1453 lines
65 KiB
Python
1453 lines
65 KiB
Python
import gc
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import importlib
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import json
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import time
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from typing import NamedTuple
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from torch.autograd.function import Function
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import argparse
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import glob
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import math
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import os
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import random
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from tqdm import tqdm
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import torch
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from torchvision import transforms
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from accelerate import Accelerator
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from accelerate.utils import set_seed
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from transformers import CLIPTokenizer
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import diffusers
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from diffusers import DDPMScheduler, StableDiffusionPipeline
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import albumentations as albu
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import numpy as np
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from PIL import Image
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import cv2
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from einops import rearrange
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from torch import einsum
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import library.model_util as model_util
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# 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
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TOKENIZER_PATH = "openai/clip-vit-large-patch14"
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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
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# checkpointファイル名
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EPOCH_STATE_NAME = "epoch-{:06d}-state"
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LAST_STATE_NAME = "last-state"
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EPOCH_FILE_NAME = "epoch-{:06d}"
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LAST_FILE_NAME = "last"
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# region dataset
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class ImageInfo():
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def __init__(self, image_key: str, num_repeats: int, caption: str, is_reg: bool, absolute_path: str) -> None:
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self.image_key: str = image_key
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self.num_repeats: int = num_repeats
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self.caption: str = caption
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self.is_reg: bool = is_reg
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self.absolute_path: str = absolute_path
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self.image_size: tuple[int, int] = None
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self.bucket_reso: tuple[int, int] = None
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self.latents: torch.Tensor = None
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self.latents_flipped: torch.Tensor = None
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self.latents_npz: str = None
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self.latents_npz_flipped: str = None
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class BucketBatchIndex(NamedTuple):
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bucket_index: int
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batch_index: int
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class BaseDataset(torch.utils.data.Dataset):
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def __init__(self, tokenizer, max_token_length, shuffle_caption, shuffle_keep_tokens, resolution, flip_aug: bool, color_aug: bool, face_crop_aug_range, debug_dataset: bool) -> None:
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super().__init__()
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self.tokenizer: CLIPTokenizer = tokenizer
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self.max_token_length = max_token_length
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self.shuffle_caption = shuffle_caption
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self.shuffle_keep_tokens = shuffle_keep_tokens
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self.width, self.height = resolution
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self.face_crop_aug_range = face_crop_aug_range
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self.flip_aug = flip_aug
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self.color_aug = color_aug
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self.debug_dataset = debug_dataset
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self.tokenizer_max_length = self.tokenizer.model_max_length if max_token_length is None else max_token_length + 2
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# augmentation
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flip_p = 0.5 if flip_aug else 0.0
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if color_aug:
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# わりと弱めの色合いaugmentation：brightness/contrastあたりは画像のpixel valueの最大値・最小値を変えてしまうのでよくないのではという想定でgamma/hueあたりを触る
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self.aug = albu.Compose([
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albu.OneOf([
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albu.HueSaturationValue(8, 0, 0, p=.5),
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albu.RandomGamma((95, 105), p=.5),
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], p=.33),
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albu.HorizontalFlip(p=flip_p)
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], p=1.)
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elif flip_aug:
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self.aug = albu.Compose([
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albu.HorizontalFlip(p=flip_p)
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], p=1.)
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else:
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self.aug = None
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self.image_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ])
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self.image_data: dict[str, ImageInfo] = {}
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def process_caption(self, caption):
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if self.shuffle_caption:
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tokens = caption.strip().split(",")
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if self.shuffle_keep_tokens is None:
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random.shuffle(tokens)
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else:
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if len(tokens) > self.shuffle_keep_tokens:
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keep_tokens = tokens[:self.shuffle_keep_tokens]
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tokens = tokens[self.shuffle_keep_tokens:]
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random.shuffle(tokens)
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tokens = keep_tokens + tokens
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caption = ",".join(tokens).strip()
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return caption
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def get_input_ids(self, caption):
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input_ids = self.tokenizer(caption, padding="max_length", truncation=True,
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max_length=self.tokenizer_max_length, return_tensors="pt").input_ids
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if self.tokenizer_max_length > self.tokenizer.model_max_length:
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input_ids = input_ids.squeeze(0)
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iids_list = []
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if self.tokenizer.pad_token_id == self.tokenizer.eos_token_id:
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# v1
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# 77以上の時は "<BOS> .... <EOS> <EOS> <EOS>" でトータル227とかになっているので、"<BOS>...<EOS>"の三連に変換する
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# 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
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for i in range(1, self.tokenizer_max_length - self.tokenizer.model_max_length + 2, self.tokenizer.model_max_length - 2): # (1, 152, 75)
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ids_chunk = (input_ids[0].unsqueeze(0),
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input_ids[i:i + self.tokenizer.model_max_length - 2],
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input_ids[-1].unsqueeze(0))
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ids_chunk = torch.cat(ids_chunk)
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iids_list.append(ids_chunk)
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else:
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# v2
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# 77以上の時は "<BOS> .... <EOS> <PAD> <PAD>..." でトータル227とかになっているので、"<BOS>...<EOS> <PAD> <PAD> ..."の三連に変換する
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for i in range(1, self.tokenizer_max_length - self.tokenizer.model_max_length + 2, self.tokenizer.model_max_length - 2):
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ids_chunk = (input_ids[0].unsqueeze(0), # BOS
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input_ids[i:i + self.tokenizer.model_max_length - 2],
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input_ids[-1].unsqueeze(0)) # PAD or EOS
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ids_chunk = torch.cat(ids_chunk)
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# 末尾が <EOS> <PAD> または <PAD> <PAD> の場合は、何もしなくてよい
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# 末尾が x <PAD/EOS> の場合は末尾を <EOS> に変える（x <EOS> なら結果的に変化なし）
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if ids_chunk[-2] != self.tokenizer.eos_token_id and ids_chunk[-2] != self.tokenizer.pad_token_id:
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ids_chunk[-1] = self.tokenizer.eos_token_id
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# 先頭が <BOS> <PAD> ... の場合は <BOS> <EOS> <PAD> ... に変える
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if ids_chunk[1] == self.tokenizer.pad_token_id:
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ids_chunk[1] = self.tokenizer.eos_token_id
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iids_list.append(ids_chunk)
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input_ids = torch.stack(iids_list) # 3,77
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return input_ids
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def register_image(self, info: ImageInfo):
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self.image_data[info.image_key] = info
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def make_buckets(self, enable_bucket, min_size, max_size):
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'''
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bucketingを行わない場合も呼び出し必須（ひとつだけbucketを作る）
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min_size and max_size are ignored when enable_bucket is False
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'''
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self.enable_bucket = enable_bucket
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print("loading image sizes.")
