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Imagen Video produces diverse and temporally-", "type": "text" } ], "index": 28 }, { "bbox": [ 69, 732, 337, 747 ], "spans": [ { "bbox": [ 69, 732, 337, 747 ], "score": 1.0, "content": "coherent videos that are well-aligned with the given prompt.", "type": "text" } ], "index": 29 } ], "index": 28.5, "bbox_fs": [ 68, 720, 541, 747 ] } ] }, { "preproc_blocks": [ { "type": "text", "bbox": [ 70, 82, 540, 201 ], "lines": [ { "bbox": [ 70, 83, 541, 95 ], "spans": [ { "bbox": [ 70, 83, 541, 95 ], "score": 1.0, "content": "Our work aims to generate videos from text. 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Imagen Video scales from prior work of 64-frame 128", "type": "text" }, { "bbox": [ 516, 235, 524, 242 ], "score": 0.62, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 525, 231, 541, 244 ], "score": 1.0, "content": "128", "type": "text" } ], "index": 12 }, { "bbox": [ 70, 243, 541, 257 ], "spans": [ { "bbox": [ 70, 243, 281, 257 ], "score": 1.0, "content": "videos at 24 frames per second to 128 frame 1280", "type": "text" }, { "bbox": [ 282, 247, 290, 254 ], "score": 0.71, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 290, 243, 541, 257 ], "score": 1.0, "content": "768 high-definition video at 24 frames per second. Imagen", "type": "text" } ], "index": 13 }, { "bbox": [ 69, 254, 542, 269 ], "spans": [ { "bbox": [ 69, 254, 542, 269 ], "score": 1.0, "content": "Video has a simple architecture: The model consists of a frozen T5 text encoder (Raffel et al., 2020), a", "type": "text" } ], "index": 14 }, { "bbox": [ 69, 267, 540, 281 ], "spans": [ { "bbox": [ 69, 267, 540, 281 ], "score": 1.0, "content": "base video diffusion model, and interleaved spatial and temporal super-resolution diffusion models. Our key", "type": "text" } ], "index": 15 }, { "bbox": [ 70, 280, 195, 292 ], "spans": [ { "bbox": [ 70, 280, 195, 292 ], "score": 1.0, "content": "contributions are as follows:", "type": "text" } ], "index": 16 } ], "index": 13 }, { "type": "text", "bbox": [ 91, 306, 542, 452 ], "lines": [ { "bbox": [ 92, 304, 541, 320 ], "spans": [ { "bbox": [ 92, 304, 541, 320 ], "score": 1.0, "content": "1. We demonstrate the simplicity and effectiveness of cascaded diffusion video models for high definition", "type": "text" } ], "index": 17 }, { "bbox": [ 106, 319, 183, 330 ], "spans": [ { "bbox": [ 106, 319, 183, 330 ], "score": 1.0, "content": "video generation.", "type": "text" } ], "index": 18 }, { "bbox": [ 92, 337, 541, 351 ], "spans": [ { "bbox": [ 92, 337, 541, 351 ], "score": 1.0, "content": "2. We confirm that recent findings in the text-to-image setting transfer to video generation, such as", "type": "text" } ], "index": 19 }, { "bbox": [ 105, 350, 457, 363 ], "spans": [ { "bbox": [ 105, 350, 457, 363 ], "score": 1.0, "content": "the effectiveness of frozen encoder text conditioning and classifier-free guidance.", "type": "text" } ], "index": 20 }, { "bbox": [ 92, 370, 541, 384 ], "spans": [ { "bbox": [ 92, 370, 541, 384 ], "score": 1.0, "content": "3. 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We demonstrate qualitative controllability in Imagen Video, such as 3D object understanding, gen-", "type": "text" } ], "index": 25 }, { "bbox": [ 105, 439, 444, 452 ], "spans": [ { "bbox": [ 105, 439, 444, 452 ], "score": 1.0, "content": "eration of text animations, and generation of videos in various artistic styles.", "type": "text" } ], "index": 26 } ], "index": 21.5 }, { "type": "title", "bbox": [ 71, 466, 168, 480 ], "lines": [ { "bbox": [ 68, 464, 169, 484 ], "spans": [ { "bbox": [ 68, 464, 169, 484 ], "score": 1.0, "content": "2 Imagen Video", "type": "text" } ], "index": 27 } ], "index": 27 }, { "type": "text", "bbox": [ 71, 492, 540, 552 ], "lines": [ { "bbox": [ 70, 493, 541, 505 ], "spans": [ { "bbox": [ 70, 493, 541, 505 ], "score": 1.0, "content": "Our model, Imagen Video, is a cascade of video diffusion models (Ho et al., 2022a;b). It consists of 7", "type": "text" } ], "index": 28 }, { "bbox": [ 69, 504, 541, 518 ], "spans": [ { "bbox": [ 69, 504, 541, 518 ], "score": 1.0, "content": "sub-models which perform text-conditional video generation, spatial super-resolution, and temporal super-", "type": "text" } ], "index": 29 }, { "bbox": [ 70, 516, 541, 530 ], "spans": [ { "bbox": [ 70, 516, 436, 530 ], "score": 1.0, "content": "resolution. With the entire cascade, Imagen Video generates high definition 1280", "type": "text" }, { "bbox": [ 436, 520, 444, 527 ], "score": 0.5, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 444, 516, 497, 530 ], "score": 1.0, "content": "768 (width", "type": "text" }, { "bbox": [ 497, 520, 504, 527 ], "score": 0.74, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 505, 516, 541, 530 ], "score": 1.0, "content": "height)", "type": "text" } ], "index": 30 }, { "bbox": [ 70, 529, 541, 541 ], "spans": [ { "bbox": [ 70, 529, 291, 541 ], "score": 1.0, "content": "videos at 24 frames per second, for 128 frames (", "type": "text" }, { "bbox": [ 292, 532, 318, 538 ], "score": 0.58, "content": "\\approx 5 . 3", "type": "inline_equation" }, { "bbox": [ 318, 529, 541, 541 ], "score": 1.0, "content": "seconds)—approximately 126 million pixels. We", "type": "text" } ], "index": 31 }, { "bbox": [ 70, 541, 490, 554 ], "spans": [ { "bbox": [ 70, 541, 490, 554 ], "score": 1.0, "content": "describe the components and techniques that constitute Imagen Video in the following sections.", "type": "text" } ], "index": 32 } ], "index": 30 }, { "type": "title", "bbox": [ 72, 566, 173, 578 ], "lines": [ { "bbox": [ 69, 564, 174, 580 ], "spans": [ { "bbox": [ 69, 564, 174, 580 ], "score": 1.0, "content": "2.1 Diffusion models", "type": "text" } ], "index": 33 } ], "index": 33 }, { "type": "text", "bbox": [ 71, 587, 540, 648 ], "lines": [ { "bbox": [ 69, 587, 541, 601 ], "spans": [ { "bbox": [ 69, 587, 541, 601 ], "score": 1.0, "content": "Imagen Video is built from diffusion models (Sohl-Dickstein et al., 2015; Song & Ermon, 2019; Ho et al.,", "type": "text" } ], "index": 34 }, { "bbox": [ 69, 599, 542, 613 ], "spans": [ { "bbox": [ 69, 599, 250, 613 ], "score": 1.0, "content": "2020) specified in continuous time (Tzen", "type": "text" }, { "bbox": [ 250, 602, 258, 610 ], "score": 0.34, "content": "\\&", "type": "inline_equation" }, { "bbox": [ 258, 599, 542, 613 ], "score": 1.0, "content": "Raginsky, 2019; Song et al., 2021; Kingma et al., 2021). We use", "type": "text" } ], "index": 35 }, { "bbox": [ 69, 610, 540, 626 ], "spans": [ { "bbox": [ 69, 610, 460, 626 ], "score": 1.0, "content": "the formulation of Kingma et al. (2021): the model is a latent variable model with latents", "type": "text" }, { "bbox": [ 460, 614, 540, 624 ], "score": 0.93, "content": "\\mathbf { z } = \\{ \\mathbf { z } _ { t } | t \\in [ 0 , 1 ] \\}", "type": "inline_equation" } ], "index": 36 }, { "bbox": [ 69, 623, 541, 637 ], "spans": [ { "bbox": [ 69, 623, 191, 637 ], "score": 1.0, "content": "following a forward process", "type": "text" }, { "bbox": [ 191, 626, 218, 636 ], "score": 0.94, "content": "q ( \\mathbf { z } | \\mathbf { x } )", "type": "inline_equation" }, { "bbox": [ 218, 623, 291, 637 ], "score": 1.0, "content": "starting at data", "type": "text" }, { "bbox": [ 291, 626, 329, 636 ], "score": 0.94, "content": "\\mathbf { x } \\sim p ( \\mathbf { x } )", "type": "inline_equation" }, { "bbox": [ 330, 623, 541, 637 ], "score": 1.0, "content": ". The forward process is a Gaussian process that", "type": "text" } ], "index": 37 }, { "bbox": [ 69, 636, 218, 649 ], "spans": [ { "bbox": [ 69, 636, 218, 649 ], "score": 1.0, "content": "satisfies the Markovian structure:", "type": "text" } ], "index": 38 } ], "index": 36 }, { "type": "interline_equation", "bbox": [ 172, 657, 440, 672 ], "lines": [ { "bbox": [ 172, 657, 440, 672 ], "spans": [ { "bbox": [ 172, 657, 440, 672 ], "score": 0.86, "content": "\\begin{array} { r } { q ( \\mathbf { z } _ { t } | \\mathbf { x } ) = \\mathcal { N } ( \\mathbf { z } _ { t } ; \\alpha _ { t } \\mathbf { x } , \\sigma _ { t } ^ { 2 } \\mathbf { I } ) , \\quad q ( \\mathbf { z } _ { t } | \\mathbf { z } _ { s } ) = \\mathcal { N } ( \\mathbf { z } _ { t } ; ( \\alpha _ { t } / \\alpha _ { s } ) \\mathbf { z } _ { s } , \\sigma _ { t | s } ^ { 2 } \\mathbf { I } ) } \\end{array}", "type": "interline_equation", "image_path": "91e32dd5f663fe175f4fe9874f7db7b0f12fcefcf05a707499a151a1035386b8.jpg" } ] } ], "index": 39, "virtual_lines": [ { "bbox": [ 172, 657, 440, 672 ], "spans": [], "index": 39 } ] }, { "type": "text", "bbox": [ 70, 681, 540, 732 ], "lines": [ { "bbox": [ 68, 680, 543, 697 ], "spans": [ { "bbox": [ 68, 680, 99, 696 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 100, 685, 158, 694 ], "score": 0.9, "content": "0 \\leq s < t \\leq 1", "type": "inline_equation" }, { "bbox": [ 158, 680, 163, 696 ], "score": 1.0, "content": ",", "type": "text" }, { "bbox": [ 163, 684, 254, 697 ], "score": 0.94, "content": "\\sigma _ { t | s } ^ { 2 } = ( 1 - e ^ { \\lambda _ { t } - \\lambda _ { s } } ) \\sigma _ { t } ^ { 2 }", "type": "inline_equation" }, { "bbox": [ 254, 680, 278, 696 ], "score": 1.0, "content": ", and", "type": "text" }, { "bbox": [ 278, 687, 302, 694 ], "score": 0.91, "content": "\\alpha _ { t } , \\sigma _ { t }", "type": "inline_equation" }, { "bbox": [ 302, 680, 543, 696 ], "score": 1.0, "content": "specify a noise schedule whose log signal-to-noise-ratio", "type": "text" } ], "index": 40 }, { "bbox": [ 71, 695, 542, 711 ], "spans": [ { "bbox": [ 71, 698, 140, 709 ], "score": 0.94, "content": "\\lambda _ { t } = \\log [ \\alpha _ { t } ^ { 2 } / \\sigma _ { t } ^ { 2 } ]", "type": "inline_equation" }, { "bbox": [ 140, 695, 273, 711 ], "score": 1.0, "content": "decreases monotonically with", "type": "text" }, { "bbox": [ 274, 700, 278, 706 ], "score": 0.87, "content": "t", "type": "inline_equation" }, { "bbox": [ 278, 695, 305, 711 ], "score": 1.0, "content": "until", "type": "text" }, { "bbox": [ 305, 698, 373, 709 ], "score": 0.94, "content": "q ( \\mathbf { z } _ { 1 } ) \\approx \\mathcal { N } ( \\mathbf { 0 } , \\mathbf { I } )", "type": "inline_equation" }, { "bbox": [ 374, 695, 542, 711 ], "score": 1.0, "content": ". We use a continuous time version of", "type": "text" } ], "index": 41 }, { "bbox": [ 70, 708, 541, 722 ], "spans": [ { "bbox": [ 70, 708, 541, 722 ], "score": 1.0, "content": "the cosine noise schedule (Nichol & Dhariwal, 2021). 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Diffusion models have also shown promise for video gener-", "type": "text" } ], "index": 4 }, { "bbox": [ 69, 141, 542, 155 ], "spans": [ { "bbox": [ 69, 141, 542, 155 ], "score": 1.0, "content": "ation (Ho et al., 2022b) at moderate resolution. Yang et al. (2022) showed autoregressive generation with", "type": "text" } ], "index": 5 }, { "bbox": [ 69, 154, 541, 168 ], "spans": [ { "bbox": [ 69, 154, 541, 168 ], "score": 1.0, "content": "a RNN-based model with conditional diffusion observations. The concurrent work of Singer et al. (2022)", "type": "text" } ], "index": 6 }, { "bbox": [ 70, 167, 540, 178 ], "spans": [ { "bbox": [ 70, 167, 540, 178 ], "score": 1.0, "content": "also applied text-to-video modelling with diffusion models, but built on a pretrained text-to-image model.", "type": "text" } ], "index": 7 }, { "bbox": [ 69, 177, 542, 192 ], "spans": [ { "bbox": [ 69, 177, 542, 192 ], "score": 1.0, "content": "Harvey et al. 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Imagen Video scales from prior work of 64-frame 128", "type": "text" }, { "bbox": [ 516, 235, 524, 242 ], "score": 0.62, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 525, 231, 541, 244 ], "score": 1.0, "content": "128", "type": "text" } ], "index": 12 }, { "bbox": [ 70, 243, 541, 257 ], "spans": [ { "bbox": [ 70, 243, 281, 257 ], "score": 1.0, "content": "videos at 24 frames per second to 128 frame 1280", "type": "text" }, { "bbox": [ 282, 247, 290, 254 ], "score": 0.71, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 290, 243, 541, 257 ], "score": 1.0, "content": "768 high-definition video at 24 frames per second. Imagen", "type": "text" } ], "index": 13 }, { "bbox": [ 69, 254, 542, 269 ], "spans": [ { "bbox": [ 69, 254, 542, 269 ], "score": 1.0, "content": "Video has a simple architecture: The model consists of a frozen T5 text encoder (Raffel et al., 2020), a", "type": "text" } ], "index": 14 }, { "bbox": [ 69, 267, 540, 281 ], "spans": [ { "bbox": [ 69, 267, 540, 281 ], "score": 1.0, "content": "base video diffusion model, and interleaved spatial and temporal super-resolution diffusion models. Our key", "type": "text" } ], "index": 15 }, { "bbox": [ 70, 280, 195, 292 ], "spans": [ { "bbox": [ 70, 280, 195, 292 ], "score": 1.0, "content": "contributions are as follows:", "type": "text" } ], "index": 16 } ], "index": 13, "bbox_fs": [ 69, 208, 542, 292 ] }, { "type": "list", "bbox": [ 91, 306, 542, 452 ], "lines": [ { "bbox": [ 92, 304, 541, 320 ], "spans": [ { "bbox": [ 92, 304, 541, 320 ], "score": 1.0, "content": "1. 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We show new findings for video diffusion models that have implications for diffusion models in", "type": "text" } ], "index": 21, "is_list_start_line": true }, { "bbox": [ 105, 383, 541, 396 ], "spans": [ { "bbox": [ 105, 383, 541, 396 ], "score": 1.0, "content": "general, such as the effectiveness of the v-prediction parameterization for sample quality and the", "type": "text" } ], "index": 22 }, { "bbox": [ 106, 395, 540, 408 ], "spans": [ { "bbox": [ 106, 395, 540, 408 ], "score": 1.0, "content": "effectiveness of progressive distillation of guided diffusion models for the text-conditioned video", "type": "text" } ], "index": 23 }, { "bbox": [ 105, 408, 189, 420 ], "spans": [ { "bbox": [ 105, 408, 189, 420 ], "score": 1.0, "content": "generation setting.", "type": "text" } ], "index": 24, "is_list_end_line": true }, { "bbox": [ 91, 426, 540, 442 ], "spans": [ { "bbox": [ 91, 426, 540, 442 ], "score": 1.0, "content": "4. We demonstrate qualitative controllability in Imagen Video, such as 3D object understanding, gen-", "type": "text" } ], "index": 25, "is_list_start_line": true }, { "bbox": [ 105, 439, 444, 452 ], "spans": [ { "bbox": [ 105, 439, 444, 452 ], "score": 1.0, "content": "eration of text animations, and generation of videos in various artistic styles.", "type": "text" } ], "index": 26, "is_list_end_line": true } ], "index": 21.5, "bbox_fs": [ 91, 304, 541, 452 ] }, { "type": "title", "bbox": [ 71, 466, 168, 480 ], "lines": [ { "bbox": [ 68, 464, 169, 484 ], "spans": [ { "bbox": [ 68, 464, 169, 484 ], "score": 1.0, "content": "2 Imagen Video", "type": "text" } ], "index": 27 } ], "index": 27 }, { "type": "text", "bbox": [ 71, 492, 540, 552 ], "lines": [ { "bbox": [ 70, 493, 541, 505 ], "spans": [ { "bbox": [ 70, 493, 541, 505 ], "score": 1.0, "content": "Our model, Imagen Video, is a cascade of video diffusion models (Ho et al., 2022a;b). It consists of 7", "type": "text" } ], "index": 28 }, { "bbox": [ 69, 504, 541, 518 ], "spans": [ { "bbox": [ 69, 504, 541, 518 ], "score": 1.0, "content": "sub-models which perform text-conditional video generation, spatial super-resolution, and temporal super-", "type": "text" } ], "index": 29 }, { "bbox": [ 70, 516, 541, 530 ], "spans": [ { "bbox": [ 70, 516, 436, 530 ], "score": 1.0, "content": "resolution. With the entire cascade, Imagen Video generates high definition 1280", "type": "text" }, { "bbox": [ 436, 520, 444, 527 ], "score": 0.5, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 444, 516, 497, 530 ], "score": 1.0, "content": "768 (width", "type": "text" }, { "bbox": [ 497, 520, 504, 527 ], "score": 0.74, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 505, 516, 541, 530 ], "score": 1.0, "content": "height)", "type": "text" } ], "index": 30 }, { "bbox": [ 70, 529, 541, 541 ], "spans": [ { "bbox": [ 70, 529, 291, 541 ], "score": 1.0, "content": "videos at 24 frames per second, for 128 frames (", "type": "text" }, { "bbox": [ 292, 532, 318, 538 ], "score": 0.58, "content": "\\approx 5 . 