File size: 238,968 Bytes
3a18eba |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419 3420 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483 3484 3485 3486 3487 3488 3489 3490 3491 3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 3642 3643 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655 3656 3657 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869 3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 3882 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 |
import tkinter as tk
import os
import sys
import sysconfig
import subprocess
from tkinter import *
from tkinter import ttk
import tkinter.filedialog as fd
import json
from tkinter import messagebox
from PIL import Image, ImageTk,ImageOps,ImageDraw
import glob
import converters
import shutil
from datetime import datetime
import pyperclip
import random
import customtkinter as ctk
import random
import subprocess
from pathlib import Path
from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionDepth2ImgPipeline
ctk.set_appearance_mode("dark")
ctk.set_default_color_theme("blue")
#work in progress code, not finished, credits will be added at a later date.
#class to make a generated image preview for the playground window, should open a new window alongside the playground window
class GeneratedImagePreview(ctk.CTkToplevel):
def __init__(self, parent, *args, **kwargs):
ctk.CTkToplevel.__init__(self, parent, *args, **kwargs)
#title
self.title("Viewfinder")
self.parent = parent
self.configure(bg_color="transparent")
#frame
self.frame = ctk.CTkFrame(self, bg_color="transparent")
self.frame.pack(fill="both", expand=True)
#add tip label
self.tip_label = ctk.CTkLabel(self.frame,text='Press the right arrow or enter to generate a new image', bg_color="transparent")
self.tip_label.pack(fill="both", expand=True)
#image
self.image_preview_label = ctk.CTkLabel(self.frame,text='', bg_color="transparent")
self.image_preview_label.pack(fill="both", expand=True)
# run on close
self.protocol("WM_DELETE_WINDOW", self.on_close)
#bind next image to right arrow
self.bind("<Right>", lambda event: self.next_image())
#bind to enter to generate a new image
self.bind("<Return>", lambda event: self.next_image())
def next_image(self, event=None):
self.parent.generate_next_image()
def on_close(self):
self.parent.generation_window = None
self.destroy()
def ingest_image(self, image):
self.geometry(f"{image.width + 50}x{image.height + 50}")
self.image_preview_label.configure(image=ctk.CTkImage(image,size=(image.width,image.height)))
#resize window
#class to make a concept top level window
class ConceptWidget(ctk.CTkFrame):
#a widget that holds a concept and opens a concept window when clicked
def __init__(self, parent, concept=None,width=150,height=150, *args, **kwargs):
ctk.CTkFrame.__init__(self, parent, *args, **kwargs)
self.parent = parent
self.concept = concept
#if concept is none, make a new concept
if self.concept == None:
self.default_image_preview = Image.open("resources/stableTuner_logo.png").resize((150, 150), Image.Resampling.LANCZOS)
#self.default_image_preview = ImageTk.PhotoImage(self.default_image_preview)
self.concept_name = "New Concept"
self.concept_data_path = ""
self.concept_class_name = ""
self.concept_class_path = ""
self.flip_p = ''
self.concept_do_not_balance = False
self.process_sub_dirs = False
self.image_preview = self.default_image_preview
#create concept
self.concept = Concept(self.concept_name, self.concept_data_path, self.concept_class_name, self.concept_class_path,self.flip_p, self.concept_do_not_balance,self.process_sub_dirs, self.image_preview, None)
else:
self.concept = concept
self.concept.image_preview = self.make_image_preview()
self.width = width
self.height = height
self.configure(fg_color='transparent',border_width=0)
self.concept_frame = ctk.CTkFrame(self, width=400, height=300,fg_color='transparent',border_width=0)
self.concept_frame.grid_columnconfigure(0, weight=1)
self.concept_frame.grid_rowconfigure(0, weight=1)
self.concept_frame.grid(row=0, column=0, sticky="nsew")
#concept image
#if self.concept.image_preview is type(str):
# self.concept.image_preview = Image.open(self.concept.image_preview)
self.concept_image_label = ctk.CTkLabel(self.concept_frame,text='',width=width,height=height, image=ctk.CTkImage(self.concept.image_preview,size=(100,100)))
self.concept_image_label.grid(row=0, column=0, sticky="nsew")
#ctk button with name as text and image as preview
self.concept_button = ctk.CTkLabel(self.concept_frame, text=self.concept.concept_name,bg_color='transparent', compound="top")
self.concept_button.grid(row=1, column=0, sticky="nsew")
#bind the button to open a concept window
self.concept_button.bind("<Button-1>", lambda event: self.open_concept_window())
self.concept_image_label.bind("<Button-1>", lambda event: self.open_concept_window())
def resize_widget(self,width,height):
self.image_preview = self.image_preview.configure(size=(width,height))
self.concept_image_label.configure(width=width,height=height,image=self.image_preview)
def make_image_preview(self):
def add_corners(im, rad):
circle = Image.new('L', (rad * 2, rad * 2), 0)
draw = ImageDraw.Draw(circle)
draw.ellipse((0, 0, rad * 2, rad * 2), fill=255)
alpha = Image.new('L', im.size, "white")
w, h = im.size
alpha.paste(circle.crop((0, 0, rad, rad)), (0, 0))
alpha.paste(circle.crop((0, rad, rad, rad * 2)), (0, h - rad))
alpha.paste(circle.crop((rad, 0, rad * 2, rad)), (w - rad, 0))
alpha.paste(circle.crop((rad, rad, rad * 2, rad * 2)), (w - rad, h - rad))
im.putalpha(alpha)
return im
path = self.concept.concept_path
icon = 'resources/stableTuner_icon.png'
#create a photoimage object of the image in the path
icon = Image.open(icon)
#resize the image
image = icon.resize((150, 150), Image.Resampling.LANCZOS)
if path != "" and path != None:
if os.path.exists(path):
files = []
#if there are sub directories
if self.concept.process_sub_dirs:
#get a list of all sub directories
sub_dirs = [f.path for f in os.scandir(path) if f.is_dir()]
#if there are sub directories
if len(sub_dirs) != 0:
#collect all images in sub directories
for sub_dir in sub_dirs:
#collect the full path of all files in the sub directory to files
files += [os.path.join(sub_dir, f) for f in os.listdir(sub_dir)]
#if there are no sub directories
else:
files = [os.path.join(path, f) for f in os.listdir(path)]
#omit sub directories
files = [f for f in files if not os.path.isdir(f)]
if len(files) != 0:
for i in range(4):
#get an image from the path
import random
#filter files for images
files = [f for f in files if (f.endswith(".jpg") or f.endswith(".png") or f.endswith(".jpeg")) and not f.endswith("-masklabel.png") and not f.endswith("-depth.png")]
if len(files) != 0:
rand = random.choice(files)
image_path = rand
#remove image_path from files
if len(files) > 4:
files.remove(rand)
#files.pop(image_path)
#open the image
#print(image_path)
image_to_add = Image.open(image_path)
#resize the image to 38x38
#resize to 150x150 closest to the original aspect ratio
image_to_add.thumbnail((75, 75), Image.Resampling.LANCZOS)
#decide where to put the image
if i == 0:
#top left
image.paste(image_to_add, (0, 0))
elif i == 1:
#top right
image.paste(image_to_add, (75, 0))
elif i == 2:
#bottom left
image.paste(image_to_add, (0, 75))
elif i == 3:
#bottom right
image.paste(image_to_add, (75, 75))
image = add_corners(image, 30)
#convert the image to a photoimage
#image.show()
newImage=ctk.CTkImage(image,size=(100,100))
#print(image)
self.image_preview = image
return image
def open_concept_window(self, event=None):
#open a concept window
self.concept_window = ConceptWindow(parent=self.parent, conceptWidget=self, concept=self.concept)
self.concept_window.mainloop()
def update_button(self):
#update the button with the new concept name
self.concept_button.configure(text=self.concept.concept_name)
#update the preview image
self.concept_image_label.configure(image=ctk.CTkImage(self.concept.image_preview,size=(100,100)))
class ConceptWindow(ctk.CTkToplevel):
#init function
def __init__(self, parent,conceptWidget,concept,*args, **kwargs):
ctk.CTkToplevel.__init__(self, parent, *args, **kwargs)
#set title
self.title("Concept Editor")
self.parent = parent
self.conceptWidget = conceptWidget
self.concept = concept
self.geometry("576x297")
self.resizable(False, False)
#self.protocol("WM_DELETE_WINDOW", self.on_close)
self.wait_visibility()
self.grab_set()
self.focus_set()
self.default_image_preview = Image.open("resources/stableTuner_icon.png").resize((150, 150), Image.Resampling.LANCZOS)
#self.default_image_preview = ImageTk.PhotoImage(self.default_image_preview)
#make a frame for the concept window
self.concept_frame = ctk.CTkFrame(self, width=600, height=300)
self.concept_frame.grid(row=0, column=0, sticky="nsew",padx=10,pady=10)
self.concept_frame_subframe=ctk.CTkFrame(self.concept_frame, width=600, height=300)
#4 column grid
#self.concept_frame.grid_columnconfigure(0, weight=1)
#self.concept_frame.grid_columnconfigure(1, weight=5)
#self.concept_frame.grid_columnconfigure(2, weight=1)
#self.concept_frame.grid_columnconfigure(3, weight=3)
#make a label for concept name
self.concept_name_label = ctk.CTkLabel(self.concept_frame_subframe, text="Dataset Token/Name:")
self.concept_name_label.grid(row=0, column=0, sticky="nsew",padx=5,pady=5)
#make a entry box for concept name
self.concept_name_entry = ctk.CTkEntry(self.concept_frame_subframe,width=200)
#create right click menu
self.concept_name_entry.bind("<Button-3>", self.create_right_click_menu)
self.concept_name_entry.grid(row=0, column=1, sticky="e",padx=5,pady=5)
self.concept_name_entry.insert(0, self.concept.concept_name)
#make a label for concept path
self.concept_path_label = ctk.CTkLabel(self.concept_frame_subframe, text="Data Path:")
self.concept_path_label.grid(row=1, column=0, sticky="nsew",padx=5,pady=5)
#make a entry box for concept path
self.concept_path_entry = ctk.CTkEntry(self.concept_frame_subframe,width=200)
#create right click menu
self.concept_path_entry.bind("<Button-3>", self.create_right_click_menu)
self.concept_path_entry.grid(row=1, column=1, sticky="e",padx=5,pady=5)
#on focus out, update the preview image
self.concept_path_entry.bind("<FocusOut>", lambda event: self.update_preview_image(self.concept_path_entry))
self.concept_path_entry.insert(0, self.concept.concept_path)
#make a button to browse for concept path
self.concept_path_button = ctk.CTkButton(self.concept_frame_subframe,width=30, text="...", command=lambda: self.browse_for_path(self.concept_path_entry))
self.concept_path_button.grid(row=1, column=2, sticky="w",padx=5,pady=5)
#make a label for Class Name
self.class_name_label = ctk.CTkLabel(self.concept_frame_subframe, text="Class Name:")
self.class_name_label.grid(row=2, column=0, sticky="nsew",padx=5,pady=5)
#make a entry box for Class Name
self.class_name_entry = ctk.CTkEntry(self.concept_frame_subframe,width=200)
#create right click menu
self.class_name_entry.bind("<Button-3>", self.create_right_click_menu)
self.class_name_entry.grid(row=2, column=1, sticky="e",padx=5,pady=5)
self.class_name_entry.insert(0, self.concept.concept_class_name)
#make a label for Class Path
self.class_path_label = ctk.CTkLabel(self.concept_frame_subframe, text="Class Path:")
self.class_path_label.grid(row=3, column=0, sticky="nsew",padx=5,pady=5)
#make a entry box for Class Path
self.class_path_entry = ctk.CTkEntry(self.concept_frame_subframe,width=200)
#create right click menu
self.class_path_entry.bind("<Button-3>", self.create_right_click_menu)
self.class_path_entry.grid(row=3, column=1, sticky="e",padx=5,pady=5)
self.class_path_entry.insert(0, self.concept.concept_class_path)
#make a button to browse for Class Path
self.class_path_button = ctk.CTkButton(self.concept_frame_subframe,width=30, text="...", command=lambda: self.browse_for_path(entry_box=self.class_path_entry))
self.class_path_button.grid(row=3, column=2, sticky="w",padx=5,pady=5)
#entry and label for flip probability
self.flip_probability_label = ctk.CTkLabel(self.concept_frame_subframe, text="Flip Probability:")
self.flip_probability_label.grid(row=4, column=0, sticky="nsew",padx=5,pady=5)
self.flip_probability_entry = ctk.CTkEntry(self.concept_frame_subframe,width=200,placeholder_text="0.0 - 1.0")
self.flip_probability_entry.grid(row=4, column=1, sticky="e",padx=5,pady=5)
if self.concept.flip_p != '':
self.flip_probability_entry.insert(0, self.concept.flip_p)
#self.flip_probability_entry.bind("<button-3>", self.create_right_click_menu)
#make a label for dataset balancingprocess_sub_dirs
self.balance_dataset_label = ctk.CTkLabel(self.concept_frame_subframe, text="Don't Balance Dataset")
self.balance_dataset_label.grid(row=5, column=0, sticky="nsew",padx=5,pady=5)
#make a switch to enable or disable dataset balancing
self.balance_dataset_switch = ctk.CTkSwitch(self.concept_frame_subframe, text="", variable=tk.BooleanVar())
self.balance_dataset_switch.grid(row=5, column=1, sticky="e",padx=5,pady=5)
if self.concept.concept_do_not_balance == True:
self.balance_dataset_switch.toggle()
self.process_sub_dirs = ctk.CTkLabel(self.concept_frame_subframe, text="Search Sub-Directories")
self.process_sub_dirs.grid(row=6, column=0, sticky="nsew",padx=5,pady=5)
#make a switch to enable or disable dataset balancing
self.process_sub_dirs_switch = ctk.CTkSwitch(self.concept_frame_subframe, text="", variable=tk.BooleanVar())
self.process_sub_dirs_switch.grid(row=6, column=1, sticky="e",padx=5,pady=5)
if self.concept.process_sub_dirs == True:
self.process_sub_dirs_switch.toggle()
#self.balance_dataset_switch.set(self.concept.concept_do_not_balance)
#add image preview
self.image_preview_label = ctk.CTkLabel(self.concept_frame_subframe,text='', width=150, height=150,image=ctk.CTkImage(self.default_image_preview,size=(150,150)))
self.image_preview_label.grid(row=0, column=4,rowspan=5, sticky="nsew",padx=5,pady=5)
if self.concept.image_preview != None or self.concept.image_preview != "":
#print(self.concept.image_preview)
self.update_preview_image(entry=None,path=None,pil_image=self.concept.image_preview)
elif self.concept.concept_data_path != "":
self.update_preview_image(entry=None,path=self.concept_data_path)
#self.image_container = self.image_preview_label.create_image(0, 0, anchor="nw", image=test_image)
#make a save button
self.save_button = ctk.CTkButton(self.concept_frame_subframe, text="Save", command=self.save)
self.save_button.grid(row=6, column=3,columnspan=3,rowspan=1, sticky="nsew",padx=10,pady=10)
#make a delete button
#self.delete_button = ctk.CTkButton(self.concept_frame_subframe, text="Delete", command=self.delete)
#self.delete_button.grid(row=6, column=3,columnspan=2, sticky="nsew")
self.concept_frame_subframe.pack(fill="both", expand=True)
#placeholder hack focus in and out of the entry box flip probability
def create_right_click_menu(self, event):
#create a menu
self.menu = Menu(self.master, tearoff=0)
self.menu.config(font=("Segoe UI", 15))
#set dark colors for the menu
self.menu.configure(bg="#2d2d2d", fg="#ffffff", activebackground="#2d2d2d", activeforeground="#ffffff")
#add commands to the menu
self.menu.add_command(label="Cut", command=lambda: self.focus_get().event_generate("<<Cut>>"))
self.menu.add_command(label="Copy", command=lambda: self.focus_get().event_generate("<<Copy>>"))
self.menu.add_command(label="Paste", command=lambda: self.focus_get().event_generate("<<Paste>>"))
self.menu.add_command(label="Select All", command=lambda: self.focus_get().event_generate("<<SelectAll>>"))
#display the menu
try:
self.menu.tk_popup(event.x_root, event.y_root)
finally:
#make sure to release the grab (Tk 8.0a1 only)
self.menu.grab_release()
def delete(self):
del self.concept
self.conceptWidget.destroy()
del self.conceptWidget
self.destroy()
#function to update image preview on change
def update_preview_image(self, entry=None, path=None, pil_image=None):
def add_corners(im, rad):
circle = Image.new('L', (rad * 2, rad * 2), 0)
draw = ImageDraw.Draw(circle)
draw.ellipse((0, 0, rad * 2, rad * 2), fill=255)
alpha = Image.new('L', im.size, "white")
w, h = im.size
alpha.paste(circle.crop((0, 0, rad, rad)), (0, 0))
alpha.paste(circle.crop((0, rad, rad, rad * 2)), (0, h - rad))
alpha.paste(circle.crop((rad, 0, rad * 2, rad)), (w - rad, 0))
alpha.paste(circle.crop((rad, rad, rad * 2, rad * 2)), (w - rad, h - rad))
im.putalpha(alpha)
return im
#check if entry has changed
if entry != None and path == None :
#get the path from the entry
path = entry.get()
#get the path from the entry
#path = event.widget.get()
#canvas = self.canvas
#image_container = self.image_container
icon = 'resources/stableTuner_icon.png'
#create a photoimage object of the image in the path
icon = Image.open(icon)
#resize the image
image = icon.resize((150, 150), Image.Resampling.LANCZOS)
if path != "" and path != None:
if os.path.exists(path):
files = []
#if there are sub directories in the path
if self.concept.process_sub_dirs or self.process_sub_dirs_switch.get() == 1:
#get a list of all sub directories
sub_dirs = [f.path for f in os.scandir(path) if f.is_dir()]
#if there are sub directories
if len(sub_dirs) != 0:
#collect all images in sub directories
for sub_dir in sub_dirs:
#collect the full path of all files in the sub directory to files
files += [os.path.join(sub_dir, f) for f in os.listdir(sub_dir)]
#if there are no sub directories
else:
files = [os.path.join(path, f) for f in os.listdir(path)]
#omit sub directories
files = [f for f in files if not os.path.isdir(f)]
if len(files) != 0:
for i in range(4):
#get an image from the path
import random
#filter files for images
files = [f for f in files if (f.endswith(".jpg") or f.endswith(".png") or f.endswith(".jpeg")) and not f.endswith("-masklabel.png") and not f.endswith("-depth.png")]
if len(files) != 0:
rand = random.choice(files)
image_path = os.path.join(path,rand)
#remove image_path from files
if len(files) > 4:
files.remove(rand)
#files.pop(image_path)
#open the image
#print(image_path)
image_to_add = Image.open(image_path)
#resize the image to 38x38
#resize to 150x150 closest to the original aspect ratio
image_to_add.thumbnail((75, 75), Image.Resampling.LANCZOS)
#decide where to put the image
if i == 0:
#top left
image.paste(image_to_add, (0, 0))
elif i == 1:
#top right
image.paste(image_to_add, (75, 0))
elif i == 2:
#bottom left
image.paste(image_to_add, (0, 75))
elif i == 3:
#bottom right
image.paste(image_to_add, (75, 75))
add_corners(image, 30)
#convert the image to a photoimage
#image.show()
if pil_image != None:
image = pil_image
#if image is of type PIL.Image.
