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622d48e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 | """ControlNet dataset: paired training images and conditioning (control) images.
Extracted from ``library.train_util`` as part of the dataset-split refactor;
imports the abstract :class:`~library.dataset.BaseDataset` and its
:class:`~library.subset.ControlNetSubset` configuration class.
"""
import logging
import os
from typing import Any, List, Optional, Sequence, Tuple
import torch
from accelerate import Accelerator
from library.dataset import (
IMAGE_TRANSFORMS,
BaseDataset,
glob_images,
load_image,
)
from library.dreambooth_dataset import DreamBoothDataset
from library.subset import ControlNetSubset, DreamBoothSubset
from library.utils import resize_image, setup_logging, trim_and_resize_if_required
setup_logging()
logger = logging.getLogger(__name__)
class ControlNetDataset(BaseDataset):
def __init__(
self,
subsets: Sequence[ControlNetSubset],
batch_size: int,
resolution,
network_multiplier: float,
enable_bucket: bool,
min_bucket_reso: int,
max_bucket_reso: int,
bucket_reso_steps: int,
bucket_no_upscale: bool,
train_inpainting: bool,
debug_dataset: bool,
validation_split: float,
validation_seed: Optional[int],
resize_interpolation: Optional[str] = None,
skip_image_resolution: Optional[Tuple[int, int]] = None,
) -> None:
super().__init__(
resolution,
network_multiplier,
train_inpainting,
debug_dataset,
resize_interpolation,
skip_image_resolution,
)
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,
False,
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,
resize_interpolation=subset.resize_interpolation,
)
db_subsets.append(db_subset)
self.dreambooth_dataset_delegate = DreamBoothDataset(
db_subsets,
True,
batch_size,
resolution,
network_multiplier,
enable_bucket,
min_bucket_reso,
max_bucket_reso,
bucket_reso_steps,
bucket_no_upscale,
1.0,
train_inpainting,
debug_dataset,
validation_split,
validation_seed,
resize_interpolation,
skip_image_resolution,
)
# 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
self.validation_split = validation_split
self.validation_seed = validation_seed
self.resize_interpolation = resize_interpolation
# 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}"
if len(extra_imgs) > 0:
logger.warning(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 set_current_strategies(self):
return self.dreambooth_dataset_delegate.set_current_strategies()
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 new_cache_latents(self, model: Any, accelerator: Accelerator):
return self.dreambooth_dataset_delegate.new_cache_latents(model, accelerator)
def new_cache_text_encoder_outputs(self, models: List[Any], is_main_process: bool):
return self.dreambooth_dataset_delegate.new_cache_text_encoder_outputs(models, 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, _, _ = trim_and_resize_if_required(
False, # TODO support random crop
cond_img,
image_info.bucket_reso,
image_info.resized_size,
resize_interpolation=image_info.resize_interpolation,
)
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 = resize_image(
cond_img,
cond_img.shape[0],
cond_img.shape[1],
target_size_hw[1],
target_size_hw[0],
self.resize_interpolation,
)
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
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