3", "type": "inline_equation" }, { "bbox": [ 318, 529, 541, 541 ], "score": 1.0, "content": "seconds)—approximately 126 million pixels. We", "type": "text" } ], "index": 31 }, { "bbox": [ 70, 541, 490, 554 ], "spans": [ { "bbox": [ 70, 541, 490, 554 ], "score": 1.0, "content": "describe the components and techniques that constitute Imagen Video in the following sections.", "type": "text" } ], "index": 32 } ], "index": 30, "bbox_fs": [ 69, 493, 541, 554 ] }, { "type": "title", "bbox": [ 72, 566, 173, 578 ], "lines": [ { "bbox": [ 69, 564, 174, 580 ], "spans": [ { "bbox": [ 69, 564, 174, 580 ], "score": 1.0, "content": "2.1 Diffusion models", "type": "text" } ], "index": 33 } ], "index": 33 }, { "type": "text", "bbox": [ 71, 587, 540, 648 ], "lines": [ { "bbox": [ 69, 587, 541, 601 ], "spans": [ { "bbox": [ 69, 587, 541, 601 ], "score": 1.0, "content": "Imagen Video is built from diffusion models (Sohl-Dickstein et al., 2015; Song & Ermon, 2019; Ho et al.,", "type": "text" } ], "index": 34 }, { "bbox": [ 69, 599, 542, 613 ], "spans": [ { "bbox": [ 69, 599, 250, 613 ], "score": 1.0, "content": "2020) specified in continuous time (Tzen", "type": "text" }, { "bbox": [ 250, 602, 258, 610 ], "score": 0.34, "content": "\\&", "type": "inline_equation" }, { "bbox": [ 258, 599, 542, 613 ], "score": 1.0, "content": "Raginsky, 2019; Song et al., 2021; Kingma et al., 2021). We use", "type": "text" } ], "index": 35 }, { "bbox": [ 69, 610, 540, 626 ], "spans": [ { "bbox": [ 69, 610, 460, 626 ], "score": 1.0, "content": "the formulation of Kingma et al. (2021): the model is a latent variable model with latents", "type": "text" }, { "bbox": [ 460, 614, 540, 624 ], "score": 0.93, "content": "\\mathbf { z } = \\{ \\mathbf { z } _ { t } | t \\in [ 0 , 1 ] \\}", "type": "inline_equation" } ], "index": 36 }, { "bbox": [ 69, 623, 541, 637 ], "spans": [ { "bbox": [ 69, 623, 191, 637 ], "score": 1.0, "content": "following a forward process", "type": "text" }, { "bbox": [ 191, 626, 218, 636 ], "score": 0.94, "content": "q ( \\mathbf { z } | \\mathbf { x } )", "type": "inline_equation" }, { "bbox": [ 218, 623, 291, 637 ], "score": 1.0, "content": "starting at data", "type": "text" }, { "bbox": [ 291, 626, 329, 636 ], "score": 0.94, "content": "\\mathbf { x } \\sim p ( \\mathbf { x } )", "type": "inline_equation" }, { "bbox": [ 330, 623, 541, 637 ], "score": 1.0, "content": ". The forward process is a Gaussian process that", "type": "text" } ], "index": 37 }, { "bbox": [ 69, 636, 218, 649 ], "spans": [ { "bbox": [ 69, 636, 218, 649 ], "score": 1.0, "content": "satisfies the Markovian structure:", "type": "text" } ], "index": 38 } ], "index": 36, "bbox_fs": [ 69, 587, 542, 649 ] }, { "type": "interline_equation", "bbox": [ 172, 657, 440, 672 ], "lines": [ { "bbox": [ 172, 657, 440, 672 ], "spans": [ { "bbox": [ 172, 657, 440, 672 ], "score": 0.86, "content": "\\begin{array} { r } { q ( \\mathbf { z } _ { t } | \\mathbf { x } ) = \\mathcal { N } ( \\mathbf { z } _ { t } ; \\alpha _ { t } \\mathbf { x } , \\sigma _ { t } ^ { 2 } \\mathbf { I } ) , \\quad q ( \\mathbf { z } _ { t } | \\mathbf { z } _ { s } ) = \\mathcal { N } ( \\mathbf { z } _ { t } ; ( \\alpha _ { t } / \\alpha _ { s } ) \\mathbf { z } _ { s } , \\sigma _ { t | s } ^ { 2 } \\mathbf { I } ) } \\end{array}", "type": "interline_equation", "image_path": "91e32dd5f663fe175f4fe9874f7db7b0f12fcefcf05a707499a151a1035386b8.jpg" } ] } ], "index": 39, "virtual_lines": [ { "bbox": [ 172, 657, 440, 672 ], "spans": [], "index": 39 } ] }, { "type": "text", "bbox": [ 70, 681, 540, 732 ], "lines": [ { "bbox": [ 68, 680, 543, 697 ], "spans": [ { "bbox": [ 68, 680, 99, 696 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 100, 685, 158, 694 ], "score": 0.9, "content": "0 \\leq s < t \\leq 1", "type": "inline_equation" }, { "bbox": [ 158, 680, 163, 696 ], "score": 1.0, "content": ",", "type": "text" }, { "bbox": [ 163, 684, 254, 697 ], "score": 0.94, "content": "\\sigma _ { t | s } ^ { 2 } = ( 1 - e ^ { \\lambda _ { t } - \\lambda _ { s } } ) \\sigma _ { t } ^ { 2 }", "type": "inline_equation" }, { "bbox": [ 254, 680, 278, 696 ], "score": 1.0, "content": ", and", "type": "text" }, { "bbox": [ 278, 687, 302, 694 ], "score": 0.91, "content": "\\alpha _ { t } , \\sigma _ { t }", "type": "inline_equation" }, { "bbox": [ 302, 680, 543, 696 ], "score": 1.0, "content": "specify a noise schedule whose log signal-to-noise-ratio", "type": "text" } ], "index": 40 }, { "bbox": [ 71, 695, 542, 711 ], "spans": [ { "bbox": [ 71, 698, 140, 709 ], "score": 0.94, "content": "\\lambda _ { t } = \\log [ \\alpha _ { t } ^ { 2 } / \\sigma _ { t } ^ { 2 } ]", "type": "inline_equation" }, { "bbox": [ 140, 695, 273, 711 ], "score": 1.0, "content": "decreases monotonically with", "type": "text" }, { "bbox": [ 274, 700, 278, 706 ], "score": 0.87, "content": "t", "type": "inline_equation" }, { "bbox": [ 278, 695, 305, 711 ], "score": 1.0, "content": "until", "type": "text" }, { "bbox": [ 305, 698, 373, 709 ], "score": 0.94, "content": "q ( \\mathbf { z } _ { 1 } ) \\approx \\mathcal { N } ( \\mathbf { 0 } , \\mathbf { I } )", "type": "inline_equation" }, { "bbox": [ 374, 695, 542, 711 ], "score": 1.0, "content": ". We use a continuous time version of", "type": "text" } ], "index": 41 }, { "bbox": [ 70, 708, 541, 722 ], "spans": [ { "bbox": [ 70, 708, 541, 722 ], "score": 1.0, "content": "the cosine noise schedule (Nichol & Dhariwal, 2021). The generative model is a learned model that matches", "type": "text" } ], "index": 42 }, { "bbox": [ 69, 720, 528, 734 ], "spans": [ { "bbox": [ 69, 720, 339, 734 ], "score": 1.0, "content": "this forward process in the reverse time direction, generating", "type": "text" }, { "bbox": [ 340, 725, 348, 732 ], "score": 0.89, "content": "\\mathbf { z } _ { t }", "type": "inline_equation" }, { "bbox": [ 349, 720, 412, 734 ], "score": 1.0, "content": "starting from", "type": "text" }, { "bbox": [ 412, 723, 434, 730 ], "score": 0.92, "content": "t = 1", "type": "inline_equation" }, { "bbox": [ 435, 720, 501, 734 ], "score": 1.0, "content": "and ending at", "type": "text" }, { "bbox": [ 501, 723, 523, 730 ], "score": 0.92, "content": "t = 0", "type": "inline_equation" }, { "bbox": [ 524, 720, 528, 734 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 43 } ], "index": 41.5, "bbox_fs": [ 68, 680, 543, 734 ] } ] }, { "preproc_blocks": [ { "type": "text", "bbox": [ 70, 82, 540, 118 ], "lines": [ { "bbox": [ 69, 81, 540, 96 ], "spans": [ { "bbox": [ 69, 81, 485, 96 ], "score": 1.0, "content": "Learning to reverse the forward process for generation can be reduced to learning to denoise", "type": "text" }, { "bbox": [ 485, 84, 540, 95 ], "score": 0.94, "content": "{ \\mathbf z } _ { t } \\sim q ( { \\mathbf z } _ { t } | { \\mathbf x } )", "type": "inline_equation" } ], "index": 0 }, { "bbox": [ 68, 91, 542, 110 ], "spans": [ { "bbox": [ 68, 91, 146, 110 ], "score": 1.0, "content": "into an estimate", "type": "text" }, { "bbox": [ 147, 96, 209, 106 ], "score": 0.94, "content": "\\hat { \\mathbf { x } } _ { \\theta } ( { \\mathbf z } _ { t } , \\lambda _ { t } ) \\approx { \\mathbf x }", "type": "inline_equation" }, { "bbox": [ 209, 91, 243, 110 ], "score": 1.0, "content": "for all", "type": "text" }, { "bbox": [ 243, 97, 247, 104 ], "score": 0.87, "content": "t", "type": "inline_equation" }, { "bbox": [ 247, 91, 542, 110 ], "score": 1.0, "content": ". Like (Song & Ermon, 2019; Ho et al., 2020) and most follow-up", "type": "text" } ], "index": 1 }, { "bbox": [ 70, 106, 397, 119 ], "spans": [ { "bbox": [ 70, 106, 397, 119 ], "score": 1.0, "content": "work, we optimize the model by minimizing a simple noise-prediction loss:", "type": "text" } ], "index": 2 } ], "index": 1 }, { "type": "interline_equation", "bbox": [ 210, 128, 400, 142 ], "lines": [ { "bbox": [ 210, 128, 400, 142 ], "spans": [ { "bbox": [ 210, 128, 400, 142 ], "score": 0.91, "content": "\\mathcal { L } ( \\mathbf { x } ) = \\mathbb { E } _ { \\epsilon \\sim \\mathcal { N } ( 0 , \\mathbf { I } ) , t \\sim U ( 0 , 1 ) } \\big [ \\| \\hat { \\epsilon } _ { \\boldsymbol { \\theta } } ( \\mathbf { z } _ { t } , \\lambda _ { t } ) - \\epsilon \\| _ { 2 } ^ { 2 } \\big ]", "type": "interline_equation", "image_path": "db4bd1a4a4cd023b03677a900aa10cb38dbaf8e9b28230ce0e6a05cc97c09a70.jpg" } ] } ], "index": 3, "virtual_lines": [ { "bbox": [ 210, 128, 400, 142 ], "spans": [], "index": 3 } ] }, { "type": "text", "bbox": [ 71, 152, 540, 189 ], "lines": [ { "bbox": [ 69, 151, 541, 168 ], "spans": [ { "bbox": [ 69, 151, 99, 168 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 99, 157, 162, 164 ], "score": 0.92, "content": "\\mathbf { z } _ { t } = \\alpha _ { t } \\mathbf { x } + \\sigma _ { t } \\mathbf { \\epsilon } ", "type": "inline_equation" }, { "bbox": [ 162, 151, 186, 168 ], "score": 1.0, "content": ", and", "type": "text" }, { "bbox": [ 186, 154, 333, 165 ], "score": 0.93, "content": "\\hat { \\epsilon } _ { \\boldsymbol { \\theta } } ( \\mathbf { z } _ { t } , \\lambda _ { t } ) = \\sigma _ { t } ^ { - 1 } ( \\mathbf { z } _ { t } - \\alpha _ { t } \\hat { \\mathbf { x } } _ { \\boldsymbol { \\theta } } ( \\mathbf { z } _ { t } , \\lambda _ { t } ) )", "type": "inline_equation" }, { "bbox": [ 333, 151, 480, 168 ], "score": 1.0, "content": ". We will drop the dependence on", "type": "text" }, { "bbox": [ 481, 156, 490, 164 ], "score": 0.91, "content": "\\lambda _ { t }", "type": "inline_equation" }, { "bbox": [ 491, 151, 541, 168 ], "score": 1.0, "content": "to simplify", "type": "text" } ], "index": 4 }, { "bbox": [ 69, 164, 541, 179 ], "spans": [ { "bbox": [ 69, 164, 353, 179 ], "score": 1.0, "content": "notation. In practice, we parameterize our models in terms of the", "type": "text" }, { "bbox": [ 354, 171, 360, 175 ], "score": 0.8, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 360, 164, 541, 179 ], "score": 1.0, "content": "-parameterization (Salimans & Ho, 2022),", "type": "text" } ], "index": 5 }, { "bbox": [ 70, 177, 308, 190 ], "spans": [ { "bbox": [ 70, 177, 171, 190 ], "score": 1.0, "content": "rather than predicting", "type": "text" }, { "bbox": [ 172, 182, 177, 187 ], "score": 0.89, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 177, 177, 191, 190 ], "score": 1.0, "content": "or", "type": "text" }, { "bbox": [ 192, 182, 198, 187 ], "score": 0.89, "content": "\\mathbf { x }", "type": "inline_equation" }, { "bbox": [ 199, 177, 308, 190 ], "score": 1.0, "content": "directly; see Section 2.4.", "type": "text" } ], "index": 6 } ], "index": 5 }, { "type": "text", "bbox": [ 70, 195, 540, 267 ], "lines": [ { "bbox": [ 69, 194, 542, 209 ], "spans": [ { "bbox": [ 69, 194, 411, 209 ], "score": 1.0, "content": "For conditional generative modeling, we provide the conditioning information", "type": "text" }, { "bbox": [ 412, 200, 417, 205 ], "score": 0.72, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 417, 194, 504, 209 ], "score": 1.0, "content": "drawn jointly with", "type": "text" }, { "bbox": [ 504, 200, 510, 205 ], "score": 0.81, "content": "\\mathbf { x }", "type": "inline_equation" }, { "bbox": [ 511, 194, 542, 209 ], "score": 1.0, "content": "to the", "type": "text" } ], "index": 7 }, { "bbox": [ 69, 207, 541, 221 ], "spans": [ { "bbox": [ 69, 207, 113, 221 ], "score": 1.0, "content": "model as", "type": "text" }, { "bbox": [ 113, 209, 154, 219 ], "score": 0.94, "content": "\\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } _ { t } )", "type": "inline_equation" }, { "bbox": [ 154, 207, 541, 221 ], "score": 1.0, "content": ". We use these conditional diffusion models for spatial and temporal super-resolution in", "type": "text" } ], "index": 8 }, { "bbox": [ 68, 218, 542, 232 ], "spans": [ { "bbox": [ 68, 218, 275, 232 ], "score": 1.0, "content": "our pipeline of diffusion models: in these cases,", "type": "text" }, { "bbox": [ 275, 224, 281, 229 ], "score": 0.6, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 281, 218, 542, 232 ], "score": 1.0, "content": "includes both the text and the previous stage low resolution", "type": "text" } ], "index": 9 }, { "bbox": [ 69, 230, 542, 244 ], "spans": [ { "bbox": [ 69, 230, 181, 244 ], "score": 1.0, "content": "video as well as a signal", "type": "text" }, { "bbox": [ 182, 233, 191, 243 ], "score": 0.92, "content": "\\lambda _ { t } ^ { \\prime }", "type": "inline_equation" }, { "bbox": [ 191, 230, 492, 244 ], "score": 1.0, "content": "that describes the strength of conditioning augmentation added to", "type": "text" }, { "bbox": [ 493, 236, 498, 241 ], "score": 0.66, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 498, 230, 542, 244 ], "score": 1.0, "content": ". Saharia", "type": "text" } ], "index": 10 }, { "bbox": [ 68, 241, 541, 256 ], "spans": [ { "bbox": [ 68, 241, 541, 256 ], "score": 1.0, "content": "et al. (2022b) found it critical to condition all the super-resolution models with the text embedding, and we", "type": "text" } ], "index": 11 }, { "bbox": [ 69, 254, 163, 268 ], "spans": [ { "bbox": [ 69, 254, 163, 268 ], "score": 1.0, "content": "follow this approach.", "type": "text" } ], "index": 12 } ], "index": 9.5 }, { "type": "text", "bbox": [ 70, 272, 540, 322 ], "lines": [ { "bbox": [ 70, 272, 541, 286 ], "spans": [ { "bbox": [ 70, 272, 541, 286 ], "score": 1.0, "content": "We use the discrete time ancestral sampler (Ho et al., 2020), with sampling variances derived from lower and", "type": "text" } ], "index": 13 }, { "bbox": [ 68, 284, 541, 298 ], "spans": [ { "bbox": [ 68, 284, 485, 298 ], "score": 1.0, "content": "upper bounds on reverse process entropy (Sohl-Dickstein et al., 2015; Ho et al., 2020; Nichol", "type": "text" }, { "bbox": [ 485, 287, 493, 295 ], "score": 0.67, "content": "\\&", "type": "inline_equation" }, { "bbox": [ 493, 284, 541, 298 ], "score": 1.0, "content": "Dhariwal,", "type": "text" } ], "index": 14 }, { "bbox": [ 69, 296, 541, 310 ], "spans": [ { "bbox": [ 69, 296, 484, 310 ], "score": 1.0, "content": "2021). This sampler can be formulated by using a reversed description of the forward process as", "type": "text" }, { "bbox": [ 485, 299, 541, 309 ], "score": 0.93, "content": "q ( \\mathbf { z } _ { s } | \\mathbf { z } _ { t } , \\mathbf { x } ) =", "type": "inline_equation" } ], "index": 15 }, { "bbox": [ 71, 307, 269, 324 ], "spans": [ { "bbox": [ 71, 310, 170, 324 ], "score": 0.92, "content": "\\mathcal { N } ( \\mathbf { z } _ { s } ; \\tilde { \\pmb { \\mu } } _ { s | t } ( \\mathbf { z } _ { t } , \\mathbf { x } ) , \\tilde { \\sigma } _ { s | t } ^ { 2 } \\mathbf { I } )", "type": "inline_equation" }, { "bbox": [ 171, 307, 208, 324 ], "score": 1.0, "content": "(noting", "type": "text" }, { "bbox": [ 208, 312, 230, 319 ], "score": 0.88, "content": "s < t", "type": "inline_equation" }, { "bbox": [ 230, 307, 269, 324 ], "score": 1.0, "content": "), where", "type": "text" } ], "index": 16 } ], "index": 14.5 }, { "type": "interline_equation", "bbox": [ 134, 333, 477, 348 ], "lines": [ { "bbox": [ 134, 333, 477, 348 ], "spans": [ { "bbox": [ 134, 333, 477, 348 ], "score": 0.89, "content": "\\tilde { \\mu } _ { s | t } ( \\mathbf { z } _ { t } , \\mathbf { x } ) = e ^ { \\lambda _ { t } - \\lambda _ { s } } ( \\alpha _ { s } / \\alpha _ { t } ) \\mathbf { z } _ { t } + ( 1 - e ^ { \\lambda _ { t } - \\lambda _ { s } } ) \\alpha _ { s } \\mathbf { x } \\quad \\mathrm { a n d } \\quad \\tilde { \\sigma } _ { s | t } ^ { 2 } = ( 1 - e ^ { \\lambda _ { t } - \\lambda _ { s } } ) \\sigma _ { s } ^ { 2 } .", "type": "interline_equation", "image_path": "a7b880d8eadfc62c0f843b873189b1cb8c4885c743fc1fd90542e4ff2258635f.jpg" } ] } ], "index": 17, "virtual_lines": [ { "bbox": [ 134, 333, 477, 348 ], "spans": [], "index": 17 } ] }, { "type": "text", "bbox": [ 71, 357, 348, 369 ], "lines": [ { "bbox": [ 70, 357, 348, 370 ], "spans": [ { "bbox": [ 70, 357, 122, 370 ], "score": 1.0, "content": "Starting at", "type": "text" }, { "bbox": [ 122, 359, 177, 370 ], "score": 0.95, "content": "{ \\bf z } _ { 1 } \\sim \\mathcal { N } ( { \\bf 0 } , { \\bf I } )", "type": "inline_equation" }, { "bbox": [ 177, 357, 348, 370 ], "score": 1.0, "content": ", the ancestral sampler follows the rule", "type": "text" } ], "index": 18 } ], "index": 18 }, { "type": "interline_equation", "bbox": [ 214, 379, 397, 400 ], "lines": [ { "bbox": [ 214, 379, 397, 400 ], "spans": [ { "bbox": [ 214, 379, 397, 400 ], "score": 0.94, "content": "\\mathbf { z } _ { s } = \\tilde { \\mu } _ { s | t } ( \\mathbf { z } _ { t } , \\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } ) ) + \\sqrt { ( \\tilde { \\sigma } _ { s | t } ^ { 2 } ) ^ { 1 - \\gamma } ( \\sigma _ { t | s } ^ { 2 } ) ^ { \\gamma } } \\epsilon", "type": "interline_equation", "image_path": "9a4f0f1639000ce1432966e3bf67c71de0417417382e4f2966860af208c6efa2.jpg" } ] } ], "index": 19, "virtual_lines": [ { "bbox": [ 214, 379, 397, 400 ], "spans": [], "index": 19 } ] }, { "type": "text", "bbox": [ 70, 408, 540, 445 ], "lines": [ { "bbox": [ 69, 407, 540, 422 ], "spans": [ { "bbox": [ 69, 407, 101, 422 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 102, 414, 107, 419 ], "score": 0.87, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 107, 407, 240, 422 ], "score": 1.0, "content": "is standard Gaussian noise,", "type": "text" }, { "bbox": [ 240, 414, 246, 421 ], "score": 0.92, "content": "\\gamma", "type": "inline_equation" }, { "bbox": [ 247, 407, 540, 422 ], "score": 1.0, "content": "is a hyperparameter that controls the stochasticity of the sam-", "type": "text" } ], "index": 20 }, { "bbox": [ 70, 420, 541, 434 ], "spans": [ { "bbox": [ 70, 420, 233, 434 ], "score": 1.0, "content": "pler (Nichol & Dhariwal, 2021), and", "type": "text" }, { "bbox": [ 234, 424, 246, 432 ], "score": 0.92, "content": "s , t", "type": "inline_equation" }, { "bbox": [ 247, 420, 452, 434 ], "score": 1.0, "content": "follow a uniformly spaced sequence from 1 to", "type": "text" }, { "bbox": [ 452, 424, 457, 431 ], "score": 0.29, "content": "0", "type": "inline_equation" }, { "bbox": [ 458, 420, 541, 434 ], "score": 1.0, "content": ". See Section 3 for", "type": "text" } ], "index": 21 }, { "bbox": [ 69, 433, 218, 447 ], "spans": [ { "bbox": [ 69, 433, 218, 447 ], "score": 1.0, "content": "sampler hyperparameter settings.", "type": "text" } ], "index": 22 } ], "index": 21 }, { "type": "text", "bbox": [ 70, 450, 540, 510 ], "lines": [ { "bbox": [ 70, 451, 540, 463 ], "spans": [ { "bbox": [ 70, 451, 540, 463 ], "score": 1.0, "content": "Alternatively, the deterministic DDIM sampler (Song et al., 2020) can be used for sampling. This sampler", "type": "text" } ], "index": 23 }, { "bbox": [ 69, 463, 540, 475 ], "spans": [ { "bbox": [ 69, 463, 540, 475 ], "score": 1.0, "content": "is a numerical integration rule for the probability flow ODE (Song et al., 2021; Salimans & Ho, 2022),", "type": "text" } ], "index": 24 }, { "bbox": [ 69, 474, 541, 487 ], "spans": [ { "bbox": [ 69, 474, 541, 487 ], "score": 1.0, "content": "which describes how a sample from a standard normal distribution can be deterministically transformed", "type": "text" } ], "index": 25 }, { "bbox": [ 70, 487, 540, 499 ], "spans": [ { "bbox": [ 70, 487, 540, 499 ], "score": 1.0, "content": "into a sample from the video data distribution using the denoising model. The DDIM sampler is useful for", "type": "text" } ], "index": 26 }, { "bbox": [ 69, 498, 372, 511 ], "spans": [ { "bbox": [ 69, 498, 372, 511 ], "score": 1.0, "content": "progressive distillation for fast sampling, as described in Section 2.7.", "type": "text" } ], "index": 27 } ], "index": 25 }, { "type": "title", "bbox": [ 71, 524, 322, 537 ], "lines": [ { "bbox": [ 69, 523, 323, 540 ], "spans": [ { "bbox": [ 69, 523, 323, 540 ], "score": 1.0, "content": "2.2 Cascaded Diffusion Models and text conditioning", "type": "text" } ], "index": 28 } ], "index": 28 }, { "type": "text", "bbox": [ 70, 546, 540, 655 ], "lines": [ { "bbox": [ 70, 547, 540, 560 ], "spans": [ { "bbox": [ 70, 547, 540, 560 ], "score": 1.0, "content": "Cascaded Diffusion Models (Ho et al., 2022a) are an effective method for scaling diffusion models to high", "type": "text" } ], "index": 29 }, { "bbox": [ 69, 559, 541, 572 ], "spans": [ { "bbox": [ 69, 559, 541, 572 ], "score": 1.0, "content": "resolution outputs, finding considerable success in both class-conditional ImageNet (Ho et al., 2022a) and", "type": "text" } ], "index": 30 }, { "bbox": [ 68, 570, 541, 584 ], "spans": [ { "bbox": [ 68, 570, 541, 584 ], "score": 1.0, "content": "text-to-image generation (Ramesh et al., 2022; Saharia et al., 2022b). Cascaded diffusion models generate", "type": "text" } ], "index": 31 }, { "bbox": [ 68, 582, 541, 596 ], "spans": [ { "bbox": [ 68, 582, 541, 596 ], "score": 1.0, "content": "an image or video at a low resolution, then sequentially increase the resolution of the image or video through", "type": "text" } ], "index": 32 }, { "bbox": [ 69, 594, 540, 607 ], "spans": [ { "bbox": [ 69, 594, 540, 607 ], "score": 1.0, "content": "a series of super-resolution diffusion models. Cascaded Diffusion Models can model very high dimensional", "type": "text" } ], "index": 33 }, { "bbox": [ 70, 607, 541, 619 ], "spans": [ { "bbox": [ 70, 607, 541, 619 ], "score": 1.0, "content": "problems while still keeping each sub-model relatively simple. Imagen (Saharia et al., 2022b) also showed", "type": "text" } ], "index": 34 }, { "bbox": [ 70, 619, 541, 632 ], "spans": [ { "bbox": [ 70, 619, 541, 632 ], "score": 1.0, "content": "that by conditioning on text embeddings from a large frozen language model in conjunction with cascaded", "type": "text" } ], "index": 35 }, { "bbox": [ 68, 630, 542, 644 ], "spans": [ { "bbox": [ 68, 630, 279, 644 ], "score": 1.0, "content": "diffusion models, one can generate high quality", "type": "text" }, { "bbox": [ 280, 633, 332, 641 ], "score": 0.88, "content": "1 0 2 4 \\times 1 0 2 4", "type": "inline_equation" }, { "bbox": [ 333, 630, 542, 644 ], "score": 1.0, "content": "images from text descriptions. In this work we", "type": "text" } ], "index": 36 }, { "bbox": [ 69, 643, 253, 656 ], "spans": [ { "bbox": [ 69, 643, 253, 656 ], "score": 1.0, "content": "extend this approach to video generation.", "type": "text" } ], "index": 37 } ], "index": 33 }, { "type": "text", "bbox": [ 70, 660, 540, 732 ], "lines": [ { "bbox": [ 70, 660, 540, 673 ], "spans": [ { "bbox": [ 70, 660, 540, 673 ], "score": 1.0, "content": "Figure 6 summarizes the entire cascading pipeline of Imagen Video. In total, we have 1 frozen text encoder, 1", "type": "text" } ], "index": 38 }, { "bbox": [ 69, 672, 541, 686 ], "spans": [ { "bbox": [ 69, 672, 541, 686 ], "score": 1.0, "content": "base video diffusion model, 3 SSR (spatial super-resolution), and 3 TSR (temporal super-resolution) models", "type": "text" } ], "index": 39 }, { "bbox": [ 76, 685, 541, 697 ], "spans": [ { "bbox": [ 76, 685, 541, 697 ], "score": 1.0, "content": "for a total of 7 video diffusion models, with a total of 11.6B diffusion model parameters. The data used to", "type": "text" } ], "index": 40 }, { "bbox": [ 69, 696, 541, 710 ], "spans": [ { "bbox": [ 69, 696, 541, 710 ], "score": 1.0, "content": "train these models is processed to the appropriate spatial and temporal resolutions by spatial resizing and", "type": "text" } ], "index": 41 }, { "bbox": [ 70, 709, 541, 721 ], "spans": [ { "bbox": [ 70, 709, 541, 721 ], "score": 1.0, "content": "frame skipping. At generation time, the SSR models increase spatial resolution for all input frames, whereas", "type": "text" } ], "index": 42 }, { "bbox": [ 69, 720, 542, 734 ], "spans": [ { "bbox": [ 69, 720, 542, 734 ], "score": 1.0, "content": "the TSR models increase temporal resolution by filling in intermediate frames between input frames. All", "type": "text" } ], "index": 43 } ], "index": 40.5 } ], "page_idx": 6, "page_size": [ 612, 792 ], "discarded_blocks": [ { "type": "discarded", "bbox": [ 72, 26, 237, 37 ], "lines": [ { "bbox": [ 71, 25, 239, 38 ], "spans": [ { "bbox": [ 71, 25, 239, 38 ], "score": 1.0, "content": "Under review as submission to TMLR", "type": "text" } ] } ] }, { "type": "discarded", "bbox": [ 302, 751, 309, 760 ], "lines": [ { "bbox": [ 301, 750, 310, 762 ], "spans": [ { "bbox": [ 301, 750, 310, 762 ], "score": 1.0, "content": "7", "type": "text" } ] } ] } ], "para_blocks": [ { "type": "text", "bbox": [ 70, 82, 540, 118 ], "lines": [ { "bbox": [ 69, 81, 540, 96 ], "spans": [ { "bbox": [ 69, 81, 485, 96 ], "score": 1.0, "content": "Learning to reverse the forward process for generation can be reduced to learning to denoise", "type": "text" }, { "bbox": [ 485, 84, 540, 95 ], "score": 0.94, "content": "{ \\mathbf z } _ { t } \\sim q ( { \\mathbf z } _ { t } | { \\mathbf x } )", "type": "inline_equation" } ], "index": 0 }, { "bbox": [ 68, 91, 542, 110 ], "spans": [ { "bbox": [ 68, 91, 146, 110 ], "score": 1.0, "content": "into an estimate", "type": "text" }, { "bbox": [ 147, 96, 209, 106 ], "score": 0.94, "content": "\\hat { \\mathbf { x } } _ { \\theta } ( { \\mathbf z } _ { t } , \\lambda _ { t } ) \\approx { \\mathbf x }", "type": "inline_equation" }, { "bbox": [ 209, 91, 243, 110 ], "score": 1.0, "content": "for all", "type": "text" }, { "bbox": [ 243, 97, 247, 104 ], "score": 0.87, "content": "t", "type": "inline_equation" }, { "bbox": [ 247, 91, 542, 110 ], "score": 1.0, "content": ". Like (Song & Ermon, 2019; Ho et al., 2020) and most follow-up", "type": "text" } ], "index": 1 }, { "bbox": [ 70, 106, 397, 119 ], "spans": [ { "bbox": [ 70, 106, 397, 119 ], "score": 1.0, "content": "work, we optimize the model by minimizing a simple noise-prediction loss:", "type": "text" } ], "index": 2 } ], "index": 1, "bbox_fs": [ 68, 81, 542, 119 ] }, { "type": "interline_equation", "bbox": [ 210, 128, 400, 142 ], "lines": [ { "bbox": [ 210, 128, 400, 142 ], "spans": [ { "bbox": [ 210, 128, 400, 142 ], "score": 0.91, "content": "\\mathcal { L } ( \\mathbf { x } ) = \\mathbb { E } _ { \\epsilon \\sim \\mathcal { N } ( 0 , \\mathbf { I } ) , t \\sim U ( 0 , 1 ) } \\big [ \\| \\hat { \\epsilon } _ { \\boldsymbol { \\theta } } ( \\mathbf { z } _ { t } , \\lambda _ { t } ) - \\epsilon \\| _ { 2 } ^ { 2 } \\big ]", "type": "interline_equation", "image_path": "db4bd1a4a4cd023b03677a900aa10cb38dbaf8e9b28230ce0e6a05cc97c09a70.jpg" } ] } ], "index": 3, "virtual_lines": [ { "bbox": [ 210, 128, 400, 142 ], "spans": [], "index": 3 } ] }, { "type": "text", "bbox": [ 71, 152, 540, 189 ], "lines": [ { "bbox": [ 69, 151, 541, 168 ], "spans": [ { "bbox": [ 69, 151, 99, 168 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 99, 157, 162, 164 ], "score": 0.92, "content": "\\mathbf { z } _ { t } = \\alpha _ { t } \\mathbf { x } + \\sigma _ { t } \\mathbf { \\epsilon } ", "type": "inline_equation" }, { "bbox": [ 162, 151, 186, 168 ], "score": 1.0, "content": ", and", "type": "text" }, { "bbox": [ 186, 154, 333, 165 ], "score": 0.93, "content": "\\hat { \\epsilon } _ { \\boldsymbol { \\theta } } ( \\mathbf { z } _ { t } , \\lambda _ { t } ) = \\sigma _ { t } ^ { - 1 } ( \\mathbf { z } _ { t } - \\alpha _ { t } \\hat { \\mathbf { x } } _ { \\boldsymbol { \\theta } } ( \\mathbf { z } _ { t } , \\lambda _ { t } ) )", "type": "inline_equation" }, { "bbox": [ 333, 151, 480, 168 ], "score": 1.0, "content": ". We will drop the dependence on", "type": "text" }, { "bbox": [ 481, 156, 490, 164 ], "score": 0.91, "content": "\\lambda _ { t }", "type": "inline_equation" }, { "bbox": [ 491, 151, 541, 168 ], "score": 1.0, "content": "to simplify", "type": "text" } ], "index": 4 }, { "bbox": [ 69, 164, 541, 179 ], "spans": [ { "bbox": [ 69, 164, 353, 179 ], "score": 1.0, "content": "notation. In practice, we parameterize our models in terms of the", "type": "text" }, { "bbox": [ 354, 171, 360, 175 ], "score": 0.8, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 360, 164, 541, 179 ], "score": 1.0, "content": "-parameterization (Salimans & Ho, 2022),", "type": "text" } ], "index": 5 }, { "bbox": [ 70, 177, 308, 190 ], "spans": [ { "bbox": [ 70, 177, 171, 190 ], "score": 1.0, "content": "rather than predicting", "type": "text" }, { "bbox": [ 172, 182, 177, 187 ], "score": 0.89, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 177, 177, 191, 190 ], "score": 1.0, "content": "or", "type": "text" }, { "bbox": [ 192, 182, 198, 187 ], "score": 0.89, "content": "\\mathbf { x }", "type": "inline_equation" }, { "bbox": [ 199, 177, 308, 190 ], "score": 1.0, "content": "directly; see Section 2.4.", "type": "text" } ], "index": 6 } ], "index": 5, "bbox_fs": [ 69, 151, 541, 190 ] }, { "type": "text", "bbox": [ 70, 195, 540, 267 ], "lines": [ { "bbox": [ 69, 194, 542, 209 ], "spans": [ { "bbox": [ 69, 194, 411, 209 ], "score": 1.0, "content": "For conditional generative modeling, we provide the conditioning information", "type": "text" }, { "bbox": [ 412, 200, 417, 205 ], "score": 0.72, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 417, 194, 504, 209 ], "score": 1.0, "content": "drawn jointly with", "type": "text" }, { "bbox": [ 504, 200, 510, 205 ], "score": 0.81, "content": "\\mathbf { x }", "type": "inline_equation" }, { "bbox": [ 511, 194, 542, 209 ], "score": 1.0, "content": "to the", "type": "text" } ], "index": 7 }, { "bbox": [ 69, 207, 541, 221 ], "spans": [ { "bbox": [ 69, 207, 113, 221 ], "score": 1.0, "content": "model as", "type": "text" }, { "bbox": [ 113, 209, 154, 219 ], "score": 0.94, "content": "\\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } _ { t } )", "type": "inline_equation" }, { "bbox": [ 154, 207, 541, 221 ], "score": 1.0, "content": ". We use these conditional diffusion models for spatial and temporal super-resolution in", "type": "text" } ], "index": 8 }, { "bbox": [ 68, 218, 542, 232 ], "spans": [ { "bbox": [ 68, 218, 275, 232 ], "score": 1.0, "content": "our pipeline of diffusion models: in these cases,", "type": "text" }, { "bbox": [ 275, 224, 281, 229 ], "score": 0.6, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 281, 218, 542, 232 ], "score": 1.0, "content": "includes both the text and the previous stage low resolution", "type": "text" } ], "index": 9 }, { "bbox": [ 69, 230, 542, 244 ], "spans": [ { "bbox": [ 69, 230, 181, 244 ], "score": 1.0, "content": "video as well as a signal", "type": "text" }, { "bbox": [ 182, 233, 191, 243 ], "score": 0.92, "content": "\\lambda _ { t } ^ { \\prime }", "type": "inline_equation" }, { "bbox": [ 191, 230, 492, 244 ], "score": 1.0, "content": "that describes the strength of conditioning augmentation added to", "type": "text" }, { "bbox": [ 493, 236, 498, 241 ], "score": 0.66, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 498, 230, 542, 244 ], "score": 1.0, "content": ". Saharia", "type": "text" } ], "index": 10 }, { "bbox": [ 68, 241, 541, 256 ], "spans": [ { "bbox": [ 68, 241, 541, 256 ], "score": 1.0, "content": "et al. (2022b) found it critical to condition all the super-resolution models with the text embedding, and we", "type": "text" } ], "index": 11 }, { "bbox": [ 69, 254, 163, 268 ], "spans": [ { "bbox": [ 69, 254, 163, 268 ], "score": 1.0, "content": "follow this approach.", "type": "text" } ], "index": 12 } ], "index": 9.5, "bbox_fs": [ 68, 194, 542, 268 ] }, { "type": "text", "bbox": [ 70, 272, 540, 322 ], "lines": [ { "bbox": [ 70, 272, 541, 286 ], "spans": [ { "bbox": [ 70, 272, 541, 286 ], "score": 1.0, "content": "We use the discrete time ancestral sampler (Ho et al., 2020), with sampling variances derived from lower and", "type": "text" } ], "index": 13 }, { "bbox": [ 68, 284, 541, 298 ], "spans": [ { "bbox": [ 68, 284, 485, 298 ], "score": 1.0, "content": "upper bounds on reverse process entropy (Sohl-Dickstein et al., 2015; Ho et al., 2020; Nichol", "type": "text" }, { "bbox": [ 485, 287, 493, 295 ], "score": 0.67, "content": "\\&", "type": "inline_equation" }, { "bbox": [ 493, 284, 541, 298 ], "score": 1.0, "content": "Dhariwal,", "type": "text" } ], "index": 14 }, { "bbox": [ 69, 296, 541, 310 ], "spans": [ { "bbox": [ 69, 296, 484, 310 ], "score": 1.0, "content": "2021). This sampler can be formulated by using a reversed description of the forward process as", "type": "text" }, { "bbox": [ 485, 299, 541, 309 ], "score": 0.93, "content": "q ( \\mathbf { z } _ { s } | \\mathbf { z } _ { t } , \\mathbf { x } ) =", "type": "inline_equation" } ], "index": 15 }, { "bbox": [ 71, 307, 269, 324 ], "spans": [ { "bbox": [ 71, 310, 170, 324 ], "score": 0.92, "content": "\\mathcal { N } ( \\mathbf { z } _ { s } ; \\tilde { \\pmb { \\mu } } _ { s | t } ( \\mathbf { z } _ { t } , \\mathbf { x } ) , \\tilde { \\sigma } _ { s | t } ^ { 2 } \\mathbf { I } )", "type": "inline_equation" }, { "bbox": [ 171, 307, 208, 324 ], "score": 1.0, "content": "(noting", "type": "text" }, { "bbox": [ 208, 312, 230, 319 ], "score": 0.88, "content": "s < t", "type": "inline_equation" }, { "bbox": [ 230, 307, 269, 324 ], "score": 1.0, "content": "), where", "type": "text" } ], "index": 16 } ], "index": 14.5, "bbox_fs": [ 68, 272, 541, 324 ] }, { "type": "interline_equation", "bbox": [ 134, 333, 477, 348 ], "lines": [ { "bbox": [ 134, 333, 477, 348 ], "spans": [ { "bbox": [ 134, 333, 477, 348 ], "score": 0.89, "content": "\\tilde { \\mu } _ { s | t } ( \\mathbf { z } _ { t } , \\mathbf { x } ) = e ^ { \\lambda _ { t } - \\lambda _ { s } } ( \\alpha _ { s } / \\alpha _ { t } ) \\mathbf { z } _ { t } + ( 1 - e ^ { \\lambda _ { t } - \\lambda _ { s } } ) \\alpha _ { s } \\mathbf { x } \\quad \\mathrm { a n d } \\quad \\tilde { \\sigma } _ { s | t } ^ { 2 } = ( 1 - e ^ { \\lambda _ { t } - \\lambda _ { s } } ) \\sigma _ { s } ^ { 2 } .", "type": "interline_equation", "image_path": "a7b880d8eadfc62c0f843b873189b1cb8c4885c743fc1fd90542e4ff2258635f.jpg" } ] } ], "index": 17, "virtual_lines": [ { "bbox": [ 134, 333, 477, 348 ], "spans": [], "index": 17 } ] }, { "type": "text", "bbox": [ 71, 357, 348, 369 ], "lines": [ { "bbox": [ 70, 357, 348, 370 ], "spans": [ { "bbox": [ 70, 357, 122, 370 ], "score": 1.0, "content": "Starting at", "type": "text" }, { "bbox": [ 122, 359, 177, 370 ], "score": 0.95, "content": "{ \\bf z } _ { 1 } \\sim \\mathcal { N } ( { \\bf 0 } , { \\bf I } )", "type": "inline_equation" }, { "bbox": [ 177, 357, 348, 370 ], "score": 1.0, "content": ", the ancestral sampler follows the rule", "type": "text" } ], "index": 18 } ], "index": 18, "bbox_fs": [ 70, 357, 348, 370 ] }, { "type": "interline_equation", "bbox": [ 214, 379, 397, 400 ], "lines": [ { "bbox": [ 214, 379, 397, 400 ], "spans": [ { "bbox": [ 214, 379, 397, 400 ], "score": 0.94, "content": "\\mathbf { z } _ { s } = \\tilde { \\mu } _ { s | t } ( \\mathbf { z } _ { t } , \\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } ) ) + \\sqrt { ( \\tilde { \\sigma } _ { s | t } ^ { 2 } ) ^ { 1 - \\gamma } ( \\sigma _ { t | s } ^ { 2 } ) ^ { \\gamma } } \\epsilon", "type": "interline_equation", "image_path": "9a4f0f1639000ce1432966e3bf67c71de0417417382e4f2966860af208c6efa2.jpg" } ] } ], "index": 19, "virtual_lines": [ { "bbox": [ 214, 379, 397, 400 ], "spans": [], "index": 19 } ] }, { "type": "text", "bbox": [ 70, 408, 540, 445 ], "lines": [ { "bbox": [ 69, 407, 540, 422 ], "spans": [ { "bbox": [ 69, 407, 101, 422 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 102, 414, 107, 419 ], "score": 0.87, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 107, 407, 240, 422 ], "score": 1.0, "content": "is standard Gaussian noise,", "type": "text" }, { "bbox": [ 240, 414, 246, 421 ], "score": 0.92, "content": "\\gamma", "type": "inline_equation" }, { "bbox": [ 247, 407, 540, 422 ], "score": 1.0, "content": "is a hyperparameter that controls the stochasticity of the sam-", "type": "text" } ], "index": 20 }, { "bbox": [ 70, 420, 541, 434 ], "spans": [ { "bbox": [ 70, 420, 233, 434 ], "score": 1.0, "content": "pler (Nichol & Dhariwal, 2021), and", "type": "text" }, { "bbox": [ 234, 424, 246, 432 ], "score": 0.92, "content": "s , t", "type": "inline_equation" }, { "bbox": [ 247, 420, 452, 434 ], "score": 1.0, "content": "follow a uniformly spaced sequence from 1 to", "type": "text" }, { "bbox": [ 452, 424, 457, 431 ], "score": 0.29, "content": "0", "type": "inline_equation" }, { "bbox": [ 458, 420, 541, 434 ], "score": 1.0, "content": ". See Section 3 for", "type": "text" } ], "index": 21 }, { "bbox": [ 69, 433, 218, 447 ], "spans": [ { "bbox": [ 69, 433, 218, 447 ], "score": 1.0, "content": "sampler hyperparameter settings.", "type": "text" } ], "index": 22 } ], "index": 21, "bbox_fs": [ 69, 407, 541, 447 ] }, { "type": "text", "bbox": [ 70, 450, 540, 510 ], "lines": [ { "bbox": [ 70, 451, 540, 463 ], "spans": [ { "bbox": [ 70, 451, 540, 463 ], "score": 1.0, "content": "Alternatively, the deterministic DDIM sampler (Song et al., 2020) can be used for sampling. This sampler", "type": "text" } ], "index": 23 }, { "bbox": [ 69, 463, 540, 475 ], "spans": [ { "bbox": [ 69, 463, 540, 475 ], "score": 1.0, "content": "is a numerical integration rule for the probability flow ODE (Song et al., 2021; Salimans & Ho, 2022),", "type": "text" } ], "index": 24 }, { "bbox": [ 69, 474, 541, 487 ], "spans": [ { "bbox": [ 69, 474, 541, 487 ], "score": 1.0, "content": "which describes how a sample from a standard normal distribution can be deterministically transformed", "type": "text" } ], "index": 25 }, { "bbox": [ 70, 487, 540, 499 ], "spans": [ { "bbox": [ 70, 487, 540, 499 ], "score": 1.0, "content": "into a sample from the video data distribution using the denoising model. The DDIM sampler is useful for", "type": "text" } ], "index": 26 }, { "bbox": [ 69, 498, 372, 511 ], "spans": [ { "bbox": [ 69, 498, 372, 511 ], "score": 1.0, "content": "progressive distillation for fast sampling, as described in Section 2.7.", "type": "text" } ], "index": 27 } ], "index": 25, "bbox_fs": [ 69, 451, 541, 511 ] }, { "type": "title", "bbox": [ 71, 524, 322, 537 ], "lines": [ { "bbox": [ 69, 523, 323, 540 ], "spans": [ { "bbox": [ 69, 523, 323, 540 ], "score": 1.0, "content": "2.2 Cascaded Diffusion Models and text conditioning", "type": "text" } ], "index": 28 } ], "index": 28 }, { "type": "text", "bbox": [ 70, 546, 540, 655 ], "lines": [ { "bbox": [ 70, 547, 540, 560 ], "spans": [ { "bbox": [ 70, 547, 540, 560 ], "score": 1.0, "content": "Cascaded Diffusion Models (Ho et al., 2022a) are an effective method for scaling diffusion models to high", "type": "text" } ], "index": 29 }, { "bbox": [ 69, 559, 541, 572 ], "spans": [ { "bbox": [ 69, 559, 541, 572 ], "score": 1.0, "content": "resolution outputs, finding considerable success in both class-conditional ImageNet (Ho et al., 2022a) and", "type": "text" } ], "index": 30 }, { "bbox": [ 68, 570, 541, 584 ], "spans": [ { "bbox": [ 68, 570, 541, 584 ], "score": 1.0, "content": "text-to-image generation (Ramesh et al., 2022; Saharia et al., 2022b). Cascaded diffusion models generate", "type": "text" } ], "index": 31 }, { "bbox": [ 68, 582, 541, 596 ], "spans": [ { "bbox": [ 68, 582, 541, 596 ], "score": 1.0, "content": "an image or video at a low resolution, then sequentially increase the resolution of the image or video through", "type": "text" } ], "index": 32 }, { "bbox": [ 69, 594, 540, 607 ], "spans": [ { "bbox": [ 69, 594, 540, 607 ], "score": 1.0, "content": "a series of super-resolution diffusion models. Cascaded Diffusion Models can model very high dimensional", "type": "text" } ], "index": 33 }, { "bbox": [ 70, 607, 541, 619 ], "spans": [ { "bbox": [ 70, 607, 541, 619 ], "score": 1.0, "content": "problems while still keeping each sub-model relatively simple. Imagen (Saharia et al., 2022b) also showed", "type": "text" } ], "index": 34 }, { "bbox": [ 70, 619, 541, 632 ], "spans": [ { "bbox": [ 70, 619, 541, 632 ], "score": 1.0, "content": "that by conditioning on text embeddings from a large frozen language model in conjunction with cascaded", "type": "text" } ], "index": 35 }, { "bbox": [ 68, 630, 542, 644 ], "spans": [ { "bbox": [ 68, 630, 279, 644 ], "score": 1.0, "content": "diffusion models, one can generate high quality", "type": "text" }, { "bbox": [ 280, 633, 332, 641 ], "score": 0.88, "content": "1 0 2 4 \\times 1 0 2 4", "type": "inline_equation" }, { "bbox": [ 333, 630, 542, 644 ], "score": 1.0, "content": "images from text descriptions. In this work we", "type": "text" } ], "index": 36 }, { "bbox": [ 69, 643, 253, 656 ], "spans": [ { "bbox": [ 69, 643, 253, 656 ], "score": 1.0, "content": "extend this approach to video generation.", "type": "text" } ], "index": 37 } ], "index": 33, "bbox_fs": [ 68, 547, 542, 656 ] }, { "type": "text", "bbox": [ 70, 660, 540, 732 ], "lines": [ { "bbox": [ 70, 660, 540, 673 ], "spans": [ { "bbox": [ 70, 660, 540, 673 ], "score": 1.0, "content": "Figure 6 summarizes the entire cascading pipeline of Imagen Video. In total, we have 1 frozen text encoder, 1", "type": "text" } ], "index": 38 }, { "bbox": [ 69, 672, 541, 686 ], "spans": [ { "bbox": [ 69, 672, 541, 686 ], "score": 1.0, "content": "base video diffusion model, 3 SSR (spatial super-resolution), and 3 TSR (temporal super-resolution) models", "type": "text" } ], "index": 39 }, { "bbox": [ 76, 685, 541, 697 ], "spans": [ { "bbox": [ 76, 685, 541, 697 ], "score": 1.0, "content": "for a total of 7 video diffusion models, with a total of 11.6B diffusion model parameters. The data used to", "type": "text" } ], "index": 40 }, { "bbox": [ 69, 696, 541, 710 ], "spans": [ { "bbox": [ 69, 696, 541, 710 ], "score": 1.0, "content": "train these models is processed to the appropriate spatial and temporal resolutions by spatial resizing and", "type": "text" } ], "index": 41 }, { "bbox": [ 70, 709, 541, 721 ], "spans": [ { "bbox": [ 70, 709, 541, 721 ], "score": 1.0, "content": "frame skipping. 