# common functions for training import argparse import ast import asyncio import datetime import importlib import json import logging import pathlib import re import shutil import time from typing import ( Dict, List, NamedTuple, Optional, Sequence, Tuple, Union, ) from accelerate import Accelerator, InitProcessGroupKwargs, DistributedDataParallelKwargs, PartialState import glob import math import os import random import hashlib import subprocess from io import BytesIO import toml from tqdm import tqdm import torch from library.device_utils import init_ipex, clean_memory_on_device init_ipex() from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import Optimizer from torchvision import transforms from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection import transformers from diffusers.optimization import SchedulerType, TYPE_TO_SCHEDULER_FUNCTION from diffusers import ( StableDiffusionPipeline, DDPMScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, LMSDiscreteScheduler, PNDMScheduler, DDIMScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, KDPM2DiscreteScheduler, KDPM2AncestralDiscreteScheduler, AutoencoderKL, ) from library import custom_train_functions from library.original_unet import UNet2DConditionModel from huggingface_hub import hf_hub_download import numpy as np from PIL import Image import imagesize import cv2 import safetensors.torch from library.lpw_stable_diffusion import StableDiffusionLongPromptWeightingPipeline import library.model_util as model_util import library.huggingface_util as huggingface_util import library.sai_model_spec as sai_model_spec import library.deepspeed_utils as deepspeed_utils from library.utils import setup_logging setup_logging() import logging logger = logging.getLogger(__name__) # from library.attention_processors import FlashAttnProcessor # from library.hypernetwork import replace_attentions_for_hypernetwork from library.original_unet import UNet2DConditionModel # Tokenizer: checkpointから読み込むのではなくあらかじめ提供されているものを使う TOKENIZER_PATH = "openai/clip-vit-large-patch14" V2_STABLE_DIFFUSION_PATH = "stabilityai/stable-diffusion-2" # ここからtokenizerだけ使う v2とv2.1はtokenizer仕様は同じ HIGH_VRAM = False # checkpointファイル名 EPOCH_STATE_NAME = "{}-{:06d}-state" EPOCH_FILE_NAME = "{}-{:06d}" EPOCH_DIFFUSERS_DIR_NAME = "{}-{:06d}" LAST_STATE_NAME = "{}-state" DEFAULT_EPOCH_NAME = "epoch" DEFAULT_LAST_OUTPUT_NAME = "last" DEFAULT_STEP_NAME = "at" STEP_STATE_NAME = "{}-step{:08d}-state" STEP_FILE_NAME = "{}-step{:08d}" STEP_DIFFUSERS_DIR_NAME = "{}-step{:08d}" # region dataset IMAGE_EXTENSIONS = [".png", ".jpg", ".jpeg", ".webp", ".bmp", ".PNG", ".JPG", ".JPEG", ".WEBP", ".BMP"] try: import pillow_avif IMAGE_EXTENSIONS.extend([".avif", ".AVIF"]) except: pass # JPEG-XL on Linux try: from jxlpy import JXLImagePlugin IMAGE_EXTENSIONS.extend([".jxl", ".JXL"]) except: pass # JPEG-XL on Windows try: import pillow_jxl IMAGE_EXTENSIONS.extend([".jxl", ".JXL"]) except: pass IMAGE_TRANSFORMS = transforms.Compose( [ transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) TEXT_ENCODER_OUTPUTS_CACHE_SUFFIX = "_te_outputs.npz" class ImageInfo: def __init__(self, image_key: str, num_repeats: int, caption: str, is_reg: bool, absolute_path: str) -> None: self.image_key: str = image_key self.num_repeats: int = num_repeats self.caption: str = caption self.is_reg: bool = is_reg self.absolute_path: str = absolute_path self.image_size: Tuple[int, int] = None self.resized_size: Tuple[int, int] = None self.bucket_reso: Tuple[int, int] = None self.latents: torch.Tensor = None self.latents_flipped: torch.Tensor = None self.latents_npz: str = None self.latents_original_size: Tuple[int, int] = None # original image size, not latents size self.latents_crop_ltrb: Tuple[int, int] = None # crop left top right bottom in original pixel size, not latents size self.cond_img_path: str = None self.image: Optional[Image.Image] = None # optional, original PIL Image # SDXL, optional self.text_encoder_outputs_npz: Optional[str] = None self.text_encoder_outputs1: Optional[torch.Tensor] = None self.text_encoder_outputs2: Optional[torch.Tensor] = None self.text_encoder_pool2: Optional[torch.Tensor] = None class BucketManager: def __init__(self, no_upscale, max_reso, min_size, max_size, reso_steps) -> None: if max_size is not None: if max_reso is not None: assert max_size >= max_reso[0], "the max_size should be larger than the width of max_reso" assert max_size >= max_reso[1], "the max_size should be larger than the height of max_reso" if min_size is not None: assert max_size >= min_size, "the max_size should be larger than the min_size" self.no_upscale = no_upscale if max_reso is None: self.max_reso = None self.max_area = None else: self.max_reso = max_reso self.max_area = max_reso[0] * max_reso[1] self.min_size = min_size self.max_size = max_size self.reso_steps = reso_steps self.resos = [] self.reso_to_id = {} self.buckets = [] # 前処理時は (image_key, image, original size, crop left/top)、学習時は image_key def add_image(self, reso, image_or_info): bucket_id = self.reso_to_id[reso] self.buckets[bucket_id].append(image_or_info) def shuffle(self): for bucket in self.buckets: random.shuffle(bucket) def sort(self): # 解像度順にソートする（表示時、メタデータ格納時の見栄えをよくするためだけ）。bucketsも入れ替えてreso_to_idも振り直す sorted_resos = self.resos.copy() sorted_resos.sort() sorted_buckets = [] sorted_reso_to_id = {} for i, reso in enumerate(sorted_resos): bucket_id = self.reso_to_id[reso] sorted_buckets.append(self.buckets[bucket_id]) sorted_reso_to_id[reso] = i self.resos = sorted_resos self.buckets = sorted_buckets self.reso_to_id = sorted_reso_to_id def make_buckets(self): resos = model_util.make_bucket_resolutions(self.max_reso, self.min_size, self.max_size, self.reso_steps) self.set_predefined_resos(resos) def set_predefined_resos(self, resos): # 規定サイズから選ぶ場合の解像度、aspect ratioの情報を格納しておく self.predefined_resos = resos.copy() self.predefined_resos_set = set(resos) self.predefined_aspect_ratios = np.array([w / h for w, h in resos]) def add_if_new_reso(self, reso): if reso not in self.reso_to_id: bucket_id = len(self.resos) self.reso_to_id[reso] = bucket_id self.resos.append(reso) self.buckets.append([]) # logger.info(reso, bucket_id, len(self.buckets)) def round_to_steps(self, x): x = int(x + 0.5) return x - x % self.reso_steps def select_bucket(self, image_width, image_height): aspect_ratio = image_width / image_height if not self.no_upscale: # 拡大および縮小を行う # 同じaspect ratioがあるかもしれないので（fine tuningで、no_upscale=Trueで前処理した場合）、解像度が同じものを優先する reso = (image_width, image_height) if reso in self.predefined_resos_set: pass else: ar_errors = self.predefined_aspect_ratios - aspect_ratio predefined_bucket_id = np.abs(ar_errors).argmin() # 当該解像度以外でaspect ratio errorが最も少ないもの reso = self.predefined_resos[predefined_bucket_id] ar_reso = reso[0] / reso[1] if aspect_ratio > ar_reso: # 横が長い→縦を合わせる scale = reso[1] / image_height else: scale = reso[0] / image_width resized_size = (int(image_width * scale + 0.5), int(image_height * scale + 0.5)) # logger.info(f"use predef, {image_width}, {image_height}, {reso}, {resized_size}") else: # 縮小のみを行う if image_width * image_height > self.max_area: # 画像が大きすぎるのでアスペクト比を保ったまま縮小することを前提にbucketを決める resized_width = math.sqrt(self.max_area * aspect_ratio) resized_height = self.max_area / resized_width assert abs(resized_width / resized_height - aspect_ratio) < 1e-2, "aspect is illegal" # リサイズ後の短辺または長辺をreso_steps単位にする：aspect ratioの差が少ないほうを選ぶ # 元のbucketingと同じロジック b_width_rounded = self.round_to_steps(resized_width) b_height_in_wr = self.round_to_steps(b_width_rounded / aspect_ratio) ar_width_rounded = b_width_rounded / b_height_in_wr b_height_rounded = self.round_to_steps(resized_height) b_width_in_hr = self.round_to_steps(b_height_rounded * aspect_ratio) ar_height_rounded = b_width_in_hr / b_height_rounded # logger.info(b_width_rounded, b_height_in_wr, ar_width_rounded) # logger.info(b_width_in_hr, b_height_rounded, ar_height_rounded) if abs(ar_width_rounded - aspect_ratio) < abs(ar_height_rounded - aspect_ratio): resized_size = (b_width_rounded, int(b_width_rounded / aspect_ratio + 0.5)) else: resized_size = (int(b_height_rounded * aspect_ratio + 0.5), b_height_rounded) # logger.info(resized_size) else: resized_size = (image_width, image_height) # リサイズは不要 # 画像のサイズ未満をbucketのサイズとする（paddingせずにcroppingする） bucket_width = resized_size[0] - resized_size[0] % self.reso_steps bucket_height = resized_size[1] - resized_size[1] % self.reso_steps # logger.info(f"use arbitrary {image_width}, {image_height}, {resized_size}, {bucket_width}, {bucket_height}") reso = (bucket_width, bucket_height) self.add_if_new_reso(reso) ar_error = (reso[0] / reso[1]) - aspect_ratio return reso, resized_size, ar_error @staticmethod def get_crop_ltrb(bucket_reso: Tuple[int, int], image_size: Tuple[int, int]): # Stability AIの前処理に合わせてcrop left/topを計算する。crop rightはflipのaugmentationのために求める # Calculate crop left/top according to the preprocessing of Stability AI. Crop right is calculated for flip augmentation. bucket_ar = bucket_reso[0] / bucket_reso[1] image_ar = image_size[0] / image_size[1] if bucket_ar > image_ar: # bucketのほうが横長→縦を合わせる resized_width = bucket_reso[1] * image_ar resized_height = bucket_reso[1] else: resized_width = bucket_reso[0] resized_height = bucket_reso[0] / image_ar crop_left = (bucket_reso[0] - resized_width) // 2 crop_top = (bucket_reso[1] - resized_height) // 2 crop_right = crop_left + resized_width crop_bottom = crop_top + resized_height return crop_left, crop_top, crop_right, crop_bottom class BucketBatchIndex(NamedTuple): bucket_index: int bucket_batch_size: int batch_index: int class AugHelper: # albumentationsへの依存をなくしたがとりあえず同じinterfaceを持たせる def __init__(self): pass def color_aug(self, image: np.ndarray): # self.color_aug_method = albu.OneOf( # [ # albu.HueSaturationValue(8, 0, 0, p=0.5), # albu.RandomGamma((95, 105), p=0.5), # ], # p=0.33, # ) hue_shift_limit = 8 # remove dependency to albumentations if random.random() <= 0.33: if random.random() > 0.5: # hue shift hsv_img = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) hue_shift = random.uniform(-hue_shift_limit, hue_shift_limit) if hue_shift < 0: hue_shift = 180 + hue_shift hsv_img[:, :, 0] = (hsv_img[:, :, 0] + hue_shift) % 180 image = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR) else: # random gamma gamma = random.uniform(0.95, 1.05) image = np.clip(image**gamma, 0, 255).astype(np.uint8) return {"image": image} def get_augmentor(self, use_color_aug: bool): # -> Optional[Callable[[np.ndarray], Dict[str, np.ndarray]]]: return self.color_aug if use_color_aug else None class BaseSubset: def __init__( self, image_dir: Optional[str], num_repeats: int, shuffle_caption: bool, caption_separator: str, keep_tokens: int, keep_tokens_separator: str, secondary_separator: Optional[str], enable_wildcard: bool, color_aug: bool, flip_aug: bool, face_crop_aug_range: Optional[Tuple[float, float]], random_crop: bool, caption_dropout_rate: float, caption_dropout_every_n_epochs: int, caption_tag_dropout_rate: float, caption_prefix: Optional[str], caption_suffix: Optional[str], token_warmup_min: int, token_warmup_step: Union[float, int], ) -> None: self.image_dir = image_dir self.num_repeats = num_repeats self.shuffle_caption = shuffle_caption self.caption_separator = caption_separator self.keep_tokens = keep_tokens self.keep_tokens_separator = keep_tokens_separator self.secondary_separator = secondary_separator self.enable_wildcard = enable_wildcard self.color_aug = color_aug self.flip_aug = flip_aug self.face_crop_aug_range = face_crop_aug_range self.random_crop = random_crop self.caption_dropout_rate = caption_dropout_rate self.caption_dropout_every_n_epochs = caption_dropout_every_n_epochs self.caption_tag_dropout_rate = caption_tag_dropout_rate self.caption_prefix = caption_prefix self.caption_suffix = caption_suffix self.token_warmup_min = token_warmup_min # step=0におけるタグの数 self.token_warmup_step = token_warmup_step # N（N<1ならN*max_train_steps）ステップ目でタグの数が最大になる self.img_count = 0 class DreamBoothSubset(BaseSubset): def __init__( self, image_dir: str, is_reg: bool, class_tokens: Optional[str], caption_extension: str, cache_info: bool, num_repeats, shuffle_caption, caption_separator: str, keep_tokens, keep_tokens_separator, secondary_separator, enable_wildcard, color_aug, flip_aug, face_crop_aug_range, random_crop, caption_dropout_rate, caption_dropout_every_n_epochs, caption_tag_dropout_rate, caption_prefix, caption_suffix, token_warmup_min, token_warmup_step, ) -> None: assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です" super().__init__( image_dir, num_repeats, shuffle_caption, caption_separator, keep_tokens, keep_tokens_separator, secondary_separator, enable_wildcard, color_aug, flip_aug, face_crop_aug_range, random_crop, caption_dropout_rate, caption_dropout_every_n_epochs, caption_tag_dropout_rate, caption_prefix, caption_suffix, token_warmup_min, token_warmup_step, ) self.is_reg = is_reg self.class_tokens = class_tokens self.caption_extension = caption_extension if self.caption_extension and not self.caption_extension.startswith("."): self.caption_extension = "." + self.caption_extension self.cache_info = cache_info def __eq__(self, other) -> bool: if not isinstance(other, DreamBoothSubset): return NotImplemented return self.image_dir == other.image_dir class FineTuningSubset(BaseSubset): def __init__( self, image_dir, metadata_file: str, num_repeats, shuffle_caption, caption_separator, keep_tokens, keep_tokens_separator, secondary_separator, enable_wildcard, color_aug, flip_aug, face_crop_aug_range, random_crop, caption_dropout_rate, caption_dropout_every_n_epochs, caption_tag_dropout_rate, caption_prefix, caption_suffix, token_warmup_min, token_warmup_step, ) -> None: assert metadata_file is not None, "metadata_file must be specified / metadata_fileは指定が必須です" super().__init__( image_dir, num_repeats, shuffle_caption, caption_separator, keep_tokens, keep_tokens_separator, secondary_separator, enable_wildcard, color_aug, flip_aug, face_crop_aug_range, random_crop, caption_dropout_rate, caption_dropout_every_n_epochs, caption_tag_dropout_rate, caption_prefix, caption_suffix, token_warmup_min, token_warmup_step, ) self.metadata_file = metadata_file def __eq__(self, other) -> bool: if not isinstance(other, FineTuningSubset): return NotImplemented return self.metadata_file == other.metadata_file class ControlNetSubset(BaseSubset): def __init__( self, image_dir: str, conditioning_data_dir: str, caption_extension: str, cache_info: bool, num_repeats, shuffle_caption, caption_separator, keep_tokens, keep_tokens_separator, secondary_separator, enable_wildcard, color_aug, flip_aug, face_crop_aug_range, random_crop, caption_dropout_rate, caption_dropout_every_n_epochs, caption_tag_dropout_rate, caption_prefix, caption_suffix, token_warmup_min, token_warmup_step, ) -> None: assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です" super().__init__( image_dir, num_repeats, shuffle_caption, caption_separator, keep_tokens, keep_tokens_separator, secondary_separator, enable_wildcard, color_aug, flip_aug, face_crop_aug_range, random_crop, caption_dropout_rate, caption_dropout_every_n_epochs, caption_tag_dropout_rate, caption_prefix, caption_suffix, token_warmup_min, token_warmup_step, ) self.conditioning_data_dir = conditioning_data_dir self.caption_extension = caption_extension if self.caption_extension and not self.caption_extension.startswith("."): self.caption_extension = "." + self.caption_extension self.cache_info = cache_info def __eq__(self, other) -> bool: if not isinstance(other, ControlNetSubset): return NotImplemented return self.image_dir == other.image_dir and self.conditioning_data_dir == other.conditioning_data_dir class BaseDataset(torch.utils.data.Dataset): def __init__( self, tokenizer: Union[CLIPTokenizer, List[CLIPTokenizer]], max_token_length: int, resolution: Optional[Tuple[int, int]], network_multiplier: float, debug_dataset: bool, ) -> None: super().__init__() self.tokenizers = tokenizer if isinstance(tokenizer, list) else [tokenizer] self.max_token_length = max_token_length # width/height is used when enable_bucket==False self.width, self.height = (None, None) if resolution is None else resolution self.network_multiplier = network_multiplier self.debug_dataset = debug_dataset self.subsets: List[Union[DreamBoothSubset, FineTuningSubset]] = [] self.token_padding_disabled = False self.tag_frequency = {} self.XTI_layers = None self.token_strings = None self.enable_bucket = False self.bucket_manager: BucketManager = None # not initialized self.min_bucket_reso = None self.max_bucket_reso = None self.bucket_reso_steps = None self.bucket_no_upscale = None self.bucket_info = None # for metadata self.tokenizer_max_length = self.tokenizers[0].model_max_length if max_token_length is None else max_token_length + 2 self.current_epoch: int = 0 # インスタンスがepochごとに新しく作られるようなので外側から渡さないとダメ self.current_step: int = 0 self.max_train_steps: int = 0 self.seed: int = 0 # augmentation self.aug_helper = AugHelper() self.image_transforms = IMAGE_TRANSFORMS self.image_data: Dict[str, ImageInfo] = {} self.image_to_subset: Dict[str, Union[DreamBoothSubset, FineTuningSubset]] = {} self.replacements = {} # caching self.caching_mode = None # None, 'latents', 'text' def set_seed(self, seed): self.seed = seed def set_caching_mode(self, mode): self.caching_mode = mode def set_current_epoch(self, epoch): if not self.current_epoch == epoch: # epochが切り替わったらバケツをシャッフルする self.shuffle_buckets() self.current_epoch = epoch def set_current_step(self, step): self.current_step = step def set_max_train_steps(self, max_train_steps): self.max_train_steps = max_train_steps def set_tag_frequency(self, dir_name, captions): frequency_for_dir = self.tag_frequency.get(dir_name, {}) self.tag_frequency[dir_name] = frequency_for_dir for caption in captions: for tag in caption.split(","): tag = tag.strip() if tag: tag = tag.lower() frequency = frequency_for_dir.get(tag, 0) frequency_for_dir[tag] = frequency + 1 def disable_token_padding(self): self.token_padding_disabled = True def enable_XTI(self, layers=None, token_strings=None): self.XTI_layers = layers self.token_strings = token_strings def add_replacement(self, str_from, str_to): self.replacements[str_from] = str_to def process_caption(self, subset: BaseSubset, caption): # caption に prefix/suffix を付ける if subset.caption_prefix: caption = subset.caption_prefix + " " + caption if subset.caption_suffix: caption = caption + " " + subset.caption_suffix # dropoutの決定：tag dropがこのメソッド内にあるのでここで行うのが良い is_drop_out = subset.caption_dropout_rate > 0 and random.random() < subset.caption_dropout_rate is_drop_out = ( is_drop_out or subset.caption_dropout_every_n_epochs > 0 and self.current_epoch % subset.caption_dropout_every_n_epochs == 0 ) if is_drop_out: caption = "" else: # process wildcards if subset.enable_wildcard: # if caption is multiline, random choice one line if "\n" in caption: caption = random.choice(caption.split("\n")) # wildcard is like '{aaa|bbb|ccc...}' # escape the curly braces like {{ or }} replacer1 = "⦅" replacer2 = "⦆" while replacer1 in caption or replacer2 in caption: replacer1 += "⦅" replacer2 += "⦆" caption = caption.replace("{{", replacer1).replace("}}", replacer2) # replace the wildcard def replace_wildcard(match): return random.choice(match.group(1).split("|")) caption = re.sub(r"\{([^}]+)\}", replace_wildcard, caption) # unescape the curly braces caption = caption.replace(replacer1, "{").replace(replacer2, "}") else: # if caption is multiline, use the first line caption = caption.split("\n")[0] if subset.shuffle_caption or subset.token_warmup_step > 0 or subset.caption_tag_dropout_rate > 0: fixed_tokens = [] flex_tokens = [] fixed_suffix_tokens = [] if ( hasattr(subset, "keep_tokens_separator") and subset.keep_tokens_separator and subset.keep_tokens_separator in caption ): fixed_part, flex_part = caption.split(subset.keep_tokens_separator, 1) if subset.keep_tokens_separator in flex_part: flex_part, fixed_suffix_part = flex_part.split(subset.keep_tokens_separator, 1) fixed_suffix_tokens = [t.strip() for t in fixed_suffix_part.split(subset.caption_separator) if t.strip()] fixed_tokens = [t.strip() for t in fixed_part.split(subset.caption_separator) if t.strip()] flex_tokens = [t.strip() for t in flex_part.split(subset.caption_separator) if t.strip()] else: tokens = [t.strip() for t in caption.strip().split(subset.caption_separator)] flex_tokens = tokens[:] if subset.keep_tokens > 0: fixed_tokens = flex_tokens[: subset.keep_tokens] flex_tokens = tokens[subset.keep_tokens :] if subset.token_warmup_step < 1: # 初回に上書きする subset.token_warmup_step = math.floor(subset.token_warmup_step * self.max_train_steps) if subset.token_warmup_step and self.current_step < subset.token_warmup_step: tokens_len = ( math.floor( (self.current_step) * ((len(flex_tokens) - subset.token_warmup_min) / (subset.token_warmup_step)) ) + subset.token_warmup_min ) flex_tokens = flex_tokens[:tokens_len] def dropout_tags(tokens): if subset.caption_tag_dropout_rate <= 0: return tokens l = [] for token in tokens: if random.random() >= subset.caption_tag_dropout_rate: l.append(token) return l if subset.shuffle_caption: random.shuffle(flex_tokens) flex_tokens = dropout_tags(flex_tokens) caption = ", ".join(fixed_tokens + flex_tokens + fixed_suffix_tokens) # process secondary separator if subset.secondary_separator: caption = caption.replace(subset.secondary_separator, subset.caption_separator) # textual inversion対応 for str_from, str_to in self.replacements.items(): if str_from == "": # replace all if type(str_to) == list: caption = random.choice(str_to) else: caption = str_to else: caption = caption.replace(str_from, str_to) return caption def get_input_ids(self, caption, tokenizer=None): if tokenizer is None: tokenizer = self.tokenizers[0] input_ids = tokenizer( caption, padding="max_length", truncation=True, max_length=self.tokenizer_max_length, return_tensors="pt" ).input_ids if self.tokenizer_max_length > tokenizer.model_max_length: input_ids = input_ids.squeeze(0) iids_list = [] if tokenizer.pad_token_id == tokenizer.eos_token_id: # v1 # 77以上の時は " .... " でトータル227とかになっているので、"..."