
index_no_updates = torch.arange(len(tokenizer)) < token_ids[0]
|
||
# print(len(index_no_updates), torch.sum(index_no_updates))
|
||
orig_embeds_params = unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
|
||
|
||
# Freeze all parameters except for the token embeddings in text encoder
|
||
text_encoder.requires_grad_(True)
|
||
text_encoder.text_model.encoder.requires_grad_(False)
|
||
text_encoder.text_model.final_layer_norm.requires_grad_(False)
|
||
text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
|
||
# text_encoder.text_model.embeddings.token_embedding.requires_grad_(True)
|
||
|
||
unet.requires_grad_(False)
|
||
unet.to(accelerator.device, dtype=weight_dtype)
|
||
if args.gradient_checkpointing: # according to TI example in Diffusers, train is required
|
||
unet.train()
|
||
else:
|
||
unet.eval()
|
||
|
||
if not cache_latents:
|
||
vae.requires_grad_(False)
|
||
vae.eval()
|
||
vae.to(accelerator.device, dtype=weight_dtype)
|
||
|
||
# 実験的機能：勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
|
||
if args.full_fp16:
|
||
train_util.patch_accelerator_for_fp16_training(accelerator)
|
||
text_encoder.to(weight_dtype)
|
||
|
||
# resumeする
|
||
if args.resume is not None:
|
||
print(f"resume training from state: {args.resume}")
|
||
accelerator.load_state(args.resume)
|
||
|
||
# epoch数を計算する
|
||
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
||
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
||
if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
|
||
args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
|
||
|
||
# 学習する
|
||
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
||
print("running training / 学習開始")
|
||
print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
|
||
print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
|
||
print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
|
||
print(f" num epochs / epoch数: {num_train_epochs}")
|
||
print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
|
||
print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
|
||
print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
|
||
print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
|
||
|
||
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
|
||
global_step = 0
|
||
|
||
noise_scheduler = DDPMScheduler(
|
||
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
|
||
)
|
||
|
||
if accelerator.is_main_process:
|
||
accelerator.init_trackers("textual_inversion")
|
||
|
||
for epoch in range(num_train_epochs):
|
||
print(f"epoch {epoch+1}/{num_train_epochs}")
|
||
current_epoch.value = epoch+1
|
||
|
||
text_encoder.train()
|
||
|
||
loss_total = 0
|
||
|
||
for step, batch in enumerate(train_dataloader):
|
||
current_step.value = global_step
|
||
with accelerator.accumulate(text_encoder):
|
||
with torch.no_grad():
|
||
if "latents" in batch and batch["latents"] is not None:
|
||
latents = batch["latents"].to(accelerator.device)
|
||
else:
|
||
# latentに変換
|
||
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
|
||
latents = latents * 0.18215
|
||
b_size = latents.shape[0]
|
||
|
||
# Get the text embedding for conditioning
|
||
input_ids = batch["input_ids"].to(accelerator.device)
|
||
# weight_dtype) use float instead of fp16/bf16 because text encoder is float
|
||
encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, torch.float)
|
||
|
||
# Sample noise that we'll add to the latents
|
||
noise = torch.randn_like(latents, device=latents.device)
|
||
if args.noise_offset:
|
||
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
||
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
|
||
|
||
# Sample a random timestep for each image
|
||
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
|
||
timesteps = timesteps.long()
|
||
|
||
# Add noise to the latents according to the noise magnitude at each timestep
|
||
# (this is the forward diffusion process)
|
||
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
||
|
||
# Predict the noise residual
|
||
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
||
|
||
if args.v_parameterization:
|
||
# v-parameterization training
|
||
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
||
else:
|
||
target = noise
|
||
|
||
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
|
||
loss = loss.mean([1, 2, 3])
|
||
|
||
if args.min_snr_gamma:
|
||
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
|
||
|
||
loss_weights = batch["loss_weights"] # 各sampleごとのweight
|
||
loss = loss * loss_weights
|
||
|
||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||
|
||
accelerator.backward(loss)
|
||
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
|
||
params_to_clip = text_encoder.get_input_embeddings().parameters()
|
||
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
||
|
||
optimizer.step()
|
||
lr_scheduler.step()
|
||
optimizer.zero_grad(set_to_none=True)
|
||
|
||
# Let's make sure we don't update any embedding weights besides the newly added token
|
||
with torch.no_grad():
|
||
unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = orig_embeds_params[
|
||
index_no_updates
|
||
]
|
||
|
||
# Checks if the accelerator has performed an optimization step behind the scenes
|
||
if accelerator.sync_gradients:
|
||
progress_bar.update(1)
|
||
global_step += 1
|
||
|
||
train_util.sample_images(
|
||
accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
|
||
)
|
||
|
||
current_loss = loss.detach().item()
|
||
if args.logging_dir is not None:
|
||
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
|
||
if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value
|
||
logs["lr/d*lr"] = (
|
||
lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].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]}
|
||
progress_bar.set_postfix(**logs)
|
||
|
||
if global_step >= args.max_train_steps:
|
||
break
|
||
|
||
if args.logging_dir is not None:
|
||
logs = {"loss/epoch": loss_total / len(train_dataloader)}
|
||
accelerator.log(logs, step=epoch + 1)
|
||
|
||
accelerator.wait_for_everyone()
|
||
|
||
updated_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone()
|
||
|
||
if args.save_every_n_epochs is not None:
|
||
model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name
|
||
|
||
def save_func():
|
||
ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + "." + args.save_model_as
|
||
ckpt_file = os.path.join(args.output_dir, ckpt_name)
|
||
print(f"saving checkpoint: {ckpt_file}")
|
||
save_weights(ckpt_file, updated_embs, save_dtype)
|
||
|
||
def remove_old_func(old_epoch_no):
|
||
old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + "." + args.save_model_as
|
||
old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
|
||
if os.path.exists(old_ckpt_file):
|
||
print(f"removing old checkpoint: {old_ckpt_file}")
|
||
os.remove(old_ckpt_file)
|
||
|
||
saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
|
||
if saving and args.save_state:
|
||
train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)
|
||
|
||
train_util.sample_images(
|
||
accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
|
||
)
|
||
|
||
# end of epoch
|
||
|
||
is_main_process = accelerator.is_main_process
|
||
if is_main_process:
|
||
text_encoder = unwrap_model(text_encoder)
|
||
|
||
accelerator.end_training()
|
||
|
||
if args.save_state:
|
||
train_util.save_state_on_train_end(args, accelerator)
|
||
|
||
updated_embs = text_encoder.get_input_embeddings().weight[token_ids].data.detach().clone()
|
||
|
||
del accelerator # この後メモリを使うのでこれは消す
|
||
|
||
if is_main_process:
|
||
os.makedirs(args.output_dir, exist_ok=True)
|
||
|
||
model_name = train_util.DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name
|
||
ckpt_name = model_name + "." + args.save_model_as
|
||
ckpt_file = os.path.join(args.output_dir, ckpt_name)
|
||
|
||
print(f"save trained model to {ckpt_file}")
|
||
save_weights(ckpt_file, updated_embs, save_dtype)
|
||
print("model saved.")
