File size: 30,459 Bytes
be82bb1 984d3e7 be82bb1 16237fc 542f65d be82bb1 a6ab21f be82bb1 542f65d be82bb1 542f65d 16237fc be82bb1 542f65d a6ab21f be82bb1 16237fc be82bb1 d1ca5a4 be82bb1 542f65d be82bb1 16237fc be82bb1 16237fc 984d3e7 542f65d 16237fc 542f65d d1ca5a4 16237fc 542f65d 16237fc d1ca5a4 16237fc d1ca5a4 16237fc d1ca5a4 16237fc d1ca5a4 16237fc c4972f8 a05998f c4972f8 d1ca5a4 16237fc d1ca5a4 16237fc d1ca5a4 16237fc d1ca5a4 16237fc d1ca5a4 be82bb1 16237fc d1ca5a4 16237fc be82bb1 16237fc 542f65d 16237fc 542f65d 16237fc 984d3e7 e46266a 984d3e7 0c8190e 16237fc 0c8190e 16237fc 0c8190e 16237fc 0c8190e 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 a6ab21f 984d3e7 542f65d 984d3e7 d1ca5a4 542f65d 16237fc 542f65d 16237fc 542f65d 16237fc d1ca5a4 16237fc d1ca5a4 16237fc d1ca5a4 16237fc d1ca5a4 16237fc 542f65d 16237fc e46266a be82bb1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 |
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"Try this Free online SD 1.5 generator with the results: https://perchance.org/fusion-ai-image-generator\n",
"\n",
" This Notebook is a Stable-diffusion tool which allows you to find similiar prompts to an existing prompt. It uses the Nearest Neighbor decoder method listed here:https://arxiv.org/pdf/2303.03032"
],
"metadata": {
"id": "cRV2YWomjMBU"
}
},
{
"cell_type": "code",
"source": [
"# @title ⚄ Initialize\n",
"\n",
"import os\n",
"home_directory = '/content/'\n",
"using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n",
"if using_Kaggle : home_directory = '/kaggle/working/'\n",
"%cd {home_directory}\n",
"\n",
"def fix_bad_symbols(txt):\n",
" result = txt\n",
" for symbol in ['^', '}', '{' , ')', '(', '[' , ']' , ':' , '=' ]:\n",
" result = result.replace(symbol,'\\\\' + symbol)\n",
" #------#\n",
" return result;\n",
"\n",
"def my_mkdirs(folder):\n",
" if os.path.exists(folder)==False:\n",
" os.makedirs(folder)\n",
"\n",
"#🔸🔹\n",
"# Load the data if not already loaded\n",
"try:\n",
" loaded\n",
"except:\n",
" from safetensors.torch import load_file , save_file\n",
" import json , torch , requests , math\n",
" import pandas as pd\n",
" from PIL import Image\n",
" #----#\n",
" %cd {home_directory}\n",
" !git clone https://huggingface.co/datasets/codeShare/fusion-t2i-generator-data\n",
" loaded = True\n",
"\n",
"from transformers import AutoTokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
"from transformers import CLIPProcessor, CLIPModel\n",
"processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n",
"model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
"logit_scale = model.logit_scale.exp() #logit_scale = 100.00000762939453\n",
"\n",
"%cd {home_directory + 'fusion-t2i-generator-data/'}\n",
"!unzip reference.zip\n",
"#------#\n",
"%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
"with open(f'reference_prompts.json', 'r') as f:\n",
" data = json.load(f)\n",
" _df = pd.DataFrame({'count': data})['count']\n",
" target_prompts = {\n",
" key : value for key, value in _df.items()\n",
" }\n",
"#------#\n",
"with open(f'reference_urls.json', 'r') as f:\n",
" data = json.load(f)\n",
" _df = pd.DataFrame({'count': data})['count']\n",
" target_urls = {\n",
" key : value for key, value in _df.items()\n",
" }\n",
"\n",
"#------#\n",
"dot_dtype = torch.float32\n",
"dim = 768\n",
"reference = torch.zeros(dim).to(dtype = dot_dtype)"
],
"metadata": {
"id": "TC5lMJrS1HCC"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Feel free to skip these cells if you do not plan on using them\n"
],
"metadata": {
"id": "Xf9zoq-Za3wi"