の三連に変換する # 1111氏のやつは , で区切る、とかしているようだが　とりあえず単純に for i in range( 1, self.tokenizer_max_length - tokenizer.model_max_length + 2, tokenizer.model_max_length - 2 ): # (1, 152, 75) ids_chunk = ( input_ids[0].unsqueeze(0), input_ids[i : i + tokenizer.model_max_length - 2], input_ids[-1].unsqueeze(0), ) ids_chunk = torch.cat(ids_chunk) iids_list.append(ids_chunk) else: # v2 or SDXL # 77以上の時は " .... ..." でトータル227とかになっているので、"... ..."の三連に変換する for i in range(1, self.tokenizer_max_length - tokenizer.model_max_length + 2, tokenizer.model_max_length - 2): ids_chunk = ( input_ids[0].unsqueeze(0), # BOS input_ids[i : i + tokenizer.model_max_length - 2], input_ids[-1].unsqueeze(0), ) # PAD or EOS ids_chunk = torch.cat(ids_chunk) # 末尾が または の場合は、何もしなくてよい # 末尾が x の場合は末尾を に変える（x なら結果的に変化なし） if ids_chunk[-2] != tokenizer.eos_token_id and ids_chunk[-2] != tokenizer.pad_token_id: ids_chunk[-1] = tokenizer.eos_token_id # 先頭が ... の場合は ... に変える if ids_chunk[1] == tokenizer.pad_token_id: ids_chunk[1] = tokenizer.eos_token_id iids_list.append(ids_chunk) input_ids = torch.stack(iids_list) # 3,77 return input_ids def register_image(self, info: ImageInfo, subset: BaseSubset): self.image_data[info.image_key] = info self.image_to_subset[info.image_key] = subset def make_buckets(self): """ bucketingを行わない場合も呼び出し必須（ひとつだけbucketを作る） min_size and max_size are ignored when enable_bucket is False """ logger.info("loading image sizes.") for info in tqdm(self.image_data.values()): if info.image_size is None: info.image_size = self.get_image_size(info.absolute_path) if self.enable_bucket: logger.info("make buckets") else: logger.info("prepare dataset") # bucketを作成し、画像をbucketに振り分ける if self.enable_bucket: if self.bucket_manager is None: # fine tuningの場合でmetadataに定義がある場合は、すでに初期化済み self.bucket_manager = BucketManager( self.bucket_no_upscale, (self.width, self.height), self.min_bucket_reso, self.max_bucket_reso, self.bucket_reso_steps, ) if not self.bucket_no_upscale: self.bucket_manager.make_buckets() else: logger.warning( "min_bucket_reso and max_bucket_reso are ignored if bucket_no_upscale is set, because bucket reso is defined by image size automatically / bucket_no_upscaleが指定された場合は、bucketの解像度は画像サイズから自動計算されるため、min_bucket_resoとmax_bucket_resoは無視されます" ) img_ar_errors = [] for image_info in self.image_data.values(): image_width, image_height = image_info.image_size image_info.bucket_reso, image_info.resized_size, ar_error = self.bucket_manager.select_bucket( image_width, image_height ) # logger.info(image_info.image_key, image_info.bucket_reso) img_ar_errors.append(abs(ar_error)) self.bucket_manager.sort() else: self.bucket_manager = BucketManager(False, (self.width, self.height), None, None, None) self.bucket_manager.set_predefined_resos([(self.width, self.height)]) # ひとつの固定サイズbucketのみ for image_info in self.image_data.values(): image_width, image_height = image_info.image_size image_info.bucket_reso, image_info.resized_size, _ = self.bucket_manager.select_bucket(image_width, image_height) for image_info in self.image_data.values(): for _ in range(image_info.num_repeats): self.bucket_manager.add_image(image_info.bucket_reso, image_info.image_key) # bucket情報を表示、格納する if self.enable_bucket: self.bucket_info = {"buckets": {}} logger.info("number of images (including repeats) / 各bucketの画像枚数（繰り返し回数を含む）") for i, (reso, bucket) in enumerate(zip(self.bucket_manager.resos, self.bucket_manager.buckets)): count = len(bucket) if count > 0: self.bucket_info["buckets"][i] = {"resolution": reso, "count": len(bucket)} logger.info(f"bucket {i}: resolution {reso}, count: {len(bucket)}") img_ar_errors = np.array(img_ar_errors) mean_img_ar_error = np.mean(np.abs(img_ar_errors)) self.bucket_info["mean_img_ar_error"] = mean_img_ar_error logger.info(f"mean ar error (without repeats): {mean_img_ar_error}") # データ参照用indexを作る。このindexはdatasetのshuffleに用いられる self.buckets_indices: List(BucketBatchIndex) = [] for bucket_index, bucket in enumerate(self.bucket_manager.buckets): batch_count = int(math.ceil(len(bucket) / self.batch_size)) for batch_index in range(batch_count): self.buckets_indices.append(BucketBatchIndex(bucket_index, self.batch_size, batch_index)) # ↓以下はbucketごとのbatch件数があまりにも増えて混乱を招くので元に戻す # 　学習時はステップ数がランダムなので、同一画像が同一batch内にあってもそれほど悪影響はないであろう、と考えられる # # # bucketが細分化されることにより、ひとつのbucketに一種類の画像のみというケースが増え、つまりそれは # # ひとつのbatchが同じ画像で占められることになるので、さすがに良くないであろう # # そのためバッチサイズを画像種類までに制限する # # ただそれでも同一画像が同一バッチに含まれる可能性はあるので、繰り返し回数が少ないほうがshuffleの品質は良くなることは間違いない？ # # TO DO 正則化画像をepochまたがりで利用する仕組み # num_of_image_types = len(set(bucket)) # bucket_batch_size = min(self.batch_size, num_of_image_types) # batch_count = int(math.ceil(len(bucket) / bucket_batch_size)) # # logger.info(bucket_index, num_of_image_types, bucket_batch_size, batch_count) # for batch_index in range(batch_count): # self.buckets_indices.append(BucketBatchIndex(bucket_index, bucket_batch_size, batch_index)) # ↑ここまで self.shuffle_buckets() self._length = len(self.buckets_indices) def shuffle_buckets(self): # set random seed for this epoch random.seed(self.seed + self.current_epoch) random.shuffle(self.buckets_indices) self.bucket_manager.shuffle() def verify_bucket_reso_steps(self, min_steps: int): assert self.bucket_reso_steps is None or self.bucket_reso_steps % min_steps == 0, ( f"bucket_reso_steps is {self.bucket_reso_steps}. it must be divisible by {min_steps}.\n" + f"bucket_reso_stepsが{self.bucket_reso_steps}です。{min_steps}で割り切れる必要があります" ) def is_latent_cacheable(self): return all([not subset.color_aug and not subset.random_crop for subset in self.subsets]) def is_text_encoder_output_cacheable(self): return all( [ not ( subset.caption_dropout_rate > 0 or subset.shuffle_caption or subset.token_warmup_step > 0 or subset.caption_tag_dropout_rate > 0 ) for subset in self.subsets ] ) def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_process=True): # マルチGPUには対応していないので、そちらはtools/cache_latents.pyを使うこと logger.info("caching latents.") image_infos = list(self.image_data.values()) # sort by resolution image_infos.sort(key=lambda info: info.bucket_reso[0] * info.bucket_reso[1]) # split by resolution batches = [] batch = [] logger.info("checking cache validity...") for info in tqdm(image_infos): subset = self.image_to_subset[info.image_key] if info.latents_npz is not None: # fine tuning dataset continue # check disk cache exists and size of latents if cache_to_disk: info.latents_npz = os.path.splitext(info.absolute_path)[0] + ".npz" if not is_main_process: # store to info only continue cache_available = is_disk_cached_latents_is_expected(info.bucket_reso, info.latents_npz, subset.flip_aug) if cache_available: # do not add to batch continue # if last member of batch has different resolution, flush the batch if len(batch) > 0 and batch[-1].bucket_reso != info.bucket_reso: batches.append(batch) batch = [] batch.append(info) # if number of data in batch is enough, flush the batch if len(batch) >= vae_batch_size: batches.append(batch) batch = [] if len(batch) > 0: batches.append(batch) if cache_to_disk and not is_main_process: # if cache to disk, don't cache latents in non-main process, set to info only return # iterate batches: batch doesn't have image, image will be loaded in cache_batch_latents and discarded logger.info("caching latents...") for batch in tqdm(batches, smoothing=1, total=len(batches)): cache_batch_latents(vae, cache_to_disk, batch, subset.flip_aug, subset.random_crop) # weight_dtypeを指定するとText Encoderそのもの、およひ出力がweight_dtypeになる # SDXLでのみ有効だが、datasetのメソッドとする必要があるので、sdxl_train_util.pyではなくこちらに実装する # SD1/2に対応するにはv2のフラグを持つ必要があるので後回し def cache_text_encoder_outputs( self, tokenizers, text_encoders, device, weight_dtype, cache_to_disk=False, is_main_process=True ): assert len(tokenizers) == 2, "only support SDXL" # latentsのキャッシュと同様に、ディスクへのキャッシュに対応する # またマルチGPUには対応していないので、そちらはtools/cache_latents.pyを使うこと logger.info("caching text encoder outputs.") image_infos = list(self.image_data.values()) logger.info("checking cache existence...") image_infos_to_cache = [] for info in tqdm(image_infos): # subset = self.image_to_subset[info.image_key] if cache_to_disk: te_out_npz = os.path.splitext(info.absolute_path)[0] + TEXT_ENCODER_OUTPUTS_CACHE_SUFFIX info.text_encoder_outputs_npz = te_out_npz if not is_main_process: # store to info only continue if os.path.exists(te_out_npz): continue image_infos_to_cache.append(info) if cache_to_disk and not is_main_process: # if cache to disk, don't cache latents in non-main process, set to info only return # prepare tokenizers and text encoders for text_encoder in text_encoders: text_encoder.to(device) if weight_dtype is not None: text_encoder.to(dtype=weight_dtype) # create batch batch = [] batches = [] for info in image_infos_to_cache: input_ids1 = self.get_input_ids(info.caption, tokenizers[0]) input_ids2 = self.get_input_ids(info.caption, tokenizers[1]) batch.append((info, input_ids1, input_ids2)) if len(batch) >= self.batch_size: batches.append(batch) batch = [] if len(batch) > 0: batches.append(batch) # iterate batches: call text encoder and cache outputs for memory or disk logger.info("caching text encoder outputs...") for batch in tqdm(batches): infos, input_ids1, input_ids2 = zip(*batch) input_ids1 = torch.stack(input_ids1, dim=0) input_ids2 = torch.stack(input_ids2, dim=0) cache_batch_text_encoder_outputs( infos, tokenizers, text_encoders, self.max_token_length, cache_to_disk, input_ids1, input_ids2, weight_dtype ) def get_image_size(self, image_path): return imagesize.get(image_path) def load_image_with_face_info(self, subset: BaseSubset, image_path: str): img = load_image(image_path) face_cx = face_cy = face_w = face_h = 0 if subset.face_crop_aug_range is not None: tokens = os.path.splitext(os.path.basename(image_path))[0].split("_") if len(tokens) >= 5: face_cx = int(tokens[-4]) face_cy = int(tokens[-3]) face_w = int(tokens[-2]) face_h = int(tokens[-1]) return img, face_cx, face_cy, face_w, face_h # いい感じに切り出す def crop_target(self, subset: BaseSubset, image, face_cx, face_cy, face_w, face_h): height, width = image.shape[0:2] if height == self.height and width == self.width: return image # 画像サイズはsizeより大きいのでリサイズする face_size = max(face_w, face_h) size = min(self.height, self.width) # 短いほう min_scale = max(self.height / height, self.width / width) # 画像がモデル入力サイズぴったりになる倍率（最小の倍率） min_scale = min(1.0, max(min_scale, size / (face_size * subset.face_crop_aug_range[1]))) # 指定した顔最小サイズ max_scale = min(1.0, max(min_scale, size / (face_size * subset.face_crop_aug_range[0]))) # 指定した顔最大サイズ if min_scale >= max_scale: # range指定がmin==max scale = min_scale else: scale = random.uniform(min_scale, max_scale) nh = int(height * scale + 0.5) nw = int(width * scale + 0.5) assert nh >= self.height and nw >= self.width, f"internal error. small scale {scale}, {width}*{height}" image = cv2.resize(image, (nw, nh), interpolation=cv2.INTER_AREA) face_cx = int(face_cx * scale + 0.5) face_cy = int(face_cy * scale + 0.5) height, width = nh, nw # 顔を中心として448*640とかへ切り出す for axis, (target_size, length, face_p) in enumerate(zip((self.height, self.width), (height, width), (face_cy, face_cx))): p1 = face_p - target_size // 2 # 顔を中心に持ってくるための切り出し位置 if subset.random_crop: # 背景も含めるために顔を中心に置く確率を高めつつずらす range = max(length - face_p, face_p) # 画像の端から顔中心までの距離の長いほう p1 = p1 + (random.randint(0, range) + random.randint(0, range)) - range # -range ~ +range までのいい感じの乱数 else: # range指定があるときのみ、すこしだけランダムに（わりと適当） if subset.face_crop_aug_range[0] != subset.face_crop_aug_range[1]: if face_size > size // 10 and face_size >= 40: p1 = p1 + random.randint(-face_size // 20, +face_size // 20) p1 = max(0, min(p1, length - target_size)) if axis == 0: image = image[p1 : p1 + target_size, :] else: image = image[:, p1 : p1 + target_size] return image def __len__(self): return self._length def __getitem__(self, index): bucket = self.bucket_manager.buckets[self.buckets_indices[index].bucket_index] bucket_batch_size = self.buckets_indices[index].bucket_batch_size image_index = self.buckets_indices[index].batch_index * bucket_batch_size if self.caching_mode is not None: # return batch for latents/text encoder outputs caching return self.get_item_for_caching(bucket, bucket_batch_size, image_index) loss_weights = [] captions = [] input_ids_list = [] input_ids2_list = [] latents_list = [] images = [] original_sizes_hw = [] crop_top_lefts = [] target_sizes_hw = [] flippeds = [] # 変数名が微妙 text_encoder_outputs1_list = [] text_encoder_outputs2_list = [] text_encoder_pool2_list = [] for image_key in bucket[image_index : image_index + bucket_batch_size]: image_info = self.image_data[image_key] subset = self.image_to_subset[image_key] loss_weights.append( self.prior_loss_weight if image_info.is_reg else 1.0 ) # in case of fine tuning, is_reg is always False flipped = subset.flip_aug and random.random() < 0.5 # not flipped or flipped with 50% chance # image/latentsを処理する if image_info.latents is not None: # cache_latents=Trueの場合 original_size = image_info.latents_original_size crop_ltrb = image_info.latents_crop_ltrb # calc values later if flipped if not flipped: latents = image_info.latents else: latents = image_info.latents_flipped image = None elif image_info.latents_npz is not None: # FineTuningDatasetまたはcache_latents_to_disk=Trueの場合 latents, original_size, crop_ltrb, flipped_latents = load_latents_from_disk(image_info.latents_npz) if flipped: latents = flipped_latents del flipped_latents latents = torch.FloatTensor(latents) image = None else: # 画像を読み込み、必要ならcropする img, face_cx, face_cy, face_w, face_h = self.load_image_with_face_info(subset, image_info.absolute_path) im_h, im_w = img.shape[0:2] if self.enable_bucket: img, original_size, crop_ltrb = trim_and_resize_if_required( subset.random_crop, img, image_info.bucket_reso, image_info.resized_size ) else: if face_cx > 0: # 顔位置情報あり img = self.crop_target(subset, img, face_cx, face_cy, face_w, face_h) elif im_h > self.height or im_w > self.width: assert ( subset.random_crop ), f"image too large, but cropping and bucketing are disabled / 画像サイズが大きいのでface_crop_aug_rangeかrandom_crop、またはbucketを有効にしてください: {image_info.absolute_path}" if im_h > self.height: p = random.randint(0, im_h - self.height) img = img[p : p + self.height] if im_w > self.width: p = random.randint(0, im_w - self.width) img = img[:, p : p + self.width] im_h, im_w = img.shape[0:2] assert ( im_h == self.height and im_w == self.width ), f"image size is small / 画像サイズが小さいようです: {image_info.absolute_path}" original_size = [im_w, im_h] crop_ltrb = (0, 0, 0, 0) # augmentation aug = self.aug_helper.get_augmentor(subset.color_aug) if aug is not None: img = aug(image=img)["image"] if flipped: img = img[:, ::-1, :].copy() # copy to avoid negative stride problem latents = None image = self.image_transforms(img) # -1.0~1.0のtorch.Tensorになる images.append(image) latents_list.append(latents) target_size = (image.shape[2], image.shape[1]) if image is not None else (latents.shape[2] * 8, latents.shape[1] * 8) if not flipped: crop_left_top = (crop_ltrb[0], crop_ltrb[1]) else: # crop_ltrb[2] is right, so target_size[0] - crop_ltrb[2] is left in flipped image crop_left_top = (target_size[0] - crop_ltrb[2], crop_ltrb[1]) original_sizes_hw.append((int(original_size[1]), int(original_size[0]))) crop_top_lefts.append((int(crop_left_top[1]), int(crop_left_top[0]))) target_sizes_hw.append((int(target_size[1]), int(target_size[0]))) flippeds.append(flipped) # captionとtext encoder outputを処理する caption = image_info.caption # default if image_info.text_encoder_outputs1 is not None: text_encoder_outputs1_list.append(image_info.text_encoder_outputs1) text_encoder_outputs2_list.append(image_info.text_encoder_outputs2) text_encoder_pool2_list.append(image_info.text_encoder_pool2) captions.append(caption) elif image_info.text_encoder_outputs_npz is not None: text_encoder_outputs1, text_encoder_outputs2, text_encoder_pool2 = load_text_encoder_outputs_from_disk( image_info.text_encoder_outputs_npz ) text_encoder_outputs1_list.append(text_encoder_outputs1) text_encoder_outputs2_list.append(text_encoder_outputs2) text_encoder_pool2_list.append(text_encoder_pool2) captions.append(caption) else: caption = self.process_caption(subset, image_info.caption) if self.XTI_layers: caption_layer = [] for layer in self.XTI_layers: token_strings_from = " ".join(self.token_strings) token_strings_to = " ".join([f"{x}_{layer}" for x in self.token_strings]) caption_ = caption.replace(token_strings_from, token_strings_to) caption_layer.append(caption_) captions.append(caption_layer) else: captions.append(caption) if not self.token_padding_disabled: # this option might be omitted in future if self.XTI_layers: token_caption = self.get_input_ids(caption_layer, self.tokenizers[0]) else: token_caption = self.get_input_ids(caption, self.tokenizers[0]) input_ids_list.append(token_caption) if len(self.tokenizers) > 1: if self.XTI_layers: token_caption2 = self.get_input_ids(caption_layer, self.tokenizers[1]) else: token_caption2 = self.get_input_ids(caption, self.tokenizers[1]) input_ids2_list.append(token_caption2) example = {} example["loss_weights"] = torch.FloatTensor(loss_weights) if len(text_encoder_outputs1_list) == 0: if self.token_padding_disabled: # padding=True means pad in the batch example["input_ids"] = self.tokenizer[0](captions, padding=True, truncation=True, return_tensors="pt").input_ids if len(self.tokenizers) > 1: example["input_ids2"] = self.tokenizer[1]( captions, padding=True, truncation=True, return_tensors="pt" ).input_ids else: example["input_ids2"] = None else: example["input_ids"] = torch.stack(input_ids_list) example["input_ids2"] = torch.stack(input_ids2_list) if len(self.tokenizers) > 1 else None example["text_encoder_outputs1_list"] = None example["text_encoder_outputs2_list"] = None example["text_encoder_pool2_list"] = None else: example["input_ids"] = None example["input_ids2"] = None # # for assertion # example["input_ids"] = torch.stack([self.get_input_ids(cap, self.tokenizers[0]) for cap in captions]) # example["input_ids2"] = torch.stack([self.get_input_ids(cap, self.tokenizers[1]) for cap in captions]) example["text_encoder_outputs1_list"] = torch.stack(text_encoder_outputs1_list) example["text_encoder_outputs2_list"] = torch.stack(text_encoder_outputs2_list) example["text_encoder_pool2_list"] = torch.stack(text_encoder_pool2_list) if images[0] is not None: images = torch.stack(images) images = images.to(memory_format=torch.contiguous_format).float() else: images = None example["images"] = images example["latents"] = torch.stack(latents_list) if latents_list[0] is not None else None example["captions"] = captions example["original_sizes_hw"] = torch.stack([torch.LongTensor(x) for x in original_sizes_hw]) example["crop_top_lefts"] = torch.stack([torch.LongTensor(x) for x in crop_top_lefts]) example["target_sizes_hw"] = torch.stack([torch.LongTensor(x) for x in target_sizes_hw]) example["flippeds"] = flippeds example["network_multipliers"] = torch.FloatTensor([self.network_multiplier] * len(captions)) if self.debug_dataset: example["image_keys"] = bucket[image_index : image_index + self.batch_size] return example def get_item_for_caching(self, bucket, bucket_batch_size, image_index): captions = [] images = [] input_ids1_list = [] input_ids2_list = [] absolute_paths = [] resized_sizes = [] bucket_reso = None flip_aug = None random_crop = None for image_key in bucket[image_index : image_index + bucket_batch_size]: image_info = self.image_data[image_key] subset = self.image_to_subset[image_key] if flip_aug is None: flip_aug = subset.flip_aug random_crop = subset.random_crop bucket_reso = image_info.bucket_reso else: assert flip_aug == subset.flip_aug, "flip_aug must be same in a batch" assert random_crop == subset.random_crop, "random_crop must be same in a batch" assert bucket_reso == image_info.bucket_reso, "bucket_reso must be same in a batch" caption = image_info.caption # TODO cache some patterns of dropping, shuffling, etc. if self.caching_mode == "latents": image = load_image(image_info.absolute_path) else: image = None if self.caching_mode == "text": input_ids1 = self.get_input_ids(caption, self.tokenizers[0]) input_ids2 = self.get_input_ids(caption, self.tokenizers[1]) else: input_ids1 = None input_ids2 = None captions.append(caption) images.append(image) input_ids1_list.append(input_ids1) input_ids2_list.append(input_ids2) absolute_paths.append(image_info.absolute_path) resized_sizes.append(image_info.resized_size) example = {} if images[0] is None: images = None example["images"] = images example["captions"] = captions example["input_ids1_list"] = input_ids1_list example["input_ids2_list"] = input_ids2_list example["absolute_paths"] = absolute_paths example["resized_sizes"] = resized_sizes example["flip_aug"] = flip_aug example["random_crop"] = random_crop example["bucket_reso"] = bucket_reso return example class DreamBoothDataset(BaseDataset): IMAGE_INFO_CACHE_FILE = "metadata_cache.json" def __init__( self, subsets: Sequence[DreamBoothSubset], batch_size: int, tokenizer, max_token_length, resolution, network_multiplier: float, enable_bucket: bool, min_bucket_reso: int, max_bucket_reso: int, bucket_reso_steps: int, bucket_no_upscale: bool, prior_loss_weight: float, debug_dataset: bool, ) -> None: super().