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for info in tqdm(self.image_data.values()):
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if info.image_size is None:
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info.image_size = self.get_image_size(info.absolute_path)
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if enable_bucket:
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print("make buckets")
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else:
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print("prepare dataset")
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# bucketingを用意する
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if enable_bucket:
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bucket_resos, bucket_aspect_ratios = model_util.make_bucket_resolutions((self.width, self.height), min_size, max_size)
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else:
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# bucketはひとつだけ、すべての画像は同じ解像度
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bucket_resos = [(self.width, self.height)]
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bucket_aspect_ratios = [self.width / self.height]
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bucket_aspect_ratios = np.array(bucket_aspect_ratios)
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# bucketを作成する
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if enable_bucket:
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img_ar_errors = []
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for image_info in self.image_data.values():
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# bucketを決める
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image_width, image_height = image_info.image_size
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aspect_ratio = image_width / image_height
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ar_errors = bucket_aspect_ratios - aspect_ratio
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bucket_id = np.abs(ar_errors).argmin()
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image_info.bucket_reso = bucket_resos[bucket_id]
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ar_error = ar_errors[bucket_id]
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img_ar_errors.append(ar_error)
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else:
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reso = (self.width, self.height)
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for image_info in self.image_data.values():
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image_info.bucket_reso = reso
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# 画像をbucketに分割する
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self.buckets: list[str] = [[] for _ in range(len(bucket_resos))]
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reso_to_index = {}
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for i, reso in enumerate(bucket_resos):
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reso_to_index[reso] = i
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for image_info in self.image_data.values():
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bucket_index = reso_to_index[image_info.bucket_reso]
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for _ in range(image_info.num_repeats):
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self.buckets[bucket_index].append(image_info.image_key)
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if enable_bucket:
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print("number of images (including repeats for DreamBooth) / 各bucketの画像枚数（DreamBoothの場合は繰り返し回数を含む）")
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for i, (reso, img_keys) in enumerate(zip(bucket_resos, self.buckets)):
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print(f"bucket {i}: resolution {reso}, count: {len(img_keys)}")
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img_ar_errors = np.array(img_ar_errors)
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print(f"mean ar error (without repeats): {np.mean(np.abs(img_ar_errors))}")
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# 参照用indexを作る
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self.buckets_indices: list(BucketBatchIndex) = []
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for bucket_index, bucket in enumerate(self.buckets):
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batch_count = int(math.ceil(len(bucket) / self.batch_size))
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for batch_index in range(batch_count):
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self.buckets_indices.append(BucketBatchIndex(bucket_index, batch_index))
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self.shuffle_buckets()
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self._length = len(self.buckets_indices)
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def shuffle_buckets(self):
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random.shuffle(self.buckets_indices)
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for bucket in self.buckets:
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random.shuffle(bucket)
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def load_image(self, image_path):
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image = Image.open(image_path)
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if not image.mode == "RGB":
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image = image.convert("RGB")
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img = np.array(image, np.uint8)
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return img
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def resize_and_trim(self, image, reso):
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image_height, image_width = image.shape[0:2]
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ar_img = image_width / image_height
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ar_reso = reso[0] / reso[1]
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if ar_img > ar_reso: # 横が長い→縦を合わせる
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scale = reso[1] / image_height
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else:
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scale = reso[0] / image_width
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resized_size = (int(image_width * scale + .5), int(image_height * scale + .5))
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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
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if resized_size[0] > reso[0]:
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trim_size = resized_size[0] - reso[0]
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image = image[:, trim_size//2:trim_size//2 + reso[0]]
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elif resized_size[1] > reso[1]:
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trim_size = resized_size[1] - reso[1]
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image = image[trim_size//2:trim_size//2 + reso[1]]
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assert image.shape[0] == reso[1] and image.shape[1] == reso[0], \
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f"internal error, illegal trimmed size: {image.shape}, {reso}"
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return image
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def cache_latents(self, vae):
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print("caching latents.")
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for info in tqdm(self.image_data.values()):
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if info.latents_npz is not None:
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info.latents = self.load_latents_from_npz(info, False)
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info.latents = torch.FloatTensor(info.latents)
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info.latents_flipped = self.load_latents_from_npz(info, True)
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info.latents_flipped = torch.FloatTensor(info.latents_flipped)
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continue
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image = self.load_image(info.absolute_path)
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image = self.resize_and_trim(image, info.bucket_reso)
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img_tensor = self.image_transforms(image)
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img_tensor = img_tensor.unsqueeze(0).to(device=vae.device, dtype=vae.dtype)
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info.latents = vae.encode(img_tensor).latent_dist.sample().squeeze(0).to("cpu")
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if self.flip_aug:
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image = image[:, ::-1].copy() # cannot convert to Tensor without copy
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img_tensor = self.image_transforms(image)
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img_tensor = img_tensor.unsqueeze(0).to(device=vae.device, dtype=vae.dtype)
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info.latents_flipped = vae.encode(img_tensor).latent_dist.sample().squeeze(0).to("cpu")
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def get_image_size(self, image_path):
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image = Image.open(image_path)
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return image.size
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def load_image_with_face_info(self, image_path: str):
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img = self.load_image(image_path)
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face_cx = face_cy = face_w = face_h = 0
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if self.face_crop_aug_range is not None:
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tokens = os.path.splitext(os.path.basename(image_path))[0].split('_')
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if len(tokens) >= 5:
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face_cx = int(tokens[-4])
