Datasets:
File size: 94,955 Bytes
03185c4 | 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 | [
{
"type": "text",
"text": "Any-to-Any Generation via Composable Diffusion ",
"text_level": 1,
"bbox": [
194,
122,
803,
147
],
"page_idx": 0
},
{
"type": "text",
"text": "Zineng Tang1∗ ",
"text_level": 1,
"bbox": [
184,
200,
285,
215
],
"page_idx": 0
},
{
"type": "text",
"text": "Mohit Bansal1† ",
"text_level": 1,
"bbox": [
705,
200,
813,
215
],
"page_idx": 0
},
{
"type": "text",
"text": "Ziyi Yang2† Chenguang ${ \\bf Z } { \\bf h } { \\bf u } ^ { 2 \\ddagger }$ Michael Zeng2 1University of North Carolina at Chapel Hill 2Microsoft Azure Cognitive Services Research https://codi-gen.github.io ",
"bbox": [
310,
199,
676,
258
],
"page_idx": 0
},
{
"type": "text",
"text": "Abstract ",
"text_level": 1,
"bbox": [
462,
294,
535,
310
],
"page_idx": 0
},
{
"type": "text",
"text": "We present Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any combination of input modalities. Unlike existing generative AI systems, CoDi can generate multiple modalities in parallel and its input is not limited to a subset of modalities like text or image. Despite the absence of training datasets for many combinations of modalities, we propose to align modalities in both the input and output space. This allows CoDi to freely condition on any input combination and generate any group of modalities, even if they are not present in the training data. CoDi employs a novel composable generation strategy which involves building a shared multimodal space by bridging alignment in the diffusion process, enabling the synchronized generation of intertwined modalities, such as temporally aligned video and audio. Highly customizable and flexible, CoDi achieves strong joint-modality generation quality, and outperforms or is on par with the unimodal state-of-the-art for single-modality synthesis. The project page with demonstrations and code is at https://codi-gen.github.io/ ",
"bbox": [
232,
329,
766,
535
],
"page_idx": 0
},
{
"type": "image",
"img_path": "images/f6d7a60ecba1b89ddec9c2b8b8bfea37a1e8ff89e67fccf04a7b8dcb4427834d.jpg",
"image_caption": [
"Figure 1: CoDi can generate various (joint) combinations of output modalities from diverse (joint) sets of inputs: video, image, audio, and text (example combinations depicted by the colored arrows). "
],
"image_footnote": [],
"bbox": [
176,
565,
820,
773
],
"page_idx": 0
},
{
"type": "text",
"text": "1 Introduction ",
"text_level": 1,
"bbox": [
174,
89,
310,
106
],
"page_idx": 1
},
{
"type": "text",
"text": "Recent years have seen the rise of powerful cross-modal models that can generate one modality from another, e.g. text-to-text [6, 37], text-to-image [13, 19, 22, 41, 44], or text-to-audio [23, 33]. However, these models are restricted in their real-world applicability where multiple modalities coexist and interact. While one can chain together modality-specific generative models in a multi-step generation setting, the generation power of each step remains inherently limited, and a serial, multistep process can be cumbersome and slow. Moreover, independently generated unimodal streams will not be consistent and aligned when stitched together in a post-processing way (e.g., synchronized video and audio). The development of a comprehensive and versatile model that can generate any combination of modalities from any set of input conditions has been eagerly anticipated, as it would more accurately capture the multimodal nature of the world and human comprehension, seamlessly consolidate information from a wide range of sources, and enable strong immersion in human-AI interactions (for example, by generating coherent video, audio, and text description at the same time). ",
"bbox": [
174,
122,
825,
289
],
"page_idx": 1
},
{
"type": "text",
"text": "In pursuit of this goal, we propose Composable Diffusion, or CoDi, the first model capable of simultaneously processing and generating arbitrary combinations of modalities as shown in Fig. 1. Training a model to take any mixture of input modalities and flexibly generate any mixture of outputs presents significant computational and data requirements, as the number of combinations for the input and output modalities scales exponentially. Also aligned training data for many groups of modalities is scarce or even non-existent, making it infeasible to train with all possible input-output combinations. To address this challenge, we propose to align multiple modalities in both the input conditioning (Section 3.2) and generation diffusion step (Section 3.4). Furthermore, a proposed “Bridging Alignment” strategy for contrastive learning (Section 3.2) allows us to efficiently model the exponential number of input-output combinations with a linear number of training objectives. ",
"bbox": [
174,
295,
825,
433
],
"page_idx": 1
},
{
"type": "text",
"text": "Building a model with any-to-any generation capacity with exceptional generation quality requires comprehensive model design and training on diverse data resources. Therefore, we build CoDi in an integrative way. First, we train a latent diffusion model (LDM) for each modality, e.g., text, image, video, and audio. These models can be trained in parallel independently, ensuring exceptional singlemodality generation quality using widely available modality-specific training data (i.e., data with one or more modalities as input and one modality as output). For conditional cross-modality generation, such as generating images using audio+language prompts, the input modalities are projected into a shared feature space (Section 3.2), and the output LDM attends to the combination of input features. This multimodal conditioning mechanism prepares the diffusion model to condition on any modality or combination of modalities without directly training for such settings. ",
"bbox": [
174,
439,
826,
578
],
"page_idx": 1
},
{
"type": "text",
"text": "The second stage of training enables the model to handle many-to-many generation strategies that involve simultaneously generating arbitrary combinations of output modalities. To the best of our knowledge, CoDi is the first AI model with this capability. This is achieved by adding a crossattention module to each diffuser, and an environment encoder $V$ to project the latent variable of different LDMs into a shared latent space (Section 3.4). Next, we freeze the parameters of the LDM, training only the cross-attention parameters and $V$ . Since the environment encoder of different modalities are aligned, an LDM can cross-attend with any group of co-generated modalities by interpolating the representation’s output by $V$ . This enables CoDi to seamlessly generate any group of modalities, without training on all possible generation combinations. This reduces the number of training objectives from exponential to linear. ",
"bbox": [
174,
584,
825,
722
],
"page_idx": 1
},
{
"type": "text",
"text": "We demonstrate the any-to-any generation capability of CoDi, including single-to-single modality generation, multi-condition generation, and the novel capacity of joint generation of multiple modalities. For example, generating synchronized video and audio given the text input prompt; or generating video given a prompt image and audio. We also provide a quantitative evaluation of CoDi using eight multimodal datasets. As the latest work from Project i-Code [55] towards Composable AI, CoDi exhibits exceptional generation quality across assorted scenarios, with synthesis quality on par or even better than single to single modality SOTA, e.g., audio generation and audio captioning. ",
"bbox": [
174,
729,
825,
827
],
"page_idx": 1
},
{
"type": "text",
"text": "2 Related Works ",
"text_level": 1,
"bbox": [
176,
849,
328,
866
],
"page_idx": 1
},
{
"type": "text",
"text": "Diffusion models (DMs) learn the data distribution by denoising and recovering the original data. Deep Diffusion Process (DDP) [45] adopts a sequence of reversible diffusion steps to model image probability distribution. It uses a reversible encoder to map the input image to a latent space and a decoder to map the latent variables to an output image. Denoising diffusion probabilistic model (DDPM) [20] uses a cascade of diffusion processes to gradually increase the complexity of the probability density function model. At each step, the model adds noise to the input image and estimates the corresponding noise level using an autoregressive model. This allows the model to capture the dependencies between adjacent pixels and generate high-quality images. Score-based generative models (SOG) [46] use the score function to model the diffusion process. [40] generates high-fidelity images conditioned on CLIP representations of text prompts. Latent diffusion model (LDM) [41] uses a VAE to encode inputs into latent space to reduce modeling dimension and improves efficiency. The motivation is that image compression can be separated into semantic space by a diffusion model and perceptual space by an autoencoder. By incorporating temporal modeling modules and cascading model architectures, video diffusion models have been built upon image diffusers to generate temporally consistent and inherent frames[14, 19, 21, 44]. Diffusion models have also been applied to other domains, such as generating audio from text and vision prompts[23, 33]. ",