__init__(tokenizer, max_token_length, resolution, network_multiplier, debug_dataset) assert resolution is not None, f"resolution is required / resolution（解像度）指定は必須です" self.batch_size = batch_size self.size = min(self.width, self.height) # 短いほう self.prior_loss_weight = prior_loss_weight self.latents_cache = None self.enable_bucket = enable_bucket if self.enable_bucket: assert ( min(resolution) >= min_bucket_reso ), f"min_bucket_reso must be equal or less than resolution / min_bucket_resoは最小解像度より大きくできません。解像度を大きくするかmin_bucket_resoを小さくしてください" assert ( max(resolution) <= max_bucket_reso ), f"max_bucket_reso must be equal or greater than resolution / max_bucket_resoは最大解像度より小さくできません。解像度を小さくするかmin_bucket_resoを大きくしてください" self.min_bucket_reso = min_bucket_reso self.max_bucket_reso = max_bucket_reso self.bucket_reso_steps = bucket_reso_steps self.bucket_no_upscale = bucket_no_upscale else: self.min_bucket_reso = None self.max_bucket_reso = None self.bucket_reso_steps = None # この情報は使われない self.bucket_no_upscale = False def read_caption(img_path, caption_extension, enable_wildcard): # captionの候補ファイル名を作る base_name = os.path.splitext(img_path)[0] base_name_face_det = base_name tokens = base_name.split("_") if len(tokens) >= 5: base_name_face_det = "_".join(tokens[:-4]) cap_paths = [base_name + caption_extension, base_name_face_det + caption_extension] caption = None for cap_path in cap_paths: if os.path.isfile(cap_path): with open(cap_path, "rt", encoding="utf-8") as f: try: lines = f.readlines() except UnicodeDecodeError as e: logger.error(f"illegal char in file (not UTF-8) / ファイルにUTF-8以外の文字があります: {cap_path}") raise e assert len(lines) > 0, f"caption file is empty / キャプションファイルが空です: {cap_path}" if enable_wildcard: caption = "\n".join([line.strip() for line in lines if line.strip() != ""]) # 空行を除く、改行で連結 else: caption = lines[0].strip() break return caption def load_dreambooth_dir(subset: DreamBoothSubset): if not os.path.isdir(subset.image_dir): logger.warning(f"not directory: {subset.image_dir}") return [], [] info_cache_file = os.path.join(subset.image_dir, self.IMAGE_INFO_CACHE_FILE) use_cached_info_for_subset = subset.cache_info if use_cached_info_for_subset: logger.info( f"using cached image info for this subset / このサブセットで、キャッシュされた画像情報を使います: {info_cache_file}" ) if not os.path.isfile(info_cache_file): logger.warning( f"image info file not found. You can ignore this warning if this is the first time to use this subset" + " / キャッシュファイルが見つかりませんでした。初回実行時はこの警告を無視してください: {metadata_file}" ) use_cached_info_for_subset = False if use_cached_info_for_subset: # json: {`img_path`:{"caption": "caption...", "resolution": [width, height]}, ...} with open(info_cache_file, "r", encoding="utf-8") as f: metas = json.load(f) img_paths = list(metas.keys()) sizes = [meta["resolution"] for meta in metas.values()] # we may need to check image size and existence of image files, but it takes time, so user should check it before training else: img_paths = glob_images(subset.image_dir, "*") sizes = [None] * len(img_paths) logger.info(f"found directory {subset.image_dir} contains {len(img_paths)} image files") if use_cached_info_for_subset: captions = [meta["caption"] for meta in metas.values()] missing_captions = [img_path for img_path, caption in zip(img_paths, captions) if caption is None or caption == ""] else: # 画像ファイルごとにプロンプトを読み込み、もしあればそちらを使う captions = [] missing_captions = [] for img_path in img_paths: cap_for_img = read_caption(img_path, subset.caption_extension, subset.enable_wildcard) if cap_for_img is None and subset.class_tokens is None: logger.warning( f"neither caption file nor class tokens are found. use empty caption for {img_path} / キャプションファイルもclass tokenも見つかりませんでした。空のキャプションを使用します: {img_path}" ) captions.append("") missing_captions.append(img_path) else: if cap_for_img is None: captions.append(subset.class_tokens) missing_captions.append(img_path) else: captions.append(cap_for_img) self.set_tag_frequency(os.path.basename(subset.image_dir), captions) # タグ頻度を記録 if missing_captions: number_of_missing_captions = len(missing_captions) number_of_missing_captions_to_show = 5 remaining_missing_captions = number_of_missing_captions - number_of_missing_captions_to_show logger.warning( f"No caption file found for {number_of_missing_captions} images. Training will continue without captions for these images. If class token exists, it will be used. / {number_of_missing_captions}枚の画像にキャプションファイルが見つかりませんでした。これらの画像についてはキャプションなしで学習を続行します。class tokenが存在する場合はそれを使います。" ) for i, missing_caption in enumerate(missing_captions): if i >= number_of_missing_captions_to_show: logger.warning(missing_caption + f"... and {remaining_missing_captions} more") break logger.warning(missing_caption) if not use_cached_info_for_subset and subset.cache_info: logger.info(f"cache image info for / 画像情報をキャッシュします : {info_cache_file}") sizes = [self.get_image_size(img_path) for img_path in tqdm(img_paths, desc="get image size")] matas = {} for img_path, caption, size in zip(img_paths, captions, sizes): matas[img_path] = {"caption": caption, "resolution": list(size)} with open(info_cache_file, "w", encoding="utf-8") as f: json.dump(matas, f, ensure_ascii=False, indent=2) logger.info(f"cache image info done for / 画像情報を出力しました : {info_cache_file}") # if sizes are not set, image size will be read in make_buckets return img_paths, captions, sizes logger.info("prepare images.") num_train_images = 0 num_reg_images = 0 reg_infos: List[Tuple[ImageInfo, DreamBoothSubset]] = [] for subset in subsets: if subset.num_repeats < 1: logger.warning( f"ignore subset with image_dir='{subset.image_dir}': num_repeats is less than 1 / num_repeatsが1を下回っているためサブセットを無視します: {subset.num_repeats}" ) continue if subset in self.subsets: logger.warning( f"ignore duplicated subset with image_dir='{subset.image_dir}': use the first one / 既にサブセットが登録されているため、重複した後発のサブセットを無視します" ) continue img_paths, captions, sizes = load_dreambooth_dir(subset) if len(img_paths) < 1: logger.warning( f"ignore subset with image_dir='{subset.image_dir}': no images found / 画像が見つからないためサブセットを無視します" ) continue if subset.is_reg: num_reg_images += subset.num_repeats * len(img_paths) else: num_train_images += subset.num_repeats * len(img_paths) for img_path, caption, size in zip(img_paths, captions, sizes): info = ImageInfo(img_path, subset.num_repeats, caption, subset.is_reg, img_path) if size is not None: info.image_size = size if subset.is_reg: reg_infos.append((info, subset)) else: self.register_image(info, subset) subset.img_count = len(img_paths) self.subsets.append(subset) logger.info(f"{num_train_images} train images with repeating.") self.num_train_images = num_train_images logger.info(f"{num_reg_images} reg images.") if num_train_images < num_reg_images: logger.warning("some of reg images are not used / 正則化画像の数が多いので、一部使用されない正則化画像があります") if num_reg_images == 0: logger.warning("no regularization images / 正則化画像が見つかりませんでした") else: # num_repeatsを計算する：どうせ大した数ではないのでループで処理する n = 0 first_loop = True while n < num_train_images: for info, subset in reg_infos: if first_loop: self.register_image(info, subset) n += info.num_repeats else: info.num_repeats += 1 # rewrite registered info n += 1 if n >= num_train_images: break first_loop = False self.num_reg_images = num_reg_images class FineTuningDataset(BaseDataset): def __init__( self, subsets: Sequence[FineTuningSubset], batch_size: int, tokenizer, max_token_length, resolution, network_multiplier: float, enable_bucket: bool, min_bucket_reso: int, max_bucket_reso: int, bucket_reso_steps: int, bucket_no_upscale: bool, debug_dataset: bool, ) -> None: super().__init__(tokenizer, max_token_length, resolution, network_multiplier, debug_dataset) self.batch_size = batch_size self.num_train_images = 0 self.num_reg_images = 0 for subset in subsets: if subset.num_repeats < 1: logger.warning( f"ignore subset with metadata_file='{subset.metadata_file}': num_repeats is less than 1 / num_repeatsが1を下回っているためサブセットを無視します: {subset.num_repeats}" ) continue if subset in self.subsets: logger.warning( f"ignore duplicated subset with metadata_file='{subset.metadata_file}': use the first one / 既にサブセットが登録されているため、重複した後発のサブセットを無視します" ) continue # メタデータを読み込む if os.path.exists(subset.metadata_file): logger.info(f"loading existing metadata: {subset.metadata_file}") with open(subset.metadata_file, "rt", encoding="utf-8") as f: metadata = json.load(f) else: raise ValueError(f"no metadata / メタデータファイルがありません: {subset.metadata_file}") if len(metadata) < 1: logger.warning( f"ignore subset with '{subset.metadata_file}': no image entries found / 画像に関するデータが見つからないためサブセットを無視します" ) continue tags_list = [] for image_key, img_md in metadata.items(): # path情報を作る abs_path = None # まず画像を優先して探す if os.path.exists(image_key): abs_path = image_key else: # わりといい加減だがいい方法が思いつかん paths = glob_images(subset.image_dir, image_key) if len(paths) > 0: abs_path = paths[0] # なければnpzを探す if abs_path is None: if os.path.exists(os.path.splitext(image_key)[0] + ".npz"): abs_path = os.path.splitext(image_key)[0] + ".npz" else: npz_path = os.path.join(subset.image_dir, image_key + ".npz") if os.path.exists(npz_path): abs_path = npz_path assert abs_path is not None, f"no image / 画像がありません: {image_key}" caption = img_md.get("caption") tags = img_md.get("tags") if caption is None: caption = tags # could be multiline tags = None if subset.enable_wildcard: # tags must be single line if tags is not None: tags = tags.replace("\n", subset.caption_separator) # add tags to each line of caption if caption is not None and tags is not None: caption = "\n".join( [f"{line}{subset.caption_separator}{tags}" for line in caption.split("\n") if line.strip() != ""] ) else: # use as is if tags is not None and len(tags) > 0: caption = caption + subset.caption_separator + tags tags_list.append(tags) if caption is None: caption = "" image_info = ImageInfo(image_key, subset.num_repeats, caption, False, abs_path) image_info.image_size = img_md.get("train_resolution") if not subset.color_aug and not subset.random_crop: # if npz exists, use them image_info.latents_npz, image_info.latents_npz_flipped = self.image_key_to_npz_file(subset, image_key) self.register_image(image_info, subset) self.num_train_images += len(metadata) * subset.num_repeats # TODO do not record tag freq when no tag self.set_tag_frequency(os.path.basename(subset.metadata_file), tags_list) subset.img_count = len(metadata) self.subsets.append(subset) # check existence of all npz files use_npz_latents = all([not (subset.color_aug or subset.random_crop) for subset in self.subsets]) if use_npz_latents: flip_aug_in_subset = False npz_any = False npz_all = True for image_info in self.image_data.values(): subset = self.image_to_subset[image_info.image_key] has_npz = image_info.latents_npz is not None npz_any = npz_any or has_npz if subset.flip_aug: has_npz = has_npz and image_info.latents_npz_flipped is not None flip_aug_in_subset = True npz_all = npz_all and has_npz if npz_any and not npz_all: break if not npz_any: use_npz_latents = False logger.warning(f"npz file does not exist. ignore npz files / npzファイルが見つからないためnpzファイルを無視します") elif not npz_all: use_npz_latents = False logger.warning( f"some of npz file does not exist. ignore npz files / いくつかのnpzファイルが見つからないためnpzファイルを無視します" ) if flip_aug_in_subset: logger.warning("maybe no flipped files / 反転されたnpzファイルがないのかもしれません") # else: # logger.info("npz files are not used with color_aug and/or random_crop / color_augまたはrandom_cropが指定されているためnpzファイルは使用されません") # check min/max bucket size sizes = set() resos = set() for image_info in self.image_data.values(): if image_info.image_size is None: sizes = None # not calculated break sizes.add(image_info.image_size[0]) sizes.add(image_info.image_size[1]) resos.add(tuple(image_info.image_size)) if sizes is None: if use_npz_latents: use_npz_latents = False logger.warning( f"npz files exist, but no bucket info in metadata. ignore npz files / メタデータにbucket情報がないためnpzファイルを無視します" ) assert ( resolution is not None ), "if metadata doesn't have bucket info, resolution is required / メタデータにbucket情報がない場合はresolutionを指定してください" self.enable_bucket = enable_bucket if self.enable_bucket: self.min_bucket_reso = min_bucket_reso self.max_bucket_reso = max_bucket_reso self.bucket_reso_steps = bucket_reso_steps self.bucket_no_upscale = bucket_no_upscale else: if not enable_bucket: logger.info("metadata has bucket info, enable bucketing / メタデータにbucket情報があるためbucketを有効にします") logger.info("using bucket info in metadata / メタデータ内のbucket情報を使います") self.enable_bucket = True assert ( not bucket_no_upscale ), "if metadata has bucket info, bucket reso is precalculated, so bucket_no_upscale cannot be used / メタデータ内にbucket情報がある場合はbucketの解像度は計算済みのため、bucket_no_upscaleは使えません" # bucket情報を初期化しておく、make_bucketsで再作成しない self.bucket_manager = BucketManager(False, None, None, None, None) self.bucket_manager.set_predefined_resos(resos) # npz情報をきれいにしておく if not use_npz_latents: for image_info in self.image_data.values(): image_info.latents_npz = image_info.latents_npz_flipped = None def image_key_to_npz_file(self, subset: FineTuningSubset, image_key): base_name = os.path.splitext(image_key)[0] npz_file_norm = base_name + ".npz" if os.path.exists(npz_file_norm): # image_key is full path npz_file_flip = base_name + "_flip.npz" if not os.path.exists(npz_file_flip): npz_file_flip = None return npz_file_norm, npz_file_flip # if not full path, check image_dir. if image_dir is None, return None if subset.image_dir is None: return None, None # image_key is relative path npz_file_norm = os.path.join(subset.image_dir, image_key + ".npz") npz_file_flip = os.path.join(subset.image_dir, image_key + "_flip.npz") if not os.path.exists(npz_file_norm): npz_file_norm = None npz_file_flip = None elif not os.path.exists(npz_file_flip): npz_file_flip = None return npz_file_norm, npz_file_flip class ControlNetDataset(BaseDataset): def __init__( self, subsets: Sequence[ControlNetSubset], batch_size: int, tokenizer, max_token_length, resolution, network_multiplier: float, enable_bucket: bool, min_bucket_reso: int, max_bucket_reso: int, bucket_reso_steps: int, bucket_no_upscale: bool, debug_dataset: float, ) -> None: super().__init__(tokenizer, max_token_length, resolution, network_multiplier, debug_dataset) db_subsets = [] for subset in subsets: assert ( not subset.random_crop ), "random_crop is not supported in ControlNetDataset / random_cropはControlNetDatasetではサポートされていません" db_subset = DreamBoothSubset( subset.image_dir, False, None, subset.caption_extension, subset.cache_info, subset.num_repeats, subset.shuffle_caption, subset.caption_separator, subset.keep_tokens, subset.keep_tokens_separator, subset.secondary_separator, subset.enable_wildcard, subset.color_aug, subset.flip_aug, subset.face_crop_aug_range, subset.random_crop, subset.caption_dropout_rate, subset.caption_dropout_every_n_epochs, subset.caption_tag_dropout_rate, subset.caption_prefix, subset.caption_suffix, subset.token_warmup_min, subset.token_warmup_step, ) db_subsets.append(db_subset) self.dreambooth_dataset_delegate = DreamBoothDataset( db_subsets, batch_size, tokenizer, max_token_length, resolution, network_multiplier, enable_bucket, min_bucket_reso, max_bucket_reso, bucket_reso_steps, bucket_no_upscale, 1.0, debug_dataset, ) # config_util等から参照される値をいれておく（若干微妙なのでなんとかしたい） self.image_data = self.dreambooth_dataset_delegate.image_data self.batch_size = batch_size self.num_train_images = self.dreambooth_dataset_delegate.num_train_images self.num_reg_images = self.dreambooth_dataset_delegate.num_reg_images # assert all conditioning data exists missing_imgs = [] cond_imgs_with_pair = set() for image_key, info in self.dreambooth_dataset_delegate.image_data.items(): db_subset = self.dreambooth_dataset_delegate.image_to_subset[image_key] subset = None for s in subsets: if s.image_dir == db_subset.image_dir: subset = s break assert subset is not None, "internal error: subset not found" if not os.path.isdir(subset.conditioning_data_dir): logger.warning(f"not directory: {subset.conditioning_data_dir}") continue img_basename = os.path.splitext(os.path.basename(info.absolute_path))[0] ctrl_img_path = glob_images(subset.conditioning_data_dir, img_basename) if len(ctrl_img_path) < 1: missing_imgs.append(img_basename) continue ctrl_img_path = ctrl_img_path[0] ctrl_img_path = os.path.abspath(ctrl_img_path) # normalize path info.cond_img_path = ctrl_img_path cond_imgs_with_pair.add(os.path.splitext(ctrl_img_path)[0]) # remove extension because Windows is case insensitive extra_imgs = [] for subset in subsets: conditioning_img_paths = glob_images(subset.conditioning_data_dir, "*") conditioning_img_paths = [os.path.abspath(p) for p in conditioning_img_paths] # normalize path extra_imgs.extend([p for p in conditioning_img_paths if os.path.splitext(p)[0] not in cond_imgs_with_pair]) assert ( len(missing_imgs) == 0 ), f"missing conditioning data for {len(missing_imgs)} images / 制御用画像が見つかりませんでした: {missing_imgs}" assert ( len(extra_imgs) == 0 ), f"extra conditioning data for {len(extra_imgs)} images / 余分な制御用画像があります: {extra_imgs}" self.conditioning_image_transforms = IMAGE_TRANSFORMS def make_buckets(self): self.dreambooth_dataset_delegate.make_buckets() self.bucket_manager = self.dreambooth_dataset_delegate.bucket_manager self.buckets_indices = self.dreambooth_dataset_delegate.buckets_indices def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_process=True): return self.dreambooth_dataset_delegate.cache_latents(vae, vae_batch_size, cache_to_disk, is_main_process) def __len__(self): return self.dreambooth_dataset_delegate.__len__() def __getitem__(self, index): example = self.dreambooth_dataset_delegate[index] bucket = self.dreambooth_dataset_delegate.bucket_manager.buckets[ self.dreambooth_dataset_delegate.buckets_indices[index].bucket_index ] bucket_batch_size = self.dreambooth_dataset_delegate.buckets_indices[index].bucket_batch_size image_index = self.dreambooth_dataset_delegate.buckets_indices[index].batch_index * bucket_batch_size conditioning_images = [] for i, image_key in enumerate(bucket[image_index : image_index + bucket_batch_size]): image_info = self.dreambooth_dataset_delegate.image_data[image_key] target_size_hw = example["target_sizes_hw"][i] original_size_hw = example["original_sizes_hw"][i] crop_top_left = example["crop_top_lefts"][i] flipped = example["flippeds"][i] cond_img = load_image(image_info.cond_img_path) if self.dreambooth_dataset_delegate.enable_bucket: assert ( cond_img.shape[0] == original_size_hw[0] and cond_img.shape[1] == original_size_hw[1] ), f"size of conditioning image is not match / 画像サイズが合いません: {image_info.absolute_path}" cond_img = cv2.resize( cond_img, image_info.resized_size, interpolation=cv2.INTER_AREA ) # INTER_AREAでやりたいのでcv2でリサイズ # TODO support random crop # 現在サポートしているcropはrandomではなく中央のみ h, w = target_size_hw ct = (cond_img.shape[0] - h) // 2 cl = (cond_img.shape[1] - w) // 2 cond_img = cond_img[ct : ct + h, cl : cl + w] else: # assert ( # cond_img.shape[0] == self.height and cond_img.shape[1] == self.width # ), f"image size is small / 画像サイズが小さいようです: {image_info.absolute_path}" # resize to target if cond_img.shape[0] != target_size_hw[0] or cond_img.shape[1] != target_size_hw[1]: cond_img = cv2.resize( cond_img, (int(target_size_hw[1]), int(target_size_hw[0])), interpolation=cv2.INTER_LANCZOS4 ) if flipped: cond_img = cond_img[:, ::-1, :].copy() # copy to avoid negative stride cond_img = self.conditioning_image_transforms(cond_img) conditioning_images.append(cond_img) example["conditioning_images"] = torch.stack(conditioning_images).to(memory_format=torch.contiguous_format).float() return example # behave as Dataset mock class DatasetGroup(torch.utils.data.ConcatDataset): def __init__(self, datasets: Sequence[Union[DreamBoothDataset, FineTuningDataset]]): self.datasets: List[Union[DreamBoothDataset, FineTuningDataset]] super().