}
},
{
"cell_type": "code",
"source": [
"# @markdown 🖼️+📝 Choose a pre-encoded reference (optional)\n",
"index = 657 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
"PROMPT_INDEX = index\n",
"prompt = target_prompts[f'{PROMPT_INDEX}']\n",
"url = target_urls[f'{PROMPT_INDEX}']\n",
"if url.find('perchance')>-1:\n",
" image = Image.open(requests.get(url, stream=True).raw)\n",
"#------#\n",
"try: reference\n",
"except: reference = torch.zeros(dim).to(dtype = dot_dtype)\n",
"if reference == '': reference = torch.zeros(dim).to(dtype = dot_dtype)\n",
"# @markdown ⚖️ 🖼️ encoding <-----?-----> 📝 encoding </div> <br>\n",
"C = 0.3 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
"log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
"%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
"references = torch.load('reference_text_and_image_encodings.pt' , weights_only=False)\n",
"reference = torch.add(reference, math.pow(10 ,log_strength-1) * C * references[index][0].dequantize().to(dtype = torch.float32))\n",
"reference = torch.add(reference, math.pow(10 ,log_strength-1) * (1-C) * references[index][1].dequantize().to(dtype = torch.float32))\n",
"references = '' # Clear up memory\n",
"ref = reference.clone().detach()\n",
"#------#\n",
"print(f'Prompt for this image : \\n\\n \"{prompt} \" \\n\\n')\n",
"image"
],
"metadata": {
"id": "BwrEs5zVB0Sb"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @markdown 🖼️ Upload your own image for use as reference via URL (optional)\n",
"URL = '' # @param {type:'string' ,placeholder:'paste an url here'}\n",
"image = Image.open(requests.get(URL, stream=True).raw)\n",
"#---------#\n",
"# Get image features\n",
"inputs = processor(images=image, return_tensors=\"pt\")\n",
"image_features = model.get_image_features(**inputs)\n",
"image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\n",
"#-----#\n",
"log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
"ref = ref + math.pow(10,log_strength-1)*image_features\n",
"image"
],
"metadata": {
"id": "IqUsiQw2HU2C"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @markdown 🖼️ Upload your own image in the /content/ folder for use as reference (optional)\n",
"FILENAME = '' # @param {type:'string' ,placeholder:'IMG_123.png'}\n",
"import cv2\n",
"image = cv2.imread(FILENAME)\n",
"image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
"\n",
"#---------#\n",
"# Get image features\n",
"inputs = processor(images=image, return_tensors=\"pt\")\n",
"image_features = model.get_image_features(**inputs)\n",
"image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\n",
"#-----#\n",
"log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
"ref = ref + math.pow(10,log_strength-1)*image_features\n",
"image"
],
"metadata": {
"id": "I_-GOwFPKkha"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Save the reference prior to running the Interrogator"
],
"metadata": {
"id": "zeu6JcM-mk9z"
}
},
{
"cell_type": "code",
"source": [
"# @title ⚄ Save the reference\n",
"try: ref\n",
"except: ref = torch.zeros(dim)\n",
"_ref = {}\n",
"_ref['weights'] = ref.to(dot_dtype)\n",
"%cd /content/\n",
"save_file(_ref , 'reference.safetensors' )"
],
"metadata": {
"id": "lOQuTPfBMK82"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @title ⚄ Run the CLIP interrogator on the saved reference\n",
"LIST_SIZE = 1000 # @param {type:'number' , placeholder:'set how large the list should be'}\n",
"START_AT = 0 # @param {type:'number' , placeholder:'set how large the list should be'}\n",
"# @markdown -----\n",
"# @markdown Select vocab\n",