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face_cy = int(tokens[-3])
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face_w = int(tokens[-2])
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face_h = int(tokens[-1])
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return img, face_cx, face_cy, face_w, face_h
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# いい感じに切り出す
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def crop_target(self, image, face_cx, face_cy, face_w, face_h):
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height, width = image.shape[0:2]
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if height == self.height and width == self.width:
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return image
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# 画像サイズはsizeより大きいのでリサイズする
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face_size = max(face_w, face_h)
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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）
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min_scale = min(1.0, max(min_scale, self.size / (face_size * self.face_crop_aug_range[1]))) # 指定した顔最小サイズ
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max_scale = min(1.0, max(min_scale, self.size / (face_size * self.face_crop_aug_range[0]))) # 指定した顔最大サイズ
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if min_scale >= max_scale: # range指定がmin==max
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scale = min_scale
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else:
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scale = random.uniform(min_scale, max_scale)
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nh = int(height * scale + .5)
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nw = int(width * scale + .5)
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assert nh >= self.height and nw >= self.width, f"internal error. small scale {scale}, {width}*{height}"
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image = cv2.resize(image, (nw, nh), interpolation=cv2.INTER_AREA)
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face_cx = int(face_cx * scale + .5)
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face_cy = int(face_cy * scale + .5)
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height, width = nh, nw
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# 顔を中心として448*640とかへ切り出す
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for axis, (target_size, length, face_p) in enumerate(zip((self.height, self.width), (height, width), (face_cy, face_cx))):
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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
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if self.random_crop:
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# 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
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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
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p1 = p1 + (random.randint(0, range) + random.randint(0, range)) - range # -range ~ +range までのいい感じの乱数
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else:
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# 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）
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if self.face_crop_aug_range[0] != self.face_crop_aug_range[1]:
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if face_size > self.size // 10 and face_size >= 40:
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p1 = p1 + random.randint(-face_size // 20, +face_size // 20)
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p1 = max(0, min(p1, length - target_size))
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if axis == 0:
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image = image[p1:p1 + target_size, :]
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else:
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image = image[:, p1:p1 + target_size]
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return image
|
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|
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def load_latents_from_npz(self, image_info: ImageInfo, flipped):
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npz_file = image_info.latents_npz_flipped if flipped else image_info.latents_npz
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return np.load(npz_file)['arr_0']
|
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|
||
def __len__(self):
|
||
return self._length
|
||
|
||
def __getitem__(self, index):
|
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if index == 0:
|
||
self.shuffle_buckets()
|
||
|
||
bucket = self.buckets[self.buckets_indices[index].bucket_index]
|
||
image_index = self.buckets_indices[index].batch_index * self.batch_size
|
||
|
||
loss_weights = []
|
||
captions = []
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||
input_ids_list = []
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||
latents_list = []
|
||
images = []
|
||
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||
for image_key in bucket[image_index:image_index + self.batch_size]:
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image_info = self.image_data[image_key]
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loss_weights.append(self.prior_loss_weight if image_info.is_reg else 1.0)
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||
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||
# image/latentsを処理する
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||
if image_info.latents is not None:
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||
latents = image_info.latents if not self.flip_aug or random.random() < .5 else image_info.latents_flipped
|
||
image = None
|
||
elif image_info.latents_npz is not None:
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||
latents = self.load_latents_from_npz(image_info, self.flip_aug and random.random() >= .5)
|
||
latents = torch.FloatTensor(latents)
|
||
image = None
|
||
else:
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||
# 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(image_info.absolute_path)
|
||
im_h, im_w = img.shape[0:2]
|
||
|
||
if self.enable_bucket:
|
||
img = self.resize_and_trim(img, image_info.bucket_reso)
|
||
else:
|
||
if face_cx > 0: # 顔位置情報あり
|
||
img = self.crop_target(img, face_cx, face_cy, face_w, face_h)
|
||
elif im_h > self.height or im_w > self.width:
|
||
assert self.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}"
|
||
|
||
# augmentation
|
||
if self.aug is not None:
|
||
img = self.aug(image=img)['image']
|
||
|
||
latents = None
|
||
image = self.image_transforms(img) # -1.0~1.0のtorch.Tensorになる
|
||
|
||
images.append(image)
|
||
latents_list.append(latents)
|
||
|
||
caption = self.process_caption(image_info.caption)
|
||
captions.append(caption)
|
||
input_ids_list.append(self.get_input_ids(caption))
|
||
|
||
example = {}
|
||
example['loss_weights'] = torch.FloatTensor(loss_weights)
|
||
example['input_ids'] = torch.stack(input_ids_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
|
||
|
||
if self.debug_dataset:
|
||
example['image_keys'] = bucket[image_index:image_index + self.batch_size]
|
||
example['captions'] = captions
|
||
return example
|
||
|
||
|
||
class DreamBoothDataset(BaseDataset):
|
||
def __init__(self, batch_size, train_data_dir, reg_data_dir, tokenizer, max_token_length, caption_extension, shuffle_caption, shuffle_keep_tokens, resolution, prior_loss_weight, flip_aug, color_aug, face_crop_aug_range, random_crop, debug_dataset) -> None:
|
||
super().__init__(tokenizer, max_token_length, shuffle_caption, shuffle_keep_tokens,
|
||
resolution, flip_aug, color_aug, face_crop_aug_range, debug_dataset)
|
||
|
||
self.batch_size = batch_size
|
||
self.size = min(self.width, self.height) # 短いほう
|
||
self.prior_loss_weight = prior_loss_weight
|
||
self.random_crop = random_crop
|
||
self.latents_cache = None
|
||
self.enable_bucket = False
|
||
|
||
def read_caption(img_path):
|
||
# 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:
|
||
lines = f.readlines()
|
||
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}"
|
||
caption = lines[0].strip()
|
||
break
|
||
return caption
|
||
|
||
def load_dreambooth_dir(dir):
|
||
if not os.path.isdir(dir):
|
||
# print(f"ignore file: {dir}")
|
||
return 0, [], []
|
||
|
||
tokens = os.path.basename(dir).split('_')
|
||
try:
|
||
n_repeats = int(tokens[0])
|
||
except ValueError as e:
|
||
print(f"ignore directory without repeats / 繰り返し回数のないディレクトリを無視します: {dir}")
|
||
return 0, [], []
|
||
|
||
caption_by_folder = '_'.join(tokens[1:])
|
||
img_paths = glob.glob(os.path.join(dir, "*.png")) + glob.glob(os.path.join(dir, "*.jpg")) + \
|
||
glob.glob(os.path.join(dir, "*.webp"))
|
||
print(f"found directory {n_repeats}_{caption_by_folder} contains {len(img_paths)} image files")
|
||
|
||
# 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 = []
|
||
for img_path in img_paths:
|
||
cap_for_img = read_caption(img_path)
|
||
captions.append(caption_by_folder if cap_for_img is None else cap_for_img)
|
||
|
||
return n_repeats, img_paths, captions
|
||
|
||
print("prepare train images.")
|
||
train_dirs = os.listdir(train_data_dir)
|
||
num_train_images = 0
|
||
for dir in train_dirs:
|
||
n_repeats, img_paths, captions = load_dreambooth_dir(os.path.join(train_data_dir, dir))
|
||
num_train_images += n_repeats * len(img_paths)
|
||
for img_path, caption in zip(img_paths, captions):
|
||
info = ImageInfo(img_path, n_repeats, caption, False, img_path)
|
||
self.register_image(info)
|
||
print(f"{num_train_images} train images with repeating.")
|
||
self.num_train_images = num_train_images
|
||
|
||
# reg imageは数を数えて学習画像と同じ枚数にする
|
||
num_reg_images = 0
|
||
if reg_data_dir:
|
||
print("prepare reg images.")
|
||
reg_infos: list[ImageInfo] = []
|
||
|
||
reg_dirs = os.listdir(reg_data_dir)
|
||
for dir in reg_dirs:
|
||
n_repeats, img_paths, captions = load_dreambooth_dir(os.path.join(reg_data_dir, dir))
|
||
num_reg_images += n_repeats * len(img_paths)
|
||
for img_path, caption in zip(img_paths, captions):
|
||
info = ImageInfo(img_path, n_repeats, caption, True, img_path)
|
||
reg_infos.append(info)
|
||
|
||
print(f"{num_reg_images} reg images.")