__init__(datasets) self.image_data = {} self.num_train_images = 0 self.num_reg_images = 0 # simply concat together # TODO: handling image_data key duplication among dataset # In practical, this is not the big issue because image_data is accessed from outside of dataset only for debug_dataset. for dataset in datasets: self.image_data.update(dataset.image_data) self.num_train_images += dataset.num_train_images self.num_reg_images += dataset.num_reg_images def add_replacement(self, str_from, str_to): for dataset in self.datasets: dataset.add_replacement(str_from, str_to) # def make_buckets(self): # for dataset in self.datasets: # dataset.make_buckets() def enable_XTI(self, *args, **kwargs): for dataset in self.datasets: dataset.enable_XTI(*args, **kwargs) def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_process=True): for i, dataset in enumerate(self.datasets): logger.info(f"[Dataset {i}]") dataset.cache_latents(vae, vae_batch_size, cache_to_disk, is_main_process) def cache_text_encoder_outputs( self, tokenizers, text_encoders, device, weight_dtype, cache_to_disk=False, is_main_process=True ): for i, dataset in enumerate(self.datasets): logger.info(f"[Dataset {i}]") dataset.cache_text_encoder_outputs(tokenizers, text_encoders, device, weight_dtype, cache_to_disk, is_main_process) def set_caching_mode(self, caching_mode): for dataset in self.datasets: dataset.set_caching_mode(caching_mode) def verify_bucket_reso_steps(self, min_steps: int): for dataset in self.datasets: dataset.verify_bucket_reso_steps(min_steps) def is_latent_cacheable(self) -> bool: return all([dataset.is_latent_cacheable() for dataset in self.datasets]) def is_text_encoder_output_cacheable(self) -> bool: return all([dataset.is_text_encoder_output_cacheable() for dataset in self.datasets]) def set_current_epoch(self, epoch): for dataset in self.datasets: dataset.set_current_epoch(epoch) def set_current_step(self, step): for dataset in self.datasets: dataset.set_current_step(step) def set_max_train_steps(self, max_train_steps): for dataset in self.datasets: dataset.set_max_train_steps(max_train_steps) def disable_token_padding(self): for dataset in self.datasets: dataset.disable_token_padding() def is_disk_cached_latents_is_expected(reso, npz_path: str, flip_aug: bool): expected_latents_size = (reso[1] // 8, reso[0] // 8) # bucket_resoはWxHなので注意 if not os.path.exists(npz_path): return False npz = np.load(npz_path) if "latents" not in npz or "original_size" not in npz or "crop_ltrb" not in npz: # old ver? return False if npz["latents"].shape[1:3] != expected_latents_size: return False if flip_aug: if "latents_flipped" not in npz: return False if npz["latents_flipped"].shape[1:3] != expected_latents_size: return False return True # 戻り値は、latents_tensor, (original_size width, original_size height), (crop left, crop top) def load_latents_from_disk( npz_path, ) -> Tuple[Optional[torch.Tensor], Optional[List[int]], Optional[List[int]], Optional[torch.Tensor]]: npz = np.load(npz_path) if "latents" not in npz: raise ValueError(f"error: npz is old format. please re-generate {npz_path}") latents = npz["latents"] original_size = npz["original_size"].tolist() crop_ltrb = npz["crop_ltrb"].tolist() flipped_latents = npz["latents_flipped"] if "latents_flipped" in npz else None return latents, original_size, crop_ltrb, flipped_latents def save_latents_to_disk(npz_path, latents_tensor, original_size, crop_ltrb, flipped_latents_tensor=None): kwargs = {} if flipped_latents_tensor is not None: kwargs["latents_flipped"] = flipped_latents_tensor.float().cpu().numpy() np.savez( npz_path, latents=latents_tensor.float().cpu().numpy(), original_size=np.array(original_size), crop_ltrb=np.array(crop_ltrb), **kwargs, ) def debug_dataset(train_dataset, show_input_ids=False): logger.info(f"Total dataset length (steps) / データセットの長さ（ステップ数）: {len(train_dataset)}") logger.info( "`S` for next step, `E` for next epoch no. , Escape for exit. / Sキーで次のステップ、Eキーで次のエポック、Escキーで中断、終了します" ) epoch = 1 while True: logger.info(f"") logger.info(f"epoch: {epoch}") steps = (epoch - 1) * len(train_dataset) + 1 indices = list(range(len(train_dataset))) random.shuffle(indices) k = 0 for i, idx in enumerate(indices): train_dataset.set_current_epoch(epoch) train_dataset.set_current_step(steps) logger.info(f"steps: {steps} ({i + 1}/{len(train_dataset)})") example = train_dataset[idx] if example["latents"] is not None: logger.info(f"sample has latents from npz file: {example['latents'].size()}") for j, (ik, cap, lw, iid, orgsz, crptl, trgsz, flpdz) in enumerate( zip( example["image_keys"], example["captions"], example["loss_weights"], example["input_ids"], example["original_sizes_hw"], example["crop_top_lefts"], example["target_sizes_hw"], example["flippeds"], ) ): logger.info( f'{ik}, size: {train_dataset.image_data[ik].image_size}, loss weight: {lw}, caption: "{cap}", original size: {orgsz}, crop top left: {crptl}, target size: {trgsz}, flipped: {flpdz}' ) if "network_multipliers" in example: print(f"network multiplier: {example['network_multipliers'][j]}") if show_input_ids: logger.info(f"input ids: {iid}") if "input_ids2" in example: logger.info(f"input ids2: {example['input_ids2'][j]}") if example["images"] is not None: im = example["images"][j] logger.info(f"image size: {im.size()}") im = ((im.numpy() + 1.0) * 127.5).astype(np.uint8) im = np.transpose(im, (1, 2, 0)) # c,H,W -> H,W,c im = im[:, :, ::-1] # RGB -> BGR (OpenCV) if "conditioning_images" in example: cond_img = example["conditioning_images"][j] logger.info(f"conditioning image size: {cond_img.size()}") cond_img = ((cond_img.numpy() + 1.0) * 127.5).astype(np.uint8) cond_img = np.transpose(cond_img, (1, 2, 0)) cond_img = cond_img[:, :, ::-1] if os.name == "nt": cv2.imshow("cond_img", cond_img) if os.name == "nt": # only windows cv2.imshow("img", im) k = cv2.waitKey() cv2.destroyAllWindows() if k == 27 or k == ord("s") or k == ord("e"): break steps += 1 if k == ord("e"): break if k == 27 or (example["images"] is None and i >= 8): k = 27 break if k == 27: break epoch += 1 def glob_images(directory, base="*"): img_paths = [] for ext in IMAGE_EXTENSIONS: if base == "*": img_paths.extend(glob.glob(os.path.join(glob.escape(directory), base + ext))) else: img_paths.extend(glob.glob(glob.escape(os.path.join(directory, base + ext)))) img_paths = list(set(img_paths)) # 重複を排除 img_paths.sort() return img_paths def glob_images_pathlib(dir_path, recursive): image_paths = [] if recursive: for ext in IMAGE_EXTENSIONS: image_paths += list(dir_path.rglob("*" + ext)) else: for ext in IMAGE_EXTENSIONS: image_paths += list(dir_path.glob("*" + ext)) image_paths = list(set(image_paths)) # 重複を排除 image_paths.sort() return image_paths class MinimalDataset(BaseDataset): def __init__(self, tokenizer, max_token_length, resolution, network_multiplier, debug_dataset=False): super().__init__(tokenizer, max_token_length, resolution, network_multiplier, debug_dataset) self.num_train_images = 0 # update in subclass self.num_reg_images = 0 # update in subclass self.datasets = [self] self.batch_size = 1 # update in subclass self.subsets = [self] self.num_repeats = 1 # update in subclass if needed self.img_count = 1 # update in subclass if needed self.bucket_info = {} self.is_reg = False self.image_dir = "dummy" # for metadata def verify_bucket_reso_steps(self, min_steps: int): pass def is_latent_cacheable(self) -> bool: return False def __len__(self): raise NotImplementedError # override to avoid shuffling buckets def set_current_epoch(self, epoch): self.current_epoch = epoch def __getitem__(self, idx): r""" The subclass may have image_data for debug_dataset, which is a dict of ImageInfo objects. Returns: example like this: for i in range(batch_size): image_key = ... # whatever hashable image_keys.append(image_key) image = ... # PIL Image img_tensor = self.image_transforms(img) images.append(img_tensor) caption = ... # str input_ids = self.get_input_ids(caption) input_ids_list.append(input_ids) captions.append(caption) images = torch.stack(images, dim=0) input_ids_list = torch.stack(input_ids_list, dim=0) example = { "images": images, "input_ids": input_ids_list, "captions": captions, # for debug_dataset "latents": None, "image_keys": image_keys, # for debug_dataset "loss_weights": torch.ones(batch_size, dtype=torch.float32), } return example """ raise NotImplementedError def load_arbitrary_dataset(args, tokenizer) -> MinimalDataset: module = ".".join(args.dataset_class.split(".")[:-1]) dataset_class = args.dataset_class.split(".")[-1] module = importlib.import_module(module) dataset_class = getattr(module, dataset_class) train_dataset_group: MinimalDataset = dataset_class(tokenizer, args.max_token_length, args.resolution, args.debug_dataset) return train_dataset_group def load_image(image_path): image = Image.open(image_path) if not image.mode == "RGB": image = image.convert("RGB") img = np.array(image, np.uint8) return img # 画像を読み込む。戻り値はnumpy.ndarray,(original width, original height),(crop left, crop top, crop right, crop bottom) def trim_and_resize_if_required( random_crop: bool, image: Image.Image, reso, resized_size: Tuple[int, int] ) -> Tuple[np.ndarray, Tuple[int, int], Tuple[int, int, int, int]]: image_height, image_width = image.shape[0:2] original_size = (image_width, image_height) # size before resize if image_width != resized_size[0] or image_height != resized_size[1]: # リサイズする image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA) # INTER_AREAでやりたいのでcv2でリサイズ image_height, image_width = image.shape[0:2] if image_width > reso[0]: trim_size = image_width - reso[0] p = trim_size // 2 if not random_crop else random.randint(0, trim_size) # logger.info(f"w {trim_size} {p}") image = image[:, p : p + reso[0]] if image_height > reso[1]: trim_size = image_height - reso[1] p = trim_size // 2 if not random_crop else random.randint(0, trim_size) # logger.info(f"h {trim_size} {p}) image = image[p : p + reso[1]] # random cropの場合のcropされた値をどうcrop left/topに反映するべきか全くアイデアがない # I have no idea how to reflect the cropped value in crop left/top in the case of random crop crop_ltrb = BucketManager.get_crop_ltrb(reso, original_size) assert image.shape[0] == reso[1] and image.shape[1] == reso[0], f"internal error, illegal trimmed size: {image.shape}, {reso}" return image, original_size, crop_ltrb def cache_batch_latents( vae: AutoencoderKL, cache_to_disk: bool, image_infos: List[ImageInfo], flip_aug: bool, random_crop: bool ) -> None: r""" requires image_infos to have: absolute_path, bucket_reso, resized_size, latents_npz optionally requires image_infos to have: image if cache_to_disk is True, set info.latents_npz flipped latents is also saved if flip_aug is True if cache_to_disk is False, set info.latents latents_flipped is also set if flip_aug is True latents_original_size and latents_crop_ltrb are also set """ images = [] for info in image_infos: image = load_image(info.absolute_path) if info.image is None else np.array(info.image, np.uint8) # TODO 画像のメタデータが壊れていて、メタデータから割り当てたbucketと実際の画像サイズが一致しない場合があるのでチェック追加要 image, original_size, crop_ltrb = trim_and_resize_if_required(random_crop, image, info.bucket_reso, info.resized_size) image = IMAGE_TRANSFORMS(image) images.append(image) info.latents_original_size = original_size info.latents_crop_ltrb = crop_ltrb img_tensors = torch.stack(images, dim=0) img_tensors = img_tensors.to(device=vae.device, dtype=vae.dtype) with torch.no_grad(): latents = vae.encode(img_tensors).latent_dist.sample().to("cpu") if flip_aug: img_tensors = torch.flip(img_tensors, dims=[3]) with torch.no_grad(): flipped_latents = vae.encode(img_tensors).latent_dist.sample().to("cpu") else: flipped_latents = [None] * len(latents) for info, latent, flipped_latent in zip(image_infos, latents, flipped_latents): # check NaN if torch.isnan(latents).any() or (flipped_latent is not None and torch.isnan(flipped_latent).any()): raise RuntimeError(f"NaN detected in latents: {info.absolute_path}") if cache_to_disk: save_latents_to_disk(info.latents_npz, latent, info.latents_original_size, info.latents_crop_ltrb, flipped_latent) else: info.latents = latent if flip_aug: info.latents_flipped = flipped_latent if not HIGH_VRAM: clean_memory_on_device(vae.device) def cache_batch_text_encoder_outputs( image_infos, tokenizers, text_encoders, max_token_length, cache_to_disk, input_ids1, input_ids2, dtype ): input_ids1 = input_ids1.to(text_encoders[0].device) input_ids2 = input_ids2.to(text_encoders[1].device) with torch.no_grad(): b_hidden_state1, b_hidden_state2, b_pool2 = get_hidden_states_sdxl( max_token_length, input_ids1, input_ids2, tokenizers[0], tokenizers[1], text_encoders[0], text_encoders[1], dtype, ) # ここでcpuに移動しておかないと、上書きされてしまう b_hidden_state1 = b_hidden_state1.detach().to("cpu") # b,n*75+2,768 b_hidden_state2 = b_hidden_state2.detach().to("cpu") # b,n*75+2,1280 b_pool2 = b_pool2.detach().to("cpu") # b,1280 for info, hidden_state1, hidden_state2, pool2 in zip(image_infos, b_hidden_state1, b_hidden_state2, b_pool2): if cache_to_disk: save_text_encoder_outputs_to_disk(info.text_encoder_outputs_npz, hidden_state1, hidden_state2, pool2) else: info.text_encoder_outputs1 = hidden_state1 info.text_encoder_outputs2 = hidden_state2 info.text_encoder_pool2 = pool2 def save_text_encoder_outputs_to_disk(npz_path, hidden_state1, hidden_state2, pool2): np.savez( npz_path, hidden_state1=hidden_state1.cpu().float().numpy(), hidden_state2=hidden_state2.cpu().float().numpy(), pool2=pool2.cpu().float().numpy(), ) def load_text_encoder_outputs_from_disk(npz_path): with np.load(npz_path) as f: hidden_state1 = torch.from_numpy(f["hidden_state1"]) hidden_state2 = torch.from_numpy(f["hidden_state2"]) if "hidden_state2" in f else None pool2 = torch.from_numpy(f["pool2"]) if "pool2" in f else None return hidden_state1, hidden_state2, pool2 # endregion # region モジュール入れ替え部 """ 高速化のためのモジュール入れ替え """ # FlashAttentionを使うCrossAttention # based on https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/memory_efficient_attention_pytorch/flash_attention.py # LICENSE MIT https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/LICENSE # constants EPSILON = 1e-6 # helper functions def exists(val): return val is not None def default(val, d): return val if exists(val) else d def model_hash(filename): """Old model hash used by stable-diffusion-webui""" try: with open(filename, "rb") as file: m = hashlib.sha256() file.seek(0x100000) m.update(file.read(0x10000)) return m.hexdigest()[0:8] except FileNotFoundError: return "NOFILE" except IsADirectoryError: # Linux? return "IsADirectory" except PermissionError: # Windows return "IsADirectory" def calculate_sha256(filename): """New model hash used by stable-diffusion-webui""" try: hash_sha256 = hashlib.sha256() blksize = 1024 * 1024 with open(filename, "rb") as f: for chunk in iter(lambda: f.read(blksize), b""): hash_sha256.update(chunk) return hash_sha256.hexdigest() except FileNotFoundError: return "NOFILE" except IsADirectoryError: # Linux? return "IsADirectory" except PermissionError: # Windows return "IsADirectory" def precalculate_safetensors_hashes(tensors, metadata): """Precalculate the model hashes needed by sd-webui-additional-networks to save time on indexing the model later.""" # Because writing user metadata to the file can change the result of # sd_models.model_hash(), only retain the training metadata for purposes of # calculating the hash, as they are meant to be immutable metadata = {k: v for k, v in metadata.items() if k.startswith("ss_")} bytes = safetensors.torch.save(tensors, metadata) b = BytesIO(bytes) model_hash = addnet_hash_safetensors(b) legacy_hash = addnet_hash_legacy(b) return model_hash, legacy_hash def addnet_hash_legacy(b): """Old model hash used by sd-webui-additional-networks for .safetensors format files""" m = hashlib.sha256() b.seek(0x100000) m.update(b.read(0x10000)) return m.hexdigest()[0:8] def addnet_hash_safetensors(b): """New model hash used by sd-webui-additional-networks for .safetensors format files""" hash_sha256 = hashlib.sha256() blksize = 1024 * 1024 b.seek(0) header = b.read(8) n = int.from_bytes(header, "little") offset = n + 8 b.seek(offset) for chunk in iter(lambda: b.read(blksize), b""): hash_sha256.update(chunk) return hash_sha256.hexdigest() def get_git_revision_hash() -> str: try: return subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=os.path.dirname(__file__)).decode("ascii").strip() except: return "(unknown)" # def replace_unet_modules(unet: diffusers.models.unet_2d_condition.UNet2DConditionModel, mem_eff_attn, xformers): # replace_attentions_for_hypernetwork() # # unet is not used currently, but it is here for future use # unet.enable_xformers_memory_efficient_attention() # return # if mem_eff_attn: # unet.set_attn_processor(FlashAttnProcessor()) # elif xformers: # unet.enable_xformers_memory_efficient_attention() # def replace_unet_cross_attn_to_xformers(): # logger.info("CrossAttention.forward has been replaced to enable xformers.") # try: # import xformers.ops # except ImportError: # raise ImportError("No xformers / xformersがインストールされていないようです") # def forward_xformers(self, x, context=None, mask=None): # h = self.heads # q_in = self.to_q(x) # context = default(context, x) # context = context.to(x.dtype) # if hasattr(self, "hypernetwork") and self.hypernetwork is not None: # context_k, context_v = self.hypernetwork.forward(x, context) # context_k = context_k.to(x.dtype) # context_v = context_v.to(x.dtype) # else: # context_k = context # context_v = context # k_in = self.to_k(context_k) # v_in = self.to_v(context_v) # q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b n h d", h=h), (q_in, k_in, v_in)) # del q_in, k_in, v_in # q = q.contiguous() # k = k.contiguous() # v = v.contiguous() # out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) # 最適なのを選んでくれる # out = rearrange(out, "b n h d -> b n (h d)", h=h) # # diffusers 0.7.0~ # out = self.to_out[0](out) # out = self.to_out[1](out) # return out # diffusers.models.attention.CrossAttention.forward = forward_xformers def replace_unet_modules(unet: UNet2DConditionModel, mem_eff_attn, xformers, sdpa): if mem_eff_attn: logger.info("Enable memory efficient attention for U-Net") unet.set_use_memory_efficient_attention(False, True) elif xformers: logger.info("Enable xformers for U-Net") try: import xformers.ops except ImportError: raise ImportError("No xformers / xformersがインストールされていないようです") unet.set_use_memory_efficient_attention(True, False) elif sdpa: logger.info("Enable SDPA for U-Net") unet.set_use_sdpa(True) """ def replace_vae_modules(vae: diffusers.models.AutoencoderKL, mem_eff_attn, xformers): # vae is not used currently, but it is here for future use if mem_eff_attn: replace_vae_attn_to_memory_efficient() elif xformers: # とりあえずDiffusersのxformersを使う。AttentionがあるのはMidBlockのみ logger.info("Use Diffusers xformers for VAE") vae.encoder.mid_block.attentions[0].set_use_memory_efficient_attention_xformers(True) vae.decoder.mid_block.attentions[0].set_use_memory_efficient_attention_xformers(True) def replace_vae_attn_to_memory_efficient(): logger.info("AttentionBlock.forward has been replaced to FlashAttention (not xformers)") flash_func = FlashAttentionFunction def forward_flash_attn(self, hidden_states): logger.info("forward_flash_attn") q_bucket_size = 512 k_bucket_size = 1024 residual = hidden_states batch, channel, height, width = hidden_states.shape # norm hidden_states = self.group_norm(hidden_states) hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2) # proj to q, k, v query_proj = self.query(hidden_states) key_proj = self.key(hidden_states) value_proj = self.value(hidden_states) query_proj, key_proj, value_proj = map( lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.num_heads), (query_proj, key_proj, value_proj) ) out = flash_func.apply(query_proj, key_proj, value_proj, None, False, q_bucket_size, k_bucket_size) out = rearrange(out, "b h n d -> b n (h d)") # compute next hidden_states hidden_states = self.proj_attn(hidden_states) hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width) # res connect and rescale hidden_states = (hidden_states + residual) / self.rescale_output_factor return hidden_states diffusers.models.attention.AttentionBlock.forward = forward_flash_attn """ # endregion # region arguments def load_metadata_from_safetensors(safetensors_file: str) -> dict: """r This method locks the file. see https://github.com/huggingface/safetensors/issues/164 If the file isn't .safetensors or doesn't have metadata, return empty dict. """ if os.path.splitext(safetensors_file)[1] != ".safetensors": return {} with safetensors.safe_open(safetensors_file, framework="pt", device="cpu") as f: metadata = f.metadata() if metadata is None: metadata = {} return metadata # this metadata is referred from train_network and various scripts, so we wrote here SS_METADATA_KEY_V2 = "ss_v2" SS_METADATA_KEY_BASE_MODEL_VERSION = "ss_base_model_version" SS_METADATA_KEY_NETWORK_MODULE = "ss_network_module" SS_METADATA_KEY_NETWORK_DIM = "ss_network_dim" SS_METADATA_KEY_NETWORK_ALPHA = "ss_network_alpha" SS_METADATA_KEY_NETWORK_ARGS = "ss_network_args" SS_METADATA_MINIMUM_KEYS = [ SS_METADATA_KEY_V2, SS_METADATA_KEY_BASE_MODEL_VERSION, SS_METADATA_KEY_NETWORK_MODULE, SS_METADATA_KEY_NETWORK_DIM, SS_METADATA_KEY_NETWORK_ALPHA, SS_METADATA_KEY_NETWORK_ARGS, ] def build_minimum_network_metadata( v2: Optional[bool], base_model: Optional[str], network_module: str, network_dim: str, network_alpha: str, network_args: Optional[dict], ): # old LoRA doesn't have base_model metadata = { SS_METADATA_KEY_NETWORK_MODULE: network_module, SS_METADATA_KEY_NETWORK_DIM: network_dim, SS_METADATA_KEY_NETWORK_ALPHA: network_alpha, } if v2 is not None: metadata[SS_METADATA_KEY_V2] = v2 if base_model is not None: metadata[SS_METADATA_KEY_BASE_MODEL_VERSION] = base_model if network_args is not None: metadata[SS_METADATA_KEY_NETWORK_ARGS] = json.dumps(network_args) return metadata def get_sai_model_spec( state_dict: dict, args: argparse.Namespace, sdxl: bool, lora: bool, textual_inversion: bool, is_stable_diffusion_ckpt: Optional[bool] = None, # None for TI and LoRA ): timestamp = time.time() v2 = args.v2 v_parameterization = args.v_parameterization reso = args.resolution title = args.metadata_title if args.metadata_title is not None else args.output_name if args.min_timestep is not None or args.max_timestep is not None: min_time_step = args.min_timestep if args.min_timestep is not None else 0 max_time_step = args.max_timestep if args.max_timestep is not None else 1000 timesteps = (min_time_step, max_time_step) else: timesteps = None metadata = sai_model_spec.build_metadata( state_dict, v2, v_parameterization, sdxl, lora, textual_inversion, timestamp, title=title, reso=reso, is_stable_diffusion_ckpt=is_stable_diffusion_ckpt, author=args.metadata_author, description=args.metadata_description, license=args.metadata_license, tags=args.metadata_tags, timesteps=timesteps, clip_skip=args.clip_skip, # None or int ) return metadata def add_sd_models_arguments(parser: argparse.ArgumentParser): # for pretrained models parser.add_argument( "--v2", action="store_true", help="load Stable Diffusion v2.0 model / Stable Diffusion 2.0のモデルを読み込む" ) parser.add_argument( "--v_parameterization", action="store_true", help="enable v-parameterization training / v-parameterization学習を有効にする" ) parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, help="pretrained model to train, directory to Diffusers model or StableDiffusion checkpoint / 