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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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122 lines
4.3 KiB
Python
122 lines
4.3 KiB
Python
import os
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import numpy as np
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import PIL
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import torch
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from PIL import Image
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from torch.utils.data import Dataset
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from torchvision import transforms
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import random
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import tqdm
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from modules import devices, shared
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import re
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re_numbers_at_start = re.compile(r"^[-\d]+\s*")
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class DatasetEntry:
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def __init__(self, filename=None, latent=None, filename_text=None):
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self.filename = filename
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self.latent = latent
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self.filename_text = filename_text
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self.cond = None
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self.cond_text = None
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class PersonalizedBase(Dataset):
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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):
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re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
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self.placeholder_token = placeholder_token
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self.batch_size = batch_size
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self.width = width
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self.height = height
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self.flip = transforms.RandomHorizontalFlip(p=flip_p)
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self.dataset = []
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with open(template_file, "r") as file:
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lines = [x.strip() for x in file.readlines()]
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self.lines = lines
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assert data_root, 'dataset directory not specified'
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cond_model = shared.sd_model.cond_stage_model
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self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
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print("Preparing dataset...")
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for path in tqdm.tqdm(self.image_paths):
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try:
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image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
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except Exception:
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continue
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text_filename = os.path.splitext(path)[0] + ".txt"
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filename = os.path.basename(path)
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if os.path.exists(text_filename):
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with open(text_filename, "r", encoding="utf8") as file:
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filename_text = file.read()
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else:
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filename_text = os.path.splitext(filename)[0]
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filename_text = re.sub(re_numbers_at_start, '', filename_text)
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if re_word:
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tokens = re_word.findall(filename_text)
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filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens)
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npimage = np.array(image).astype(np.uint8)
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npimage = (npimage / 127.5 - 1.0).astype(np.float32)
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torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)
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torchdata = torch.moveaxis(torchdata, 2, 0)
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init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
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init_latent = init_latent.to(devices.cpu)
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entry = DatasetEntry(filename=path, filename_text=filename_text, latent=init_latent)
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if include_cond:
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entry.cond_text = self.create_text(filename_text)
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entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
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self.dataset.append(entry)
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assert len(self.dataset) > 0, "No images have been found in the dataset."
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self.length = len(self.dataset) * repeats // batch_size
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self.initial_indexes = np.arange(len(self.dataset))
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self.indexes = None
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self.shuffle()
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def shuffle(self):
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self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0]).numpy()]
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def create_text(self, filename_text):
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text = random.choice(self.lines)
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text = text.replace("[name]", self.placeholder_token)
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text = text.replace("[filewords]", filename_text)
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return text
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def __len__(self):
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return self.length
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def __getitem__(self, i):
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res = []
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for j in range(self.batch_size):
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position = i * self.batch_size + j
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if position % len(self.indexes) == 0:
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self.shuffle()
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index = self.indexes[position % len(self.indexes)]
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entry = self.dataset[index]
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if entry.cond is None:
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entry.cond_text = self.create_text(entry.filename_text)
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res.append(entry)
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return res
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