File size: 8,210 Bytes
4878ed5 |
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 |
import base64
import json
import warnings
import numpy as np
import zlib
from PIL import Image, ImageDraw
import torch
class EmbeddingEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, torch.Tensor):
return {'TORCHTENSOR': obj.cpu().detach().numpy().tolist()}
return json.JSONEncoder.default(self, obj)
class EmbeddingDecoder(json.JSONDecoder):
def __init__(self, *args, **kwargs):
json.JSONDecoder.__init__(self, *args, object_hook=self.object_hook, **kwargs)
def object_hook(self, d):
if 'TORCHTENSOR' in d:
return torch.from_numpy(np.array(d['TORCHTENSOR']))
return d
def embedding_to_b64(data):
d = json.dumps(data, cls=EmbeddingEncoder)
return base64.b64encode(d.encode())
def embedding_from_b64(data):
d = base64.b64decode(data)
return json.loads(d, cls=EmbeddingDecoder)
def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
while True:
seed = (a * seed + c) % m
yield seed % 255
def xor_block(block):
g = lcg()
randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)
def style_block(block, sequence):
im = Image.new('RGB', (block.shape[1], block.shape[0]))
draw = ImageDraw.Draw(im)
i = 0
for x in range(-6, im.size[0], 8):
for yi, y in enumerate(range(-6, im.size[1], 8)):
offset = 0
if yi % 2 == 0:
offset = 4
shade = sequence[i % len(sequence)]
i += 1
draw.ellipse((x+offset, y, x+6+offset, y+6), fill=(shade, shade, shade))
fg = np.array(im).astype(np.uint8) & 0xF0
return block ^ fg
def insert_image_data_embed(image, data):
d = 3
data_compressed = zlib.compress(json.dumps(data, cls=EmbeddingEncoder).encode(), level=9)
data_np_ = np.frombuffer(data_compressed, np.uint8).copy()
data_np_high = data_np_ >> 4
data_np_low = data_np_ & 0x0F
h = image.size[1]
next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h))
next_size = next_size + ((h*d)-(next_size % (h*d)))
data_np_low = np.resize(data_np_low, next_size)
data_np_low = data_np_low.reshape((h, -1, d))
data_np_high = np.resize(data_np_high, next_size)
data_np_high = data_np_high.reshape((h, -1, d))
edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
data_np_low = style_block(data_np_low, sequence=edge_style)
data_np_low = xor_block(data_np_low)
data_np_high = style_block(data_np_high, sequence=edge_style[::-1])
data_np_high = xor_block(data_np_high)
im_low = Image.fromarray(data_np_low, mode='RGB')
im_high = Image.fromarray(data_np_high, mode='RGB')
background = Image.new('RGB', (image.size[0]+im_low.size[0]+im_high.size[0]+2, image.size[1]), (0, 0, 0))
background.paste(im_low, (0, 0))
background.paste(image, (im_low.size[0]+1, 0))
background.paste(im_high, (im_low.size[0]+1+image.size[0]+1, 0))
return background
def crop_black(img, tol=0):
mask = (img > tol).all(2)
mask0, mask1 = mask.any(0), mask.any(1)
col_start, col_end = mask0.argmax(), mask.shape[1]-mask0[::-1].argmax()
row_start, row_end = mask1.argmax(), mask.shape[0]-mask1[::-1].argmax()
return img[row_start:row_end, col_start:col_end]
def extract_image_data_embed(image):
d = 3
outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F
black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)
if black_cols[0].shape[0] < 2:
print('No Image data blocks found.')
return None
data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8)
data_block_upper = outarr[:, black_cols[0].max()+1:, :].astype(np.uint8)
data_block_lower = xor_block(data_block_lower)
data_block_upper = xor_block(data_block_upper)
data_block = (data_block_upper << 4) | (data_block_lower)
data_block = data_block.flatten().tobytes()
data = zlib.decompress(data_block)
return json.loads(data, cls=EmbeddingDecoder)
def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
from modules.images import get_font
if textfont:
warnings.warn(
'passing in a textfont to caption_image_overlay is deprecated and does nothing',
DeprecationWarning,
stacklevel=2,
)
from math import cos
image = srcimage.copy()
fontsize = 32
factor = 1.5
gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
for y in range(image.size[1]):
mag = 1-cos(y/image.size[1]*factor)
mag = max(mag, 1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
gradient.putpixel((0, y), (0, 0, 0, int(mag*255)))
image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
draw = ImageDraw.Draw(image)
font = get_font(fontsize)
padding = 10
_, _, w, h = draw.textbbox((0, 0), title, font=font)
fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
font = get_font(fontsize)
_, _, w, h = draw.textbbox((0, 0), title, font=font)
draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
_, _, w, h = draw.textbbox((0, 0), footerLeft, font=font)
fontsize_left = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
_, _, w, h = draw.textbbox((0, 0), footerMid, font=font)
fontsize_mid = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
_, _, w, h = draw.textbbox((0, 0), footerRight, font=font)
fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
font = get_font(min(fontsize_left, fontsize_mid, fontsize_right))
draw.text((padding, image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
draw.text((image.size[0]/2, image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))
draw.text((image.size[0]-padding, image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255, 255, 255, 230))
return image
if __name__ == '__main__':
testEmbed = Image.open('test_embedding.png')
data = extract_image_data_embed(testEmbed)
assert data is not None
data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])
assert data is not None
image = Image.new('RGBA', (512, 512), (255, 255, 200, 255))
cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}}
embedded_image = insert_image_data_embed(cap_image, test_embed)
retrived_embed = extract_image_data_embed(embedded_image)
assert str(retrived_embed) == str(test_embed)
embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)
assert embedded_image == embedded_image2
g = lcg()
shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
reference_random = [253, 242, 127, 44, 157, 27, 239, 133, 38, 79, 167, 4, 177,
95, 130, 79, 78, 14, 52, 215, 220, 194, 126, 28, 240, 179,
160, 153, 149, 50, 105, 14, 21, 218, 199, 18, 54, 198, 193,
38, 128, 19, 53, 195, 124, 75, 205, 12, 6, 145, 0, 28,
30, 148, 8, 45, 218, 171, 55, 249, 97, 166, 12, 35, 0,
41, 221, 122, 215, 170, 31, 113, 186, 97, 119, 31, 23, 185,
66, 140, 30, 41, 37, 63, 137, 109, 216, 55, 159, 145, 82,
204, 86, 73, 222, 44, 198, 118, 240, 97]
assert shared_random == reference_random
hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
assert 12731374 == hunna_kay_random_sum
|