学習元モデル、Diffusers形式モデルのディレクトリまたはStableDiffusionのckptファイル", ) parser.add_argument( "--tokenizer_cache_dir", type=str, default=None, help="directory for caching Tokenizer (for offline training) / Tokenizerをキャッシュするディレクトリ（ネット接続なしでの学習のため）", ) def add_optimizer_arguments(parser: argparse.ArgumentParser): parser.add_argument( "--optimizer_type", type=str, default="", help="Optimizer to use / オプティマイザの種類: AdamW (default), AdamW8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, Lion8bit, PagedLion8bit, Lion, SGDNesterov, SGDNesterov8bit, DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, AdaFactor", ) # backward compatibility parser.add_argument( "--use_8bit_adam", action="store_true", help="use 8bit AdamW optimizer (requires bitsandbytes) / 8bit Adamオプティマイザを使う（bitsandbytesのインストールが必要）", ) parser.add_argument( "--use_lion_optimizer", action="store_true", help="use Lion optimizer (requires lion-pytorch) / Lionオプティマイザを使う（ lion-pytorch のインストールが必要）", ) parser.add_argument("--learning_rate", type=float, default=2.0e-6, help="learning rate / 学習率") parser.add_argument( "--max_grad_norm", default=1.0, type=float, help="Max gradient norm, 0 for no clipping / 勾配正規化の最大norm、0でclippingを行わない", ) parser.add_argument( "--optimizer_args", type=str, default=None, nargs="*", help='additional arguments for optimizer (like "weight_decay=0.01 betas=0.9,0.999 ...") / オプティマイザの追加引数（例： "weight_decay=0.01 betas=0.9,0.999 ..."）', ) parser.add_argument("--lr_scheduler_type", type=str, default="", help="custom scheduler module / 使用するスケジューラ") parser.add_argument( "--lr_scheduler_args", type=str, default=None, nargs="*", help='additional arguments for scheduler (like "T_max=100") / スケジューラの追加引数（例： "T_max100"）', ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help="scheduler to use for learning rate / 学習率のスケジューラ: linear, cosine, cosine_with_restarts, polynomial, constant (default), constant_with_warmup, adafactor", ) parser.add_argument( "--lr_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler (default is 0) / 学習率のスケジューラをウォームアップするステップ数（デフォルト0）", ) parser.add_argument( "--lr_scheduler_num_cycles", type=int, default=1, help="Number of restarts for cosine scheduler with restarts / cosine with restartsスケジューラでのリスタート回数", ) parser.add_argument( "--lr_scheduler_power", type=float, default=1, help="Polynomial power for polynomial scheduler / polynomialスケジューラでのpolynomial power", ) def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: bool): parser.add_argument( "--output_dir", type=str, default=None, help="directory to output trained model / 学習後のモデル出力先ディレクトリ" ) parser.add_argument( "--output_name", type=str, default=None, help="base name of trained model file / 学習後のモデルの拡張子を除くファイル名" ) parser.add_argument( "--huggingface_repo_id", type=str, default=None, help="huggingface repo name to upload / huggingfaceにアップロードするリポジトリ名", ) parser.add_argument( "--huggingface_repo_type", type=str, default=None, help="huggingface repo type to upload / huggingfaceにアップロードするリポジトリの種類", ) parser.add_argument( "--huggingface_path_in_repo", type=str, default=None, help="huggingface model path to upload files / huggingfaceにアップロードするファイルのパス", ) parser.add_argument("--huggingface_token", type=str, default=None, help="huggingface token / huggingfaceのトークン") parser.add_argument( "--huggingface_repo_visibility", type=str, default=None, help="huggingface repository visibility ('public' for public, 'private' or None for private) / huggingfaceにアップロードするリポジトリの公開設定（'public'で公開、'private'またはNoneで非公開）", ) parser.add_argument( "--save_state_to_huggingface", action="store_true", help="save state to huggingface / huggingfaceにstateを保存する" ) parser.add_argument( "--resume_from_huggingface", action="store_true", help="resume from huggingface (ex: --resume {repo_id}/{path_in_repo}:{revision}:{repo_type}) / huggingfaceから学習を再開する(例: --resume {repo_id}/{path_in_repo}:{revision}:{repo_type})", ) parser.add_argument( "--async_upload", action="store_true", help="upload to huggingface asynchronously / huggingfaceに非同期でアップロードする", ) parser.add_argument( "--save_precision", type=str, default=None, choices=[None, "float", "fp16", "bf16"], help="precision in saving / 保存時に精度を変更して保存する", ) parser.add_argument( "--save_every_n_epochs", type=int, default=None, help="save checkpoint every N epochs / 学習中のモデルを指定エポックごとに保存する", ) parser.add_argument( "--save_every_n_steps", type=int, default=None, help="save checkpoint every N steps / 学習中のモデルを指定ステップごとに保存する", ) parser.add_argument( "--save_n_epoch_ratio", type=int, default=None, help="save checkpoint N epoch ratio (for example 5 means save at least 5 files total) / 学習中のモデルを指定のエポック割合で保存する（たとえば5を指定すると最低5個のファイルが保存される）", ) parser.add_argument( "--save_last_n_epochs", type=int, default=None, help="save last N checkpoints when saving every N epochs (remove older checkpoints) / 指定エポックごとにモデルを保存するとき最大Nエポック保存する（古いチェックポイントは削除する）", ) parser.add_argument( "--save_last_n_epochs_state", type=int, default=None, help="save last N checkpoints of state (overrides the value of --save_last_n_epochs)/ 最大Nエポックstateを保存する（--save_last_n_epochsの指定を上書きする）", ) parser.add_argument( "--save_last_n_steps", type=int, default=None, help="save checkpoints until N steps elapsed (remove older checkpoints if N steps elapsed) / 指定ステップごとにモデルを保存するとき、このステップ数経過するまで保存する（このステップ数経過したら削除する）", ) parser.add_argument( "--save_last_n_steps_state", type=int, default=None, help="save states until N steps elapsed (remove older states if N steps elapsed, overrides --save_last_n_steps) / 指定ステップごとにstateを保存するとき、このステップ数経過するまで保存する（このステップ数経過したら削除する。--save_last_n_stepsを上書きする）", ) parser.add_argument( "--save_state", action="store_true", help="save training state additionally (including optimizer states etc.) when saving model / optimizerなど学習状態も含めたstateをモデル保存時に追加で保存する", ) parser.add_argument( "--save_state_on_train_end", action="store_true", help="save training state (including optimizer states etc.) on train end / optimizerなど学習状態も含めたstateを学習完了時に保存する", ) parser.add_argument("--resume", type=str, default=None, help="saved state to resume training / 学習再開するモデルのstate") parser.add_argument("--train_batch_size", type=int, default=1, help="batch size for training / 学習時のバッチサイズ") parser.add_argument( "--max_token_length", type=int, default=None, choices=[None, 150, 225], help="max token length of text encoder (default for 75, 150 or 225) / text encoderのトークンの最大長（未指定で75、150または225が指定可）", ) parser.add_argument( "--mem_eff_attn", action="store_true", help="use memory efficient attention for CrossAttention / CrossAttentionに省メモリ版attentionを使う", ) parser.add_argument( "--torch_compile", action="store_true", help="use torch.compile (requires PyTorch 2.0) / torch.compile を使う" ) parser.add_argument( "--dynamo_backend", type=str, default="inductor", # available backends: # https://github.com/huggingface/accelerate/blob/d1abd59114ada8ba673e1214218cb2878c13b82d/src/accelerate/utils/dataclasses.py#L376-L388C5 # https://pytorch.org/docs/stable/torch.compiler.html choices=["eager", "aot_eager", "inductor", "aot_ts_nvfuser", "nvprims_nvfuser", "cudagraphs", "ofi", "fx2trt", "onnxrt"], help="dynamo backend type (default is inductor) / dynamoのbackendの種類（デフォルトは inductor）", ) parser.add_argument("--xformers", action="store_true", help="use xformers for CrossAttention / CrossAttentionにxformersを使う") parser.add_argument( "--sdpa", action="store_true", help="use sdpa for CrossAttention (requires PyTorch 2.0) / CrossAttentionにsdpaを使う（PyTorch 2.0が必要）", ) parser.add_argument( "--vae", type=str, default=None, help="path to checkpoint of vae to replace / VAEを入れ替える場合、VAEのcheckpointファイルまたはディレクトリ", ) parser.add_argument("--max_train_steps", type=int, default=1600, help="training steps / 学習ステップ数") parser.add_argument( "--max_train_epochs", type=int, default=None, help="training epochs (overrides max_train_steps) / 学習エポック数（max_train_stepsを上書きします）", ) parser.add_argument( "--max_data_loader_n_workers", type=int, default=8, help="max num workers for DataLoader (lower is less main RAM usage, faster epoch start and slower data loading) / DataLoaderの最大プロセス数（小さい値ではメインメモリの使用量が減りエポック間の待ち時間が減りますが、データ読み込みは遅くなります）", ) parser.add_argument( "--persistent_data_loader_workers", action="store_true", help="persistent DataLoader workers (useful for reduce time gap between epoch, but may use more memory) / DataLoader のワーカーを持続させる (エポック間の時間差を少なくするのに有効だが、より多くのメモリを消費する可能性がある)", ) parser.add_argument("--seed", type=int, default=None, help="random seed for training / 学習時の乱数のseed") parser.add_argument( "--gradient_checkpointing", action="store_true", help="enable gradient checkpointing / grandient checkpointingを有効にする" ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass / 学習時に逆伝播をする前に勾配を合計するステップ数", ) parser.add_argument( "--mixed_precision", type=str, default="no", choices=["no", "fp16", "bf16"], help="use mixed precision / 混合精度を使う場合、その精度", ) parser.add_argument("--full_fp16", action="store_true", help="fp16 training including gradients / 勾配も含めてfp16で学習する") parser.add_argument( "--full_bf16", action="store_true", help="bf16 training including gradients / 勾配も含めてbf16で学習する" ) # TODO move to SDXL training, because it is not supported by SD1/2 parser.add_argument("--fp8_base", action="store_true", help="use fp8 for base model / base modelにfp8を使う") parser.add_argument( "--ddp_timeout", type=int, default=None, help="DDP timeout (min, None for default of accelerate) / DDPのタイムアウト（分、Noneでaccelerateのデフォルト）", ) parser.add_argument( "--ddp_gradient_as_bucket_view", action="store_true", help="enable gradient_as_bucket_view for DDP / DDPでgradient_as_bucket_viewを有効にする", ) parser.add_argument( "--ddp_static_graph", action="store_true", help="enable static_graph for DDP / DDPでstatic_graphを有効にする", ) parser.add_argument( "--clip_skip", type=int, default=None, help="use output of nth layer from back of text encoder (n>=1) / text encoderの後ろからn番目の層の出力を用いる（nは1以上）", ) parser.add_argument( "--logging_dir", type=str, default=None, help="enable logging and output TensorBoard log to this directory / ログ出力を有効にしてこのディレクトリにTensorBoard用のログを出力する", ) parser.add_argument( "--log_with", type=str, default=None, choices=["tensorboard", "wandb", "all"], help="what logging tool(s) to use (if 'all', TensorBoard and WandB are both used) / ログ出力に使用するツール (allを指定するとTensorBoardとWandBの両方が使用される)", ) parser.add_argument( "--log_prefix", type=str, default=None, help="add prefix for each log directory / ログディレクトリ名の先頭に追加する文字列" ) parser.add_argument( "--log_tracker_name", type=str, default=None, help="name of tracker to use for logging, default is script-specific default name / ログ出力に使用するtrackerの名前、省略時はスクリプトごとのデフォルト名", ) parser.add_argument( "--wandb_run_name", type=str, default=None, help="The name of the specific wandb session / wandb ログに表示される特定の実行の名前", ) parser.add_argument( "--log_tracker_config", type=str, default=None, help="path to tracker config file to use for logging / ログ出力に使用するtrackerの設定ファイルのパス", ) parser.add_argument( "--wandb_api_key", type=str, default=None, help="specify WandB API key to log in before starting training (optional). / WandB APIキーを指定して学習開始前にログインする（オプション）", ) parser.add_argument( "--noise_offset", type=float, default=None, help="enable noise offset with this value (if enabled, around 0.1 is recommended) / Noise offsetを有効にしてこの値を設定する（有効にする場合は0.1程度を推奨）", ) parser.add_argument( "--noise_offset_random_strength", action="store_true", help="use random strength between 0~noise_offset for noise offset. / noise offsetにおいて、0からnoise_offsetの間でランダムな強度を使用します。", ) parser.add_argument( "--multires_noise_iterations", type=int, default=None, help="enable multires noise with this number of iterations (if enabled, around 6-10 is recommended) / Multires noiseを有効にしてこのイテレーション数を設定する（有効にする場合は6-10程度を推奨）", ) parser.add_argument( "--ip_noise_gamma", type=float, default=None, help="enable input perturbation noise. used for regularization. recommended value: around 0.1 (from arxiv.org/abs/2301.11706) " + "/ input perturbation noiseを有効にする。正則化に使用される。推奨値: 0.1程度 (arxiv.org/abs/2301.11706 より)", ) parser.add_argument( "--ip_noise_gamma_random_strength", action="store_true", help="Use random strength between 0~ip_noise_gamma for input perturbation noise." + "/ input perturbation noiseにおいて、0からip_noise_gammaの間でランダムな強度を使用します。", ) # parser.add_argument( # "--perlin_noise", # type=int, # default=None, # help="enable perlin noise and set the octaves / perlin noiseを有効にしてoctavesをこの値に設定する", # ) parser.add_argument( "--multires_noise_discount", type=float, default=0.3, help="set discount value for multires noise (has no effect without --multires_noise_iterations) / Multires noiseのdiscount値を設定する（--multires_noise_iterations指定時のみ有効）", ) parser.add_argument( "--adaptive_noise_scale", type=float, default=None, help="add `latent mean absolute value * this value` to noise_offset (disabled if None, default) / latentの平均値の絶対値 * この値をnoise_offsetに加算する（Noneの場合は無効、デフォルト）", ) parser.add_argument( "--zero_terminal_snr", action="store_true", help="fix noise scheduler betas to enforce zero terminal SNR / noise schedulerのbetasを修正して、zero terminal SNRを強制する", ) parser.add_argument( "--min_timestep", type=int, default=None, help="set minimum time step for U-Net training (0~999, default is 0) / U-Net学習時のtime stepの最小値を設定する（0~999で指定、省略時はデフォルト値(0)） ", ) parser.add_argument( "--max_timestep", type=int, default=None, help="set maximum time step for U-Net training (1~1000, default is 1000) / U-Net学習時のtime stepの最大値を設定する（1~1000で指定、省略時はデフォルト値(1000)）", ) parser.add_argument( "--loss_type", type=str, default="l2", choices=["l2", "huber", "smooth_l1"], help="The type of loss function to use (L2, Huber, or smooth L1), default is L2 / 使用する損失関数の種類（L2、Huber、またはsmooth L1）、デフォルトはL2", ) parser.add_argument( "--huber_schedule", type=str, default="snr", choices=["constant", "exponential", "snr"], help="The scheduling method for Huber loss (constant, exponential, or SNR-based). Only used when loss_type is 'huber' or 'smooth_l1'. default is snr" + " / Huber損失のスケジューリング方法（constant、exponential、またはSNRベース）。loss_typeが'huber'または'smooth_l1'の場合に有効、デフォルトは snr", ) parser.add_argument( "--huber_c", type=float, default=0.1, help="The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type. default is 0.1 / Huber損失のパラメータ。loss_typeがhuberまたはsmooth l1の場合に有効。デフォルトは0.1", ) parser.add_argument( "--lowram", action="store_true", help="enable low RAM optimization. e.g. load models to VRAM instead of RAM (for machines which have bigger VRAM than RAM such as Colab and Kaggle) / メインメモリが少ない環境向け最適化を有効にする。たとえばVRAMにモデルを読み込む等（ColabやKaggleなどRAMに比べてVRAMが多い環境向け）", ) parser.add_argument( "--highvram", action="store_true", help="disable low VRAM optimization. e.g. do not clear CUDA cache after each latent caching (for machines which have bigger VRAM) " + "/ VRAMが少ない環境向け最適化を無効にする。たとえば各latentのキャッシュ後のCUDAキャッシュクリアを行わない等（VRAMが多い環境向け）", ) parser.add_argument( "--sample_every_n_steps", type=int, default=None, help="generate sample images every N steps / 学習中のモデルで指定ステップごとにサンプル出力する", ) parser.add_argument( "--sample_at_first", action="store_true", help="generate sample images before training / 学習前にサンプル出力する" ) parser.add_argument( "--sample_every_n_epochs", type=int, default=None, help="generate sample images every N epochs (overwrites n_steps) / 学習中のモデルで指定エポックごとにサンプル出力する（ステップ数指定を上書きします）", ) parser.add_argument( "--sample_prompts", type=str, default=None, help="file for prompts to generate sample images / 学習中モデルのサンプル出力用プロンプトのファイル", ) parser.add_argument( "--sample_sampler", type=str, default="ddim", choices=[ "ddim", "pndm", "lms", "euler", "euler_a", "heun", "dpm_2", "dpm_2_a", "dpmsolver", "dpmsolver++", "dpmsingle", "k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", ], help=f"sampler (scheduler) type for sample images / サンプル出力時のサンプラー（スケジューラ）の種類", ) parser.add_argument( "--config_file", type=str, default=None, help="using .toml instead of args to pass hyperparameter / ハイパーパラメータを引数ではなく.tomlファイルで渡す", ) parser.add_argument( "--output_config", action="store_true", help="output command line args to given .toml file / 引数を.tomlファイルに出力する" ) # SAI Model spec parser.add_argument( "--metadata_title", type=str, default=None, help="title for model metadata (default is output_name) / メタデータに書き込まれるモデルタイトル、省略時はoutput_name", ) parser.add_argument( "--metadata_author", type=str, default=None, help="author name for model metadata / メタデータに書き込まれるモデル作者名", ) parser.add_argument( "--metadata_description", type=str, default=None, help="description for model metadata / メタデータに書き込まれるモデル説明", ) parser.add_argument( "--metadata_license", type=str, default=None, help="license for model metadata / メタデータに書き込まれるモデルライセンス", ) parser.add_argument( "--metadata_tags", type=str, default=None, help="tags for model metadata, separated by comma / メタデータに書き込まれるモデルタグ、カンマ区切り", ) if support_dreambooth: # DreamBooth training parser.add_argument( "--prior_loss_weight", type=float, default=1.0, help="loss weight for regularization images / 正則化画像のlossの重み" ) def add_masked_loss_arguments(parser: argparse.ArgumentParser): parser.add_argument( "--conditioning_data_dir", type=str, default=None, help="conditioning data directory / 条件付けデータのディレクトリ", ) parser.add_argument( "--masked_loss", action="store_true", help="apply mask for calculating loss. conditioning_data_dir is required for dataset. / 損失計算時にマスクを適用する。datasetにはconditioning_data_dirが必要", ) # verify command line args for training def verify_command_line_training_args(args: argparse.Namespace): # if wandb is enabled, the command line is exposed to the public # check whether sensitive options are included in the command line arguments # if so, warn or inform the user to move them to the configuration file # wandbが有効な場合、コマンドラインが公開される # 学習用のコマンドライン引数に敏感なオプションが含まれているかどうかを確認し、 # 含まれている場合は設定ファイルに移動するようにユーザーに警告または通知する wandb_enabled = args.log_with is not None and args.log_with != "tensorboard" # "all" or "wandb" if not wandb_enabled: return sensitive_args = ["wandb_api_key", "huggingface_token"] sensitive_path_args = [ "pretrained_model_name_or_path", "vae", "tokenizer_cache_dir", "train_data_dir", "conditioning_data_dir", "reg_data_dir", "output_dir", "logging_dir", ] for arg in sensitive_args: if getattr(args, arg, None) is not None: logger.warning( f"wandb is enabled, but option `{arg}` is included in the command line. Because the command line is exposed to the public, it is recommended to move it to the `.toml` file." + f" / wandbが有効で、かつオプション `{arg}` がコマンドラインに含まれています。コマンドラインは公開されるため、`.toml`ファイルに移動することをお勧めします。" ) # if path is absolute, it may include sensitive information for arg in sensitive_path_args: if getattr(args, arg, None) is not None and os.path.isabs(getattr(args, arg)): logger.info( f"wandb is enabled, but option `{arg}` is included in the command line and it is an absolute path. Because the command line is exposed to the public, it is recommended to move it to the `.toml` file or use relative path." + f" / wandbが有効で、かつオプション `{arg}` がコマンドラインに含まれており、絶対パスです。コマンドラインは公開されるため、`.toml`ファイルに移動するか、相対パスを使用することをお勧めします。" ) if getattr(args, "config_file", None) is not None: logger.info( f"wandb is enabled, but option `config_file` is included in the command line. Because the command line is exposed to the public, please be careful about the information included in the path." + f" / wandbが有効で、かつオプション `config_file` がコマンドラインに含まれています。コマンドラインは公開されるため、パスに含まれる情報にご注意ください。" ) # other sensitive options if args.huggingface_repo_id is not None and args.huggingface_repo_visibility != "public": logger.info( f"wandb is enabled, but option huggingface_repo_id is included in the command line and huggingface_repo_visibility is not 'public'. Because the command line is exposed to the public, it is recommended to move it to the `.toml` file." + f" / wandbが有効で、かつオプション huggingface_repo_id がコマンドラインに含まれており、huggingface_repo_visibility が 'public' ではありません。コマンドラインは公開されるため、`.toml`ファイルに移動することをお勧めします。" ) def verify_training_args(args: argparse.Namespace): r""" Verify training arguments. Also reflect highvram option to global variable 学習用引数を検証する。あわせて highvram オプションの指定をグローバル変数に反映する """ if args.highvram: print("highvram is enabled / highvramが有効です") global HIGH_VRAM HIGH_VRAM = True if args.v_parameterization and not args.v2: logger.warning( "v_parameterization should be with v2 not v1 or sdxl / v1やsdxlでv_parameterizationを使用することは想定されていません" ) if args.v2 and args.clip_skip is not None: logger.warning("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません") if args.cache_latents_to_disk and not args.cache_latents: args.cache_latents = True logger.warning( "cache_latents_to_disk is enabled, so cache_latents is also enabled / cache_latents_to_diskが有効なため、cache_latentsを有効にします" ) # noise_offset, perlin_noise, multires_noise_iterations cannot be enabled at the same time # # Listを使って数えてもいいけど並べてしまえ # if args.noise_offset is not None and args.multires_noise_iterations is not None: # raise ValueError( # "noise_offset and multires_noise_iterations cannot be enabled at the same time / noise_offsetとmultires_noise_iterationsを同時に有効にできません" # ) # if args.noise_offset is not None and args.perlin_noise is not None: # raise ValueError("noise_offset and perlin_noise cannot be enabled at the same time / noise_offsetとperlin_noiseは同時に有効にできません") # if args.perlin_noise is not None and args.multires_noise_iterations is not None: # raise ValueError( # "perlin_noise and multires_noise_iterations cannot be enabled at the same time / perlin_noiseとmultires_noise_iterationsを同時に有効にできません" # ) if args.adaptive_noise_scale is not None and args.noise_offset is None: raise ValueError("adaptive_noise_scale requires noise_offset / adaptive_noise_scaleを使用するにはnoise_offsetが必要です") if args.scale_v_pred_loss_like_noise_pred and not args.v_parameterization: raise ValueError( "scale_v_pred_loss_like_noise_pred can be enabled only with v_parameterization / scale_v_pred_loss_like_noise_predはv_parameterizationが有効なときのみ有効にできます" ) if args.v_pred_like_loss and args.v_parameterization: raise ValueError( "v_pred_like_loss cannot be enabled with v_parameterization / v_pred_like_lossはv_parameterizationが有効なときには有効にできません" ) if args.zero_terminal_snr and not args.v_parameterization: logger.warning( f"zero_terminal_snr is enabled, but v_parameterization is not enabled. training will be unexpected" + " / zero_terminal_snrが有効ですが、v_parameterizationが有効ではありません。