|
||
if num_train_images < num_reg_images:
|
||
print("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:
|
||
print("no regularization images / 正則化画像が見つかりませんでした")
|
||
else:
|
||
n = 0
|
||
while n < num_train_images:
|
||
for info in reg_infos:
|
||
self.register_image(info)
|
||
n += info.num_repeats
|
||
if n >= num_train_images: # reg画像にnum_repeats>1のときはまずありえないので考慮しない
|
||
break
|
||
|
||
self.num_reg_images = num_reg_images
|
||
|
||
|
||
class FineTuningDataset(BaseDataset):
|
||
def __init__(self, metadata, batch_size, train_data_dir, tokenizer, max_token_length, shuffle_caption, shuffle_keep_tokens, resolution, flip_aug, color_aug, face_crop_aug_range, dataset_repeats, debug_dataset) -> None:
|
||
super().__init__(tokenizer, max_token_length, shuffle_caption, shuffle_keep_tokens,
|
||
resolution, flip_aug, color_aug, face_crop_aug_range, debug_dataset)
|
||
|
||
self.metadata = metadata
|
||
self.train_data_dir = train_data_dir
|
||
self.batch_size = batch_size
|
||
|
||
for image_key, img_md in metadata.items():
|
||
# path情報を作る
|
||
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
|
||
abs_path = (glob.glob(os.path.join(train_data_dir, f"{image_key}.png")) + glob.glob(os.path.join(train_data_dir, f"{image_key}.jpg")) +
|
||
glob.glob(os.path.join(train_data_dir, f"{image_key}.webp")))
|
||
assert len(abs_path) >= 1, f"no image / 画像がありません: {abs_path}"
|
||
abs_path = abs_path[0]
|
||
|
||
caption = img_md.get('caption')
|
||
tags = img_md.get('tags')
|
||
if caption is None:
|
||
caption = tags
|
||
elif tags is not None and len(tags) > 0:
|
||
caption = caption + ', ' + tags
|
||
assert caption is not None and len(caption) > 0, f"caption or tag is required / キャプションまたはタグは必須です:{abs_path}"
|
||
|
||
image_info = ImageInfo(image_key, dataset_repeats, caption, False, abs_path)
|
||
image_info.image_size = img_md.get('train_resolution')
|
||
|
||
if not self.color_aug:
|
||
# if npz exists, use them
|
||
image_info.latents_npz, image_info.latents_npz_flipped = self.image_key_to_npz_file(image_key)
|
||
|
||
self.register_image(image_info)
|
||
self.num_train_images = len(metadata) * dataset_repeats
|
||
self.num_reg_images = 0
|
||
|
||
# check existence of all npz files
|
||
if not self.color_aug:
|
||
npz_any = False
|
||
npz_all = True
|
||
for image_info in self.image_data.values():
|
||
has_npz = image_info.latents_npz is not None
|
||
npz_any = npz_any or has_npz
|
||
|
||
if self.flip_aug:
|
||
has_npz = has_npz and image_info.latents_npz_flipped is not None
|
||
npz_all = npz_all and has_npz
|
||
|
||
if npz_any and not npz_all:
|
||
break
|
||
|
||
if not npz_any:
|
||
print(f"npz file does not exist. make latents with VAE / npzファイルが見つからないためVAEを使ってlatentsを取得します")
|
||
elif not npz_all:
|
||
print(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")
|
||
for image_info in self.image_data.values():
|
||
image_info.latents_npz = image_info.latents_npz_flipped = None
|
||
|
||
# check min/max bucket size
|
||
sizes = 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])
|
||
|
||
if sizes is None:
|
||
self.min_bucket_reso = self.max_bucket_reso = None # set as not calculated
|
||
else:
|
||
self.min_bucket_reso = min(sizes)
|
||
self.max_bucket_reso = max(sizes)
|
||
|
||
def image_key_to_npz_file(self, 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
|
||
|
||
# image_key is relative path
|
||
npz_file_norm = os.path.join(self.train_data_dir, image_key + '.npz')
|
||
npz_file_flip = os.path.join(self.train_data_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
|
||
|
||
# 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
|
||
|
||
# flash attention forwards and backwards
|
||
|
||
# https://arxiv.org/abs/2205.14135
|
||
|
||
|
||
class FlashAttentionFunction(Function):
|
||
@ staticmethod
|
||
@ torch.no_grad()
|
||
def forward(ctx, q, k, v, mask, causal, q_bucket_size, k_bucket_size):
|
||
""" Algorithm 2 in the paper """
|
||
|
||
device = q.device
|
||
dtype = q.dtype
|
||
max_neg_value = -torch.finfo(q.dtype).max
|
||
qk_len_diff = max(k.shape[-2] - q.shape[-2], 0)
|
||
|
||
o = torch.zeros_like(q)
|
||
all_row_sums = torch.zeros((*q.shape[:-1], 1), dtype=dtype, device=device)
|
||
all_row_maxes = torch.full((*q.shape[:-1], 1), max_neg_value, dtype=dtype, device=device)
|
||
|
||
scale = (q.shape[-1] ** -0.5)
|
||
|
||
if not exists(mask):
|
||
mask = (None,) * math.ceil(q.shape[-2] / q_bucket_size)
|
||
else:
|
||
mask = rearrange(mask, 'b n -> b 1 1 n')
|
||
mask = mask.split(q_bucket_size, dim=-1)
|
||
|
||
row_splits = zip(
|
||
q.split(q_bucket_size, dim=-2),
|
||
o.split(q_bucket_size, dim=-2),
|
||
mask,
|
||
all_row_sums.split(q_bucket_size, dim=-2),
|
||
all_row_maxes.split(q_bucket_size, dim=-2),
|
||
)
|
||
|
||
for ind, (qc, oc, row_mask, row_sums, row_maxes) in enumerate(row_splits):
|
||
q_start_index = ind * q_bucket_size - qk_len_diff
|
||
|
||
col_splits = zip(
|
||
k.split(k_bucket_size, dim=-2),
|
||
v.split(k_bucket_size, dim=-2),
|
||
)
|
||
|
||
for k_ind, (kc, vc) in enumerate(col_splits):
|
||
k_start_index = k_ind * k_bucket_size
|
||
|
||
attn_weights = einsum('... i d, ... j d -> ... i j', qc, kc) * scale
|
||
|
||
if exists(row_mask):
|
||
attn_weights.masked_fill_(~row_mask, max_neg_value)
|
||
|
||
if causal and q_start_index < (k_start_index + k_bucket_size - 1):
|
||
causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype=torch.bool,
|
||
device=device).triu(q_start_index - k_start_index + 1)
|
||
attn_weights.masked_fill_(causal_mask, max_neg_value)
|
||
|
||
block_row_maxes = attn_weights.amax(dim=-1, keepdims=True)
|
||
attn_weights -= block_row_maxes
|
||
exp_weights = torch.exp(attn_weights)
|
||
|
||
if exists(row_mask):
|
||
exp_weights.masked_fill_(~row_mask, 0.)