"bbox": [
174,
882,
823,
911
],
"page_idx": 1
},
{
"type": "image",
"img_path": "images/fd34f6f54041b6f9329e8b6d950176f23a2bd0ce1819fa8d9711a4b7d903ec66.jpg",
"image_caption": [
"Figure 2: CoDi model architecture: (a) We first train individual diffusion model with aligned prompt encoder by “Bridging Alignment”; (b) Diffusion models learn to attend with each other via “Latent Alignment”; (c) CoDi achieves any-to-any generation with a linear number of training objectives. "
],
"image_footnote": [],
"bbox": [
173,
87,
821,
415
],
"page_idx": 2
},
{
"type": "text",
"text": "",
"bbox": [
174,
496,
825,
689
],
"page_idx": 2
},
{
"type": "text",
"text": "Multimodal modeling has experienced rapid advancement recently, with researchers striving to build uniform representations of multiple modalities using a single model to achieve more comprehensive cross-modal understanding. Vision transformers [11], featuring diverse model architectures and training techniques, have been applied to various downstream tasks such as vision Q&A and image captioning. Multimodal encoders have also proven successful in vision-language [1, 8, 57], videoaudio [47] and video-speech-language [55, 56] domains. Aligning data from different modalities is an active research area [12, 38], with promising applications in cross-modality retrieval and building uniform multimodal representations [33, 35, 41]. ",
"bbox": [
174,
695,
825,
806
],
"page_idx": 2
},
{
"type": "text",
"text": "3 Methodology ",
"text_level": 1,
"bbox": [
174,
825,
313,
843
],
"page_idx": 2
},
{
"type": "text",
"text": "3.1 Preliminary: Latent Diffusion Model ",
"text_level": 1,
"bbox": [
174,
857,
470,
872
],
"page_idx": 2
},
{
"type": "text",
"text": "Diffusion models (DM) represent a class of generative models that learn data distributions $p ( { \\pmb x } )$ by simulating the diffusion of information over time. During training, random noise is iteratively added to $_ { \\textbf { \\em x } }$ , while the model learns to denoise the examples. For inference, the model denoises data points sampled from simple distributions such as Gaussian. Latent diffusion models (LDM) [41] learn the distribution of the latent variable $_ { z }$ corresponding to $_ { \\textbf { \\em x } }$ , significantly reducing computational cost by decreasing the data dimension. ",
"bbox": [
174,
883,
821,
911
],
"page_idx": 2
},
{
"type": "text",
"text": "",
"bbox": [
174,
90,
825,
147
],
"page_idx": 3
},
{
"type": "text",
"text": "In LDM, an autoencoder is first trained to reconstruct $_ { \\textbf { \\em x } }$ , i.e., $\\hat { \\pmb { x } } = D ( E ( \\pmb { x } ) )$ , where $E$ and $D$ denote the encoder and decoder, respectively. The latent variable $z = E ( { \\pmb x } )$ is iteratively diffused over time steps $t$ based on a variance schedule $\\beta _ { 1 } , \\ldots , \\beta _ { T }$ , i.e., $q ( z _ { t } | z _ { t - 1 } ) = \\mathcal { N } ( z _ { t } ; \\sqrt { 1 - \\beta _ { t } } z _ { t - 1 } , \\beta _ { t } I )$ [20, 45]. ",
"bbox": [
173,
152,
825,
209
],
"page_idx": 3
},
{
"type": "text",
"text": "The forward process allows the random sampling of ${ \\boldsymbol { z } } _ { t }$ at any timestep in a closed form [20, 45]: $\\boldsymbol { z } _ { t } = \\alpha _ { t } \\boldsymbol { z } + \\sigma _ { t } \\boldsymbol { \\epsilon }$ , where $\\epsilon \\sim \\mathcal { N } ( 0 , I )$ , $\\alpha _ { t } : = 1 - \\beta _ { t }$ and $\\begin{array} { r } { \\sigma _ { t } : = \\dot { 1 } - \\prod _ { s = 1 } ^ { t } \\dot { \\alpha } _ { s } } \\end{array}$ . The diffuser learns how to denoise from $\\left\\{ { z } _ { t } \\right\\}$ to recover $_ z$ . Following the reparameterization method proposed in [20], the denoising training objective can be expressed as [41]: ",
"bbox": [
173,
215,
825,
273
],
"page_idx": 3
},
{
"type": "equation",
"img_path": "images/c0b6a42a7dddc81d5d528ed8a349c84f1a8e8e52d97b6eb0c26878eaf69606c7.jpg",
"text": "$$\n\\begin{array} { r } { \\mathcal { L } _ { D } = \\mathbb { E } _ { z , \\epsilon , t } \\Vert \\epsilon - \\epsilon _ { \\theta } ( z _ { t } , t , C ( \\pmb { y } ) ) \\Vert _ { 2 } ^ { 2 } . } \\end{array}\n$$",
"text_format": "latex",
"bbox": [
377,
279,
620,
297
],
"page_idx": 3
},
{
"type": "text",
"text": "In data generation, the denoising process can be realized through reparameterized Gaussian sampling: ",
"bbox": [
174,
304,
826,
320
],
"page_idx": 3
},
{
"type": "equation",
"img_path": "images/cd64023bfb25f4aca1f1f00e845397403302ab368fd5956a291fe57286f62be3.jpg",
"text": "$$\np ( z _ { t - 1 } | z _ { t } ) = \\mathcal { N } \\left( z _ { t - 1 } ; \\frac { 1 } { \\sqrt { \\alpha _ { t } } } \\left( z _ { t } - \\frac { \\beta _ { t } } { \\sqrt { \\sigma _ { t } } } \\epsilon _ { \\theta } \\right) , \\beta _ { t } I \\right) .\n$$",
"text_format": "latex",
"bbox": [
316,
329,
679,
364
],
"page_idx": 3
},
{
"type": "text",
"text": "In $\\mathcal { L } _ { D }$ , the diffusion time step $t \\sim \\mathcal { U } [ 1 , T ]$ ; $\\epsilon _ { \\theta }$ is a denoising model with UNet backbone parameterized by $\\theta ; { \\boldsymbol { y } }$ represents the conditional variable that can be used to control generation; $C$ is the prompt encoder. The conditioning mechanism is implemented by first featurizing $\\textbf { { y } }$ into $C ( \\boldsymbol { y } )$ , then the UNet $\\epsilon _ { \\theta }$ conditions on $C ( \\boldsymbol { y } )$ via cross-attention, as described in [41]. Distinct from previous works, our model can condition on any combinations of modalities of text, image, video and audio. Details are presented in the following section. ",
"bbox": [
173,
368,
826,
452
],
"page_idx": 3
},
{
"type": "text",
"text": "3.2 Composable Multimodal Conditioning ",
"text_level": 1,
"bbox": [
176,
469,
480,
484
],
"page_idx": 3
},
{
"type": "text",
"text": "To enable our model to condition on any combination of input/prompt modalities, we align the prompt encoder of text, image, video and audio (denoted by $C _ { t }$ , $C _ { i }$ , $C _ { v }$ , and $C _ { a }$ , respectively) to project the input from any modality into the same space. Multimodal conditioning can then be conveniently achieved by interpolating the representations of each modality $m$ : $\\begin{array} { r } { C ( x _ { t } , \\bar { x _ { i } } , x _ { v } , x _ { a } ) = \\sum _ { m } \\alpha _ { m } C ( \\bar { m } ) } \\end{array}$ for $m \\in \\ b { x } _ { t } , \\ b { x } _ { i } , \\ b { x } _ { v } , \\ b { x } _ { a }$ , with $\\textstyle \\sum _ { m } \\alpha _ { m } = 1$ . Through simple weighted interpolation of aligned embeddings, we enable models trained with single-conditioning (i.e., with only one input) to perform zero-shot multi-conditioning (i.e., with multiple inputs). This process is illustrated in Fig. 2 (a)(2). ",
"bbox": [
173,
494,
825,
593
],
"page_idx": 3
},
{
"type": "text",
"text": "Optimizing all four prompt encoders simultaneously in a combinatorial manner is computationally heavy, with $\\mathcal { O } ( n ^ { 2 } )$ pairs. Additionally, for certain dual modalities, well-aligned paired datasets are limited or unavailable e.g., image-audio pairs. To address this challenge, we propose a simple and effective technique called \"Bridging Alignment\" to efficiently align conditional encoders. As shown in Fig. 2 (a)(1), we choose the text modality as the \"bridging\" modality due to its ubiquitous presence in paired data, such as text-image, text-video, and text-audio pairs. We begin with a pretrained text-image paired encoder, i.e., CLIP [38]. We then train audio and video prompt encoders on audio-text and video-text paired datasets using contrastive learning, with text and image encoder weights frozen. ",
"bbox": [
173,
598,
825,
723
],
"page_idx": 3
},
{
"type": "text",
"text": "In this way, all four modalities are aligned in the feature space. As shown in Section 5.2, CoDi can effectively leverage and combine the complementary information present in any combination of modalities to generate more accurate and comprehensive outputs. The high generation quality remains unaffected with respect to the number of prompt modalities. As we will discuss in subsequent sections, we continue to apply Bridging Alignment to align the latent space of LDMs with different modalities to achieve joint multimodal generation. ",
"bbox": [
174,
729,
825,
813
],
"page_idx": 3
},
{
"type": "text",
"text": "3.3 Composable Diffusion ",
"text_level": 1,
"bbox": [
174,
829,
367,
844
],
"page_idx": 3
},
{
"type": "text",
