mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-21 07:30:02 +08:00
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
|
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
|
||
|
|
|
||
|
|
|
||
|
|
class PersonalizedBase(Dataset):
|
||
|
|
def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
|
||
|
|
|
||
|
|
self.placeholder_token = placeholder_token
|
||
|
|
|
||
|
|
self.size = 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'
|
||
|
|
|
||
|
|
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):
|
||
|
|
image = Image.open(path)
|
||
|
|
image = image.convert('RGB')
|
||
|
|
image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
|
||
|
|
|
||
|
|
filename = os.path.basename(path)
|
||
|
|
filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-')
|
||
|
|
filename_tokens = [token for token in filename_tokens if token.isalpha()]
|
||
|
|
|
||
|
|
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()
|
||
|
|
|
||
|
|
self.dataset.append((init_latent, filename_tokens))
|
||
|
|
|
||
|
|
self.length = len(self.dataset) * repeats
|
||
|
|
|
||
|
|
self.initial_indexes = np.arange(self.length) % len(self.dataset)
|
||
|
|
self.indexes = None
|
||
|
|
self.shuffle()
|
||
|
|
|
||
|
|
def shuffle(self):
|
||
|
|
self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
return self.length
|
||
|
|
|
||
|
|
def __getitem__(self, i):
|
||
|
|
if i % len(self.dataset) == 0:
|
||
|
|
self.shuffle()
|
||
|
|
|
||
|
|
index = self.indexes[i % len(self.indexes)]
|
||
|
|
x, filename_tokens = self.dataset[index]
|
||
|
|
|
||
|
|
text = random.choice(self.lines)
|
||
|
|
text = text.replace("[name]", self.placeholder_token)
|
||
|
|
text = text.replace("[filewords]", ' '.join(filename_tokens))
|
||
|
|
|
||
|
|
return x, text
|