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In", "type": "text" } ], "index": 33 }, { "bbox": [ 70, 666, 542, 680 ], "spans": [ { "bbox": [ 70, 666, 542, 680 ], "score": 1.0, "content": "particular, it facilitates parallel training of different models in the cascade, as it reduces the sensitivity to", "type": "text" } ], "index": 34 }, { "bbox": [ 68, 677, 541, 692 ], "spans": [ { "bbox": [ 68, 677, 541, 692 ], "score": 1.0, "content": "domain gaps between the output of one stage of the cascade and the inputs used in training the subsequent", "type": "text" } ], "index": 35 }, { "bbox": [ 69, 690, 99, 705 ], "spans": [ { "bbox": [ 69, 690, 99, 705 ], "score": 1.0, "content": "stage.", "type": "text" } ], "index": 36 } ], "index": 33.5 }, { "type": "text", "bbox": [ 70, 708, 538, 732 ], "lines": [ { "bbox": [ 69, 708, 541, 721 ], "spans": [ { "bbox": [ 69, 708, 541, 721 ], "score": 1.0, "content": "Following Ho et al. 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Our use of v-parameterization also has the benefit of faster convergence of sample", "type": "text" } ], "index": 28 }, { "bbox": [ 69, 582, 212, 596 ], "spans": [ { "bbox": [ 69, 582, 212, 596 ], "score": 1.0, "content": "quality metrics: see Section 3.3.", "type": "text" } ], "index": 29 } ], "index": 26, "bbox_fs": [ 69, 511, 542, 596 ] }, { "type": "title", "bbox": [ 72, 608, 222, 621 ], "lines": [ { "bbox": [ 69, 606, 224, 624 ], "spans": [ { "bbox": [ 69, 606, 224, 624 ], "score": 1.0, "content": "2.5 Conditioning Augmentation", "type": "text" } ], "index": 30 } ], "index": 30 }, { "type": "text", "bbox": [ 70, 630, 540, 703 ], "lines": [ { "bbox": [ 70, 631, 541, 643 ], "spans": [ { "bbox": [ 70, 631, 541, 643 ], "score": 1.0, "content": "We use noise conditioning augmentation (Ho et al., 2022a) for all our temporal and spatial super-resolution", "type": "text" } ], "index": 31 }, { "bbox": [ 70, 643, 541, 655 ], "spans": [ { "bbox": [ 70, 643, 541, 655 ], "score": 1.0, "content": "models. Noise conditioning augmentation has been found to be critical for cascaded diffusion models for", "type": "text" } ], "index": 32 }, { "bbox": [ 70, 654, 541, 667 ], "spans": [ { "bbox": [ 70, 654, 541, 667 ], "score": 1.0, "content": "class-conditional generation (Ho et al., 2022a) as well as text-to-image models (Saharia et al., 2022b). In", "type": "text" } ], "index": 33 }, { "bbox": [ 70, 666, 542, 680 ], "spans": [ { "bbox": [ 70, 666, 542, 680 ], "score": 1.0, "content": "particular, it facilitates parallel training of different models in the cascade, as it reduces the sensitivity to", "type": "text" } ], "index": 34 }, { "bbox": [ 68, 677, 541, 692 ], "spans": [ { "bbox": [ 68, 677, 541, 692 ], "score": 1.0, "content": "domain gaps between the output of one stage of the cascade and the inputs used in training the subsequent", "type": "text" } ], "index": 35 }, { "bbox": [ 69, 690, 99, 705 ], "spans": [ { "bbox": [ 69, 690, 99, 705 ], "score": 1.0, "content": "stage.", "type": "text" } ], "index": 36 } ], "index": 33.5, "bbox_fs": [ 68, 631, 542, 705 ] }, { "type": "text", "bbox": [ 70, 708, 538, 732 ], "lines": [ { "bbox": [ 69, 708, 541, 721 ], "spans": [ { "bbox": [ 69, 708, 541, 721 ], "score": 1.0, "content": "Following Ho et al. (2022a), we apply Gaussian noise augmentation with a random signal-to-noise ratio to", "type": "text" } ], "index": 37 }, { "bbox": [ 70, 720, 541, 733 ], "spans": [ { "bbox": [ 70, 720, 541, 733 ], "score": 1.0, "content": "the conditioning input video during training, and this sampled signal-to-noise ratio is provided to the model", "type": "text" } ], "index": 38 }, { "bbox": [ 69, 82, 541, 94 ], "spans": [ { "bbox": [ 69, 82, 541, 94 ], "score": 1.0, "content": "as well. At sampling time we use a fixed signal-to-noise ratio such as 3 or 5, representing a small amount of", "type": "text", "cross_page": true } ], "index": 0 }, { "bbox": [ 68, 93, 541, 108 ], "spans": [ { "bbox": [ 68, 93, 541, 108 ], "score": 1.0, "content": "augmentation that aids in removing artifacts in the samples from the previous stage while preserving most", "type": "text", "cross_page": true } ], "index": 1 }, { "bbox": [ 69, 105, 144, 119 ], "spans": [ { "bbox": [ 69, 105, 144, 119 ], "score": 1.0, "content": "of the structure.", "type": "text", "cross_page": true } ], "index": 2 } ], "index": 37.5, "bbox_fs": [ 69, 708, 541, 733 ] } ] }, { "preproc_blocks": [ { "type": "text", "bbox": [ 71, 82, 540, 118 ], "lines": [ { "bbox": [ 69, 82, 541, 94 ], "spans": [ { "bbox": [ 69, 82, 541, 94 ], "score": 1.0, "content": "as well. At sampling time we use a fixed signal-to-noise ratio such as 3 or 5, representing a small amount of", "type": "text" } ], "index": 0 }, { "bbox": [ 68, 93, 541, 108 ], "spans": [ { "bbox": [ 68, 93, 541, 108 ], "score": 1.0, "content": "augmentation that aids in removing artifacts in the samples from the previous stage while preserving most", "type": "text" } ], "index": 1 }, { "bbox": [ 69, 105, 144, 119 ], "spans": [ { "bbox": [ 69, 105, 144, 119 ], "score": 1.0, "content": "of the structure.", "type": "text" } ], "index": 2 } ], "index": 1 }, { "type": "title", "bbox": [ 72, 131, 222, 144 ], "lines": [ { "bbox": [ 69, 129, 224, 147 ], "spans": [ { "bbox": [ 69, 129, 224, 147 ], "score": 1.0, "content": "2.6 Video-Image Joint Training", "type": "text" } ], "index": 3 } ], "index": 3 }, { "type": "text", "bbox": [ 70, 153, 540, 297 ], "lines": [ { "bbox": [ 69, 152, 542, 167 ], "spans": [ { "bbox": [ 69, 152, 542, 167 ], "score": 1.0, "content": "We follow Ho et al. (2022b) in jointly training all the models in the Imagen Video pipeline on images", "type": "text" } ], "index": 4 }, { "bbox": [ 69, 164, 541, 179 ], "spans": [ { "bbox": [ 69, 164, 541, 179 ], "score": 1.0, "content": "and videos. During training, individual images are treated as single frame videos. We achieve this by", "type": "text" } ], "index": 5 }, { "bbox": [ 68, 176, 542, 191 ], "spans": [ { "bbox": [ 68, 176, 542, 191 ], "score": 1.0, "content": "packing individual independent images into a sequence of the same length as a video, and bypass the", "type": "text" } ], "index": 6 }, { "bbox": [ 69, 189, 540, 202 ], "spans": [ { "bbox": [ 69, 189, 540, 202 ], "score": 1.0, "content": "temporal convolution residual blocks by masking out their computation path. Similarly, we disable cross-", "type": "text" } ], "index": 7 }, { "bbox": [ 69, 201, 542, 215 ], "spans": [ { "bbox": [ 69, 201, 542, 215 ], "score": 1.0, "content": "frame temporal attention by applying masking to the temporal attention maps. This strategy allows us to", "type": "text" } ], "index": 8 }, { "bbox": [ 69, 212, 542, 227 ], "spans": [ { "bbox": [ 69, 212, 542, 227 ], "score": 1.0, "content": "use to train our video models on image-text datasets that are significantly larger and more diverse than", "type": "text" } ], "index": 9 }, { "bbox": [ 69, 224, 541, 238 ], "spans": [ { "bbox": [ 69, 224, 541, 238 ], "score": 1.0, "content": "available video-text datasets. Consistent with Ho et al. (2022b), we observe that joint training with images", "type": "text" } ], "index": 10 }, { "bbox": [ 70, 237, 541, 250 ], "spans": [ { "bbox": [ 70, 237, 541, 250 ], "score": 1.0, "content": "significantly increases the overall quality of video samples. Another interesting artifact of joint training is the", "type": "text" } ], "index": 11 }, { "bbox": [ 70, 249, 541, 262 ], "spans": [ { "bbox": [ 70, 249, 541, 262 ], "score": 1.0, "content": "knowledge transfer from images to videos. For instance, while training on natural video data only enables", "type": "text" } ], "index": 12 }, { "bbox": [ 69, 260, 542, 275 ], "spans": [ { "bbox": [ 69, 260, 542, 275 ], "score": 1.0, "content": "the model to learn dynamics in natural settings, the model can learn about different image styles (such as", "type": "text" } ], "index": 13 }, { "bbox": [ 70, 273, 541, 285 ], "spans": [ { "bbox": [ 70, 273, 541, 285 ], "score": 1.0, "content": "sketch, painting, etc.) by training on images. As a result, this joint training enables the model to generate", "type": "text" } ], "index": 14 }, { "bbox": [ 69, 284, 400, 297 ], "spans": [ { "bbox": [ 69, 284, 400, 297 ], "score": 1.0, "content": "interesting video dynamics in different styles. See Fig. 8 for such examples.", "type": "text" } ], "index": 15 } ], "index": 9.5 }, { "type": "title", "bbox": [ 72, 308, 214, 321 ], "lines": [ { "bbox": [ 70, 308, 216, 322 ], "spans": [ { "bbox": [ 70, 308, 216, 322 ], "score": 1.0, "content": "2.6.1 Classifier Free Guidance", "type": "text" } ], "index": 16 } ], "index": 16 }, { "type": "text", "bbox": [ 71, 328, 540, 365 ], "lines": [ { "bbox": [ 70, 328, 541, 342 ], "spans": [ { "bbox": [ 70, 328, 541, 342 ], "score": 1.0, "content": "We found classifier free guidance (Ho & Salimans, 2021) to be critical for generating high fidelity samples", "type": "text" } ], "index": 17 }, { "bbox": [ 71, 341, 540, 353 ], "spans": [ { "bbox": [ 71, 341, 540, 353 ], "score": 1.0, "content": "which respect a given text prompt. This is consistent with earlier results on text-to-image models (Nichol", "type": "text" } ], "index": 18 }, { "bbox": [ 69, 352, 385, 366 ], "spans": [ { "bbox": [ 69, 352, 385, 366 ], "score": 1.0, "content": "et al., 2021; Ramesh et al., 2022; Saharia et al., 2022b; Yu et al., 2022).", "type": "text" } ], "index": 19 } ], "index": 18 }, { "type": "text", "bbox": [ 70, 370, 540, 419 ], "lines": [ { "bbox": [ 69, 370, 542, 384 ], "spans": [ { "bbox": [ 69, 370, 272, 384 ], "score": 1.0, "content": "In the conditional generation setting, the data", "type": "text" }, { "bbox": [ 273, 376, 279, 380 ], "score": 0.84, "content": "\\mathbf { x }", "type": "inline_equation" }, { "bbox": [ 279, 370, 435, 384 ], "score": 1.0, "content": "is generated conditional on a signal", "type": "text" }, { "bbox": [ 436, 376, 441, 380 ], "score": 0.62, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 442, 370, 542, 384 ], "score": 1.0, "content": ", which here represents", "type": "text" } ], "index": 20 }, { "bbox": [ 67, 381, 542, 398 ], "spans": [ { "bbox": [ 67, 381, 542, 398 ], "score": 1.0, "content": "a contextualized embedding of the text prompt, and a conditional diffusion model can be trained by using", "type": "text" } ], "index": 21 }, { "bbox": [ 69, 393, 541, 408 ], "spans": [ { "bbox": [ 69, 393, 119, 408 ], "score": 1.0, "content": "this signal", "type": "text" }, { "bbox": [ 119, 400, 124, 405 ], "score": 0.72, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 125, 393, 331, 408 ], "score": 1.0, "content": "as an additional input to the denoising model", "type": "text" }, { "bbox": [ 331, 397, 368, 407 ], "score": 0.95, "content": "\\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } )", "type": "inline_equation" }, { "bbox": [ 368, 393, 541, 408 ], "score": 1.0, "content": ". After training, Ho & Salimans (2021)", "type": "text" } ], "index": 22 }, { "bbox": [ 69, 405, 486, 421 ], "spans": [ { "bbox": [ 69, 405, 420, 421 ], "score": 1.0, "content": "find that sample quality can be improved by adjusting the denoising prediction", "type": "text" }, { "bbox": [ 420, 408, 457, 419 ], "score": 0.94, "content": "\\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } )", "type": "inline_equation" }, { "bbox": [ 457, 405, 486, 421 ], "score": 1.0, "content": "using", "type": "text" } ], "index": 23 } ], "index": 21.5 }, { "type": "interline_equation", "bbox": [ 221, 428, 390, 440 ], "lines": [ { "bbox": [ 221, 428, 390, 440 ], "spans": [ { "bbox": [ 221, 428, 390, 440 ], "score": 0.91, "content": "\\widetilde { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ) = ( 1 + w ) \\widehat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ) - w \\widehat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } ) ,", "type": "interline_equation", "image_path": "06f3a1abdc1bdf6b554de868a970a4328c5556b6f081c78cb6270b02b7c614fc.jpg" } ] } ], "index": 24, "virtual_lines": [ { "bbox": [ 221, 428, 390, 440 ], "spans": [], "index": 24 } ] }, { "type": "text", "bbox": [ 71, 448, 540, 522 ], "lines": [ { "bbox": [ 69, 447, 542, 463 ], "spans": [ { "bbox": [ 69, 447, 101, 463 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 101, 454, 109, 459 ], "score": 0.88, "content": "w", "type": "inline_equation" }, { "bbox": [ 109, 447, 228, 463 ], "score": 1.0, "content": "is the guidance strength,", "type": "text" }, { "bbox": [ 228, 451, 265, 461 ], "score": 0.94, "content": "\\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } )", "type": "inline_equation" }, { "bbox": [ 265, 447, 407, 463 ], "score": 1.0, "content": "is the conditional model, and", "type": "text" }, { "bbox": [ 407, 451, 511, 461 ], "score": 0.91, "content": "\\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } ) ~ = ~ \\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ~ = ~ \\varnothing )", "type": "inline_equation" }, { "bbox": [ 512, 447, 542, 463 ], "score": 1.0, "content": "is an", "type": "text" } ], "index": 25 }, { "bbox": [ 68, 459, 542, 476 ], "spans": [ { "bbox": [ 68, 459, 542, 476 ], "score": 1.0, "content": "unconditional model. The unconditional model is jointly trained with the conditional model by dropping", "type": "text" } ], "index": 26 }, { "bbox": [ 69, 472, 541, 487 ], "spans": [ { "bbox": [ 69, 472, 187, 487 ], "score": 1.0, "content": "out the conditioning input", "type": "text" }, { "bbox": [ 187, 478, 192, 483 ], "score": 0.82, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 192, 472, 407, 487 ], "score": 1.0, "content": ". The predictions of the adjusted denoising model", "type": "text" }, { "bbox": [ 408, 475, 444, 485 ], "score": 0.94, "content": "\\tilde { \\bf x } _ { \\theta } ( { \\bf z } _ { t } , { \\bf c } )", "type": "inline_equation" }, { "bbox": [ 445, 472, 541, 487 ], "score": 1.0, "content": "are clipped to respect", "type": "text" } ], "index": 27 }, { "bbox": [ 70, 485, 540, 497 ], "spans": [ { "bbox": [ 70, 485, 540, 497 ], "score": 1.0, "content": "the range of possible pixel values, which we discuss in more detail in the next section. Note that the linear", "type": "text" } ], "index": 28 }, { "bbox": [ 69, 496, 541, 510 ], "spans": [ { "bbox": [ 69, 496, 360, 510 ], "score": 1.0, "content": "transformation in Equation 5 can equivalently be performed in", "type": "text" }, { "bbox": [ 361, 502, 367, 506 ], "score": 0.57, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 367, 496, 399, 510 ], "score": 1.0, "content": "-space", "type": "text" }, { "bbox": [ 399, 498, 541, 509 ], "score": 0.91, "content": "\\left( \\tilde { \\mathbf { v } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ) = ( 1 + w ) \\hat { \\mathbf { v } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ) - \\right.", "type": "inline_equation" } ], "index": 29 }, { "bbox": [ 71, 508, 338, 522 ], "spans": [ { "bbox": [ 71, 510, 108, 521 ], "score": 0.91, "content": "w \\hat { \\mathbf { v } } _ { \\theta } ( \\mathbf { z } _ { t } )", "type": "inline_equation" }, { "bbox": [ 109, 508, 126, 522 ], "score": 1.0, "content": ") or", "type": "text" }, { "bbox": [ 127, 514, 132, 518 ], "score": 0.86, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 132, 508, 164, 522 ], "score": 1.0, "content": "-space", "type": "text" }, { "bbox": [ 164, 510, 333, 521 ], "score": 0.91, "content": "\\tilde { \\epsilon } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ) = ( 1 + w ) \\hat { \\epsilon } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ) - w \\hat { \\epsilon } _ { \\theta } ( \\mathbf { z } _ { t } ) )", "type": "inline_equation" }, { "bbox": [ 334, 508, 338, 522 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 30 } ], "index": 27.5 }, { "type": "text", "bbox": [ 71, 526, 540, 586 ], "lines": [ { "bbox": [ 69, 525, 541, 541 ], "spans": [ { "bbox": [ 69, 525, 90, 541 ], "score": 1.0, "content": "For", "type": "text" }, { "bbox": [ 90, 529, 119, 537 ], "score": 0.91, "content": "w > 0", "type": "inline_equation" }, { "bbox": [ 120, 525, 531, 541 ], "score": 1.0, "content": "this adjustment has the effect of over-emphasizing the effect of conditioning on the signal", "type": "text" }, { "bbox": [ 531, 532, 537, 537 ], "score": 0.66, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 537, 525, 541, 541 ], "score": 