"general = False # @param {type:\"boolean\"}\n",
"civit9 = True # @param {type:\"boolean\"}\n",
"fanfic1 = False # @param {type:\"boolean\"}\n",
"fanfic2 = False # @param {type:\"boolean\"}\n",
"# @markdown -----\n",
"# @title ⚄ New interrogator code using quantized text corpus\n",
"%cd /content/\n",
"_ref = load_file('reference.safetensors' )\n",
"ref = _ref['weights'].to(dot_dtype)\n",
"# @markdown 📝 Enhance/Penalize Similarity and skip items containing word(s)\n",
"POS1 = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
"POS2 = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
"NEG = ''# @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
"SKIP = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
"min_wordcount = 0 # @param {type:\"slider\", min:0, max:20, step:1}\n",
"def isBlacklisted(_txt):\n",
" blacklist = SKIP.lower().replace('</w>' , ' ').replace('{' , '').replace('}' , '').replace('|' , ',').strip()\n",
" if blacklist == '': return False\n",
" txt = _txt.lower().strip()\n",
" if len(txt)<min_wordcount: return True\n",
" if txt.isnumeric(): return True\n",
" #-----#\n",
" for item in list(blacklist.split(',')):\n",
" if item.strip() == '' : continue\n",
" if txt.find(item.strip())> -1 : return True\n",
" #------#\n",
" found = False\n",
" alphabet = 'abcdefghijklmnopqrstuvxyz'\n",
" for letter in alphabet:\n",
" found = txt.find(letter)>-1\n",
" if found:break\n",
" #------#\n",
" return not found\n",
"# @markdown -----\n",
"# @markdown logarithmic prompt strength x for value 10^(x-1)\n",
"_POS1 = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
"_POS2 = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
"_NEG = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
"# @markdown -----\n",
"for _item in POS1.split(','):\n",
" item = _item.strip()\n",
" if item == '':continue\n",
" inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
" ref = ref + math.pow(10,_POS1-1) * model.get_text_features(**inputs)[0]\n",
"#-------#\n",
"for _item in POS2.split(','):\n",
" item = _item.strip()\n",
" if item == '':continue\n",
" inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
" ref = ref + math.pow(10,_POS2-1) * model.get_text_features(**inputs)[0]\n",
"#-------#\n",
"for _item in NEG.split(','):\n",
" item = _item.strip()\n",
" if item == '':continue\n",
" inputs = tokenizer(text = item.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n",
" ref = ref + math.pow(10,_NEG-1) * model.get_text_features(**inputs)[0]\n",
"#------#\n",
"ref = (ref/ref.norm(p=2, dim=-1, keepdim=True)).to(dtype = dot_dtype)\n",
"vocab_to_load = ''\n",
"if (general): vocab_to_load = vocab_to_load + 'general , '\n",
"if (civit9): vocab_to_load = vocab_to_load + 'civit9 , '\n",
"if (fanfic1): vocab_to_load = vocab_to_load + 'fanfic1 , '\n",
"if (fanfic2): vocab_to_load = vocab_to_load + 'fanfic2 , '\n",
"vocab_to_load = (vocab_to_load +'}').replace(' , }' , '')\n",
"multi = vocab_to_load.find(',')>-1\n",
"\n",
"#-----#\n",
"prompts_folder = f'{home_directory}fusion-t2i-generator-data/vocab-v2/text'\n",
"encodings_folder = f'{home_directory}fusion-t2i-generator-data/vocab-v2/text_encodings'\n",
"#----#\n",
"scale = 0.0043\n",
"size = 0\n",
"#------#\n",
"total_items = 0\n",
"for filename in os.listdir(prompts_folder):\n",
" if (not general and filename.find('general')>-1):continue\n",
" if (not civit9 and filename.find('civit9')>-1):continue\n",
" if (not fanfic1 and filename.find('fanfic1')>-1):continue\n",
" if (not fanfic2 and filename.find('fanfic2')>-1):continue\n",
