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  1. sd_token_similarity_calculator.ipynb +137 -2193
sd_token_similarity_calculator.ipynb CHANGED
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1731
  {
@@ -1746,10 +34,10 @@
1746
  "# Load the tokens into the colab\n",
1747
  "!git clone https://huggingface.co/datasets/codeShare/sd_tokens\n",
1748
  "import torch\n",
1749
- "from torch import linalg as LA\n",
1750
  "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
 
1751
  "%cd /content/sd_tokens\n",
1752
- "token = torch.load('sd15_tensors.pt', map_location=device, weights_only=True)\n",
1753
  "#-----#\n",
1754
  "VOCAB_FILENAME = 'tokens_most_similiar_to_girl'\n",
1755
  "ACTIVE_IMG = ''\n",
@@ -1852,10 +140,29 @@
1852
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1853
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1854
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1855
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1856
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1857
- "execution_count": null,
1858
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1859
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1860
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1861
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@@ -2005,470 +312,10 @@
2005
  "# See this link for additional stuff to do with shelve: https://docs.python.org/3/library/shelve.html"
2006
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2076
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2118
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2120
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2121
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2126
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2127
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2129
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2130
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2131
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2132
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2133
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2134
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2135
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2136
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2137
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2138
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2140
- }
2141
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2142
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2143
- },
2144
- {
2145
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2146
- "name": "stderr",
2147
- "text": [
2148
- "<ipython-input-2-f767de8bdd18>:22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
2149
- " A = torch.tensor(token[id_A])\n",
2150
- "<ipython-input-2-f767de8bdd18>:28: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
2151
- " C = torch.tensor(token[id_C])\n",
2152
- "<ipython-input-2-f767de8bdd18>:84: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
2153
- " B = torch.tensor(token[id_B])\n"
2154
- ]
2155
- },
2156
- {
2157
- "output_type": "stream",
2158
- "name": "stdout",
2159
- "text": [
2160
- "[49406, 49407]\n",
2161
- "Tokenized prompt 'mix_with' tensor C is a random valued tensor with no ID\n",
2162
- "The similarity between A 'banana ' and C 'token C of random type' is -1.97 %\n",
2163
- "No operation\n",
2164
- "Calculated all cosine-similarities between the token banana with Id_A = 8922 with the the rest of the 49407 tokens as a 1x49407 tensor\n",
2165
- "//---//\n",
2166
- "\n",
2167
- "Here is the result : \n",
2168
- "\n",
2169
- "banana \n",
2170
- "similiarity = 100.0 %\n",
2171
- "--------\n",
2172
- "bananas \n",
2173
- "similiarity = 38.93 %\n",
2174
- "--------\n",
2175
- "banan-\n",
2176
- "similiarity = 30.8 %\n",
2177
- "--------\n",
2178
- " \n",
2179
- "similiarity = 27.12 %\n",
2180
- "--------\n",
2181
- "pineapple \n",