|
||
|
||
block_row_sums = exp_weights.sum(dim=-1, keepdims=True).clamp(min=EPSILON)
|
||
|
||
new_row_maxes = torch.maximum(block_row_maxes, row_maxes)
|
||
|
||
exp_values = einsum('... i j, ... j d -> ... i d', exp_weights, vc)
|
||
|
||
exp_row_max_diff = torch.exp(row_maxes - new_row_maxes)
|
||
exp_block_row_max_diff = torch.exp(block_row_maxes - new_row_maxes)
|
||
|
||
new_row_sums = exp_row_max_diff * row_sums + exp_block_row_max_diff * block_row_sums
|
||
|
||
oc.mul_((row_sums / new_row_sums) * exp_row_max_diff).add_((exp_block_row_max_diff / new_row_sums) * exp_values)
|
||
|
||
row_maxes.copy_(new_row_maxes)
|
||
row_sums.copy_(new_row_sums)
|
||
|
||
ctx.args = (causal, scale, mask, q_bucket_size, k_bucket_size)
|
||
ctx.save_for_backward(q, k, v, o, all_row_sums, all_row_maxes)
|
||
|
||
return o
|
||
|
||
@ staticmethod
|
||
@ torch.no_grad()
|
||
def backward(ctx, do):
|
||
""" Algorithm 4 in the paper """
|
||
|
||
causal, scale, mask, q_bucket_size, k_bucket_size = ctx.args
|
||
q, k, v, o, l, m = ctx.saved_tensors
|
||
|
||
device = q.device
|
||
|
||
max_neg_value = -torch.finfo(q.dtype).max
|
||
qk_len_diff = max(k.shape[-2] - q.shape[-2], 0)
|
||
|
||
dq = torch.zeros_like(q)
|
||
dk = torch.zeros_like(k)
|
||
dv = torch.zeros_like(v)
|
||
|
||
row_splits = zip(
|
||
q.split(q_bucket_size, dim=-2),
|
||
o.split(q_bucket_size, dim=-2),
|
||
do.split(q_bucket_size, dim=-2),
|
||
mask,
|
||
l.split(q_bucket_size, dim=-2),
|
||
m.split(q_bucket_size, dim=-2),
|
||
dq.split(q_bucket_size, dim=-2)
|
||
)
|
||
|
||
for ind, (qc, oc, doc, row_mask, lc, mc, dqc) in enumerate(row_splits):
|
||
q_start_index = ind * q_bucket_size - qk_len_diff
|
||
|
||
col_splits = zip(
|
||
k.split(k_bucket_size, dim=-2),
|
||
v.split(k_bucket_size, dim=-2),
|
||
dk.split(k_bucket_size, dim=-2),
|
||
dv.split(k_bucket_size, dim=-2),
|
||
)
|
||
|
||
for k_ind, (kc, vc, dkc, dvc) in enumerate(col_splits):
|
||
k_start_index = k_ind * k_bucket_size
|
||
|
||
attn_weights = einsum('... i d, ... j d -> ... i j', qc, kc) * scale
|
||
|
||
if causal and q_start_index < (k_start_index + k_bucket_size - 1):
|
||
causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype=torch.bool,
|
||
device=device).triu(q_start_index - k_start_index + 1)
|
||
attn_weights.masked_fill_(causal_mask, max_neg_value)
|
||
|
||
exp_attn_weights = torch.exp(attn_weights - mc)
|
||
|
||
if exists(row_mask):
|
||
exp_attn_weights.masked_fill_(~row_mask, 0.)
|
||
|
||
p = exp_attn_weights / lc
|
||
|
||
dv_chunk = einsum('... i j, ... i d -> ... j d', p, doc)
|
||
dp = einsum('... i d, ... j d -> ... i j', doc, vc)
|
||
|
||
D = (doc * oc).sum(dim=-1, keepdims=True)
|
||
ds = p * scale * (dp - D)
|
||
|
||
dq_chunk = einsum('... i j, ... j d -> ... i d', ds, kc)
|
||
dk_chunk = einsum('... i j, ... i d -> ... j d', ds, qc)
|
||
|
||
dqc.add_(dq_chunk)
|
||
dkc.add_(dk_chunk)
|
||
dvc.add_(dv_chunk)
|
||
|
||
return dq, dk, dv, None, None, None, None
|
||
|
||
|
||
def replace_unet_modules(unet: diffusers.models.unet_2d_condition.UNet2DConditionModel, mem_eff_attn, xformers):
|
||
if mem_eff_attn:
|
||
replace_unet_cross_attn_to_memory_efficient()
|
||
elif xformers:
|
||
replace_unet_cross_attn_to_xformers()
|
||
|
||
|
||
def replace_unet_cross_attn_to_memory_efficient():
|
||
print("Replace CrossAttention.forward to use FlashAttention")
|
||
flash_func = FlashAttentionFunction
|
||
|
||
def forward_flash_attn(self, x, context=None, mask=None):
|
||
q_bucket_size = 512
|
||
k_bucket_size = 1024
|
||
|
||
h = self.heads
|
||
q = self.to_q(x)
|
||
|
||
context = context if context is not None else x
|
||
context = context.to(x.dtype)
|
||
k = self.to_k(context)
|
||
v = self.to_v(context)
|
||
del context, x
|
||
|
||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
|
||
|
||
out = flash_func.apply(q, k, v, mask, False, q_bucket_size, k_bucket_size)
|
||
|
||
out = rearrange(out, 'b h n d -> b n (h d)')
|
||
|
||
# diffusers 0.7.0~
|
||
out = self.to_out[0](out)
|
||
out = self.to_out[1](out)
|
||
return out
|
||
|
||
diffusers.models.attention.CrossAttention.forward = forward_flash_attn
|
||
|
||
|
||
def replace_unet_cross_attn_to_xformers():
|
||
print("Replace CrossAttention.forward to use 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)
|
||
|
||
k_in = self.to_k(context)
|
||
v_in = self.to_v(context)
|
||
|
||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in)) # new format
|
||
# q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) # legacy format
|
||
del q_in, k_in, v_in
|
||
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)
|
||
# out = rearrange(out, '(b h) n 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
|
||
# endregion
|
||
|
||
|
||
def collate_fn(examples):
|
||
return examples[0]
|
||
|
||
|
||
def train(args):
|
||
cache_latents = args.cache_latents
|
||
|
||
# latentsをキャッシュする場合のオプション設定を確認する
|
||
if cache_latents:
|
||
assert not args.color_aug, "when caching latents, color_aug cannot be used / latentをキャッシュするときはcolor_augは使えません"
|
||
|
||
# その他のオプション設定を確認する
|
||
if args.v_parameterization and not args.v2:
|
||
print("v_parameterization should be with v2 / v1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:
|
||
print("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")
|
||
|
||
use_dreambooth_method = args.in_json is None
|
||
|
||
# モデル形式のオプション設定を確認する：
|
||
load_stable_diffusion_format = os.path.isfile(args.pretrained_model_name_or_path)
|
||
|
||
# 乱数系列を初期化する
|
||
if args.seed is not None:
|
||
set_seed(args.seed)
|
||
|
||
# tokenizerを読み込む
|
||
print("prepare tokenizer")
|
||
if args.v2:
|
||
tokenizer = CLIPTokenizer.from_pretrained(V2_STABLE_DIFFUSION_PATH, subfolder="tokenizer")
|
||
else:
|
||
tokenizer = CLIPTokenizer.from_pretrained(TOKENIZER_PATH)
|
||
|
||
if args.max_token_length is not None:
|
||
print(f"update token length: {args.max_token_length}")
|
||
|
||
# 学習データを用意する
|
||
assert args.resolution is not None, f"resolution is required / resolution（解像度）を指定してください"
|
||
resolution = tuple([int(r) for r in args.resolution.split(',')])
|
||
if len(resolution) == 1:
|
||
resolution = (resolution[0], resolution[0])
|
||
assert len(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:
|
||
face_crop_aug_range = tuple([float(r) for r in args.face_crop_aug_range.split(',')])
|
||
assert len(
|
||
face_crop_aug_range) == 2, 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:
|
||
face_crop_aug_range = None
|
||
|
||
# データセットを準備する
|
||
if use_dreambooth_method:
|
||
print("Use DreamBooth method.")