newImage=ctk.CTkImage(image,size=(150,150))
self.image_preview = image
self.image_preview_label.configure(image=newImage)
#function to browse for concept path
def browse_for_path(self,entry_box):
#get the path from the user
path = fd.askdirectory()
#set the path to the entry box
#delete entry box text
entry_box.focus_set()
entry_box.delete(0, tk.END)
entry_box.insert(0, path)
self.focus_set()
#save the concept
def save(self):
#get the concept name
concept_name = self.concept_name_entry.get()
#get the concept path
concept_path = self.concept_path_entry.get()
#get the class name
class_name = self.class_name_entry.get()
#get the class path
class_path = self.class_path_entry.get()
#get the flip probability
flip_p = self.flip_probability_entry.get()
#get the dataset balancing
balance_dataset = self.balance_dataset_switch.get()
#create the concept
process_sub_dirs = self.process_sub_dirs_switch.get()
#image preview
image_preview = self.image_preview
#get the main window
image_preview_label = self.image_preview_label
#update the concept
self.concept.update(concept_name, concept_path, class_name, class_path,flip_p,balance_dataset,process_sub_dirs,image_preview,image_preview_label)
self.conceptWidget.update_button()
#close the window
self.destroy()
#class of the concept
class Concept:
def __init__(self, concept_name, concept_path, class_name, class_path,flip_p, balance_dataset=None,process_sub_dirs=None,image_preview=None, image_container=None):
if concept_name == None:
concept_name = ""
if concept_path == None:
concept_path = ""
if class_name == None:
class_name = ""
if class_path == None:
class_path = ""
if flip_p == None:
flip_p = ""
if balance_dataset == None:
balance_dataset = False
if process_sub_dirs == None:
process_sub_dirs = False
if image_preview == None:
image_preview = ""
if image_container == None:
image_container = ""
self.concept_name = concept_name
self.concept_path = concept_path
self.concept_class_name = class_name
self.concept_class_path = class_path
self.flip_p = flip_p
self.concept_do_not_balance = balance_dataset
self.image_preview = image_preview
self.image_container = image_container
self.process_sub_dirs = process_sub_dirs
#update the concept
def update(self, concept_name, concept_path, class_name, class_path,flip_p,balance_dataset,process_sub_dirs, image_preview, image_container):
self.concept_name = concept_name
self.concept_path = concept_path
self.concept_class_name = class_name
self.concept_class_path = class_path
self.flip_p = flip_p
self.image_preview = image_preview
self.image_container = image_container
self.concept_do_not_balance = balance_dataset
self.image_preview = image_preview
self.image_container = image_container
self.process_sub_dirs = process_sub_dirs
#get the cocept details
def get_details(self):
return self.concept_name, self.concept_path, self.concept_class_name, self.concept_class_path,self.flip_p, self.concept_do_not_balance,self.process_sub_dirs, self.image_preview, self.image_container
#class to make popup right click menu with select all, copy, paste, cut, and delete when right clicked on an entry box
class DynamicGrid(ctk.CTkFrame):
def __init__(self, parent, *args, **kwargs):
ctk.CTkFrame.__init__(self, parent, *args, **kwargs)
self.text = tk.Text(self, wrap="char", borderwidth=0, highlightthickness=0,
state="disabled")
self.text.pack(fill="both", expand=True)
self.boxes = []
def add_box(self, color=None):
#bg = color if color else random.choice(("red", "orange", "green", "blue", "violet"))
box = ctk.CTkFrame(self.text,width=100, height=100)
#add a ctkbutton to the frame
#ctk.CTkButton(box,text="test",command=lambda:print("test")).pack()
#add a ctklabel to the frame
ctk.CTkLabel(box,text="test").pack()
#add a ctkentry to the frame
ctk.CTkEntry(box).pack()
#add a ctkcombobox to the frame
#add a button remove the frame
ctk.CTkButton(box,text="remove",command=lambda:self.remove_box(box)).pack()
self.boxes.append(box)
self.text.configure(state="normal")
self.text.window_create("end", window=box)
self.text.configure(state="disabled")
def remove_box(self,box):
self.boxes.remove(box)
box.destroy()
self.text.configure(state="normal")
self.text.delete("1.0", "end")
for box in self.boxes:
self.text.window_create("end", window=box)
self.text.configure(state="disabled")
#class to make a title bar for the window instead of the default one with the minimize, maximize, and close buttons
class ScrollableFrame(ttk.Frame):
def __init__(self, container, *args, **kwargs):
super().__init__(container, *args, **kwargs)
#self.pack(fill="both", expand=True)
self.grid(row=0,column=0,sticky="nsew")
s = ttk.Style()
s.configure('new.TFrame', background='#242424',borderwidth=0,highlightthickness=0)
self.configure(style='new.TFrame')
self.canvas = tk.Canvas(self,bg='#242424')
self.canvas.config(bg="#333333",highlightthickness=0,borderwidth=0,highlightbackground="#333333")
self.scrollbar = ctk.CTkScrollbar(
self, orientation="vertical", command=self.canvas.yview,bg_color="#333333",
width=10, corner_radius=10)
#s = ttk.Style()
#s.configure('new.TFrame', background='#242424',borderwidth=0,highlightthickness=0)
self.scrollable_frame = ttk.Frame(self.canvas,style='new.TFrame')
self.scrollable_frame.grid_columnconfigure(0, weight=1)
self.scrollable_frame.grid_columnconfigure(1, weight=1)
#set background color of the scrollable frame
#self.scrollable_frame.config(background="#333333")
self.scrollable_frame.bind("<Configure>",
lambda *args, **kwargs: self.canvas.configure(
scrollregion=self.canvas.bbox("all")))
#resize the scrollable frame to the size of the window capped at 1000x1000
self.scrollable_frame.bind("<Configure>", lambda e: self.canvas.configure(width=min(750, e.width), height=min(750, e.height)))
self.bind_all("<MouseWheel>", self._on_mousewheel)
self.bind("<Destroy>",
lambda *args, **kwargs: self.unbind_all("<MouseWheel>"))
self.canvas.create_window((0, 0), window=self.scrollable_frame, anchor="nw")
self.canvas.configure(yscrollcommand=self.scrollbar.set)
self.canvas.pack(side="left", fill="both", expand=True)
self.scrollbar.pack(side="right", fill="y")
def _on_mousewheel(self, event):
self.canvas.yview_scroll(-1 * round(event.delta / 120), "units")
def update_scroll_region(self):
self.canvas.configure(scrollregion=self.canvas.bbox("all"))
class CreateToolTip(object):
"""
create a tooltip for a given widget
"""
def __init__(self, widget, text='widget info'):
self.waittime = 500 #miliseconds
self.wraplength = 180 #pixels
self.widget = widget
#parent of the widget
#hack to get the master of the app
self.parent = widget.winfo_toplevel()
self.text = text
self.widget.bind("<Enter>", self.enter)
self.widget.bind("<Leave>", self.leave)
self.widget.bind("<ButtonPress>", self.leave)
self.id = None
self.tw = None
def enter(self, event=None):
self.schedule()
def leave(self, event=None):
self.unschedule()
self.hidetip()
def schedule(self):
self.unschedule()
self.id = self.widget.after(self.waittime, self.showtip)
def unschedule(self):
id = self.id
self.id = None
if id:
self.widget.after_cancel(id)
def showtip(self, event=None):
x = y = 0
x, y, cx, cy = self.widget.bbox("insert")
x += self.widget.winfo_rootx() + 50
y += self.widget.winfo_rooty() + 50
# creates a toplevel window
self.tw = ctk.CTkToplevel(self.widget)
#self.tw.wm_attributes("-topmost", 1)
#self.parent.wm_attributes("-topmost", 0)
# Leaves only the label and removes the app window
self.tw.wm_overrideredirect(True)
self.tw.wm_geometry("+%d+%d" % (x, y))
#top most
label = ctk.CTkLabel(self.tw, text=self.text, justify='left',
wraplength = self.wraplength)
label.pack(padx=10, pady=10 )
def hidetip(self):
tw = self.tw
self.tw= None
if tw:
tw.destroy()
class App(ctk.CTk):
def __init__(self):
super().__init__()
try:
latest_git_hash = subprocess.check_output(["git", "ls-remote", "http://github.com/RossM/StableTuner.git","main"], cwd=Path(__file__).resolve().parent).strip().decode()[0:7]
#check if configs folder exists
print("Latest git hash: " + latest_git_hash)
except:
pass
if not os.path.exists("configs"):
os.makedirs("configs")
self.grid_columnconfigure(1, weight=1)
self.grid_columnconfigure((2, 3), weight=0)
self.grid_rowconfigure((0, 1, 2), weight=1)
self.geometry(f"{1100}x{685}")
self.stableTune_icon =PhotoImage(master=self,file = "resources/stableTuner_icon.png")
self.iconphoto(False, self.stableTune_icon)
self.dark_mode_var = "#1e2124"
self.dark_purple_mode_var = "#1B0F1B"
self.dark_mode_title_var = "#7289da"
self.dark_mode_button_pressed_var = "#BB91B6"
self.dark_mode_button_var = "#8ea0e1"
self.dark_mode_text_var = "#c6c7c8"
self.title("StableTuner")
self.configure(cursor="left_ptr")
#resizable window
self.resizable(True, True)
self.create_default_variables()
#check if stableTuner.cfg exists
if not os.path.exists("configs/stableTuner_hash.cfg"):
#create stableTuner.cfg and write the latest git hash
with open("configs/stableTuner_hash.cfg", "w") as f:
f.write(latest_git_hash)
else:
#read stableTuner.cfg
with open("configs/stableTuner_hash.cfg", "r") as f:
old_git_hash = f.read()
try:
#check if the latest git hash is the same as the one in stableTuner.cfg
if latest_git_hash != old_git_hash:
#if not the same, delete the old stableTuner.cfg and create a new one with the latest git hash
self.update_available = True
except:
self.update_available = False
self.sidebar_frame = ctk.CTkFrame(self, width=140, corner_radius=0)
self.sidebar_frame.grid(row=0, column=0, rowspan=10, sticky="nsew")
self.logo_img = ctk.CTkImage(Image.open("resources/stableTuner_logo.png").resize((300, 300), Image.Resampling.LANCZOS),size=(80,80))
self.logo_img = ctk.CTkLabel(self.sidebar_frame, image=self.logo_img, text='', height=50,width=50, font=ctk.CTkFont(size=15, weight="bold"))
self.logo_img.grid(row=0, column=0, padx=20, pady=20)
self.logo_label = ctk.CTkLabel(self.sidebar_frame, text="StableTuner", font=ctk.CTkFont(size=20, weight="bold"))
self.logo_label.place(x=90, y=105, anchor="n")
self.empty_label = ctk.CTkLabel(self.sidebar_frame, text="", font=ctk.CTkFont(size=20, weight="bold"))
self.empty_label.grid(row=1, column=0, padx=0, pady=0)
self.sidebar_button_1 = ctk.CTkButton(self.sidebar_frame,text='General Settings',command=self.general_nav_button_event)
self.sidebar_button_1.grid(row=2, column=0, padx=20, pady=5)
self.sidebar_button_2 = ctk.CTkButton(self.sidebar_frame,text='Trainer Settings',command=self.training_nav_button_event)
self.sidebar_button_2.grid(row=3, column=0, padx=20, pady=5)
self.sidebar_button_3 = ctk.CTkButton(self.sidebar_frame,text='Dataset Settings',command=self.dataset_nav_button_event)
self.sidebar_button_3.grid(row=4, column=0, padx=20, pady=5)
self.sidebar_button_4 = ctk.CTkButton(self.sidebar_frame,text='Sampling Settings',command=self.sampling_nav_button_event)
self.sidebar_button_4.grid(row=5, column=0, padx=20, pady=5)
self.sidebar_button_5 = ctk.CTkButton(self.sidebar_frame,text='Data',command=self.data_nav_button_event)
self.sidebar_button_5.grid(row=6, column=0, padx=20, pady=5)
self.sidebar_button_6 = ctk.CTkButton(self.sidebar_frame,text='Model Playground',command=self.playground_nav_button_event)
self.sidebar_button_6.grid(row=7, column=0, padx=20, pady=5)
self.sidebar_button_7 = ctk.CTkButton(self.sidebar_frame,text='Toolbox',command=self.toolbox_nav_button_event)
self.sidebar_button_7.grid(row=8, column=0, padx=20, pady=5)
#empty label
self.empty_label = ctk.CTkLabel(self.sidebar_frame, text="", font=ctk.CTkFont(size=20, weight="bold"))
self.empty_label.grid(row=9, column=0, padx=0, pady=0)
#empty label
if self.update_available:
self.sidebar_button_11 = ctk.CTkButton(self.sidebar_frame,text='Update Available',fg_color='red',hover_color='darkred',command=self.update_ST)
self.sidebar_button_11.grid(row=12, column=0, padx=20, pady=5)
else:
self.empty_label = ctk.CTkLabel(self.sidebar_frame, text="", font=ctk.CTkFont(size=20, weight="bold"))
self.empty_label.grid(row=10, column=0, padx=0, pady=0)
#empty label
self.empty_label = ctk.CTkLabel(self.sidebar_frame, text="", font=ctk.CTkFont(size=20, weight="bold"))
self.empty_label.grid(row=11, column=0, padx=0, pady=0)
self.sidebar_button_11 = ctk.CTkButton(self.sidebar_frame,text='Caption Buddy',command=self.caption_buddy)
self.sidebar_button_11.grid(row=13, column=0, padx=20, pady=5)
self.sidebar_button_12 = ctk.CTkButton(self.sidebar_frame,text='Start Training!', command=lambda : self.process_inputs(export=False))
self.sidebar_button_12.bind("<Button-3>", self.create_right_click_menu_export)
self.sidebar_button_12.grid(row=14, column=0, padx=20, pady=5)
self.general_frame = ctk.CTkFrame(self, width=140, corner_radius=0,fg_color='transparent')
self.general_frame.grid_columnconfigure(0, weight=5)
self.general_frame.grid_columnconfigure(1, weight=10)
self.general_frame_subframe = ctk.CTkFrame(self.general_frame,width=300, corner_radius=20)
self.general_frame_subframe.grid(row=2, column=0,sticky="nsew", padx=20, pady=20)
self.general_frame_subframe_side_guide = ctk.CTkFrame(self.general_frame,width=250, corner_radius=20)
self.general_frame_subframe_side_guide.grid(row=2, column=1,sticky="nsew", padx=20, pady=20)
self.create_general_settings_widgets()
self.apply_general_style_to_widgets(self.general_frame_subframe)
self.override_general_style_widgets()
self.training_frame_finetune = ctk.CTkFrame(self, width=400, corner_radius=0,fg_color='transparent')
self.training_frame_finetune.grid_columnconfigure(0, weight=1)
self.training_frame_finetune_subframe = ctk.CTkFrame(self.training_frame_finetune,width=400,height=1500, corner_radius=20)
self.training_frame_finetune_subframe.grid_columnconfigure(0, weight=1)
self.training_frame_finetune_subframe.grid_columnconfigure(1, weight=1)
self.training_frame_finetune_subframe.grid(row=2, column=0,sticky="nsew", padx=20, pady=20)
self.create_trainer_settings_widgets()
self.grid_train_settings()
self.apply_general_style_to_widgets(self.training_frame_finetune_subframe)
self.override_training_style_widgets()
self.dataset_frame = ctk.CTkFrame(self, width=140, corner_radius=0,fg_color='transparent')
self.dataset_frame.grid_columnconfigure(0, weight=1)
self.dataset_frame_subframe = ctk.CTkFrame(self.dataset_frame,width=400, corner_radius=20)
self.dataset_frame_subframe.grid(row=2, column=0,sticky="nsew", padx=20, pady=20)
self.create_dataset_settings_widgets()
self.apply_general_style_to_widgets(self.dataset_frame_subframe)
self.sampling_frame = ctk.CTkFrame(self, width=140, corner_radius=0,fg_color='transparent')
self.sampling_frame.grid_columnconfigure(0, weight=1)
self.sampling_frame_subframe = ctk.CTkFrame(self.sampling_frame,width=400, corner_radius=20)
self.sampling_frame_subframe.grid(row=2, column=0,sticky="nsew", padx=20, pady=20)
self.create_sampling_settings_widgets()
self.apply_general_style_to_widgets(self.sampling_frame_subframe)
self.data_frame = ctk.CTkFrame(self, width=140, corner_radius=0,fg_color='transparent')
self.data_frame.grid_columnconfigure(0, weight=1)
self.data_frame_subframe = ctk.CTkFrame(self.data_frame,width=400, corner_radius=20)
self.data_frame_subframe.grid(row=2, column=0,sticky="nsew", padx=20, pady=5)
self.create_data_settings_widgets()
self.apply_general_style_to_widgets(self.data_frame_subframe)
self.data_frame_concepts_subframe = ctk.CTkFrame(self.data_frame,width=400, corner_radius=20)
self.data_frame_concepts_subframe.grid(row=3, column=0,sticky="nsew", padx=20, pady=5)
self.playground_frame = ctk.CTkFrame(self, width=140, corner_radius=0,fg_color='transparent')
self.playground_frame.grid_columnconfigure(0, weight=1)
self.playground_frame_subframe = ctk.CTkFrame(self.playground_frame,width=400, corner_radius=20)
self.playground_frame_subframe.grid(row=2, column=0,sticky="nsew", padx=20, pady=20)
self.playground_frame_subframe.grid_columnconfigure(0, weight=1)
self.playground_frame_subframe.grid_columnconfigure(1, weight=3)
self.playground_frame_subframe.grid_columnconfigure(2, weight=1)
self.create_plyaground_widgets()
self.apply_general_style_to_widgets(self.playground_frame_subframe)
self.override_playground_widgets_style()
self.toolbox_frame = ctk.CTkFrame(self, width=140, corner_radius=0,fg_color='transparent')
self.toolbox_frame.grid_columnconfigure(0, weight=1)
self.toolbox_frame_subframe = ctk.CTkFrame(self.toolbox_frame,width=400, corner_radius=20)
self.toolbox_frame_subframe.grid(row=2, column=0,sticky="nsew", padx=20, pady=20)
self.create_toolbox_widgets()
self.apply_general_style_to_widgets(self.toolbox_frame_subframe)
self.select_frame_by_name('general')
self.update()
if os.path.exists("stabletune_last_run.json"):
try:
self.load_config(file_name="stabletune_last_run.json")
#try loading the latest generated model to playground entry
self.find_latest_generated_model(self.play_model_entry)
#convert to ckpt if option is wanted
if self.execute_post_conversion == True:
#construct unique name
epoch = self.play_model_entry.get().split(os.sep)[-1]
name_of_model = self.play_model_entry.get().split(os.sep)[-2]
res = self.resolution_var.get()
#time and date
#format time and date to %month%day%hour%minute
now = datetime.now()
dt_string = now.strftime("%m-%d-%H-%M")
#construct name
name = name_of_model+'_'+res+"_"+dt_string+"_"+epoch
#print(self.play_model_entry.get())
#if self.play_model_entry.get() is a directory and all required folders exist
if os.path.isdir(self.play_model_entry.get()) and all([os.path.exists(os.path.join(self.play_model_entry.get(), folder)) for folder in self.required_folders]):
#print("all folders exist")
self.convert_to_ckpt(model_path=self.play_model_entry.get(), output_path=self.output_path_entry.get(),name=name)
#self.convert_to_ckpt(model_path=self.play_model_entry.get(), output_path=self.output_path_entry.get(),name=name)
#open stabletune_last_run.json and change convert_to_ckpt_after_training to False
with open("stabletune_last_run.json", "r") as f:
data = json.load(f)
data["execute_post_conversion"] = False
with open("stabletune_last_run.json", "w") as f:
json.dump(data, f, indent=4)
except Exception as e:
print(e)
pass
else:
pass
def create_default_variables(self):
self.possible_resolutions = ["256", "320", "384", "448", "512", "576", "640", "704", "768", "832", "896", "960", "1024","1088", "1152", "1216", "1280", "1344", "1408", "1472", "1536", "1600", "1664", "1728", "1792", "1856", "1920", '1984', '2048']
self.play_current_image = None
self.update_available = False
self.shuffle_dataset_per_epoch = False
self.batch_prompt_sampling_num_prompts = '0'
self.save_safetensors = False
self.attention = 'xformers'
self.attention_types = ['xformers','Flash Attention']
self.model_variant = 'Regular'
self.model_variants = ['Regular', 'Inpaint','Depth2Img']
self.masked_training = False
self.normalize_masked_area_loss = True
self.unmasked_probability = '0%'
self.fallback_mask_prompt = ''
self.max_denoising_strength = '100%'
self.required_folders = ["vae", "unet", "tokenizer", "text_encoder"]
self.aspect_ratio_bucketing_mode = 'Dynamic Fill'
self.dynamic_bucketing_mode = 'Duplicate'
self.play_keep_seed = False
self.use_ema = False
self.clip_penultimate = False
self.conditional_dropout = ''
self.cloud_toggle = False
self.generation_window = None
self.concept_widgets = []
self.sample_prompts = []
self.number_of_sample_prompts = len(self.sample_prompts)
self.sample_prompt_labels = []
self.input_model_path = "stabilityai/stable-diffusion-2-1-base"
self.vae_model_path = ""
self.output_path = "models/new_model"
self.send_telegram_updates = False
self.telegram_token = "TOKEN"
self.telegram_chat_id = "ID"
self.seed_number = 3434554
self.resolution = 512
self.batch_size = 24
self.num_train_epochs = 100
self.accumulation_steps = 1
self.mixed_precision = "fp16"
self.learning_rate = "3e-6"
self.learning_rate_schedule = "constant"
self.learning_rate_warmup_steps = 0
self.concept_list_json_path = "concept_list.json"
self.save_and_sample_every_x_epochs = 5
self.train_text_encoder = True
self.use_8bit_adam = True
self.use_gradient_checkpointing = True
self.num_class_images = 200
self.add_class_images_to_training = False
self.sample_batch_size = 1
self.save_sample_controlled_seed = []
self.delete_checkpoints_when_full_drive = True
self.use_image_names_as_captions = True
self.shuffle_captions = False
self.use_offset_noise = False
self.offset_noise_weight = 0.1
self.use_gan = False
self.gan_weight = 0.05
self.num_samples_to_generate = 1
self.auto_balance_concept_datasets = False
self.sample_width = 512
self.sample_height = 512
#self.save_latents_cache = True
self.regenerate_latents_cache = False
self.use_aspect_ratio_bucketing = True
self.do_not_use_latents_cache = True
self.with_prior_reservation = False
self.prior_loss_weight = 1.0
self.sample_random_aspect_ratio = False
self.add_controlled_seed_to_sample = []
self.sample_on_training_start = True
self.concept_template = {'instance_prompt': 'subject', 'class_prompt': 'a photo of class', 'instance_data_dir':'./data/subject','class_data_dir':'./data/subject_class'}
self.concepts = []
self.play_input_model_path = ""
self.play_postive_prompt = ""
self.play_negative_prompt = ""
self.play_seed = -1
self.play_num_samples = 1
self.play_sample_width = 512
self.play_sample_height = 512
self.play_cfg = 7.5
self.play_steps = 25
self.schedulers = ["DPMSolverMultistepScheduler", "PNDMScheduler", 'DDIMScheduler','EulerAncestralDiscreteScheduler','EulerDiscreteScheduler']
self.quick_select_models = ["Stable Diffusion 1.4", "Stable Diffusion 1.5", "Stable Diffusion 1.5 Inpaint", "Stable Diffusion 2 Base (512)", "Stable Diffusion 2 (768)", 'Stable Diffusion 2 Inpaint','Stable Diffusion 2 Depth2Img', 'Stable Diffusion 2.1 Base (512)', "Stable Diffusion 2.1 (768)"]
self.play_scheduler = 'DPMSolverMultistepScheduler'
self.pipe = None
self.current_model = None
self.play_save_image_button = None
self.dataset_repeats = 1
self.limit_text_encoder = 0
self.use_text_files_as_captions = True
self.ckpt_sd_version = None
self.convert_to_ckpt_after_training = False
self.execute_post_conversion = False
self.preview_images = []
self.disable_cudnn_benchmark = True
self.sample_step_interval = 500
self.use_lion = False
def select_frame_by_name(self, name):
# set button color for selected button
self.sidebar_button_1.configure(fg_color=("gray75", "gray25") if name == "general" else "transparent")
self.sidebar_button_2.configure(fg_color=("gray75", "gray25") if name == "training" else "transparent")
self.sidebar_button_3.configure(fg_color=("gray75", "gray25") if name == "dataset" else "transparent")
self.sidebar_button_4.configure(fg_color=("gray75", "gray25") if name == "sampling" else "transparent")
self.sidebar_button_5.configure(fg_color=("gray75", "gray25") if name == "data" else "transparent")
self.sidebar_button_6.configure(fg_color=("gray75", "gray25") if name == "playground" else "transparent")
self.sidebar_button_7.configure(fg_color=("gray75", "gray25") if name == "toolbox" else "transparent")
# show selected frame
if name == "general":
self.general_frame.grid(row=0, column=1, sticky="nsew")
else:
self.general_frame.grid_forget()
if name == "training":
self.training_frame_finetune.grid(row=0, column=1, sticky="nsew")
else:
self.training_frame_finetune.grid_forget()
if name == "dataset":
self.dataset_frame.grid(row=0, column=1, sticky="nsew")
else:
self.dataset_frame.grid_forget()
if name == "sampling":
self.sampling_frame.grid(row=0, column=1, sticky="nsew")
else:
self.sampling_frame.grid_forget()
if name == "data":
self.data_frame.grid(row=0, column=1, sticky="nsew")
else:
self.data_frame.grid_forget()
if name == "playground":
self.playground_frame.grid(row=0, column=1, sticky="nsew")
else:
self.playground_frame.grid_forget()
if name == "toolbox":
self.toolbox_frame.grid(row=0, column=1, sticky="nsew")
else:
self.toolbox_frame.grid_forget()
def general_nav_button_event(self):
self.select_frame_by_name("general")
def training_nav_button_event(self):
self.select_frame_by_name("training")
def dataset_nav_button_event(self):
self.select_frame_by_name("dataset")
def sampling_nav_button_event(self):
self.select_frame_by_name("sampling")
def data_nav_button_event(self):
self.select_frame_by_name("data")
def playground_nav_button_event(self):
self.select_frame_by_name("playground")
def toolbox_nav_button_event(self):
self.select_frame_by_name("toolbox")
#create a right click menu for entry widgets
def create_right_click_menu(self, event):
#create a menu
self.menu = Menu(self.master, tearoff=0)
self.menu.config(font=("Segoe UI", 15))
#set dark colors for the menu
self.menu.configure(bg="#2d2d2d", fg="#ffffff", activebackground="#2d2d2d", activeforeground="#ffffff")
#add commands to the menu
self.menu.add_command(label="Cut", command=lambda: self.focus_get().event_generate("<<Cut>>"))
self.menu.add_command(label="Copy", command=lambda: self.focus_get().event_generate("<<Copy>>"))
self.menu.add_command(label="Paste", command=lambda: self.focus_get().event_generate("<<Paste>>"))
self.menu.add_command(label="Select All", command=lambda: self.focus_get().event_generate("<<SelectAll>>"))
#display the menu
try:
self.menu.tk_popup(event.x_root, event.y_root)
finally:
#make sure to release the grab (Tk 8.0a1 only)
self.menu.grab_release()
def create_right_click_menu_export(self, event):
#create a menu
self.menu = Menu(self.master, tearoff=0)
#set menu size and font size
self.menu.config(font=("Segoe UI", 15))
#set dark colors for the menu
self.menu.configure(bg="#2d2d2d", fg="#ffffff", activebackground="#2d2d2d", activeforeground="#ffffff")
#add commands to the menu
self.menu.add_command(label="Export Trainer Command for Windows", command=lambda: self.process_inputs(export='Win'))
self.menu.add_command(label="Copy Trainer Command for Linux", command=lambda: self.process_inputs(export='LinuxCMD'))
#display the menu
try:
self.menu.tk_popup(event.x_root, event.y_root)
finally:
#make sure to release the grab (Tk 8.0a1 only)
self.menu.grab_release()
def create_left_click_menu_config(self, event):
#create a menu
self.menu = Menu(self.master, tearoff=0)
#set menu size and font size
self.menu.config(font=("Segoe UI", 15))
#set dark colors for the menu
self.menu.configure(bg="#2d2d2d", fg="#ffffff", activebackground="#2d2d2d", activeforeground="#ffffff")
#add commands to the menu
self.menu.add_command(label="Load Config", command=self.load_config)
self.menu.add_command(label="Save Config", command=self.save_config)
#display the menu
try:
self.menu.tk_popup(event.x_root, event.y_root)
finally:
#make sure to release the grab (Tk 8.0a1 only)
self.menu.grab_release()
def quick_select_model(self,*args):
val = self.quick_select_var.get()
if val != "Click to select model":
#clear input_model_path_entry
self.input_model_path_entry.delete(0, tk.END)
if val == 'Stable Diffusion 1.4':
self.input_model_path_entry.insert(0,"CompVis/stable-diffusion-v1-4")
self.model_variant_var.set("Regular")
elif val == 'Stable Diffusion 1.5':
self.input_model_path_entry.insert(0,"runwayml/stable-diffusion-v1-5")
self.model_variant_var.set("Regular")
elif val == 'Stable Diffusion 1.5 Inpaint':
self.input_model_path_entry.insert(0,"runwayml/stable-diffusion-inpainting")
self.model_variant_var.set("Inpaint")
elif val == 'Stable Diffusion 2 Base (512)':
self.input_model_path_entry.insert(0,"stabilityai/stable-diffusion-2-base")
self.model_variant_var.set("Regular")
elif val == 'Stable Diffusion 2 (768)':