2182
- "similiarity = 19.7 %\n",
2183
- "--------\n",
2184
- "chicken \n",
2185
- "similiarity = 19.24 %\n",
2186
- "--------\n",
2187
- "potassium \n",
2188
- "similiarity = 19.21 %\n",
2189
- "--------\n",
2190
- "sausage \n",
2191
- "similiarity = 19.07 %\n",
2192
- "--------\n",
2193
- "lemon \n",
2194
- "similiarity = 18.82 %\n",
2195
- "--------\n",
2196
- "orange \n",
2197
- "similiarity = 18.42 %\n",
2198
- "--------\n",
2199
- "peanut \n",
2200
- "similiarity = 17.84 %\n",
2201
- "--------\n",
2202
- "parachute \n",
2203
- "similiarity = 17.19 %\n",
2204
- "--------\n",
2205
- "duck-\n",
2206
- "similiarity = 16.8 %\n",
2207
- "--------\n",
2208
- "yellow \n",
2209
- "similiarity = 16.21 %\n",
2210
- "--------\n",
2211
- "grape \n",
2212
- "similiarity = 16.19 %\n",
2213
- "--------\n",
2214
- "kangaroo \n",
2215
- "similiarity = 16.13 %\n",
2216
- "--------\n",
2217
- "apple \n",
2218
- "similiarity = 16.13 %\n",
2219
- "--------\n",
2220
- "tangerine \n",
2221
- "similiarity = 16.08 %\n",
2222
- "--------\n",
2223
- "giraffe \n",
2224
- "similiarity = 16.04 %\n",
2225
- "--------\n",
2226
- "mango \n",
2227
- "similiarity = 16.03 %\n",
2228
- "--------\n",
2229
- "rubber \n",
2230
- "similiarity = 15.95 %\n",
2231
- "--------\n",
2232
- "bamboo \n",
2233
- "similiarity = 15.88 %\n",
2234
- "--------\n",
2235
- "umbrella \n",
2236
- "similiarity = 15.82 %\n",
2237
- "--------\n",
2238
- "nutella \n",
2239
- "similiarity = 15.69 %\n",
2240
- "--------\n",
2241
- "ferrari \n",
2242
- "similiarity = 15.69 %\n",
2243
- "--------\n",
2244
- "oranges \n",
2245
- "similiarity = 15.65 %\n",
2246
- "--------\n",
2247
- "peanuts \n",
2248
- "similiarity = 15.62 %\n",
2249
- "--------\n",
2250
- "ali \n",
2251
- "similiarity = 15.49 %\n",
2252
- "--------\n",
2253
- "cucumber \n",
2254
- "similiarity = 15.32 %\n",
2255
- "--------\n",
2256
- "potato \n",
2257
- "similiarity = 15.22 %\n",
2258
- "--------\n",
2259
- "monkey \n",
2260
- "similiarity = 15.2 %\n",
2261
- "--------\n",
2262
- "croissant \n",
2263
- "similiarity = 15.18 %\n",
2264
- "--------\n",
2265
- "papaya \n",
2266
- "similiarity = 15.17 %\n",
2267
- "--------\n",
2268
- "christmas \n",
2269
- "similiarity = 15.12 %\n",
2270
- "--------\n",
2271
- "sandwich \n",
2272
- "similiarity = 15.0 %\n",
2273
- "--------\n",
2274
- "rainbow \n",
2275
- "similiarity = 14.98 %\n",
2276
- "--------\n",
2277
- "tomato \n",
2278
- "similiarity = 14.96 %\n",
2279
- "--------\n",
2280
- "martini \n",
2281
- "similiarity = 14.93 %\n",
2282
- "--------\n",
2283
- "cabaret \n",
2284
- "similiarity = 14.83 %\n",
2285
- "--------\n",
2286
- "ginger \n",
2287
- "similiarity = 14.82 %\n",
2288
- "--------\n",
2289
- "animal \n",
2290
- "similiarity = 14.76 %\n",
2291
- "--------\n",
2292
- "vanilla \n",
2293
- "similiarity = 14.73 %\n",
2294
- "--------\n",
2295
- "mustache \n",
2296
- "similiarity = 14.64 %\n",
2297
- "--------\n",
2298
- "lime \n",
2299
- "similiarity = 14.62 %\n",
2300
- "--------\n",
2301
- "sickle \n",
2302
- "similiarity = 14.6 %\n",
2303
- "--------\n",
2304
- "vista \n",
2305
- "similiarity = 14.53 %\n",
2306
- "--------\n",
2307
- "coconut \n",
2308
- "similiarity = 14.52 %\n",
2309
- "--------\n",
2310
- "kara \n",
2311
- "similiarity = 14.46 %\n",
2312
- "--------\n",
2313
- "alligator \n",
2314
- "similiarity = 14.39 %\n",
2315
- "--------\n",
2316
- "blueberry \n",
2317
- "similiarity = 14.34 %\n",
2318
- "--------\n",
2319
- "squirrel \n",
2320
- "similiarity = 14.29 %\n",
2321
- "--------\n",
2322
- "atore \n",
2323
- "similiarity = 14.19 %\n",
2324
- "--------\n",
2325
- "watermelon \n",
2326
- "similiarity = 14.13 %\n",
2327
- "--------\n",
2328
- "nana \n",
2329
- "similiarity = 14.09 %\n",
2330
- "--------\n",
2331
- "latex \n",
2332
- "similiarity = 14.08 %\n",
2333
- "--------\n",
2334
- "agricultural \n",
2335
- "similiarity = 14.02 %\n",
2336
- "--------\n",
2337
- "zucchini \n",
2338
