From 50be33e953be93c40814262c6dbce36e66004528 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 12 Oct 2022 13:13:25 +0100 Subject: [PATCH] formatting --- modules/textual_inversion/image_embedding.py | 170 ++++++++++--------- 1 file changed, 91 insertions(+), 79 deletions(-) diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index 1224fb4..898ce3b 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -2,122 +2,134 @@ import base64 import json import numpy as np import zlib -from PIL import Image,PngImagePlugin,ImageDraw,ImageFont +from PIL import Image, PngImagePlugin, ImageDraw, ImageFont from fonts.ttf import Roboto import torch + class EmbeddingEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, torch.Tensor): - return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()} + 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) + 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) + 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 + 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) + 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])) + +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) ) + 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): + +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_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 - + 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))) + 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_low = data_np_low.reshape((h, -1, d)) data_np_high.resize(next_size) - data_np_high = data_np_high.reshape((h,-1,d)) + 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) + 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') + 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)) + 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 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) + 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 = 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) + return json.loads(data, cls=EmbeddingDecoder) -def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None): + +def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None): from math import cos image = srcimage.copy() @@ -130,11 +142,11 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo textfont = Roboto factor = 1.5 - gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0)) + 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))) + 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) @@ -142,41 +154,41 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo 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) + _, _, 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), 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) + _, _, 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)) + 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)) + 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)) + + 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)))}} + test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}} embedded_image = insert_image_data_embed(cap_image, test_embed) @@ -191,16 +203,16 @@ if __name__ == '__main__': 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, + 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 + assert shared_random == reference_random hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())