1.0, "content": ",", "type": "text" } ], "index": 31 }, { "bbox": [ 69, 537, 541, 552 ], "spans": [ { "bbox": [ 69, 537, 541, 552 ], "score": 1.0, "content": "which tends to produce samples of lower diversity but higher quality compared to sampling from the regular", "type": "text" } ], "index": 32 }, { "bbox": [ 69, 550, 541, 564 ], "spans": [ { "bbox": [ 69, 550, 541, 564 ], "score": 1.0, "content": "conditional model (Ho & Salimans, 2021). The method can be interpreted as a way to guide the samples", "type": "text" } ], "index": 33 }, { "bbox": [ 70, 562, 542, 576 ], "spans": [ { "bbox": [ 70, 562, 259, 576 ], "score": 1.0, "content": "towards areas where an implicit classifier", "type": "text" }, { "bbox": [ 259, 564, 289, 575 ], "score": 0.94, "content": "p ( \\mathbf { c } | \\mathbf { z } _ { t } )", "type": "inline_equation" }, { "bbox": [ 289, 562, 542, 576 ], "score": 1.0, "content": "has high likelihood; as such, it is an adaptation of the", "type": "text" } ], "index": 34 }, { "bbox": [ 70, 574, 397, 588 ], "spans": [ { "bbox": [ 70, 574, 322, 588 ], "score": 1.0, "content": "explicit classifier guidance method proposed by Dhariwal", "type": "text" }, { "bbox": [ 323, 577, 331, 585 ], "score": 0.39, "content": "\\&", "type": "inline_equation" }, { "bbox": [ 331, 574, 397, 588 ], "score": 1.0, "content": "Nichol (2022).", "type": "text" } ], "index": 35 } ], "index": 33 }, { "type": "title", "bbox": [ 71, 598, 217, 610 ], "lines": [ { "bbox": [ 69, 596, 218, 613 ], "spans": [ { "bbox": [ 69, 596, 218, 613 ], "score": 1.0, "content": "2.6.2 Large Guidance Weights", "type": "text" } ], "index": 36 } ], "index": 36 }, { "type": "text", "bbox": [ 70, 618, 540, 703 ], "lines": [ { "bbox": [ 70, 618, 542, 632 ], "spans": [ { "bbox": [ 70, 618, 290, 632 ], "score": 1.0, "content": "When using large guidance weights, the resulting", "type": "text" }, { "bbox": [ 290, 621, 327, 631 ], "score": 0.94, "content": "\\tilde { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } )", "type": "inline_equation" }, { "bbox": [ 327, 618, 542, 632 ], "score": 1.0, "content": "must be projected back to the possible range of", "type": "text" } ], "index": 37 }, { "bbox": [ 68, 630, 542, 644 ], "spans": [ { "bbox": [ 68, 630, 542, 644 ], "score": 1.0, "content": "pixel values at every sampling step to prevent train-test mismatch. When using large guidance weights, the", "type": "text" } ], "index": 38 }, { "bbox": [ 68, 642, 542, 656 ], "spans": [ { "bbox": [ 68, 642, 542, 656 ], "score": 1.0, "content": "standard approach, i.e., clipping the values to the right range (e.g., np.clip(x, -1, 1)), leads to significant", "type": "text" } ], "index": 39 }, { "bbox": [ 69, 654, 541, 668 ], "spans": [ { "bbox": [ 69, 654, 541, 668 ], "score": 1.0, "content": "saturation artifacts in the generated videos. A similar effect was observed in Saharia et al. (2022b) for text-", "type": "text" } ], "index": 40 }, { "bbox": [ 69, 666, 541, 680 ], "spans": [ { "bbox": [ 69, 666, 541, 680 ], "score": 1.0, "content": "to-image generation. Saharia et al. (2022b) use dynamic thresholding to alleviate this saturation issue.", "type": "text" } ], "index": 41 }, { "bbox": [ 70, 678, 541, 692 ], "spans": [ { "bbox": [ 70, 678, 480, 692 ], "score": 1.0, "content": "Specifically, dynamic clipping involves clipping the image to a dynamically chosen threshold", "type": "text" }, { "bbox": [ 481, 684, 486, 688 ], "score": 0.26, "content": "\\mathbf { s }", "type": "inline_equation" }, { "bbox": [ 486, 678, 541, 692 ], "score": 1.0, "content": "followed by", "type": "text" } ], "index": 42 }, { "bbox": [ 69, 690, 368, 704 ], "spans": [ { "bbox": [ 69, 690, 118, 704 ], "score": 1.0, "content": "scaling by", "type": "text" }, { "bbox": [ 118, 695, 123, 700 ], "score": 0.62, "content": "\\tt s", "type": "inline_equation" }, { "bbox": [ 123, 690, 368, 704 ], "score": 1.0, "content": "(i.e., np.clip(x, -s, s) / s) (Saharia et al., 2022b).", "type": "text" } ], "index": 43 } ], "index": 40 }, { "type": "text", "bbox": [ 70, 708, 537, 732 ], "lines": [ { "bbox": [ 70, 708, 538, 721 ], "spans": [ { "bbox": [ 70, 708, 538, 721 ], "score": 1.0, "content": "Although dynamic clipping can help with over-saturation, we did not find it sufficient in initial experiments.", "type": "text" } ], "index": 44 }, { "bbox": [ 70, 720, 538, 732 ], "spans": [ { "bbox": [ 70, 720, 261, 732 ], "score": 1.0, "content": "We therefore also experiment with letting", "type": "text" }, { "bbox": [ 262, 725, 269, 730 ], "score": 0.88, "content": "w", "type": "inline_equation" }, { "bbox": [ 270, 720, 538, 732 ], "score": 1.0, "content": "oscillate between a high and a low guidance weight at each", "type": "text" } ], "index": 45 } ], "index": 44.5 } ], "page_idx": 9, "page_size": [ 612, 792 ], "discarded_blocks": [ { "type": "discarded", "bbox": [ 72, 27, 237, 37 ], "lines": [ { "bbox": [ 70, 25, 239, 38 ], "spans": [ { "bbox": [ 70, 25, 239, 38 ], "score": 1.0, "content": "Under review as submission to TMLR", "type": "text" } ] } ] }, { "type": "discarded", "bbox": [ 300, 751, 311, 760 ], "lines": [ { "bbox": [ 299, 750, 313, 763 ], "spans": [ { "bbox": [ 299, 750, 313, 763 ], "score": 1.0, "content": "10", "type": "text" } ] } ] } ], "para_blocks": [ { "type": "text", "bbox": [ 71, 82, 540, 118 ], "lines": [], "index": 1, "bbox_fs": [ 68, 82, 541, 119 ], "lines_deleted": true }, { "type": "title", "bbox": [ 72, 131, 222, 144 ], "lines": [ { "bbox": [ 69, 129, 224, 147 ], "spans": [ { "bbox": [ 69, 129, 224, 147 ], "score": 1.0, "content": "2.6 Video-Image Joint Training", "type": "text" } ], "index": 3 } ], "index": 3 }, { "type": "text", "bbox": [ 70, 153, 540, 297 ], "lines": [ { "bbox": [ 69, 152, 542, 167 ], "spans": [ { "bbox": [ 69, 152, 542, 167 ], "score": 1.0, "content": "We follow Ho et al. (2022b) in jointly training all the models in the Imagen Video pipeline on images", "type": "text" } ], "index": 4 }, { "bbox": [ 69, 164, 541, 179 ], "spans": [ { "bbox": [ 69, 164, 541, 179 ], "score": 1.0, "content": "and videos. During training, individual images are treated as single frame videos. We achieve this by", "type": "text" } ], "index": 5 }, { "bbox": [ 68, 176, 542, 191 ], "spans": [ { "bbox": [ 68, 176, 542, 191 ], "score": 1.0, "content": "packing individual independent images into a sequence of the same length as a video, and bypass the", "type": "text" } ], "index": 6 }, { "bbox": [ 69, 189, 540, 202 ], "spans": [ { "bbox": [ 69, 189, 540, 202 ], "score": 1.0, "content": "temporal convolution residual blocks by masking out their computation path. Similarly, we disable cross-", "type": "text" } ], "index": 7 }, { "bbox": [ 69, 201, 542, 215 ], "spans": [ { "bbox": [ 69, 201, 542, 215 ], "score": 1.0, "content": "frame temporal attention by applying masking to the temporal attention maps. This strategy allows us to", "type": "text" } ], "index": 8 }, { "bbox": [ 69, 212, 542, 227 ], "spans": [ { "bbox": [ 69, 212, 542, 227 ], "score": 1.0, "content": "use to train our video models on image-text datasets that are significantly larger and more diverse than", "type": "text" } ], "index": 9 }, { "bbox": [ 69, 224, 541, 238 ], "spans": [ { "bbox": [ 69, 224, 541, 238 ], "score": 1.0, "content": "available video-text datasets. Consistent with Ho et al. (2022b), we observe that joint training with images", "type": "text" } ], "index": 10 }, { "bbox": [ 70, 237, 541, 250 ], "spans": [ { "bbox": [ 70, 237, 541, 250 ], "score": 1.0, "content": "significantly increases the overall quality of video samples. Another interesting artifact of joint training is the", "type": "text" } ], "index": 11 }, { "bbox": [ 70, 249, 541, 262 ], "spans": [ { "bbox": [ 70, 249, 541, 262 ], "score": 1.0, "content": "knowledge transfer from images to videos. For instance, while training on natural video data only enables", "type": "text" } ], "index": 12 }, { "bbox": [ 69, 260, 542, 275 ], "spans": [ { "bbox": [ 69, 260, 542, 275 ], "score": 1.0, "content": "the model to learn dynamics in natural settings, the model can learn about different image styles (such as", "type": "text" } ], "index": 13 }, { "bbox": [ 70, 273, 541, 285 ], "spans": [ { "bbox": [ 70, 273, 541, 285 ], "score": 1.0, "content": "sketch, painting, etc.) by training on images. As a result, this joint training enables the model to generate", "type": "text" } ], "index": 14 }, { "bbox": [ 69, 284, 400, 297 ], "spans": [ { "bbox": [ 69, 284, 400, 297 ], "score": 1.0, "content": "interesting video dynamics in different styles. See Fig. 8 for such examples.", "type": "text" } ], "index": 15 } ], "index": 9.5, "bbox_fs": [ 68, 152, 542, 297 ] }, { "type": "title", "bbox": [ 72, 308, 214, 321 ], "lines": [ { "bbox": [ 70, 308, 216, 322 ], "spans": [ { "bbox": [ 70, 308, 216, 322 ], "score": 1.0, "content": "2.6.1 Classifier Free Guidance", "type": "text" } ], "index": 16 } ], "index": 16 }, { "type": "text", "bbox": [ 71, 328, 540, 365 ], "lines": [ { "bbox": [ 70, 328, 541, 342 ], "spans": [ { "bbox": [ 70, 328, 541, 342 ], "score": 1.0, "content": "We found classifier free guidance (Ho & Salimans, 2021) to be critical for generating high fidelity samples", "type": "text" } ], "index": 17 }, { "bbox": [ 71, 341, 540, 353 ], "spans": [ { "bbox": [ 71, 341, 540, 353 ], "score": 1.0, "content": "which respect a given text prompt. This is consistent with earlier results on text-to-image models (Nichol", "type": "text" } ], "index": 18 }, { "bbox": [ 69, 352, 385, 366 ], "spans": [ { "bbox": [ 69, 352, 385, 366 ], "score": 1.0, "content": "et al., 2021; Ramesh et al., 2022; Saharia et al., 2022b; Yu et al., 2022).", "type": "text" } ], "index": 19 } ], "index": 18, "bbox_fs": [ 69, 328, 541, 366 ] }, { "type": "text", "bbox": [ 70, 370, 540, 419 ], "lines": [ { "bbox": [ 69, 370, 542, 384 ], "spans": [ { "bbox": [ 69, 370, 272, 384 ], "score": 1.0, "content": "In the conditional generation setting, the data", "type": "text" }, { "bbox": [ 273, 376, 279, 380 ], "score": 0.84, "content": "\\mathbf { x }", "type": "inline_equation" }, { "bbox": [ 279, 370, 435, 384 ], "score": 1.0, "content": "is generated conditional on a signal", "type": "text" }, { "bbox": [ 436, 376, 441, 380 ], "score": 0.62, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 442, 370, 542, 384 ], "score": 1.0, "content": ", which here represents", "type": "text" } ], "index": 20 }, { "bbox": [ 67, 381, 542, 398 ], "spans": [ { "bbox": [ 67, 381, 542, 398 ], "score": 1.0, "content": "a contextualized embedding of the text prompt, and a conditional diffusion model can be trained by using", "type": "text" } ], "index": 21 }, { "bbox": [ 69, 393, 541, 408 ], "spans": [ { "bbox": [ 69, 393, 119, 408 ], "score": 1.0, "content": "this signal", "type": "text" }, { "bbox": [ 119, 400, 124, 405 ], "score": 0.72, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 125, 393, 331, 408 ], "score": 1.0, "content": "as an additional input to the denoising model", "type": "text" }, { "bbox": [ 331, 397, 368, 407 ], "score": 0.95, "content": "\\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } )", "type": "inline_equation" }, { "bbox": [ 368, 393, 541, 408 ], "score": 1.0, "content": ". After training, Ho & Salimans (2021)", "type": "text" } ], "index": 22 }, { "bbox": [ 69, 405, 486, 421 ], "spans": [ { "bbox": [ 69, 405, 420, 421 ], "score": 1.0, "content": "find that sample quality can be improved by adjusting the denoising prediction", "type": "text" }, { "bbox": [ 420, 408, 457, 419 ], "score": 0.94, "content": "\\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } )", "type": "inline_equation" }, { "bbox": [ 457, 405, 486, 421 ], "score": 1.0, "content": "using", "type": "text" } ], "index": 23 } ], "index": 21.5, "bbox_fs": [ 67, 370, 542, 421 ] }, { "type": "interline_equation", "bbox": [ 221, 428, 390, 440 ], "lines": [ { "bbox": [ 221, 428, 390, 440 ], "spans": [ { "bbox": [ 221, 428, 390, 440 ], "score": 0.91, "content": "\\widetilde { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ) = ( 1 + w ) \\widehat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ) - w \\widehat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } ) ,", "type": "interline_equation", "image_path": "06f3a1abdc1bdf6b554de868a970a4328c5556b6f081c78cb6270b02b7c614fc.jpg" } ] } ], "index": 24, "virtual_lines": [ { "bbox": [ 221, 428, 390, 440 ], "spans": [], "index": 24 } ] }, { "type": "text", "bbox": [ 71, 448, 540, 522 ], "lines": [ { "bbox": [ 69, 447, 542, 463 ], "spans": [ { "bbox": [ 69, 447, 101, 463 ], "score": 1.0, "content": "where", "type": "text" }, { "bbox": [ 101, 454, 109, 459 ], "score": 0.88, "content": "w", "type": "inline_equation" }, { "bbox": [ 109, 447, 228, 463 ], "score": 1.0, "content": "is the guidance strength,", "type": "text" }, { "bbox": [ 228, 451, 265, 461 ], "score": 0.94, "content": "\\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } )", "type": "inline_equation" }, { "bbox": [ 265, 447, 407, 463 ], "score": 1.0, "content": "is the conditional model, and", "type": "text" }, { "bbox": [ 407, 451, 511, 461 ], "score": 0.91, "content": "\\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } ) ~ = ~ \\hat { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ~ = ~ \\varnothing )", "type": "inline_equation" }, { "bbox": [ 512, 447, 542, 463 ], "score": 1.0, "content": "is an", "type": "text" } ], "index": 25 }, { "bbox": [ 68, 459, 542, 476 ], "spans": [ { "bbox": [ 68, 459, 542, 476 ], "score": 1.0, "content": "unconditional model. The unconditional model is jointly trained with the conditional model by dropping", "type": "text" } ], "index": 26 }, { "bbox": [ 69, 472, 541, 487 ], "spans": [ { "bbox": [ 69, 472, 187, 487 ], "score": 1.0, "content": "out the conditioning input", "type": "text" }, { "bbox": [ 187, 478, 192, 483 ], "score": 0.82, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 192, 472, 407, 487 ], "score": 1.0, "content": ". The predictions of the adjusted denoising model", "type": "text" }, { "bbox": [ 408, 475, 444, 485 ], "score": 0.94, "content": "\\tilde { \\bf x } _ { \\theta } ( { \\bf z } _ { t } , { \\bf c } )", "type": "inline_equation" }, { "bbox": [ 445, 472, 541, 487 ], "score": 1.0, "content": "are clipped to respect", "type": "text" } ], "index": 27 }, { "bbox": [ 70, 485, 540, 497 ], "spans": [ { "bbox": [ 70, 485, 540, 497 ], "score": 1.0, "content": "the range of possible pixel values, which we discuss in more detail in the next section. Note that the linear", "type": "text" } ], "index": 28 }, { "bbox": [ 69, 496, 541, 510 ], "spans": [ { "bbox": [ 69, 496, 360, 510 ], "score": 1.0, "content": "transformation in Equation 5 can equivalently be performed in", "type": "text" }, { "bbox": [ 361, 502, 367, 506 ], "score": 0.57, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 367, 496, 399, 510 ], "score": 1.0, "content": "-space", "type": "text" }, { "bbox": [ 399, 498, 541, 509 ], "score": 0.91, "content": "\\left( \\tilde { \\mathbf { v } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ) = ( 1 + w ) \\hat { \\mathbf { v } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ) - \\right.", "type": "inline_equation" } ], "index": 29 }, { "bbox": [ 71, 508, 338, 522 ], "spans": [ { "bbox": [ 71, 510, 108, 521 ], "score": 0.91, "content": "w \\hat { \\mathbf { v } } _ { \\theta } ( \\mathbf { z } _ { t } )", "type": "inline_equation" }, { "bbox": [ 109, 508, 126, 522 ], "score": 1.0, "content": ") or", "type": "text" }, { "bbox": [ 127, 514, 132, 518 ], "score": 0.86, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 132, 508, 164, 522 ], "score": 1.0, "content": "-space", "type": "text" }, { "bbox": [ 164, 510, 333, 521 ], "score": 0.91, "content": "\\tilde { \\epsilon } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ) = ( 1 + w ) \\hat { \\epsilon } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } ) - w \\hat { \\epsilon } _ { \\theta } ( \\mathbf { z } _ { t } ) )", "type": "inline_equation" }, { "bbox": [ 334, 508, 338, 522 ], "score": 1.0, "content": ".", "type": "text" } ], "index": 30 } ], "index": 27.5, "bbox_fs": [ 68, 447, 542, 522 ] }, { "type": "text", "bbox": [ 71, 526, 540, 586 ], "lines": [ { "bbox": [ 69, 525, 541, 541 ], "spans": [ { "bbox": [ 69, 525, 90, 541 ], "score": 1.0, "content": "For", "type": "text" }, { "bbox": [ 90, 529, 119, 537 ], "score": 0.91, "content": "w > 0", "type": "inline_equation" }, { "bbox": [ 120, 525, 531, 541 ], "score": 1.0, "content": "this adjustment has the effect of over-emphasizing the effect of conditioning on the signal", "type": "text" }, { "bbox": [ 531, 532, 537, 537 ], "score": 0.66, "content": "\\mathbf { c }", "type": "inline_equation" }, { "bbox": [ 537, 525, 541, 541 ], "score": 1.0, "content": ",", "type": "text" } ], "index": 31 }, { "bbox": [ 69, 537, 541, 552 ], "spans": [ { "bbox": [ 69, 537, 541, 552 ], "score": 1.0, "content": "which tends to produce samples of lower diversity but higher quality compared to sampling from the regular", "type": "text" } ], "index": 32 }, { "bbox": [ 69, 550, 541, 564 ], "spans": [ { "bbox": [ 69, 550, 541, 564 ], "score": 1.0, "content": "conditional model (Ho & Salimans, 2021). The method can be interpreted as a way to guide the samples", "type": "text" } ], "index": 33 }, { "bbox": [ 70, 562, 542, 576 ], "spans": [ { "bbox": [ 70, 562, 259, 576 ], "score": 1.0, "content": "towards areas where an implicit classifier", "type": "text" }, { "bbox": [ 259, 564, 289, 575 ], "score": 0.94, "content": "p ( \\mathbf { c } | \\mathbf { z } _ { t } )", "type": "inline_equation" }, { "bbox": [ 289, 562, 542, 576 ], "score": 1.0, "content": "has high likelihood; as such, it is an adaptation of the", "type": "text" } ], "index": 34 }, { "bbox": [ 70, 574, 397, 588 ], "spans": [ { "bbox": [ 70, 574, 322, 588 ], "score": 1.0, "content": "explicit classifier guidance method proposed by Dhariwal", "type": "text" }, { "bbox": [ 323, 577, 331, 585 ], "score": 0.39, "content": "\\&", "type": "inline_equation" }, { "bbox": [ 331, 574, 397, 588 ], "score": 1.0, "content": "Nichol (2022).", "type": "text" } ], "index": 35 } ], "index": 33, "bbox_fs": [ 69, 525, 542, 588 ] }, { "type": "title", "bbox": [ 71, 598, 217, 610 ], "lines": [ { "bbox": [ 69, 596, 218, 613 ], "spans": [ { "bbox": [ 69, 596, 218, 613 ], "score": 1.0, "content": "2.6.2 Large Guidance Weights", "type": "text" } ], "index": 36 } ], "index": 36 }, { "type": "text", "bbox": [ 70, 618, 540, 703 ], "lines": [ { "bbox": [ 70, 618, 542, 632 ], "spans": [ { "bbox": [ 70, 618, 290, 632 ], "score": 1.0, "content": "When using large guidance weights, the resulting", "type": "text" }, { "bbox": [ 290, 621, 327, 631 ], "score": 0.94, "content": "\\tilde { \\mathbf { x } } _ { \\theta } ( \\mathbf { z } _ { t } , \\mathbf { c } )", "type": "inline_equation" }, { "bbox": [ 327, 618, 542, 632 ], "score": 1.0, "content": "must be projected back to the possible range of", "type": "text" } ], "index": 37 }, { "bbox": [ 68, 630, 542, 644 ], "spans": [ { "bbox": [ 68, 630, 542, 644 ], "score": 1.0, "content": "pixel values at every sampling step to prevent train-test mismatch. When using large guidance weights, the", "type": "text" } ], "index": 38 }, { "bbox": [ 68, 642, 542, 656 ], "spans": [ { "bbox": [ 68, 642, 542, 656 ], "score": 1.0, "content": "standard approach, i.e., clipping the values to the right range (e.g., np.clip(x, -1, 1)), leads to significant", "type": "text" } ], "index": 39 }, { "bbox": [ 69, 654, 541, 668 ], "spans": [ { "bbox": [ 69, 654, 541, 668 ], "score": 1.0, "content": "saturation artifacts in the generated videos. A similar effect was observed in Saharia et al. (2022b) for text-", "type": "text" } ], "index": 40 }, { "bbox": [ 69, 666, 541, 680 ], "spans": [ { "bbox": [ 69, 666, 541, 680 ], "score": 1.0, "content": "to-image generation. Saharia et al. (2022b) use dynamic thresholding to alleviate this saturation issue.", "type": "text" } ], "index": 41 }, { "bbox": [ 70, 678, 541, 692 ], "spans": [ { "bbox": [ 70, 678, 480, 692 ], "score": 1.0, "content": "Specifically, dynamic clipping involves clipping the image to a dynamically chosen threshold", "type": "text" }, { "bbox": [ 481, 684, 486, 688 ], "score": 0.26, "content": "\\mathbf { s }", "type": "inline_equation" }, { "bbox": [ 486, 678, 541, 692 ], "score": 1.0, "content": "followed by", "type": "text" } ], "index": 42 }, { "bbox": [ 69, 690, 368, 704 ], "spans": [ { "bbox": [ 69, 690, 118, 704 ], "score": 1.0, "content": "scaling by", "type": "text" }, { "bbox": [ 118, 695, 123, 700 ], "score": 0.62, "content": "\\tt s", "type": "inline_equation" }, { "bbox": [ 123, 690, 368, 704 ], "score": 1.0, "content": "(i.e., np.clip(x, -s, s) / s) (Saharia et al., 2022b).", "type": "text" } ], "index": 43 } ], "index": 40, "bbox_fs": [ 68, 618, 542, 704 ] }, { "type": "text", "bbox": [ 70, 708, 537, 732 ], "lines": [ { "bbox": [ 70, 708, 538, 721 ], "spans": [ { "bbox": [ 70, 708, 538, 721 ], "score": 1.0, "content": "Although dynamic clipping can help with over-saturation, we did not find it sufficient in initial experiments.", "type": "text" } ], "index": 44 }, { "bbox": [ 70, 720, 538, 732 ], "spans": [ { "bbox": [ 70, 720, 261, 732 ], "score": 1.0, "content": "We therefore also experiment with letting", "type": "text" }, { "bbox": [ 262, 725, 269, 730 ], "score": 0.88, "content": "w", "type": "inline_equation" }, { "bbox": [ 270, 720, 538, 732 ], "score": 1.0, "content": "oscillate between a high and a low guidance weight at each", "type": "text" } ], "index": 45 }, { "bbox": [ 68, 80, 542, 97 ], "spans": [ { "bbox": [ 68, 80, 542, 97 ], "score": 1.0, "content": "alternating sampling step, which we find significantly helps with these saturation issues. We call this sampling", "type": "text", "cross_page": true } ], "index": 0 }, { "bbox": [ 69, 94, 541, 108 ], "spans": [ { "bbox": [ 69, 94, 541, 108 ], "score": 1.0, "content": "technique oscillating guidance. Specifically, we use a constant high guidance weight for a certain number", "type": "text", "cross_page": true } ], "index": 1 }, { "bbox": [ 69, 105, 541, 119 ], "spans": [ { "bbox": [ 69, 105, 541, 119 ], "score": 1.0, "content": "of initial sampling steps, followed by oscillation between high and low guidance weights: this oscillation", "type": "text", "cross_page": true } ], "index": 2 }, { "bbox": [ 69, 118, 541, 132 ], "spans": [ { "bbox": [ 69, 118, 541, 132 ], "score": 1.0, "content": "is implemented simply by alternating between a large weight (such as 15) and a small weight (such as 1)", "type": "text", "cross_page": true } ], "index": 3 }, { "bbox": [ 68, 128, 542, 145 ], "spans": [ { "bbox": [ 68, 128, 542, 145 ], "score": 1.0, "content": "over the course of sampling. We hypothesize that a constant high guidance weight at the start of sampling", "type": "text", "cross_page": true } ], "index": 4 }, { "bbox": [ 69, 142, 541, 155 ], "spans": [ { "bbox": [ 69, 142, 541, 155 ], "score": 1.0, "content": "helps break modes with heavy emphasis on text, while oscillating between high and low guidance weights", "type": "text", "cross_page": true } ], "index": 5 }, { "bbox": [ 69, 154, 542, 167 ], "spans": [ { "bbox": [ 69, 154, 542, 167 ], "score": 1.0, "content": "helps maintain a strong text alignment (via high guidance sampling step) while limiting saturation artifacts", "type": "text", "cross_page": true } ], "index": 6 }, { "bbox": [ 70, 166, 542, 180 ], "spans": [ { "bbox": [ 70, 166, 542, 180 ], "score": 1.0, "content": "(via low guidance sampling step). We however observed no improvement in sample fidelity and more visual", "type": "text", "cross_page": true } ], "index": 7 }, { "bbox": [ 69, 178, 541, 192 ], "spans": [ { "bbox": [ 69, 178, 354, 192 ], "score": 1.0, "content": "artifacts when applying oscillating guidance to models past the 80", "type": "text", "cross_page": true }, { "bbox": [ 354, 182, 362, 189 ], "score": 0.29, "content": "\\times", "type": "inline_equation", "cross_page": true }, { "bbox": [ 362, 178, 541, 192 ], "score": 1.0, "content": "48 spatial resolution. Thus we only apply", "type": "text", "cross_page": true } ], "index": 8 }, { "bbox": [ 70, 190, 337, 202 ], "spans": [ { "bbox": [ 70, 190, 337, 202 ], "score": 1.0, "content": "oscillating guidance to the base and the first two SR models.", "type": "text", "cross_page": true } ], "index": 9 } ], "index": 44.5, "bbox_fs": [ 70, 708, 538, 732 ] } ] }, { "preproc_blocks": [ { "type": "text", "bbox": [ 70, 82, 540, 201 ], "lines": [ { "bbox": [ 68, 80, 542, 97 ], "spans": [ { "bbox": [ 68, 80, 542, 97 ], "score": 1.0, "content": "alternating sampling step, which we find significantly helps with these saturation issues. We call this sampling", "type": "text" } ], "index": 0 }, { "bbox": [ 69, 94, 541, 108 ], "spans": [ { "bbox": [ 69, 94, 541, 108 ], "score": 1.0, "content": "technique oscillating guidance. Specifically, we use a constant high guidance weight for a certain number", "type": "text" } ], "index": 1 }, { "bbox": [ 69, 105, 541, 119 ], "spans": [ { "bbox": [ 69, 105, 541, 119 ], "score": 1.0, "content": "of initial sampling steps, followed by oscillation between high and low guidance weights: this oscillation", "type": "text" } ], "index": 2 }, { "bbox": [ 69, 118, 541, 132 ], "spans": [ { "bbox": [ 69, 118, 541, 132 ], "score": 1.0, "content": "is implemented simply by alternating between a large weight (such as 15) and a small weight (such as 1)", "type": "text" } ], "index": 3 }, { "bbox": [ 68, 128, 542, 145 ], "spans": [ { "bbox": [ 68, 128, 542, 145 ], "score": 1.0, "content": "over the course of sampling. We hypothesize that a constant high guidance weight at the start of sampling", "type": "text" } ], "index": 4 }, { "bbox": [ 69, 142, 541, 155 ], "spans": [ { "bbox": [ 69, 142, 541, 155 ], "score": 1.0, "content": "helps break modes with heavy emphasis on text, while oscillating between high and low guidance weights", "type": "text" } ], "index": 5 }, { "bbox": [ 69, 154, 542, 167 ], "spans": [ { "bbox": [ 69, 154, 542, 167 ], "score": 1.0, "content": "helps maintain a strong text alignment (via high guidance sampling step) while limiting saturation artifacts", "type": "text" } ], "index": 6 }, { "bbox": [ 70, 166, 542, 180 ], "spans": [ { "bbox": [ 70, 166, 542, 180 ], "score": 1.0, "content": "(via low guidance sampling step). We however observed no improvement in sample fidelity and more visual", "type": "text" } ], "index": 7 }, { "bbox": [ 69, 178, 541, 192 ], "spans": [ { "bbox": [ 69, 178, 354, 192 ], "score": 1.0, "content": "artifacts when applying oscillating guidance to models past the 80", "type": "text" }, { "bbox": [ 354, 182, 362, 189 ], "score": 0.29, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 362, 178, 541, 192 ], "score": 1.0, "content": "48 spatial resolution. Thus we only apply", "type": "text" } ], "index": 8 }, { "bbox": [ 70, 190, 337, 202 ], "spans": [ { "bbox": [ 70, 190, 337, 202 ], "score": 1.0, "content": "oscillating guidance to the base and the first two SR models.", "type": "text" } ], "index": 9 } ], "index": 4.5 }, { "type": "title", "bbox": [ 71, 215, 385, 227 ], "lines": [ { "bbox": [ 69, 214, 387, 231 ], "spans": [ { "bbox": [ 69, 214, 387, 231 ], "score": 1.0, "content": "2.7 Progressive Distillation with Guidance and Stochastic Samplers", "type": "text" } ], "index": 10 } ], "index": 10 }, { "type": "text", "bbox": [ 71, 236, 540, 321 ], "lines": [ { "bbox": [ 70, 237, 541, 250 ], "spans": [ { "bbox": [ 70, 237, 541, 250 ], "score": 1.0, "content": "Salimans & Ho (2022) proposed progressive distillation to enable fast sampling of diffusion models. This", "type": "text" } ], "index": 11 }, { "bbox": [ 69, 249, 540, 262 ], "spans": [ { "bbox": [ 69, 249, 540, 262 ], "score": 1.0, "content": "method distills a trained deterministic DDIM sampler (Song et al., 2020) to a diffusion model that takes", "type": "text" } ], "index": 12 }, { "bbox": [ 69, 261, 541, 274 ], "spans": [ { "bbox": [ 69, 261, 541, 274 ], "score": 1.0, "content": "many fewer sampling steps, without losing much perceptual quality. At each iteration of the distillation", "type": "text" } ], "index": 13 }, { "bbox": [ 69, 273, 541, 286 ], "spans": [ { "bbox": [ 69, 273, 123, 286 ], "score": 1.0, "content": "process, an", "type": "text" }, { "bbox": [ 123, 276, 133, 283 ], "score": 0.91, "content": "N", "type": "inline_equation" }, { "bbox": [ 133, 273, 370, 286 ], "score": 1.0, "content": "-step DDIM sampler is distilled to a new model with", "type": "text" }, { "bbox": [ 370, 275, 388, 285 ], "score": 0.84, "content": "N / 2", "type": "inline_equation" }, { "bbox": [ 389, 273, 541, 286 ], "score": 1.0, "content": "-steps. This procedure is repeated", "type": "text" } ], "index": 14 }, { "bbox": [ 70, 285, 541, 298 ], "spans": [ { "bbox": [ 70, 285, 541, 298 ], "score": 1.0, "content": "by halving the required sampling steps each iteration. Meng et al. (2022) extend this approach to samplers", "type": "text" } ], "index": 15 }, { "bbox": [ 69, 296, 541, 309 ], "spans": [ { "bbox": [ 69, 296, 541, 309 ], "score": 1.0, "content": "with guidance, and propose a new stochastic sampler for use with distilled models. Here we show that this", "type": "text" } ], "index": 16 }, { "bbox": [ 69, 310, 296, 322 ], "spans": [ { "bbox": [ 69, 310, 296, 322 ], "score": 1.0, "content": "approach also works very well for video generation.", "type": "text" } ], "index": 17 } ], "index": 14 }, { "type": "text", "bbox": [ 71, 326, 540, 387 ], "lines": [ { "bbox": [ 71, 327, 541, 339 ], "spans": [ { "bbox": [ 71, 327, 541, 339 ], "score": 1.0, "content": "We use a two-stage distillation approach to distill a DDIM sampler (Song et al., 2020) with classifier-free", "type": "text" } ], "index": 18 }, { "bbox": [ 69, 339, 541, 352 ], "spans": [ { "bbox": [ 69, 339, 541, 352 ], "score": 1.0, "content": "guidance. At the first stage, we learn a single diffusion model that matches the combined output from", "type": "text" } ], "index": 19 }, { "bbox": [ 69, 350, 542, 364 ], "spans": [ { "bbox": [ 69, 350, 542, 364 ], "score": 1.0, "content": "the jointly trained conditional and unconditional diffusion models, where the combination coefficients are", "type": "text" } ], "index": 20 }, { "bbox": [ 70, 362, 542, 376 ], "spans": [ { "bbox": [ 70, 362, 542, 376 ], "score": 1.0, "content": "determined by the guidance weight. Then we apply progressive distillation to that single model to produce", "type": "text" } ], "index": 21 }, { "bbox": [ 69, 374, 328, 388 ], "spans": [ { "bbox": [ 69, 374, 328, 388 ], "score": 1.0, "content": "models requiring fewer sampling steps at the second stage.", "type": "text" } ], "index": 22 } ], "index": 20 }, { "type": "text", "bbox": [ 71, 393, 540, 464 ], "lines": [ { "bbox": [ 70, 392, 541, 406 ], "spans": [ { "bbox": [ 70, 392, 235, 406 ], "score": 1.0, "content": "After distillation, we use a stochastic", "type": "text" }, { "bbox": [ 236, 395, 245, 402 ], "score": 0.91, "content": "N", "type": "inline_equation" }, { "bbox": [ 245, 392, 541, 406 ], "score": 1.0, "content": "-step sampler: At each step, we first apply one deterministic DDIM", "type": "text" } ], "index": 23 }, { "bbox": [ 69, 405, 542, 418 ], "spans": [ { "bbox": [ 69, 405, 363, 418 ], "score": 1.0, "content": "update with twice the original step size (i.e., the same step size as a", "type": "text" }, { "bbox": [ 364, 406, 383, 417 ], "score": 0.91, "content": "N / 2", "type": "inline_equation" }, { "bbox": [ 383, 405, 542, 418 ], "score": 1.0, "content": "-step sampler), and then we perform", "type": "text" } ], "index": 24 }, { "bbox": [ 69, 416, 542, 430 ], "spans": [ { "bbox": [ 69, 416, 542, 430 ], "score": 1.0, "content": "one stochastic step backward (i.e., perturbed with noise following the forward diffusion process) with the", "type": "text" } ], "index": 25 }, { "bbox": [ 69, 428, 542, 442 ], "spans": [ { "bbox": [ 69, 428, 542, 442 ], "score": 1.0, "content": "original step size, inspired by Karras et al. (2022). See Meng et al. (2022) for more details. Using this", "type": "text" } ], "index": 26 }, { "bbox": [ 68, 440, 542, 454 ], "spans": [ { "bbox": [ 68, 440, 542, 454 ], "score": 1.0, "content": "approach, we are able to distill all 7 video diffusion models down to just 8 sampling steps per model without", "type": "text" } ], "index": 27 }, { "bbox": [ 69, 452, 250, 466 ], "spans": [ { "bbox": [ 69, 452, 250, 466 ], "score": 1.0, "content": "any noticeable loss in perceptual quality.", "type": "text" } ], "index": 28 } ], "index": 25.5 }, { "type": "title", "bbox": [ 71, 479, 159, 493 ], "lines": [ { "bbox": [ 68, 477, 161, 497 ], "spans": [ { "bbox": [ 68, 477, 161, 497 ], "score": 1.0, "content": "3 Experiments", "type": "text" } ], "index": 29 } ], "index": 29 }, { "type": "text", "bbox": [ 70, 505, 540, 625 ], "lines": [ { "bbox": [ 69, 505, 541, 519 ], "spans": [ { "bbox": [ 69, 505, 541, 519 ], "score": 1.0, "content": "We train our models on a combination of an internal dataset consisting of 14 million video-text pairs and", "type": "text" } ], "index": 30 }, { "bbox": [ 69, 516, 541, 531 ], "spans": [ { "bbox": [ 69, 516, 541, 531 ], "score": 1.0, "content": "60 million image-text pairs, and the publicly available LAION-400M image-text dataset (Schuhmann et al.,", "type": "text" } ], "index": 31 }, { "bbox": [ 68, 528, 542, 543 ], "spans": [ { "bbox": [ 68, 528, 542, 543 ], "score": 1.0, "content": "2021). To process the data into a form suitable for training our cascading pipeline, we spatially resize", "type": "text" } ], "index": 32 }, { "bbox": [ 69, 541, 541, 555 ], "spans": [ { "bbox": [ 69, 541, 541, 555 ], "score": 1.0, "content": "images and videos using antialiased bilinear resizing, and we temporally resize videos by skipping frames.", "type": "text" } ], "index": 33 }, { "bbox": [ 69, 553, 541, 567 ], "spans": [ { "bbox": [ 69, 553, 541, 567 ], "score": 1.0, "content": "Throughout our model development process, we evaluated Imagen Video on several different metrics, such", "type": "text" } ], "index": 34 }, { "bbox": [ 68, 564, 542, 580 ], "spans": [ { "bbox": [ 68, 564, 542, 580 ], "score": 1.0, "content": "as FID on individual frames (Heusel et al., 2017), FVD (Unterthiner et al., 2019) for temporal consistency,", "type": "text" } ], "index": 35 }, { "bbox": [ 69, 577, 542, 591 ], "spans": [ { "bbox": [ 69, 577, 542, 591 ], "score": 1.0, "content": "and frame-wise CLIP scores (Hessel et al., 2021; Park et al., 2021) for video-text alignment. Below, we", "type": "text" } ], "index": 36 }, { "bbox": [ 70, 589, 542, 603 ], "spans": [ { "bbox": [ 70, 589, 542, 603 ], "score": 1.0, "content": "explore the capabilities of our model and investigate its performance in regards to 1) scaling up the number", "type": "text" } ], "index": 37 }, { "bbox": [ 69, 601, 542, 615 ], "spans": [ { "bbox": [ 69, 601, 542, 615 ], "score": 1.0, "content": "of parameters in our model, 2) changing the parameterization of our model, and 3) distilling our models so", "type": "text" } ], "index": 38 }, { "bbox": [ 69, 612, 221, 627 ], "spans": [ { "bbox": [ 69, 612, 221, 627 ], "score": 1.0, "content": "that they are fast to sample from.", "type": "text" } ], "index": 39 } ], "index": 34.5 }, { "type": "title", "bbox": [ 72, 639, 262, 651 ], "lines": [ { "bbox": [ 69, 638, 263, 653 ], "spans": [ { "bbox": [ 69, 638, 263, 653 ], "score": 1.0, "content": "3.1 Unique video generation capabilities", "type": "text" } ], "index": 40 } ], "index": 40 }, { "type": "text", "bbox": [ 71, 660, 540, 732 ], "lines": [ { "bbox": [ 70, 659, 541, 675 ], "spans": [ { "bbox": [ 70, 659, 541, 675 ], "score": 1.0, "content": "We find that Imagen Video is capable of generating high fidelity video, and that it possesses several unique", "type": "text" } ], "index": 41 }, { "bbox": [ 69, 672, 541, 686 ], "spans": [ { "bbox": [ 69, 672, 541, 686 ], "score": 1.0, "content": "capabilities that are not traditionally found in unstructured generative models learned purely from data. For", "type": "text" } ], "index": 42 }, { "bbox": [ 68, 682, 542, 700 ], "spans": [ { "bbox": [ 68, 682, 542, 700 ], "score": 1.0, "content": "example, Fig. 8 shows that our model is capable of generating videos with artistic styles learned from image", "type": "text" } ], "index": 43 }, { "bbox": [ 69, 695, 541, 710 ], "spans": [ { "bbox": [ 69, 695, 541, 710 ], "score": 1.0, "content": "information, such as videos in the style of van Gogh paintings or watercolor paintings. Fig. 9 shows that", "type": "text" } ], "index": 44 }, { "bbox": [ 70, 709, 541, 721 ], "spans": [ { "bbox": [ 70, 709, 541, 721 ], "score": 1.0, "content": "Imagen Video possesses an understanding of 3D structure, as it is capable of generating videos of objects", "type": "text" } ], "index": 45 }, { "bbox": [ 69, 720, 541, 734 ], "spans": [ { "bbox": [ 69, 720, 541, 734 ], "score": 1.0, "content": "rotating while roughly preserving structure. While the 3D consistency over the course of rotation is