self.input_model_path_entry.insert(0,"stabilityai/stable-diffusion-2")
self.resolution_var.set("768")
self.sample_height_entry.delete(0, tk.END)
self.sample_height_entry.insert(0,"768")
self.sample_width_entry.delete(0, tk.END)
self.sample_width_entry.insert(0,"768")
self.model_variant_var.set("Regular")
elif val == 'Stable Diffusion 2 Inpaint':
self.input_model_path_entry.insert(0,"stabilityai/stable-diffusion-2-inpainting")
self.model_variant_var.set("Inpaint")
elif val == 'Stable Diffusion 2 Depth2Img':
self.input_model_path_entry.insert(0,"stabilityai/stable-diffusion-2-depth")
self.model_variant_var.set("Depth2Img")
elif val == 'Stable Diffusion 2.1 Base (512)':
self.input_model_path_entry.insert(0,"stabilityai/stable-diffusion-2-1-base")
self.model_variant_var.set("Regular")
elif val == 'Stable Diffusion 2.1 (768)':
self.input_model_path_entry.insert(0,"stabilityai/stable-diffusion-2-1")
self.resolution_var.set("768")
self.sample_height_entry.delete(0, tk.END)
self.sample_height_entry.insert(0,"768")
self.sample_width_entry.delete(0, tk.END)
self.sample_width_entry.insert(0,"768")
self.model_variant_var.set("Regular")
def override_training_style_widgets(self):
for i in self.training_frame_finetune_subframe.children.values():
if 'ctkbutton' in str(i):
i.grid(padx=5, pady=5,sticky="w")
if 'ctkoptionmenu' in str(i):
i.grid(padx=10, pady=5,sticky="w")
if 'ctkentry' in str(i):
i.configure(width=160)
i.grid(padx=10, pady=5,sticky="w")
i.bind("<Button-3>", self.create_right_click_menu)
if 'ctkswitch' in str(i):
i.configure(text='')
i.grid(padx=10, pady=5,sticky="")
if 'ctklabel' in str(i):
i.grid(padx=10, pady=5,sticky="w")
def override_playground_widgets_style(self):
self.playground_title.grid(row=0, column=0, padx=20, pady=20)
self.play_model_label.grid(row=0, column=0, sticky="nsew")
self.play_model_entry.grid(row=0, column=1, sticky="nsew")
self.play_prompt_label.grid(row=1, column=0, sticky="nsew")
self.play_prompt_entry.grid(row=1, column=1,columnspan=2, sticky="nsew")
self.play_negative_prompt_label.grid(row=2, column=0, sticky="nsew")
self.play_negative_prompt_entry.grid(row=2, column=1,columnspan=2, sticky="nsew")
self.play_seed_label.grid(row=3, column=0, sticky="nsew")
self.play_seed_entry.grid(row=3, column=1, sticky="w")
self.play_keep_seed_checkbox.grid(row=3, column=1)
self.play_steps_label.grid(row=4, column=0, sticky="nsew")
self.play_steps_slider.grid(row=4, column=1, sticky="nsew")
self.play_scheduler_label.grid(row=5, column=0, sticky="nsew")
self.play_scheduler_option_menu.grid(row=5, column=1, sticky="nsew")
self.play_resolution_label.grid(row=6,rowspan=2, column=0, sticky="nsew")
self.play_resolution_label_height.grid(row=6, column=1, sticky="w")
self.play_resolution_label_width.grid(row=6, column=1, sticky="e")
self.play_resolution_slider_height.grid(row=7, column=1, sticky="w")
self.play_resolution_slider_width.grid(row=7, column=1, sticky="e")
self.play_resolution_slider_height.set(self.play_sample_height)
self.play_cfg_label.grid(row=8, column=0, sticky="nsew")
self.play_cfg_slider.grid(row=8, column=1, sticky="nsew")
self.play_toolbox_label.grid(row=9, column=0, sticky="nsew")
self.play_generate_image_button.grid(row=10, column=0, columnspan=2, sticky="nsew")
self.play_convert_to_ckpt_button.grid(row=9, column=1, columnspan=1, sticky="w")
def override_general_style_widgets(self):
pass
def apply_general_style_to_widgets(self,frame):
for i in frame.children.values():
if 'ctkbutton' in str(i):
i.grid(padx=5, pady=10,sticky="w")
if 'ctkoptionmenu' in str(i):
i.grid(padx=10, pady=10,sticky="w")
if 'ctkentry' in str(i):
i.configure(width=160)
i.grid(padx=10, pady=5,sticky="w")
i.bind("<Button-3>", self.create_right_click_menu)
if 'ctkswitch' in str(i):
i.configure(text='')
i.grid(padx=10, pady=10,sticky="")
if 'ctklabel' in str(i):
i.grid(padx=10,sticky="w")
def grid_train_settings(self):
#define grid row and column
self.training_frame_finetune_subframe.grid_columnconfigure(0, weight=2)
self.training_frame_finetune_subframe.grid_columnconfigure(1, weight=1)
self.training_frame_finetune_subframe.grid_columnconfigure(2, weight=2)
self.training_frame_finetune_subframe.grid_columnconfigure(3, weight=1)
rows = 14
columns = 4
widgets = self.training_frame_finetune_subframe.children.values()
#organize widgets in grid
curRow = 0
curColumn = 0
#make widgets a list
widgets = list(widgets)[1:]
#find ctkcanvas in widgets and remove it
for i in widgets:
if 'ctkcanvas' in str(i):
widgets.remove(i)
#create pairs of widgets
pairs = []
for i in range(0,len(widgets),2):
pairs.append([widgets[i],widgets[i+1]])
for p in pairs:
p[0].grid(row=curRow, column=curColumn, sticky="w",padx=1,pady=1)
p[1].grid(row=curRow, column=curColumn+1, sticky="w",padx=1,pady=1)
curRow += 1
if curRow == rows:
curRow = 0
curColumn += 2
def dreambooth_mode(self):
try:
if self.dreambooth_mode_selected:
self.dreambooth_mode_selected.destroy()
except:
pass
try:
if self.fine_tune_mode_selected:
self.fine_tune_mode_selected.destroy()
#re-enable previous disabled widgets
self.with_prior_loss_preservation_checkbox.configure(state='normal')
self.with_prior_loss_preservation_label.configure(state='normal')
self.prior_loss_preservation_weight_entry.configure(state='normal')
self.prior_loss_preservation_weight_label.configure(state='normal')
self.with_prior_loss_preservation_var.set(1)
except:
pass
self.dreambooth_mode_selected = ctk.CTkLabel(self.general_frame_subframe_side_guide,fg_color='transparent', text="Dreambooth it is!\n I disabled irrelevant features for you.", font=ctk.CTkFont(size=14))
self.dreambooth_mode_selected.pack(side="top", fill="x", expand=False, padx=10, pady=10)
self.use_text_files_as_captions_checkbox.configure(state='disabled')
self.use_text_files_as_captions_label.configure(state='disabled')
self.use_text_files_as_captions_var.set(0)
#self.use_text_files_as_captions_checkbox.set(0)
self.use_image_names_as_captions_label.configure(state='disabled')
self.use_image_names_as_captions_checkbox.configure(state='disabled')
self.use_image_names_as_captions_var.set(0)
#self.use_image_names_as_captions_checkbox.set(0)
self.shuffle_captions_label.configure(state='disabled')
self.shuffle_captions_checkbox.configure(state='disabled')
self.shuffle_captions_var.set(0)
#self.shuffle_captions_checkbox.set(0)
self.add_class_images_to_dataset_checkbox.configure(state='disabled')
self.add_class_images_to_dataset_label.configure(state='disabled')
self.add_class_images_to_dataset_var.set(0)
#self.add_class_images_to_dataset_checkbox.set(0)
pass
def fine_tune_mode(self):
try:
if self.dreambooth_mode_selected:
self.dreambooth_mode_selected.destroy()
#re-enable checkboxes
self.use_text_files_as_captions_checkbox.configure(state='normal')
self.use_text_files_as_captions_label.configure(state='normal')
self.use_image_names_as_captions_label.configure(state='normal')
self.use_image_names_as_captions_checkbox.configure(state='normal')
self.shuffle_captions_label.configure(state='normal')
self.shuffle_captions_checkbox.configure(state='normal')
self.add_class_images_to_dataset_checkbox.configure(state='normal')
self.add_class_images_to_dataset_label.configure(state='normal')
self.use_text_files_as_captions_var.set(1)
self.use_image_names_as_captions_var.set(1)
self.shuffle_captions_var.set(0)
self.add_class_images_to_dataset_var.set(0)
except:
pass
try:
if self.fine_tune_mode_selected:
self.fine_tune_mode_selected.destroy()
except:
pass
self.fine_tune_mode_selected = ctk.CTkLabel(self.general_frame_subframe_side_guide,fg_color='transparent', text="Let's Fine-Tune!\n I disabled irrelevant features for you.", font=ctk.CTkFont(size=14))
self.fine_tune_mode_selected.pack(side="top", fill="x", expand=False, padx=10, pady=10)
self.with_prior_loss_preservation_checkbox.configure(state='disabled')
self.with_prior_loss_preservation_label.configure(state='disabled')
#self.with_prior_loss_preservation_checkbox.set(0)
self.prior_loss_preservation_weight_label.configure(state='disabled')
self.prior_loss_preservation_weight_entry.configure(state='disabled')
self.with_prior_loss_preservation_var.set(0)
#self.prior_loss_preservation_weight_entry.set(1.0)
pass
'''
def lora_mode(self):
self.lora_mode_selected = ctk.CTkLabel(self.general_frame_subframe_side_guide,fg_color='transparent', text="Lora it is!\n I disabled irrelevant features for you.", font=ctk.CTkFont(size=14))
self.lora_mode_selected.pack(side="top", fill="x", expand=False, padx=10, pady=10)
pass
'''
def create_general_settings_widgets(self):
self.general_frame_title = ctk.CTkLabel(self.general_frame, text="General Settings", font=ctk.CTkFont(size=20, weight="bold"))
self.general_frame_title.grid(row=0, column=0,columnspan=2, padx=20, pady=20)
#self.tip_label = ctk.CTkLabel(self.general_frame, text="Tip: Hover over settings for information", font=ctk.CTkFont(size=14))
#self.tip_label.grid(row=1, column=0, sticky="nsew")
self.general_frame_sidebar_title = ctk.CTkLabel(self.general_frame_subframe_side_guide,fg_color='transparent', text="Welcome!", font=ctk.CTkFont(size=20, weight="bold"))
#self.general_frame_sidebar_title.grid(row=0, column=0, sticky="nsew")
self.general_frame_sidebar_title.pack(side="top", fill="x", expand=False, padx=10, pady=10)
#text
self.general_frame_sidebar_text = ctk.CTkLabel(self.general_frame_subframe_side_guide,fg_color='transparent', text="Welcome To StableTuner\nHow do you want to train today?", font=ctk.CTkFont(size=14))
self.general_frame_sidebar_text.pack(side="top", fill="x", expand=False, padx=10, pady=10)
#add dreambooth button
self.dreambooth_button = ctk.CTkButton(self.general_frame_subframe_side_guide, text="Dreambooth", command=self.dreambooth_mode)
self.dreambooth_button.pack(side="top", fill="x", expand=False, padx=10, pady=10)
#add fine-tune button
self.fine_tune_button = ctk.CTkButton(self.general_frame_subframe_side_guide, text="Fine-Tune", command=self.fine_tune_mode)
self.fine_tune_button.pack(side="top", fill="x", expand=False, padx=10, pady=10)
#add LORA button with disabled state
#self.lora_button = ctk.CTkButton(self.general_frame_subframe_side_guide, text="LORA", command=self.lora_mode, state="disabled")
#self.lora_button.pack(side="top", fill="x", expand=False, padx=10, pady=10)
self.quick_select_var = tk.StringVar(self.master)
self.quick_select_var.set('Quick Select Base Model')
self.quick_select_dropdown = ctk.CTkOptionMenu(self.general_frame_subframe, variable=self.quick_select_var, values=self.quick_select_models, command=self.quick_select_model,dynamic_resizing=False, width=200)
self.quick_select_dropdown.grid(row=0, column=0, sticky="nsew")
self.load_config_button = ctk.CTkButton(self.general_frame_subframe, text="Load/Save Config")
#bind the load config button to a function
self.load_config_button.bind("<Button-1>", lambda event: self.create_left_click_menu_config(event))
self.load_config_button.grid(row=0, column=1, sticky="nsew")
#create another button to resume from latest checkpoint
self.input_model_path_resume_button = ctk.CTkButton(self.general_frame_subframe, text="Resume From Last Session",width=50, command=lambda : self.find_latest_generated_model(self.input_model_path_entry))
self.input_model_path_resume_button.grid(row=0, column=2, sticky="nsew")
self.input_model_path_label = ctk.CTkLabel(self.general_frame_subframe, text="Input Model / HuggingFace Repo")
input_model_path_label_ttp = CreateToolTip(self.input_model_path_label, "The path to the diffusers model to use. Can be a local path or a HuggingFace repo path.")
self.input_model_path_label.grid(row=1, column=0, sticky="nsew")
self.input_model_path_entry = ctk.CTkEntry(self.general_frame_subframe,width=30)
self.input_model_path_entry.grid(row=1, column=1, sticky="nsew")
self.input_model_path_entry.insert(0, self.input_model_path)
#make a button to open a file dialog
self.input_model_path_button = ctk.CTkButton(self.general_frame_subframe,width=30, text="...", command=self.choose_model)
self.input_model_path_button.grid(row=1, column=2, sticky="w")
self.vae_model_path_label = ctk.CTkLabel(self.general_frame_subframe, text="VAE model path / HuggingFace Repo")
vae_model_path_label_ttp = CreateToolTip(self.vae_model_path_label, "OPTINAL The path to the VAE model to use. Can be a local path or a HuggingFace repo path.")
self.vae_model_path_label.grid(row=2, column=0, sticky="nsew")
self.vae_model_path_entry = ctk.CTkEntry(self.general_frame_subframe)
self.vae_model_path_entry.grid(row=2, column=1, sticky="nsew")
self.vae_model_path_entry.insert(0, self.vae_model_path)
#make a button to open a file dialog
self.vae_model_path_button = ctk.CTkButton(self.general_frame_subframe,width=30, text="...", command=lambda: self.open_file_dialog(self.vae_model_path_entry))
self.vae_model_path_button.grid(row=2, column=2, sticky="w")
self.output_path_label = ctk.CTkLabel(self.general_frame_subframe, text="Output Path")
output_path_label_ttp = CreateToolTip(self.output_path_label, "The path to the output directory. If it doesn't exist, it will be created.")
self.output_path_label.grid(row=3, column=0, sticky="nsew")
self.output_path_entry = ctk.CTkEntry(self.general_frame_subframe)
self.output_path_entry.grid(row=3, column=1, sticky="nsew")
self.output_path_entry.insert(0, self.output_path)
#make a button to open a file dialog
self.output_path_button = ctk.CTkButton(self.general_frame_subframe,width=30, text="...", command=lambda: self.open_file_dialog(self.output_path_entry))
self.output_path_button.grid(row=3, column=2, sticky="w")
self.convert_to_ckpt_after_training_label = ctk.CTkLabel(self.general_frame_subframe, text="Convert to CKPT after training?")
convert_to_ckpt_label_ttp = CreateToolTip(self.convert_to_ckpt_after_training_label, "Convert the model to a tensorflow checkpoint after training.")
self.convert_to_ckpt_after_training_label.grid(row=4, column=0, sticky="nsew")
self.convert_to_ckpt_after_training_var = tk.IntVar()
self.convert_to_ckpt_after_training_checkbox = ctk.CTkSwitch(self.general_frame_subframe,text='',variable=self.convert_to_ckpt_after_training_var)
self.convert_to_ckpt_after_training_checkbox.grid(row=4, column=1, sticky="nsew",padx=10)
#use telegram updates dark mode
self.send_telegram_updates_label = ctk.CTkLabel(self.general_frame_subframe, text="Send Telegram Updates")
send_telegram_updates_label_ttp = CreateToolTip(self.send_telegram_updates_label, "Use Telegram updates to monitor training progress, must have a Telegram bot set up.")
self.send_telegram_updates_label.grid(row=6, column=0, sticky="nsew")
#create checkbox to toggle telegram updates and show telegram token and chat id
self.send_telegram_updates_var = tk.IntVar()
self.send_telegram_updates_checkbox = ctk.CTkSwitch(self.general_frame_subframe,variable=self.send_telegram_updates_var, command=self.toggle_telegram_settings)
self.send_telegram_updates_checkbox.grid(row=6, column=1, sticky="nsew")
#create telegram token dark mode
self.telegram_token_label = ctk.CTkLabel(self.general_frame_subframe, text="Telegram Token", state="disabled")
telegram_token_label_ttp = CreateToolTip(self.telegram_token_label, "The Telegram token for your bot.")
self.telegram_token_label.grid(row=7, column=0, sticky="nsew")
self.telegram_token_entry = ctk.CTkEntry(self.general_frame_subframe, state="disabled")
self.telegram_token_entry.grid(row=7, column=1,columnspan=3, sticky="nsew")
self.telegram_token_entry.insert(0, self.telegram_token)
#create telegram chat id dark mode
self.telegram_chat_id_label = ctk.CTkLabel(self.general_frame_subframe, text="Telegram Chat ID", state="disabled")
telegram_chat_id_label_ttp = CreateToolTip(self.telegram_chat_id_label, "The Telegram chat ID to send updates to.")
self.telegram_chat_id_label.grid(row=8, column=0, sticky="nsew")
self.telegram_chat_id_entry = ctk.CTkEntry(self.general_frame_subframe, state="disabled")
self.telegram_chat_id_entry.grid(row=8, column=1,columnspan=3, sticky="nsew")
self.telegram_chat_id_entry.insert(0, self.telegram_chat_id)
#add a switch to toggle runpod mode
self.cloud_mode_label = ctk.CTkLabel(self.general_frame_subframe, text="Cloud Training Export")
cloud_mode_label_ttp = CreateToolTip(self.cloud_mode_label, "Cloud mode will package up a quick trainer session for RunPod/Colab etc.")
self.cloud_mode_label.grid(row=9, column=0, sticky="nsew")
self.cloud_mode_var = tk.IntVar()
self.cloud_mode_checkbox = ctk.CTkSwitch(self.general_frame_subframe,variable=self.cloud_mode_var, command=self.toggle_runpod_mode)
self.cloud_mode_checkbox.grid(row=9, column=1, sticky="nsew")
def toggle_runpod_mode(self):
toggle = self.cloud_mode_var.get()
#flip self.toggle
if toggle == True:
toggle = False
self.sidebar_button_12.configure(text='Export for Cloud!')
else:
toggle = True
self.sidebar_button_12.configure(text='Start Training!')
def create_trainer_settings_widgets(self):
self.training_frame_finetune_title = ctk.CTkLabel(self.training_frame_finetune, text="Training Settings", font=ctk.CTkFont(size=20, weight="bold"))
self.training_frame_finetune_title.grid(row=0, column=0, padx=20, pady=20)
#add a model variant dropdown
self.model_variant_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Model Variant")
model_variant_label_ttp = CreateToolTip(self.model_variant_label, "The model type you're training.")
self.model_variant_label.grid(row=0, column=0, sticky="nsew")
self.model_variant_var = tk.StringVar()
self.model_variant_var.set(self.model_variant)
self.model_variant_dropdown = ctk.CTkOptionMenu(self.training_frame_finetune_subframe, values=self.model_variants, variable=self.model_variant_var)
#add attention optionMenu
self.attention_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Attention")
attention_label_ttp = CreateToolTip(self.attention_label, "The attention type to use. Flash Attention may enable lower VRAM training but Xformers will be faster and better for bigger batch sizes.")
self.attention_label.grid(row=1, column=0, sticky="nsew")
self.attention_var = tk.StringVar()
self.attention_var.set(self.attention)
self.attention_dropdown = ctk.CTkOptionMenu(self.training_frame_finetune_subframe, values=self.attention_types, variable=self.attention_var)
#add a batch size entry
#add a seed entry
self.seed_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Seed")
seed_label_ttp = CreateToolTip(self.seed_label, "The seed to use for training.")
#self.seed_label.grid(row=1, column=0, sticky="nsew")
self.seed_entry = ctk.CTkEntry(self.training_frame_finetune_subframe)
#self.seed_entry.grid(row=1, column=1, sticky="nsew")
self.seed_entry.insert(0, self.seed_number)
#create resolution dark mode dropdown
self.resolution_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Resolution")
resolution_label_ttp = CreateToolTip(self.resolution_label, "The resolution of the images to train on.")
#self.resolution_label.grid(row=2, column=0, sticky="nsew")
self.resolution_var = tk.StringVar()
self.resolution_var.set(self.resolution)
self.resolution_dropdown = ctk.CTkOptionMenu(self.training_frame_finetune_subframe, variable=self.resolution_var, values=self.possible_resolutions)
#self.resolution_dropdown.grid(row=2, column=1, sticky="nsew")
#create train batch size dark mode dropdown with values from 1 to 60
self.train_batch_size_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Train Batch Size")
train_batch_size_label_ttp = CreateToolTip(self.train_batch_size_label, "The batch size to use for training.")
#self.train_batch_size_label.grid(row=3, column=0, sticky="nsew")
self.train_batch_size_var = tk.StringVar()
self.train_batch_size_var.set(self.batch_size)
#make a list of values from 1 to 60 that are strings
#train_batch_size_values =
self.train_batch_size_dropdown = ctk.CTkOptionMenu(self.training_frame_finetune_subframe, variable=self.train_batch_size_var, values=[str(i) for i in range(1,61)])
#self.train_batch_size_dropdown.grid(row=3, column=1, sticky="nsew")
#create train epochs dark mode
self.train_epochs_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Train Epochs")
train_epochs_label_ttp = CreateToolTip(self.train_epochs_label, "The number of epochs to train for. An epoch is one pass through the entire dataset.")
#self.train_epochs_label.grid(row=4, column=0, sticky="nsew")
self.train_epochs_entry = ctk.CTkEntry(self.training_frame_finetune_subframe)
#self.train_epochs_entry.grid(row=4, column=1, sticky="nsew")
self.train_epochs_entry.insert(0, self.num_train_epochs)
#create mixed precision dark mode dropdown
self.mixed_precision_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Mixed Precision")
mixed_precision_label_ttp = CreateToolTip(self.mixed_precision_label, "Use mixed precision training to speed up training, FP16 is recommended but requires a GPU with Tensor Cores. TF32 is recommended for RTX 30 series GPUs and newer.")
#self.mixed_precision_label.grid(row=5, column=0, sticky="nsew")
self.mixed_precision_var = tk.StringVar()
self.mixed_precision_var.set(self.mixed_precision)
self.mixed_precision_dropdown = ctk.CTkOptionMenu(self.training_frame_finetune_subframe, variable=self.mixed_precision_var,values=["bf16","fp16","fp32","tf32"])
#self.mixed_precision_dropdown.grid(row=5, column=1, sticky="nsew")
#create use 8bit adam checkbox
self.use_8bit_adam_var = tk.IntVar()
self.use_8bit_adam_var.set(self.use_8bit_adam)
#create label
self.use_8bit_adam_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Use 8bit Adam")
use_8bit_adam_label_ttp = CreateToolTip(self.use_8bit_adam_label, "Use 8bit Adam to speed up training, requires bytsandbytes.")
#self.use_8bit_adam_label.grid(row=6, column=0, sticky="nsew")
#create checkbox
self.use_8bit_adam_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.use_8bit_adam_var,text='')
#create use LION optimizer checkbox
self.use_lion_var = tk.IntVar()
self.use_lion_var.set(self.use_lion)
#create label
self.use_lion_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Use LION")
use_lion_label_ttp = CreateToolTip(self.use_lion_label, "Use LION optimizer to speed up training, requires triton.")
#self.use_lion_label.grid(row=7, column=0, sticky="nsew")
#create checkbox
self.use_lion_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.use_lion_var,text='Use LION Optimizer')
#self.use_8bit_adam_checkbox.grid(row=6, column=1, sticky="nsew")
#create use gradient checkpointing checkbox
self.use_gradient_checkpointing_var = tk.IntVar()
self.use_gradient_checkpointing_var.set(self.use_gradient_checkpointing)
#create label
self.use_gradient_checkpointing_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Use Gradient Checkpointing")
use_gradient_checkpointing_label_ttp = CreateToolTip(self.use_gradient_checkpointing_label, "Use gradient checkpointing to reduce RAM usage.")
#self.use_gradient_checkpointing_label.grid(row=7, column=0, sticky="nsew")
#create checkbox
self.use_gradient_checkpointing_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.use_gradient_checkpointing_var)
#self.use_gradient_checkpointing_checkbox.grid(row=7, column=1, sticky="nsew")
#create gradient accumulation steps dark mode dropdown with values from 1 to 60
self.gradient_accumulation_steps_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Gradient Accumulation Steps")
gradient_accumulation_steps_label_ttp = CreateToolTip(self.gradient_accumulation_steps_label, "The number of gradient accumulation steps to use, this is useful for training with limited GPU memory.")
#self.gradient_accumulation_steps_label.grid(row=8, column=0, sticky="nsew")
self.gradient_accumulation_steps_var = tk.StringVar()
self.gradient_accumulation_steps_var.set(self.accumulation_steps)
self.gradient_accumulation_steps_dropdown = ctk.CTkOptionMenu(self.training_frame_finetune_subframe, variable=self.gradient_accumulation_steps_var, values=['1','2','3','4','5','6','7','8','9','10'])
#self.gradient_accumulation_steps_dropdown.grid(row=8, column=1, sticky="nsew")
#create learning rate dark mode entry
self.learning_rate_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Learning Rate")
learning_rate_label_ttp = CreateToolTip(self.learning_rate_label, "The learning rate to use for training.")
#self.learning_rate_label.grid(row=9, column=0, sticky="nsew")
self.learning_rate_entry = ctk.CTkEntry(self.training_frame_finetune_subframe)
#self.learning_rate_entry.grid(row=9, column=1, sticky="nsew")
self.learning_rate_entry.insert(0, self.learning_rate)
#create learning rate scheduler dropdown
self.learning_rate_scheduler_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Learning Rate Scheduler")
learning_rate_scheduler_label_ttp = CreateToolTip(self.learning_rate_scheduler_label, "The learning rate scheduler to use for training.")
#self.learning_rate_scheduler_label.grid(row=10, column=0, sticky="nsew")
self.learning_rate_scheduler_var = tk.StringVar()
self.learning_rate_scheduler_var.set(self.learning_rate_schedule)
self.learning_rate_scheduler_dropdown = ctk.CTkOptionMenu(self.training_frame_finetune_subframe, variable=self.learning_rate_scheduler_var, values=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"])
#self.learning_rate_scheduler_dropdown.grid(row=10, column=1, sticky="nsew")
#create num warmup steps dark mode entry
self.num_warmup_steps_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="LR Warmup Steps")
num_warmup_steps_label_ttp = CreateToolTip(self.num_warmup_steps_label, "The number of warmup steps to use for the learning rate scheduler.")
#self.num_warmup_steps_label.grid(row=11, column=0, sticky="nsew")
self.num_warmup_steps_entry = ctk.CTkEntry(self.training_frame_finetune_subframe)
#self.num_warmup_steps_entry.grid(row=11, column=1, sticky="nsew")
self.num_warmup_steps_entry.insert(0, self.learning_rate_warmup_steps)
#create use latent cache checkbox
#self.use_latent_cache_var = tk.IntVar()
#self.use_latent_cache_var.set(self.do_not_use_latents_cache)
#create label
#self.use_latent_cache_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Use Latent Cache")
#use_latent_cache_label_ttp = CreateToolTip(self.use_latent_cache_label, "Cache the latents to speed up training.")
#self.use_latent_cache_label.grid(row=12, column=0, sticky="nsew")
#create checkbox
#self.use_latent_cache_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.use_latent_cache_var)
#self.use_latent_cache_checkbox.grid(row=12, column=1, sticky="nsew")
#create save latent cache checkbox
#self.save_latent_cache_var = tk.IntVar()
#self.save_latent_cache_var.set(self.save_latents_cache)
#create label
#self.save_latent_cache_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Save Latent Cache")
#save_latent_cache_label_ttp = CreateToolTip(self.save_latent_cache_label, "Save the latents cache to disk after generation, will be remade if batch size changes.")
#self.save_latent_cache_label.grid(row=13, column=0, sticky="nsew")
#create checkbox
#self.save_latent_cache_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.save_latent_cache_var)
#self.save_latent_cache_checkbox.grid(row=13, column=1, sticky="nsew")
#create regnerate latent cache checkbox
self.regenerate_latent_cache_var = tk.IntVar()
self.regenerate_latent_cache_var.set(self.regenerate_latents_cache)
#create label
self.regenerate_latent_cache_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Regenerate Latent Cache")
regenerate_latent_cache_label_ttp = CreateToolTip(self.regenerate_latent_cache_label, "Force the latents cache to be regenerated.")
