Spaces:
Configuration error
Configuration error
| 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 | |