not", "type": "text" } ], "index": 46 } ], "index": 43.5 } ], "page_idx": 10, "page_size": [ 612, 792 ], "discarded_blocks": [ { "type": "discarded", "bbox": [ 72, 27, 237, 36 ], "lines": [ { "bbox": [ 70, 25, 238, 38 ], "spans": [ { "bbox": [ 70, 25, 238, 38 ], "score": 1.0, "content": "Under review as submission to TMLR", "type": "text" } ] } ] }, { "type": "discarded", "bbox": [ 300, 751, 310, 760 ], "lines": [ { "bbox": [ 299, 750, 312, 764 ], "spans": [ { "bbox": [ 299, 750, 312, 764 ], "score": 1.0, "content": "", "type": "text", "height": 14, "width": 13 } ] } ] } ], "para_blocks": [ { "type": "text", "bbox": [ 70, 82, 540, 201 ], "lines": [], "index": 4.5, "bbox_fs": [ 68, 80, 542, 202 ], "lines_deleted": true }, { "type": "title", "bbox": [ 71, 215, 385, 227 ], "lines": [ { "bbox": [ 69, 214, 387, 231 ], "spans": [ { "bbox": [ 69, 214, 387, 231 ], "score": 1.0, "content": "2.7 Progressive Distillation with Guidance and Stochastic Samplers", "type": "text" } ], "index": 10 } ], "index": 10 }, { "type": "text", "bbox": [ 71, 236, 540, 321 ], "lines": [ { "bbox": [ 70, 237, 541, 250 ], "spans": [ { "bbox": [ 70, 237, 541, 250 ], "score": 1.0, "content": "Salimans & Ho (2022) proposed progressive distillation to enable fast sampling of diffusion models. This", "type": "text" } ], "index": 11 }, { "bbox": [ 69, 249, 540, 262 ], "spans": [ { "bbox": [ 69, 249, 540, 262 ], "score": 1.0, "content": "method distills a trained deterministic DDIM sampler (Song et al., 2020) to a diffusion model that takes", "type": "text" } ], "index": 12 }, { "bbox": [ 69, 261, 541, 274 ], "spans": [ { "bbox": [ 69, 261, 541, 274 ], "score": 1.0, "content": "many fewer sampling steps, without losing much perceptual quality. At each iteration of the distillation", "type": "text" } ], "index": 13 }, { "bbox": [ 69, 273, 541, 286 ], "spans": [ { "bbox": [ 69, 273, 123, 286 ], "score": 1.0, "content": "process, an", "type": "text" }, { "bbox": [ 123, 276, 133, 283 ], "score": 0.91, "content": "N", "type": "inline_equation" }, { "bbox": [ 133, 273, 370, 286 ], "score": 1.0, "content": "-step DDIM sampler is distilled to a new model with", "type": "text" }, { "bbox": [ 370, 275, 388, 285 ], "score": 0.84, "content": "N / 2", "type": "inline_equation" }, { "bbox": [ 389, 273, 541, 286 ], "score": 1.0, "content": "-steps. This procedure is repeated", "type": "text" } ], "index": 14 }, { "bbox": [ 70, 285, 541, 298 ], "spans": [ { "bbox": [ 70, 285, 541, 298 ], "score": 1.0, "content": "by halving the required sampling steps each iteration. Meng et al. (2022) extend this approach to samplers", "type": "text" } ], "index": 15 }, { "bbox": [ 69, 296, 541, 309 ], "spans": [ { "bbox": [ 69, 296, 541, 309 ], "score": 1.0, "content": "with guidance, and propose a new stochastic sampler for use with distilled models. Here we show that this", "type": "text" } ], "index": 16 }, { "bbox": [ 69, 310, 296, 322 ], "spans": [ { "bbox": [ 69, 310, 296, 322 ], "score": 1.0, "content": "approach also works very well for video generation.", "type": "text" } ], "index": 17 } ], "index": 14, "bbox_fs": [ 69, 237, 541, 322 ] }, { "type": "text", "bbox": [ 71, 326, 540, 387 ], "lines": [ { "bbox": [ 71, 327, 541, 339 ], "spans": [ { "bbox": [ 71, 327, 541, 339 ], "score": 1.0, "content": "We use a two-stage distillation approach to distill a DDIM sampler (Song et al., 2020) with classifier-free", "type": "text" } ], "index": 18 }, { "bbox": [ 69, 339, 541, 352 ], "spans": [ { "bbox": [ 69, 339, 541, 352 ], "score": 1.0, "content": "guidance. At the first stage, we learn a single diffusion model that matches the combined output from", "type": "text" } ], "index": 19 }, { "bbox": [ 69, 350, 542, 364 ], "spans": [ { "bbox": [ 69, 350, 542, 364 ], "score": 1.0, "content": "the jointly trained conditional and unconditional diffusion models, where the combination coefficients are", "type": "text" } ], "index": 20 }, { "bbox": [ 70, 362, 542, 376 ], "spans": [ { "bbox": [ 70, 362, 542, 376 ], "score": 1.0, "content": "determined by the guidance weight. Then we apply progressive distillation to that single model to produce", "type": "text" } ], "index": 21 }, { "bbox": [ 69, 374, 328, 388 ], "spans": [ { "bbox": [ 69, 374, 328, 388 ], "score": 1.0, "content": "models requiring fewer sampling steps at the second stage.", "type": "text" } ], "index": 22 } ], "index": 20, "bbox_fs": [ 69, 327, 542, 388 ] }, { "type": "text", "bbox": [ 71, 393, 540, 464 ], "lines": [ { "bbox": [ 70, 392, 541, 406 ], "spans": [ { "bbox": [ 70, 392, 235, 406 ], "score": 1.0, "content": "After distillation, we use a stochastic", "type": "text" }, { "bbox": [ 236, 395, 245, 402 ], "score": 0.91, "content": "N", "type": "inline_equation" }, { "bbox": [ 245, 392, 541, 406 ], "score": 1.0, "content": "-step sampler: At each step, we first apply one deterministic DDIM", "type": "text" } ], "index": 23 }, { "bbox": [ 69, 405, 542, 418 ], "spans": [ { "bbox": [ 69, 405, 363, 418 ], "score": 1.0, "content": "update with twice the original step size (i.e., the same step size as a", "type": "text" }, { "bbox": [ 364, 406, 383, 417 ], "score": 0.91, "content": "N / 2", "type": "inline_equation" }, { "bbox": [ 383, 405, 542, 418 ], "score": 1.0, "content": "-step sampler), and then we perform", "type": "text" } ], "index": 24 }, { "bbox": [ 69, 416, 542, 430 ], "spans": [ { "bbox": [ 69, 416, 542, 430 ], "score": 1.0, "content": "one stochastic step backward (i.e., perturbed with noise following the forward diffusion process) with the", "type": "text" } ], "index": 25 }, { "bbox": [ 69, 428, 542, 442 ], "spans": [ { "bbox": [ 69, 428, 542, 442 ], "score": 1.0, "content": "original step size, inspired by Karras et al. (2022). See Meng et al. (2022) for more details. Using this", "type": "text" } ], "index": 26 }, { "bbox": [ 68, 440, 542, 454 ], "spans": [ { "bbox": [ 68, 440, 542, 454 ], "score": 1.0, "content": "approach, we are able to distill all 7 video diffusion models down to just 8 sampling steps per model without", "type": "text" } ], "index": 27 }, { "bbox": [ 69, 452, 250, 466 ], "spans": [ { "bbox": [ 69, 452, 250, 466 ], "score": 1.0, "content": "any noticeable loss in perceptual quality.", "type": "text" } ], "index": 28 } ], "index": 25.5, "bbox_fs": [ 68, 392, 542, 466 ] }, { "type": "title", "bbox": [ 71, 479, 159, 493 ], "lines": [ { "bbox": [ 68, 477, 161, 497 ], "spans": [ { "bbox": [ 68, 477, 161, 497 ], "score": 1.0, "content": "3 Experiments", "type": "text" } ], "index": 29 } ], "index": 29 }, { "type": "text", "bbox": [ 70, 505, 540, 625 ], "lines": [ { "bbox": [ 69, 505, 541, 519 ], "spans": [ { "bbox": [ 69, 505, 541, 519 ], "score": 1.0, "content": "We train our models on a combination of an internal dataset consisting of 14 million video-text pairs and", "type": "text" } ], "index": 30 }, { "bbox": [ 69, 516, 541, 531 ], "spans": [ { "bbox": [ 69, 516, 541, 531 ], "score": 1.0, "content": "60 million image-text pairs, and the publicly available LAION-400M image-text dataset (Schuhmann et al.,", "type": "text" } ], "index": 31 }, { "bbox": [ 68, 528, 542, 543 ], "spans": [ { "bbox": [ 68, 528, 542, 543 ], "score": 1.0, "content": "2021). To process the data into a form suitable for training our cascading pipeline, we spatially resize", "type": "text" } ], "index": 32 }, { "bbox": [ 69, 541, 541, 555 ], "spans": [ { "bbox": [ 69, 541, 541, 555 ], "score": 1.0, "content": "images and videos using antialiased bilinear resizing, and we temporally resize videos by skipping frames.", "type": "text" } ], "index": 33 }, { "bbox": [ 69, 553, 541, 567 ], "spans": [ { "bbox": [ 69, 553, 541, 567 ], "score": 1.0, "content": "Throughout our model development process, we evaluated Imagen Video on several different metrics, such", "type": "text" } ], "index": 34 }, { "bbox": [ 68, 564, 542, 580 ], "spans": [ { "bbox": [ 68, 564, 542, 580 ], "score": 1.0, "content": "as FID on individual frames (Heusel et al., 2017), FVD (Unterthiner et al., 2019) for temporal consistency,", "type": "text" } ], "index": 35 }, { "bbox": [ 69, 577, 542, 591 ], "spans": [ { "bbox": [ 69, 577, 542, 591 ], "score": 1.0, "content": "and frame-wise CLIP scores (Hessel et al., 2021; Park et al., 2021) for video-text alignment. Below, we", "type": "text" } ], "index": 36 }, { "bbox": [ 70, 589, 542, 603 ], "spans": [ { "bbox": [ 70, 589, 542, 603 ], "score": 1.0, "content": "explore the capabilities of our model and investigate its performance in regards to 1) scaling up the number", "type": "text" } ], "index": 37 }, { "bbox": [ 69, 601, 542, 615 ], "spans": [ { "bbox": [ 69, 601, 542, 615 ], "score": 1.0, "content": "of parameters in our model, 2) changing the parameterization of our model, and 3) distilling our models so", "type": "text" } ], "index": 38 }, { "bbox": [ 69, 612, 221, 627 ], "spans": [ { "bbox": [ 69, 612, 221, 627 ], "score": 1.0, "content": "that they are fast to sample from.", "type": "text" } ], "index": 39 } ], "index": 34.5, "bbox_fs": [ 68, 505, 542, 627 ] }, { "type": "title", "bbox": [ 72, 639, 262, 651 ], "lines": [ { "bbox": [ 69, 638, 263, 653 ], "spans": [ { "bbox": [ 69, 638, 263, 653 ], "score": 1.0, "content": "3.1 Unique video generation capabilities", "type": "text" } ], "index": 40 } ], "index": 40 }, { "type": "text", "bbox": [ 71, 660, 540, 732 ], "lines": [ { "bbox": [ 70, 659, 541, 675 ], "spans": [ { "bbox": [ 70, 659, 541, 675 ], "score": 1.0, "content": "We find that Imagen Video is capable of generating high fidelity video, and that it possesses several unique", "type": "text" } ], "index": 41 }, { "bbox": [ 69, 672, 541, 686 ], "spans": [ { "bbox": [ 69, 672, 541, 686 ], "score": 1.0, "content": "capabilities that are not traditionally found in unstructured generative models learned purely from data. 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Smooth animation.", "type": "text" } ], "index": 23 }, { "bbox": [ 69, 702, 540, 715 ], "spans": [ { "bbox": [ 69, 702, 540, 715 ], "score": 1.0, "content": "Figure 10: Snapshots of frames from videos generated by Imagen Video demonstrating the ability of the", "type": "text" } ], "index": 24 }, { "bbox": [ 69, 713, 367, 727 ], "spans": [ { "bbox": [ 69, 713, 367, 727 ], "score": 1.0, "content": "model to render a variety of text with different style and dynamics.", "type": "text" } ], "index": 25 } ], "index": 24 } ], "index": 22.5 } ] }, { "preproc_blocks": [ { "type": "text", "bbox": [ 71, 82, 540, 142 ], "lines": [ { "bbox": [ 70, 83, 540, 94 ], "spans": [ { "bbox": [ 70, 83, 540, 94 ], "score": 1.0, "content": "exact, we believe Imagen Video shows that video models can serve as effective priors for methods that do", "type": "text" } ], "index": 0 }, { "bbox": [ 69, 94, 541, 107 ], "spans": [ { "bbox": [ 69, 94, 541, 107 ], "score": 1.0, "content": "force 3D consistency. 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We see", "type": "text" } ], "index": 2 }, { "bbox": [ 68, 117, 541, 132 ], "spans": [ { "bbox": [ 68, 117, 541, 132 ], "score": 1.0, "content": "results such as these as an exciting indication of how general purpose generative models such as Imagen", "type": "text" } ], "index": 3 }, { "bbox": [ 70, 129, 423, 144 ], "spans": [ { "bbox": [ 70, 129, 423, 144 ], "score": 1.0, "content": "Video can significantly decrease the difficulty of high quality content generation.", "type": "text" } ], "index": 4 } ], "index": 2 }, { "type": "title", "bbox": [ 71, 154, 129, 167 ], "lines": [ { "bbox": [ 68, 151, 132, 171 ], "spans": [ { "bbox": [ 68, 151, 132, 171 ], "score": 1.0, "content": "3.2 Scaling", "type": "text" } ], "index": 5 } ], "index": 5 }, { "type": "text", "bbox": [ 71, 176, 540, 248 ], "lines": [ { "bbox": [ 69, 175, 541, 190 ], "spans": [ { "bbox": [ 69, 175, 541, 190 ], "score": 1.0, "content": "In Figure 11 we show that our base video model strongly benefits from scaling up the parameter count of the", "type": "text" } ], "index": 6 }, { "bbox": [ 70, 187, 541, 201 ], "spans": [ { "bbox": [ 70, 187, 541, 201 ], "score": 1.0, "content": "video U-Net. We performed this scaling by increasing the base channel count and depth of the network. This", "type": "text" } ], "index": 7 }, { "bbox": [ 70, 200, 540, 212 ], "spans": [ { "bbox": [ 70, 200, 540, 212 ], "score": 1.0, "content": "result is contrary to the text-to-image U-Net scaling results by Saharia et al. (2022b), which found limited", "type": "text" } ], "index": 8 }, { "bbox": [ 69, 212, 541, 226 ], "spans": [ { "bbox": [ 69, 212, 541, 226 ], "score": 1.0, "content": "benefit from diffusion model scaling when measured by image-text sample quality scores. We conclude that", "type": "text" } ], "index": 9 }, { "bbox": [ 69, 223, 541, 238 ], "spans": [ { "bbox": [ 69, 223, 541, 238 ], "score": 1.0, "content": "video modeling is a harder task for which performance is not yet saturated at current model sizes, implying", "type": "text" } ], "index": 10 }, { "bbox": [ 70, 235, 337, 250 ], "spans": [ { "bbox": [ 70, 235, 337, 250 ], "score": 1.0, "content": "future benefits to further model scaling for video generation.", "type": "text" } ], "index": 11 } ], "index": 8.5 }, { "type": "image", "bbox": [ 73, 257, 523, 368 ], "blocks": [ { "type": "image_body", "bbox": [ 73, 257, 523, 368 ], "group_id": 0, "lines": [ { "bbox": [ 73, 257, 523, 368 ], "spans": [ { "bbox": [ 73, 257, 523, 368 ], "score": 0.838, "type": "image", "image_path": "789e8cc4f7559686e6db1efe096b011df90ad9bd57a151db67d7f5d533171a51.jpg" } ] } ], "index": 13, "virtual_lines": [ { "bbox": [ 73, 257, 523, 294.0 ], "spans": [], "index": 12 }, { "bbox": [ 73, 294.0, 523, 331.0 ], "spans": [], "index": 13 }, { "bbox": [ 73, 331.0, 523, 368.0 ], "spans": [], "index": 14 } ] }, { "type": "image_caption", "bbox": [ 70, 377, 541, 413 ], "group_id": 0, "lines": [ { "bbox": [ 69, 376, 541, 390 ], "spans": [ { "bbox": [ 69, 376, 270, 390 ], "score": 1.0, "content": "Figure 11: Scaling Comparison for the base", "type": "text" }, { "bbox": [ 271, 378, 318, 388 ], "score": 0.77, "content": "1 6 \\times 4 0 \\times 2 4", "type": "inline_equation" }, { "bbox": [ 318, 376, 541, 390 ], "score": 1.0, "content": "video model on FVD and CLIP scores (on 0-100", "type": "text" } ], "index": 15 }, { "bbox": [ 69, 388, 541, 402 ], "spans": [ { "bbox": [ 69, 388, 541, 402 ], "score": 1.0, "content": "scale). Both FVD and CLIP scores are computed on 4096 video samples. We see clear signs of improvement", "type": "text" } ], "index": 16 }, { "bbox": [ 69, 399, 376, 414 ], "spans": [ { "bbox": [ 69, 399, 376, 414 ], "score": 1.0, "content": "on both metrics when scaling from 500M to 1.6B to 5.6B parameters.", "type": "text" } ], "index": 17 } ], "index": 16 } ], "index": 14.5 }, { "type": "title", "bbox": [ 71, 431, 279, 443 ], "lines": [ { "bbox": [ 69, 430, 280, 446 ], "spans": [ { "bbox": [ 69, 430, 280, 446 ], "score": 1.0, "content": "3.3 Comparing prediction parameterizations", "type": "text" } ], "index": 18 } ], "index": 18 }, { "type": "text", "bbox": [ 70, 452, 540, 549 ], "lines": [ { "bbox": [ 69, 452, 542, 467 ], "spans": [ { "bbox": [ 69, 452, 272, 467 ], "score": 1.0, "content": "In early experiments we found that training", "type": "text" }, { "bbox": [ 272, 458, 278, 463 ], "score": 0.52, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 278, 452, 542, 467 ], "score": 1.0, "content": "-prediction models (Ho et al., 2020) performed worse than", "type": "text" } ], "index": 19 }, { "bbox": [ 71, 464, 541, 478 ], "spans": [ { "bbox": [ 71, 470, 78, 475 ], "score": 0.53, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 78, 464, 541, 478 ], "score": 1.0, "content": "-prediction (Salimans & Ho, 2022) especially at high resolutions. Specifically, for high resolution SSR", "type": "text" } ], "index": 20 }, { "bbox": [ 68, 476, 541, 490 ], "spans": [ { "bbox": [ 68, 476, 188, 490 ], "score": 1.0, "content": "models, we observed that", "type": "text" }, { "bbox": [ 189, 482, 194, 487 ], "score": 0.48, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 194, 476, 541, 490 ], "score": 1.0, "content": "-prediction converges relatively slowly in terms of sample quality metrics and", "type": "text" } ], "index": 21 }, { "bbox": [ 68, 488, 541, 502 ], "spans": [ { "bbox": [ 68, 488, 541, 502 ], "score": 1.0, "content": "suffers from color shift and color inconsistency across frames in the generated videos. Fig. 12 shows the", "type": "text" } ], "index": 22 }, { "bbox": [ 69, 501, 541, 514 ], "spans": [ { "bbox": [ 69, 501, 164, 514 ], "score": 1.0, "content": "comparison between", "type": "text" }, { "bbox": [ 164, 506, 169, 511 ], "score": 0.84, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 169, 501, 240, 514 ], "score": 1.0, "content": "-prediction and", "type": "text" }, { "bbox": [ 240, 506, 246, 511 ], "score": 0.65, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 247, 501, 331, 514 ], "score": 1.0, "content": "-prediction on a 80", "type": "text" }, { "bbox": [ 332, 505, 339, 511 ], "score": 0.61, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 340, 501, 382, 514 ], "score": 1.0, "content": "48 → 320", "type": "text" }, { "bbox": [ 383, 505, 390, 511 ], "score": 0.67, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 390, 501, 541, 514 ], "score": 1.0, "content": "192 video spatial super-resolution", "type": "text" } ], "index": 23 }, { "bbox": [ 69, 512, 541, 526 ], "spans": [ { "bbox": [ 69, 512, 161, 526 ], "score": 1.0, "content": "task. It is clear that", "type": "text" }, { "bbox": [ 162, 518, 167, 523 ], "score": 0.85, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 167, 512, 390, 526 ], "score": 1.0, "content": "-parameterization produces worse generations than", "type": "text" }, { "bbox": [ 391, 518, 397, 523 ], "score": 0.65, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 397, 512, 541, 526 ], "score": 1.0, "content": "-parameterization. Fig. 13 shows", "type": "text" } ], "index": 24 }, { "bbox": [ 69, 524, 541, 539 ], "spans": [ { "bbox": [ 69, 524, 541, 539 ], "score": 1.0, "content": "the quantitative comparison between the two parameterizations as a function of training steps. We observe", "type": "text" } ], "index": 25 }, { "bbox": [ 69, 536, 412, 550 ], "spans": [ { "bbox": [ 69, 536, 92, 550 ], "score": 1.0, "content": "that", "type": "text" }, { "bbox": [ 93, 542, 99, 546 ], "score": 0.83, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 100, 536, 326, 550 ], "score": 1.0, "content": "parameterization converges much