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 = 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 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