- "similiarity = 14.0 %\n",
2339
- "--------\n",
2340
- "saxophone \n",
2341
- "similiarity = 13.93 %\n",
2342
- "--------\n",
2343
- "mozzarella \n",
2344
- "similiarity = 13.91 %\n",
2345
- "--------\n",
2346
- "eggplant \n",
2347
- "similiarity = 13.9 %\n",
2348
- "--------\n",
2349
- "pickle \n",
2350
- "similiarity = 13.89 %\n",
2351
- "--------\n",
2352
- "tortilla \n",
2353
- "similiarity = 13.88 %\n",
2354
- "--------\n",
2355
- "maniac \n",
2356
- "similiarity = 13.84 %\n",
2357
- "--------\n",
2358
- "milk \n",
2359
- "similiarity = 13.83 %\n",
2360
- "--------\n",
2361
- "cellphone \n",
2362
- "similiarity = 13.78 %\n",
2363
- "--------\n",
2364
- "duck \n",
2365
- "similiarity = 13.73 %\n",
2366
- "--------\n",
2367
- "umbrel-\n",
2368
- "similiarity = 13.71 %\n",
2369
- "--------\n",
2370
- "fanny \n",
2371
- "similiarity = 13.69 %\n",
2372
- "--------\n",
2373
- "twister \n",
2374
- "similiarity = 13.67 %\n",
2375
- "--------\n",
2376
- "moustache \n",
2377
- "similiarity = 13.66 %\n",
2378
- "--------\n",
2379
- "manafort \n",
2380
- "similiarity = 13.66 %\n",
2381
- "--------\n",
2382
- "grapefruit \n",
2383
- "similiarity = 13.6 %\n",
2384
- "--------\n",
2385
- "broom \n",
2386
- "similiarity = 13.59 %\n",
2387
- "--------\n",
2388
- "scorpion \n",
2389
- "similiarity = 13.59 %\n",
2390
- "--------\n",
2391
- "fruit-\n",
2392
- "similiarity = 13.57 %\n",
2393
- "--------\n",
2394
- "agan-\n",
2395
- "similiarity = 13.53 %\n",
2396
- "--------\n",
2397
- "sunflower \n",
2398
- "similiarity = 13.49 %\n",
2399
- "--------\n",
2400
- "banc-\n",
2401
- "similiarity = 13.46 %\n",
2402
- "--------\n",
2403
- "literature \n",
2404
- "similiarity = 13.45 %\n",
2405
- "--------\n",
2406
- "pelican \n",
2407
- "similiarity = 13.43 %\n",
2408
- "--------\n",
2409
- "breakfast \n",
2410
- "similiarity = 13.42 %\n",
2411
- "--------\n",
2412
- "pear \n",
2413
- "similiarity = 13.42 %\n",
2414
- "--------\n",
2415
- "orange-\n",
2416
- "similiarity = 13.4 %\n",
2417
- "--------\n",
2418
- "monet \n",
2419
- "similiarity = 13.4 %\n",
2420
- "--------\n",
2421
- "snake \n",
2422
- "similiarity = 13.32 %\n",
2423
- "--------\n",
2424
- "vampire \n",
2425
- "similiarity = 13.32 %\n",
2426
- "--------\n",
2427
- "cinnamon \n",
2428
- "similiarity = 13.3 %\n",
2429
- "--------\n",
2430
- "strawberries \n",
2431
- "similiarity = 13.29 %\n",
2432
- "--------\n",
2433
- "butternut \n",
2434
- "similiarity = 13.22 %\n",
2435
- "--------\n",
2436
- "sausages \n",
2437
- "similiarity = 13.22 %\n",
2438
- "--------\n",
2439
- "iphone \n",
2440
- "similiarity = 13.21 %\n",
2441
- "--------\n",
2442
- "egg-\n",
2443
- "similiarity = 13.2 %\n",
2444
- "--------\n",
2445
- "capu-\n",
2446
- "similiarity = 13.2 %\n",
2447
- "--------\n",
2448
- "mannequin \n",
2449
- "similiarity = 13.19 %\n",
2450
- "--------\n",
2451
- "cucumbers \n",
2452
- "similiarity = 13.16 %\n",
2453
- "--------\n",
2454
- "champagne \n",
2455
- "similiarity = 13.15 %\n",
2456
- "--------\n",
2457
- "triangle \n",
2458
- "similiarity = 13.14 %\n",
2459
- "--------\n",
2460
- "apples \n",
2461
- "similiarity = 13.09 %\n",
2462
- "--------\n",
2463
- "dynamite \n",
2464
- "similiarity = 13.08 %\n",
2465
- "--------\n",
2466
- "chocolate \n",
2467
- "similiarity = 13.08 %\n",
2468
- "--------\n"
2469
- ]
2470
- }
2471
- ]
2472
  },
2473
  {
2474
  "cell_type": "markdown",
@@ -2535,7 +382,7 @@
2535
  "height": 1000
2536
  }
2537
  },
2538
- "execution_count": 3,
2539
  "outputs": [
2540
  {
2541
  "output_type": "display_data",
@@ -2633,7 +480,7 @@
2633
  "base_uri": "https://localhost:8080/"
2634
  }
2635
  },
2636
- "execution_count": 5,
2637
  "outputs": [
2638
  {
2639
  "output_type": "stream",
@@ -3026,28 +873,125 @@
3026
  {
3027
  "cell_type": "code",
3028
  "source": [
3029
- "# @title Make your own text_encodings .db file for later use (rate is roughly 1K encodings per minute, so plan accordingly)\n",
 
