1 files changed, 121 insertions, 0 deletions
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
new file mode 100644
index 00000000..23bb4b6a
--- /dev/null
+++ b/modules/textual_inversion/dataset.py
@@ -0,0 +1,121 @@
+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) > 1, "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])]
+
+    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