"text": "Training an end-to-end anything-to-anything model requires extensive learning on various data resources. The model also needs to maintain generation quality for all synthesis flows. To address these challenges, CoDi is designed to be composable and integrative, allowing individual modalityspecific models to be built independently and then smoothly integrated later. Specifically, we start by independently training image, video, audio, and text LDMs. These diffusion models then efficiently learn to attend across modalities for joint multimodal generation (Section 3.4) by a novel mechanism named “latent alignment”. ",
"bbox": [
174,
856,
825,
911
],
"page_idx": 3
},
{
"type": "text",
"text": "",
"bbox": [
176,
90,
823,
132
],
"page_idx": 4
},
{
"type": "text",
"text": "Image Diffusion Model. The image LDM follows the same structure as Stable Diffusion 1.5 [41] and is initialized with the same weights. Reusing the weights transfers the knowledge and exceptional generation fidelity of Stable Diffusion trained on large-scale high-quality image datasets to CoDi. ",
"bbox": [
174,
146,
825,
189
],
"page_idx": 4
},
{
"type": "text",
"text": "Video Diffusion Model. To model the temporal properties of videos and simultaneously maintain vision generation quality, we construct the video diffuser by extending the image diffuser with temporal modules. Specifically, we insert pseudo-temporal attention before the residual block [13]. However, we argue that pseudo-temporal attention only enables video frames to globally attend to each other by flattening the pixels (height, width dimension) to batch dimension, resulting in a lack of cross-frame interaction between local pixels. We argue that this results in the common temporal-inconsistency issue in video generation that locations, shapes, colors, etc. of objects can be inconsistent across generated frames. To address this problem, we propose adapting the latent shift method [2] that performs temporal-spatial shifts on latent features in accordance with temporal attention. We divide the video by the hidden dimension into $k = 8$ chunks, and for each chunk $i = 0$ to 7, we shift the temporal dimension forward by $i$ positions. Further details will be provided in the appendix. ",
"bbox": [
173,
203,
825,
368
],
"page_idx": 4
},
{
"type": "text",
"text": "Audio Diffusion Model. To enable flexible cross-modality attention in joint generation, the audio diffuser is designed to have a similar architecture to vision diffusers, where the mel-spectrogram can be naturally viewed as an image with 1 channel. We use a VAE encoder to encode the melspectrogram of audio to a compressed latent space. In audio synthesis, a VAE decoder maps the latent variable to the mel-spectrogram, and a vocoder generates the audio sample from the mel-spectrogram. We employ the audio VAE from [33] and the vocoder from [27]. ",
"bbox": [
174,
382,
825,
465
],
"page_idx": 4
},
{
"type": "text",
"text": "Text Diffusion Model. The VAE of the text LDM is OPTIMUS [29], and its encoder and decoder are [9] and GPT-2 [39], respectively. For the denoising UNet, unlike the one in image diffusion, the 2D convolution in residual blocks is replaced with 1D convolution [53]. ",
"bbox": [
174,
479,
823,
522
],
"page_idx": 4
},
{
"type": "text",
"text": "3.4 Joint Multimodal Generation by Latent Alignment ",
"text_level": 1,
"bbox": [
174,
536,
565,
551
],
"page_idx": 4
},
{
"type": "text",
"text": "The final step is to enable cross-attention between diffusion flows in joint generation, i.e., generating two or more modalities simultaneously. This is achieved by adding cross-modal attention sublayers to the UNet $\\epsilon _ { \\theta }$ (Fig. 2 (b)(2)). Specifically, consider a diffusion model of modality $A$ that cross-attends with another modality $B$ . Let the latent variables of modalities $m _ { A }$ and $m _ { B }$ at diffusion step $t$ be denoted as $ { \\boldsymbol { z } } _ { t } ^ { A }$ and $\\hat { z _ { t } ^ { B } }$ , respectively. The proposed “Latent Alignment” technique is such that a modality-specific environment encoder $V _ { B }$ first projects $ { \\boldsymbol { z } } _ { t } ^ { B }$ into a shared latent space for different modalities. Then, in each layer of the UNet for modality $A$ , a cross-attention sublayer attends to $V _ { B } \\big ( z _ { t } ^ { B } \\big )$ . For the diffusion model of modality $A$ , the training objective in Eq. (1) now becomes: ",
"bbox": [
173,
561,
825,
674
],
"page_idx": 4
},
{
"type": "equation",
"img_path": "images/543f8049db9dbb90f598c7dea6ef4aa9f7c34c5bc297bb830ab29bb93064c182.jpg",
"text": "$$\n\\mathcal { L } _ { C r o s s } ^ { A } = \\mathbb { E } _ { z , \\epsilon , t } \\Vert \\epsilon - \\epsilon _ { \\theta _ { c } } ( z _ { t } ^ { A } , V _ { B } ( z _ { t } ^ { B } ) , t , C ( \\pmb { y } ) ) \\Vert _ { 2 } ^ { 2 } ,\n$$",
"text_format": "latex",
"bbox": [
328,
690,
668,
710
],
"page_idx": 4
},
{
"type": "text",
"text": "where $\\theta _ { c }$ denotes the weights of cross-attention modules in the UNet. ",
"bbox": [
169,
717,
630,
731
],
"page_idx": 4
},
{
"type": "text",
"text": "The training objective of $A + B$ joint generation is $\\mathcal { L } _ { C r o s s } ^ { A } + \\mathcal { L } _ { C r o s s } ^ { B }$ . $V ( \\cdot )$ of different modalities are trained to be aligned with contrastive learning. Since $z _ { t } ^ { A }$ and $z _ { t } ^ { B }$ at any time step can be sampled with closed form in the diffusion process Section 3.1, one can conveniently train the contrastive learning together with $\\mathcal { L } _ { C r o s s }$ . The purpose of $V$ is to achieve the generation of any combination of modalities (in polynomial) by training on a linear number of joint-generation tasks. For example, if we have trained the joint generation of modalities $A , B$ , and $B$ , $C$ independently, then we have $V _ { A } ( z _ { t } ^ { A } )$ , $V _ { B } \\big ( z _ { t } ^ { B } \\big )$ , and $V _ { C } ( z _ { t } ^ { C } )$ aligned. Therefore, CoDi can seamlessly achieve joint generation of modalities $A$ and $C$ without any additional training. Moreover, such design automatically effortlessly enables joint generation of modalities $A$ , $B$ , and $C$ concurrently. Specifically, UNet of $A$ can cross-attend with the interpolation of $V _ { B } \\big ( z _ { t } ^ { B } \\big )$ , and $V _ { C } ( z _ { t } ^ { C } )$ , although CoDi has not been trained with such task. ",
"bbox": [
173,
736,
825,
877
],
"page_idx": 4
},
{
"type": "text",
"text": "As shown in Fig. 2(b)(3), we follow similar designs to the \"Bridging Alignment\" in training joint generation: (1) We first train the cross-attention weights in the image and text diffusers, as well as their environment encoders $V$ , on text-image paired data. (2) We freeze the weights of the text diffuser and train the environment encoder and cross-attention weights of the audio diffuser on text-audio paired data. (3) Finally we freeze the audio diffuser and its environment encoder, and train the joint generation of the video modality on audio-video paired data. As demonstrated in Section 5.3, although only trained on three paired joint generation tasks (i.e, Text $^ +$ Audio, Text+Image, and Video+Audio), CoDi is capable of generating assorted combinations of modalities simultaneously that are unseen in training, e.g., joint image-text-audio generation in Fig. 5. ",
"bbox": [
173,
882,
821,
911
],
"page_idx": 4
},
{
"type": "table",
"img_path": "images/a1227e1b3cb165e5b75d57d2176ab2a10da01b03a47cc44ccf5819cbf35e61b5.jpg",
"table_caption": [
"Table 1: Training tasks (CT stands for “contrastive learning” to align prompt encoders) and datasets with corresponding statistics. \\* denotes the number of accessible examples in the original datasets. "
],
"table_footnote": [],
"table_body": "<table><tr><td>Categories</td><td>Tasks</td><td>Datasets</td><td># of samples</td><td>Domain</td></tr><tr><td>Image + Text</td><td>Image-→Text,Text-→Image Text-→Image+Text</td><td>Laion400M [42]</td><td>400M</td><td>Open</td></tr><tr><td>Audio + Text</td><td>Text→Audio,Audio-→Text, Text-→Audio+Text,Audio-Text CT</td><td>AudioSet [16] AudioCaps [24] Freesound 500K BBC Sound Effect</td><td>900K* 46K 2.5M 30K</td><td>YouTube YouTube Public audio samples Authentic natural sound</td></tr><tr><td>Audiovisual</td><td>Image→Audio,Image→Video+Audio</td><td>AudioSet SoundNet [3]</td><td>900K* 1.0M*</td><td>YouTube Flickr, natural sound</td></tr><tr><td>Video</td><td>Text-→Video,Image→Video, Video-Text CT</td><td>Webvid10M[4] HD-Villa-100M [54]</td><td>10.7M 100M</td><td>Short videos YouTube</td></tr></table>",
"bbox": [
174,
122,
821,
276
],
"page_idx": 5
},
{
"type": "image",
"img_path": "images/493f24169a89b5450efae4d85c3805ebc13a132fcf5e28a3a488cbd564530264.jpg",
"image_caption": [
"Figure 3: Single-to-single modality generation. Clockwise from top left: text image, image text, image video, audio image. "
],
"image_footnote": [],
"bbox": [
174,
291,
821,
493
],
"page_idx": 5
