File size: 7,111 Bytes
1a14066
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
import random
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union

from PIL import Image
from torch import zeros_like
from torch.utils.data import Dataset
from torchvision import transforms
import glob
from lora_diffusion.preprocess_files import face_mask_google_mediapipe

OBJECT_TEMPLATE = [
    "a photo of a {}",
    "a rendering of a {}",
    "a cropped photo of the {}",
    "the photo of a {}",
    "a photo of a clean {}",
    "a photo of a dirty {}",
    "a dark photo of the {}",
    "a photo of my {}",
    "a photo of the cool {}",
    "a close-up photo of a {}",
    "a bright photo of the {}",
    "a cropped photo of a {}",
    "a photo of the {}",
    "a good photo of the {}",
    "a photo of one {}",
    "a close-up photo of the {}",
    "a rendition of the {}",
    "a photo of the clean {}",
    "a rendition of a {}",
    "a photo of a nice {}",
    "a good photo of a {}",
    "a photo of the nice {}",
    "a photo of the small {}",
    "a photo of the weird {}",
    "a photo of the large {}",
    "a photo of a cool {}",
    "a photo of a small {}",
]

STYLE_TEMPLATE = [
    "a painting in the style of {}",
    "a rendering in the style of {}",
    "a cropped painting in the style of {}",
    "the painting in the style of {}",
    "a clean painting in the style of {}",
    "a dirty painting in the style of {}",
    "a dark painting in the style of {}",
    "a picture in the style of {}",
    "a cool painting in the style of {}",
    "a close-up painting in the style of {}",
    "a bright painting in the style of {}",
    "a cropped painting in the style of {}",
    "a good painting in the style of {}",
    "a close-up painting in the style of {}",
    "a rendition in the style of {}",
    "a nice painting in the style of {}",
    "a small painting in the style of {}",
    "a weird painting in the style of {}",
    "a large painting in the style of {}",
]

NULL_TEMPLATE = ["{}"]

TEMPLATE_MAP = {
    "object": OBJECT_TEMPLATE,
    "style": STYLE_TEMPLATE,
    "null": NULL_TEMPLATE,
}


def _randomset(lis):
    ret = []
    for i in range(len(lis)):
        if random.random() < 0.5:
            ret.append(lis[i])
    return ret


def _shuffle(lis):

    return random.sample(lis, len(lis))


def _get_cutout_holes(
    height,
    width,
    min_holes=8,
    max_holes=32,
    min_height=16,
    max_height=128,
    min_width=16,
    max_width=128,
):
    holes = []
    for _n in range(random.randint(min_holes, max_holes)):
        hole_height = random.randint(min_height, max_height)
        hole_width = random.randint(min_width, max_width)
        y1 = random.randint(0, height - hole_height)
        x1 = random.randint(0, width - hole_width)
        y2 = y1 + hole_height
        x2 = x1 + hole_width
        holes.append((x1, y1, x2, y2))
    return holes


def _generate_random_mask(image):
    mask = zeros_like(image[:1])
    holes = _get_cutout_holes(mask.shape[1], mask.shape[2])
    for (x1, y1, x2, y2) in holes:
        mask[:, y1:y2, x1:x2] = 1.0
    if random.uniform(0, 1) < 0.25:
        mask.fill_(1.0)
    masked_image = image * (mask < 0.5)
    return mask, masked_image


class PivotalTuningDatasetCapation(Dataset):
    """
    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
    It pre-processes the images and the tokenizes prompts.
    """

    def __init__(
        self,
        images,
        caption,
        tokenizer,
        token_map: Optional[dict] = None,
        use_template: Optional[str] = None,
        size=512,
        h_flip=True,
        color_jitter=False,
        resize=True,
        use_mask_captioned_data=False,
        use_face_segmentation_condition=False,
        train_inpainting=False,
        blur_amount: int = 70,
    ):
        self.size = size
        self.tokenizer = tokenizer
        self.resize = resize
        self.train_inpainting = train_inpainting

        assert not (
            use_mask_captioned_data and use_template
        ), "Can't use both mask caption data and template."

        # Prepare the instance images
        # self.instance_images_path = None
        self.images = images
        self.captions = [caption] * len(images)

        self.use_mask = use_face_segmentation_condition or use_mask_captioned_data
        self.use_mask_captioned_data = use_mask_captioned_data

        self.num_instance_images = len(self.images)
        self.token_map = token_map

        self.use_template = use_template
        if use_template is not None:
            self.templates = TEMPLATE_MAP[use_template]

        self._length = self.num_instance_images

        self.h_flip = h_flip
        self.image_transforms = transforms.Compose(
            [
                transforms.Resize(
                    size, interpolation=transforms.InterpolationMode.BILINEAR
                )
                if resize
                else transforms.Lambda(lambda x: x),
                transforms.ColorJitter(0.1, 0.1)
                if color_jitter
                else transforms.Lambda(lambda x: x),
                transforms.CenterCrop(size),
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
        )

        self.blur_amount = blur_amount

    def __len__(self):
        return self._length

    def __getitem__(self, index):

        example = {}
        instance_image = self.images[index % self.num_instance_images]
        if not instance_image.mode == "RGB":
            instance_image = instance_image.convert("RGB")
        example["instance_images"] = self.image_transforms(instance_image)

        if self.train_inpainting:
            (
                example["instance_masks"],
                example["instance_masked_images"],
            ) = _generate_random_mask(example["instance_images"])

        if self.use_template:
            assert self.token_map is not None

            input_tok = list(self.token_map.values())[0]

            text = random.choice(self.templates).format(input_tok)
            
        else:
            text = self.captions[index % self.num_instance_images].strip()

            if self.token_map is not None:
                for token, value in self.token_map.items():
                    text = text.replace(token, value)

        print(text)

        if self.use_mask:
            example["mask"] = (
                self.image_transforms(
                    Image.open(self.mask_path[index % self.num_instance_images])
                )
                * 0.5
                + 1.0
            )

        if self.h_flip and random.random() > 0.5:
            hflip = transforms.RandomHorizontalFlip(p=1)

            example["instance_images"] = hflip(example["instance_images"])
            if self.use_mask:
                example["mask"] = hflip(example["mask"])

        example["instance_prompt_ids"] = self.tokenizer(
            text,
            padding="do_not_pad",
            truncation=True,
            max_length=self.tokenizer.model_max_length,
        ).input_ids

        return example