stable-diffusion-webui/modules/textual_inversion/image_embedding.py

221 lines
8.3 KiB
Python

import base64
import json
import numpy as np
import zlib
from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
from fonts.ttf import Roboto
import torch
from modules.shared import opts
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, object_hook=self.object_hook, *args, **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.resize(next_size)
data_np_low = data_np_low.reshape((h, -1, d))
data_np_high.resize(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 math import cos
image = srcimage.copy()
fontsize = 32
if textfont is None:
try:
textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
textfont = opts.font or Roboto
except Exception:
textfont = Roboto
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 = ImageFont.truetype(textfont, 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 = ImageFont.truetype(textfont, 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 = ImageFont.truetype(textfont, 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