},
{
"type": "text",
"text": "",
"bbox": [
174,
561,
825,
660
],
"page_idx": 5
},
{
"type": "text",
"text": "4 Experiments ",
"text_level": 1,
"bbox": [
174,
680,
312,
696
],
"page_idx": 5
},
{
"type": "text",
"text": "4.1 Training Objectives and Datasets ",
"text_level": 1,
"bbox": [
176,
712,
442,
727
],
"page_idx": 5
},
{
"type": "text",
"text": "We list training tasks of CoDi in Table 1, including single modality synthesis, joint multimodal generation, and contrastive learning to align prompt encoders. Table 1 provides an overview of the datasets, tasks, number of samples, and domain. Datasets are from the following domains: image $^ +$ text (e.g. image with caption), audio $^ +$ text (e.g. audio with description), audio $^ +$ video (e.g. video with sound), and video $^ +$ text (e.g. video with description). As one may have noticed, the language modality appears in most datasets and domains. This echos the idea of using text as the bridge modality to be able to extrapolate and generate new unseen combinations such as audio and image bridged by text, as mentioned in Section 3.2 and Section 3.4. Due to space limit, more details on training datasets and can be found in Appendix C, model architecture details in Appendix Appendix A.1, and training details in Appendix B. ",
"bbox": [
173,
738,
825,
877
],
"page_idx": 5
},
{
"type": "text",
"text": "Image $^ +$ Text. We use a recently developed large-scale image caption dataset, Laion400M [42]. This image-text paired data allows us to train with tasks text image, image text, and the joint generation of image and text. For the joint generation task, we propose to train with text image+text, where the prompt text is the truncated image caption, and the output text is the original caption. Since the condition information is incomplete, the text and image diffuser will need to learn to attend with each other through the joint generation process. ",
"bbox": [
173,
883,
823,
911
],
"page_idx": 5
},
{
"type": "table",
"img_path": "images/f0713a5e3868134fdb21d162b330910771c9fe92eb4e6a85f24933ece1e1781b.jpg",
"table_caption": [
"Table 2: COCO-caption [32] FID scores for text-to-image generation. "
],
"table_footnote": [],
"table_body": "<table><tr><td>Method</td><td>FID↓</td></tr><tr><td>CogView [10]</td><td>27.10</td></tr><tr><td>GLIDE [36]</td><td>12.24</td></tr><tr><td>Make-a-Scene [15]</td><td>11.84</td></tr><tr><td>LDM [41]</td><td>12.63</td></tr><tr><td>Stable Diffusion-1.4</td><td>11.21</td></tr><tr><td>Stable Diffusion-1.5</td><td>11.12</td></tr><tr><td>Versatile Diffusion [53]</td><td>11.10</td></tr><tr><td>CoDi (Ours)</td><td>11.26</td></tr></table>",
"bbox": [
178,
137,
370,
257
],
"page_idx": 6
},
{
"type": "table",
"img_path": "images/092e9968c4819972025aaf57bfda705e16f8eed1cfa742fa9dda77afc438a0d5.jpg",
"table_caption": [
"Table 3: MSR-VTT text-to-video Table 4: UCF-101 text-to-video generation performance. generation performance. "
],
"table_footnote": [],
"table_body": "<table><tr><td>Method</td><td>Zero-Shot</td><td>CLIPSIM ↑</td></tr><tr><td>GODIVA [50]</td><td>No</td><td>0.2402</td></tr><tr><td>NUWA [51]</td><td>No</td><td>0.2439</td></tr><tr><td>CogVideo [22]</td><td>Yes</td><td>0.2631</td></tr><tr><td>Make-A-Video [44]</td><td>Yes</td><td>0.3049</td></tr><tr><td>Video LDM[5]</td><td>Yes</td><td>0.2929</td></tr><tr><td>CoDi(Ours)</td><td>Yes</td><td>0.2890</td></tr></table>",
"bbox": [
390,
152,
609,
231
],
"page_idx": 6
},
{
"type": "table",
"img_path": "images/89d31c9fff04f8286fa349bee00035aa85492d934c65c9e6e455e2a373e24edf.jpg",
"table_caption": [],
"table_footnote": [],
"table_body": "<table><tr><td>Method</td><td>IS(1)</td><td>FVD (↑)</td></tr><tr><td>Cog Video (Chinese)</td><td>23.55</td><td>751.34</td></tr><tr><td>CogVideo (English)</td><td>25.27</td><td>701.59</td></tr><tr><td>Make-A-Video</td><td>33.00</td><td>367.23</td></tr><tr><td>Video LDM</td><td>33.45</td><td>550.61</td></tr><tr><td>CoDi(Ours)</td><td>32.88</td><td>596.34</td></tr></table>",
"bbox": [
617,
154,
821,
229
],
"page_idx": 6
},
{
"type": "table",
"img_path": "images/6cdd7e474acf79309e07b53bb583c906924dd31228adbed791c23115f83c02ab.jpg",
"table_caption": [
"Table 5: The comparison between our audio diffuser and baseline TTA generation models. Evaluation is conducted on AudioCaps test set. AS, AC, FSD, BBC, and SDN stand for AudioSet, AudioCaps, Freesound, BBC Sound Effect, and Soundnet. "
],
"table_footnote": [],
"table_body": "<table><tr><td>Model</td><td>Datasets</td><td>FD↓</td><td>IS个</td><td>KL←</td><td>FAD↓</td><td>OVL ↑</td><td>REL个</td></tr><tr><td>Ground truth</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>83.61</td><td>80.11</td></tr><tr><td>DiffSound</td><td>AS+AC</td><td>47.68</td><td>4.01</td><td>2.52</td><td>7.75</td><td>45.00</td><td>43.83</td></tr><tr><td>AudioGen</td><td>AS +AC+8others</td><td>=</td><td>-</td><td>2.09</td><td>3.13</td><td>-</td><td>=</td></tr><tr><td>AudioLDM-L-Full</td><td>AS+AC+FSD+BBC</td><td>23.31</td><td>8.13</td><td>1.59</td><td>1.96</td><td>65.91</td><td>65.97</td></tr><tr><td>CoDi(Ours)</td><td>AS+AC+FSD+BBC+SDN</td><td>22.90</td><td>8.77</td><td>1.40</td><td>1.80</td><td>66.87</td><td>67.60</td></tr></table>",
"bbox": [
205,
316,
790,
401
],
"page_idx": 6
},
{
"type": "table",
"img_path": "images/3b08dd3a07ff80c69c148aa0491f160ff27946a1af6b4a57632f75d5ff7af62b.jpg",
"table_caption": [
"Table 6: COCO image captioning scores comparison. "
],
"table_footnote": [],
"table_body": "<table><tr><td>Model</td><td>B@4</td><td>METEOR</td><td>CIDEr</td></tr><tr><td colspan=\"4\">Autoregressive Model</td></tr><tr><td>Oscar [31]</td><td>36.58</td><td>30.4</td><td>124.12</td></tr><tr><td>ClipCap [35]</td><td>32.15</td><td>27.1</td><td>108.35</td></tr><tr><td>OFA [49]</td><td>44.9</td><td>32.5</td><td>154.9</td></tr><tr><td>BLIP2 [30]</td><td>43.7</td><td>-</td><td>145.8</td></tr><tr><td colspan=\"4\">Diffusion Model</td></tr><tr><td>DDCap [59]</td><td>35.0</td><td>28.2</td><td>117.8</td></tr><tr><td>SCD-Net [34]</td><td>39.4</td><td>29.2</td><td>131.6</td></tr><tr><td>CoDi (Ours)</td><td>40.2</td><td>31.0</td><td>149.9</td></tr></table>",
"bbox": [
176,
449,
377,
554
],
"page_idx": 6
},
{
"type": "table",
"img_path": "images/d48a4e4d621172d01f3a4f8bc34da6f60c2863153ae2635e211633ead5c366e1.jpg",
"table_caption": [
"Table 7: AudioCaps audio captioning scores comparison. "
],
"table_footnote": [],
"table_body": "<table><tr><td>Model</td><td>SPIDEr</td><td>CIDEr</td><td>SPICE</td></tr><tr><td>AudioCaps [24]</td><td>0.369</td><td>0.593</td><td>0.144</td></tr><tr><td>BART-Finetune [17]</td><td>0.465</td><td>0.753</td><td>0.176</td></tr><tr><td>VALOR[7]</td><td></td><td>0.741</td><td></td></tr><tr><td>AL-MixGen [25]</td><td>0.466</td><td>0.755</td><td>0.177</td></tr><tr><td>CoDi (Ours)</td><td>0.480</td><td>0.789</td><td>0.182</td></tr></table>",
"bbox": [
390,
472,
598,
534
],
"page_idx": 6
},
{
"type": "table",
"img_path": "images/555831ba6bc0c59839fc917171599939764c30018cb4c379929213d7ca95eaf8.jpg",
"table_caption": [
"Table 8: MSRVTT video captioning scores comparison. "
],
"table_footnote": [],
"table_body": "<table><tr><td>Model</td><td>B@4</td><td>METEOR</td><td>CIDEr</td></tr><tr><td>ORG-TRL[58]</td><td>43.6</td><td>28.8</td><td>50.9</td></tr><tr><td>MV-GPT[43]</td><td>48.9</td><td>38.7</td><td>60.0</td></tr><tr><td>GIT[48]</td><td>54.8</td><td>33.1</td><td>75.9</td></tr><tr><td>mPLUG-2 [52]</td><td>57.8</td><td>34.9</td><td>80.3</td></tr><tr><td>CoDi(Ours)</td><td>52.1</td><td>32.5</td><td>74.4</td></tr></table>",
"bbox": [
611,
470,
808,
534
],
"page_idx": 6
},
{
"type": "text",
"text": "",
"bbox": [
174,
587,
825,
642
],
"page_idx": 6
},
{
"type": "text",
"text": "Audio $^ +$ Text. We curated a new dataset, Freesound 500K, by crawling 500K audio samples together with tags and descriptions from the Freesound website. We also use AudioSet [42] with 2 million human-labeled 10-second sound clips from YouTube videos and AudioCaps [24] with 46K audiotext pairs derived from the AudioSet dataset. Audio samples are clipped into 10-second segments for training purposes. The paired audio $^ +$ text data enables us to train text audio, audio text, text audio $^ +$ text generation, and audio-text contrastive learning. Similar to image $^ +$ text joint generation, in text audio $^ +$ text, text prompt is the truncated text, and the output is the original text. ",
"bbox": [
173,
648,
825,
746
],
"page_idx": 6
},
{
"type": "text",
"text": "Video. We use the following diverse and high-quality video datasets to train video generation and video prompt encoder. WebVid [4], a large-scale dataset of web videos together with descriptions; HD-Villa-100M [54] with high resolution YouTube videos of at least 720P. We perform text video and video-text contrastive learning task with WebVid. We use HD-Villa-100M for image video generation where the middle frame is the input image. ",
"bbox": [
174,
752,
825,
821
],
"page_idx": 6
},
{
"type": "text",