学習結果は想定外になる可能性があります" ) if args.sample_every_n_epochs is not None and args.sample_every_n_epochs <= 0: logger.warning( "sample_every_n_epochs is less than or equal to 0, so it will be disabled / sample_every_n_epochsに0以下の値が指定されたため無効になります" ) args.sample_every_n_epochs = None if args.sample_every_n_steps is not None and args.sample_every_n_steps <= 0: logger.warning( "sample_every_n_steps is less than or equal to 0, so it will be disabled / sample_every_n_stepsに0以下の値が指定されたため無効になります" ) args.sample_every_n_steps = None def add_dataset_arguments( parser: argparse.ArgumentParser, support_dreambooth: bool, support_caption: bool, support_caption_dropout: bool ): # dataset common parser.add_argument( "--train_data_dir", type=str, default=None, help="directory for train images / 学習画像データのディレクトリ" ) parser.add_argument( "--cache_info", action="store_true", help="cache meta information (caption and image size) for faster dataset loading. only available for DreamBooth" + " / メタ情報（キャプションとサイズ）をキャッシュしてデータセット読み込みを高速化する。DreamBooth方式のみ有効", ) parser.add_argument( "--shuffle_caption", action="store_true", help="shuffle separated caption / 区切られたcaptionの各要素をshuffleする" ) parser.add_argument("--caption_separator", type=str, default=",", help="separator for caption / captionの区切り文字") parser.add_argument( "--caption_extension", type=str, default=".caption", help="extension of caption files / 読み込むcaptionファイルの拡張子" ) parser.add_argument( "--caption_extention", type=str, default=None, help="extension of caption files (backward compatibility) / 読み込むcaptionファイルの拡張子（スペルミスを残してあります）", ) parser.add_argument( "--keep_tokens", type=int, default=0, help="keep heading N tokens when shuffling caption tokens (token means comma separated strings) / captionのシャッフル時に、先頭からこの個数のトークンをシャッフルしないで残す（トークンはカンマ区切りの各部分を意味する）", ) parser.add_argument( "--keep_tokens_separator", type=str, default="", help="A custom separator to divide the caption into fixed and flexible parts. Tokens before this separator will not be shuffled. If not specified, '--keep_tokens' will be used to determine the fixed number of tokens." + " / captionを固定部分と可変部分に分けるためのカスタム区切り文字。この区切り文字より前のトークンはシャッフルされない。指定しない場合、'--keep_tokens'が固定部分のトークン数として使用される。", ) parser.add_argument( "--secondary_separator", type=str, default=None, help="a secondary separator for caption. This separator is replaced to caption_separator after dropping/shuffling caption" + " / captionのセカンダリ区切り文字。この区切り文字はcaptionのドロップやシャッフル後にcaption_separatorに置き換えられる", ) parser.add_argument( "--enable_wildcard", action="store_true", help="enable wildcard for caption (e.g. '{image|picture|rendition}') / captionのワイルドカードを有効にする（例：'{image|picture|rendition}'）", ) parser.add_argument( "--caption_prefix", type=str, default=None, help="prefix for caption text / captionのテキストの先頭に付ける文字列", ) parser.add_argument( "--caption_suffix", type=str, default=None, help="suffix for caption text / captionのテキストの末尾に付ける文字列", ) parser.add_argument( "--color_aug", action="store_true", help="enable weak color augmentation / 学習時に色合いのaugmentationを有効にする" ) parser.add_argument( "--flip_aug", action="store_true", help="enable horizontal flip augmentation / 学習時に左右反転のaugmentationを有効にする" ) parser.add_argument( "--face_crop_aug_range", type=str, default=None, help="enable face-centered crop augmentation and its range (e.g. 2.0,4.0) / 学習時に顔を中心とした切り出しaugmentationを有効にするときは倍率を指定する（例：2.0,4.0）", ) parser.add_argument( "--random_crop", action="store_true", help="enable random crop (for style training in face-centered crop augmentation) / ランダムな切り出しを有効にする（顔を中心としたaugmentationを行うときに画風の学習用に指定する）", ) parser.add_argument( "--debug_dataset", action="store_true", help="show images for debugging (do not train) / デバッグ用に学習データを画面表示する（学習は行わない）", ) parser.add_argument( "--resolution", type=str, default=None, help="resolution in training ('size' or 'width,height') / 学習時の画像解像度（'サイズ'指定、または'幅,高さ'指定）", ) parser.add_argument( "--cache_latents", action="store_true", help="cache latents to main memory to reduce VRAM usage (augmentations must be disabled) / VRAM削減のためにlatentをメインメモリにcacheする（augmentationは使用不可） ", ) parser.add_argument( "--vae_batch_size", type=int, default=1, help="batch size for caching latents / latentのcache時のバッチサイズ" ) parser.add_argument( "--cache_latents_to_disk", action="store_true", help="cache latents to disk to reduce VRAM usage (augmentations must be disabled) / VRAM削減のためにlatentをディスクにcacheする（augmentationは使用不可）", ) parser.add_argument( "--enable_bucket", action="store_true", help="enable buckets for multi aspect ratio training / 複数解像度学習のためのbucketを有効にする", ) parser.add_argument("--min_bucket_reso", type=int, default=256, help="minimum resolution for buckets / bucketの最小解像度") parser.add_argument("--max_bucket_reso", type=int, default=1024, help="maximum resolution for buckets / bucketの最大解像度") parser.add_argument( "--bucket_reso_steps", type=int, default=64, help="steps of resolution for buckets, divisible by 8 is recommended / bucketの解像度の単位、8で割り切れる値を推奨します", ) parser.add_argument( "--bucket_no_upscale", action="store_true", help="make bucket for each image without upscaling / 画像を拡大せずbucketを作成します", ) parser.add_argument( "--token_warmup_min", type=int, default=1, help="start learning at N tags (token means comma separated strinfloatgs) / タグ数をN個から増やしながら学習する", ) parser.add_argument( "--token_warmup_step", type=float, default=0, help="tag length reaches maximum on N steps (or N*max_train_steps if N<1) / N（N<1ならN*max_train_steps）ステップでタグ長が最大になる。デフォルトは0（最初から最大）", ) parser.add_argument( "--dataset_class", type=str, default=None, help="dataset class for arbitrary dataset (package.module.Class) / 任意のデータセットを用いるときのクラス名 (package.module.Class)", ) if support_caption_dropout: # Textual Inversion はcaptionのdropoutをsupportしない # いわゆるtensorのDropoutと紛らわしいのでprefixにcaptionを付けておく　every_n_epochsは他と平仄を合わせてdefault Noneに parser.add_argument( "--caption_dropout_rate", type=float, default=0.0, help="Rate out dropout caption(0.0~1.0) / captionをdropoutする割合" ) parser.add_argument( "--caption_dropout_every_n_epochs", type=int, default=0, help="Dropout all captions every N epochs / captionを指定エポックごとにdropoutする", ) parser.add_argument( "--caption_tag_dropout_rate", type=float, default=0.0, help="Rate out dropout comma separated tokens(0.0~1.0) / カンマ区切りのタグをdropoutする割合", ) if support_dreambooth: # DreamBooth dataset parser.add_argument( "--reg_data_dir", type=str, default=None, help="directory for regularization images / 正則化画像データのディレクトリ" ) if support_caption: # caption dataset parser.add_argument( "--in_json", type=str, default=None, help="json metadata for dataset / データセットのmetadataのjsonファイル" ) parser.add_argument( "--dataset_repeats", type=int, default=1, help="repeat dataset when training with captions / キャプションでの学習時にデータセットを繰り返す回数", ) def add_sd_saving_arguments(parser: argparse.ArgumentParser): parser.add_argument( "--save_model_as", type=str, default=None, choices=[None, "ckpt", "safetensors", "diffusers", "diffusers_safetensors"], help="format to save the model (default is same to original) / モデル保存時の形式（未指定時は元モデルと同じ）", ) parser.add_argument( "--use_safetensors", action="store_true", help="use safetensors format to save (if save_model_as is not specified) / checkpoint、モデルをsafetensors形式で保存する（save_model_as未指定時）", ) def read_config_from_file(args: argparse.Namespace, parser: argparse.ArgumentParser): if not args.config_file: return args config_path = args.config_file + ".toml" if not args.config_file.endswith(".toml") else args.config_file if args.output_config: # check if config file exists if os.path.exists(config_path): logger.error(f"Config file already exists. Aborting... / 出力先の設定ファイルが既に存在します: {config_path}") exit(1) # convert args to dictionary args_dict = vars(args) # remove unnecessary keys for key in ["config_file", "output_config", "wandb_api_key"]: if key in args_dict: del args_dict[key] # get default args from parser default_args = vars(parser.parse_args([])) # remove default values: cannot use args_dict.items directly because it will be changed during iteration for key, value in list(args_dict.items()): if key in default_args and value == default_args[key]: del args_dict[key] # convert Path to str in dictionary for key, value in args_dict.items(): if isinstance(value, pathlib.Path): args_dict[key] = str(value) # convert to toml and output to file with open(config_path, "w") as f: toml.dump(args_dict, f) logger.info(f"Saved config file / 設定ファイルを保存しました: {config_path}") exit(0) if not os.path.exists(config_path): logger.info(f"{config_path} not found.") exit(1) logger.info(f"Loading settings from {config_path}...") with open(config_path, "r", encoding="utf-8") as f: config_dict = toml.load(f) # combine all sections into one ignore_nesting_dict = {} for section_name, section_dict in config_dict.items(): # if value is not dict, save key and value as is if not isinstance(section_dict, dict): ignore_nesting_dict[section_name] = section_dict continue # if value is dict, save all key and value into one dict for key, value in section_dict.items(): ignore_nesting_dict[key] = value config_args = argparse.Namespace(**ignore_nesting_dict) args = parser.parse_args(namespace=config_args) args.config_file = os.path.splitext(args.config_file)[0] logger.info(args.config_file) return args # endregion # region utils def resume_from_local_or_hf_if_specified(accelerator, args): if not args.resume: return if not args.resume_from_huggingface: logger.info(f"resume training from local state: {args.resume}") accelerator.load_state(args.resume) return logger.info(f"resume training from huggingface state: {args.resume}") repo_id = args.resume.split("/")[0] + "/" + args.resume.split("/")[1] path_in_repo = "/".join(args.resume.split("/")[2:]) revision = None repo_type = None if ":" in path_in_repo: divided = path_in_repo.split(":") if len(divided) == 2: path_in_repo, revision = divided repo_type = "model" else: path_in_repo, revision, repo_type = divided logger.info(f"Downloading state from huggingface: {repo_id}/{path_in_repo}@{revision}") list_files = huggingface_util.list_dir( repo_id=repo_id, subfolder=path_in_repo, revision=revision, token=args.huggingface_token, repo_type=repo_type, ) async def download(filename) -> str: def task(): return hf_hub_download( repo_id=repo_id, filename=filename, revision=revision, repo_type=repo_type, token=args.huggingface_token, ) return await asyncio.get_event_loop().run_in_executor(None, task) loop = asyncio.get_event_loop() results = loop.run_until_complete(asyncio.gather(*[download(filename=filename.rfilename) for filename in list_files])) if len(results) == 0: raise ValueError( "No files found in the specified repo id/path/revision / 指定されたリポジトリID/パス/リビジョンにファイルが見つかりませんでした" ) dirname = os.path.dirname(results[0]) accelerator.load_state(dirname) def get_optimizer(args, trainable_params): # "Optimizer to use: AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, Lion8bit, PagedLion8bit, DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, Adafactor" optimizer_type = args.optimizer_type if args.use_8bit_adam: assert ( not args.use_lion_optimizer ), "both option use_8bit_adam and use_lion_optimizer are specified / use_8bit_adamとuse_lion_optimizerの両方のオプションが指定されています" assert ( optimizer_type is None or optimizer_type == "" ), "both option use_8bit_adam and optimizer_type are specified / use_8bit_adamとoptimizer_typeの両方のオプションが指定されています" optimizer_type = "AdamW8bit" elif args.use_lion_optimizer: assert ( optimizer_type is None or optimizer_type == "" ), "both option use_lion_optimizer and optimizer_type are specified / use_lion_optimizerとoptimizer_typeの両方のオプションが指定されています" optimizer_type = "Lion" if optimizer_type is None or optimizer_type == "": optimizer_type = "AdamW" optimizer_type = optimizer_type.lower() # 引数を分解する optimizer_kwargs = {} if args.optimizer_args is not None and len(args.optimizer_args) > 0: for arg in args.optimizer_args: key, value = arg.split("=") value = ast.literal_eval(value) # value = value.split(",") # for i in range(len(value)): # if value[i].lower() == "true" or value[i].lower() == "false": # value[i] = value[i].lower() == "true" # else: # value[i] = ast.float(value[i]) # if len(value) == 1: # value = value[0] # else: # value = tuple(value) optimizer_kwargs[key] = value # logger.info(f"optkwargs {optimizer}_{kwargs}") lr = args.learning_rate optimizer = None if optimizer_type == "Lion".lower(): try: import lion_pytorch except ImportError: raise ImportError("No lion_pytorch / lion_pytorch がインストールされていないようです") logger.info(f"use Lion optimizer | {optimizer_kwargs}") optimizer_class = lion_pytorch.Lion optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) elif optimizer_type.endswith("8bit".lower()): try: import bitsandbytes as bnb except ImportError: raise ImportError("No bitsandbytes / bitsandbytesがインストールされていないようです") if optimizer_type == "AdamW8bit".lower(): logger.info(f"use 8-bit AdamW optimizer | {optimizer_kwargs}") optimizer_class = bnb.optim.AdamW8bit optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) elif optimizer_type == "SGDNesterov8bit".lower(): logger.info(f"use 8-bit SGD with Nesterov optimizer | {optimizer_kwargs}") if "momentum" not in optimizer_kwargs: logger.warning( f"8-bit SGD with Nesterov must be with momentum, set momentum to 0.9 / 8-bit SGD with Nesterovはmomentum指定が必須のため0.9に設定します" ) optimizer_kwargs["momentum"] = 0.9 optimizer_class = bnb.optim.SGD8bit optimizer = optimizer_class(trainable_params, lr=lr, nesterov=True, **optimizer_kwargs) elif optimizer_type == "Lion8bit".lower(): logger.info(f"use 8-bit Lion optimizer | {optimizer_kwargs}") try: optimizer_class = bnb.optim.Lion8bit except AttributeError: raise AttributeError( "No Lion8bit. The version of bitsandbytes installed seems to be old. Please install 0.38.0 or later. / Lion8bitが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.38.0以上をインストールしてください" ) elif optimizer_type == "PagedAdamW8bit".lower(): logger.info(f"use 8-bit PagedAdamW optimizer | {optimizer_kwargs}") try: optimizer_class = bnb.optim.PagedAdamW8bit except AttributeError: raise AttributeError( "No PagedAdamW8bit. The version of bitsandbytes installed seems to be old. Please install 0.39.0 or later. / PagedAdamW8bitが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.39.0以上をインストールしてください" ) elif optimizer_type == "PagedLion8bit".lower(): logger.info(f"use 8-bit Paged Lion optimizer | {optimizer_kwargs}") try: optimizer_class = bnb.optim.PagedLion8bit except AttributeError: raise AttributeError( "No PagedLion8bit. The version of bitsandbytes installed seems to be old. Please install 0.39.0 or later. / PagedLion8bitが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.39.0以上をインストールしてください" ) optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) elif optimizer_type == "PagedAdamW".lower(): logger.info(f"use PagedAdamW optimizer | {optimizer_kwargs}") try: import bitsandbytes as bnb except ImportError: raise ImportError("No bitsandbytes / bitsandbytesがインストールされていないようです") try: optimizer_class = bnb.optim.PagedAdamW except AttributeError: raise AttributeError( "No PagedAdamW. The version of bitsandbytes installed seems to be old. Please install 0.39.0 or later. / PagedAdamWが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.39.0以上をインストールしてください" ) optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) elif optimizer_type == "PagedAdamW32bit".lower(): logger.info(f"use 32-bit PagedAdamW optimizer | {optimizer_kwargs}") try: import bitsandbytes as bnb except ImportError: raise ImportError("No bitsandbytes / bitsandbytesがインストールされていないようです") try: optimizer_class = bnb.optim.PagedAdamW32bit except AttributeError: raise AttributeError( "No PagedAdamW32bit. The version of bitsandbytes installed seems to be old. Please install 0.39.0 or later. / PagedAdamW32bitが定義されていません。インストールされているbitsandbytesのバージョンが古いようです。0.39.0以上をインストールしてください" ) optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) elif optimizer_type == "SGDNesterov".lower(): logger.info(f"use SGD with Nesterov optimizer | {optimizer_kwargs}") if "momentum" not in optimizer_kwargs: logger.info( f"SGD with Nesterov must be with momentum, set momentum to 0.9 / SGD with Nesterovはmomentum指定が必須のため0.9に設定します" ) optimizer_kwargs["momentum"] = 0.9 optimizer_class = torch.optim.SGD optimizer = optimizer_class(trainable_params, lr=lr, nesterov=True, **optimizer_kwargs) elif optimizer_type.startswith("DAdapt".lower()) or optimizer_type == "Prodigy".lower(): # check lr and lr_count, and logger.info warning actual_lr = lr lr_count = 1 if type(trainable_params) == list and type(trainable_params[0]) == dict: lrs = set() actual_lr = trainable_params[0].get("lr", actual_lr) for group in trainable_params: lrs.add(group.get("lr", actual_lr)) lr_count = len(lrs) if actual_lr <= 0.1: logger.warning( f"learning rate is too low. If using D-Adaptation or Prodigy, set learning rate around 1.0 / 学習率が低すぎるようです。D-AdaptationまたはProdigyの使用時は1.0前後の値を指定してください: lr={actual_lr}" ) logger.warning("recommend option: lr=1.0 / 推奨は1.0です") if lr_count > 1: logger.warning( f"when multiple learning rates are specified with dadaptation (e.g. for Text Encoder and U-Net), only the first one will take effect / D-AdaptationまたはProdigyで複数の学習率を指定した場合（Text EncoderとU-Netなど）、最初の学習率のみが有効になります: lr={actual_lr}" ) if optimizer_type.startswith("DAdapt".lower()): # DAdaptation family # check dadaptation is installed try: import dadaptation import dadaptation.experimental as experimental except ImportError: raise ImportError("No dadaptation / dadaptation がインストールされていないようです") # set optimizer if optimizer_type == "DAdaptation".lower() or optimizer_type == "DAdaptAdamPreprint".lower(): optimizer_class = experimental.DAdaptAdamPreprint logger.info(f"use D-Adaptation AdamPreprint optimizer | {optimizer_kwargs}") elif optimizer_type == "DAdaptAdaGrad".lower(): optimizer_class = dadaptation.DAdaptAdaGrad logger.info(f"use D-Adaptation AdaGrad optimizer | {optimizer_kwargs}") elif optimizer_type == "DAdaptAdam".lower(): optimizer_class = dadaptation.DAdaptAdam logger.info(f"use D-Adaptation Adam optimizer | {optimizer_kwargs}") elif optimizer_type == "DAdaptAdan".lower(): optimizer_class = dadaptation.DAdaptAdan logger.info(f"use D-Adaptation Adan optimizer | {optimizer_kwargs}") elif optimizer_type == "DAdaptAdanIP".lower(): optimizer_class = experimental.DAdaptAdanIP logger.info(f"use D-Adaptation AdanIP optimizer | {optimizer_kwargs}") elif optimizer_type == "DAdaptLion".lower(): optimizer_class = dadaptation.DAdaptLion logger.info(f"use D-Adaptation Lion optimizer | {optimizer_kwargs}") elif optimizer_type == "DAdaptSGD".lower(): optimizer_class = dadaptation.DAdaptSGD logger.info(f"use D-Adaptation SGD optimizer | {optimizer_kwargs}") else: raise ValueError(f"Unknown optimizer type: {optimizer_type}") optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) else: # Prodigy # check Prodigy is installed try: import prodigyopt except ImportError: raise ImportError("No Prodigy / Prodigy がインストールされていないようです") logger.info(f"use Prodigy optimizer | {optimizer_kwargs}") optimizer_class = prodigyopt.Prodigy optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) elif optimizer_type == "Adafactor".lower(): # 引数を確認して適宜補正する if "relative_step" not in optimizer_kwargs: optimizer_kwargs["relative_step"] = True # default if not optimizer_kwargs["relative_step"] and optimizer_kwargs.get("warmup_init", False): logger.info( f"set relative_step to True because warmup_init is True / warmup_initがTrueのためrelative_stepをTrueにします" ) optimizer_kwargs["relative_step"] = True logger.info(f"use Adafactor optimizer | {optimizer_kwargs}") if optimizer_kwargs["relative_step"]: logger.info(f"relative_step is true / relative_stepがtrueです") if lr != 0.0: logger.warning(f"learning rate is used as initial_lr / 指定したlearning rateはinitial_lrとして使用されます") args.learning_rate = None # trainable_paramsがgroupだった時の処理：lrを削除する if type(trainable_params) == list and type(trainable_params[0]) == dict: has_group_lr = False for group in trainable_params: p = group.pop("lr", None) has_group_lr = has_group_lr or (p is not None) if has_group_lr: # 一応argsを無効にしておく TODO 依存関係が逆転してるのであまり望ましくない logger.warning(f"unet_lr and text_encoder_lr are ignored / unet_lrとtext_encoder_lrは無視されます") args.unet_lr = None args.text_encoder_lr = None if args.lr_scheduler != "adafactor": logger.info(f"use adafactor_scheduler / スケジューラにadafactor_schedulerを使用します") args.lr_scheduler = f"adafactor:{lr}" # ちょっと微妙だけど lr = None else: if args.max_grad_norm != 0.0: logger.warning( f"because max_grad_norm is set, clip_grad_norm is enabled. consider set to 0 / max_grad_normが設定されているためclip_grad_normが有効になります。0に設定して無効にしたほうがいいかもしれません" ) if args.lr_scheduler != "constant_with_warmup": logger.warning(f"constant_with_warmup will be good / スケジューラはconstant_with_warmupが良いかもしれません") if optimizer_kwargs.get("clip_threshold", 1.0) != 1.0: logger.warning(f"clip_threshold=1.0 will be good / clip_thresholdは1.0が良いかもしれません") optimizer_class = transformers.optimization.Adafactor optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) elif optimizer_type == "AdamW".lower(): logger.info(f"use AdamW optimizer | {optimizer_kwargs}") optimizer_class = torch.optim.AdamW optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) if optimizer is None: # 任意のoptimizerを使う optimizer_type = args.optimizer_type # lowerでないやつ（微妙） logger.info(f"use {optimizer_type} | {optimizer_kwargs}") if "." not in optimizer_type: optimizer_module = torch.optim else: values = optimizer_type.split(".") optimizer_module = importlib.import_module(".".join(values[:-1])) optimizer_type = values[-1] optimizer_class = getattr(optimizer_module, optimizer_type) optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs) optimizer_name = optimizer_class.