 
 
3030
  "from transformers import AutoTokenizer\n",
3031
  "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
3032
  "from transformers import CLIPProcessor, CLIPModel\n",
3033
  "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n",
3034
- "model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
3035
  "\n",
3036
  "# Save results as .db file\n",
3037
  "import shelve\n",
3038
- "d = shelve.open('my_text_encodings')\n",
3039
- "for index in range(NUM_PREFIX):\n",
3040
- " inputs = tokenizer(text = get_prefix(index)+'girl ', padding=True, return_tensors=\"pt\")\n",
3041
- " text_features = model.get_text_features(**inputs)\n",
3042
- " d[f'{index}'] = text_features\n",
3043
  "#----#\n",
3044
- "\n",
3045
  "d.close() #close the file\n",
3046
  "\n"
3047
  ],
3048
  "metadata": {
3049
  "id": "9ZiTsF9jV0TV"
3050
  },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3051
  "execution_count": null,
3052
  "outputs": []
3053
  },
 
3
  "nbformat_minor": 0,
4
  "metadata": {
5
  "colab": {
6
+ "provenance": [],
7
+ "gpuType": "T4"
8
  },
9
  "kernelspec": {
10
  "name": "python3",
 
13
  "language_info": {
14
  "name": "python"
15
  },
16
+ "accelerator": "GPU"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
17
  },
18
  "cells": [
19
  {
 
34
  "# Load the tokens into the colab\n",
35
  "!git clone https://huggingface.co/datasets/codeShare/sd_tokens\n",
36
  "import torch\n",
 