"text": "Audiovisual. Web videos are a natural aligned audio-video data resource. However, many existing datasets, e.g., ACAV100M [28], feature heavily on videos of human speech rather than natural sounds. Therefore, we leverage sound-oriented datasets AudioSet and SoundNet [3] for joint audio-video generation. For image audio $^ +$ video, we use the middle frame of the target video as the input prompt image. We also use the middle frame as the prompt input to train the model to generate the audio, i.e., image audio. ",
"bbox": [
174,
828,
825,
911
],
"page_idx": 6
},
{
"type": "image",
"img_path": "images/dc63eb6c668a08077a3c76c77a471ccad92d8b97c381a63765856efb970b42b0.jpg",
"image_caption": [
"Figure 4: Generation with multiple input modality conditions. Top to bottom: text+audio image, text+audio video, video+audio text. "
],
"image_footnote": [],
"bbox": [
174,
89,
823,
468
],
"page_idx": 7
},
{
"type": "text",
"text": "5 Evaluation Results ",
"text_level": 1,
"bbox": [
174,
531,
362,
549
],
"page_idx": 7
},
{
"type": "text",
"text": "In this section, we will evaluate the model generation quality in different settings including single modality generation, multi-condition generation, and multi-output joint generation. We provide both quantitative benchmarking on evaluation datasets as well as qualitative visualization demonstrations. ",
"bbox": [
176,
564,
825,
607
],
"page_idx": 7
},
{
"type": "text",
"text": "5.1 Single Modality Generation Results ",
"text_level": 1,
"bbox": [
176,
625,
460,
640
],
"page_idx": 7
},
{
"type": "text",
"text": "We first show example demo in Fig. 3, where we present various single to single modality generation. Then, we evaluate the synthesis quality of the unimodal generation on text, image, video, and audio. CoDi achieves SOTA on audio captions and audio generation, as shown in Table 7 and Table 5. Notably for the first time in the field, CoDi, a diffusion-base model, exhibits comparable performance on image captioning with autoregressive transformer-based SOTA (Table 6). CoDi is the first diffusion-model based for video captioning Table 8. On image and video generation, CoDi performs competitively with state-of-the-art (Tables 2 to 4). This gives us strong starting points for multi-condition and multi-output generation that will be presented next in Section 5.2 and Section 5.3. ",
"bbox": [
173,
651,
825,
762
],
"page_idx": 7
},
{
"type": "text",
"text": "We demonstrate in Section 3.2 that CoDi is capable of integrating representation from different modalities in the generation. Thus, we first show multi-condition generation demo as shown in Fig. 4. ",
"bbox": [
173,
768,
823,
796
],
"page_idx": 7
},
{
"type": "text",
"text": "5.2 Multi-Condition Generation Results ",
"text_level": 1,
"bbox": [
176,
814,
464,
830
],
"page_idx": 7
},
{
"type": "text",
"text": "For quantitative evaluation, we focus on multiple inputs to image synthesis output since the evaluation metric for this case (FID) does not require specific modality inputs like text. We test with several input combinations including text $^ +$ image, text $^ +$ audio, image $^ +$ audio, text $^ +$ video, as well as three inputs text $^ +$ audio $^ +$ image. We test on the validation set of AudioCaps [24] since all four modalities are present in this dataset. The prompt image input is the middle frame of the video. As shown in ",
"bbox": [
174,
842,
823,
911
],
"page_idx": 7
},
{
"type": "table",
"img_path": "images/e4534d7ebcfba76448956e498aa36d45e0b4f106882850134d22e24e5043a3c9.jpg",
"table_caption": [
"Table 9: CoDi is capable of generating high quality output (image in this case) from various combinations of prompt modalities. "
],
"table_footnote": [],
"table_body": "<table><tr><td>Inputs</td><td>FID↓</td></tr><tr><td> Single-modality Prompt</td><td></td></tr><tr><td>Text</td><td>14.2</td></tr><tr><td>Audio</td><td>14.3</td></tr><tr><td> Dual-modality Prompt</td><td></td></tr><tr><td>Text+Audio</td><td>14.9</td></tr></table>",
"bbox": [
236,
137,
441,
227
],
"page_idx": 8
},
{
"type": "table",
"img_path": "images/5dac823453343eca2eec8a73d3324d6d6bf7776e7f743d995f77fa1083127e85.jpg",
"table_caption": [
"Table 10: MSR-VTT text-to-video generation performance. "
],
"table_footnote": [],
"table_body": "<table><tr><td>Inputs</td><td>CLIPSIM个</td></tr><tr><td> Single-modality Prompt</td><td></td></tr><tr><td>Text</td><td>0.2890</td></tr><tr><td> Dual-modality Prompt</td><td></td></tr><tr><td>Text+Audio</td><td>0.2912</td></tr><tr><td>Text+Image</td><td>0.2891</td></tr><tr><td>Text+Audio+Image</td><td>0.2923</td></tr></table>",
"bbox": [
562,
122,
805,
227
],
"page_idx": 8
},
{
"type": "image",
"img_path": "images/570523fa62e48124b6e7ee0927d72eb815a36ea17ca483b39355fc5b79c4dd8d.jpg",
"image_caption": [
"Figure 5: Joint generation of multiple output modalities by CoDi. From top to bottom: text video+audio, tex image+text+audio, text+audio+image video+audio. "
],
"image_footnote": [],
"bbox": [
178,
253,
818,
597
],
"page_idx": 8
},
{
"type": "text",
"text": "Table 9, CoDi achieves high image generation quality given assorted groups of input modalities. We also test with several input combinations with video as output including text, text $^ +$ audio, image $^ +$ image, as well as text $^ +$ audio $^ +$ image. We also test on MSRVTT [24] since all four modalities are present in this dataset. Similarly, the prompt image input is the middle frame of the video. As shown in Table 10, CoDi achieves high video and ground truth text similarity given assorted groups of input modalities. Again our model does not need to train on multi-condition generation like text $^ +$ audio or text $^ +$ image. Through bridging alignment and composable multimodal conditioning as proposed in Section 3.2, our model trained on single condition can zero-shot infer on multiple conditions. ",
"bbox": [
173,
662,
825,
775
],
"page_idx": 8
},
{
"type": "text",
"text": "5.3 Multi-Output Joint Generation Results ",
"text_level": 1,
"bbox": [
174,
791,
485,
808
],
"page_idx": 8
},
{
"type": "text",
"text": "For joint multimodal generation, we first demonstrate high-quality multimodal output joint generation demo as shown in Fig. 5. For quantitative evaluation, there is no existing evaluation metric since we are the first model that can simultaneously generate across all 4 modalities. Therefore, we propose the following metric SIM that quantifies the coherence and consistency between the two generated modalities by cosine similarity of embeddings: ",
"bbox": [
174,
818,
825,
887
],
"page_idx": 8
},
{
"type": "equation",
"img_path": "images/442991e83372788db1166c87e1f118b81b1449278a900571ed564c3772d81cd4.jpg",
"text": "$$\n\\operatorname { S I M } ( A , B ) = \\cos { ( C _ { A } ( A ) , C _ { B } ( B ) ) }\n$$",
"text_format": "latex",
"bbox": [
379,
895,
619,
912
],
"page_idx": 8
},
{
"type": "table",
"img_path": "images/7c6e18d97f95273627bd5cdb16fd3a210522268a86a54bd9275114265361cbb0.jpg",
"table_caption": [
"Table 11: Similarity scores between generated modalities. The number on the left of $\" / \"$ represents the similarity score of independent generation, and the right it represents the case of joint generation. Jointly generated outputs consistently show stronger coherence. "
],
"table_footnote": [],
"table_body": "<table><tr><td>Inputs</td><td>SIM-IT</td><td>SIM-AT</td><td>SIM-VT</td><td>SIM-VA</td></tr><tr><td colspan=\"5\"> Two Joint Outputs</td></tr><tr><td>Audio → Image+Text</td><td>0.251 / 0.260</td><td></td><td></td><td></td></tr><tr><td>Image→Audio+Text</td><td>■</td><td>0.244 / 0.256</td><td></td><td></td></tr><tr><td>Text →Video+Audio</td><td></td><td></td><td></td><td>0.240 / 0.255</td></tr><tr><td>Audio →Video+Text</td><td></td><td></td><td>0.256 / 0.261</td><td></td></tr><tr><td colspan=\"5\"> Three Joint Outputs</td></tr><tr><td>Text-→ Video+Image+Audio 0.256/0.270 0.240/0.257</td><td></td><td></td><td></td><td>0.240 / 0.257</td></tr><tr><td colspan=\"5\"> Multi-Inputs-Outputs</td></tr><tr><td>Text+Image -→ Video+Audio</td><td></td><td></td><td></td><td>0.247 / 0.259</td></tr></table>",
"bbox": [
178,
136,
816,
305
],
"page_idx": 9
},
{
"type": "text",
"text": "where $A$ , $B$ are the generated modalities, and $C _ { A }$ and $C _ { B }$ are aligned encoders that project $A$ and $B$ to the same space. We use the prompt encoder as described in Section 3.2. This metric aims to compute the cosine similarity of the embedding of two modalities using contrastive learned prompt encoders. Thus, the higher the metric, the more aligned and similar the generated modalities are. ",
"bbox": [
174,
332,
825,
388
],
"page_idx": 9
},
{
"type": "text",