#self.regenerate_latent_cache_label.grid(row=14, column=0, sticky="nsew")
#create checkbox
self.regenerate_latent_cache_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.regenerate_latent_cache_var)
#self.regenerate_latent_cache_checkbox.grid(row=14, column=1, sticky="nsew")
#create train text encoder checkbox
self.train_text_encoder_var = tk.IntVar()
self.train_text_encoder_var.set(self.train_text_encoder)
#create label
self.train_text_encoder_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Train Text Encoder")
train_text_encoder_label_ttp = CreateToolTip(self.train_text_encoder_label, "Train the text encoder along with the UNET.")
#self.train_text_encoder_label.grid(row=15, column=0, sticky="nsew")
#create checkbox
self.train_text_encoder_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.train_text_encoder_var)
#self.train_text_encoder_checkbox.grid(row=15, column=1, sticky="nsew")
#create limit text encoder encoder entry
self.clip_penultimate_var = tk.IntVar()
self.clip_penultimate_var.set(self.clip_penultimate)
#create label
self.clip_penultimate_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Clip Penultimate")
clip_penultimate_label_ttp = CreateToolTip(self.clip_penultimate_label, "Train using the Penultimate layer of the text encoder.")
#create checkbox
self.clip_penultimate_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.clip_penultimate_var)
self.limit_text_encoder_var = tk.StringVar()
self.limit_text_encoder_var.set(self.limit_text_encoder)
#create label
self.limit_text_encoder_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Limit Text Encoder")
limit_text_encoder_label_ttp = CreateToolTip(self.limit_text_encoder_label, "Stop training the text encoder after this many epochs, use % to train for a percentage of the total epochs.")
#self.limit_text_encoder_label.grid(row=16, column=0, sticky="nsew")
#create entry
self.limit_text_encoder_entry = ctk.CTkEntry(self.training_frame_finetune_subframe, textvariable=self.limit_text_encoder_var)
#self.limit_text_encoder_entry.grid(row=16, column=1, sticky="nsew")
#create checkbox disable cudnn benchmark
self.disable_cudnn_benchmark_var = tk.IntVar()
self.disable_cudnn_benchmark_var.set(self.disable_cudnn_benchmark)
#create label for checkbox
self.disable_cudnn_benchmark_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="EXPERIMENTAL: Disable cuDNN Benchmark")
disable_cudnn_benchmark_label_ttp = CreateToolTip(self.disable_cudnn_benchmark_label, "Disable cuDNN benchmarking, may offer 2x performance on some systems and stop OOM errors.")
#self.disable_cudnn_benchmark_label.grid(row=17, column=0, sticky="nsew")
#create checkbox
self.disable_cudnn_benchmark_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.disable_cudnn_benchmark_var)
#self.disable_cudnn_benchmark_checkbox.grid(row=17, column=1, sticky="nsew")
#add conditional dropout entry
self.conditional_dropout_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Conditional Dropout")
conditional_dropout_label_ttp = CreateToolTip(self.conditional_dropout_label, "Precentage of probability to drop out a caption token to train the model to be more robust to missing words.")
self.conditional_dropout_entry = ctk.CTkEntry(self.training_frame_finetune_subframe)
self.conditional_dropout_entry.insert(0, self.conditional_dropout)
#create use EMA switch
self.use_ema_var = tk.IntVar()
self.use_ema_var.set(self.use_ema)
#create label
self.use_ema_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Use EMA")
use_ema_label_ttp = CreateToolTip(self.use_ema_label, "Use Exponential Moving Average to smooth the training paramaters. Will increase VRAM usage.")
#self.use_ema_label.grid(row=18, column=0, sticky="nsew")
#create checkbox
self.use_ema_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.use_ema_var)
#create with prior loss preservation checkbox
self.with_prior_loss_preservation_var = tk.IntVar()
self.with_prior_loss_preservation_var.set(self.with_prior_reservation)
#create label
self.with_prior_loss_preservation_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="With Prior Loss Preservation")
with_prior_loss_preservation_label_ttp = CreateToolTip(self.with_prior_loss_preservation_label, "Use the prior loss preservation method. part of Dreambooth.")
self.with_prior_loss_preservation_label.grid(row=19, column=0, sticky="nsew")
#create checkbox
self.with_prior_loss_preservation_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.with_prior_loss_preservation_var)
self.with_prior_loss_preservation_checkbox.grid(row=19, column=1, sticky="nsew")
#create prior loss preservation weight entry
self.prior_loss_preservation_weight_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Weight")
prior_loss_preservation_weight_label_ttp = CreateToolTip(self.prior_loss_preservation_weight_label, "The weight of the prior loss preservation loss.")
self.prior_loss_preservation_weight_label.grid(row=19, column=1, sticky="e")
self.prior_loss_preservation_weight_entry = ctk.CTkEntry(self.training_frame_finetune_subframe)
self.prior_loss_preservation_weight_entry.grid(row=19, column=3, sticky="w")
self.prior_loss_preservation_weight_entry.insert(0, self.prior_loss_weight)
#create contrasting light and color checkbox
self.use_offset_noise_var = tk.IntVar()
self.use_offset_noise_var.set(self.use_offset_noise)
#create label
self.offset_noise_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="With Offset Noise")
offset_noise_label_ttp = CreateToolTip(self.offset_noise_label, "Apply offset noise to latents to learn image contrast.")
self.offset_noise_label.grid(row=20, column=0, sticky="nsew")
#create checkbox
self.offset_noise_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.use_offset_noise_var)
self.offset_noise_checkbox.grid(row=20, column=1, sticky="nsew")
#create prior loss preservation weight entry
self.offset_noise_weight_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="Offset Noise Weight")
offset_noise_weight_label_ttp = CreateToolTip(self.offset_noise_weight_label, "The weight of the offset noise.")
self.offset_noise_weight_label.grid(row=20, column=1, sticky="e")
self.offset_noise_weight_entry = ctk.CTkEntry(self.training_frame_finetune_subframe)
self.offset_noise_weight_entry.grid(row=20, column=3, sticky="w")
self.offset_noise_weight_entry.insert(0, self.offset_noise_weight)
# GAN training
self.use_gan_var = tk.IntVar()
self.use_gan_var.set(self.use_gan)
#create label
self.gan_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="With GAN")
gan_label_ttp = CreateToolTip(self.gan_label, "Use GAN (experimental).")
#create checkbox
self.gan_checkbox = ctk.CTkSwitch(self.training_frame_finetune_subframe, variable=self.use_gan_var)
self.gan_checkbox.grid(row=21, column=1, sticky="nsew")
#create GAN weight entry
self.gan_weight_label = ctk.CTkLabel(self.training_frame_finetune_subframe, text="GAN Weight")
gan_weight_label_ttp = CreateToolTip(self.gan_weight_label, "The weight of the GAN.")
self.gan_weight_label.grid(row=21, column=1, sticky="e")
self.gan_weight_entry = ctk.CTkEntry(self.training_frame_finetune_subframe)
self.gan_weight_entry.grid(row=21, column=3, sticky="w")
self.gan_weight_entry.insert(0, self.gan_weight)
def create_dataset_settings_widgets(self):
#self.dataset_settings_label = ctk.CTkLabel(self.dataset_tab, text="Dataset Settings", font=("Arial", 12, "bold"))
#self.dataset_settings_label.grid(row=0, column=0, sticky="nsew")
self.dataset_frame_title = ctk.CTkLabel(self.dataset_frame, text="Dataset Settings", font=ctk.CTkFont(size=20, weight="bold"))
self.dataset_frame_title.grid(row=0, column=0, padx=20, pady=20, sticky="nsew")
#create use text files as captions checkbox
self.use_text_files_as_captions_var = tk.IntVar()
self.use_text_files_as_captions_var.set(self.use_text_files_as_captions)
#create label
self.use_text_files_as_captions_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Use Text Files as Captions")
use_text_files_as_captions_label_ttp = CreateToolTip(self.use_text_files_as_captions_label, "Use the text files as captions for training, text files must have same name as image, instance prompt/token will be ignored.")
self.use_text_files_as_captions_label.grid(row=1, column=0, sticky="nsew")
#create checkbox
self.use_text_files_as_captions_checkbox = ctk.CTkSwitch(self.dataset_frame_subframe, variable=self.use_text_files_as_captions_var)
self.use_text_files_as_captions_checkbox.grid(row=1, column=1, sticky="nsew")
# create use image names as captions checkbox
self.use_image_names_as_captions_var = tk.IntVar()
self.use_image_names_as_captions_var.set(self.use_image_names_as_captions)
# create label
self.use_image_names_as_captions_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Use Image Names as Captions")
use_image_names_as_captions_label_ttp = CreateToolTip(self.use_image_names_as_captions_label, "Use the image names as captions for training, instance prompt/token will be ignored.")
self.use_image_names_as_captions_label.grid(row=2, column=0, sticky="nsew")
# create checkbox
self.use_image_names_as_captions_checkbox = ctk.CTkSwitch(self.dataset_frame_subframe, variable=self.use_image_names_as_captions_var)
self.use_image_names_as_captions_checkbox.grid(row=2, column=1, sticky="nsew")
# create shuffle captions checkbox
self.shuffle_captions_var = tk.IntVar()
self.shuffle_captions_var.set(self.shuffle_captions)
# create label
self.shuffle_captions_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Shuffle Captions")
shuffle_captions_label_ttp = CreateToolTip(self.shuffle_captions_label, "Randomize the order of tags in a caption. Tags are separated by ','. Used for training with booru-style captions.")
self.shuffle_captions_label.grid(row=3, column=0, sticky="nsew")
# create checkbox
self.shuffle_captions_checkbox = ctk.CTkSwitch(self.dataset_frame_subframe, variable=self.shuffle_captions_var)
self.shuffle_captions_checkbox.grid(row=3, column=1, sticky="nsew")
# create auto balance dataset checkbox
self.auto_balance_dataset_var = tk.IntVar()
self.auto_balance_dataset_var.set(self.auto_balance_concept_datasets)
# create label
self.auto_balance_dataset_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Auto Balance Dataset")
auto_balance_dataset_label_ttp = CreateToolTip(self.auto_balance_dataset_label, "Will use the concept with the least amount of images to balance the dataset by removing images from the other concepts.")
self.auto_balance_dataset_label.grid(row=4, column=0, sticky="nsew")
# create checkbox
self.auto_balance_dataset_checkbox = ctk.CTkSwitch(self.dataset_frame_subframe, variable=self.auto_balance_dataset_var)
self.auto_balance_dataset_checkbox.grid(row=4, column=1, sticky="nsew")
#create add class images to dataset checkbox
self.add_class_images_to_dataset_var = tk.IntVar()
self.add_class_images_to_dataset_var.set(self.add_class_images_to_training)
#create label
self.add_class_images_to_dataset_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Add Class Images to Dataset")
add_class_images_to_dataset_label_ttp = CreateToolTip(self.add_class_images_to_dataset_label, "Will add class images without prior preservation to the dataset.")
self.add_class_images_to_dataset_label.grid(row=5, column=0, sticky="nsew")
#create checkbox
self.add_class_images_to_dataset_checkbox = ctk.CTkSwitch(self.dataset_frame_subframe, variable=self.add_class_images_to_dataset_var)
self.add_class_images_to_dataset_checkbox.grid(row=5, column=1, sticky="nsew")
#create number of class images entry
self.number_of_class_images_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Number of Class Images")
number_of_class_images_label_ttp = CreateToolTip(self.number_of_class_images_label, "The number of class images to add to the dataset, if they don't exist in the class directory they will be generated.")
self.number_of_class_images_label.grid(row=6, column=0, sticky="nsew")
self.number_of_class_images_entry = ctk.CTkEntry(self.dataset_frame_subframe)
self.number_of_class_images_entry.grid(row=6, column=1, sticky="nsew")
self.number_of_class_images_entry.insert(0, self.num_class_images)
#create dataset repeat entry
self.dataset_repeats_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Dataset Repeats")
dataset_repeat_label_ttp = CreateToolTip(self.dataset_repeats_label, "The number of times to repeat the dataset, this will increase the number of images in the dataset.")
self.dataset_repeats_label.grid(row=7, column=0, sticky="nsew")
self.dataset_repeats_entry = ctk.CTkEntry(self.dataset_frame_subframe)
self.dataset_repeats_entry.grid(row=7, column=1, sticky="nsew")
self.dataset_repeats_entry.insert(0, self.dataset_repeats)
#add use_aspect_ratio_bucketing checkbox
self.use_aspect_ratio_bucketing_var = tk.IntVar()
self.use_aspect_ratio_bucketing_var.set(self.use_aspect_ratio_bucketing)
#create label
self.use_aspect_ratio_bucketing_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Use Aspect Ratio Bucketing")
use_aspect_ratio_bucketing_label_ttp = CreateToolTip(self.use_aspect_ratio_bucketing_label, "Will use aspect ratio bucketing, may improve aspect ratio generations.")
self.use_aspect_ratio_bucketing_label.grid(row=8, column=0, sticky="nsew")
#create checkbox
self.use_aspect_ratio_bucketing_checkbox = ctk.CTkSwitch(self.dataset_frame_subframe, variable=self.use_aspect_ratio_bucketing_var)
self.use_aspect_ratio_bucketing_checkbox.grid(row=8, column=1, sticky="nsew")
#do something on checkbox click
self.use_aspect_ratio_bucketing_checkbox.bind("<Button-1>", self.aspect_ratio_mode_toggles)
#option menu to select aspect ratio bucketing mode
self.aspect_ratio_bucketing_mode_var = tk.StringVar()
self.aspect_ratio_bucketing_mode_var.set(self.aspect_ratio_bucketing_mode)
self.aspect_ratio_bucketing_mode_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Aspect Ratio Bucketing Mode")
aspect_ratio_bucketing_mode_label_ttp = CreateToolTip(self.aspect_ratio_bucketing_mode_label, "Select what the Auto Bucketing will do in case the bucket doesn't match the batch size, dynamic will choose the least amount of adding/removing of images per bucket.")
self.aspect_ratio_bucketing_mode_label.grid(row=9, column=0, sticky="nsew")
self.aspect_ratio_bucketing_mode_option_menu = ctk.CTkOptionMenu(self.dataset_frame_subframe, variable=self.aspect_ratio_bucketing_mode_var, values=['Dynamic Fill', 'Drop Fill', 'Duplicate Fill'])
self.aspect_ratio_bucketing_mode_option_menu.grid(row=9, column=1, sticky="nsew")
#option menu to select dynamic bucketing mode (if enabled)
self.dynamic_bucketing_mode_var = tk.StringVar()
self.dynamic_bucketing_mode_var.set(self.dynamic_bucketing_mode)
self.dynamic_bucketing_mode_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Dynamic Preference")
dynamic_bucketing_mode_label_ttp = CreateToolTip(self.dynamic_bucketing_mode_label, "If you're using dynamic mode, choose what you prefer in the case that dropping and duplicating are the same amount of images.")
self.dynamic_bucketing_mode_label.grid(row=10, column=0, sticky="nsew")
self.dynamic_bucketing_mode_option_menu = ctk.CTkOptionMenu(self.dataset_frame_subframe, variable=self.dynamic_bucketing_mode_var, values=['Duplicate', 'Drop'])
self.dynamic_bucketing_mode_option_menu.grid(row=10, column=1, sticky="nsew")
#add shuffle dataset per epoch checkbox
self.shuffle_dataset_per_epoch_var = tk.IntVar()
self.shuffle_dataset_per_epoch_var.set(self.shuffle_dataset_per_epoch)
#create label
self.shuffle_dataset_per_epoch_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Shuffle Dataset Per Epoch")
shuffle_dataset_per_epoch_label_ttp = CreateToolTip(self.shuffle_dataset_per_epoch_label, "Will shuffle the dataset per epoch, may improve training.")
self.shuffle_dataset_per_epoch_label.grid(row=1, column=2, sticky="nsew")
#create checkbox
self.shuffle_dataset_per_epoch_checkbox = ctk.CTkSwitch(self.dataset_frame_subframe, variable=self.shuffle_dataset_per_epoch_var)
self.shuffle_dataset_per_epoch_checkbox.grid(row=1, column=3, sticky="nsew")
#masked training
self.masked_training_var = tk.IntVar()
self.masked_training_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Masked Training")
masked_training_label_ttp = CreateToolTip(self.masked_training_label, "Enable training on masked areas of the dataset.")
self.masked_training_checkbox = ctk.CTkSwitch(self.dataset_frame_subframe, variable=self.masked_training_var)
self.masked_training_var.set(self.masked_training)
self.masked_training_label.grid(row=2, column=2, sticky="nsew")
self.masked_training_checkbox.grid(row=2, column=3, sticky="nsew")
#normalize masked area loss
self.normalize_masked_area_loss_var = tk.IntVar()
self.normalize_masked_area_loss_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Normalize Masked Area Loss")
normalize_masked_area_loss_label_ttp = CreateToolTip(self.normalize_masked_area_loss_label, "Normalize loss values based on the masked area of images.")
self.normalize_masked_area_loss_checkbox = ctk.CTkSwitch(self.dataset_frame_subframe, variable=self.normalize_masked_area_loss_var)
self.normalize_masked_area_loss_var.set(self.normalize_masked_area_loss)
self.normalize_masked_area_loss_label.grid(row=3, column=2, sticky="nsew")
self.normalize_masked_area_loss_checkbox.grid(row=3, column=3, sticky="nsew")
#unmasked probability
self.unmasked_probability_var = tk.StringVar()
self.unmasked_probability_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Unmasked Steps")
unmasked_probability_label_ttp = CreateToolTip(self.unmasked_probability_label, "Fraction of steps to train on unmasked images.")
self.unmasked_probability_var.set(self.unmasked_probability)
self.unmasked_probability_entry = ctk.CTkEntry(self.dataset_frame_subframe, textvariable=self.unmasked_probability_var)
self.unmasked_probability_label.grid(row=4, column=2, sticky="nsew")
self.unmasked_probability_entry.grid(row=4, column=3, sticky="nsew")
#unmasked probability
self.max_denoising_strength_var = tk.StringVar()
self.max_denoising_strength_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Max Denoising Strength")
max_denoising_strength_label_ttp = CreateToolTip(self.max_denoising_strength_label, "Max denoising factor to train on. Set this to 70%-80% for masked training and to reduce overfitting. 100% is the default behavior for training on up to fully noisy images.")
self.max_denoising_strength_var.set(self.max_denoising_strength)
self.max_denoising_strength_entry = ctk.CTkEntry(self.dataset_frame_subframe, textvariable=self.max_denoising_strength_var)
self.max_denoising_strength_label.grid(row=5, column=2, sticky="nsew")
self.max_denoising_strength_entry.grid(row=5, column=3, sticky="nsew")
#fallback mask prompt
self.fallback_mask_prompt_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Fallback Mask Prompt")
fallback_mask_prompt_label_ttp = CreateToolTip(self.fallback_mask_prompt_label, "A prompt used for masking images without a mask.")
self.fallback_mask_prompt_entry = ctk.CTkEntry(self.dataset_frame_subframe)
self.fallback_mask_prompt_entry.insert(0, self.fallback_mask_prompt)
self.fallback_mask_prompt_label.grid(row=6, column=2, sticky="nsew")
self.fallback_mask_prompt_entry.grid(row=6, column=3, sticky="nsew")
#add download dataset entry
#add a switch to duplicate fill bucket
#self.duplicate_fill_buckets_var = tk.IntVar()
#self.duplicate_fill_buckets_var.set(self.duplicate_fill_buckets)
#create label
#self.duplicate_fill_buckets_label = ctk.CTkLabel(self.dataset_frame_subframe, text="Force Fill Buckets with Duplicates")
#duplicate_fill_buckets_label_ttp = CreateToolTip(self.duplicate_fill_buckets_label, "Will duplicate to fill buckets, enable this to avoid buckets dropping images.")
#self.duplicate_fill_buckets_label.grid(row=8, column=0, sticky="nsew")
#create checkbox
#self.duplicate_fill_buckets_checkbox = ctk.CTkSwitch(self.dataset_frame_subframe, variable=self.duplicate_fill_buckets_var)
#self.duplicate_fill_buckets_checkbox.grid(row=8, column=1, sticky="nsew")
#self.use_aspect_ratio_bucketing_checkbox.bind("<Button-1>", self.duplicate_fill_buckets_label.configure(state="disabled"))
#self.use_aspect_ratio_bucketing_checkbox.bind("<Button-1>", self.duplicate_fill_buckets_checkbox.configure(state="disabled"))
def create_sampling_settings_widgets(self):
self.sampling_title = ctk.CTkLabel(self.sampling_frame, text="Sampling Settings", font=ctk.CTkFont(size=20, weight="bold"))
self.sampling_title.grid(row=0, column=0, padx=20, pady=20)
#create sample every n steps entry
self.sample_step_interval_label = ctk.CTkLabel(self.sampling_frame_subframe, text="Sample Every N Steps")
sample_step_interval_label_ttp = CreateToolTip(self.sample_step_interval_label, "Will sample the model every N steps.")
self.sample_step_interval_label.grid(row=1, column=0, sticky="nsew")
self.sample_step_interval_entry = ctk.CTkEntry(self.sampling_frame_subframe)
self.sample_step_interval_entry.grid(row=1, column=1, sticky="nsew")
self.sample_step_interval_entry.insert(0, self.sample_step_interval)
#create saver every n epochs entry
self.save_every_n_epochs_label = ctk.CTkLabel(self.sampling_frame_subframe, text="Save and sample Every N Epochs")
save_every_n_epochs_label_ttp = CreateToolTip(self.save_every_n_epochs_label, "Will save and sample the model every N epochs.")
self.save_every_n_epochs_label.grid(row=2, column=0, sticky="nsew")
self.save_every_n_epochs_entry = ctk.CTkEntry(self.sampling_frame_subframe)
self.save_every_n_epochs_entry.grid(row=2, column=1, sticky="nsew")
self.save_every_n_epochs_entry.insert(0, self.save_and_sample_every_x_epochs)
#create number of samples to generate entry
self.number_of_samples_to_generate_label = ctk.CTkLabel(self.sampling_frame_subframe, text="Number of Samples to Generate")
number_of_samples_to_generate_label_ttp = CreateToolTip(self.number_of_samples_to_generate_label, "The number of samples to generate per prompt.")
self.number_of_samples_to_generate_label.grid(row=3, column=0, sticky="nsew")
self.number_of_samples_to_generate_entry = ctk.CTkEntry(self.sampling_frame_subframe)
self.number_of_samples_to_generate_entry.grid(row=3, column=1, sticky="nsew")
self.number_of_samples_to_generate_entry.insert(0, self.num_samples_to_generate)
#create sample width entry
self.sample_width_label = ctk.CTkLabel(self.sampling_frame_subframe, text="Sample Width")
sample_width_label_ttp = CreateToolTip(self.sample_width_label, "The width of the generated samples.")
self.sample_width_label.grid(row=4, column=0, sticky="nsew")
self.sample_width_entry = ctk.CTkEntry(self.sampling_frame_subframe)
self.sample_width_entry.grid(row=4, column=1, sticky="nsew")
self.sample_width_entry.insert(0, self.sample_width)
#create sample height entry
self.sample_height_label = ctk.CTkLabel(self.sampling_frame_subframe, text="Sample Height")
sample_height_label_ttp = CreateToolTip(self.sample_height_label, "The height of the generated samples.")
self.sample_height_label.grid(row=5, column=0, sticky="nsew")
self.sample_height_entry = ctk.CTkEntry(self.sampling_frame_subframe)
self.sample_height_entry.grid(row=5, column=1, sticky="nsew")
self.sample_height_entry.insert(0, self.sample_height)
#create a checkbox to sample_on_training_start
self.sample_on_training_start_var = tk.IntVar()
self.sample_on_training_start_var.set(self.sample_on_training_start)
#create label
self.sample_on_training_start_label = ctk.CTkLabel(self.sampling_frame_subframe, text="Sample On Training Start")
sample_on_training_start_label_ttp = CreateToolTip(self.sample_on_training_start_label, "Will save and sample the model on training start, useful for debugging and comparison.")
self.sample_on_training_start_label.grid(row=6, column=0, sticky="nsew")
#create checkbox
self.sample_on_training_start_checkbox = ctk.CTkSwitch(self.sampling_frame_subframe, variable=self.sample_on_training_start_var)
self.sample_on_training_start_checkbox.grid(row=6, column=1, sticky="nsew")
#create sample random aspect ratio checkbox
self.sample_random_aspect_ratio_var = tk.IntVar()
self.sample_random_aspect_ratio_var.set(self.sample_random_aspect_ratio)
#create label
self.sample_random_aspect_ratio_label = ctk.CTkLabel(self.sampling_frame_subframe, text="Sample Random Aspect Ratio")
sample_random_aspect_ratio_label_ttp = CreateToolTip(self.sample_random_aspect_ratio_label, "Will generate samples with random aspect ratios, useful to check aspect ratio bucketing.")
self.sample_random_aspect_ratio_label.grid(row=7, column=0, sticky="nsew")
#create checkbox
self.sample_random_aspect_ratio_checkbox = ctk.CTkSwitch(self.sampling_frame_subframe, variable=self.sample_random_aspect_ratio_var)
self.sample_random_aspect_ratio_checkbox.grid(row=7, column=1, sticky="nsew")
#create an optionmenu to select a number of desired prompts to sample from the batch
self.batch_prompt_sampling_optionmenu_var = tk.StringVar()
self.batch_prompt_sampling_optionmenu_var.set(self.batch_prompt_sampling_num_prompts)
self.batch_prompt_sampling_label = ctk.CTkLabel(self.sampling_frame_subframe, text="Batch Prompt Sampling")
self.batch_prompt_sampling_label.grid(row=8, column=0, sticky="nsew")
self.batch_prompt_sampling_optionmenu = ctk.CTkOptionMenu(self.sampling_frame_subframe, variable=self.batch_prompt_sampling_optionmenu_var, values=['0','1','2','3','4','5','6','7','8','9','10'])
self.batch_prompt_sampling_optionmenu_ttp = CreateToolTip(self.batch_prompt_sampling_label, "Will try to sample prompts/tokens from the batch to use as prompts for the samples.")
