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import os
import numpy as np
import PIL
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
import random
import tqdm
from modules import devices, shared
import re
re_numbers_at_start = re.compile(r"^[-\d]+\s*")
class DatasetEntry:
def __init__(self, filename=None, latent=None, filename_text=None):
self.filename = filename
self.latent = latent
self.filename_text = filename_text
self.cond = None
self.cond_text = None
class PersonalizedBase(Dataset):
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
self.placeholder_token = placeholder_token
self.batch_size = batch_size
self.width = width
self.height = height
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'
cond_model = shared.sd_model.cond_stage_model
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
print("Preparing dataset...")
for path in tqdm.tqdm(self.image_paths):
try:
image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
except Exception:
continue
text_filename = 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).to(device=device, dtype=torch.float32)
torchdata = torch.moveaxis(torchdata, 2, 0)
init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
init_latent = init_latent.to(devices.cpu)
entry = DatasetEntry(filename=path, filename_text=filename_text, latent=init_latent)
if include_cond:
entry.cond_text = self.create_text(filename_text)
entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
self.dataset.append(entry)
assert len(self.dataset) > 0, "No images have been found in the dataset."
self.length = len(self.dataset) * repeats // batch_size
self.initial_indexes = np.arange(len(self.dataset))
self.indexes = None
self.shuffle()
def shuffle(self):
self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0]).numpy()]
def create_text(self, filename_text):
text = random.choice(self.lines)
text = text.replace("[name]", self.placeholder_token)
text = text.replace("[filewords]", filename_text)
return text
def __len__(self):
return self.length
def __getitem__(self, i):
res = []
for j in range(self.batch_size):
position = i * self.batch_size + j
if position % len(self.indexes) == 0:
self.shuffle()
index = self.indexes[position % len(self.indexes)]
entry = self.dataset[index]
if entry.cond is None:
entry.cond_text = self.create_text(entry.filename_text)
res.append(entry)
return res
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