"text": "To demonstrate the effectiveness of joint generation, assume the prompt modality is $P$ , we compare $\\mathrm { S I M } ( A , B )$ of $A$ and $B$ generated separately vs. jointly, i.e., $\\{ P \\ { \\overset { - } { \\to } } \\ A , \\ P \\ { \\overset { - } { \\to } } \\ B \\}$ vs. $\\{ P $ $A + B \\}$ . The benchmark is the validation set of AudioCaps [24]. We test on the following settings, audio image+text, image audio+text, and text video+audio, image video+audio. audio video+text, audio text+video+image, text video+image+audio, where the image prompt is the middle frame of the video clip. As shown in Table 11, joint generation (similarity shown on the right side of $\" / \"$ ) consistently outperforms independent generation (on the left side of $\" / \"$ ). ",
"bbox": [
174,
395,
825,
492
],
"page_idx": 9
},
{
"type": "text",
"text": "6 Conclusion ",
"text_level": 1,
"bbox": [
174,
513,
299,
530
],
"page_idx": 9
},
{
"type": "text",
"text": "In this paper, we present Composable Diffusion (CoDi), a groundbreaking model in multimodal generation that is capable of processing and simultaneously generating modalities across text, image, video, and audio. Our approach enables the synergistic generation of high-quality and coherent outputs spanning various modalities, from assorted combinations of input modalities. Through extensive experiments, we demonstrate CoDi’s remarkable capabilities in flexibly generating single or multiple modalities from a wide range of inputs. Our work marks a significant step towards more engaging and holistic human-computer interactions, establishing a solid foundation for future investigations in generative artificial intelligence. ",
"bbox": [
174,
546,
825,
657
],
"page_idx": 9
},
{
"type": "text",
"text": "Limitations & Broader Impacts. See Appendix D for the discussion. ",
"bbox": [
174,
664,
635,
679
],
"page_idx": 9
},
{
"type": "text",
"text": "Acknowledgement ",
"text_level": 1,
"bbox": [
176,
700,
330,
717
],
"page_idx": 9
},
{
"type": "text",
"text": "We would like to thank Bei Liu for HD-VILA-100M data support. We also thank Shi Dong, Mahmoud Khademi, Junheng Hao, Yuwei Fang, Yichong Xu and Azure Cognitive Services Research team members for their feedback. ",
"bbox": [
176,
733,
825,
775
],
"page_idx": 9
},
{
"type": "text",
"text": "References ",
"text_level": 1,
"bbox": [
174,
796,
266,
811
],
"page_idx": 9
},
{
"type": "text",
"text": "[1] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning. Advances in Neural Information Processing Systems, 35:23716–23736, 2022. 3 \n[2] Jie An, Songyang Zhang, Harry Yang, Sonal Gupta, Jia-Bin Huang, Jiebo Luo, and Xi Yin. Latent-shift: Latent diffusion with temporal shift for efficient text-to-video generation. arXiv preprint arXiv:2304.08477, 2023. 5, 15, 16 ",
"bbox": [
178,
819,
826,
911
],
"page_idx": 9
},
{
"type": "text",
"text": "[3] Yusuf Aytar, Carl Vondrick, and Antonio Torralba. Soundnet: Learning sound representations from unlabeled video. Advances in neural information processing systems, 29, 2016. 6, 7 ",
"bbox": [
178,
92,
823,
118
],
"page_idx": 10
},
{
"type": "text",
"text": "[4] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1728–1738, 2021. 6, 7, 16 ",
"bbox": [
179,
128,
820,
167
],
"page_idx": 10
},
{
"type": "text",
"text": "[5] Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. arXiv preprint arXiv:2304.08818, 2023. 7 ",
"bbox": [
178,
176,
821,
215
],
"page_idx": 10
},
{
"type": "text",
"text": "[6] Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712, 2023. 2 ",
"bbox": [
178,
226,
823,
265
],
"page_idx": 10
},
{
"type": "text",
"text": "[7] Sihan Chen, Xingjian He, Longteng Guo, Xinxin Zhu, Weining Wang, Jinhui Tang, and Jing Liu. Valor: Vision-audio-language omni-perception pretraining model and dataset. arXiv preprint arXiv:2304.08345, 2023. 7 ",
"bbox": [
178,
275,
823,
314
],
"page_idx": 10
},
{
"type": "text",
"text": "[8] Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal. Unifying vision-and-language tasks via text generation. In International Conference on Machine Learning, pages 1931–1942. PMLR, 2021. 3 ",
"bbox": [
178,
323,
821,
351
],
"page_idx": 10
},
{
"type": "text",
"text": "[9] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 5 ",
"bbox": [
178,
359,
823,
387
],
"page_idx": 10
},
{
"type": "text",
"text": "[10] Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, and Jie Tang. Cogview: Mastering text-to-image generation via transformers. arXiv preprint arXiv:2105.13290, 2021. 7 ",
"bbox": [
173,
395,
823,
435
],
"page_idx": 10
},
{
"type": "text",
"text": "[11] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10687–10696, 2021. 3 ",
"bbox": [
173,
445,
821,
497
],
"page_idx": 10
},
{
"type": "text",
"text": "[12] Benjamin Elizalde, Soham Deshmukh, Mahmoud Al Ismail, and Huaming Wang. Clap: Learning audio concepts from natural language supervision. arXiv preprint arXiv:2206.04769, 2022. 3 ",
"bbox": [
173,
506,
823,
534
],
"page_idx": 10
},
{
"type": "text",
"text": "[13] Patrick Esser, Johnathan Chiu, Parmida Atighehchian, Jonathan Granskog, and Anastasis Germanidis. Structure and content-guided video synthesis with diffusion models. arXiv preprint arXiv:2302.03011, 2023. 2, 5 ",
"bbox": [
173,
542,
825,
582
],
"page_idx": 10
},
{
"type": "text",
"text": "[14] Patrick Esser, Johnathan Chiu, Parmida Atighehchian, Jonathan Granskog, and Anastasis Germanidis. Structure and content-guided video synthesis with diffusion models. arXiv preprint arXiv:2302.03011, 2023. 3 ",
"bbox": [
171,
592,
825,
631
],
"page_idx": 10
},
{
"type": "text",
"text": "[15] Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman. Make-a-scene: Scene-based text-to-image generation with human priors. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XV, pages 89–106. Springer, 2022. 7 [16] Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter. Audio set: An ontology and human-labeled dataset for audio events. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 776–780. IEEE, 2017. 6 ",
"bbox": [
171,
640,
823,
680
],
"page_idx": 10
},
{
"type": "text",
"text": "",
"bbox": [
173,
689,
828,
741
],
"page_idx": 10
},
{
"type": "text",
"text": "[17] Félix Gontier, Romain Serizel, and Christophe Cerisara. Automated audio captioning by fine-tuning bart with audioset tags. In Detection and Classification of Acoustic Scenes and Events-DCASE 2021, 2021. 7 [18] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. 15 ",
"bbox": [
171,
751,
823,
777
],
"page_idx": 10
},
{
"type": "text",
"text": "",
"bbox": [
173,
786,
825,
825
],
"page_idx": 10
},
{
"type": "text",
"text": "[19] Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303, 2022. 2, 3 ",
"bbox": [
173,
835,
823,
876
],
"page_idx": 10
},
{
"type": "text",
"text": "[20] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840–6851, 2020. 3, 4 ",
"bbox": [
169,
885,
825,
911
],
"page_idx": 10
},
{
"type": "text",
"text": "[21] Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. arXiv preprint arXiv:2204.03458, 2022. 3, 15 ",
"bbox": [
171,
92,
825,
118
],
"page_idx": 11
},
{
"type": "text",
"text": "[22] Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, and Jie Tang. Cogvideo: Large-scale pretraining for text-to-video generation via transformers. arXiv preprint arXiv:2205.15868, 2022. 2, 7 ",
"bbox": [
173,
127,
821,
155
],
"page_idx": 11
},
{
"type": "text",
"text": "[23] Rongjie Huang, Jiawei Huang, Dongchao Yang, Yi Ren, Luping Liu, Mingze Li, Zhenhui Ye, Jinglin Liu, Xiang Yin, and Zhou Zhao. Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models. arXiv preprint arXiv:2301.12661, 2023. 2, 3 ",
"bbox": [
174,
162,
821,
202
],
"page_idx": 11
},
{
"type": "text",
"text": "[24] Chris Dongjoo Kim, Byeongchang Kim, Hyunmin Lee, and Gunhee Kim. Audiocaps: Generating captions for audios in the wild. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 119–132, 2019. 6, 7, 8, 9, 10 ",
"bbox": [
173,
210,
825,
262
],
"page_idx": 11
},
{
"type": "text",
"text": "[25] Eungbeom Kim, Jinhee Kim, Yoori Oh, Kyungsu Kim, Minju Park, Jaeheon Sim, Jinwoo Lee, and Kyogu Lee. Improving audio-language learning with mixgen and multi-level test-time augmentation. arXiv preprint arXiv:2210.17143, 2022. 7 ",
"bbox": [
171,
272,
823,
310
],
"page_idx": 11
},
{
"type": "text",
"text": "[26] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 15 ",
"bbox": [
173,
319,
823,
345
],
"page_idx": 11
},
{
"type": "text",
"text": "[27] Jungil Kong, Jaehyeon Kim, and Jaekyoung Bae. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. Advances in Neural Information Processing Systems, 33:17022–17033, 2020. 5 ",
"bbox": [
174,
356,
825,
393
],
"page_idx": 11
},
{
"type": "text",
"text": "[28] Sangho Lee, Jiwan Chung, Youngjae Yu, Gunhee Kim, Thomas Breuel, Gal Chechik, and Yale Song. Acav $1 0 0 \\mathrm { m }$ : Automatic curation of large-scale datasets for audio-visual video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10274–10284, 2021. 7 [29] Chunyuan Li, Xiang Gao, Yuan Li, Baolin Peng, Xiujun Li, Yizhe Zhang, and Jianfeng Gao. Optimus: Organizing sentences via pre-trained modeling of a latent space. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4678–4699, 2020. 5 ",
"bbox": [
173,
404,
823,
443
],
"page_idx": 11
},
{
"type": "text",