|
||
|
||
|
||
def save_weights(file, updated_embs, save_dtype):
|
||
state_dict = {"emb_params": updated_embs}
|
||
|
||
if save_dtype is not None:
|
||
for key in list(state_dict.keys()):
|
||
v = state_dict[key]
|
||
v = v.detach().clone().to("cpu").to(save_dtype)
|
||
state_dict[key] = v
|
||
|
||
if os.path.splitext(file)[1] == ".safetensors":
|
||
from safetensors.torch import save_file
|
||
|
||
save_file(state_dict, file)
|
||
else:
|
||
torch.save(state_dict, file) # can be loaded in Web UI
|
||
|
||
|
||
def load_weights(file):
|
||
if os.path.splitext(file)[1] == ".safetensors":
|
||
from safetensors.torch import load_file
|
||
|
||
data = load_file(file)
|
||
else:
|
||
# compatible to Web UI's file format
|
||
data = torch.load(file, map_location="cpu")
|
||
if type(data) != dict:
|
||
raise ValueError(f"weight file is not dict / 重みファイルがdict形式ではありません: {file}")
|
||
|
||
if "string_to_param" in data: # textual inversion embeddings
|
||
data = data["string_to_param"]
|
||
if hasattr(data, "_parameters"): # support old PyTorch?
|
||
data = getattr(data, "_parameters")
|
||
|
||
emb = next(iter(data.values()))
|
||
if type(emb) != torch.Tensor:
|
||
raise ValueError(f"weight file does not contains Tensor / 重みファイルのデータがTensorではありません: {file}")
|
||
|
||
if len(emb.size()) == 1:
|
||
emb = emb.unsqueeze(0)
|
||
|
||
return emb
|
||
|
||
|
||
def setup_parser() -> argparse.ArgumentParser:
|
||
parser = argparse.ArgumentParser()
|
||
|
||
train_util.add_sd_models_arguments(parser)
|
||
train_util.add_dataset_arguments(parser, True, True, False)
|
||
train_util.add_training_arguments(parser, True)
|
||
train_util.add_optimizer_arguments(parser)
|
||
config_util.add_config_arguments(parser)
|
||
custom_train_functions.add_custom_train_arguments(parser)
|
||
|
||
parser.add_argument(
|
||
"--save_model_as",
|
||
type=str,
|
||
default="pt",
|
||
choices=[None, "ckpt", "pt", "safetensors"],
|
||
help="format to save the model (default is .pt) / モデル保存時の形式（デフォルトはpt）",
|
||
)
|
||
|
||
parser.add_argument("--weights", type=str, default=None, help="embedding weights to initialize / 学習するネットワークの初期重み")
|
||
parser.add_argument(
|
||
"--num_vectors_per_token", type=int, default=1, help="number of vectors per token / トークンに割り当てるembeddingsの要素数"
|
||
)
|
||
parser.add_argument(
|
||
"--token_string",
|
||
type=str,
|
||
default=None,
|
||
help="token string used in training, must not exist in tokenizer / 学習時に使用されるトークン文字列、tokenizerに存在しない文字であること",
|
||
)
|
||
parser.add_argument("--init_word", type=str, default=None, help="words to initialize vector / ベクトルを初期化に使用する単語、複数可")
|
||
parser.add_argument(
|
||
"--use_object_template",
|
||
action="store_true",
|
||
help="ignore caption and use default templates for object / キャプションは使わずデフォルトの物体用テンプレートで学習する",
|
||
)
|
||
parser.add_argument(
|
||
"--use_style_template",
|
||
action="store_true",
|
||
help="ignore caption and use default templates for stype / キャプションは使わずデフォルトのスタイル用テンプレートで学習する",
|
||
)
|
||
|
||
return parser
|
||
|
||
|
||
if __name__ == "__main__":
|
||
parser = setup_parser()
|
||
|
||
args = parser.parse_args()
|
||
args = train_util.read_config_from_file(args, parser)
|
||
|
||
train(args)
|