|
||
train_dataset = DreamBoothDataset(args.train_batch_size, args.train_data_dir, args.reg_data_dir,
|
||
tokenizer, args.max_token_length, args.caption_extension, args.shuffle_caption, args.keep_tokens,
|
||
resolution, args.prior_loss_weight, args.flip_aug, args.color_aug, face_crop_aug_range, args.random_crop, args.debug_dataset)
|
||
else:
|
||
print("Train with captions.")
|
||
|
||
# メタデータを読み込む
|
||
if os.path.exists(args.in_json):
|
||
print(f"loading existing metadata: {args.in_json}")
|
||
with open(args.in_json, "rt", encoding='utf-8') as f:
|
||
metadata = json.load(f)
|
||
else:
|
||
print(f"no metadata / メタデータファイルがありません: {args.in_json}")
|
||
return
|
||
|
||
if args.color_aug:
|
||
print(f"latents in npz is ignored when color_aug is True / color_augを有効にした場合、npzファイルのlatentsは無視されます")
|
||
|
||
train_dataset = FineTuningDataset(metadata, args.train_batch_size, args.train_data_dir,
|
||
tokenizer, args.max_token_length, args.shuffle_caption, args.keep_tokens,
|
||
resolution, args.flip_aug, args.color_aug, face_crop_aug_range, args.dataset_repeats, args.debug_dataset)
|
||
|
||
if train_dataset.min_bucket_reso is not None and (args.enable_bucket or train_dataset.min_bucket_reso != train_dataset.max_bucket_reso):
|
||
print(f"using bucket info in metadata / メタデータ内のbucket情報を使います")
|
||
args.min_bucket_reso = train_dataset.min_bucket_reso
|
||
args.max_bucket_reso = train_dataset.max_bucket_reso
|
||
args.enable_bucket = True
|
||
print(f"min bucket reso: {args.min_bucket_reso}, max bucket reso: {args.max_bucket_reso}")
|
||
|
||
if args.enable_bucket:
|
||
assert min(resolution) >= args.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) <= args.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"
|
||
|
||
train_dataset.make_buckets(args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso)
|
||
|
||
if args.debug_dataset:
|
||
print(f"Total dataset length (steps) / データセットの長さ（ステップ数）: {len(train_dataset)}")
|
||
print("Escape for exit. / Escキーで中断、終了します")
|
||
k = 0
|
||
for example in train_dataset:
|
||
if example['latents'] is not None:
|
||
print("sample has latents from npz file")
|
||
for j, (ik, cap, lw) in enumerate(zip(example['image_keys'], example['captions'], example['loss_weights'])):
|
||
print(f'{ik}, size: {train_dataset.image_data[ik].image_size}, caption: "{cap}", loss weight: {lw}')
|
||
if example['images'] is not None:
|
||
im = example['images'][j]
|
||
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)
|
||
cv2.imshow("img", im)
|
||
k = cv2.waitKey()
|
||
cv2.destroyAllWindows()
|
||
if k == 27:
|
||
break
|
||
if k == 27 or example['images'] is None:
|
||
break
|
||
return
|
||
|
||
if len(train_dataset) == 0:
|
||
print("No data found. Please verify arguments / 画像がありません。引数指定を確認してください")
|
||
return
|
||
|
||
# acceleratorを準備する
|
||
print("prepare accelerator")
|
||
if args.logging_dir is None:
|
||
log_with = None
|
||
logging_dir = None
|
||
else:
|
||
log_with = "tensorboard"
|
||
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())
|
||
|
||
accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision,
|
||
log_with=log_with, logging_dir=logging_dir)
|
||
|
||
# accelerateの互換性問題を解決する
|
||
accelerator_0_15 = True
|
||
try:
|
||
accelerator.unwrap_model("dummy", True)
|
||
print("Using accelerator 0.15.0 or above.")
|
||
except TypeError:
|
||
accelerator_0_15 = False
|
||
|
||
def unwrap_model(model):
|
||
if accelerator_0_15:
|
||
return accelerator.unwrap_model(model, True)
|
||
return accelerator.unwrap_model(model)
|
||
|
||
# mixed precisionに対応した型を用意しておき適宜castする
|
||
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
|
||
|
||
# モデルを読み込む
|
||
if load_stable_diffusion_format:
|
||
print("load StableDiffusion checkpoint")
|
||
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.pretrained_model_name_or_path)
|
||
else:
|
||
print("load Diffusers pretrained models")
|
||
pipe = StableDiffusionPipeline.from_pretrained(args.pretrained_model_name_or_path, tokenizer=None, safety_checker=None)
|
||
text_encoder = pipe.text_encoder
|
||
vae = pipe.vae
|
||
unet = pipe.unet
|
||
del pipe
|
||
|
||
# VAEを読み込む
|
||
if args.vae is not None:
|
||
vae = model_util.load_vae(args.vae, weight_dtype)
|
||
print("additional VAE loaded")
|
||
|
||
# モデルに xformers とか memory efficient attention を組み込む
|
||
replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
|
||
|
||
# 学習を準備する
|
||
if cache_latents:
|
||
vae.to(accelerator.device, dtype=weight_dtype)
|
||
vae.requires_grad_(False)
|
||
vae.eval()
|
||
with torch.no_grad():
|
||
train_dataset.cache_latents(vae)
|
||
vae.to("cpu")
|
||
if torch.cuda.is_available():
|
||
torch.cuda.empty_cache()
|
||
gc.collect()
|
||
|
||
# prepare network
|
||
print("import network module:", args.network_module)
|
||
network_module = importlib.import_module(args.network_module)
|
||
|
||
net_kwargs = {}
|
||
if args.network_args is not None:
|
||
for net_arg in args.network_args:
|
||
key, value = net_arg.split('=')
|
||
net_kwargs[key] = value
|
||
|
||
network = network_module.create_network(1.0, args.network_dim, vae, text_encoder, unet, **net_kwargs)
|
||
if network is None:
|
||
return
|
||
|
||
if args.network_weights is not None:
|
||
print("load network weights from:", args.network_weights)
|
||
network.load_weights(args.network_weights)
|
||
|
||
train_unet = not args.network_train_text_encoder_only
|
||
train_text_encoder = not args.network_train_unet_only
|
||
network.apply_to(text_encoder, unet, train_text_encoder, train_unet)
|
||
|
||
if args.gradient_checkpointing:
|
||
unet.enable_gradient_checkpointing()
|
||
text_encoder.gradient_checkpointing_enable()
|
||
network.enable_gradient_checkpointing() # may have no effect
|
||
|
||
# 学習に必要なクラスを準備する
|
||
print("prepare optimizer, data loader etc.")