__module__ + "." + optimizer_class.__name__ optimizer_args = ",".join([f"{k}={v}" for k, v in optimizer_kwargs.items()]) return optimizer_name, optimizer_args, optimizer # Modified version of get_scheduler() function from diffusers.optimizer.get_scheduler # Add some checking and features to the original function. def get_scheduler_fix(args, optimizer: Optimizer, num_processes: int): """ Unified API to get any scheduler from its name. """ name = args.lr_scheduler num_warmup_steps: Optional[int] = args.lr_warmup_steps num_training_steps = args.max_train_steps * num_processes # * args.gradient_accumulation_steps num_cycles = args.lr_scheduler_num_cycles power = args.lr_scheduler_power lr_scheduler_kwargs = {} # get custom lr_scheduler kwargs if args.lr_scheduler_args is not None and len(args.lr_scheduler_args) > 0: for arg in args.lr_scheduler_args: key, value = arg.split("=") value = ast.literal_eval(value) lr_scheduler_kwargs[key] = value def wrap_check_needless_num_warmup_steps(return_vals): if num_warmup_steps is not None and num_warmup_steps != 0: raise ValueError(f"{name} does not require `num_warmup_steps`. Set None or 0.") return return_vals # using any lr_scheduler from other library if args.lr_scheduler_type: lr_scheduler_type = args.lr_scheduler_type logger.info(f"use {lr_scheduler_type} | {lr_scheduler_kwargs} as lr_scheduler") if "." not in lr_scheduler_type: # default to use torch.optim lr_scheduler_module = torch.optim.lr_scheduler else: values = lr_scheduler_type.split(".") lr_scheduler_module = importlib.import_module(".".join(values[:-1])) lr_scheduler_type = values[-1] lr_scheduler_class = getattr(lr_scheduler_module, lr_scheduler_type) lr_scheduler = lr_scheduler_class(optimizer, **lr_scheduler_kwargs) return wrap_check_needless_num_warmup_steps(lr_scheduler) if name.startswith("adafactor"): assert ( type(optimizer) == transformers.optimization.Adafactor ), f"adafactor scheduler must be used with Adafactor optimizer / adafactor schedulerはAdafactorオプティマイザと同時に使ってください" initial_lr = float(name.split(":")[1]) # logger.info(f"adafactor scheduler init lr {initial_lr}") return wrap_check_needless_num_warmup_steps(transformers.optimization.AdafactorSchedule(optimizer, initial_lr)) name = SchedulerType(name) schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name] if name == SchedulerType.CONSTANT: return wrap_check_needless_num_warmup_steps(schedule_func(optimizer, **lr_scheduler_kwargs)) if name == SchedulerType.PIECEWISE_CONSTANT: return schedule_func(optimizer, **lr_scheduler_kwargs) # step_rules and last_epoch are given as kwargs # All other schedulers require `num_warmup_steps` if num_warmup_steps is None: raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.") if name == SchedulerType.CONSTANT_WITH_WARMUP: return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, **lr_scheduler_kwargs) # All other schedulers require `num_training_steps` if num_training_steps is None: raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.") if name == SchedulerType.COSINE_WITH_RESTARTS: return schedule_func( optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, num_cycles=num_cycles, **lr_scheduler_kwargs, ) if name == SchedulerType.POLYNOMIAL: return schedule_func( optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, power=power, **lr_scheduler_kwargs ) return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, **lr_scheduler_kwargs) def prepare_dataset_args(args: argparse.Namespace, support_metadata: bool): # backward compatibility if args.caption_extention is not None: args.caption_extension = args.caption_extention args.caption_extention = None # assert args.resolution is not None, f"resolution is required / resolution（解像度）を指定してください" if args.resolution is not None: args.resolution = tuple([int(r) for r in args.resolution.split(",")]) if len(args.resolution) == 1: args.resolution = (args.resolution[0], args.resolution[0]) assert ( len(args.resolution) == 2 ), f"resolution must be 'size' or 'width,height' / resolution（解像度）は'サイズ'または'幅','高さ'で指定してください: {args.resolution}" if args.face_crop_aug_range is not None: args.face_crop_aug_range = tuple([float(r) for r in args.face_crop_aug_range.split(",")]) assert ( len(args.face_crop_aug_range) == 2 and args.face_crop_aug_range[0] <= args.face_crop_aug_range[1] ), f"face_crop_aug_range must be two floats / face_crop_aug_rangeは'下限,上限'で指定してください: {args.face_crop_aug_range}" else: args.face_crop_aug_range = None if support_metadata: if args.in_json is not None and (args.color_aug or args.random_crop): logger.warning( f"latents in npz is ignored when color_aug or random_crop is True / color_augまたはrandom_cropを有効にした場合、npzファイルのlatentsは無視されます" ) def load_tokenizer(args: argparse.Namespace): logger.info("prepare tokenizer") original_path = V2_STABLE_DIFFUSION_PATH if args.v2 else TOKENIZER_PATH tokenizer: CLIPTokenizer = None if args.tokenizer_cache_dir: local_tokenizer_path = os.path.join(args.tokenizer_cache_dir, original_path.replace("/", "_")) if os.path.exists(local_tokenizer_path): logger.info(f"load tokenizer from cache: {local_tokenizer_path}") tokenizer = CLIPTokenizer.from_pretrained(local_tokenizer_path) # same for v1 and v2 if tokenizer is None: if args.v2: tokenizer = CLIPTokenizer.from_pretrained(original_path, subfolder="tokenizer") else: tokenizer = CLIPTokenizer.from_pretrained(original_path) if hasattr(args, "max_token_length") and args.max_token_length is not None: logger.info(f"update token length: {args.max_token_length}") if args.tokenizer_cache_dir and not os.path.exists(local_tokenizer_path): logger.info(f"save Tokenizer to cache: {local_tokenizer_path}") tokenizer.save_pretrained(local_tokenizer_path) return tokenizer def prepare_accelerator(args: argparse.Namespace): """ this function also prepares deepspeed plugin """ if args.logging_dir is None: logging_dir = None else: log_prefix = "" if args.log_prefix is None else args.log_prefix logging_dir = args.logging_dir + "/" + log_prefix + time.strftime("%Y%m%d%H%M%S", time.localtime()) if args.log_with is None: if logging_dir is not None: log_with = "tensorboard" else: log_with = None else: log_with = args.log_with if log_with in ["tensorboard", "all"]: if logging_dir is None: raise ValueError( "logging_dir is required when log_with is tensorboard / Tensorboardを使う場合、logging_dirを指定してください" ) if log_with in ["wandb", "all"]: try: import wandb except ImportError: raise ImportError("No wandb / wandb がインストールされていないようです") if logging_dir is not None: os.makedirs(logging_dir, exist_ok=True) os.environ["WANDB_DIR"] = logging_dir if args.wandb_api_key is not None: wandb.login(key=args.wandb_api_key) # torch.compile のオプション。 NO の場合は torch.compile は使わない dynamo_backend = "NO" if args.torch_compile: dynamo_backend = args.dynamo_backend kwargs_handlers = ( InitProcessGroupKwargs(timeout=datetime.timedelta(minutes=args.ddp_timeout)) if args.ddp_timeout else None, ( DistributedDataParallelKwargs( gradient_as_bucket_view=args.ddp_gradient_as_bucket_view, static_graph=args.ddp_static_graph ) if args.ddp_gradient_as_bucket_view or args.ddp_static_graph else None ), ) kwargs_handlers = list(filter(lambda x: x is not None, kwargs_handlers)) deepspeed_plugin = deepspeed_utils.prepare_deepspeed_plugin(args) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with=log_with, project_dir=logging_dir, kwargs_handlers=kwargs_handlers, dynamo_backend=dynamo_backend, deepspeed_plugin=deepspeed_plugin, ) print("accelerator device:", accelerator.device) return accelerator def prepare_dtype(args: argparse.Namespace): weight_dtype = torch.float32 if args.mixed_precision == "fp16": weight_dtype = torch.float16 elif args.mixed_precision == "bf16": weight_dtype = torch.bfloat16 save_dtype = None if args.save_precision == "fp16": save_dtype = torch.float16 elif args.save_precision == "bf16": save_dtype = torch.bfloat16 elif args.save_precision == "float": save_dtype = torch.float32 return weight_dtype, save_dtype def _load_target_model(args: argparse.Namespace, weight_dtype, device="cpu", unet_use_linear_projection_in_v2=False): name_or_path = args.pretrained_model_name_or_path name_or_path = os.path.realpath(name_or_path) if os.path.islink(name_or_path) else name_or_path load_stable_diffusion_format = os.path.isfile(name_or_path) # determine SD or Diffusers if load_stable_diffusion_format: logger.info(f"load StableDiffusion checkpoint: {name_or_path}") text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint( args.v2, name_or_path, device, unet_use_linear_projection_in_v2=unet_use_linear_projection_in_v2 ) else: # Diffusers model is loaded to CPU logger.info(f"load Diffusers pretrained models: {name_or_path}") try: pipe = StableDiffusionPipeline.from_pretrained(name_or_path, tokenizer=None, safety_checker=None) except EnvironmentError as ex: logger.error( f"model is not found as a file or in Hugging Face, perhaps file name is wrong? / 指定したモデル名のファイル、またはHugging Faceのモデルが見つかりません。ファイル名が誤っているかもしれません: {name_or_path}" ) raise ex text_encoder = pipe.text_encoder vae = pipe.vae unet = pipe.unet del pipe # Diffusers U-Net to original U-Net # TODO *.ckpt/*.safetensorsのv2と同じ形式にここで変換すると良さそう # logger.info(f"unet config: {unet.config}") original_unet = UNet2DConditionModel( unet.config.sample_size, unet.config.attention_head_dim, unet.config.cross_attention_dim, unet.config.use_linear_projection, unet.config.upcast_attention, ) original_unet.load_state_dict(unet.state_dict()) unet = original_unet logger.info("U-Net converted to original U-Net") # VAEを読み込む if args.vae is not None: vae = model_util.load_vae(args.vae, weight_dtype) logger.info("additional VAE loaded") return text_encoder, vae, unet, load_stable_diffusion_format def load_target_model(args, weight_dtype, accelerator, unet_use_linear_projection_in_v2=False): for pi in range(accelerator.state.num_processes): if pi == accelerator.state.local_process_index: logger.info(f"loading model for process {accelerator.state.local_process_index}/{accelerator.state.num_processes}") text_encoder, vae, unet, load_stable_diffusion_format = _load_target_model( args, weight_dtype, accelerator.device if args.lowram else "cpu", unet_use_linear_projection_in_v2=unet_use_linear_projection_in_v2, ) # work on low-ram device if args.lowram: text_encoder.to(accelerator.device) unet.to(accelerator.device) vae.to(accelerator.device) clean_memory_on_device(accelerator.device) accelerator.wait_for_everyone() return text_encoder, vae, unet, load_stable_diffusion_format def patch_accelerator_for_fp16_training(accelerator): org_unscale_grads = accelerator.scaler._unscale_grads_ def _unscale_grads_replacer(optimizer, inv_scale, found_inf, allow_fp16): return org_unscale_grads(optimizer, inv_scale, found_inf, True) accelerator.scaler._unscale_grads_ = _unscale_grads_replacer def get_hidden_states(args: argparse.Namespace, input_ids, tokenizer, text_encoder, weight_dtype=None): # with no_token_padding, the length is not max length, return result immediately if input_ids.size()[-1] != tokenizer.model_max_length: return text_encoder(input_ids)[0] # input_ids: b,n,77 b_size = input_ids.size()[0] input_ids = input_ids.reshape((-1, tokenizer.model_max_length)) # batch_size*3, 77 if args.clip_skip is None: encoder_hidden_states = text_encoder(input_ids)[0] else: enc_out = text_encoder(input_ids, output_hidden_states=True, return_dict=True) encoder_hidden_states = enc_out["hidden_states"][-args.clip_skip] encoder_hidden_states = text_encoder.text_model.final_layer_norm(encoder_hidden_states) # bs*3, 77, 768 or 1024 encoder_hidden_states = encoder_hidden_states.reshape((b_size, -1, encoder_hidden_states.shape[-1])) if args.max_token_length is not None: if args.v2: # v2: ... ... の三連を ... ... へ戻す　正直この実装でいいのかわからん states_list = [encoder_hidden_states[:, 0].unsqueeze(1)] # for i in range(1, args.max_token_length, tokenizer.model_max_length): chunk = encoder_hidden_states[:, i : i + tokenizer.model_max_length - 2] # の後から 最後の前まで if i > 0: for j in range(len(chunk)): if input_ids[j, 1] == tokenizer.eos_token: # 空、つまり ...のパターン chunk[j, 0] = chunk[j, 1] # 次の の値をコピーする states_list.append(chunk) # の後から の前まで states_list.append(encoder_hidden_states[:, -1].unsqueeze(1)) # か のどちらか encoder_hidden_states = torch.cat(states_list, dim=1) else: # v1: ... の三連を ... へ戻す states_list = [encoder_hidden_states[:, 0].unsqueeze(1)] # for i in range(1, args.max_token_length, tokenizer.model_max_length): states_list.append( encoder_hidden_states[:, i : i + tokenizer.model_max_length - 2] ) # の後から の前まで states_list.append(encoder_hidden_states[:, -1].unsqueeze(1)) # encoder_hidden_states = torch.cat(states_list, dim=1) if weight_dtype is not None: # this is required for additional network training encoder_hidden_states = encoder_hidden_states.to(weight_dtype) return encoder_hidden_states def pool_workaround( text_encoder: CLIPTextModelWithProjection, last_hidden_state: torch.Tensor, input_ids: torch.Tensor, eos_token_id: int ): r""" workaround for CLIP's pooling bug: it returns the hidden states for the max token id as the pooled output instead of the hidden states for the EOS token If we use Textual Inversion, we need to use the hidden states for the EOS token as the pooled output Original code from CLIP's pooling function: \# text_embeds.shape = [batch_size, sequence_length, transformer.width] \# take features from the eot embedding (eot_token is the highest number in each sequence) \# casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14 pooled_output = last_hidden_state[ torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device), input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1), ] """ # input_ids: b*n,77 # find index for EOS token # Following code is not working if one of the input_ids has multiple EOS tokens (very odd case) # eos_token_index = torch.where(input_ids == eos_token_id)[1] # eos_token_index = eos_token_index.to(device=last_hidden_state.device) # Create a mask where the EOS tokens are eos_token_mask = (input_ids == eos_token_id).int() # Use argmax to find the last index of the EOS token for each element in the batch eos_token_index = torch.argmax(eos_token_mask, dim=1) # this will be 0 if there is no EOS token, it's fine eos_token_index = eos_token_index.to(device=last_hidden_state.device) # get hidden states for EOS token pooled_output = last_hidden_state[torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device), eos_token_index] # apply projection: projection may be of different dtype than last_hidden_state pooled_output = text_encoder.text_projection(pooled_output.to(text_encoder.text_projection.weight.dtype)) pooled_output = pooled_output.to(last_hidden_state.dtype) return pooled_output def get_hidden_states_sdxl( max_token_length: int, input_ids1: torch.Tensor, input_ids2: torch.Tensor, tokenizer1: CLIPTokenizer, tokenizer2: CLIPTokenizer, text_encoder1: CLIPTextModel, text_encoder2: CLIPTextModelWithProjection, weight_dtype: Optional[str] = None, accelerator: Optional[Accelerator] = None, ): # input_ids: b,n,77 -> b*n, 77 b_size = input_ids1.size()[0] input_ids1 = input_ids1.reshape((-1, tokenizer1.model_max_length)) # batch_size*n, 77 input_ids2 = input_ids2.reshape((-1, tokenizer2.model_max_length)) # batch_size*n, 77 # text_encoder1 enc_out = text_encoder1(input_ids1, output_hidden_states=True, return_dict=True) hidden_states1 = enc_out["hidden_states"][11] # text_encoder2 enc_out = text_encoder2(input_ids2, output_hidden_states=True, return_dict=True) hidden_states2 = enc_out["hidden_states"][-2] # penuultimate layer # pool2 = enc_out["text_embeds"] unwrapped_text_encoder2 = text_encoder2 if accelerator is None else accelerator.unwrap_model(text_encoder2) pool2 = pool_workaround(unwrapped_text_encoder2, enc_out["last_hidden_state"], input_ids2, tokenizer2.eos_token_id) # b*n, 77, 768 or 1280 -> b, n*77, 768 or 1280 n_size = 1 if max_token_length is None else max_token_length // 75 hidden_states1 = hidden_states1.reshape((b_size, -1, hidden_states1.shape[-1])) hidden_states2 = hidden_states2.reshape((b_size, -1, hidden_states2.shape[-1])) if max_token_length is not None: # bs*3, 77, 768 or 1024 # encoder1: ... の三連を ... へ戻す states_list = [hidden_states1[:, 0].unsqueeze(1)] # for i in range(1, max_token_length, tokenizer1.model_max_length): states_list.append(hidden_states1[:, i : i + tokenizer1.model_max_length - 2]) # の後から の前まで states_list.append(hidden_states1[:, -1].unsqueeze(1)) # hidden_states1 = torch.cat(states_list, dim=1) # v2: ... ... の三連を ... ... へ戻す　正直この実装でいいのかわからん states_list = [hidden_states2[:, 0].unsqueeze(1)] # for i in range(1, max_token_length, tokenizer2.model_max_length): chunk = hidden_states2[:, i : i + tokenizer2.model_max_length - 2] # の後から 最後の前まで # this causes an error: # RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation # if i > 1: # for j in range(len(chunk)): # batch_size # if input_ids2[n_index + j * n_size, 1] == tokenizer2.eos_token_id: # 空、つまり ...のパターン # chunk[j, 0] = chunk[j, 1] # 次の の値をコピーする states_list.append(chunk) # の後から の前まで states_list.append(hidden_states2[:, -1].unsqueeze(1)) # か のどちらか hidden_states2 = torch.cat(states_list, dim=1) # pool はnの最初のものを使う pool2 = pool2[::n_size] if weight_dtype is not None: # this is required for additional network training hidden_states1 = hidden_states1.to(weight_dtype) hidden_states2 = hidden_states2.to(weight_dtype) return hidden_states1, hidden_states2, pool2 def default_if_none(value, default): return default if value is None else value def get_epoch_ckpt_name(args: argparse.Namespace, ext: str, epoch_no: int): model_name = default_if_none(args.output_name, DEFAULT_EPOCH_NAME) return EPOCH_FILE_NAME.format(model_name, epoch_no) + ext def get_step_ckpt_name(args: argparse.Namespace, ext: str, step_no: int): model_name = default_if_none(args.output_name, DEFAULT_STEP_NAME) return STEP_FILE_NAME.format(model_name, step_no) + ext def get_last_ckpt_name(args: argparse.Namespace, ext: str): model_name = default_if_none(args.output_name, DEFAULT_LAST_OUTPUT_NAME) return model_name + ext def get_remove_epoch_no(args: argparse.Namespace, epoch_no: int): if args.save_last_n_epochs is None: return None remove_epoch_no = epoch_no - args.save_every_n_epochs * args.save_last_n_epochs if remove_epoch_no < 0: return None return remove_epoch_no def get_remove_step_no(args: argparse.Namespace, step_no: int): if args.save_last_n_steps is None: return None # last_n_steps前のstep_noから、save_every_n_stepsの倍数のstep_noを計算して削除する # save_every_n_steps=10, save_last_n_steps=30の場合、50step目には30step分残し、10step目を削除する remove_step_no = step_no - args.save_last_n_steps - 1 remove_step_no = remove_step_no - (remove_step_no % args.save_every_n_steps) if remove_step_no < 0: return None return remove_step_no # epochとstepの保存、メタデータにepoch/stepが含まれ引数が同じになるため、統合している # on_epoch_end: Trueならepoch終了時、Falseならstep経過時 def save_sd_model_on_epoch_end_or_stepwise( args: argparse.Namespace, on_epoch_end: bool, accelerator, src_path: str, save_stable_diffusion_format: bool, use_safetensors: bool, save_dtype: torch.dtype, epoch: int, num_train_epochs: int, global_step: int, text_encoder, unet, vae, ): def sd_saver(ckpt_file, epoch_no, global_step): sai_metadata = get_sai_model_spec(None, args, False, False, False, is_stable_diffusion_ckpt=True) model_util.save_stable_diffusion_checkpoint( args.v2, ckpt_file, text_encoder, unet, src_path, epoch_no, global_step, sai_metadata, save_dtype, vae ) def diffusers_saver(out_dir): model_util.save_diffusers_checkpoint( args.v2, out_dir, text_encoder, unet, src_path, vae=vae, use_safetensors=use_safetensors ) save_sd_model_on_epoch_end_or_stepwise_common( args, on_epoch_end, accelerator, save_stable_diffusion_format, use_safetensors, epoch, num_train_epochs, global_step, sd_saver, diffusers_saver, ) def save_sd_model_on_epoch_end_or_stepwise_common( args: argparse.Namespace, on_epoch_end: bool, accelerator, save_stable_diffusion_format: bool, use_safetensors: bool, epoch: int, num_train_epochs: int, global_step: int, sd_saver, diffusers_saver, ): if on_epoch_end: epoch_no = epoch + 1 saving = epoch_no % args.save_every_n_epochs == 0 and epoch_no < num_train_epochs if not saving: return model_name = default_if_none(args.output_name, DEFAULT_EPOCH_NAME) remove_no = get_remove_epoch_no(args, epoch_no) else: # 保存するか否かは呼び出し側で判断済み model_name = default_if_none(args.output_name, DEFAULT_STEP_NAME) epoch_no = epoch # 例: 最初のepochの途中で保存したら0になる、SDモデルに保存される remove_no = get_remove_step_no(args, global_step) os.makedirs(args.output_dir, exist_ok=True) if save_stable_diffusion_format: ext = ".safetensors" if use_safetensors else ".ckpt" if on_epoch_end: ckpt_name = get_epoch_ckpt_name(args, ext, epoch_no) else: ckpt_name = get_step_ckpt_name(args, ext, global_step) ckpt_file = os.path.join(args.output_dir, ckpt_name) logger.info("") logger.info(f"saving checkpoint: {ckpt_file}") sd_saver(ckpt_file, epoch_no, global_step) if args.huggingface_repo_id is not None: huggingface_util.upload(args, ckpt_file, "/" + ckpt_name) # remove older checkpoints if remove_no is not None: if on_epoch_end: remove_ckpt_name = get_epoch_ckpt_name(args, ext, remove_no) else: remove_ckpt_name = get_step_ckpt_name(args, ext, remove_no) remove_ckpt_file = os.path.join(args.output_dir, remove_ckpt_name) if