self.batch_prompt_sampling_optionmenu.grid(row=8, column=1, sticky="nsew")
#create add sample prompt button
self.add_sample_prompt_button = ctk.CTkButton(self.sampling_frame_subframe, text="Add Sample Prompt", command=self.add_sample_prompt)
add_sample_prompt_button_ttp = CreateToolTip(self.add_sample_prompt_button, "Add a sample prompt to the list.")
self.add_sample_prompt_button.grid(row=1, column=2, sticky="nsew")
#create remove sample prompt button
self.remove_sample_prompt_button = ctk.CTkButton(self.sampling_frame_subframe, text="Remove Sample Prompt", command=self.remove_sample_prompt)
remove_sample_prompt_button_ttp = CreateToolTip(self.remove_sample_prompt_button, "Remove a sample prompt from the list.")
self.remove_sample_prompt_button.grid(row=1, column=3, sticky="nsew")
#for every prompt in self.sample_prompts, create a label and entry
self.sample_prompt_labels = []
self.sample_prompt_entries = []
self.sample_prompt_row = 2
for i in range(len(self.sample_prompts)):
#create label
self.sample_prompt_labels.append(ctk.CTkLabel(self.sampling_frame_subframe, text="Sample Prompt " + str(i)))
self.sample_prompt_labels[i].grid(row=self.sample_prompt_row + i, column=2, sticky="nsew")
#create entry
self.sample_prompt_entries.append(ctk.CTkEntry(self.sampling_frame_subframe, width=70))
self.sample_prompt_entries[i].grid(row=self.sample_prompt_row + i, column=3, sticky="nsew")
self.sample_prompt_entries[i].insert(0, self.sample_prompts[i])
for i in self.sample_prompt_entries:
i.bind("<Button-3>", self.create_right_click_menu)
self.controlled_sample_row = 2 + len(self.sample_prompts)
#create a button to add controlled seed sample
self.add_controlled_seed_sample_button = ctk.CTkButton(self.sampling_frame_subframe, text="Add Controlled Seed Sample", command=self.add_controlled_seed_sample)
add_controlled_seed_sample_button_ttp = CreateToolTip(self.add_controlled_seed_sample_button, "Will generate a sample using the seed at every save interval.")
self.add_controlled_seed_sample_button.grid(row=self.controlled_sample_row + len(self.sample_prompts), column=2, sticky="nsew")
#create a button to remove controlled seed sample
self.remove_controlled_seed_sample_button = ctk.CTkButton(self.sampling_frame_subframe, text="Remove Controlled Seed Sample", command=self.remove_controlled_seed_sample)
remove_controlled_seed_sample_button_ttp = CreateToolTip(self.remove_controlled_seed_sample_button, "Will remove the last controlled seed sample.")
self.remove_controlled_seed_sample_button.grid(row=self.controlled_sample_row + len(self.sample_prompts), column=3, sticky="nsew")
#for every controlled seed sample in self.controlled_seed_samples, create a label and entry
self.controlled_seed_sample_labels = []
self.controlled_seed_sample_entries = []
self.controlled_seed_buttons = [self.add_controlled_seed_sample_button, self.remove_controlled_seed_sample_button]
for i in range(len(self.add_controlled_seed_to_sample)):
#create label
self.controlled_seed_sample_labels.append(ctk.CTkLabel(self.sampling_frame_subframe, text="Controlled Seed Sample " + str(i)))
self.controlled_seed_sample_labels[i].grid(row=self.controlled_sample_row + len(self.sample_prompts) + i, column=2, sticky="nsew")
#create entry
self.controlled_seed_sample_entries.append(ctk.CTkEntry(self.sampling_frame_subframe))
self.controlled_seed_sample_entries[i].grid(row=self.controlled_sample_row + len(self.sample_prompts) + i, column=3, sticky="nsew")
self.controlled_seed_sample_entries[i].insert(0, self.add_controlled_seed_to_sample[i])
for i in self.controlled_seed_sample_entries:
i.bind("<Button-3>", self.create_right_click_menu)
def create_data_settings_widgets(self):
#add concept settings label
self.data_frame_title = ctk.CTkLabel(self.data_frame, text='Data Settings', font=ctk.CTkFont(size=20, weight="bold"))
self.data_frame_title.grid(row=0, column=0,columnspan=2, padx=20, pady=20)
#add load concept from json button
#add empty label
empty = ctk.CTkLabel(self.data_frame_subframe, text="",width=40)
empty.grid(row=1, column=0, sticky="nsew")
self.load_concept_from_json_button = ctk.CTkButton(self.data_frame_subframe, text="Load Concepts From JSON", command=self.load_concept_from_json)
self.load_concept_from_json_button.grid(row=1, column=1, sticky="e")
load_concept_from_json_button_ttp = CreateToolTip(self.load_concept_from_json_button, "Load concepts from a JSON file, compatible with Shivam's concept list.")
#self.load_concept_from_json_button.grid(row=1, column=0, sticky="nsew")
#add save concept to json button
self.save_concept_to_json_button = ctk.CTkButton(self.data_frame_subframe, text="Save Concepts To JSON", command=self.save_concept_to_json)
self.save_concept_to_json_button.grid(row=1, column=2, sticky="e")
save_concept_to_json_button_ttp = CreateToolTip(self.save_concept_to_json_button, "Save concepts to a JSON file, compatible with Shivam's concept list.")
#self.save_concept_to_json_button.grid(row=1, column=1, sticky="nsew")
#create a button to add concept
self.add_concept_button = ctk.CTkButton(self.data_frame_subframe, text="Add Concept", command=self.add_new_concept,width=50)
self.add_concept_button.grid(row=1, column=3, sticky="e")
#self.add_concept_button.grid(row=2, column=0, sticky="nsew")
#create a button to remove concept
self.remove_concept_button = ctk.CTkButton(self.data_frame_subframe, text="Remove Concept", command=self.remove_new_concept,width=50)
self.remove_concept_button.grid(row=1, column=4, sticky="e")
#self.remove_concept_button.grid(row=2, column=1, sticky="nsew")
self.previous_page_button = ctk.CTkButton(self.data_frame_subframe, text="Previous Page", command=self.next_concept_page,width=50, state="disabled")
self.previous_page_button.grid(row=1, column=5, sticky="e")
#self.remove_concept_button.grid(row=2, column=1, sticky="nsew")
self.next_page_button = ctk.CTkButton(self.data_frame_subframe, text="Next Page", command=self.next_concept_page,width=50, state="disabled")
self.next_page_button.grid(row=1, column=6, sticky="e")
#self.remove_concept_button.grid(row=2, column=1, sticky="nsew")
#self.concept_entries = []
#self.concept_labels = []
#self.concept_file_dialog_buttons = []
def next_concept_page(self):
self.concept_page += 1
self.update_concept_page()
def create_plyaground_widgets(self):
self.playground_title = ctk.CTkLabel(self.playground_frame, text="Model Playground", font=ctk.CTkFont(size=20, weight="bold"))
#add play model entry with button to open file dialog
self.play_model_label = ctk.CTkLabel(self.playground_frame_subframe, text="Diffusers Model Directory")
self.play_model_entry = ctk.CTkEntry(self.playground_frame_subframe,placeholder_text="CTkEntry")
self.play_model_entry.insert(0, self.play_input_model_path)
self.play_model_file_dialog_button = ctk.CTkButton(self.playground_frame_subframe, text="...",width=5, command=lambda: self.open_file_dialog(self.play_model_entry))
self.play_model_file_dialog_button.grid(row=0, column=2, sticky="w")
#add a prompt entry to play tab
self.play_prompt_label = ctk.CTkLabel(self.playground_frame_subframe, text="Prompt")
self.play_prompt_entry = ctk.CTkEntry(self.playground_frame_subframe)
self.play_prompt_entry.insert(0, self.play_postive_prompt)
#add a negative prompt entry to play tab
self.play_negative_prompt_label = ctk.CTkLabel(self.playground_frame_subframe, text="Negative Prompt")
self.play_negative_prompt_entry = ctk.CTkEntry(self.playground_frame_subframe, width=40)
self.play_negative_prompt_entry.insert(0, self.play_negative_prompt)
#add a seed entry to play tab
self.play_seed_label = ctk.CTkLabel(self.playground_frame_subframe, text="Seed")
self.play_seed_entry = ctk.CTkEntry(self.playground_frame_subframe)
self.play_seed_entry.insert(0, self.play_seed)
#add a keep seed checkbox next to seed entry
self.play_keep_seed_var = tk.IntVar()
self.play_keep_seed_var.set(self.play_keep_seed)
self.play_keep_seed_checkbox = ctk.CTkCheckBox(self.playground_frame_subframe, text="Keep Seed", variable=self.play_keep_seed_var)
#add a temperature slider from 0.1 to 1.0
#create a steps slider from 1 to 100
self.play_steps_label = ctk.CTkLabel(self.playground_frame_subframe, text=f"Steps: {self.play_steps}")
self.play_steps_slider = ctk.CTkSlider(self.playground_frame_subframe, from_=1, to=150, number_of_steps=149, command= lambda x: self.play_steps_label.configure(text="Steps: " + str(int(self.play_steps_slider.get()))))
#on slider change update the value
#self.play_steps_slider.bind("<Configure>", self.play_steps_label.configure(text="Steps: " + str(self.play_steps_slider.get())))
self.play_steps_slider.set(self.play_steps)
#add a scheduler selection box
self.play_scheduler_label = ctk.CTkLabel(self.playground_frame_subframe, text="Scheduler")
self.play_scheduler_variable = tk.StringVar(self.playground_frame_subframe)
self.play_scheduler_variable.set(self.play_scheduler)
self.play_scheduler_option_menu = ctk.CTkOptionMenu(self.playground_frame_subframe, variable=self.play_scheduler_variable, values=self.schedulers)
#add resoltuion slider from 256 to 1024 in increments of 64 for width and height
self.play_resolution_label = ctk.CTkLabel(self.playground_frame_subframe, text="Resolution")
self.play_resolution_label_height = ctk.CTkLabel(self.playground_frame_subframe, text=f"Height: {self.play_sample_height}")
self.play_resolution_label_width = ctk.CTkLabel(self.playground_frame_subframe, text=f"Width: {self.play_sample_width}")
#add sliders for height and width
#make a list of resolutions from 256 to 2048 in increments of 64
#play_resolutions = []
#for i in range(256,2049,64):
# play_resolutions.append(str(i))
self.play_resolution_slider_height = ctk.CTkSlider(self.playground_frame_subframe,from_=128, to=2048, number_of_steps=30, command= lambda x: self.play_resolution_label_height.configure(text="Height: " + str(int(self.play_resolution_slider_height.get()))))
self.play_resolution_slider_width = ctk.CTkSlider(self.playground_frame_subframe, from_=128, to=2048, number_of_steps=30, command= lambda x: self.play_resolution_label_width.configure(text="Width: " + str(int(self.play_resolution_slider_width.get()))))
self.play_resolution_slider_width.set(self.play_sample_width)
self.play_resolution_slider_height.set(self.play_sample_height)
#add a cfg slider 0.5 to 25 in increments of 0.5
self.play_cfg_label = ctk.CTkLabel(self.playground_frame_subframe, text=f"CFG: {self.play_cfg}")
self.play_cfg_slider = ctk.CTkSlider(self.playground_frame_subframe, from_=0.5, to=25, number_of_steps=49, command= lambda x: self.play_cfg_label.configure(text="CFG: " + str(self.play_cfg_slider.get())))
self.play_cfg_slider.set(self.play_cfg)
#add Toolbox label
self.play_toolbox_label = ctk.CTkLabel(self.playground_frame_subframe, text="Toolbox")
self.play_generate_image_button = ctk.CTkButton(self.playground_frame_subframe, text="Generate Image", command=lambda: self.play_generate_image(self.play_model_entry.get(), self.play_prompt_entry.get(), self.play_negative_prompt_entry.get(), self.play_seed_entry.get(), self.play_scheduler_variable.get(), int(self.play_resolution_slider_height.get()), int(self.play_resolution_slider_width.get()), self.play_cfg_slider.get(), self.play_steps_slider.get()))
#create a canvas to display the generated image
#self.play_image_canvas = tk.Canvas(self.playground_frame_subframe, width=512, height=512, highlightthickness=0)
#self.play_image_canvas.grid(row=11, column=0, columnspan=3, sticky="nsew")
#create a button to generate image
self.play_prompt_entry.bind("<Return>", lambda event: self.play_generate_image(self.play_model_entry.get(), self.play_prompt_entry.get(), self.play_negative_prompt_entry.get(), self.play_seed_entry.get(), self.play_scheduler_variable.get(), int(self.play_resolution_slider_height.get()), int(self.play_resolution_slider_width.get()), self.play_cfg_slider.get(), self.play_steps_slider.get()))
self.play_negative_prompt_entry.bind("<Return>", lambda event: self.play_generate_image(self.play_model_entry.get(), self.play_prompt_entry.get(), self.play_negative_prompt_entry.get(), self.play_seed_entry.get(), self.play_scheduler_variable.get(), int(self.play_resolution_slider_height.get()), int(self.play_resolution_slider_width.get()), self.play_cfg_slider.get(), self.play_steps_slider.get()))
#add convert to ckpt button
self.play_convert_to_ckpt_button = ctk.CTkButton(self.playground_frame_subframe, text="Convert To CKPT", command=lambda:self.convert_to_ckpt(model_path=self.play_model_entry.get()))
#add interative generation button to act as a toggle
#convert to safetensors button
#self.play_interactive_generation_button_bool = tk.BooleanVar()
#self.play_interactive_generation_button = ctk.CTkButton(self.playground_frame_subframe, text="Interactive Generation", command=self.interactive_generation_button)
#self.play_interactive_generation_button_bool.set(False)#add play model entry with button to open file dialog
def create_toolbox_widgets(self):
#add label to tools tab
self.toolbox_title = ctk.CTkLabel(self.toolbox_frame, text="Toolbox", font=ctk.CTkFont(size=20, weight="bold"))
self.toolbox_title.grid(row=0, column=0, padx=20, pady=20)
#empty row
#self.empty_row = ctk.CTkLabel(self.toolbox_frame_subframe, text="")
#self.empty_row.grid(row=1, column=0, sticky="nsew")
#add a label model tools title
self.model_tools_label = ctk.CTkLabel(self.toolbox_frame_subframe, text="Model Tools", font=ctk.CTkFont(size=20, weight="bold"))
self.model_tools_label.grid(row=2, column=0,columnspan=3, sticky="nsew",pady=10)
#empty row
#self.empty_row = ctk.CTkLabel(self.toolbox_frame_subframe, text="")
#self.empty_row.grid(row=3, column=0, sticky="nsew")
#add a button to convert to ckpt
self.convert_to_ckpt_button = ctk.CTkButton(self.toolbox_frame_subframe, text="Convert Diffusers To CKPT", command=lambda:self.convert_to_ckpt())
self.convert_to_ckpt_button.grid(row=4, column=0, columnspan=1, sticky="nsew")
#convert to safetensors button
self.convert_to_safetensors_button = ctk.CTkButton(self.toolbox_frame_subframe, text="Convert Diffusers To SafeTensors", command=lambda:self.convert_to_safetensors())
self.convert_to_safetensors_button.grid(row=4, column=1, columnspan=1, sticky="nsew")
#add a button to convert ckpt to diffusers
self.convert_ckpt_to_diffusers_button = ctk.CTkButton(self.toolbox_frame_subframe, text="Convert CKPT To Diffusers", command=lambda:self.convert_ckpt_to_diffusers())
self.convert_ckpt_to_diffusers_button.grid(row=4, column=2, columnspan=1, sticky="nsew")
#empty row
self.empty_row = ctk.CTkLabel(self.toolbox_frame_subframe, text="")
self.empty_row.grid(row=6, column=0, sticky="nsew")
#add a label dataset tools title
self.dataset_tools_label = ctk.CTkLabel(self.toolbox_frame_subframe, text="Dataset Tools", font=ctk.CTkFont(size=20, weight="bold"))
self.dataset_tools_label.grid(row=7, column=0,columnspan=3, sticky="nsew")
#add a button for Caption Buddy
#self.caption_buddy_button = ctk.CTkButton(self.toolbox_frame_subframe, text="Launch Caption Buddy",font=("Helvetica", 10, "bold"), command=lambda:self.caption_buddy())
#self.caption_buddy_button.grid(row=8, column=0, columnspan=3, sticky="nsew")
self.download_dataset_label = ctk.CTkLabel(self.toolbox_frame_subframe, text="Clone Dataset from HF")
download_dataset_label_ttp = CreateToolTip(self.download_dataset_label, "Will git clone a HF dataset repo")
self.download_dataset_label.grid(row=9, column=0, sticky="nsew")
self.download_dataset_entry = ctk.CTkEntry(self.toolbox_frame_subframe)
self.download_dataset_entry.grid(row=9, column=1, sticky="nsew")
#add download dataset button
self.download_dataset_button = ctk.CTkButton(self.toolbox_frame_subframe, text="Download Dataset", command=self.download_dataset)
self.download_dataset_button.grid(row=9, column=2, sticky="nsew")
def find_latest_generated_model(self,entry=None):
last_output_path = self.output_path_entry.get()
last_num_epochs = self.train_epochs_entry.get()
last_model_path = last_output_path + os.sep + last_num_epochs
#convert last_model_path seperators to the correct ones for the os
last_model_path = last_model_path.replace("/", os.sep)
last_model_path = last_model_path.replace("\\", os.sep)
#check if the output path is valid
if last_output_path != "":
#check if the output path exists
if os.path.exists(last_output_path):
#check if the output path has a model in it
if os.path.exists(last_model_path):
#check if the model is a ckpt
if all(x in os.listdir(last_model_path) for x in self.required_folders):
# print(newest_dir)
last_model_path = last_model_path.replace("/", os.sep).replace("\\", os.sep)
if entry:
entry.delete(0, tk.END)
entry.insert(0, last_model_path)
return
else:
newest_dirs = sorted(glob.iglob(last_output_path + os.sep + '*'), key=os.path.getctime, reverse=True)
#remove anything that is not a dir
newest_dirs = [x for x in newest_dirs if os.path.isdir(x)]
#sort newest_dirs by date
for newest_dir in newest_dirs:
#check if the newest dir has all the required folders
if all(x in os.listdir(newest_dir) for x in self.required_folders):
last_model_path = newest_dir.replace("/", os.sep).replace("\\", os.sep)
if entry:
entry.delete(0, tk.END)
entry.insert(0, last_model_path)
return
else:
newest_dirs = sorted(glob.iglob(last_output_path + os.sep + '*'), key=os.path.getctime, reverse=True)
newest_dirs = [x for x in newest_dirs if os.path.isdir(x)]
#sort newest_dirs by date
for newest_dir in newest_dirs:
#check if the newest dir has all the required folders
if all(x in os.listdir(newest_dir) for x in self.required_folders):
last_model_path = newest_dir.replace("/", os.sep).replace("\\", os.sep)
if entry:
entry.delete(0, tk.END)
entry.insert(0, last_model_path)
return
else:
return
else:
return
def update_ST(self):
#git
new_version = subprocess.check_output(["git", "ls-remote", "http://github.com/RossM/StableTuner.git","main"], cwd=Path(__file__).resolve().parent).strip().decode()[0:7]
#open the stabletuner_hash.cfg file
#update the stabletuner_hash.cfg file
with open("configs/stabletuner_hash.cfg", "w") as f:
f.write(new_version)
#update the stabletuner
#self.update_stabletuner()
#git pull and wait for it to finish
subprocess.run(["git", "stash"], cwd=Path(__file__).resolve().parent)
subprocess.run(["git", "pull"], cwd=Path(__file__).resolve().parent)
print('pulled')
#restart the app
restart(self)
def packageForCloud(self):
#check if there's an export folder in the cwd and if not create one
if not os.path.exists("exports"):
os.mkdir("exports")
exportDir = self.export_name
if not os.path.exists("exports" + os.sep + exportDir):
os.mkdir("exports" + os.sep + exportDir)
else:
#remove the old export folder
shutil.rmtree("exports" + os.sep + exportDir)
os.mkdir("exports" + os.sep + exportDir)
self.full_export_path = "exports" + os.sep + exportDir
os.mkdir(self.full_export_path + os.sep + 'output')
os.mkdir(self.full_export_path + os.sep + 'datasets')
#check if self.model_path is a directory
if os.path.isdir(self.model_path):
#get the directory name
model_name = os.path.basename(self.model_path)
#check if model_name can be an int
try:
model_name = int(model_name)
#get the parent directory name
model_name = os.path.basename(os.path.dirname(self.model_path))
except:
pass
#create a folder in the export folder with the model name
if not os.path.exists(self.full_export_path + os.sep + 'input_model'+ os.sep):
os.mkdir(self.full_export_path + os.sep + 'input_model'+ os.sep)
if not os.path.exists(self.full_export_path + os.sep + 'input_model'+ os.sep + model_name):
os.mkdir(self.full_export_path + os.sep + 'input_model'+ os.sep + model_name)
#copy the model to the export folder
shutil.copytree(self.model_path, self.full_export_path + os.sep +'input_model'+ os.sep+ model_name + os.sep,dirs_exist_ok=True)
self.model_path= 'input_model' + '/' + model_name
if os.path.isdir(self.vae_path):
#get the directory name
vae_name = os.path.basename(self.vae_path)
#create a folder in the export folder with the model name
if not os.path.exists(self.full_export_path + os.sep + 'input_vae_model'+ os.sep + vae_name):
os.mkdir(self.full_export_path + os.sep + 'input_vae_model'+ os.sep + vae_name)
#copy the model to the export folder
shutil.copytree(self.vae_path, self.full_export_path + os.sep +'input_vae_model'+ os.sep+ vae_name + os.sep + vae_name,dirs_exist_ok=True)
self.vae_path= 'input_vae_model' + '/' + vae_name
if self.output_path == '':
self.output_path = 'output'
else:
#get the dirname
output_name = os.path.basename(self.output_path)
#create a folder in the export folder with the model name
if not os.path.exists(self.full_export_path + os.sep + 'output'+ os.sep + output_name):
os.mkdir(self.full_export_path + os.sep + 'output'+ os.sep + output_name)
self.output_path = 'output' + '/' + output_name
#loop through the concepts and add them to the export folder
concept_counter = 0
new_concepts = []
for concept in self.concepts:
concept_counter += 1
concept_data_dir = os.path.basename(concept['instance_data_dir'])
#concept is a dict
#get the concept name
concept_name = concept['instance_prompt']
#if concept_name is ''
if concept_name == '':
concept_name = 'concept_' + str(concept_counter)
#create a folder in the export/datasets folder with the concept name
#if not os.path.exists(self.full_export_path + os.sep + 'datasets'+ os.sep + concept_name):
# os.mkdir(self.full_export_path + os.sep + 'datasets'+ os.sep + concept_name)
#copy the concept to the export folder
shutil.copytree(concept['instance_data_dir'], self.full_export_path + os.sep + 'datasets'+ os.sep + concept_data_dir ,dirs_exist_ok=True)
concept_class_name = concept['class_prompt']
if concept_class_name == '':
#if class_data_dir is ''
if concept['class_data_dir'] != '':
concept_class_name = 'class_' + str(concept_counter)
#create a folder in the export/datasets folder with the concept name
if not os.path.exists(self.full_export_path + os.sep + 'datasets'+ os.sep + concept_class_name):
os.mkdir(self.full_export_path + os.sep + 'datasets'+ os.sep + concept_class_name)
#copy the concept to the export folder
shutil.copytree(concept['class_data_dir'], self.full_export_path + os.sep + 'datasets'+ os.sep + concept_class_name+ os.sep,dirs_exist_ok=True)
else:
if concept['class_data_dir'] != '':
#create a folder in the export/datasets folder with the concept name
if not os.path.exists(self.full_export_path + os.sep + 'datasets'+ os.sep + concept_class_name):
os.mkdir(self.full_export_path + os.sep + 'datasets'+ os.sep + concept_class_name)
#copy the concept to the export folder
shutil.copytree(concept['class_data_dir'], self.full_export_path + os.sep + 'datasets'+ os.sep + concept_class_name+ os.sep,dirs_exist_ok=True)
#create a new concept dict
new_concept = {}
new_concept['instance_prompt'] = concept_name
new_concept['instance_data_dir'] = 'datasets' + '/' + concept_data_dir
new_concept['class_prompt'] = concept_class_name
new_concept['class_data_dir'] = 'datasets' + '/' + concept_class_name if concept_class_name != '' else ''
new_concept['do_not_balance'] = concept['do_not_balance']
new_concept['use_sub_dirs'] = concept['use_sub_dirs']
new_concepts.append(new_concept)
#make scripts folder
self.save_concept_to_json(filename=self.full_export_path + os.sep + 'stabletune_concept_list.json', preMadeConcepts=new_concepts)
if not os.path.exists(self.full_export_path + os.sep + 'scripts'):
os.mkdir(self.full_export_path + os.sep + 'scripts')
#copy the scripts/trainer.py the scripts folder
shutil.copy('scripts' + os.sep + 'trainer.py', self.full_export_path + os.sep + 'scripts' + os.sep + 'trainer.py')
#copy trainer_utils.py to the scripts folder
shutil.copy('scripts' + os.sep + 'trainer_util.py', self.full_export_path + os.sep + 'scripts' + os.sep + 'trainer_util.py')
#copy converters.py to the scripts folder
shutil.copy('scripts' + os.sep + 'converters.py', self.full_export_path + os.sep + 'scripts' + os.sep + 'converters.py')
#copy model_util.py to the scripts folder
shutil.copy('scripts' + os.sep + 'model_util.py', self.full_export_path + os.sep + 'scripts' + os.sep + 'model_util.py')
#copy clip_seg to the scripts folder
shutil.copy('scripts' + os.sep + 'clip_segmentation.py', self.full_export_path + os.sep + 'scripts' + os.sep + 'clip_segmentation.py')
def caption_buddy(self):
import captionBuddy
#self.master.overrideredirect(False)
self.iconify()
#cb_root = tk.Tk()
cb_icon =PhotoImage(master=self,file = "resources/stableTuner_icon.png")
#cb_root.iconphoto(False, cb_icon)
app2 = captionBuddy.ImageBrowser(self)
app2.iconphoto(False, cb_icon)
#app = app2.mainloop()
#check if app2 is running
#self.master.overrideredirect(True)
#self.master.deiconify()
def aspect_ratio_mode_toggles(self, *args):
if self.use_aspect_ratio_bucketing_var.get() == 1:
self.with_prior_loss_preservation_var.set(0)
self.with_prior_loss_preservation_checkbox.configure(state="disabled")
self.aspect_ratio_bucketing_mode_label.configure(state="normal")
self.aspect_ratio_bucketing_mode_option_menu.configure(state="normal")
self.dynamic_bucketing_mode_label.configure(state="normal")
self.dynamic_bucketing_mode_option_menu.configure(state="normal")
else:
self.with_prior_loss_preservation_checkbox.configure(state="normal")
self.aspect_ratio_bucketing_mode_label.configure(state="disabled")
self.aspect_ratio_bucketing_mode_option_menu.configure(state="disabled")
self.dynamic_bucketing_mode_label.configure(state="disabled")
self.dynamic_bucketing_mode_option_menu.configure(state="disabled")
def download_dataset(self):
#get the dataset name
#import datasets
from git import Repo
folder = fd.askdirectory()
dataset_name = self.download_dataset_entry.get()
url = "https://huggingface.co/datasets/" + dataset_name if "/" not in dataset_name[0] else "/" + dataset_name
Repo.clone_from(url, folder)
#dataset = load_dataset(dataset_name)
#for each item in the dataset save it to a file in a folder with the name of the dataset
#create the folder
#get user to pick a folder
#git clone hugging face repo
#using
def interactive_generation_button(self):
#get state of button
button_state = self.play_interactive_generation_button_bool.get()
#flip the state of the button
self.play_interactive_generation_button_bool.set(not button_state)
#if the button is now true
if self.play_interactive_generation_button_bool.get():
#change the background of the button to green
#self.play_interactive_generation_button.configure()
pass
else:
#change the background of the button to normal
pass
#self.play_interactive_generation_button.configure(fg=self.dark_mode_title_var)
def play_save_image(self):
file = fd.asksaveasfilename(defaultextension=".png", filetypes=[("PNG", "*.png")])
#check if png in file name
if ".png" not in file and file != "" and self.play_current_image:
file = file + ".png"
self.play_current_image.save(file)
def generate_next_image(self):
self.play_generate_image(self.play_model_entry.get(), self.play_prompt_entry.get(), self.play_negative_prompt_entry.get(), self.play_seed_entry.get(), self.play_scheduler_variable.get(), int(self.play_resolution_slider_height.get()), int(self.play_resolution_slider_width.get()), self.play_cfg_slider.get(), self.play_steps_slider.get())
def play_generate_image(self, model, prompt, negative_prompt, seed, scheduler, sample_height, sample_width, cfg, steps):
import diffusers
import torch
from diffusers.utils.import_utils import is_xformers_available
self.play_height = sample_height
self.play_width = sample_width
#interactive = self.play_interactive_generation_button_bool.get()
#update generate image button text
if self.pipe is None or self.play_model_entry.get() != self.current_model:
if self.pipe is not None:
del self.pipe
#clear torch cache
torch.cuda.empty_cache()
self.play_generate_image_button["text"] = "Loading Model, Please stand by..."