|
||
|
||
# 8-bit Adamを使う
|
||
if args.use_8bit_adam:
|
||
try:
|
||
import bitsandbytes as bnb
|
||
except ImportError:
|
||
raise ImportError("No bitsand bytes / bitsandbytesがインストールされていないようです")
|
||
print("use 8-bit Adam optimizer")
|
||
optimizer_class = bnb.optim.AdamW8bit
|
||
else:
|
||
optimizer_class = torch.optim.AdamW
|
||
|
||
trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)
|
||
|
||
# betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略
|
||
optimizer = optimizer_class(trainable_params, lr=args.learning_rate)
|
||
|
||
# dataloaderを準備する
|
||
# DataLoaderのプロセス数：0はメインプロセスになる
|
||
n_workers = min(8, os.cpu_count() - 1) # cpu_count-1 ただし最大8
|
||
train_dataloader = torch.utils.data.DataLoader(
|
||
train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers)
|
||
|
||
# lr schedulerを用意する
|
||
lr_scheduler = diffusers.optimization.get_scheduler(
|
||
args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps)
|
||
|
||
# 実験的機能：勾配も含めたfp16学習を行う モデル全体をfp16にする
|
||
if args.full_fp16:
|
||
assert args.mixed_precision == "fp16", "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
|
||
print("enable full fp16 training.")
|
||
# unet.to(weight_dtype)
|
||
# text_encoder.to(weight_dtype)
|
||
network.to(weight_dtype)
|
||
|
||
# acceleratorがなんかよろしくやってくれるらしい
|
||
if train_unet and train_text_encoder:
|
||
unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||
unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler)
|
||
elif train_unet:
|
||
unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||
unet, network, optimizer, train_dataloader, lr_scheduler)
|
||
elif train_text_encoder:
|
||
text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||
text_encoder, network, optimizer, train_dataloader, lr_scheduler)
|
||
else:
|
||
network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||
network, optimizer, train_dataloader, lr_scheduler)
|
||
|
||
unet.requires_grad_(False)
|
||
unet.to(accelerator.device, dtype=weight_dtype)
|
||
unet.eval()
|
||
text_encoder.requires_grad_(False)
|
||
text_encoder.to(accelerator.device, dtype=weight_dtype)
|
||
text_encoder.eval()
|
||
|
||
network.prepare_grad_etc(text_encoder, unet)
|
||
|
||
if not cache_latents:
|
||
vae.requires_grad_(False)
|
||
vae.eval()
|
||
vae.to(accelerator.device, dtype=weight_dtype)
|
||
|
||
# 実験的機能：勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
|
||
if args.full_fp16:
|
||
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
|
||
|
||
# resumeする
|
||
if args.resume is not None:
|
||
print(f"resume training from state: {args.resume}")
|
||
accelerator.load_state(args.resume)
|
||
|
||
# epoch数を計算する
|
||
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
||
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
||
|
||
# 学習する
|
||
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
||
print("running training / 学習開始")
|
||
print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset.num_train_images}")
|
||
print(f" num reg images / 正則化画像の数: {train_dataset.num_reg_images}")
|
||
print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
|
||
print(f" num epochs / epoch数: {num_train_epochs}")
|
||
print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
|
||
print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
|
||
print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
|
||
print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
|
||
|
||
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
|
||
global_step = 0
|
||
|
||
noise_scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
|
||
num_train_timesteps=1000, clip_sample=False)
|
||
|
||
if accelerator.is_main_process:
|
||
accelerator.init_trackers("network_train")
|
||
|
||
for epoch in range(num_train_epochs):
|
||
print(f"epoch {epoch+1}/{num_train_epochs}")
|
||
|
||
# 指定したステップ数までText Encoderを学習する：epoch最初の状態
|
||
network.on_epoch_start(text_encoder, unet)
|
||
|
||
loss_total = 0
|
||
for step, batch in enumerate(train_dataloader):
|
||
with accelerator.accumulate(network):
|
||
with torch.no_grad():
|
||
# latentに変換
|
||
if batch["latents"] is not None:
|
||
latents = batch["latents"].to(accelerator.device)
|
||
else:
|
||
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
|
||
latents = latents * 0.18215
|
||
b_size = latents.shape[0]
|
||
|
||
with torch.set_grad_enabled(train_text_encoder):
|
||
# Get the text embedding for conditioning
|
||
input_ids = batch["input_ids"].to(accelerator.device)
|
||
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 = encoder_hidden_states.to(weight_dtype) # なぜかこれが必要
|
||
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: <BOS>...<EOS> <PAD> ... の三連を <BOS>...<EOS> <PAD> ... 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)] # <BOS>
|
||
for i in range(1, args.max_token_length, tokenizer.model_max_length):
|
||
chunk = encoder_hidden_states[:, i:i + tokenizer.model_max_length - 2] # <BOS> 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 <BOS> <EOS> <PAD> ...のパターン
|
||
chunk[j, 0] = chunk[j, 1] # 次の <PAD> の値をコピーする
|
||
states_list.append(chunk) # <BOS> の後から <EOS> の前まで
|
||
states_list.append(encoder_hidden_states[:, -1].unsqueeze(1)) # <EOS> か <PAD> のどちらか
|
||
encoder_hidden_states = torch.cat(states_list, dim=1)
|
||
else:
|
||
# v1: <BOS>...<EOS> の三連を <BOS>...<EOS> へ戻す
|
||
states_list = [encoder_hidden_states[:, 0].unsqueeze(1)] # <BOS>
|
||
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]) # <BOS> の後から <EOS> の前まで
|
||
states_list.append(encoder_hidden_states[:, -1].unsqueeze(1)) # <EOS>
|
||
encoder_hidden_states = torch.cat(states_list, dim=1)
|
||
|
||
# Sample noise that we'll add to the latents
|
||
noise = torch.randn_like(latents, device=latents.device)
|
||
|
||
# Sample a random timestep for each image
|
||
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
|
||
timesteps = timesteps.long()
|
||
|
||
# Add noise to the latents according to the noise magnitude at each timestep
|
||
# (this is the forward diffusion process)
|
||
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
||
|
||
# Predict the noise residual
|
||
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
||
|
||
if args.v_parameterization:
|
||
# v-parameterization training
|
||
# Diffusers 0.10.0からv_parameterizationの学習に対応したのでそちらを使う
|
||
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
||
else:
|
||
target = noise
|