os.path.exists(remove_ckpt_file): logger.info(f"removing old checkpoint: {remove_ckpt_file}") os.remove(remove_ckpt_file) else: if on_epoch_end: out_dir = os.path.join(args.output_dir, EPOCH_DIFFUSERS_DIR_NAME.format(model_name, epoch_no)) else: out_dir = os.path.join(args.output_dir, STEP_DIFFUSERS_DIR_NAME.format(model_name, global_step)) logger.info("") logger.info(f"saving model: {out_dir}") diffusers_saver(out_dir) if args.huggingface_repo_id is not None: huggingface_util.upload(args, out_dir, "/" + model_name) # remove older checkpoints if remove_no is not None: if on_epoch_end: remove_out_dir = os.path.join(args.output_dir, EPOCH_DIFFUSERS_DIR_NAME.format(model_name, remove_no)) else: remove_out_dir = os.path.join(args.output_dir, STEP_DIFFUSERS_DIR_NAME.format(model_name, remove_no)) if os.path.exists(remove_out_dir): logger.info(f"removing old model: {remove_out_dir}") shutil.rmtree(remove_out_dir) if args.save_state: if on_epoch_end: save_and_remove_state_on_epoch_end(args, accelerator, epoch_no) else: save_and_remove_state_stepwise(args, accelerator, global_step) 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) logger.info("") logger.info(f"saving state at epoch {epoch_no}") os.makedirs(args.output_dir, exist_ok=True) state_dir = os.path.join(args.output_dir, EPOCH_STATE_NAME.format(model_name, epoch_no)) accelerator.save_state(state_dir) if args.save_state_to_huggingface: logger.info("uploading state to huggingface.") huggingface_util.upload(args, state_dir, "/" + EPOCH_STATE_NAME.format(model_name, epoch_no)) last_n_epochs = args.save_last_n_epochs_state if args.save_last_n_epochs_state else args.save_last_n_epochs if last_n_epochs is not None: remove_epoch_no = epoch_no - args.save_every_n_epochs * last_n_epochs state_dir_old = os.path.join(args.output_dir, EPOCH_STATE_NAME.format(model_name, remove_epoch_no)) if os.path.exists(state_dir_old): logger.info(f"removing old state: {state_dir_old}") shutil.rmtree(state_dir_old) def save_and_remove_state_stepwise(args: argparse.Namespace, accelerator, step_no): model_name = default_if_none(args.output_name, DEFAULT_STEP_NAME) logger.info("") logger.info(f"saving state at step {step_no}") os.makedirs(args.output_dir, exist_ok=True) state_dir = os.path.join(args.output_dir, STEP_STATE_NAME.format(model_name, step_no)) accelerator.save_state(state_dir) if args.save_state_to_huggingface: logger.info("uploading state to huggingface.") huggingface_util.upload(args, state_dir, "/" + STEP_STATE_NAME.format(model_name, step_no)) last_n_steps = args.save_last_n_steps_state if args.save_last_n_steps_state else args.save_last_n_steps if last_n_steps is not None: # last_n_steps前のstep_noから、save_every_n_stepsの倍数のstep_noを計算して削除する remove_step_no = step_no - last_n_steps - 1 remove_step_no = remove_step_no - (remove_step_no % args.save_every_n_steps) if remove_step_no > 0: state_dir_old = os.path.join(args.output_dir, STEP_STATE_NAME.format(model_name, remove_step_no)) if os.path.exists(state_dir_old): logger.info(f"removing old state: {state_dir_old}") shutil.rmtree(state_dir_old) def save_state_on_train_end(args: argparse.Namespace, accelerator): model_name = default_if_none(args.output_name, DEFAULT_LAST_OUTPUT_NAME) logger.info("") logger.info("saving last state.") os.makedirs(args.output_dir, exist_ok=True) state_dir = os.path.join(args.output_dir, LAST_STATE_NAME.format(model_name)) accelerator.save_state(state_dir) if args.save_state_to_huggingface: logger.info("uploading last state to huggingface.") huggingface_util.upload(args, state_dir, "/" + LAST_STATE_NAME.format(model_name)) def save_sd_model_on_train_end( args: argparse.Namespace, src_path: str, save_stable_diffusion_format: bool, use_safetensors: bool, save_dtype: torch.dtype, epoch: int, global_step: int, text_encoder, unet, vae, ): def sd_saver(ckpt_file, epoch_no, global_step): sai_metadata = get_sai_model_spec(None, args, False, False, False, is_stable_diffusion_ckpt=True) model_util.save_stable_diffusion_checkpoint( args.v2, ckpt_file, text_encoder, unet, src_path, epoch_no, global_step, sai_metadata, save_dtype, vae ) def diffusers_saver(out_dir): model_util.save_diffusers_checkpoint( args.v2, out_dir, text_encoder, unet, src_path, vae=vae, use_safetensors=use_safetensors ) save_sd_model_on_train_end_common( args, save_stable_diffusion_format, use_safetensors, epoch, global_step, sd_saver, diffusers_saver ) def save_sd_model_on_train_end_common( args: argparse.Namespace, save_stable_diffusion_format: bool, use_safetensors: bool, epoch: int, global_step: int, sd_saver, diffusers_saver, ): model_name = default_if_none(args.output_name, DEFAULT_LAST_OUTPUT_NAME) if save_stable_diffusion_format: os.makedirs(args.output_dir, exist_ok=True) ckpt_name = model_name + (".safetensors" if use_safetensors else ".ckpt") ckpt_file = os.path.join(args.output_dir, ckpt_name) logger.info(f"save trained model as StableDiffusion checkpoint to {ckpt_file}") sd_saver(ckpt_file, epoch, global_step) if args.huggingface_repo_id is not None: huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=True) else: out_dir = os.path.join(args.output_dir, model_name) os.makedirs(out_dir, exist_ok=True) logger.info(f"save trained model as Diffusers to {out_dir}") diffusers_saver(out_dir) if args.huggingface_repo_id is not None: huggingface_util.upload(args, out_dir, "/" + model_name, force_sync_upload=True) def get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, b_size, device): # TODO: if a huber loss is selected, it will use constant timesteps for each batch # as. In the future there may be a smarter way if args.loss_type == "huber" or args.loss_type == "smooth_l1": timesteps = torch.randint(min_timestep, max_timestep, (1,), device="cpu") timestep = timesteps.item() if args.huber_schedule == "exponential": alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps huber_c = math.exp(-alpha * timestep) elif args.huber_schedule == "snr": alphas_cumprod = noise_scheduler.alphas_cumprod[timestep] sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5 huber_c = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c elif args.huber_schedule == "constant": huber_c = args.huber_c else: raise NotImplementedError(f"Unknown Huber loss schedule {args.huber_schedule}!") timesteps = timesteps.repeat(b_size).to(device) elif args.loss_type == "l2": timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device=device) huber_c = 1 # may be anything, as it's not used else: raise NotImplementedError(f"Unknown loss type {args.loss_type}") timesteps = timesteps.long() return timesteps, huber_c def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents): # Sample noise that we'll add to the latents noise = torch.randn_like(latents, device=latents.device) if args.noise_offset: if args.noise_offset_random_strength: noise_offset = torch.rand(1, device=latents.device) * args.noise_offset else: noise_offset = args.noise_offset noise = custom_train_functions.apply_noise_offset(latents, noise, noise_offset, args.adaptive_noise_scale) if args.multires_noise_iterations: noise = custom_train_functions.pyramid_noise_like( noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount ) # Sample a random timestep for each image b_size = latents.shape[0] min_timestep = 0 if args.min_timestep is None else args.min_timestep max_timestep = noise_scheduler.config.num_train_timesteps if args.max_timestep is None else args.max_timestep timesteps, huber_c = get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, b_size, latents.device) # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) if args.ip_noise_gamma: if args.ip_noise_gamma_random_strength: strength = torch.rand(1, device=latents.device) * args.ip_noise_gamma else: strength = args.ip_noise_gamma noisy_latents = noise_scheduler.add_noise(latents, noise + strength * torch.randn_like(latents), timesteps) else: noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) return noise, noisy_latents, timesteps, huber_c # NOTE: if you're using the scheduled version, huber_c has to depend on the timesteps already def conditional_loss( model_pred: torch.Tensor, target: torch.Tensor, reduction: str = "mean", loss_type: str = "l2", huber_c: float = 0.1 ): if loss_type == "l2": loss = torch.nn.functional.mse_loss(model_pred, target, reduction=reduction) elif loss_type == "huber": loss = 2 * huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c) if reduction == "mean": loss = torch.mean(loss) elif reduction == "sum": loss = torch.sum(loss) elif loss_type == "smooth_l1": loss = 2 * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c) if reduction == "mean": loss = torch.mean(loss) elif reduction == "sum": loss = torch.sum(loss) else: raise NotImplementedError(f"Unsupported Loss Type {loss_type}") return loss def append_lr_to_logs(logs, lr_scheduler, optimizer_type, including_unet=True): names = [] if including_unet: names.append("unet") names.append("text_encoder1") names.append("text_encoder2") append_lr_to_logs_with_names(logs, lr_scheduler, optimizer_type, names) def append_lr_to_logs_with_names(logs, lr_scheduler, optimizer_type, names): lrs = lr_scheduler.get_last_lr() for lr_index in range(len(lrs)): name = names[lr_index] logs["lr/" + name] = float(lrs[lr_index]) if optimizer_type.lower().startswith("DAdapt".lower()) or optimizer_type.lower() == "Prodigy".lower(): logs["lr/d*lr/" + name] = ( lr_scheduler.optimizers[-1].param_groups[lr_index]["d"] * lr_scheduler.optimizers[-1].param_groups[lr_index]["lr"] ) # scheduler: SCHEDULER_LINEAR_START = 0.00085 SCHEDULER_LINEAR_END = 0.0120 SCHEDULER_TIMESTEPS = 1000 SCHEDLER_SCHEDULE = "scaled_linear" def get_my_scheduler( *, sample_sampler: str, v_parameterization: bool, ): sched_init_args = {} if sample_sampler == "ddim": scheduler_cls = DDIMScheduler elif sample_sampler == "ddpm": # ddpmはおかしくなるのでoptionから外してある scheduler_cls = DDPMScheduler elif sample_sampler == "pndm": scheduler_cls = PNDMScheduler elif sample_sampler == "lms" or sample_sampler == "k_lms": scheduler_cls = LMSDiscreteScheduler elif sample_sampler == "euler" or sample_sampler == "k_euler": scheduler_cls = EulerDiscreteScheduler elif sample_sampler == "euler_a" or sample_sampler == "k_euler_a": scheduler_cls = EulerAncestralDiscreteScheduler elif sample_sampler == "dpmsolver" or sample_sampler == "dpmsolver++": scheduler_cls = DPMSolverMultistepScheduler sched_init_args["algorithm_type"] = sample_sampler elif sample_sampler == "dpmsingle": scheduler_cls = DPMSolverSinglestepScheduler elif sample_sampler == "heun": scheduler_cls = HeunDiscreteScheduler elif sample_sampler == "dpm_2" or sample_sampler == "k_dpm_2": scheduler_cls = KDPM2DiscreteScheduler elif sample_sampler == "dpm_2_a" or sample_sampler == "k_dpm_2_a": scheduler_cls = KDPM2AncestralDiscreteScheduler else: scheduler_cls = DDIMScheduler if v_parameterization: sched_init_args["prediction_type"] = "v_prediction" scheduler = scheduler_cls( num_train_timesteps=SCHEDULER_TIMESTEPS, beta_start=SCHEDULER_LINEAR_START, beta_end=SCHEDULER_LINEAR_END, beta_schedule=SCHEDLER_SCHEDULE, **sched_init_args, ) # clip_sample=Trueにする if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is False: # logger.info("set clip_sample to True") scheduler.config.clip_sample = True return scheduler def sample_images(*args, **kwargs): return sample_images_common(StableDiffusionLongPromptWeightingPipeline, *args, **kwargs) def line_to_prompt_dict(line: str) -> dict: # subset of gen_img_diffusers prompt_args = line.split(" --") prompt_dict = {} prompt_dict["prompt"] = prompt_args[0] for parg in prompt_args: try: m = re.match(r"w (\d+)", parg, re.IGNORECASE) if m: prompt_dict["width"] = int(m.group(1)) continue m = re.match(r"h (\d+)", parg, re.IGNORECASE) if m: prompt_dict["height"] = int(m.group(1)) continue m = re.match(r"d (\d+)", parg, re.IGNORECASE) if m: prompt_dict["seed"] = int(m.group(1)) continue m = re.match(r"s (\d+)", parg, re.IGNORECASE) if m: # steps prompt_dict["sample_steps"] = max(1, min(1000, int(m.group(1)))) continue m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE) if m: # scale prompt_dict["scale"] = float(m.group(1)) continue m = re.match(r"n (.+)", parg, re.IGNORECASE) if m: # negative prompt prompt_dict["negative_prompt"] = m.group(1) continue m = re.match(r"ss (.+)", parg, re.IGNORECASE) if m: prompt_dict["sample_sampler"] = m.group(1) continue m = re.match(r"cn (.+)", parg, re.IGNORECASE) if m: prompt_dict["controlnet_image"] = m.group(1) continue except ValueError as ex: logger.error(f"Exception in parsing / 解析エラー: {parg}") logger.error(ex) return prompt_dict def sample_images_common( pipe_class, accelerator: Accelerator, args: argparse.Namespace, epoch, steps, device, vae, tokenizer, text_encoder, unet, prompt_replacement=None, controlnet=None, ): """ StableDiffusionLongPromptWeightingPipelineの改造版を使うようにしたので、clip skipおよびプロンプトの重みづけに対応した """ if steps == 0: if not args.sample_at_first: return else: if args.sample_every_n_steps is None and args.sample_every_n_epochs is None: return if args.sample_every_n_epochs is not None: # sample_every_n_steps は無視する if epoch is None or epoch % args.sample_every_n_epochs != 0: return else: if steps % args.sample_every_n_steps != 0 or epoch is not None: # steps is not divisible or end of epoch return logger.info("") logger.info(f"generating sample images at step / サンプル画像生成 ステップ: {steps}") if not os.path.isfile(args.sample_prompts): logger.error(f"No prompt file / プロンプトファイルがありません: {args.sample_prompts}") return distributed_state = PartialState() # for multi gpu distributed inference. this is a singleton, so it's safe to use it here org_vae_device = vae.device # CPUにいるはず vae.to(distributed_state.device) # distributed_state.device is same as accelerator.device # unwrap unet and text_encoder(s) unet = accelerator.unwrap_model(unet) if isinstance(text_encoder, (list, tuple)): text_encoder = [accelerator.unwrap_model(te) for te in text_encoder] else: text_encoder = accelerator.unwrap_model(text_encoder) # read prompts if args.sample_prompts.endswith(".txt"): with open(args.sample_prompts, "r", encoding="utf-8") as f: lines = f.readlines() prompts = [line.strip() for line in lines if len(line.strip()) > 0 and line[0] != "#"] elif args.sample_prompts.endswith(".toml"): with open(args.sample_prompts, "r", encoding="utf-8") as f: data = toml.load(f) prompts = [dict(**data["prompt"], **subset) for subset in data["prompt"]["subset"]] elif args.sample_prompts.endswith(".json"): with open(args.sample_prompts, "r", encoding="utf-8") as f: prompts = json.load(f) # schedulers: dict = {} cannot find where this is used default_scheduler = get_my_scheduler( sample_sampler=args.sample_sampler, v_parameterization=args.v_parameterization, ) pipeline = pipe_class( text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer, scheduler=default_scheduler, safety_checker=None, feature_extractor=None, requires_safety_checker=False, clip_skip=args.clip_skip, ) pipeline.to(distributed_state.device) save_dir = args.output_dir + "/sample" os.makedirs(save_dir, exist_ok=True) # preprocess prompts for i in range(len(prompts)): prompt_dict = prompts[i] if isinstance(prompt_dict, str): prompt_dict = line_to_prompt_dict(prompt_dict) prompts[i] = prompt_dict assert isinstance(prompt_dict, dict) # Adds an enumerator to the dict based on prompt position. Used later to name image files. Also cleanup of extra data in original prompt dict. prompt_dict["enum"] = i prompt_dict.pop("subset", None) # save random state to restore later rng_state = torch.get_rng_state() cuda_rng_state = None try: cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None except Exception: pass if distributed_state.num_processes <= 1: # If only one device is available, just use the original prompt list. We don't need to care about the distribution of prompts. with torch.no_grad(): for prompt_dict in prompts: sample_image_inference( accelerator, args, pipeline, save_dir, prompt_dict, epoch, steps, prompt_replacement, controlnet=controlnet ) else: # Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processes available (number of devices available) # prompt_dicts are assigned to lists based on order of processes, to attempt to time the image creation time to match enum order. Probably only works when steps and sampler are identical. per_process_prompts = [] # list of lists for i in range(distributed_state.num_processes): per_process_prompts.append(prompts[i :: distributed_state.num_processes]) with torch.no_grad(): with distributed_state.split_between_processes(per_process_prompts) as prompt_dict_lists: for prompt_dict in prompt_dict_lists[0]: sample_image_inference( accelerator, args, pipeline, save_dir, prompt_dict, epoch, steps, prompt_replacement, controlnet=controlnet ) # clear pipeline and cache to reduce vram usage del pipeline # I'm not sure which of these is the correct way to clear the memory, but accelerator's device is used in the pipeline, so I'm using it here. # with torch.cuda.device(torch.cuda.current_device()): # torch.cuda.empty_cache() clean_memory_on_device(accelerator.device) torch.set_rng_state(rng_state) if cuda_rng_state is not None: torch.cuda.set_rng_state(cuda_rng_state) vae.to(org_vae_device) def sample_image_inference( accelerator: Accelerator, args: argparse.Namespace, pipeline, save_dir, prompt_dict, epoch, steps, prompt_replacement, controlnet=None, ): assert isinstance(prompt_dict, dict) negative_prompt = prompt_dict.get("negative_prompt") sample_steps = prompt_dict.get("sample_steps", 30) width = prompt_dict.get("width", 512) height = prompt_dict.get("height", 512) scale = prompt_dict.get("scale", 7.5) seed = prompt_dict.get("seed") controlnet_image = prompt_dict.get("controlnet_image") prompt: str = prompt_dict.get("prompt", "") sampler_name: str = prompt_dict.get("sample_sampler", args.sample_sampler) if prompt_replacement is not None: prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1]) if negative_prompt is not None: negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1]) if seed is not None: torch.manual_seed(seed) torch.cuda.manual_seed(seed) else: # True random sample image generation torch.seed() torch.cuda.seed() scheduler = get_my_scheduler( sample_sampler=sampler_name, v_parameterization=args.v_parameterization, ) pipeline.scheduler = scheduler if controlnet_image is not None: controlnet_image = Image.open(controlnet_image).convert("RGB") controlnet_image = controlnet_image.resize((width, height), Image.LANCZOS) height = max(64, height - height % 8) # round to divisible by 8 width = max(64, width - width % 8) # round to divisible by 8 logger.info(f"prompt: {prompt}") logger.info(f"negative_prompt: {negative_prompt}") logger.info(f"height: {height}") logger.info(f"width: {width}") logger.info(f"sample_steps: {sample_steps}") logger.info(f"scale: {scale}") logger.info(f"sample_sampler: {sampler_name}") if seed is not None: logger.info(f"seed: {seed}") with accelerator.autocast(): latents = pipeline( prompt=prompt, height=height, width=width, num_inference_steps=sample_steps, guidance_scale=scale, negative_prompt=negative_prompt, controlnet=controlnet, controlnet_image=controlnet_image, ) with torch.cuda.device(torch.cuda.current_device()): torch.cuda.empty_cache() image = pipeline.latents_to_image(latents)[0] # adding accelerator.wait_for_everyone() here should sync up and ensure that sample images are saved in the same order as the original prompt list # but adding 'enum' to the filename should be enough ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime()) num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}" seed_suffix = "" if seed is None else f"_{seed}" i: int = prompt_dict["enum"] img_filename = f"{'' if args.output_name is None else args.output_name + '_'}{num_suffix}_{i:02d}_{ts_str}{seed_suffix}.png" image.save(os.path.join(save_dir, img_filename)) # wandb有効時のみログを送信 try: wandb_tracker = accelerator.get_tracker("wandb") try: import wandb except ImportError: # 事前に一度確認するのでここはエラー出ないはず raise ImportError("No wandb / wandb がインストールされていないようです") wandb_tracker.log({f"sample_{i}": wandb.Image(image)}) except: # wandb 無効時 pass # endregion # region 前処理用 class ImageLoadingDataset(torch.utils.data.Dataset): def __init__(self, image_paths): self.images = image_paths def __len__(self): return len(self.images) def __getitem__(self, idx): img_path = self.images[idx] try: image = Image.open(img_path).convert("RGB") # convert to tensor temporarily so dataloader will accept it tensor_pil = transforms.functional.pil_to_tensor(image) except Exception as e: logger.error(f"Could not load image path / 画像を読み込めません: {img_path}, error: {e}") return None return (tensor_pil, img_path) # endregion # collate_fn用 epoch,stepはmultiprocessing.Value class collator_class: def __init__(self, epoch, step, dataset): self.current_epoch = epoch self.current_step = step self.dataset = dataset # not used if worker_info is not None, in case of multiprocessing def __call__(self, examples): worker_info = torch.utils.data.get_worker_info() # worker_info is None in the main process if worker_info is not None: dataset = worker_info.dataset else: dataset = self.dataset # set epoch and step dataset.set_current_epoch(self.current_epoch.value) dataset.set_current_step(self.current_step.value) return examples[0] class LossRecorder: def __init__(self): self.loss_list: List[float] = [] self.loss_total: float = 0.0 def add(self, *, epoch: int, step: int, loss: float) -> None: if epoch == 0: self.loss_list.append(loss) else: self.loss_total -= self.loss_list[step] self.loss_list[step] = loss self.loss_total += loss @property def moving_average(self) -> float: return self.loss_total / len(self.loss_list)