more faster than", "type": "text" }, { "bbox": [ 326, 542, 331, 546 ], "score": 0.85, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 331, 536, 412, 550 ], "score": 1.0, "content": "parameterization.", "type": "text" } ], "index": 26 } ], "index": 22.5 }, { "type": "title", "bbox": [ 71, 561, 250, 573 ], "lines": [ { "bbox": [ 69, 559, 251, 576 ], "spans": [ { "bbox": [ 69, 559, 251, 576 ], "score": 1.0, "content": "3.4 Perceptual quality and distillation", "type": "text" } ], "index": 27 } ], "index": 27 }, { "type": "text", "bbox": [ 70, 582, 540, 654 ], "lines": [ { "bbox": [ 69, 581, 541, 596 ], "spans": [ { "bbox": [ 69, 581, 541, 596 ], "score": 1.0, "content": "In Table 1 we report perceptual quality metrics (CLIP score and CLIP R-Precision) for our model samples,", "type": "text" } ], "index": 28 }, { "bbox": [ 69, 595, 542, 609 ], "spans": [ { "bbox": [ 69, 595, 434, 609 ], "score": 1.0, "content": "as well as for their distilled version. Samples are generated and evaluated at 192", "type": "text" }, { "bbox": [ 435, 598, 443, 605 ], "score": 0.26, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 443, 595, 542, 609 ], "score": 1.0, "content": "320 resolution for 128", "type": "text" } ], "index": 29 }, { "bbox": [ 69, 606, 541, 621 ], "spans": [ { "bbox": [ 69, 606, 541, 621 ], "score": 1.0, "content": "frames at 24 frames per second. For CLIP score, we take the average score over all frames. For CLIP", "type": "text" } ], "index": 30 }, { "bbox": [ 69, 618, 542, 632 ], "spans": [ { "bbox": [ 69, 618, 371, 632 ], "score": 1.0, "content": "R-Precision (Park et al., 2021) we compute the top-1 accuracy (i.e.", "type": "text" }, { "bbox": [ 371, 621, 399, 628 ], "score": 0.9, "content": "R = 1", "type": "inline_equation" }, { "bbox": [ 399, 618, 542, 632 ], "score": 1.0, "content": "), treating the frames of a video", "type": "text" } ], "index": 31 }, { "bbox": [ 69, 630, 541, 644 ], "spans": [ { "bbox": [ 69, 630, 541, 644 ], "score": 1.0, "content": "sample as images sharing the same text label (the prompt). We repeat these over four different runs and", "type": "text" } ], "index": 32 }, { "bbox": [ 68, 642, 233, 657 ], "spans": [ { "bbox": [ 68, 642, 233, 657 ], "score": 1.0, "content": "report the mean and standard error.", "type": "text" } ], "index": 33 } ], "index": 30.5 }, { "type": "text", "bbox": [ 70, 660, 540, 732 ], "lines": [ { "bbox": [ 69, 658, 541, 675 ], "spans": [ { "bbox": [ 69, 658, 541, 675 ], "score": 1.0, "content": "We find that distillation provides a very favorable trade-off between sampling time and perceptual quality:", "type": "text" } ], "index": 34 }, { "bbox": [ 70, 672, 542, 686 ], "spans": [ { "bbox": [ 70, 672, 212, 686 ], "score": 1.0, "content": "the distilled cascade is about 18", "type": "text" }, { "bbox": [ 212, 676, 219, 683 ], "score": 0.82, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 219, 672, 542, 686 ], "score": 1.0, "content": "faster, while producing videos of similar quality to the samples from the", "type": "text" } ], "index": 35 }, { "bbox": [ 69, 685, 542, 697 ], "spans": [ { "bbox": [ 69, 685, 374, 697 ], "score": 1.0, "content": "original models. In terms of FLOPs, the distilled models are about 36", "type": "text" }, { "bbox": [ 375, 688, 382, 695 ], "score": 0.68, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 382, 685, 542, 697 ], "score": 1.0, "content": "more efficient: The original cascade", "type": "text" } ], "index": 36 }, { "bbox": [ 69, 696, 541, 710 ], "spans": [ { "bbox": [ 69, 696, 541, 710 ], "score": 1.0, "content": "evaluates each model twice (in parallel) to apply classifier-free guidance, while our distilled models do not,", "type": "text" } ], "index": 37 }, { "bbox": [ 68, 707, 541, 722 ], "spans": [ { "bbox": [ 68, 707, 541, 722 ], "score": 1.0, "content": "since they distilled the effect of guidance into a single model. We provide samples from our original and", "type": "text" } ], "index": 38 }, { "bbox": [ 69, 720, 268, 733 ], "spans": [ { "bbox": [ 69, 720, 268, 733 ], "score": 1.0, "content": "distilled cascade in Figure 14 for illustration.", "type": "text" } ], "index": 39 } ], "index": 36.5 } ], "page_idx": 12, "page_size": [ 612, 792 ], "discarded_blocks": [ { "type": "discarded", "bbox": [ 72, 27, 237, 37 ], "lines": [ { "bbox": [ 70, 26, 239, 38 ], "spans": [ { "bbox": [ 70, 26, 239, 38 ], "score": 1.0, "content": "Under review as submission to TMLR", "type": "text" } ] } ] }, { "type": "discarded", "bbox": [ 300, 751, 311, 760 ], "lines": [ { "bbox": [ 299, 750, 313, 763 ], "spans": [ { "bbox": [ 299, 750, 313, 763 ], "score": 1.0, "content": "13", "type": "text" } ] } ] } ], "para_blocks": [ { "type": "text", "bbox": [ 71, 82, 540, 142 ], "lines": [ { "bbox": [ 70, 83, 540, 94 ], "spans": [ { "bbox": [ 70, 83, 540, 94 ], "score": 1.0, "content": "exact, we believe Imagen Video shows that video models can serve as effective priors for methods that do", "type": "text" } ], "index": 0 }, { "bbox": [ 69, 94, 541, 107 ], "spans": [ { "bbox": [ 69, 94, 541, 107 ], "score": 1.0, "content": "force 3D consistency. Fig. 10 shows that Imagen Video is also reliably capable of generating text in a wide", "type": "text" } ], "index": 1 }, { "bbox": [ 70, 106, 541, 119 ], "spans": [ { "bbox": [ 70, 106, 541, 119 ], "score": 1.0, "content": "variety of animation styles, some of which would be difficult to animate using traditional tools. We see", "type": "text" } ], "index": 2 }, { "bbox": [ 68, 117, 541, 132 ], "spans": [ { "bbox": [ 68, 117, 541, 132 ], "score": 1.0, "content": "results such as these as an exciting indication of how general purpose generative models such as Imagen", "type": "text" } ], "index": 3 }, { "bbox": [ 70, 129, 423, 144 ], "spans": [ { "bbox": [ 70, 129, 423, 144 ], "score": 1.0, "content": "Video can significantly decrease the difficulty of high quality content generation.", "type": "text" } ], "index": 4 } ], "index": 2, "bbox_fs": [ 68, 83, 541, 144 ] }, { "type": "title", "bbox": [ 71, 154, 129, 167 ], "lines": [ { "bbox": [ 68, 151, 132, 171 ], "spans": [ { "bbox": [ 68, 151, 132, 171 ], "score": 1.0, "content": "3.2 Scaling", "type": "text" } ], "index": 5 } ], "index": 5 }, { "type": "text", "bbox": [ 71, 176, 540, 248 ], "lines": [ { "bbox": [ 69, 175, 541, 190 ], "spans": [ { "bbox": [ 69, 175, 541, 190 ], "score": 1.0, "content": "In Figure 11 we show that our base video model strongly benefits from scaling up the parameter count of the", "type": "text" } ], "index": 6 }, { "bbox": [ 70, 187, 541, 201 ], "spans": [ { "bbox": [ 70, 187, 541, 201 ], "score": 1.0, "content": "video U-Net. We performed this scaling by increasing the base channel count and depth of the network. This", "type": "text" } ], "index": 7 }, { "bbox": [ 70, 200, 540, 212 ], "spans": [ { "bbox": [ 70, 200, 540, 212 ], "score": 1.0, "content": "result is contrary to the text-to-image U-Net scaling results by Saharia et al. (2022b), which found limited", "type": "text" } ], "index": 8 }, { "bbox": [ 69, 212, 541, 226 ], "spans": [ { "bbox": [ 69, 212, 541, 226 ], "score": 1.0, "content": "benefit from diffusion model scaling when measured by image-text sample quality scores. We conclude that", "type": "text" } ], "index": 9 }, { "bbox": [ 69, 223, 541, 238 ], "spans": [ { "bbox": [ 69, 223, 541, 238 ], "score": 1.0, "content": "video modeling is a harder task for which performance is not yet saturated at current model sizes, implying", "type": "text" } ], "index": 10 }, { "bbox": [ 70, 235, 337, 250 ], "spans": [ { "bbox": [ 70, 235, 337, 250 ], "score": 1.0, "content": "future benefits to further model scaling for video generation.", "type": "text" } ], "index": 11 } ], "index": 8.5, "bbox_fs": [ 69, 175, 541, 250 ] }, { "type": "image", "bbox": [ 73, 257, 523, 368 ], "blocks": [ { "type": "image_body", "bbox": [ 73, 257, 523, 368 ], "group_id": 0, "lines": [ { "bbox": [ 73, 257, 523, 368 ], "spans": [ { "bbox": [ 73, 257, 523, 368 ], "score": 0.838, "type": "image", "image_path": "789e8cc4f7559686e6db1efe096b011df90ad9bd57a151db67d7f5d533171a51.jpg" } ] } ], "index": 13, "virtual_lines": [ { "bbox": [ 73, 257, 523, 294.0 ], "spans": [], "index": 12 }, { "bbox": [ 73, 294.0, 523, 331.0 ], "spans": [], "index": 13 }, { "bbox": [ 73, 331.0, 523, 368.0 ], "spans": [], "index": 14 } ] }, { "type": "image_caption", "bbox": [ 70, 377, 541, 413 ], "group_id": 0, "lines": [ { "bbox": [ 69, 376, 541, 390 ], "spans": [ { "bbox": [ 69, 376, 270, 390 ], "score": 1.0, "content": "Figure 11: Scaling Comparison for the base", "type": "text" }, { "bbox": [ 271, 378, 318, 388 ], "score": 0.77, "content": "1 6 \\times 4 0 \\times 2 4", "type": "inline_equation" }, { "bbox": [ 318, 376, 541, 390 ], "score": 1.0, "content": "video model on FVD and CLIP scores (on 0-100", "type": "text" } ], "index": 15 }, { "bbox": [ 69, 388, 541, 402 ], "spans": [ { "bbox": [ 69, 388, 541, 402 ], "score": 1.0, "content": "scale). Both FVD and CLIP scores are computed on 4096 video samples. We see clear signs of improvement", "type": "text" } ], "index": 16 }, { "bbox": [ 69, 399, 376, 414 ], "spans": [ { "bbox": [ 69, 399, 376, 414 ], "score": 1.0, "content": "on both metrics when scaling from 500M to 1.6B to 5.6B parameters.", "type": "text" } ], "index": 17 } ], "index": 16 } ], "index": 14.5 }, { "type": "title", "bbox": [ 71, 431, 279, 443 ], "lines": [ { "bbox": [ 69, 430, 280, 446 ], "spans": [ { "bbox": [ 69, 430, 280, 446 ], "score": 1.0, "content": "3.3 Comparing prediction parameterizations", "type": "text" } ], "index": 18 } ], "index": 18 }, { "type": "text", "bbox": [ 70, 452, 540, 549 ], "lines": [ { "bbox": [ 69, 452, 542, 467 ], "spans": [ { "bbox": [ 69, 452, 272, 467 ], "score": 1.0, "content": "In early experiments we found that training", "type": "text" }, { "bbox": [ 272, 458, 278, 463 ], "score": 0.52, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 278, 452, 542, 467 ], "score": 1.0, "content": "-prediction models (Ho et al., 2020) performed worse than", "type": "text" } ], "index": 19 }, { "bbox": [ 71, 464, 541, 478 ], "spans": [ { "bbox": [ 71, 470, 78, 475 ], "score": 0.53, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 78, 464, 541, 478 ], "score": 1.0, "content": "-prediction (Salimans & Ho, 2022) especially at high resolutions. Specifically, for high resolution SSR", "type": "text" } ], "index": 20 }, { "bbox": [ 68, 476, 541, 490 ], "spans": [ { "bbox": [ 68, 476, 188, 490 ], "score": 1.0, "content": "models, we observed that", "type": "text" }, { "bbox": [ 189, 482, 194, 487 ], "score": 0.48, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 194, 476, 541, 490 ], "score": 1.0, "content": "-prediction converges relatively slowly in terms of sample quality metrics and", "type": "text" } ], "index": 21 }, { "bbox": [ 68, 488, 541, 502 ], "spans": [ { "bbox": [ 68, 488, 541, 502 ], "score": 1.0, "content": "suffers from color shift and color inconsistency across frames in the generated videos. Fig. 12 shows the", "type": "text" } ], "index": 22 }, { "bbox": [ 69, 501, 541, 514 ], "spans": [ { "bbox": [ 69, 501, 164, 514 ], "score": 1.0, "content": "comparison between", "type": "text" }, { "bbox": [ 164, 506, 169, 511 ], "score": 0.84, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 169, 501, 240, 514 ], "score": 1.0, "content": "-prediction and", "type": "text" }, { "bbox": [ 240, 506, 246, 511 ], "score": 0.65, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 247, 501, 331, 514 ], "score": 1.0, "content": "-prediction on a 80", "type": "text" }, { "bbox": [ 332, 505, 339, 511 ], "score": 0.61, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 340, 501, 382, 514 ], "score": 1.0, "content": "48 → 320", "type": "text" }, { "bbox": [ 383, 505, 390, 511 ], "score": 0.67, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 390, 501, 541, 514 ], "score": 1.0, "content": "192 video spatial super-resolution", "type": "text" } ], "index": 23 }, { "bbox": [ 69, 512, 541, 526 ], "spans": [ { "bbox": [ 69, 512, 161, 526 ], "score": 1.0, "content": "task. It is clear that", "type": "text" }, { "bbox": [ 162, 518, 167, 523 ], "score": 0.85, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 167, 512, 390, 526 ], "score": 1.0, "content": "-parameterization produces worse generations than", "type": "text" }, { "bbox": [ 391, 518, 397, 523 ], "score": 0.65, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 397, 512, 541, 526 ], "score": 1.0, "content": "-parameterization. Fig. 13 shows", "type": "text" } ], "index": 24 }, { "bbox": [ 69, 524, 541, 539 ], "spans": [ { "bbox": [ 69, 524, 541, 539 ], "score": 1.0, "content": "the quantitative comparison between the two parameterizations as a function of training steps. We observe", "type": "text" } ], "index": 25 }, { "bbox": [ 69, 536, 412, 550 ], "spans": [ { "bbox": [ 69, 536, 92, 550 ], "score": 1.0, "content": "that", "type": "text" }, { "bbox": [ 93, 542, 99, 546 ], "score": 0.83, "content": "\\mathbf { v }", "type": "inline_equation" }, { "bbox": [ 100, 536, 326, 550 ], "score": 1.0, "content": "parameterization converges much more faster than", "type": "text" }, { "bbox": [ 326, 542, 331, 546 ], "score": 0.85, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 331, 536, 412, 550 ], "score": 1.0, "content": "parameterization.", "type": "text" } ], "index": 26 } ], "index": 22.5, "bbox_fs": [ 68, 452, 542, 550 ] }, { "type": "title", "bbox": [ 71, 561, 250, 573 ], "lines": [ { "bbox": [ 69, 559, 251, 576 ], "spans": [ { "bbox": [ 69, 559, 251, 576 ], "score": 1.0, "content": "3.4 Perceptual quality and distillation", "type": "text" } ], "index": 27 } ], "index": 27 }, { "type": "text", "bbox": [ 70, 582, 540, 654 ], "lines": [ { "bbox": [ 69, 581, 541, 596 ], "spans": [ { "bbox": [ 69, 581, 541, 596 ], "score": 1.0, "content": "In Table 1 we report perceptual quality metrics (CLIP score and CLIP R-Precision) for our model samples,", "type": "text" } ], "index": 28 }, { "bbox": [ 69, 595, 542, 609 ], "spans": [ { "bbox": [ 69, 595, 434, 609 ], "score": 1.0, "content": "as well as for their distilled version. Samples are generated and evaluated at 192", "type": "text" }, { "bbox": [ 435, 598, 443, 605 ], "score": 0.26, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 443, 595, 542, 609 ], "score": 1.0, "content": "320 resolution for 128", "type": "text" } ], "index": 29 }, { "bbox": [ 69, 606, 541, 621 ], "spans": [ { "bbox": [ 69, 606, 541, 621 ], "score": 1.0, "content": "frames at 24 frames per second. For CLIP score, we take the average score over all frames. For CLIP", "type": "text" } ], "index": 30 }, { "bbox": [ 69, 618, 542, 632 ], "spans": [ { "bbox": [ 69, 618, 371, 632 ], "score": 1.0, "content": "R-Precision (Park et al., 2021) we compute the top-1 accuracy (i.e.", "type": "text" }, { "bbox": [ 371, 621, 399, 628 ], "score": 0.9, "content": "R = 1", "type": "inline_equation" }, { "bbox": [ 399, 618, 542, 632 ], "score": 1.0, "content": "), treating the frames of a video", "type": "text" } ], "index": 31 }, { "bbox": [ 69, 630, 541, 644 ], "spans": [ { "bbox": [ 69, 630, 541, 644 ], "score": 1.0, "content": "sample as images sharing the same text label (the prompt). We repeat these over four different runs and", "type": "text" } ], "index": 32 }, { "bbox": [ 68, 642, 233, 657 ], "spans": [ { "bbox": [ 68, 642, 233, 657 ], "score": 1.0, "content": "report the mean and standard error.", "type": "text" } ], "index": 33 } ], "index": 30.5, "bbox_fs": [ 68, 581, 542, 657 ] }, { "type": "text", "bbox": [ 70, 660, 540, 732 ], "lines": [ { "bbox": [ 69, 658, 541, 675 ], "spans": [ { "bbox": [ 69, 658, 541, 675 ], "score": 1.0, "content": "We find that distillation provides a very favorable trade-off between sampling time and perceptual quality:", "type": "text" } ], "index": 34 }, { "bbox": [ 70, 672, 542, 686 ], "spans": [ { "bbox": [ 70, 672, 212, 686 ], "score": 1.0, "content": "the distilled cascade is about 18", "type": "text" }, { "bbox": [ 212, 676, 219, 683 ], "score": 0.82, "content": "\\times", "type": "inline_equation" }, { "bbox": [ 219, 672, 542, 686 ], "score": 1.0, "content": "faster, while producing videos of similar quality to the samples from the", "type": "text" } ], "index": 35 }, { "bbox": [ 69, 685, 542, 697 ], "spans": [ { "bbox": [ 69, 685, 374, 697 ], "score": 1.0, "content": "original models. 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We observe that the sample quality of the", "type": "text" }, { "bbox": [ 358, 541, 363, 545 ], "score": 0.84, "content": "\\epsilon", "type": "inline_equation" }, { "bbox": [ 363, 534, 542, 549 ], "score": 1.0, "content": "-prediction model converges much more", "type": "text" } ], "index": 16 }, { "bbox": [ 70, 548, 261, 559 ], "spans": [ { "bbox": [ 70, 548, 261, 559 ], "score": 1.0, "content": "slowly than that of the v-prediction model.", "type": "text" } ], "index": 17 } ], "index": 15.5 } ], "index": 13.0 }, { "type": "title", "bbox": [ 70, 583, 267, 598 ], "lines": [ { "bbox": [ 68, 582, 268, 600 ], "spans": [ { "bbox": [ 68, 582, 268, 600 ], "score": 1.0, "content": "4 Limitations and Societal Impact", "type": "text" } ], "index": 18 } ], "index": 18 }, { "type": "text", "bbox": [ 70, 612, 540, 732 ], "lines": [ { "bbox": [ 68, 612, 541, 627 ], "spans": [ { "bbox": [ 68, 612, 541, 627 ], "score": 1.0, "content": "Generative modeling has made tremendous progress, especially in recent text-to-image models (Saharia", "type": "text" } ], "index": 19 }, { "bbox": [ 69, 624, 541, 638 ], "spans": [ { "bbox": [ 69, 624, 541, 638 ], "score": 1.0, "content": "et al., 2022b; Ramesh et al., 2022; Rombach et al., 2022). 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Guidance wBase Steps SR StepsCLIP ScoreCLIP R-PrecisionSampling Time
constant=625612825.19±.0392.12±.53618 sec
oscillate(15,1)25612825.02±.0889.91±.96618 sec
constant=6256825.29±.0590.88±.50135 sec
oscillate(15,1)256825.15±.0988.78±.69135 sec
constant=68825.03±.0589.68±.3835 sec
oscillate(15,1)8825.12±.0790.97±.4635 sec
ground truth24.2786.18
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Guidance wBase Steps SR StepsCLIP ScoreCLIP R-PrecisionSampling Time
constant=625612825.19±.0392.12±.53618 sec
oscillate(15,1)25612825.02±.0889.91±.96618 sec
constant=6256825.29±.0590.88±.50135 sec
oscillate(15,1)256825.15±.0988.78±.69135 sec
constant=68825.03±.0589.68±.3835 sec
oscillate(15,1)8825.12±.0790.97±.4635 sec
ground truth24.2786.18
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