||
|
||
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
|
||
loss = loss.mean([1, 2, 3])
|
||
|
||
loss_weights = batch["loss_weights"] # 各sampleごとのweight
|
||
loss = loss * loss_weights
|
||
|
||
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
|
||
|
||
accelerator.backward(loss)
|
||
if accelerator.sync_gradients:
|
||
params_to_clip = network.get_trainable_params()
|
||
accelerator.clip_grad_norm_(params_to_clip, 1.0) # args.max_grad_norm)
|
||
|
||
optimizer.step()
|
||
lr_scheduler.step()
|
||
optimizer.zero_grad(set_to_none=True)
|
||
|
||
# Checks if the accelerator has performed an optimization step behind the scenes
|
||
if accelerator.sync_gradients:
|
||
progress_bar.update(1)
|
||
global_step += 1
|
||
|
||
current_loss = loss.detach().item()
|
||
if args.logging_dir is not None:
|
||
logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
|
||
accelerator.log(logs, step=global_step)
|
||
|
||
loss_total += current_loss
|
||
avr_loss = loss_total / (step+1)
|
||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||
progress_bar.set_postfix(**logs)
|
||
|
||
if global_step >= args.max_train_steps:
|
||
break
|
||
|
||
if args.logging_dir is not None:
|
||
logs = {"epoch_loss": loss_total / len(train_dataloader)}
|
||
accelerator.log(logs, step=epoch+1)
|
||
|
||
accelerator.wait_for_everyone()
|
||
|
||
if args.save_every_n_epochs is not None:
|
||
if (epoch + 1) % args.save_every_n_epochs == 0 and (epoch + 1) < num_train_epochs:
|
||
print("saving checkpoint.")
|
||
os.makedirs(args.output_dir, exist_ok=True)
|
||
ckpt_file = os.path.join(args.output_dir, EPOCH_FILE_NAME.format(epoch + 1) + '.' + args.save_model_as)
|
||
unwrap_model(network).save_weights(ckpt_file, save_dtype)
|
||
|
||
if args.save_state:
|
||
print("saving state.")
|
||
accelerator.save_state(os.path.join(args.output_dir, EPOCH_STATE_NAME.format(epoch + 1)))
|
||
|
||
is_main_process = accelerator.is_main_process
|
||
if is_main_process:
|
||
network = unwrap_model(network)
|
||
|
||
accelerator.end_training()
|
||
|
||
if args.save_state:
|
||
print("saving last state.")
|
||
os.makedirs(args.output_dir, exist_ok=True)
|
||
accelerator.save_state(os.path.join(args.output_dir, LAST_STATE_NAME))
|
||
|
||
del accelerator # この後メモリを使うのでこれは消す
|
||
|
||
if is_main_process:
|
||
os.makedirs(args.output_dir, exist_ok=True)
|
||
ckpt_file = os.path.join(args.output_dir, LAST_FILE_NAME + '.' + args.save_model_as)
|
||
print(f"save trained model to {ckpt_file}")
|
||
network.save_weights(ckpt_file, save_dtype)
|
||
print("model saved.")
|
||
|
||
|
||
if __name__ == '__main__':
|
||
# torch.cuda.set_per_process_memory_fraction(0.48)
|
||
parser = argparse.ArgumentParser()
|
||
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("--network_weights", type=str, default=None,
|
||
help="pretrained weights for network / 学習するネットワークの初期重み")
|
||
parser.add_argument("--shuffle_caption", action="store_true",
|
||
help="shuffle comma-separated caption / コンマで区切られたcaptionの各要素をshuffleする")
|
||
parser.add_argument("--keep_tokens", type=int, default=None,
|
||
help="keep heading N tokens when shuffling caption tokens / 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")
|
||
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("--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")
|
||
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("--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("--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")
|
||
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("--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_model_as", type=str, default="pt", choices=[None, "ckpt", "pt", "safetensors"],
|
||
help="format to save the model (default is .pt) / モデル保存時の形式（デフォルトはpt）")
|
||
parser.add_argument("--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_state", action="store_true",
|
||
help="save training state additionally (including optimizer states etc.) / optimizerなど学習状態も含めたstateを追加で保存する")
|
||
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("--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("--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("--use_8bit_adam", action="store_true",
|
||
help="use 8bit Adam 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("--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("--xformers", action="store_true",
|
||
help="use xformers for CrossAttention / CrossAttentionにxformersを使う")
|
||
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("--cache_latents", action="store_true",
|
||
help="cache latents to reduce memory (augmentations must be disabled) / メモリ削減のためにlatentを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("--learning_rate", type=float, default=2.0e-6, help="learning rate / 学習率")
|
||
parser.add_argument("--unet_lr", type=float, default=None, help="learning rate for U-Net / U-Netの学習率")
|
||
parser.add_argument("--text_encoder_lr", type=float, default=None, help="learning rate for Text Encoder / Text Encoderの学習率")
|
||
parser.add_argument("--max_train_steps", type=int, default=1600, help="training steps / 学習ステップ数")
|
||
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")
|
||
# parser.add_argument("--stop_text_encoder_training", type=int, default=None,
|
||
# help="steps to stop text encoder training / Text Encoderの学習を止めるステップ数")
|
||
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("--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_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("--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")
|
||
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("--network_module", type=str, default=None, help='network module to train / 学習対象のネットワークのモジュール')
|
||
parser.add_argument("--network_dim", type=int, default=None,
|
||
help='network dimensions (depends on each network) / モジュールの次元数（ネットワークにより定義は異なります）')
|
||
parser.add_argument("--network_args", type=str, default=None, nargs='*',
|
||
help='additional argmuments for network (key=value) / ネットワークへの追加の引数')
|
||
parser.add_argument("--network_train_unet_only", action="store_true", help="only training U-Net part / U-Net関連部分のみ学習する")
|
||
parser.add_argument("--network_train_text_encoder_only", action="store_true",
|
||
help="only training Text Encoder part / Text Encoder関連部分のみ学習する")
|
||
|
||
args = parser.parse_args()
|
||
train(args) |