#self.play_generate_image_button.configure(fg="red")
self.play_generate_image_button.update()
self.pipe = diffusers.DiffusionPipeline.from_pretrained(model,torch_dtype=torch.float16,safety_checker=None)
if isinstance(self.pipe, StableDiffusionPipeline):
self.play_model_variant = 'base'
if isinstance(self.pipe, StableDiffusionInpaintPipeline):
self.play_model_variant = 'inpainting'
if isinstance(self.pipe, StableDiffusionDepth2ImgPipeline):
self.play_model_variant = 'depth2img'
self.pipe.to('cuda')
self.current_model = model
if scheduler == 'DPMSolverMultistepScheduler':
scheduler = diffusers.DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
if scheduler == 'PNDMScheduler':
scheduler = diffusers.PNDMScheduler.from_config(self.pipe.scheduler.config)
if scheduler == 'DDIMScheduler':
scheduler = diffusers.DDIMScheduler.from_config(self.pipe.scheduler.config)
if scheduler == 'EulerAncestralDiscreteScheduler':
scheduler = diffusers.EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
if scheduler == 'EulerDiscreteScheduler':
scheduler = diffusers.EulerDiscreteScheduler.from_config(self.pipe.scheduler.config)
self.pipe.scheduler = scheduler
if is_xformers_available():
try:
self.pipe.enable_xformers_memory_efficient_attention()
except Exception as e:
print(
"Could not enable memory efficient attention. Make sure xformers is installed"
f" correctly and a GPU is available: {e}"
)
def displayInterImg(step: int, timestep: int, latents: torch.FloatTensor):
#tensor to image
img = self.pipe.decode_latents(latents)
image = self.pipe.numpy_to_pil(img)[0]
#convert to PIL image
self.play_current_image = ctk.CTkImage(image)
#if step == 0:
#self.play_image_canvas.configure(width=self.play_width, height=self.play_height)
#if self.play_width < self.master.winfo_width():
#self.play_width = self.master.winfo_width()
#self.master.geometry(f"{self.play_width}x{self.play_height+300}")
#self.play_image = self.play_image_canvas.create_image(0, 0, anchor="nw", image=self.play_current_image)
#self.play_image_canvas.update()
#update image
self.play_image_canvas.itemconfig(self.play_image, image=self.play_current_image)
self.play_image_canvas.update()
with torch.autocast("cuda"), torch.inference_mode():
del self.play_current_image
torch.cuda.empty_cache()
if seed == "" or seed == " ":
seed = -1
seed = int(seed)
if seed == -1 or seed == 0 or self.play_keep_seed_var.get() == 0:
#random seed
seed = random.randint(0, 10000000)
self.play_seed_entry.delete(0, "end")
self.play_seed_entry.insert(0, seed)
generator = torch.Generator("cuda").manual_seed(seed)
#self.play_generate_image_button["text"] = "Generating, Please stand by..."
#self.play_generate_image_button.configure(fg=self.dark_mode_title_var)
#self.play_generate_image_button.update()
if self.play_model_variant == 'base':
image = self.pipe(prompt=prompt, negative_prompt=negative_prompt, height=int(sample_height), width=int(sample_width), guidance_scale=cfg, num_inference_steps=int(steps), generator=generator).images[0]
if self.play_model_variant == 'inpainting':
conditioning_image = torch.zeros(1, 3, int(sample_height), int(sample_width))
mask = torch.ones(1, 1, int(sample_height), int(sample_width))
image = self.pipe(prompt, conditioning_image, mask, height=int(sample_height), width=int(sample_width), guidance_scale=cfg, num_inference_steps=int(steps), generator=generator).images[0]
if self.play_model_variant == 'depth2img':
test_image = Image.new('RGB', (int(sample_width), int(sample_height)), (255, 255, 255))
image = self.pipe(prompt, image=test_image, height=int(sample_height), width=int(sample_width), guidance_scale=cfg, num_inference_steps=int(steps), strength=1.0, generator=generator).images[0]
self.play_current_image = image
#image is PIL image
if self.generation_window is None:
self.generation_window = GeneratedImagePreview(self)
self.generation_window.ingest_image(self.play_current_image)
#focus
self.generation_window.focus_set()
#image = ctk.CTkImage(image)
#self.play_image_canvas.configure(width=sample_width, height=sample_height)
#self.play_image_canvas.create_image(0, 0, anchor="nw", image=image)
#self.play_image_canvas.image = image
#resize app to fit image, add current height to image height
#if sample width is lower than current width, use current width
#if sample_width < self.master.winfo_width():
# sample_width = self.master.winfo_width()
#self.master.geometry(f"{sample_width}x{sample_height+self.tabsSizes[5][1]}")
#refresh the window
if self.play_save_image_button == None:
self.play_save_image_button = ctk.CTkButton(self.playground_frame_subframe, text="Save Image", command=self.play_save_image)
self.play_save_image_button.grid(row=10, column=2, columnspan=1, sticky="ew", padx=5, pady=5)
#self.master.update()
#self.play_generate_image_button["text"] = "Generate Image"
#normal text
#self.play_generate_image_button.configure(fg=self.dark_mode_text_var)
def convert_ckpt_to_diffusers(self,ckpt_path=None, output_path=None):
if ckpt_path is None:
ckpt_path = fd.askopenfilename(initialdir=os.getcwd(),title = "Select CKPT file",filetypes = (("ckpt files","*.ckpt"),("all files","*.*")))
if output_path is None:
#file dialog to save diffusers model
output_path = fd.askdirectory(initialdir=os.getcwd(), title="Select where to save Diffusers Model Directory")
version, prediction = self.get_sd_version(ckpt_path)
#self.convert_model_dialog = ctk.CTkToplevel(self, takefocus=True)
#self.convert_model_dialog.title("Converting model")
#label
#empty_label = ctk.CTkLabel(self.convert_model_dialog, text="")
#empty_label.pack()
#label = ctk.CTkLabel(self.convert_model_dialog, text="Converting CKPT to Diffusers. Please wait...")
#label.pack()
#self.convert_model_dialog.geometry("300x70")
#self.convert_model_dialog.resizable(False, False)
#self.convert_model_dialog.grab_set()
#self.convert_model_dialog.focus_set()
#self.update()
convert = converters.Convert_SD_to_Diffusers(ckpt_path,output_path,prediction_type=prediction,version=version)
#self.convert_model_dialog.destroy()
def convert_to_ckpt(self,model_path=None, output_path=None,name=None):
if model_path is None:
model_path = fd.askdirectory(initialdir=self.output_path_entry.get(), title="Select Diffusers Model Directory")
#check if model path has vae,unet,text_encoder,tokenizer,scheduler and args.json and model_index.json
if output_path is None:
output_path = fd.asksaveasfilename(initialdir=os.getcwd(),title = "Save CKPT file",filetypes = (("ckpt files","*.ckpt"),("all files","*.*")))
if not os.path.exists(model_path) and not os.path.exists(os.path.join(model_path,"vae")) and not os.path.exists(os.path.join(model_path,"unet")) and not os.path.exists(os.path.join(model_path,"text_encoder")) and not os.path.exists(os.path.join(model_path,"tokenizer")) and not os.path.exists(os.path.join(model_path,"scheduler")) and not os.path.exists(os.path.join(model_path,"args.json")) and not os.path.exists(os.path.join(model_path,"model_index.json")):
messagebox.showerror("Error", "Couldn't find model structure in path")
return
#check if ckpt in output path
if name != None:
output_path = os.path.join(output_path,name+".ckpt")
if not output_path.endswith(".ckpt") and output_path != "":
#add ckpt to output path
output_path = output_path + ".ckpt"
if not output_path or output_path == "":
return
self.convert_model_dialog = ctk.CTkToplevel(self)
self.convert_model_dialog.title("Converting model")
#label
empty_label = ctk.CTkLabel(self.convert_model_dialog, text="")
empty_label.pack()
label = ctk.CTkLabel(self.convert_model_dialog, text="Converting Diffusers to CKPT. Please wait...")
label.pack()
self.convert_model_dialog.geometry("300x70")
self.convert_model_dialog.resizable(False, False)
self.convert_model_dialog.grab_set()
self.convert_model_dialog.focus_set()
self.update()
converters.Convert_Diffusers_to_SD(model_path, output_path)
self.convert_model_dialog.destroy()
#messagebox.showinfo("Conversion Complete", "Conversion Complete")
def convert_to_safetensors(self,model_path=None, output_path=None,name=None):
if model_path is None:
model_path = fd.askdirectory(initialdir=self.output_path_entry.get(), title="Select Diffusers Model Directory")
#check if model path has vae,unet,text_encoder,tokenizer,scheduler and args.json and model_index.json
if output_path is None:
output_path = fd.asksaveasfilename(initialdir=os.getcwd(),title = "Save Safetensors file",filetypes = (("safetensors files","*.safetensors"),("all files","*.*")))
if not os.path.exists(model_path) and not os.path.exists(os.path.join(model_path,"vae")) and not os.path.exists(os.path.join(model_path,"unet")) and not os.path.exists(os.path.join(model_path,"text_encoder")) and not os.path.exists(os.path.join(model_path,"tokenizer")) and not os.path.exists(os.path.join(model_path,"scheduler")) and not os.path.exists(os.path.join(model_path,"args.json")) and not os.path.exists(os.path.join(model_path,"model_index.json")):
messagebox.showerror("Error", "Couldn't find model structure in path")
return
#check if ckpt in output path
if name != None:
output_path = os.path.join(output_path,name+".safetensors")
if not output_path.endswith(".safetensors") and output_path != "":
#add ckpt to output path
output_path = output_path + ".safetensors"
if not output_path or output_path == "":
return
self.convert_model_dialog = ctk.CTkToplevel(self)
self.convert_model_dialog.title("Converting model")
#label
empty_label = ctk.CTkLabel(self.convert_model_dialog, text="")
empty_label.pack()
label = ctk.CTkLabel(self.convert_model_dialog, text="Converting Diffusers to CKPT. Please wait...")
label.pack()
self.convert_model_dialog.geometry("300x70")
self.convert_model_dialog.resizable(False, False)
self.convert_model_dialog.grab_set()
self.convert_model_dialog.focus_set()
self.update()
converters.Convert_Diffusers_to_SD(model_path, output_path)
self.convert_model_dialog.destroy()
#messagebox.showinfo("Conversion Complete", "Conversion Complete")
#function to act as a callback when the user adds a new concept data path to generate a new preview image
def update_preview_image(self, event):
#check if entry has changed
indexOfEntry = 0
for concept_entry in self.concept_entries:
if event.widget in concept_entry:
indexOfEntry = self.concept_entries.index(concept_entry)
#stop the loop
break
#get the path from the entry
path = event.widget.get()
canvas = self.preview_images[indexOfEntry][0]
image_container = self.preview_images[indexOfEntry][1]
icon = 'resources/stableTuner_icon.png'
#create a photoimage object of the image in the path
icon = Image.open(icon)
#resize the image
image = icon.resize((150, 150), Image.Resampling.LANCZOS)
if path != "":
if os.path.exists(path):
files = os.listdir(path)
for i in range(4):
#get an image from the path
import random
#filter files for images
files = [f for f in files if f.endswith(".jpg") or f.endswith(".png") or f.endswith(".jpeg")]
if len(files) != 0:
rand = random.choice(files)
image_path = os.path.join(path,rand)
#remove image_path from files
if len(files) > 4:
files.remove(rand)
#files.pop(image_path)
#open the image
#print(image_path)
image_to_add = Image.open(image_path)
#resize the image to 38x38
#resize to 150x150 closest to the original aspect ratio
image_to_add.thumbnail((150, 150), Image.Resampling.LANCZOS)
#decide where to put the image
if i == 0:
#top left
image.paste(image_to_add, (0, 0))
elif i == 1:
#top right
image.paste(image_to_add, (76, 0))
elif i == 2:
#bottom left
image.paste(image_to_add, (0, 76))
elif i == 3:
#bottom right
image.paste(image_to_add, (76, 76))
#convert the image to a photoimage
#image.show()
newImage=ctk.CTkImage(image)
self.preview_images[indexOfEntry][2] = newImage
canvas.itemconfig(image_container, image=newImage)
def remove_new_concept(self):
#get the last concept widget
if len(self.concept_widgets) > 0:
concept_widget = self.concept_widgets[-1]
#remove it from the list
self.concept_widgets.remove(concept_widget)
#destroy the widget
concept_widget.destroy()
#repack the widgets
#self.repack_concepts()
def add_new_concept(self,concept=None):
#create a new concept
#for concept in self.concept_widgets check if concept was deleted
#if it was, remove it from the list
row=0
column=len(self.concept_widgets)
if len(self.concept_widgets) > 6:
row=1
concept_widget = ConceptWidget(self.data_frame_concepts_subframe, concept,width=100,height=100)
width=100
height=100
column=len(self.concept_widgets)-7
if len(self.concept_widgets) > 13:
row=2
concept_widget = ConceptWidget(self.data_frame_concepts_subframe, concept,width=100,height=100)
height=100
width=100
column=len(self.concept_widgets)-14
if len(self.concept_widgets) > 20:
messagebox.showerror("Error", "You can only have 21 concepts")
return
else:
concept_widget = ConceptWidget(self.data_frame_concepts_subframe, concept,width=100,height=100)
#print(row)
concept_widget.grid(row=row, column=column, sticky="e",padx=13, pady=10)
self.concept_widgets.append(concept_widget)
self.update()
#print(len(self.concept_widgets))
#if row == 2:
# for concept in self.concept_widgets:
# concept.resize_widget(width, height)
def add_concept(self, inst_prompt_val=None, class_prompt_val=None, inst_data_path_val=None, class_data_path_val=None, do_not_balance_val=False):
#create a title for the new concept
concept_title = ctk.CTkLabel(self.data_frame_concepts_subframe, text="Concept " + str(len(self.concept_labels)+1), font=("Helvetica", 10, "bold"), bg_color='#333333')
concept_title.grid(row=3 + (len(self.concept_labels)*6), column=0, sticky="nsew")
#create instance prompt label
ins_prompt_label = ctk.CTkLabel(self.data_frame_concepts_subframe, text="Token/Prompt", bg_color='#333333')
ins_prompt_label_ttp = CreateToolTip(ins_prompt_label, "The token for the concept, will be ignored if use image names as captions is checked.")
ins_prompt_label.grid(row=4 + (len(self.concept_labels)*6), column=0, sticky="nsew")
#create instance prompt entry
ins_prompt_entry = ctk.CTkEntry(self.data_frame_concepts_subframe, bg_color='#333333')
ins_prompt_entry.grid(row=4 + (len(self.concept_labels)*6), column=1, sticky="nsew")
if inst_prompt_val != None:
ins_prompt_entry.insert(0, inst_prompt_val)
#create class prompt label
class_prompt_label = ctk.CTkLabel(self.data_frame_concepts_subframe, text="Class Prompt", bg_color='#333333')
class_prompt_label_ttp = CreateToolTip(class_prompt_label, "The prompt will be used to generate class images and train the class images if added to dataset")
class_prompt_label.grid(row=5 + (len(self.concept_labels)*6), column=0, sticky="nsew")
#create class prompt entry
class_prompt_entry = ctk.CTkEntry(self.data_frame_concepts_subframe,width=50, bg_color='#333333')
class_prompt_entry.grid(row=5 + (len(self.concept_labels)*6), column=1, sticky="nsew")
if class_prompt_val != None:
class_prompt_entry.insert(0, class_prompt_val)
#create instance data path label
ins_data_path_label = ctk.CTkLabel(self.data_frame_concepts_subframe, text="Training Data Directory", bg_color='#333333')
ins_data_path_label_ttp = CreateToolTip(ins_data_path_label, "The path to the folder containing the concept's images.")
ins_data_path_label.grid(row=6 + (len(self.concept_labels)*6), column=0, sticky="nsew")
#create instance data path entry
ins_data_path_entry = ctk.CTkEntry(self.data_frame_concepts_subframe,width=50, bg_color='#333333')
ins_data_path_entry.bind("<FocusOut>", self.update_preview_image)
#bind to insert
ins_data_path_entry.grid(row=6 + (len(self.concept_labels)*6), column=1, sticky="nsew")
if inst_data_path_val != None:
#focus on the entry
ins_data_path_entry.insert(0, inst_data_path_val)
ins_data_path_entry.focus_set()
#focus on main window
self.focus_set()
#add a button to open a file dialog to select the instance data path
ins_data_path_file_dialog_button = ctk.CTkButton(self.data_frame_concepts_subframe, text="...", command=lambda: self.open_file_dialog(ins_data_path_entry), bg_color='#333333')
ins_data_path_file_dialog_button.grid(row=6 + (len(self.concept_labels)*6), column=2, sticky="nsew")
#create class data path label
class_data_path_label = ctk.CTkLabel(self.data_frame_concepts_subframe, text="Class Data Directory", bg_color='#333333')
class_data_path_label_ttp = CreateToolTip(class_data_path_label, "The path to the folder containing the concept's class images.")
class_data_path_label.grid(row=7 + (len(self.concept_labels)*6), column=0, sticky="nsew")
#add a button to open a file dialog to select the class data path
class_data_path_file_dialog_button = ctk.CTkButton(self.data_frame_concepts_subframe, text="...", command=lambda: self.open_file_dialog(class_data_path_entry), bg_color='#333333')
class_data_path_file_dialog_button.grid(row=7 + (len(self.concept_labels)*6), column=2, sticky="nsew")
#create class data path entry
class_data_path_entry = ctk.CTkEntry(self.data_frame_concepts_subframe, bg_color='#333333')
class_data_path_entry.grid(row=7 + (len(self.concept_labels)*6), column=1, sticky="nsew")
if class_data_path_val != None:
class_data_path_entry.insert(0, class_data_path_val)
#add a checkbox to do not balance dataset
do_not_balance_dataset_var = tk.IntVar()
#label for checkbox
do_not_balance_dataset_label = ctk.CTkLabel(self.data_frame_concepts_subframe, text="Do not balance dataset", bg_color='#333333')
do_not_balance_dataset_label_ttp = CreateToolTip(do_not_balance_dataset_label, "If checked, the dataset will not be balanced. this settings overrides the global auto balance setting, if there's a concept you'd like to train without balance while the others will.")
do_not_balance_dataset_label.grid(row=8 + (len(self.concept_labels)*6), column=0, sticky="nsew")
do_not_balance_dataset_checkbox = ctk.CTkSwitch(self.data_frame_concepts_subframe, variable=do_not_balance_dataset_var, bg_color='#333333')
do_not_balance_dataset_checkbox.grid(row=8 + (len(self.concept_labels)*6), column=1, sticky="nsew")
do_not_balance_dataset_var.set(0)
#create a preview of the images in the path on the right side of the concept
#create a frame to hold the images
#empty column to separate the images from the rest of the concept
#sep = ctk.CTkLabel(self.data_frame_concepts_subframe,padx=3, text="").grid(row=4 + (len(self.concept_labels)*6), column=3, sticky="nsew", bg_color='#333333')
image_preview_frame = ctk.CTkFrame(self.data_frame_concepts_subframe)
image_preview_frame.grid(row=4 + (len(self.concept_labels)*6), column=4, rowspan=4, sticky="ne")
#create a label for the images
#image_preview_label = ctk.CTkLabel(image_preview_frame, text="Image Preview")
#image_preview_label.grid(row=0, column=0, sticky="nsew")
#create a canvas to hold the images
image_preview_canvas = tk.Canvas(image_preview_frame)
#flat border
image_preview_canvas.configure(border=0, relief='flat', highlightthickness=0)
#canvas size is 100x100
image_preview_canvas.configure(width=150, height=150, bg='#333333')
image_preview_canvas.grid(row=0, column=0, sticky="nsew")
#debug test, image preview just white
#if there's a path in the entry, show the images in the path
#grab stableTuner_icon.png from the resources folder
icon = 'resources/stableTuner_icon.png'
#create a photoimage object of the image in the path
icon = Image.open(icon)
#resize the image
image = icon.resize((150, 150), Image.Resampling.LANCZOS)
image_preview = ImageTk.PhotoImage(image)
if inst_data_path_val != None:
if os.path.exists(inst_data_path_val):
del image_preview
#get 4 images from the path
#create a host image
image = Image.new("RGB", (150, 150), "white")
files = os.listdir(inst_data_path_val)
if len(files) > 0:
for i in range(4):
#get an image from the path
import random
#filter files for images
files = [f for f in files if f.endswith(".jpg") or f.endswith(".png") or f.endswith(".jpeg")]
rand = random.choice(files)
image_path = os.path.join(inst_data_path_val,rand)
#remove image_path from files
if len(files) > 4:
files.remove(rand)
#files.pop(image_path)
#open the image
#print(image_path)
image_to_add = Image.open(image_path)
#resize the image to 38x38
#resize to 150x150 closest to the original aspect ratio
image_to_add.thumbnail((150, 150), Image.Resampling.LANCZOS)
#decide where to put the image
if i == 0:
#top left
image.paste(image_to_add, (0, 0))
elif i == 1:
#top right
image.paste(image_to_add, (76, 0))
elif i == 2:
#bottom left
image.paste(image_to_add, (0, 76))
elif i == 3:
#bottom right
image.paste(image_to_add, (76, 76))
#convert the image to a photoimage
#image.show()
image_preview = ctk.CTkImage(image)
#add the image to the canvas
image_container = image_preview_canvas.create_image(0, 0, anchor="nw", image=image_preview)
self.preview_images.append([image_preview_canvas,image_container,image_preview])
image_preview_frame.update()
if do_not_balance_val != False:
do_not_balance_dataset_var.set(1)
#combine all the entries into a list
concept_entries = [ins_prompt_entry, class_prompt_entry, ins_data_path_entry, class_data_path_entry,do_not_balance_dataset_var,do_not_balance_dataset_checkbox]
for i in concept_entries[:4]:
i.bind("<Button-3>", self.create_right_click_menu)
#add the list to the list of concept entries
self.concept_entries.append(concept_entries)
#add the title to the list of concept titles
self.concept_labels.append([concept_title, ins_prompt_label, class_prompt_label, ins_data_path_label, class_data_path_label,do_not_balance_dataset_label,image_preview_frame])
self.concepts.append({"instance_prompt": ins_prompt_entry, "class_prompt": class_prompt_entry, "instance_data_dir": ins_data_path_entry, "class_data_dir": class_data_path_entry,'do_not_balance': do_not_balance_dataset_var})
self.concept_file_dialog_buttons.append([ins_data_path_file_dialog_button, class_data_path_file_dialog_button])
#self.canvas.configure(scrollregion=self.canvas.bbox("all"))
def get_sd_version(self,file_path):
import torch
if 'ckpt' in file_path:
checkpoint = torch.load(file_path, map_location="cpu")
else:
from safetensors.torch import load_file
checkpoint = load_file(file_path)
#checkpoint = torch.load(file_path)
answer = messagebox.askyesno("V-Model?", "Is this model using V-Parameterization? (based on SD2.x 768 model)")
if answer == True:
prediction = "vprediction"
else:
prediction = "epsilon"
key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
if "state_dict" in checkpoint.keys():
checkpoint = checkpoint["state_dict"]
if key_name in checkpoint and checkpoint[key_name].shape[-1] == 1024:
version = "v2"
else:
version = "v1"
del checkpoint
return version, prediction
def choose_model(self):
"""Opens a file dialog and to choose either a model or a model folder."""