"text": "",
"bbox": [
173,
452,
825,
492
],
"page_idx": 11
},
{
"type": "text",
"text": "[30] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023. 7 ",
"bbox": [
171,
500,
825,
526
],
"page_idx": 11
},
{
"type": "text",
"text": "[31] Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al. Oscar: Object-semantics aligned pre-training for vision-language tasks. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXX 16, pages 121–137. Springer, 2020. 7 ",
"bbox": [
171,
535,
826,
587
],
"page_idx": 11
},
{
"type": "text",
"text": "[32] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740–755. Springer, 2014. 7 ",
"bbox": [
173,
595,
823,
647
],
"page_idx": 11
},
{
"type": "text",
"text": "[33] Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, and Mark D Plumbley. Audioldm: Text-to-audio generation with latent diffusion models. arXiv preprint arXiv:2301.12503, 2023. 2, 3, 5 ",
"bbox": [
174,
656,
823,
695
],
"page_idx": 11
},
{
"type": "text",
"text": "[34] Jianjie Luo, Yehao Li, Yingwei Pan, Ting Yao, Jianlin Feng, Hongyang Chao, and Tao Mei. Semanticconditional diffusion networks for image captioning. arXiv preprint arXiv:2212.03099, 2022. 7 ",
"bbox": [
171,
704,
823,
732
],
"page_idx": 11
},
{
"type": "text",
"text": "[35] Ron Mokady, Amir Hertz, and Amit H Bermano. Clipcap: Clip prefix for image captioning. arXiv preprint arXiv:2111.09734, 2021. 3, 7 ",
"bbox": [
173,
741,
823,
767
],
"page_idx": 11
},
{
"type": "text",
"text": "[36] Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741, 2021. 7 ",
"bbox": [
173,
776,
823,
815
],
"page_idx": 11
},
{
"type": "text",
"text": "[37] Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng. Automatic prompt optimization with\" gradient descent\" and beam search. arXiv preprint arXiv:2305.03495, 2023. 2 ",
"bbox": [
171,
824,
823,
851
],
"page_idx": 11
},
{
"type": "text",
"text": "[38] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021. 3, 4 ",
"bbox": [
174,
859,
825,
911
],
"page_idx": 11
},
{
"type": "text",
"text": "[39] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 5 ",
"bbox": [
169,
92,
823,
118
],
"page_idx": 12
},
{
"type": "text",
"text": "[40] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022. 3 ",
"bbox": [
173,
127,
823,
154
],
"page_idx": 12
},
{
"type": "text",
"text": "[41] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684–10695, 2022. 2, 3, 4, 5, 7 ",
"bbox": [
173,
162,
821,
202
],
"page_idx": 12
},
{
"type": "text",
"text": "[42] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade W Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev. LAION-5b: An open large-scale dataset for training next generation image-text models. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022. 6, 7 ",
"bbox": [
173,
210,
825,
275
],
"page_idx": 12
},
{
"type": "text",
"text": "[43] Paul Hongsuck Seo, Arsha Nagrani, Anurag Arnab, and Cordelia Schmid. End-to-end generative pretraining for multimodal video captioning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17959–17968, 2022. 7 ",
"bbox": [
171,
284,
823,
323
],
"page_idx": 12
},
{
"type": "text",
"text": "[44] Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. arXiv preprint arXiv:2209.14792, 2022. 2, 3, 7 ",
"bbox": [
173,
332,
823,
371
],
"page_idx": 12
},
{
"type": "text",
"text": "[45] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256–2265. PMLR, 2015. 2, 4 ",
"bbox": [
174,
380,
821,
419
],
"page_idx": 12
},
{
"type": "text",
"text": "[46] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations, 2021. 3 ",
"bbox": [
173,
428,
821,
467
],
"page_idx": 12
},
{
"type": "text",
"text": "[47] Zineng Tang, Jaemin Cho, Yixin Nie, and Mohit Bansal. TVLT: Textless vision-language transformer. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022. 3 ",
"bbox": [
171,
474,
823,
515
],
"page_idx": 12
},
{
"type": "text",
"text": "[48] Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, and Lijuan Wang. Git: A generative image-to-text transformer for vision and language. arXiv preprint arXiv:2205.14100, 2022. 7 ",
"bbox": [
173,
523,
823,
563
],
"page_idx": 12
},
{
"type": "text",
"text": "[49] Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. Unifying architectures, tasks, and modalities through a simple sequence-tosequence learning framework. arXiv preprint arXiv:2202.03052, 2022. 7 ",
"bbox": [
171,
570,
823,
611
],
"page_idx": 12
},
{
"type": "text",
"text": "[50] Chenfei Wu, Lun Huang, Qianxi Zhang, Binyang Li, Lei Ji, Fan Yang, Guillermo Sapiro, and Nan Duan. Godiva: Generating open-domain videos from natural descriptions. arXiv preprint arXiv:2104.14806, 2021. 7 ",
"bbox": [
171,
619,
825,
659
],
"page_idx": 12
},
{
"type": "text",
"text": "[51] Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, and Nan Duan. Nüwa: Visual synthesis pre-training for neural visual world creation. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVI, pages 720–736. Springer, 2022. 7 ",
"bbox": [
173,
667,
828,
718
],
"page_idx": 12
},
{
"type": "text",
"text": "[52] Haiyang Xu, Qinghao Ye, Ming Yan, Yaya Shi, Jiabo Ye, Yuanhong Xu, Chenliang Li, Bin Bi, Qi Qian, Wei Wang, et al. mplug-2: A modularized multi-modal foundation model across text, image and video. arXiv preprint arXiv:2302.00402, 2023. 7 ",
"bbox": [
171,
728,
823,
767
],
"page_idx": 12
},
{
"type": "text",
"text": "[53] Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, and Humphrey Shi. Versatile diffusion: Text, images and variations all in one diffusion model. arXiv preprint arXiv:2211.08332, 2022. 5, 7 ",
"bbox": [
171,
776,
825,
803
],
"page_idx": 12
},
{
"type": "text",
"text": "[54] Hongwei Xue, Tiankai Hang, Yanhong Zeng, Yuchong Sun, Bei Liu, Huan Yang, Jianlong Fu, and Baining Guo. Advancing high-resolution video-language representation with large-scale video transcriptions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5036–5045, 2022. 6, 7, 16 ",
"bbox": [
173,
811,
825,
863
],
"page_idx": 12
},
{
"type": "text",
"text": "[55] Ziyi Yang, Yuwei Fang, Chenguang Zhu, Reid Pryzant, Dongdong Chen, Yu Shi, Yichong Xu, Yao Qian, Mei Gao, Yi-Ling Chen, et al. i-code: An integrative and composable multimodal learning framework. arXiv preprint arXiv:2205.01818, 2022. 2, 3 ",
"bbox": [
171,
872,
823,
911
],
"page_idx": 12
},
{
"type": "text",
"text": "[56] Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, and Yejin Choi. Merlot reserve: Neural script knowledge through vision and language and sound. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16375–16387, 2022. 3 \n[57] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34:23634–23651, 2021. 3 \n[58] Ziqi Zhang, Yaya Shi, Chunfeng Yuan, Bing Li, Peijin Wang, Weiming Hu, and Zheng-Jun Zha. Object relational graph with teacher-recommended learning for video captioning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13278–13288, 2020. 7 \n[59] Zixin Zhu, Yixuan Wei, Jianfeng Wang, Zhe Gan, Zheng Zhang, Le Wang, Gang Hua, Lijuan Wang, Zicheng Liu, and Han Hu. Exploring discrete diffusion models for image captioning. arXiv preprint arXiv:2211.11694, 2022. 7 ",
"bbox": [
171,
92,
826,
286
],
"page_idx": 13
},
{
"type": "text",
"text": "A Model Architecture and Configuration ",
"text_level": 1,
"bbox": [
174,
89,
532,
107
],
"page_idx": 14
},
{
"type": "text",
"text": "A.1 Overview ",
"text_level": 1,
"bbox": [
174,
122,
282,
137
],
"page_idx": 14
},
{
"type": "text",
"text": "In this section, we provide more details on the model architecture as shown in Table 12, where each modality specific diffuser is based on UNet architecture with different variations detailed in the table. Another notable difference is the video architecture where we add temporal attention and temporal shift as discussed in Section 3.3 and we will discuss its detail in the next section. ",
"bbox": [
174,
148,
825,
204
],
"page_idx": 14
},
{
"type": "table",
"img_path": "images/0f1eb37557b3404ed3d97b4e9270dbc9d4725f2fb42c6ba33e4d45037b6bd75d.jpg",
"table_caption": [
"Table 12: Hyperparameters for our diffusion models. Note the video and image generation uses the same diffuser. "
],
"table_footnote": [],
