File size: 9,256 Bytes
4c022fe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
41fdef7
4c022fe
 
 
 
 
 
 
 
 
 
 
 
41fdef7
 
4c022fe
 
 
 
 
 
 
 
 
 
 
 
 
 
41fdef7
4c022fe
 
 
 
 
 
 
 
 
 
 
 
 
 
41fdef7
4c022fe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
41fdef7
4c022fe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
41fdef7
 
4c022fe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import os
import json
import torch
import random
import numpy as np

COLORS = {
    'brown': [165, 42, 42],
    'red': [255, 0, 0],
    'pink': [253, 108, 158],
    'orange': [255, 165, 0],
    'yellow': [255, 255, 0],
    'purple': [128, 0, 128],
    'green': [0, 128, 0],
    'blue': [0, 0, 255],
    'white': [255, 255, 255],
    'gray': [128, 128, 128],
    'black': [0, 0, 0],
}


def seed_everything(seed):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)


def hex_to_rgb(hex_string, return_nearest_color=False):
    r"""
    Covert Hex triplet to RGB triplet.
    """
    # Remove '#' symbol if present
    hex_string = hex_string.lstrip('#')
    # Convert hex values to integers
    red = int(hex_string[0:2], 16)
    green = int(hex_string[2:4], 16)
    blue = int(hex_string[4:6], 16)
    rgb = torch.FloatTensor((red, green, blue))[None, :, None, None]/255.
    if return_nearest_color:
        nearest_color = find_nearest_color(rgb)
        return rgb.cuda(), nearest_color
    return rgb.cuda()


def find_nearest_color(rgb):
    r"""
    Find the nearest neighbor color given the RGB value.
    """
    if isinstance(rgb, list) or isinstance(rgb, tuple):
        rgb = torch.FloatTensor(rgb)[None, :, None, None]/255.
    color_distance = torch.FloatTensor([np.linalg.norm(
        rgb - torch.FloatTensor(COLORS[color])[None, :, None, None]/255.) for color in COLORS.keys()])
    nearest_color = list(COLORS.keys())[torch.argmin(color_distance).item()]
    return nearest_color


def font2style(font):
    r"""
    Convert the font name to the style name.
    """
    return {'mirza': 'Claud Monet, impressionism, oil on canvas',
            'roboto': 'Ukiyoe',
            'cursive': 'Cyber Punk, futuristic, blade runner, william gibson, trending on artstation hq',
            'sofia': 'Pop Art, masterpiece, andy warhol',
            'slabo': 'Vincent Van Gogh',
            'inconsolata': 'Pixel Art, 8 bits, 16 bits',
            'ubuntu': 'Rembrandt',
            'Monoton': 'neon art, colorful light, highly details, octane render',
            'Akronim': 'Abstract Cubism, Pablo Picasso', }[font]


def parse_json(json_str):
    r"""
    Convert the JSON string to attributes.
    """
    # initialze region-base attributes.
    base_text_prompt = ''
    style_text_prompts = []
    footnote_text_prompts = []
    footnote_target_tokens = []
    color_text_prompts = []
    color_rgbs = []
    color_names = []
    size_text_prompts_and_sizes = []

    # parse the attributes from JSON.
    prev_style = None
    prev_color_rgb = None
    use_grad_guidance = False
    for span in json_str['ops']:
        text_prompt = span['insert'].rstrip('\n')
        base_text_prompt += span['insert'].rstrip('\n')
        if text_prompt == ' ':
            continue
        if 'attributes' in span:
            if 'font' in span['attributes']:
                style = font2style(span['attributes']['font'])
                if prev_style == style:
                    prev_text_prompt = style_text_prompts[-1].split('in the style of')[
                        0]
                    style_text_prompts[-1] = prev_text_prompt + \
                        ' ' + text_prompt + f' in the style of {style}'
                else:
                    style_text_prompts.append(
                        text_prompt + f' in the style of {style}')
                prev_style = style
            else:
                prev_style = None
            if 'link' in span['attributes']:
                footnote_text_prompts.append(span['attributes']['link'])
                footnote_target_tokens.append(text_prompt)
            font_size = 1
            if 'size' in span['attributes'] and 'strike' not in span['attributes']:
                font_size = float(span['attributes']['size'][:-2])/3.
            elif 'size' in span['attributes'] and 'strike' in span['attributes']:
                font_size = -float(span['attributes']['size'][:-2])/3.
            elif 'size' not in span['attributes'] and 'strike' not in span['attributes']:
                font_size = 1
            if 'color' in span['attributes']:
                use_grad_guidance = True
                color_rgb, nearest_color = hex_to_rgb(
                    span['attributes']['color'], True)
                if prev_color_rgb == color_rgb:
                    prev_text_prompt = color_text_prompts[-1]
                    color_text_prompts[-1] = prev_text_prompt + \
                        ' ' + text_prompt
                else:
                    color_rgbs.append(color_rgb)
                    color_names.append(nearest_color)
                    color_text_prompts.append(text_prompt)
            if font_size != 1:
                size_text_prompts_and_sizes.append([text_prompt, font_size])
    return base_text_prompt, style_text_prompts, footnote_text_prompts, footnote_target_tokens,\
        color_text_prompts, color_names, color_rgbs, size_text_prompts_and_sizes, use_grad_guidance


