"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 {}",

"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 {}",

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

use_dreambooth_method = args.in_json is None

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_id = tokenizer.encode(args.init_word, add_special_tokens=False)

assert len(

init_token_id) == 1, f"init word {args.init_word} is not converted to single token / 初期化単語が二つ以上のトークンに変換されます。別の単語を使ってください"

init_token_id = init_token_id[0]

else:

init_token_id = 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_id is not None:

for token_id in token_ids:

token_embeds[token_id] = token_embeds[init_token_id]

# 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

if use_dreambooth_method:

print("Use DreamBooth method.")

train_dataset = DreamBoothDataset(args.train_batch_size, args.train_data_dir, args.reg_data_dir,

tokenizer, args.max_token_length, args.caption_extension, args.shuffle_caption, args.keep_tokens,

args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,

args.bucket_reso_steps, args.bucket_no_upscale,

args.prior_loss_weight, args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop, args.train_inpainting, args.debug_dataset)

else:

print("Train with captions.")

train_dataset = FineTuningDataset(args.in_json, args.train_batch_size, args.train_data_dir,

tokenizer, args.max_token_length, args.shuffle_caption, args.keep_tokens,

args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,

args.bucket_reso_steps, args.bucket_no_upscale,

args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop,

args.dataset_repeats, args.train_inpainting, args.debug_dataset)

# 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.add_replacement("", captions)

elif args.num_vectors_per_token > 1:

replace_to = " ".join(token_strings)

train_dataset.add_replacement(args.token_string, replace_to)

train_dataset.make_buckets()

if args.debug_dataset:

train_util.debug_dataset(train_dataset, show_input_ids=True)

return

if len(train_dataset) == 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

# 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.cache_latents(vae)

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.")

# 8-bit Adamを使う

if args.use_8bit_adam:

try:

import bitsandbytes as bnb

except ImportError:

raise ImportError("No bitsand bytes / bitsandbytesがインストールされていないようです")

print("use 8-bit Adam optimizer")

optimizer_class = bnb.optim.AdamW8bit

else:

optimizer_class = torch.optim.AdamW

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

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)

# 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 * len(train_dataloader)

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)

# 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.num_train_images}")

print(f" num reg images / 正則化画像の数: {train_dataset.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}")

train_dataset.set_current_epoch(epoch 1)

text_encoder.train()

loss_total = 0

bef_epo_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone()

for step, batch in enumerate(train_dataloader):

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

if batch["masks"] is not None:

masked_latents = vae.encode(

batch["masked_images"].reshape(batch["images"].shape).to(dtype=weight_dtype)

).latent_dist.sample()

masked_latents = masked_latents * 0.18215

masks = batch["masks"]

# Resize the mask to latents shape as we concatenate the mask to the latents

mask = torch.stack(

[

torch.nn.functional.interpolate(mask, size=(args.resolution // 8, args.resolution // 8))

for mask in masks

]

)

mask = mask.reshape(-1, 1, args.resolution // 8, args.resolution // 8)

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)

# 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)

if batch["masks"] is not None:

# Concatenate the noised latents with the mask and the masked latents

noisy_latents = torch.cat([noisy_latents, mask, masked_latents], dim=1)

# 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])

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:

params_to_clip = text_encoder.get_input_embeddings().parameters()

accelerator.clip_grad_norm_(params_to_clip, 1.0) # 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

current_loss = loss.detach().item()

if args.logging_dir is not None:

logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}

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()

# d = updated_embs - bef_epo_embs

# print(bef_epo_embs.size(), updated_embs.size(), d.mean(), d.min())

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)

# 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)

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:

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)