"table_body": "<table><tr><td>Modality</td><td>Video (Image) LDM</td><td>Audio LDM</td><td>Text LDM</td></tr><tr><td colspan=\"4\">Hyperparameter</td></tr><tr><td>Architecture</td><td>LDM</td><td>LDM</td><td>LDM</td></tr><tr><td>z-shape</td><td>4× #frames × 64× 64</td><td>8× 256×16</td><td>768×1×1</td></tr><tr><td>Channels</td><td>320</td><td>320</td><td>320</td></tr><tr><td>Depth</td><td>4</td><td>2</td><td>2</td></tr><tr><td>Channel multiplier</td><td>1,2,4,4</td><td>1,2,4,4</td><td>1,2,4,4</td></tr><tr><td>Attention resolutions</td><td>64,32,16</td><td>64,32,16</td><td>64,32,16</td></tr><tr><td>Head channels</td><td>32</td><td>32</td><td>32</td></tr><tr><td>Number of heads</td><td>8</td><td>8</td><td>8</td></tr><tr><td>CA embed dim</td><td>768</td><td>768</td><td>768</td></tr><tr><td>CA resolutions</td><td>64,32,16</td><td>64,32,16</td><td>64,32,16</td></tr><tr><td>Autoencoders</td><td>AutoKL</td><td>AudioLDM</td><td>Optimus</td></tr><tr><td>Weight initialization</td><td>Stable Diffusion-1.4</td><td>-</td><td>Versatile Diffusion</td></tr><tr><td>Parameterization</td><td>E</td><td>E</td><td>E</td></tr><tr><td>Learning rate</td><td>2e-5</td><td>5e-6</td><td>5e-5</td></tr><tr><td>Total batch size</td><td>256</td><td>1024</td><td>1024</td></tr><tr><td colspan=\"4\">Diffusion Setup</td></tr><tr><td>Diffusion steps</td><td>1000</td><td>1000</td><td>1000</td></tr><tr><td>Noise schedule</td><td>Linear</td><td>Linear</td><td>Linear</td></tr><tr><td>β</td><td>0.00085</td><td>0.00085</td><td>0.00085</td></tr><tr><td>阳</td><td>0.0120</td><td>0.0120</td><td>0.0120</td></tr><tr><td colspan=\"4\">Sampling Parameters</td></tr><tr><td>Sampler</td><td>DDIM</td><td>DDIM</td><td>DDIM</td></tr><tr><td>Steps</td><td>50</td><td>50</td><td>50</td></tr><tr><td>n</td><td>1.0</td><td>1.0</td><td>1.0</td></tr><tr><td>Guidance scale</td><td>2.0</td><td>7.5</td><td>2.0</td></tr></table>",
"bbox": [
220,
251,
769,
616
],
"page_idx": 14
},
{
"type": "text",
"text": "A.2 Video LDM Architecture ",
"text_level": 1,
"bbox": [
174,
648,
392,
662
],
"page_idx": 14
},
{
"type": "text",
"text": "Except for the base image UNet architecture, we also add temporal attention and temporal shift [2] before each residual block. Following VDM [21], the temporal attention is a transformer attention module where we flatten the height and width dimension to batch size dimension and the self-attention is performed on the time dimension. The temporal shift is illustrated in Fig. 6 where we first split channels into $k$ chunks. Then, we shift the channel dimension numbered 0 to $k - 1$ by temporal dimension from 0 to $k - 1$ times respectively. Eventually, we concatenate the shifted chunks by the hidden dimension. Note that we use $k = 3$ in the illustration for simplicity but $k = 8$ in our implementation. We then add a convolution layer before the temporal shift module. Finally, we use residual connection [18] and add the output to the input before the convolution layer. The complete video UNet layer is shown in Fig. 7. ",
"bbox": [
173,
674,
825,
814
],
"page_idx": 14
},
{
"type": "text",
"text": "B Model Training ",
"text_level": 1,
"bbox": [
174,
835,
339,
853
],
"page_idx": 14
},
{
"type": "text",
"text": "Prompt Encoders Training. As discussed in Section 3.2, we use bridging alignment to perform contrastive learning between all prompt encoders. We use Adam [26] optimizer with learning rate 1e-4 and weight decay 1e-4. ",
"bbox": [
174,
869,
823,
911
],
"page_idx": 14
},
{
"type": "image",
"img_path": "images/e11082636d4b4499d75ad6f7451413683dac74d3ebb613e652b9ea9f4c995784.jpg",
"image_caption": [
"Figure 6: Temporal shift [2] illustration. $C , H .$ , $W$ represent channel, height, width, respectively. The vertical line represents time steps from $t - 1 , t$ , and $t + 1$ . The grey blocks denote “padding tensors”. "
],
"image_footnote": [],
"bbox": [
222,
87,
772,
337
],
"page_idx": 15
},
{
"type": "image",
"img_path": "images/4409bc09bf832575915923690ca96fa4e7ccd04266897f8300b3a97df95ceeee.jpg",
"image_caption": [
"Figure 7: Video UNet layer architecture details including normalization & activation, 2D temporal attention, followed by temporal shift and 1D spatial convolution. "
],
"image_footnote": [],
"bbox": [
348,
388,
643,
500
],
"page_idx": 15
},
{
"type": "text",
"text": "Diffusion Model Training. We train diffusion model with training objectives and hyperparameters detailed in Table 1 and Table 12. For video LDM, we adopt a more specific training curriculum. We adopt curriculum learning on frame resolution and frames-per-second (FPS). First, the diffuser is trained on the WebVid dataset of a 256-frame resolution, with the training objective being textconditioned video generation. The training clips are sampled from 2-second video chunks with 4 FPS. Second, the model is further trained on HDVILLA and ACAV datasets, with a 512-frame resolution and 8 FPS, and the training objective is image-conditioned video generation (the image is a randomly sampled frame of the clip). Each training clip contains 16 frames sampled from a 2-second video chunk with 8 FPS. ",
"bbox": [
174,
564,
826,
690
],
"page_idx": 15
},
{
"type": "text",
"text": "Joint Generation Training. As discussed in Section 3.2, we train joint generation by aligning environment encoders and optimize cross-attention layers only in the diffusion models. We use Adam optimizer with learning rate 1e-5 and weight decay 1e-4. ",
"bbox": [
174,
705,
823,
747
],
"page_idx": 15
},
{
"type": "text",
"text": "C Training Datasets ",
"text_level": 1,
"bbox": [
174,
767,
357,
784
],
"page_idx": 15
},
{
"type": "text",
"text": "In this section, we introduce more details about the video and audiovisual training datasets. ",
"bbox": [
169,
797,
769,
813
],
"page_idx": 15
},
{
"type": "text",
"text": "Video. WebVid [4] is a large-scale dataset of web videos with diverse content, spanning over 40 categories such as sports, cooking, and travel. It contains over 1.2 million video clips (all without sound) that are all at least 30 seconds in duration with video descriptions. We perform text video and video-text contrastive learning task with this dataset. HD-Villa-100M [54] is a large-scale video dataset with over 100 million video clips sourced from YouTube. The dataset covers a wide range of video categories and includes high-quality videos with a resolution of at least 720P. Since it lacks curated video description and we use the middle frame as image input to perform image video generation. ",
"bbox": [
174,
827,
825,
911
],
"page_idx": 15
},
{
"type": "text",
"text": "",
"bbox": [
173,
90,
823,
119
],
"page_idx": 16
},
{
"type": "text",
"text": "Audiovisual. SoundNet originally contains over two million sounds and spans a wide range of categories including music, animal sounds, natural sounds, and environmental sounds. We collected all currently accessible 1M videos. ",
"bbox": [
174,
133,
825,
176
],
"page_idx": 16
},
{
"type": "text",
"text": "D Limitations & Broader Impacts ",
"text_level": 1,
"bbox": [
176,
195,
473,
213
],
"page_idx": 16
},
{
"type": "text",
"text": "While the paper primarily focuses on the technical advancements and potential applications of CoDi, we also consider potential negative social impacts that could arise from the development and deployment of such technology. These impacts can include: ",
"bbox": [
176,
227,
823,
268
],
"page_idx": 16
},
{
"type": "text",
"text": "Deepfakes and Misinformation. As part of a common issue for generative AI models, the ability of CoDi to generate realistic and synchronized multimodal outputs also raises concerns about the creation and dissemination of deepfakes. Malicious actors could exploit this technology to create highly convincing fake content, such as fabricated videos or audio clips, which can be used for misinformation, fraud, or other harmful purposes. ",
"bbox": [
174,
284,
825,
353
],
"page_idx": 16
},
{
"type": "text",
"text": "Bias and Stereotyping. If the training data used for CoDi is biased or contains stereotypes, the generated multimodal outputs may also reflect these. ",
"bbox": [
173,
367,
823,
396
],
"page_idx": 16
},
{
"type": "text",
"text": "E License ",
"text_level": 1,
"bbox": [
174,
415,
272,
433
],
"page_idx": 16
},
{
"type": "text",
"text": "We will publicly release our code and checkpoints. We cite licenses from the individual dataset or package we use from the community and provide the following links for references. ",
"bbox": [
176,
446,
823,
474
],
"page_idx": 16
},
{
"type": "text",
"text": "LAION-400M: Creative Common CC-BY 4.0 ",
"bbox": [
176,
481,
483,
494
],
"page_idx": 16
},
{
"type": "text",
"text": "AudioSet: Creative Common CC-BY 4.0 ",
"bbox": [
176,
502,
449,
516
],
"page_idx": 16
},
{
"type": "text",
"text": "AudioCaps: MIT ",
"bbox": [
174,
522,
294,
536
],
"page_idx": 16
},
{
"type": "text",
"text": "Freesound: Creative Commons ",
"bbox": [
174,
542,
387,
558
],
"page_idx": 16
},
{
"type": "text",
"text": "BBC Sound Effect: The BBC’s Content Licence ",
"bbox": [
176,
563,
500,
578
],
"page_idx": 16
},
{
"type": "text",
"text": "SoundNet: MIT ",
"bbox": [
174,
584,
285,
598
],
"page_idx": 16
},
{
"type": "text",
"text": "Webvid10M: Webvid ",
"bbox": [
174,
604,
320,
619
],
"page_idx": 16
},
{
"type": "text",
"text": "HD-Villa-100M: Research Use of Data Agreement v1.0 ",
"bbox": [
178,
626,
544,
640
],
"page_idx": 16
},
{
"type": "text",
"text": "PyTorch: BSD-style ",
"bbox": [
174,
646,
315,
661
],
"page_idx": 16
},
{
"type": "text",
"text": "Huggingface Transformers: Apache ",
"bbox": [
174,
667,
424,
683
],
"page_idx": 16
},
{
"type": "text",
"text": "Torchvision: BSD 3-Clause ",
"bbox": [
174,
688,
361,
702
],
"page_idx": 16
},
{
"type": "text",
"text": "Torchaudio: BSD 2-Clause ",
"bbox": [
174,
708,
361,
723
],
"page_idx": 16
}
] |