" size = size + LIST_SIZE\n",
"#-------#\n",
"similiar_sims = torch.zeros(size)\n",
"similiar_prompts = {}\n",
"_index = 0\n",
"#-------#\n",
"similiar_encodings = {}\n",
"for filename in os.listdir(prompts_folder):\n",
" if (not general and filename.find('general')>-1):continue\n",
" if (not civit9 and filename.find('civit9')>-1):continue\n",
" if (not fanfic1 and filename.find('fanfic1')>-1):continue\n",
" if (not fanfic2 and filename.find('fanfic2')>-1):continue\n",
" #------#\n",
" root_filename = filename.replace('.json', '')\n",
" %cd {prompts_folder}\n",
" prompts = {}\n",
" with open(f'{root_filename}.json', 'r') as f:\n",
" data = json.load(f).items()\n",
" for key,value in data:\n",
" prompts[key] = value\n",
" num_items = int(prompts['num_items'])\n",
" total_items = total_items + num_items\n",
"\n",
" #------#\n",
" try:vocab_loaded\n",
" except:\n",
" vocab_loaded = 'first'\n",
" #-----#\n",
"\n",
" if vocab_loaded == 'first' or (vocab_loaded != vocab_to_load and not multi):\n",
" %cd {encodings_folder}\n",
" _text_encodings = load_file(f'{root_filename}.safetensors')['weights'].to(torch.uint8)\n",
" text_encodings = torch.zeros(num_items , dim)\n",
" tmp = torch.ones(dim).to(dot_dtype)\n",
" for index in range(num_items):\n",
" text_encodings[index] = torch.sub(_text_encodings[index][1:dim+1].to(dot_dtype) , tmp , alpha= _text_encodings[index][0].to(dot_dtype))\n",
" vocab_loaded = vocab_to_load\n",
" #------#\n",
"\n",
"\n",
" sims = torch.matmul(text_encodings*scale, ref.t())\n",
" sorted , indices = torch.sort(sims , dim=0 , descending = True)\n",
" #-----#\n",
" for index in range(LIST_SIZE + START_AT):\n",
" if index<START_AT: continue\n",
" key = indices[index].item()\n",
" try:prompt = prompts[f'{key}']\n",
" except:continue\n",
" if(isBlacklisted(prompt)):continue\n",
" #-------#\n",
" similiar_sims[_index] = torch.tensor(round(sims[key].item(), 5))\n",
" similiar_prompts[f'{_index}'] = prompt\n",
" _index = _index + 1\n",
" #-------#\n",
" continue\n",
"#---------#\n",
"print(f'\\n\\nProcessed entire list of {total_items} items to find closest match. Saved closest matching indices {START_AT} to {START_AT + LIST_SIZE} as the dict \"similiar_prompts\" with {LIST SIZE} items. \\n\\n')\n"
],
"metadata": {
"id": "kOYZ8Ajn-DD8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"\n",
"# @title ⚄ Printing results from text corpus\n",
"sorted , indices = torch.sort(similiar_sims , dim=0 , descending = True)\n",
"include_similiarity = False # @param {type:\"boolean\"}\n",
"print_as_list = False # @param {type:\"boolean\"}\n",
"N = 7 # @param {type:\"slider\", min:0, max:10, step:1}\n",
"\n",
"if(print_as_list):\n",
" for index in range(LIST_SIZE):\n",
" key = indices[index].item()\n",
" sim = similiar_sims[key].item()\n",
" prompt = similiar_prompts[f'{key}']\n",
" if include_similiarity :print(f'{prompt} - {round(sim*100,1)} %')\n",
" else: print(f'{prompt}')\n",
"#-------#\n",
"else:\n",
" prompt = ''\n",
" for iter in range(N):\n",
" prompt = prompt + '{'\n",
" for index in range(LIST_SIZE):\n",
" key = indices[index].item()\n",
" sim = similiar_sims[key].item()\n",
" prompt = prompt + fix_bad_symbols(similiar_prompts[f'{key}']) + '|'\n",
" #-----#\n",
" prompt = (prompt + '}').replace('|}', '} ')\n",
" #------#\n",
" print(f'\\ Similiar prompts: \\n\\n {prompt} \\n\\n')\n",
" image\n",
"#-----#\n"
],
"metadata": {
"id": "XOMkIKc9-wZz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"OTHER STUFF BELOW - Code for the modules below are work-in-progress."