#open file dialog and show only ckpt and json files and folders
file_path = fd.askopenfilename(filetypes=[("Model", "*.ckpt"), ("Model", "*.json"), ("Model", "*.safetensors")])
#file_path = fd.askopenfilename() model_index.json
if file_path == "":
return
#check if the file is a json file
if file_path.endswith("model_index.json"):
#check if the file is a model index file
#check if folder has folders for: vae, unet, tokenizer, text_encoder
model_dir = os.path.dirname(file_path)
for folder in self.required_folders:
if not os.path.isdir(os.path.join(model_dir, folder)):
#show error message
messagebox.showerror("Error", "The selected model is missing the {} folder.".format(folder))
return
file_path = model_dir
#if the file is not a model index file
if file_path.endswith(".ckpt") or file_path.endswith(".safetensors"):
sd_file = file_path
version, prediction = self.get_sd_version(sd_file)
#create a directory under the models folder with the name of the ckpt file
model_name = os.path.basename(file_path).split(".")[0]
#get the path of the script
script_path = os.getcwd()
#get the path of the models folder
models_path = os.path.join(script_path, "models")
#if no models_path exists, create it
if not os.path.isdir(models_path):
os.mkdir(models_path)
#create the path of the new model folder
model_path = os.path.join(models_path, model_name)
#check if the model folder already exists
if os.path.isdir(model_path) and os.path.isfile(os.path.join(model_path, "model_index.json")):
file_path = model_path
else:
#create the model folder
if os.path.isdir(model_path):
shutil.rmtree(model_path)
os.mkdir(model_path)
#converter
#show a dialog to inform the user that the model is being converted
self.convert_model_dialog = ctk.CTkToplevel(self)
self.convert_model_dialog.title("Converting model")
#label
empty_label = ctk.CTkLabel(self.convert_model_dialog, text="")
empty_label.pack()
label = ctk.CTkLabel(self.convert_model_dialog, text="Converting CKPT to Diffusers. Please wait...")
label.pack()
self.convert_model_dialog.geometry("300x70")
self.convert_model_dialog.resizable(False, False)
self.convert_model_dialog.grab_set()
self.convert_model_dialog.focus_set()
self.update()
convert = converters.Convert_SD_to_Diffusers(sd_file,model_path,prediction_type=prediction,version=version)
self.convert_model_dialog.destroy()
file_path = model_path
self.input_model_path_entry.delete(0, tk.END)
self.input_model_path_entry.insert(0, file_path)
def open_file_dialog(self, entry):
"""Opens a file dialog and sets the entry to the selected file."""
indexOfEntry = None
file_path = fd.askdirectory()
#get the entry name
entry.delete(0, tk.END)
entry.insert(0, file_path)
#focus on the entry
entry.focus_set()
#unset the focus on the button
#self.master.focus_set()
def save_concept_to_json(self,filename=None,preMadeConcepts=None):
#dialog box to select the file to save to
if filename == None:
file = fd.asksaveasfile(mode='w', defaultextension=".json", filetypes=[("JSON", "*.json")])
#check if file has json extension
if 'json' not in file.name:
file.name = file.name + '.json'
else:
file = open(filename, 'w')
if file != None:
if preMadeConcepts == None:
concepts = []
for widget in self.concept_widgets:
concept = widget.concept
concept_dict = {'instance_prompt' : concept.concept_name, 'class_prompt' : concept.concept_class_name, 'instance_data_dir' : concept.concept_path, 'class_data_dir' : concept.concept_class_path,'flip_p' : concept.flip_p, 'do_not_balance' : concept.concept_do_not_balance, 'use_sub_dirs' : concept.process_sub_dirs}
concepts.append(concept_dict)
if file != None:
#write the json to the file
json.dump(concepts, file, indent=4)
#close the file
file.close()
else:
json.dump(preMadeConcepts, file, indent=4)
#close the file
file.close()
def load_concept_from_json(self):
#
#dialog
concept_json = fd.askopenfilename(title = "Select file",filetypes = (("json files","*.json"),("all files","*.*")))
for i in range(len(self.concept_widgets)):
self.remove_new_concept()
self.concept_entries = []
self.concept_labels = []
self.concepts = []
with open(concept_json, "r") as f:
concept_json = json.load(f)
for concept in concept_json:
#print(concept)
if 'flip_p' not in concept:
concept['flip_p'] = ''
concept = Concept(concept_name=concept["instance_prompt"], class_name=concept["class_prompt"], concept_path=concept["instance_data_dir"], class_path=concept["class_data_dir"],flip_p=concept['flip_p'],balance_dataset=concept["do_not_balance"], process_sub_dirs=concept["use_sub_dirs"])
self.add_new_concept(concept) #self.canvas.configure(scrollregion=self.canvas.bbox("all"))
self.update()
return concept_json
def remove_concept(self):
#remove the last concept
if len(self.concept_labels) > 0:
for entry in self.concept_entries[-1]:
#if the entry is an intvar
if isinstance(entry, tk.IntVar):
#delete the entry
del entry
else:
entry.destroy()
for label in self.concept_labels[-1]:
label.destroy()
for button in self.concept_file_dialog_buttons[-1]:
button.destroy()
self.concept_entries.pop()
self.concept_labels.pop()
self.concepts.pop()
self.concept_file_dialog_buttons.pop()
self.preview_images.pop()
#self.canvas.configure(scrollregion=self.canvas.bbox("all"))
def remove_new_concept(self):
#remove the last concept
#print(self.concept_widgets)
if len(self.concept_widgets) > 0:
self.concept_widgets[-1].destroy()
self.concept_widgets.pop()
#self.preview_images.pop()
#self.canvas.configure(scrollregion=self.canvas.bbox("all"))
def toggle_telegram_settings(self):
#print(self.send_telegram_updates_var.get())
if self.send_telegram_updates_var.get() == 1:
self.telegram_token_label.configure(state="normal")
self.telegram_token_entry.configure(state="normal")
self.telegram_chat_id_label.configure(state="normal")
self.telegram_chat_id_entry.configure(state="normal")
else:
self.telegram_token_label.configure(state="disabled")
self.telegram_token_entry.configure(state="disabled")
self.telegram_chat_id_label.configure(state="disabled")
self.telegram_chat_id_entry.configure(state="disabled")
def add_controlled_seed_sample(self,value=""):
if len(self.controlled_seed_sample_labels) <= 4:
self.controlled_seed_sample_labels.append(ctk.CTkLabel(self.sampling_frame_subframe,bg_color='transparent' ,text="Controlled Seed Sample " + str(len(self.controlled_seed_sample_labels)+1)))
self.controlled_seed_sample_labels[-1].grid(row=self.controlled_sample_row + len(self.sample_prompts) + len(self.controlled_seed_sample_labels), column=2, padx=10, pady=5,sticky="nwes")
#create entry
entry = ctk.CTkEntry(self.sampling_frame_subframe,width=250)
entry.bind("<Button-3>",self.create_right_click_menu)
self.controlled_seed_sample_entries.append(entry)
self.controlled_seed_sample_entries[-1].grid(row=self.controlled_sample_row + len(self.sample_prompts) + len(self.controlled_seed_sample_entries), column=3, padx=10, pady=5,sticky="w")
if value != "":
self.controlled_seed_sample_entries[-1].insert(0, value)
self.add_controlled_seed_to_sample.append(value)
#self.canvas.configure(scrollregion=self.canvas.bbox("all"))
def remove_controlled_seed_sample(self):
#get the entry and label to remove
if len(self.controlled_seed_sample_labels) > 0:
self.controlled_seed_sample_labels[-1].destroy()
self.controlled_seed_sample_labels.pop()
self.controlled_seed_sample_entries[-1].destroy()
self.controlled_seed_sample_entries.pop()
self.add_controlled_seed_to_sample.pop()
#self.canvas.configure(scrollregion=self.canvas.bbox("all"))
def remove_sample_prompt(self):
if len(self.sample_prompt_labels) > 0:
#remove the last label and entry
#get entry value
self.sample_prompt_labels[-1].destroy()
self.sample_prompt_entries[-1].destroy()
#remove the last label and entry from the lists
self.sample_prompt_labels.pop()
self.sample_prompt_entries.pop()
#remove the last value from the list
self.sample_prompts.pop()
#print(self.sample_prompts)
#print(self.sample_prompt_entries)
#self.canvas.configure(scrollregion=self.canvas.bbox("all"))
for i in self.controlled_seed_buttons:
#push to next row
i.grid(row=i.grid_info()["row"] - 1, column=i.grid_info()["column"], sticky="nsew")
for i in self.controlled_seed_sample_labels:
#push to next row
i.grid(row=i.grid_info()["row"] - 1, column=i.grid_info()["column"], sticky="nsew")
for i in self.controlled_seed_sample_entries:
#push to next row
i.grid(row=i.grid_info()["row"] - 1, column=i.grid_info()["column"], sticky="nsew")
def add_sample_prompt(self,value=""):
#add a new label and entry
if len(self.sample_prompt_entries) <= 4:
self.sample_prompt_labels.append(ctk.CTkLabel(self.sampling_frame_subframe, text="Sample Prompt " + str(len(self.sample_prompt_labels)+1),bg_color='transparent'))
self.sample_prompt_labels[-1].grid(row=self.sample_prompt_row + len(self.sample_prompt_labels) - 1, column=2, padx=10, pady=5,sticky="nsew")
entry = ctk.CTkEntry(self.sampling_frame_subframe,width=250)
entry.bind("<Button-3>", self.create_right_click_menu)
self.sample_prompt_entries.append(entry)
self.sample_prompt_entries[-1].grid(row=self.sample_prompt_row + len(self.sample_prompt_labels) - 1, column=3, padx=10, pady=5,sticky="nsew")
if value != "":
self.sample_prompt_entries[-1].insert(0, value)
#update the sample prompts list
self.sample_prompts.append(value)
for i in self.controlled_seed_buttons:
#push to next row
i.grid(row=i.grid_info()["row"] + 1, column=i.grid_info()["column"], sticky="nsew")
for i in self.controlled_seed_sample_labels:
#push to next row
i.grid(row=i.grid_info()["row"] + 1, column=i.grid_info()["column"], sticky="nsew")
for i in self.controlled_seed_sample_entries:
#push to next row
i.grid(row=i.grid_info()["row"] + 1, column=i.grid_info()["column"], sticky="nsew")
#print(self.sample_prompts)
#print(self.sample_prompt_entries)
#update canvas scroll region
#self.canvas.configure(scrollregion=self.canvas.bbox("all"))
def update_sample_prompts(self):
#update the sample prompts list
self.sample_prompts = []
for i in range(len(self.sample_prompt_entries)):
self.sample_prompts.append(self.sample_prompt_entries[i].get())
def update_controlled_seed_sample(self):
#update the sample prompts list
self.add_controlled_seed_to_sample = []
for i in range(len(self.controlled_seed_sample_entries)):
self.add_controlled_seed_to_sample.append(self.controlled_seed_sample_entries[i].get())
self.update()
def update_concepts(self):
#update the concepts list
#if the first index is a dict
if isinstance(self.concepts, dict):
return
self.concepts = []
for i in range(len(self.concept_widgets)):
concept = self.concept_widgets[i].concept
self.concepts.append({'instance_prompt' : concept.concept_name, 'class_prompt' : concept.concept_class_name, 'instance_data_dir' : concept.concept_path, 'class_data_dir' : concept.concept_class_path,'flip_p' : concept.flip_p, 'do_not_balance' : concept.concept_do_not_balance, 'use_sub_dirs' : concept.process_sub_dirs})
def save_config(self, config_file=None):
#save the configure file
import json
#create a dictionary of all the variables
#ask the user for a file name
if config_file == None:
file_name = fd.asksaveasfilename(title = "Select file",filetypes = (("json files","*.json"),("all files","*.*")))
#check if json in file name
if ".json" not in file_name:
file_name += ".json"
else:
file_name = config_file
configure = {}
self.update_controlled_seed_sample()
self.update_sample_prompts()
self.update_concepts()
configure["concepts"] = self.concepts
#print(self.concepts)
configure["sample_prompts"] = self.sample_prompts
configure['add_controlled_seed_to_sample'] = self.add_controlled_seed_to_sample
configure["model_path"] = self.input_model_path_entry.get()
configure["vae_path"] = self.vae_model_path_entry.get()
configure["output_path"] = self.output_path_entry.get()
configure["send_telegram_updates"] = self.send_telegram_updates_var.get()
configure["telegram_token"] = self.telegram_token_entry.get()
configure["telegram_chat_id"] = self.telegram_chat_id_entry.get()
configure["resolution"] = self.resolution_var.get()
configure["batch_size"] = self.train_batch_size_var.get()
configure["train_epocs"] = self.train_epochs_entry.get()
configure["mixed_precision"] = self.mixed_precision_var.get()
configure["use_8bit_adam"] = self.use_8bit_adam_var.get()
configure["use_gradient_checkpointing"] = self.use_gradient_checkpointing_var.get()
configure["accumulation_steps"] = self.gradient_accumulation_steps_var.get()
configure["learning_rate"] = self.learning_rate_entry.get()
configure["warmup_steps"] = self.num_warmup_steps_entry.get()
configure["learning_rate_scheduler"] = self.learning_rate_scheduler_var.get()
#configure["use_latent_cache"] = self.use_latent_cache_var.get()
#configure["save_latent_cache"] = self.save_latent_cache_var.get()
configure["regenerate_latent_cache"] = self.regenerate_latent_cache_var.get()
configure["train_text_encoder"] = self.train_text_encoder_var.get()
configure["with_prior_loss_preservation"] = self.with_prior_loss_preservation_var.get()
configure["prior_loss_preservation_weight"] = self.prior_loss_preservation_weight_entry.get()
configure["use_image_names_as_captions"] = self.use_image_names_as_captions_var.get()
configure["shuffle_captions"] = self.shuffle_captions_var.get()
configure["auto_balance_concept_datasets"] = self.auto_balance_dataset_var.get()
configure["add_class_images_to_dataset"] = self.add_class_images_to_dataset_var.get()
configure["number_of_class_images"] = self.number_of_class_images_entry.get()
configure["save_every_n_epochs"] = self.save_every_n_epochs_entry.get()
configure["number_of_samples_to_generate"] = self.number_of_samples_to_generate_entry.get()
configure["sample_height"] = self.sample_height_entry.get()
configure["sample_width"] = self.sample_width_entry.get()
configure["sample_random_aspect_ratio"] = self.sample_random_aspect_ratio_var.get()
configure['sample_on_training_start'] = self.sample_on_training_start_var.get()
configure['concepts'] = self.concepts
configure['aspect_ratio_bucketing'] = self.use_aspect_ratio_bucketing_var.get()
configure['seed'] = self.seed_entry.get()
configure['dataset_repeats'] = self.dataset_repeats_entry.get()
configure['limit_text_encoder_training'] = self.limit_text_encoder_entry.get()
configure['use_text_files_as_captions'] = self.use_text_files_as_captions_var.get()
configure['ckpt_version'] = self.ckpt_sd_version
configure['convert_to_ckpt_after_training'] = self.convert_to_ckpt_after_training_var.get()
configure['execute_post_conversion'] = self.convert_to_ckpt_after_training_var.get()
configure['disable_cudnn_benchmark'] = self.disable_cudnn_benchmark_var.get()
configure['sample_step_interval'] = self.sample_step_interval_entry.get()
configure['conditional_dropout'] = self.conditional_dropout_entry.get()
configure["clip_penultimate"] = self.clip_penultimate_var.get()
configure['use_ema'] = self.use_ema_var.get()
configure['aspect_ratio_bucketing_mode'] = self.aspect_ratio_bucketing_mode_var.get()
configure['dynamic_bucketing_mode'] = self.dynamic_bucketing_mode_var.get()
configure['model_variant'] = self.model_variant_var.get()
configure['masked_training'] = self.masked_training_var.get()
configure['normalize_masked_area_loss'] = self.normalize_masked_area_loss_var.get()
configure['unmasked_probability'] = self.unmasked_probability_var.get()
configure['max_denoising_strength'] = self.max_denoising_strength_var.get()
configure['fallback_mask_prompt'] = self.fallback_mask_prompt_entry.get()
configure['attention'] = self.attention_var.get()
configure['batch_prompt_sampling'] = int(self.batch_prompt_sampling_optionmenu_var.get())
configure['shuffle_dataset_per_epoch'] = self.shuffle_dataset_per_epoch_var.get()
configure['use_offset_noise'] = self.use_offset_noise_var.get()
configure['offset_noise_weight'] = self.offset_noise_weight_entry.get()
configure['use_gan'] = self.use_gan_var.get()
configure['gan_weight'] = self.gan_weight_entry.get()
configure['use_lion'] = self.use_lion_var.get()
#save the configure file
#if the file exists, delete it
if os.path.exists(file_name):
os.remove(file_name)
with open(file_name, "w",encoding='utf-8') as f:
json.dump(configure, f, indent=4)
f.close()
def load_config(self,file_name=None):
#load the configure file
#ask the user for a file name
if file_name == None:
file_name = fd.askopenfilename(title = "Select file",filetypes = (("json files","*.json"),("all files","*.*")))
if file_name == "":
return
#load the configure file
with open(file_name, "r",encoding='utf-8') as f:
configure = json.load(f)
#load concepts
try:
for i in range(len(self.concept_widgets)):
self.remove_new_concept()
self.concept_entries = []
self.concept_labels = []
self.concepts = []
for i in range(len(configure["concepts"])):
inst_prompt = configure["concepts"][i]["instance_prompt"]
class_prompt = configure["concepts"][i]["class_prompt"]
inst_data_dir = configure["concepts"][i]["instance_data_dir"]
class_data_dir = configure["concepts"][i]["class_data_dir"]
if 'flip_p' not in configure["concepts"][i]:
print(configure["concepts"][i].keys())
configure["concepts"][i]['flip_p'] = ''
flip_p = configure["concepts"][i]["flip_p"]
balance_dataset = configure["concepts"][i]["do_not_balance"]
process_sub_dirs = configure["concepts"][i]["use_sub_dirs"]
concept = Concept(concept_name=inst_prompt, class_name=class_prompt, concept_path=inst_data_dir, class_path=class_data_dir,flip_p=flip_p,balance_dataset=balance_dataset,process_sub_dirs=process_sub_dirs)
self.add_new_concept(concept)
except Exception as e:
print(e)
pass
#destroy all the current labels and entries
for i in range(len(self.sample_prompt_labels)):
self.sample_prompt_labels[i].destroy()
self.sample_prompt_entries[i].destroy()
for i in range(len(self.controlled_seed_sample_labels)):
self.controlled_seed_sample_labels[i].destroy()
self.controlled_seed_sample_entries[i].destroy()
self.sample_prompt_labels = []
self.sample_prompt_entries = []
self.controlled_seed_sample_labels = []
self.controlled_seed_sample_entries = []
#set the variables
for i in range(len(configure["sample_prompts"])):
self.add_sample_prompt(value=configure["sample_prompts"][i])
for i in range(len(configure['add_controlled_seed_to_sample'])):
self.add_controlled_seed_sample(value=configure['add_controlled_seed_to_sample'][i])
self.input_model_path_entry.delete(0, tk.END)
self.input_model_path_entry.insert(0, configure["model_path"])
self.vae_model_path_entry.delete(0, tk.END)
self.vae_model_path_entry.insert(0, configure["vae_path"])
self.output_path_entry.delete(0, tk.END)
self.output_path_entry.insert(0, configure["output_path"])
self.send_telegram_updates_var.set(configure["send_telegram_updates"])
if configure["send_telegram_updates"]:
self.telegram_token_entry.configure(state='normal')
self.telegram_chat_id_entry.configure(state='normal')
self.telegram_token_label.configure(state='normal')
self.telegram_chat_id_label.configure(state='normal')
self.telegram_token_entry.delete(0, tk.END)
self.telegram_token_entry.insert(0, configure["telegram_token"])
self.telegram_chat_id_entry.delete(0, tk.END)
self.telegram_chat_id_entry.insert(0, configure["telegram_chat_id"])
self.resolution_var.set(configure["resolution"])
self.train_batch_size_var.set(configure["batch_size"])
self.train_epochs_entry.delete(0, tk.END)
self.train_epochs_entry.insert(0, configure["train_epocs"])
self.mixed_precision_var.set(configure["mixed_precision"])
self.use_8bit_adam_var.set(configure["use_8bit_adam"])
self.use_gradient_checkpointing_var.set(configure["use_gradient_checkpointing"])
self.gradient_accumulation_steps_var.set(configure["accumulation_steps"])
self.learning_rate_entry.delete(0, tk.END)
self.learning_rate_entry.insert(0, configure["learning_rate"])
self.num_warmup_steps_entry.delete(0, tk.END)
self.num_warmup_steps_entry.insert(0, configure["warmup_steps"])
self.learning_rate_scheduler_var.set(configure["learning_rate_scheduler"])
#self.use_latent_cache_var.set(configure["use_latent_cache"])
#self.save_latent_cache_var.set(configure["save_latent_cache"])
self.regenerate_latent_cache_var.set(configure["regenerate_latent_cache"])
self.train_text_encoder_var.set(configure["train_text_encoder"])
self.with_prior_loss_preservation_var.set(configure["with_prior_loss_preservation"])
self.prior_loss_preservation_weight_entry.delete(0, tk.END)
self.prior_loss_preservation_weight_entry.insert(0, configure["prior_loss_preservation_weight"])
self.use_image_names_as_captions_var.set(configure["use_image_names_as_captions"])
self.shuffle_captions_var.set(configure["shuffle_captions"])
self.auto_balance_dataset_var.set(configure["auto_balance_concept_datasets"])
self.add_class_images_to_dataset_var.set(configure["add_class_images_to_dataset"])
self.number_of_class_images_entry.delete(0, tk.END)
self.number_of_class_images_entry.insert(0, configure["number_of_class_images"])
self.save_every_n_epochs_entry.delete(0, tk.END)
self.save_every_n_epochs_entry.insert(0, configure["save_every_n_epochs"])
self.number_of_samples_to_generate_entry.delete(0, tk.END)
self.number_of_samples_to_generate_entry.insert(0, configure["number_of_samples_to_generate"])
self.sample_height_entry.delete(0, tk.END)
self.sample_height_entry.insert(0, configure["sample_height"])
self.sample_width_entry.delete(0, tk.END)
self.sample_width_entry.insert(0, configure["sample_width"])
self.sample_random_aspect_ratio_var.set(configure["sample_random_aspect_ratio"])
self.sample_on_training_start_var.set(configure["sample_on_training_start"])
self.use_aspect_ratio_bucketing_var.set(configure["aspect_ratio_bucketing"])
self.seed_entry.delete(0, tk.END)
self.seed_entry.insert(0, configure["seed"])
self.dataset_repeats_entry.delete(0, tk.END)
self.dataset_repeats_entry.insert(0, configure["dataset_repeats"])
self.limit_text_encoder_entry.delete(0, tk.END)
if configure["limit_text_encoder_training"] != '0':
self.limit_text_encoder_entry.insert(0, configure["limit_text_encoder_training"])
self.use_text_files_as_captions_var.set(configure["use_text_files_as_captions"])
self.convert_to_ckpt_after_training_var.set(configure["convert_to_ckpt_after_training"])
if configure["execute_post_conversion"]:
self.execute_post_conversion = True
self.disable_cudnn_benchmark_var.set(configure["disable_cudnn_benchmark"])
self.sample_step_interval_entry.delete(0, tk.END)
self.sample_step_interval_entry.insert(0, configure["sample_step_interval"])
self.conditional_dropout_entry.delete(0, tk.END)
self.conditional_dropout_entry.insert(0, configure["conditional_dropout"])
self.clip_penultimate_var.set(configure["clip_penultimate"])
self.use_ema_var.set(configure["use_ema"])
if configure["aspect_ratio_bucketing"]:
self.aspect_ratio_bucketing_mode_label.configure(state='normal')
self.aspect_ratio_bucketing_mode_option_menu.configure(state='normal')
self.dynamic_bucketing_mode_label.configure(state='normal')
self.dynamic_bucketing_mode_option_menu.configure(state='normal')
else:
self.aspect_ratio_bucketing_mode_label.configure(state='disabled')
self.aspect_ratio_bucketing_mode_option_menu.configure(state='disabled')
self.dynamic_bucketing_mode_label.configure(state='disabled')
self.dynamic_bucketing_mode_option_menu.configure(state='disabled')
self.model_variant_var.set(configure["model_variant"])
self.masked_training_var.set(configure["masked_training"])
self.normalize_masked_area_loss_var.set(configure["normalize_masked_area_loss"])
self.unmasked_probability_var.set(configure["unmasked_probability"])
self.max_denoising_strength_var.set(configure["max_denoising_strength"])
self.fallback_mask_prompt_entry.delete(0, tk.END)
self.fallback_mask_prompt_entry.insert(0, configure["fallback_mask_prompt"])
self.aspect_ratio_bucketing_mode_var.set(configure["aspect_ratio_bucketing_mode"])
self.dynamic_bucketing_mode_var.set(configure["dynamic_bucketing_mode"])
self.attention_var.set(configure["attention"])
self.batch_prompt_sampling_optionmenu_var.set(str(configure['batch_prompt_sampling']))
self.shuffle_dataset_per_epoch_var.set(configure["shuffle_dataset_per_epoch"])
self.use_offset_noise_var.set(configure["use_offset_noise"])
self.offset_noise_weight_entry.delete(0, tk.END)
self.offset_noise_weight_entry.insert(0, configure["offset_noise_weight"])
self.use_gan_var.set(configure["use_gan"])
self.gan_weight_entry.delete(0, tk.END)
self.gan_weight_entry.insert(0, configure["gan_weight"])
self.use_lion_var.set(configure["use_lion"])
self.update()
def process_inputs(self,export=None):
#collect and process all the inputs
self.update_controlled_seed_sample()
self.update_sample_prompts()
self.save_concept_to_json(filename='stabletune_concept_list.json')
self.update_concepts()
for i in range(len(self.sample_prompts)):
self.sample_prompts.append(self.sample_prompts[i])
for i in range(len(self.add_controlled_seed_to_sample)):
self.add_controlled_seed_to_sample.append(self.add_controlled_seed_to_sample[i])
self.model_path = self.input_model_path_entry.get()
self.vae_path = self.vae_model_path_entry.get()
self.output_path = self.output_path_entry.get()
self.send_telegram_updates = self.send_telegram_updates_var.get()
self.telegram_token = self.telegram_token_entry.get()
self.telegram_chat_id = self.telegram_chat_id_entry.get()
self.resolution = self.resolution_var.get()
self.batch_size = self.train_batch_size_var.get()
self.train_epocs = self.train_epochs_entry.get()
self.mixed_precision = self.mixed_precision_var.get()
self.use_8bit_adam = self.use_8bit_adam_var.get()
self.use_gradient_checkpointing = self.use_gradient_checkpointing_var.get()
self.accumulation_steps = self.gradient_accumulation_steps_var.get()
self.learning_rate = self.learning_rate_entry.get()
self.warmup_steps = self.num_warmup_steps_entry.get()
self.learning_rate_scheduler = self.learning_rate_scheduler_var.get()
#self.use_latent_cache = self.use_latent_cache_var.get()
#self.save_latent_cache = self.save_latent_cache_var.get()
self.regenerate_latent_cache = self.regenerate_latent_cache_var.get()
self.train_text_encoder = self.train_text_encoder_var.get()
self.with_prior_loss_preservation = self.with_prior_loss_preservation_var.get()
self.prior_loss_preservation_weight = self.prior_loss_preservation_weight_entry.get()
self.use_image_names_as_captions = self.use_image_names_as_captions_var.get()
self.shuffle_captions = self.shuffle_captions_var.get()
self.auto_balance_concept_datasets = self.auto_balance_dataset_var.get()
self.add_class_images_to_dataset = self.add_class_images_to_dataset_var.get()
self.number_of_class_images = self.number_of_class_images_entry.get()
self.save_every_n_epochs = self.save_every_n_epochs_entry.get()
self.number_of_samples_to_generate = self.number_of_samples_to_generate_entry.get()
self.sample_height = self.sample_height_entry.get()
self.sample_width = self.sample_width_entry.get()
self.sample_random_aspect_ratio = self.sample_random_aspect_ratio_var.get()
self.sample_on_training_start = self.sample_on_training_start_var.get()
self.concept_list_json_path = 'stabletune_concept_list.json'
self.use_aspect_ratio_bucketing = self.use_aspect_ratio_bucketing_var.get()
self.seed_number = self.seed_entry.get()
self.dataset_repeats = self.dataset_repeats_entry.get()
self.limit_text_encoder = self.limit_text_encoder_entry.get()
self.use_text_files_as_captions = self.use_text_files_as_captions_var.get()
self.convert_to_ckpt_after_training = self.convert_to_ckpt_after_training_var.get()
self.disable_cudnn_benchmark = self.disable_cudnn_benchmark_var.get()
self.sample_step_interval = self.sample_step_interval_entry.get()
self.cloud_mode = self.cloud_mode_var.get()
self.conditional_dropout = self.conditional_dropout_entry.get()
self.clip_penultimate = self.clip_penultimate_var.get()
self.use_ema = self.use_ema_var.get()
self.aspect_ratio_bucketing_mode = self.aspect_ratio_bucketing_mode_var.get()
self.dynamic_bucketing_mode = self.dynamic_bucketing_mode_var.get()
self.model_variant = self.model_variant_var.get()
self.masked_training = self.masked_training_var.get()
self.normalize_masked_area_loss = self.normalize_masked_area_loss_var.get()
self.unmasked_probability = self.unmasked_probability_var.get()
self.max_denoising_strength = self.max_denoising_strength_var.get()
self.fallback_mask_prompt = self.fallback_mask_prompt_entry.get()
self.attention = self.attention_var.get()
self.batch_prompt_sampling = int(self.batch_prompt_sampling_optionmenu_var.get())
self.shuffle_dataset_per_epoch = self.shuffle_dataset_per_epoch_var.get()
self.use_offset_noise = self.use_offset_noise_var.get()
self.offset_noise_weight = self.offset_noise_weight_entry.get()
self.use_gan = self.use_gan_var.get()
self.gan_weight = self.gan_weight_entry.get()
self.use_lion = self.use_lion_var.get()
mode = 'normal'
if self.cloud_mode == False and export == None:
#check if output path exists
if os.path.exists(self.output_path) == True:
#check if output path is empty
if len(os.listdir(self.output_path)) > 0:
#show a messagebox asking if the user wants to overwrite the output path
overwrite = messagebox.askyesno("Overwrite Output Path", "The output path is not empty. Do you want to overwrite it?")