def get_region_diffusion_input(model, base_text_prompt, style_text_prompts, footnote_text_prompts,
                               footnote_target_tokens, color_text_prompts, color_names):
    r"""
    Algorithm 1 in the paper.
    """
    region_text_prompts = []
    region_target_token_ids = []
    base_tokens = model.tokenizer._tokenize(base_text_prompt)
    # process the style text prompt
    for text_prompt in style_text_prompts:
        region_text_prompts.append(text_prompt)
        region_target_token_ids.append([])
        style_tokens = model.tokenizer._tokenize(
            text_prompt.split('in the style of')[0])
        for style_token in style_tokens:
            region_target_token_ids[-1].append(
                base_tokens.index(style_token)+1)

    # process the complementary text prompt
    for footnote_text_prompt, text_prompt in zip(footnote_text_prompts, footnote_target_tokens):
        region_target_token_ids.append([])
        region_text_prompts.append(footnote_text_prompt)
        style_tokens = model.tokenizer._tokenize(text_prompt)
        for style_token in style_tokens:
            region_target_token_ids[-1].append(
                base_tokens.index(style_token)+1)

    # process the color text prompt
    for color_text_prompt, color_name in zip(color_text_prompts, color_names):
        region_target_token_ids.append([])
        region_text_prompts.append(color_name+' '+color_text_prompt)
        style_tokens = model.tokenizer._tokenize(color_text_prompt)
        for style_token in style_tokens:
            region_target_token_ids[-1].append(
                base_tokens.index(style_token)+1)

    # process the remaining tokens without any attributes
    region_text_prompts.append(base_text_prompt)
    region_target_token_ids_all = [
        id for ids in region_target_token_ids for id in ids]
    target_token_ids_rest = [id for id in range(
        1, len(base_tokens)+1) if id not in region_target_token_ids_all]
    region_target_token_ids.append(target_token_ids_rest)

    region_target_token_ids = [torch.LongTensor(
        obj_token_id) for obj_token_id in region_target_token_ids]
    return region_text_prompts, region_target_token_ids, base_tokens


def get_attention_control_input(model, base_tokens, size_text_prompts_and_sizes):
    r"""
    Control the token impact using font sizes.
    """
    word_pos = []
    font_sizes = []
    for text_prompt, font_size in size_text_prompts_and_sizes:
        size_tokens = model.tokenizer._tokenize(text_prompt)
        for size_token in size_tokens:
            word_pos.append(base_tokens.index(size_token)+1)
            font_sizes.append(font_size)
    if len(word_pos) > 0:
        word_pos = torch.LongTensor(word_pos).cuda()
        font_sizes = torch.FloatTensor(font_sizes).cuda()
    else:
        word_pos = None
        font_sizes = None
    text_format_dict = {
        'word_pos': word_pos,
        'font_size': font_sizes,
    }
    return text_format_dict


def get_gradient_guidance_input(model, base_tokens, color_text_prompts, color_rgbs, text_format_dict,
                                guidance_start_step=999, color_guidance_weight=1):
    r"""
    Control the token impact using font sizes.
    """
    color_target_token_ids = []
    for text_prompt in color_text_prompts:
        color_target_token_ids.append([])
        color_tokens = model.tokenizer._tokenize(text_prompt)
        for color_token in color_tokens:
            color_target_token_ids[-1].append(base_tokens.index(color_token)+1)
    color_target_token_ids_all = [
        id for ids in color_target_token_ids for id in ids]
    color_target_token_ids_rest = [id for id in range(
        1, len(base_tokens)+1) if id not in color_target_token_ids_all]
    color_target_token_ids.append(color_target_token_ids_rest)
    color_target_token_ids = [torch.LongTensor(
        obj_token_id) for obj_token_id in color_target_token_ids]

    text_format_dict['target_RGB'] = color_rgbs
    text_format_dict['guidance_start_step'] = guidance_start_step
    text_format_dict['color_guidance_weight'] = color_guidance_weight
    return text_format_dict, color_target_token_ids