File size: 10,198 Bytes
681adff |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 |
import os
import numpy as np
import PIL
import torch
from PIL import Image
from torch.utils.data import Dataset, DataLoader, Sampler
from torchvision import transforms
from collections import defaultdict
from random import shuffle, choices
import random
import tqdm
from modules import devices, shared
import re
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
re_numbers_at_start = re.compile(r"^[-\d]+\s*")
class DatasetEntry:
def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, weight=None):
self.filename = filename
self.filename_text = filename_text
self.weight = weight
self.latent_dist = latent_dist
self.latent_sample = latent_sample
self.cond = cond
self.cond_text = cond_text
self.pixel_values = pixel_values
class PersonalizedBase(Dataset):
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
re_word = re.compile(shared.opts.dataset_filename_word_regex) if shared.opts.dataset_filename_word_regex else None
self.placeholder_token = placeholder_token
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
self.dataset = []
with open(template_file, "r") as file:
lines = [x.strip() for x in file.readlines()]
self.lines = lines
assert data_root, 'dataset directory not specified'
assert os.path.isdir(data_root), "Dataset directory doesn't exist"
assert os.listdir(data_root), "Dataset directory is empty"
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
self.shuffle_tags = shuffle_tags
self.tag_drop_out = tag_drop_out
groups = defaultdict(list)
print("Preparing dataset...")
for path in tqdm.tqdm(self.image_paths):
alpha_channel = None
if shared.state.interrupted:
raise Exception("interrupted")
try:
image = Image.open(path)
#Currently does not work for single color transparency
#We would need to read image.info['transparency'] for that
if use_weight and 'A' in image.getbands():
alpha_channel = image.getchannel('A')
image = image.convert('RGB')
if not varsize:
image = image.resize((width, height), PIL.Image.BICUBIC)
except Exception:
continue
text_filename = f"{os.path.splitext(path)[0]}.txt"
filename = os.path.basename(path)
if os.path.exists(text_filename):
with open(text_filename, "r", encoding="utf8") as file:
filename_text = file.read()
else:
filename_text = os.path.splitext(filename)[0]
filename_text = re.sub(re_numbers_at_start, '', filename_text)
if re_word:
tokens = re_word.findall(filename_text)
filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens)
npimage = np.array(image).astype(np.uint8)
npimage = (npimage / 127.5 - 1.0).astype(np.float32)
torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
latent_sample = None
with devices.autocast():
latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
#Perform latent sampling, even for random sampling.
#We need the sample dimensions for the weights
if latent_sampling_method == "deterministic":
if isinstance(latent_dist, DiagonalGaussianDistribution):
# Works only for DiagonalGaussianDistribution
latent_dist.std = 0
else:
latent_sampling_method = "once"
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
if use_weight and alpha_channel is not None:
channels, *latent_size = latent_sample.shape
weight_img = alpha_channel.resize(latent_size)
npweight = np.array(weight_img).astype(np.float32)
#Repeat for every channel in the latent sample
weight = torch.tensor([npweight] * channels).reshape([channels] + latent_size)
#Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.
weight -= weight.min()
weight /= weight.mean()
elif use_weight:
#If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
weight = torch.ones(latent_sample.shape)
else:
weight = None
if latent_sampling_method == "random":
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
else:
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, weight=weight)
if not (self.tag_drop_out != 0 or self.shuffle_tags):
entry.cond_text = self.create_text(filename_text)
if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
with devices.autocast():
entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
groups[image.size].append(len(self.dataset))
self.dataset.append(entry)
del torchdata
del latent_dist
del latent_sample
del weight
self.length = len(self.dataset)
self.groups = list(groups.values())
assert self.length > 0, "No images have been found in the dataset."
self.batch_size = min(batch_size, self.length)
self.gradient_step = min(gradient_step, self.length // self.batch_size)
self.latent_sampling_method = latent_sampling_method
if len(groups) > 1:
print("Buckets:")
for (w, h), ids in sorted(groups.items(), key=lambda x: x[0]):
print(f" {w}x{h}: {len(ids)}")
print()
def create_text(self, filename_text):
text = random.choice(self.lines)
tags = filename_text.split(',')
if self.tag_drop_out != 0:
tags = [t for t in tags if random.random() > self.tag_drop_out]
if self.shuffle_tags:
random.shuffle(tags)
text = text.replace("[filewords]", ','.join(tags))
text = text.replace("[name]", self.placeholder_token)
return text
def __len__(self):
return self.length
def __getitem__(self, i):
entry = self.dataset[i]
if self.tag_drop_out != 0 or self.shuffle_tags:
entry.cond_text = self.create_text(entry.filename_text)
if self.latent_sampling_method == "random":
entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu)
return entry
class GroupedBatchSampler(Sampler):
def __init__(self, data_source: PersonalizedBase, batch_size: int):
super().__init__(data_source)
n = len(data_source)
self.groups = data_source.groups
self.len = n_batch = n // batch_size
expected = [len(g) / n * n_batch * batch_size for g in data_source.groups]
self.base = [int(e) // batch_size for e in expected]
self.n_rand_batches = nrb = n_batch - sum(self.base)
self.probs = [e%batch_size/nrb/batch_size if nrb>0 else 0 for e in expected]
self.batch_size = batch_size
def __len__(self):
return self.len
def __iter__(self):
b = self.batch_size
for g in self.groups:
shuffle(g)
batches = []
for g in self.groups:
batches.extend(g[i*b:(i+1)*b] for i in range(len(g) // b))
for _ in range(self.n_rand_batches):
rand_group = choices(self.groups, self.probs)[0]
batches.append(choices(rand_group, k=b))
shuffle(batches)
yield from batches
class PersonalizedDataLoader(DataLoader):
def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False):
super(PersonalizedDataLoader, self).__init__(dataset, batch_sampler=GroupedBatchSampler(dataset, batch_size), pin_memory=pin_memory)
if latent_sampling_method == "random":
self.collate_fn = collate_wrapper_random
else:
self.collate_fn = collate_wrapper
class BatchLoader:
def __init__(self, data):
self.cond_text = [entry.cond_text for entry in data]
self.cond = [entry.cond for entry in data]
self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
if all(entry.weight is not None for entry in data):
self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)
else:
self.weight = None
#self.emb_index = [entry.emb_index for entry in data]
#print(self.latent_sample.device)
def pin_memory(self):
self.latent_sample = self.latent_sample.pin_memory()
return self
def collate_wrapper(batch):
return BatchLoader(batch)
class BatchLoaderRandom(BatchLoader):
def __init__(self, data):
super().__init__(data)
def pin_memory(self):
return self
def collate_wrapper_random(batch):
return BatchLoaderRandom(batch)
|