Use euler_a for samples in learning scripts; backported improvement from Dreambooth to Textual Inversion

2 files changed, 46 insertions, 53 deletions

diff --git a/data/dreambooth/csv.py b/data/dreambooth/csv.py
index 99bcf12..1676d35 100644
--- a/data/dreambooth/csv.py
+++ b/data/dreambooth/csv.py
@@ -2,7 +2,6 @@ import math
 import os
 import pandas as pd
 from pathlib import Path
-import PIL
 import pytorch_lightning as pl
 from PIL import Image
 from torch.utils.data import Dataset, DataLoader, random_split
diff --git a/data/textual_inversion/csv.py b/data/textual_inversion/csv.py
index 0d1e96e..f306c7a 100644
--- a/data/textual_inversion/csv.py
+++ b/data/textual_inversion/csv.py
@@ -1,11 +1,10 @@
 import os
 import numpy as np
 import pandas as pd
-import random
-import PIL
+from pathlib import Path
+import math
 import pytorch_lightning as pl
 from PIL import Image
-import torch
 from torch.utils.data import Dataset, DataLoader, random_split
 from torchvision import transforms
 
@@ -13,29 +12,32 @@ from torchvision import transforms
 class CSVDataModule(pl.LightningDataModule):
     def __init__(self,
                  batch_size,
-                 data_root,
+                 data_file,
                  tokenizer,
                  size=512,
                  repeats=100,
                  interpolation="bicubic",
                  placeholder_token="*",
-                 flip_p=0.5,
                  center_crop=False):
         super().__init__()
 
-        self.data_root = data_root
+        self.data_file = Path(data_file)
+
+        if not self.data_file.is_file():
+            raise ValueError("data_file must be a file")
+
+        self.data_root = self.data_file.parent
         self.tokenizer = tokenizer
         self.size = size
         self.repeats = repeats
         self.placeholder_token = placeholder_token
         self.center_crop = center_crop
-        self.flip_p = flip_p
         self.interpolation = interpolation
 
         self.batch_size = batch_size
 
     def prepare_data(self):
-        metadata = pd.read_csv(f'{self.data_root}/list.csv')
+        metadata = pd.read_csv(self.data_file)
         image_paths = [os.path.join(self.data_root, f_path) for f_path in metadata['image'].values]
         captions = [caption for caption in metadata['caption'].values]
         skips = [skip for skip in metadata['skip'].values]
@@ -47,9 +49,9 @@ class CSVDataModule(pl.LightningDataModule):
         self.data_train, self.data_val = random_split(self.data_full, [train_set_size, valid_set_size])
 
         train_dataset = CSVDataset(self.data_train, self.tokenizer, size=self.size, repeats=self.repeats, interpolation=self.interpolation,
-                                   flip_p=self.flip_p, placeholder_token=self.placeholder_token, center_crop=self.center_crop)
+                                   placeholder_token=self.placeholder_token, center_crop=self.center_crop)
         val_dataset = CSVDataset(self.data_val, self.tokenizer, size=self.size, interpolation=self.interpolation,
-                                 flip_p=self.flip_p, placeholder_token=self.placeholder_token, center_crop=self.center_crop)
+                                 placeholder_token=self.placeholder_token, center_crop=self.center_crop)
         self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True)
         self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size)
 
@@ -67,48 +69,54 @@ class CSVDataset(Dataset):
                  size=512,
                  repeats=1,
                  interpolation="bicubic",
-                 flip_p=0.5,
                  placeholder_token="*",
                  center_crop=False,
+                 batch_size=1,
                  ):
 
         self.data = data
         self.tokenizer = tokenizer
-
-        self.num_images = len(self.data)
-        self._length = self.num_images * repeats
-
         self.placeholder_token = placeholder_token
+        self.batch_size = batch_size
+        self.cache = {}
 
-        self.size = size
-        self.center_crop = center_crop
-        self.interpolation = {"linear": PIL.Image.LINEAR,
-                              "bilinear": PIL.Image.BILINEAR,
-                              "bicubic": PIL.Image.BICUBIC,
-                              "lanczos": PIL.Image.LANCZOS,
-                              }[interpolation]
-        self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+        self.num_instance_images = len(self.data)
+        self._length = self.num_instance_images * repeats
 
-        self.cache = {}
+        self.interpolation = {"linear": transforms.InterpolationMode.NEAREST,
+                              "bilinear": transforms.InterpolationMode.BILINEAR,
+                              "bicubic": transforms.InterpolationMode.BICUBIC,
+                              "lanczos": transforms.InterpolationMode.LANCZOS,
+                              }[interpolation]
+        self.image_transforms = transforms.Compose(
+            [
+                transforms.Resize(size, interpolation=self.interpolation),
+                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
+                transforms.RandomHorizontalFlip(),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
 
     def __len__(self):
-        return self._length
+        return math.ceil(self._length / self.batch_size) * self.batch_size
 
-    def get_example(self, i, flipped):
-        image_path, text = self.data[i % self.num_images]
+    def get_example(self, i):
+        image_path, text = self.data[i % self.num_instance_images]
 
         if image_path in self.cache:
             return self.cache[image_path]
 
         example = {}
-        image = Image.open(image_path)
 
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
+        instance_image = Image.open(image_path)
+        if not instance_image.mode == "RGB":
+            instance_image = instance_image.convert("RGB")
 
         text = text.format(self.placeholder_token)
 
-        example["prompt"] = text
+        example["prompts"] = text
+        example["pixel_values"] = instance_image
         example["input_ids"] = self.tokenizer(
             text,
             padding="max_length",
@@ -117,29 +125,15 @@ class CSVDataset(Dataset):
             return_tensors="pt",
         ).input_ids[0]
 
-        # default to score-sde preprocessing
-        img = np.array(image).astype(np.uint8)
-
-        if self.center_crop:
-            crop = min(img.shape[0], img.shape[1])
-            h, w, = img.shape[0], img.shape[1]
-            img = img[(h - crop) // 2:(h + crop) // 2,
-                      (w - crop) // 2:(w + crop) // 2]
-
-        image = Image.fromarray(img)
-        image = image.resize((self.size, self.size),
-                             resample=self.interpolation)
-        image = self.flip(image)
-        image = np.array(image).astype(np.uint8)
-        image = (image / 127.5 - 1.0).astype(np.float32)
-
-        example["key"] = "-".join([image_path, "-", str(flipped)])
-        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
-
         self.cache[image_path] = example
         return example
 
     def __getitem__(self, i):
-        flipped = random.choice([False, True])
-        example = self.get_example(i, flipped)
+        example = {}
+        unprocessed_example = self.get_example(i)
+
+        example["prompts"] = unprocessed_example["prompts"]
+        example["input_ids"] = unprocessed_example["input_ids"]
+        example["pixel_values"] = self.image_transforms(unprocessed_example["pixel_values"])
+
         return example