],
"metadata": {
"id": "FRIqYJDEebpf"
}
},
{
"cell_type": "markdown",
"source": [
"The savefile can be used here : https://perchance.org/fusion-ai-image-generator"
],
"metadata": {
"id": "JldNmWy1iyvK"
}
},
{
"cell_type": "code",
"source": [
"# @title \t⚄ Create fusion-generator .json savefile from result\n",
"filename = 'blank.json'\n",
"path = '/content/text-to-image-prompts/fusion/'\n",
"\n",
"print(f'reading {filename}....')\n",
"_index = 0\n",
"%cd {path}\n",
"with open(f'{filename}', 'r') as f:\n",
" data = json.load(f)\n",
"#------#\n",
"_df = pd.DataFrame({'count': data})['count']\n",
"_savefile = {\n",
" key : value for key, value in _df.items()\n",
"}\n",
"#------#\n",
"from safetensors.torch import load_file\n",
"import json , os , torch\n",
"import pandas as pd\n",
"#----#\n",
"def my_mkdirs(folder):\n",
" if os.path.exists(folder)==False:\n",
" os.makedirs(folder)\n",
"#------#\n",
"savefile_prompt = ''\n",
"for i in range(N) : savefile_prompt = savefile_prompt + ' ' + __prompts\n",
"_savefile['main'] = savefile_prompt.replace('\\n', ' ').replace(' ', ' ').replace(' ', ' ')\n",
"#------#\n",
"save_filename = f'fusion_C05_X7_1000_{PROMPT_INDEX}.json'\n",
"output_folder = '/content/output/savefiles/'\n",
"my_mkdirs(output_folder)\n",
"#-----#\n",
"%cd {output_folder}\n",
"print(f'Saving segment {save_filename} to {output_folder}...')\n",
"with open(save_filename, 'w') as f:\n",
" json.dump(_savefile, f)\n"
],
"metadata": {
"id": "Q7vpNAXQilbf",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @title \t⚄ Create a savefile-set from the entire range of pre-encoded items\n",
"\n",
"# @markdown 📥 Load the data (only required one time)\n",
"load_the_data = True # @param {type:\"boolean\"}\n",
"\n",
"import math\n",
"from safetensors.torch import load_file\n",
"import json , os , torch\n",
"import pandas as pd\n",
"from PIL import Image\n",
"import requests\n",
"\n",
"def my_mkdirs(folder):\n",
" if os.path.exists(folder)==False:\n",
" os.makedirs(folder)\n",
"\n",
"# @markdown ⚖️ Set the value for C in the reference <br> <br> sim = C* text_enc + image_enc*(1-C) <br><br>\n",
"\n",
"C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
"\n",
"# @markdown 🚫 Penalize similarity to this prompt(optional)\n",
"if(load_the_data):\n",
" target_prompts , target_text_encodings , urls , target_image_encodings , NUM_ITEMS = getPromptsAndLinks('/content/text-to-image-prompts/fusion')\n",
" from transformers import AutoTokenizer\n",
" tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
" from transformers import CLIPProcessor, CLIPModel\n",
" processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n",
" model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
" logit_scale = model.logit_scale.exp() #logit_scale = 100.00000762939453\n",
"#---------#\n",
"\n",
"filename = 'blank.json'\n",
"path = '/content/text-to-image-prompts/fusion/'\n",
"print(f'reading {filename}....')\n",
"_index = 0\n",
"%cd {path}\n",
"with open(f'{filename}', 'r') as f:\n",
" data = json.load(f)\n",
"#------#\n",
"_df = pd.DataFrame({'count': data})['count']\n",
"_blank = {\n",
" key : value for key, value in _df.items()\n",
"}\n",
"#------#\n",
"\n",
"root_savefile_name = 'fusion_C05_X7'\n",
"\n",
"%cd /content/\n",
"output_folder = '/content/output/savefiles/'\n",
"my_mkdirs(output_folder)\n",
"my_mkdirs('/content/output2/savefiles/')\n",