if overwrite == False:
return
else:
#delete the contents of the output path but the logs or 0 directory
for file in os.listdir(self.output_path):
if file != 'logs' and file != '0':
if os.path.isdir(self.output_path + '/' + file) == True:
shutil.rmtree(self.output_path + '/' + file)
else:
os.remove(self.output_path + '/' + file)
if self.cloud_mode == True or export == 'LinuxCMD':
if export == 'LinuxCMD':
mode = 'LinuxCMD'
export='Linux'
#create a sessionName for the cloud based on the output path name and the time
#format time and date to %month%day%hour%minute
now = datetime.now()
dt_string = now.strftime("%m-%d-%H-%M")
self.export_name = self.output_path.split('/')[-1].split('\\')[-1] + '_' + dt_string
self.packageForCloud()
if int(self.train_epocs) == 0 or self.train_epocs == '':
messagebox.showerror("Error", "Number of training epochs must be greater than 0")
return
#open stabletune_concept_list.json
if os.path.exists('stabletune_last_run.json'):
try:
with open('stabletune_last_run.json') as f:
self.last_run = json.load(f)
if self.regenerate_latent_cache == False:
if self.last_run["concepts"] == self.concepts:
#check if resolution is the same
try:
#try because I keep adding stuff to the json file and it may error out for peeps
if self.last_run["resolution"] != self.resolution or self.use_text_files_as_captions != self.last_run['use_text_files_as_captions'] or self.last_run['dataset_repeats'] != self.dataset_repeats or self.last_run["batch_size"] != self.batch_size or self.last_run["train_text_encoder"] != self.train_text_encoder or self.last_run["use_image_names_as_captions"] != self.use_image_names_as_captions or self.last_run["shuffle_captions"] != self.shuffle_captions or self.last_run["auto_balance_concept_datasets"] != self.auto_balance_concept_datasets or self.last_run["add_class_images_to_dataset"] != self.add_class_images_to_dataset or self.last_run["number_of_class_images"] != self.number_of_class_images or self.last_run["aspect_ratio_bucketing"] != self.use_aspect_ratio_bucketing or self.last_run["masked_training"] != self.masked_training:
self.regenerate_latent_cache = True
#show message
messagebox.showinfo("StableTuner", "Configuration changed, regenerating latent cache")
except:
print("Error trying to see if regenerating latent cache is needed, this means it probably needs to be regenerated and ST was updated recently.")
pass
else:
messagebox.showinfo("StableTuner", "Configuration changed, regenerating latent cache")
self.regenerate_latent_cache = True
else:
messagebox.showinfo("StableTuner", "Warning: Regenerating latent cache is enabled, will regenerate latent cache")
except Exception as e:
print(e)
print("Error checking last run, regenerating latent cache")
self.regenerate_latent_cache = True
#create a bat file to run the training
if self.mixed_precision == 'fp16' or self.mixed_precision == 'bf16':
batBase = f'accelerate "launch" "--mixed_precision={self.mixed_precision}" "scripts/trainer.py"'
if export == 'Linux':
batBase = f'accelerate launch --mixed_precision="{self.mixed_precision}" scripts/trainer.py'
else:
if self.mixed_precision == 'fp32':
batBase = 'accelerate "launch" "--mixed_precision=no" "scripts/trainer.py"'
if export == 'Linux':
batBase = f'accelerate launch --mixed_precision="no" scripts/trainer.py'
elif self.mixed_precision == 'tf32':
batBase = 'accelerate "launch" "--mixed_precision=no" "scripts/trainer.py"'
if export == 'Linux':
batBase = f'accelerate launch --mixed_precision="no" scripts/trainer.py'
if self.shuffle_dataset_per_epoch == True:
if export == 'Linux':
batBase += ' --shuffle_per_epoch'
else:
batBase += ' "--shuffle_per_epoch"'
if self.batch_prompt_sampling != 0:
if export == 'Linux':
batBase += f' --sample_from_batch={self.batch_prompt_sampling}'
else:
batBase += f' "--sample_from_batch={self.batch_prompt_sampling}"'
if self.attention == 'xformers':
if export == 'Linux':
batBase += ' --attention="xformers"'
else:
batBase += ' "--attention=xformers" '
elif self.attention == 'Flash Attention':
if export == 'Linux':
batBase += ' --attention="flash_attention"'
else:
batBase += ' "--attention=flash_attention" '
if self.model_variant == 'Regular':
if export == 'Linux':
batBase += ' --model_variant="base"'
else:
batBase += ' "--model_variant=base" '
elif self.model_variant == 'Inpaint':
if export == 'Linux':
batBase += ' --model_variant="inpainting"'
else:
batBase += ' "--model_variant=inpainting" '
elif self.model_variant == 'Depth2Img':
if export == 'Linux':
batBase += ' --model_variant="depth2img"'
else:
batBase += ' "--model_variant=depth2img" '
if self.masked_training == True:
if export == 'Linux':
batBase += ' --masked_training '
else:
batBase += ' "--masked_training" '
if self.normalize_masked_area_loss == True:
if export == 'Linux':
batBase += ' --normalize_masked_area_loss '
else:
batBase += ' "--normalize_masked_area_loss" '
try:
# if unmasked_probability is a percentage calculate what epoch to stop at
if '%' in self.unmasked_probability:
percent = float(self.unmasked_probability.replace('%', ''))
fraction = percent / 100
if export == 'Linux':
batBase += f' --unmasked_probability={fraction}'
else:
batBase += f' "--unmasked_probability={fraction}" '
elif '%' not in self.unmasked_probability and self.unmasked_probability.strip() != '' and self.unmasked_probability != '0':
if export == 'Linux':
batBase += f' --unmasked_probability={self.unmasked_probability}'
else:
batBase += f' "--unmasked_probability={self.unmasked_probability}" '
except:
pass
try:
# if max_denoising_strength is a percentage calculate what epoch to stop at
if '%' in self.max_denoising_strength:
percent = float(self.max_denoising_strength.replace('%', ''))
fraction = percent / 100
if export == 'Linux':
batBase += f' --max_denoising_strength={fraction}'
else:
batBase += f' "--max_denoising_strength={fraction}" '
elif '%' not in self.max_denoising_strength and self.max_denoising_strength.strip() != '' and self.max_denoising_strength != '0':
if export == 'Linux':
batBase += f' --max_denoising_strength={self.max_denoising_strength}'
else:
batBase += f' "--max_denoising_strength={self.max_denoising_strength}" '
except:
pass
if self.fallback_mask_prompt != '':
if export == 'Linux':
batBase += f' --add_mask_prompt="{self.fallback_mask_prompt}"'
else:
batBase += f' "--add_mask_prompt={self.fallback_mask_prompt}" '
if self.disable_cudnn_benchmark == True:
if export == 'Linux':
batBase += ' --disable_cudnn_benchmark'
else:
batBase += ' "--disable_cudnn_benchmark" '
if self.use_text_files_as_captions == True:
if export == 'Linux':
batBase += ' --use_text_files_as_captions'
else:
batBase += ' "--use_text_files_as_captions" '
if int(self.sample_step_interval) != 0 or self.sample_step_interval != '' or self.sample_step_interval != ' ':
if export == 'Linux':
batBase += f' --sample_step_interval={self.sample_step_interval}'
else:
batBase += f' "--sample_step_interval={self.sample_step_interval}" '
try:
#if limit_text_encoder is a percentage calculate what epoch to stop at
if '%' in self.limit_text_encoder:
percent = float(self.limit_text_encoder.replace('%',''))
stop_epoch = int((int(self.train_epocs) * percent) / 100)
if export == 'Linux':
batBase += f' --stop_text_encoder_training={stop_epoch}'
else:
batBase += f' "--stop_text_encoder_training={stop_epoch}" '
elif '%' not in self.limit_text_encoder and self.limit_text_encoder.strip() != '' and self.limit_text_encoder != '0':
if export == 'Linux':
batBase += f' --stop_text_encoder_training={self.limit_text_encoder}'
else:
batBase += f' "--stop_text_encoder_training={self.limit_text_encoder}" '
except:
pass
if export=='Linux':
batBase += f' --pretrained_model_name_or_path="{self.model_path}" '
batBase += f' --pretrained_vae_name_or_path="{self.vae_path}" '
batBase += f' --output_dir="{self.output_path}" '
batBase += f' --seed={self.seed_number} '
batBase += f' --resolution={self.resolution} '
batBase += f' --train_batch_size={self.batch_size} '
batBase += f' --num_train_epochs={self.train_epocs} '
else:
batBase += f' "--pretrained_model_name_or_path={self.model_path}" '
batBase += f' "--pretrained_vae_name_or_path={self.vae_path}" '
batBase += f' "--output_dir={self.output_path}" '
batBase += f' "--seed={self.seed_number}" '
batBase += f' "--resolution={self.resolution}" '
batBase += f' "--train_batch_size={self.batch_size}" '
batBase += f' "--num_train_epochs={self.train_epocs}" '
if self.mixed_precision == 'fp16' or self.mixed_precision == 'bf16' or self.mixed_precision == 'tf32':
if export == 'Linux':
batBase += f' --mixed_precision="{self.mixed_precision}"'
else:
batBase += f' "--mixed_precision={self.mixed_precision}" '
if self.use_aspect_ratio_bucketing:
if export == 'Linux':
batBase += ' --use_bucketing'
else:
batBase += f' "--use_bucketing" '
if self.aspect_ratio_bucketing_mode == 'Dynamic Fill':
com = 'dynamic'
if self.aspect_ratio_bucketing_mode == 'Drop Fill':
com = 'truncate'
if self.aspect_ratio_bucketing_mode == 'Duplicate Fill':
com = 'add'
if export == 'Linux':
batBase += f' --aspect_mode="{com}"'
else:
batBase += f' "--aspect_mode={com}" '
if self.dynamic_bucketing_mode == 'Duplicate':
com = 'add'
if self.dynamic_bucketing_mode == 'Drop':
com = 'truncate'
if export == 'Linux':
batBase += f' --aspect_mode_action_preference="{com}"'
else:
batBase += f' "--aspect_mode_action_preference={com}" '
if self.use_8bit_adam == True:
if export == 'Linux':
batBase += ' --use_8bit_adam'
else:
batBase += f' "--use_8bit_adam" '
if self.use_gradient_checkpointing == True:
if export == 'Linux':
batBase += ' --gradient_checkpointing'
else:
batBase += f' "--gradient_checkpointing" '
if self.use_lion == True:
if export == 'Linux':
batBase += ' --use_lion'
else:
batBase += f' "--use_lion" '
if export == 'Linux':
batBase += f' --gradient_accumulation_steps={self.accumulation_steps}'
batBase += f' --learning_rate={self.learning_rate}'
batBase += f' --lr_warmup_steps={self.warmup_steps}'
batBase += f' --lr_scheduler="{self.learning_rate_scheduler}"'
else:
batBase += f' "--gradient_accumulation_steps={self.accumulation_steps}" '
batBase += f' "--learning_rate={self.learning_rate}" '
batBase += f' "--lr_warmup_steps={self.warmup_steps}" '
batBase += f' "--lr_scheduler={self.learning_rate_scheduler}" '
if self.regenerate_latent_cache == True:
if export == 'Linux':
batBase += ' --regenerate_latent_cache'
else:
batBase += f' "--regenerate_latent_cache" '
if self.train_text_encoder == True:
if export == 'Linux':
batBase += ' --train_text_encoder'
else:
batBase += f' "--train_text_encoder" '
if self.with_prior_loss_preservation == True and self.use_aspect_ratio_bucketing == False:
if export == 'Linux':
batBase += ' --with_prior_preservation'
batBase += f' --prior_loss_weight={self.prior_loss_preservation_weight}'
else:
batBase += f' "--with_prior_preservation" '
batBase += f' "--prior_loss_weight={self.prior_loss_preservation_weight}" '
elif self.with_prior_loss_preservation == True and self.use_aspect_ratio_bucketing == True:
print('loss preservation isnt supported with aspect ratio bucketing yet, sorry!')
if self.use_image_names_as_captions == True:
if export == 'Linux':
batBase += ' --use_image_names_as_captions'
else:
batBase += f' "--use_image_names_as_captions" '
if self.shuffle_captions == True:
if export == 'Linux':
batBase += ' --shuffle_captions'
else:
batBase += f' "--shuffle_captions" '
if self.use_offset_noise == True:
if export == 'Linux':
batBase += f' --with_offset_noise'
batBase += f' --offset_noise_weight={self.offset_noise_weight}'
else:
batBase += f' "--with_offset_noise" '
batBase += f' "--offset_noise_weight={self.offset_noise_weight}" '
if self.use_gan == True:
if export == 'Linux':
batBase += f' --with_gan'
batBase += f' --gan_weight={self.gan_weight}'
else:
batBase += f' "--with_gan" '
batBase += f' "--gan_weight={self.gan_weight}" '
if self.auto_balance_concept_datasets == True:
if export == 'Linux':
batBase += ' --auto_balance_concept_datasets'
else:
batBase += f' "--auto_balance_concept_datasets" '
if self.add_class_images_to_dataset == True and self.with_prior_loss_preservation == False:
if export == 'Linux':
batBase += ' --add_class_images_to_dataset'
else:
batBase += f' "--add_class_images_to_dataset" '
if export == 'Linux':
batBase += f' --concepts_list="{self.concept_list_json_path}"'
batBase += f' --num_class_images={self.number_of_class_images}'
batBase += f' --save_every_n_epoch={self.save_every_n_epochs}'
batBase += f' --n_save_sample={self.number_of_samples_to_generate}'
batBase += f' --sample_height={self.sample_height}'
batBase += f' --sample_width={self.sample_width}'
batBase += f' --dataset_repeats={self.dataset_repeats}'
else:
batBase += f' "--concepts_list={self.concept_list_json_path}" '
batBase += f' "--num_class_images={self.number_of_class_images}" '
batBase += f' "--save_every_n_epoch={self.save_every_n_epochs}" '
batBase += f' "--n_save_sample={self.number_of_samples_to_generate}" '
batBase += f' "--sample_height={self.sample_height}" '
batBase += f' "--sample_width={self.sample_width}" '
batBase += f' "--dataset_repeats={self.dataset_repeats}" '
if self.sample_random_aspect_ratio == True:
if export == 'Linux':
batBase += ' --sample_aspect_ratios'
else:
batBase += f' "--sample_aspect_ratios" '
if self.send_telegram_updates == True:
if export == 'Linux':
batBase += ' --send_telegram_updates'
batBase += f' --telegram_token="{self.telegram_token}"'
batBase += f' --telegram_chat_id="{self.telegram_chat_id}"'
else:
batBase += f' "--send_telegram_updates" '
batBase += f' "--telegram_token={self.telegram_token}" '
batBase += f' "--telegram_chat_id={self.telegram_chat_id}" '
#remove duplicates from self.sample_prompts
self.sample_prompts = list(dict.fromkeys(self.sample_prompts))
#remove duplicates from self.add_controlled_seed_to_sample
self.add_controlled_seed_to_sample = list(dict.fromkeys(self.add_controlled_seed_to_sample))
for i in range(len(self.sample_prompts)):
if export == 'Linux':
batBase += f' --add_sample_prompt="{self.sample_prompts[i]}"'
else:
batBase += f' "--add_sample_prompt={self.sample_prompts[i]}" '
for i in range(len(self.add_controlled_seed_to_sample)):
if export == 'Linux':
batBase += f' --save_sample_controlled_seed={self.add_controlled_seed_to_sample[i]}'
else:
batBase += f' "--save_sample_controlled_seed={self.add_controlled_seed_to_sample[i]}" '
if self.sample_on_training_start == True:
if export == 'Linux':
batBase += ' --sample_on_training_start'
else:
batBase += f' "--sample_on_training_start" '
if len(self.conditional_dropout) > 0 and self.conditional_dropout != ' ' and self.conditional_dropout != '0':
#if % is in the string, remove it
if '%' in self.conditional_dropout:
self.conditional_dropout = self.conditional_dropout.replace('%', '')
#convert to float from percentage string
self.conditional_dropout = float(self.conditional_dropout) / 100
else:
#check if float
try:
#check if value is above 1.0
if float(self.conditional_dropout) > 1.0:
#divide by 100
self.conditional_dropout = float(self.conditional_dropout) / 100
else:
self.conditional_dropout = float(self.conditional_dropout)
except:
print('Error: Conditional Dropout must be a percent between 0 and 100, or a decimal between 0 and 1.')
#print(self.conditional_dropout)
#if self.coniditional dropout is a float
if isinstance(self.conditional_dropout, float):
if export == 'Linux':
batBase += f' --conditional_dropout={self.conditional_dropout}'
else:
batBase += f' "--conditional_dropout={self.conditional_dropout}" '
#save configure
if self.clip_penultimate == True:
if export == 'Linux':
batBase += ' --clip_penultimate'
else:
batBase += f' "--clip_penultimate" '
if self.use_ema == True:
if export == 'Linux':
batBase += ' --use_ema'
else:
batBase += f' "--use_ema" '
self.save_config('stabletune_last_run.json')
#check if output folder exists
if os.path.exists(self.output_path) == False:
#create everything leading up to output folder
os.makedirs(self.output_path)
#get unique name for config file
now = datetime.now()
dt_string = now.strftime("%m-%d-%H-%M")
#construct name
config_log_name = 'stabletuner'+'_'+str(self.resolution)+"_e"+str(self.train_epocs)+"_"+dt_string+'.json'
self.save_config(os.path.join(self.output_path, config_log_name))
if export == False:
#save the bat file
with open("scripts/train.bat", "w", encoding="utf-8") as f:
f.write(batBase)
#close the window
self.destroy()
#run the bat file
self.quit()
train = os.system(r".\scripts\train.bat")
#if exit code is 0, then the training was successful
if train == 0:
app = App()
app.mainloop()
#if user closed the window or keyboard interrupt, then cancel conversion
elif train == 1:
os.system("pause")
#restart the app
elif export == 'win':
with open("train.bat", "w", encoding="utf-8") as f:
f.write(batBase)
#show message
messagebox.showinfo("Export", "Exported to train.bat")
elif mode == 'LinuxCMD':
#copy batBase to clipboard
trainer_index = batBase.find('trainer.py')+11
batStart = batBase[:trainer_index]
batCommands = batBase[trainer_index:]
#split on -- and remove the first element
batCommands = batCommands.split('--')
batBase = batStart+' \\\n'
for command in batCommands[1:]:
#add the -- back
if command != batCommands[-1]:
command = ' --'+command+'\\'+'\n'
else:
command = ' --'+command
batBase += command
pyperclip.copy('!'+batBase)
shutil.rmtree(self.full_export_path)
messagebox.showinfo("Export", "Copied new training command to clipboard.")
return
elif export == 'Linux' and self.cloud_mode == True:
notebook = 'resources/stableTuner_notebook.ipynb'
#load the notebook as a dictionary
with open(notebook) as f:
nb = json.load(f)
#get the last cell
#find the cell with the source that contains changeMe
#format batBase so it won't be one line
#find index in batBase of the trainer.py
trainer_index = batBase.find('trainer.py')+11
batStart = batBase[:trainer_index]
batCommands = batBase[trainer_index:]
#split on -- and remove the first element
batCommands = batCommands.split('--')
batBase = batStart+' \\\n'
for command in batCommands[1:]:
#add the -- back
if command != batCommands[-1]:
command = ' --'+command+'\\'+'\n'
else:
command = ' --'+command
batBase += command
for i in range(len(nb['cells'])):
if 'changeMe' in nb['cells'][i]['source']:
code_cell = nb['cells'][i]
index = i
code_cell['source'] = '!'+batBase
#replace the last cell with the new one
nb['cells'][index] = code_cell
break
#save the notebook to the export folder
shutil.copy('requirements.txt', self.full_export_path)
#zip up everything in export without the folder itself
shutil.make_archive('payload', 'zip', self.full_export_path)
#move the zip file to the export folder
shutil.move('payload.zip', self.full_export_path)
#save the notebook to the export folder
with open(self.full_export_path+os.sep+'stableTuner_notebook.ipynb', 'w') as f:
json.dump(nb, f)
#delete everything in the export folder except the zip file and the notebook
for file in os.listdir(self.full_export_path):
if file.endswith('.zip') or file.endswith('.ipynb'):
continue
else:
#if it's a folder, delete it
if os.path.isdir(self.full_export_path+os.sep+file):
shutil.rmtree(self.full_export_path+os.sep+file)
#if it's a file, delete it
else:
os.remove(self.full_export_path+os.sep+file)
#show message
messagebox.showinfo("Success", f"Your cloud\linux payload is ready to go!\nSaved to: {self.full_export_path}\n\nUpload the files and run the notebook to start training.")
def restart(instance):
instance.destroy()
#os.startfile(os.getcwd()+'/scripts/configuration_gui.py')
app = App()
app.mainloop()
#root = ctk.CTk()
app = App()
app.mainloop()
|