mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
93 lines
3.0 KiB
Python
93 lines
3.0 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
|
|
from modules import devices, shared
|
|
import re
|
|
|
|
re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
|
|
|
|
|
|
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):
|
|
|
|
self.placeholder_token = placeholder_token
|
|
|
|
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
|
|
|
|
filename = os.path.basename(path)
|
|
filename_tokens = os.path.splitext(filename)[0]
|
|
filename_tokens = re_tag.findall(filename_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)
|
|
|
|
if include_cond:
|
|
text = self.create_text(filename_tokens)
|
|
cond = cond_model([text]).to(devices.cpu)
|
|
else:
|
|
cond = None
|
|
|
|
self.dataset.append((init_latent, filename_tokens, cond))
|
|
|
|
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 create_text(self, filename_tokens):
|
|
text = random.choice(self.lines)
|
|
text = text.replace("[name]", self.placeholder_token)
|
|
text = text.replace("[filewords]", ' '.join(filename_tokens))
|
|
return text
|
|
|
|
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, cond = self.dataset[index]
|
|
|
|
text = self.create_text(filename_tokens)
|
|
return x, text, cond
|