"my_mkdirs('/content/output3/savefiles/')\n",
"my_mkdirs('/content/output4/savefiles/')\n",
"my_mkdirs('/content/output5/savefiles/')\n",
"my_mkdirs('/content/output6/savefiles/')\n",
"my_mkdirs('/content/output7/savefiles/')\n",
"my_mkdirs('/content/output8/savefiles/')\n",
"my_mkdirs('/content/output9/savefiles/')\n",
"my_mkdirs('/content/output10/savefiles/')\n",
"my_mkdirs('/content/output11/savefiles/')\n",
"my_mkdirs('/content/output12/savefiles/')\n",
"my_mkdirs('/content/output13/savefiles/')\n",
"\n",
"\n",
"NEG = '' # @param {type:'string'}\n",
"strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.1}\n",
"\n",
"for index in range(1667):\n",
"\n",
" PROMPT_INDEX = index\n",
" prompt = target_prompts[f'{index}']\n",
" url = urls[f'{index}']\n",
" if url.find('perchance')>-1:\n",
" image = Image.open(requests.get(url, stream=True).raw)\n",
" else: continue #print(\"(No image for this ID)\")\n",
"\n",
" print(f\"no. {PROMPT_INDEX} : '{prompt}'\")\n",
" text_features_A = target_text_encodings[f'{index}']\n",
" image_features_A = target_image_encodings[f'{index}']\n",
" # text-similarity\n",
" sims = C * torch.matmul(text_tensor, text_features_A.t())\n",
"\n",
" neg_sims = 0*sims\n",
" if(NEG != ''):\n",
" # Get text features for user input\n",
" inputs = tokenizer(text = NEG, padding=True, return_tensors=\"pt\")\n",
" text_features_NEG = model.get_text_features(**inputs)\n",
" text_features_NEG = text_features_A/text_features_A.norm(p=2, dim=-1, keepdim=True)\n",
" # text-similarity\n",
" neg_sims = strength*torch.matmul(text_tensor, text_features_NEG.t())\n",
" #------#\n",
"\n",
" # plus image-similarity\n",
" sims = sims + (1-C) * torch.matmul(text_tensor, image_features_A.t()) * logit_scale\n",
"\n",
" # minus NEG-similarity\n",
" sims = sims - neg_sims\n",
"\n",
" # Sort the items\n",
" sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
"\n",
" # @markdown Repeat output N times\n",
" RANGE = 1000\n",
" NUM_CHUNKS = 10+\n",
" separator = '|'\n",
" _savefiles = {}\n",
" #-----#\n",
" for chunk in range(NUM_CHUNKS):\n",
" if chunk=<10:continue\n",
" start_at_index = chunk * RANGE\n",
" _prompts = ''\n",
" for _index in range(start_at_index + RANGE):\n",
" if _index < start_at_index : continue\n",
" index = indices[_index].item()\n",
" prompt = prompts[f'{index}']\n",
" _prompts = _prompts.replace(prompt + separator,'')\n",
" _prompts = _prompts + prompt + separator\n",
" #------#\n",
" _prompts = fix_bad_symbols(_prompts)\n",
" _prompts = ('{' + _prompts + '}').replace(separator + '}', '}')\n",
" _savefiles[f'{chunk}'] = _prompts\n",
" #---------#\n",
" save_filename = f'{root_savefile_name}_{start_at_index + RANGE}_{PROMPT_INDEX}.json'\n",
"\n",
"\n",
" if (chunk=<20 && chunk>10): %cd '/content/output2/savefiles/'\n",
" if (chunk<=30 && chunk>20): %cd '/content/output3/savefiles/'\n",
" if (chunk=<40 && chunk>30): %cd '/content/output4/savefiles/'\n",
" if (chunk<=50 && chunk>40): %cd '/content/output5/savefiles/'\n",
" if (chunk=<60 && chunk>50): %cd '/content/output6/savefiles/'\n",
" if (chunk<=70 && chunk>60): %cd '/content/output7/savefiles/'\n",
" if (chunk=<80 && chunk>70): %cd '/content/output8/savefiles/'\n",
" if (chunk<=90 && chunk>80): %cd '/content/output9/savefiles/'\n",
" if (chunk=<100 && chunk>90): %cd '/content/output10/savefiles/'\n",
" if (chunk<=110 && chunk>100): %cd '/content/output11/savefiles/'\n",