37
  "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
38
+ "from torch import linalg as LA\n",
39
  "%cd /content/sd_tokens\n",
40
+ "token = torch.load('sd15_tensors.pt', map_location= torch.device('cpu'), weights_only=True)\n",
41
  "#-----#\n",
42
  "VOCAB_FILENAME = 'tokens_most_similiar_to_girl'\n",
43
  "ACTIVE_IMG = ''\n",
 
140
  ],
141
  "metadata": {
142
  "id": "Ch9puvwKH1s3",
143
+ "collapsed": true,
144
+ "colab": {
145
+ "base_uri": "https://localhost:8080/"
146
+ },
147
+ "outputId": "2333a33b-1344-4a14-bee6-060d98167715"
148
  },
149
+ "execution_count": 1,
150
+ "outputs": [
151
+ {
152
+ "output_type": "stream",
153
+ "name": "stdout",
154
+ "text": [
155
+ "Cloning into 'sd_tokens'...\n",
156
+ "remote: Enumerating objects: 72, done.\u001b[K\n",
157
+ "remote: Counting objects: 100% (69/69), done.\u001b[K\n",
158
+ "remote: Compressing objects: 100% (69/69), done.\u001b[K\n",
159
+ "remote: Total 72 (delta 24), reused 0 (delta 0), pack-reused 3 (from 1)\u001b[K\n",
160
+ "Unpacking objects: 100% (72/72), 1.34 MiB | 1.65 MiB/s, done.\n",
161
+ "Filtering content: 100% (10/10), 899.76 MiB | 50.12 MiB/s, done.\n",
162
+ "/content/sd_tokens\n"
163
+ ]
164
+ }
165
+ ]
166
  },
167
  {
168
  "cell_type": "code",
 
312
  "# See this link for additional stuff to do with shelve: https://docs.python.org/3/library/shelve.html"
313
  ],
314
  "metadata": {
315
+ "id": "iWeFnT1gAx6A"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
316
  },
317
+ "execution_count": null,
318
+ "outputs": []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
319
  },
320
  {
321
  "cell_type": "markdown",
 
382
  "height": 1000
383
  }
384
  },
385
+ "execution_count": null,
386
  "outputs": [
387
  {
388
  "output_type": "display_data",
 
480
  "base_uri": "https://localhost:8080/"
481
  }
482
  },
483
+ "execution_count": null,
484
  "outputs": [
485
  {
486
  "output_type": "stream",
 
873
  {
874
  "cell_type": "code",
875
  "source": [
876
+ "# @title Make your own text_encodings .db file for later use\n",
877
+ "\n",
878
+ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
879
+ "\n",
880
  "from transformers import AutoTokenizer\n",
881
  "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
882
  "from transformers import CLIPProcessor, CLIPModel\n",
883
  "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n",
884
+ "model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\").to(device)\n",
885
  "\n",
886
  "# Save results as .db file\n",
887
  "import shelve\n",
888
+ "d = shelve.open('green_-encoded_suffix')\n",
889
+ "for index in range(NUM_SUFFIX):\n",
890
+ " inputs = tokenizer(text = 'green '+get_suffix(index), padding=True, return_tensors=\"pt\").to(device)\n",
891
+ " text_features = model.get_text_features(**inputs).to(device)\n",
892
+ " d[f'{index}'] = text_features.to('cpu')\n",
893
  "#----#\n",
 