" if (chunk=<120 && chunk>110): %cd '/content/output12/savefiles/'\n",
" if (chunk<=130 && chunk>120): %cd '/content/output13/savefiles/'\n",
"\n",
"\n",
" #------#\n",
" print(f'Saving savefile {save_filename} to {output_folder}...')\n",
" with open(save_filename, 'w') as f:\n",
" json.dump(_savefiles, f)\n",
" #---------#\n",
" continue\n",
"#-----------#"
],
"metadata": {
"id": "x1uAVXZEoL0T",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Determine if this notebook is running on Colab or Kaggle\n",
"#Use https://www.kaggle.com/ if Google Colab GPU is busy\n",
"home_directory = '/content/'\n",
"using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n",
"if using_Kaggle : home_directory = '/kaggle/working/'\n",
"%cd {home_directory}\n",
"#-------#\n",
"\n",
"# @title Download the text_encodings as .zip\n",
"import os\n",
"%cd {home_directory}\n",
"#os.remove(f'{home_directory}results.zip')\n",
"root_output_folder = home_directory + 'output/'\n",
"zip_dest = f'/content/results.zip' #drive/MyDrive\n",
"!zip -r {zip_dest} {root_output_folder}"
],
"metadata": {
"id": "zivBNrw9uSVD",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @title \t⚄ Quick fix for normalizing encoded text corpus tensors\n",
"\n",
"import os\n",
"my_mkdirs('/content/output')\n",
"my_mkdirs('/content/output/text_encodings')\n",
"\n",
"for filename in os.listdir(f'{prompts_folder}'):\n",
" %cd {prompts_folder}\n",
" prompts = {}\n",
" with open(f'{filename}', 'r') as f:\n",
" data = json.load(f).items()\n",
" for key,value in data:\n",
" prompts[key] = value\n",
" #------#\n",
" num_items = int(prompts['num_items'])\n",
"\n",
" %cd {encodings_folder}\n",
" enc_filename = filename.replace('json', 'safetensors')\n",
" _text_encodings = load_file(f'{enc_filename}')['weights'].to(torch.uint8)\n",
" text_encodings = torch.zeros(num_items , dim)\n",
" tmp = torch.ones(dim)\n",
" tmp2 = torch.tensor(1/0.0043)\n",
" zero_point = 0\n",
" for index in range(num_items):\n",
" text_encodings[index] = torch.tensor(0.0043) * torch.sub(_text_encodings[index][1:dim+1] , tmp , alpha= _text_encodings[index][0]).to(torch.float32)\n",
" text_encodings[index] = tmp2*text_encodings[index]/text_encodings[index].norm(p=2, dim=-1, keepdim = True)\n",
" test = torch.round( torch.add(text_encodings[index],tmp*zero_point))\n",
" less_than_zero = test<0\n",
" while(torch.any(less_than_zero).item()):\n",
" zero_point = zero_point + 1\n",
" test = torch.round( torch.add(text_encodings[index],tmp*zero_point))\n",
" less_than_zero = test<0\n",
" #------#\n",
" _text_encodings[index][0] = zero_point\n",
" _text_encodings[index][1:dim+1] = test\n",
" #-------#\n",
" %cd /content/output/text_encodings\n",
"\n",
" tmp = {}\n",
" tmp['weights'] = _text_encodings.to(torch.uint8)\n",
" tmp['num_items'] = torch.tensor(num_items).to(torch.uint8)\n",
" tmp['scale'] = torch.tensor(0.0043)\n",
" save_file(tmp , f'{enc_filename}')\n",
"#------#"
],
"metadata": {
"cellView": "form",
"id": "9qgHW1Wr7kZn"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Check the average value for this set\n",
"sims = torch.matmul(vocab_encodings.dequantize(),average.t())\n",
"sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
"for index in range(10):\n",
" print(prompts[f'{indices[index].item()}'])"
],
"metadata": {
"id": "XNHz0hfhHRUu"
},
"execution_count": null,
"outputs": []
}
]
} |