894
  "d.close() #close the file\n",
895
  "\n"
896
  ],
897
  "metadata": {
898
  "id": "9ZiTsF9jV0TV"
899
  },
900
+ "execution_count": 6,
901
+ "outputs": []
902
+ },
903
+ {
904
+ "cell_type": "code",
905
+ "source": [
906
+ "# Save results as .db file\n",
907
+ "import shelve\n",
908
+ "d = shelve.open('encoded_suffix-_knee')\n",
909
+ "for index in range(NUM_SUFFIX):\n",
910
+ " inputs = tokenizer(text = get_suffix(index)+' knee', padding=True, return_tensors=\"pt\").to(device)\n",
911
+ " text_features = model.get_text_features(**inputs).to(device)\n",
912
+ " d[f'{index}'] = text_features.to('cpu')\n",
913
+ "#----#\n",
914
+ "d.close() #close the file"
915
+ ],
916
+ "metadata": {
917
+ "id": "9h6Rw72Pd307"
918
+ },
919
+ "execution_count": null,
920
+ "outputs": []
921
+ },
922
+ {
923
+ "cell_type": "code",
924
+ "source": [
925
+ "# Save results as .db file\n",
926
+ "import shelve\n",
927
+ "d = shelve.open('encoded_suffix-_nose')\n",
928
+ "for index in range(NUM_SUFFIX):\n",
929
+ " inputs = tokenizer(text = get_suffix(index)+' nose', padding=True, return_tensors=\"pt\").to(device)\n",
930
+ " text_features = model.get_text_features(**inputs).to(device)\n",
931
+ " d[f'{index}'] = text_features.to('cpu')\n",
932
+ "#----#\n",
933
+ "d.close() #close the file"
934
+ ],
935
+ "metadata": {
936
+ "id": "UG4M1-Tqd8Ly"
937
+ },
938
+ "execution_count": null,
939
+ "outputs": []
940
+ },
941
+ {
942
+ "cell_type": "code",
943
+ "source": [
944
+ "# Save results as .db file\n",
945
+ "import shelve\n",
946
+ "d = shelve.open('encoded_suffix-_hair')\n",
947
+ "for index in range(NUM_SUFFIX):\n",
948
+ " inputs = tokenizer(text = get_suffix(index)+' hair', padding=True, return_tensors=\"pt\").to(device)\n",
949
+ " text_features = model.get_text_features(**inputs).to(device)\n",
950
+ " d[f'{index}'] = text_features.to('cpu')\n",
951
+ "#----#\n",
952
+ "d.close() #close the file"
953
+ ],
954
+ "metadata": {
955
+ "id": "GxkmAz6-eJgY"
956
+ },
957
+ "execution_count": null,
958
+ "outputs": []
959
+ },
960
+ {
961
+ "cell_type": "code",
962
+ "source": [
963
+ "# Save results as .db file\n",
964
+ "import shelve\n",
965
+ "d = shelve.open('encoded_suffix-_background')\n",
966
+ "for index in range(NUM_SUFFIX):\n",
967
+ " inputs = tokenizer(text = get_suffix(index)+' background', padding=True, return_tensors=\"pt\").to(device)\n",
968
+ " text_features = model.get_text_features(**inputs).to(device)\n",
969
+ " d[f'{index}'] = text_features.to('cpu')\n",
970
+ "#----#\n",
971
+ "d.close() #close the file"
972
+ ],
973
+ "metadata": {
974
+ "id": "Sdi8Gw1BfL-0"
975
+ },
976
+ "execution_count": null,
977
+ "outputs": []
978
+ },
979
+ {
980
+ "cell_type": "code",
981
+ "source": [
982
+ "# Save results as .db file\n",
983
+ "import shelve\n",
984
+ "d = shelve.open('in_a_-encoded_suffix')\n",
985
+ "for index in range(NUM_SUFFIX):\n",
986
+ " inputs = tokenizer(text = 'in a '+get_suffix(index), padding=True, return_tensors=\"pt\").to(device)\n",
987
+ " text_features = model.get_text_features(**inputs).to(device)\n",
988
+ " d[f'{index}'] = text_features.to('cpu')\n",
989
+ "#----#\n",
990
+ "d.close() #close the file"
991
+ ],
992
+ "metadata": {
993
+ "id": "FztUKiyegN2F"
994
+ },
995
  "execution_count": null,
996
  "outputs": []
997
  },