{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/show-1-marrying-pixel-and-latent-diffusion","title":"Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation","arxiv_id":"2309.15818","date":"2023-09-27","proceeding":null,"authors":["David Junhao Zhang","Jay Zhangjie Wu","Jia-Wei Liu","Rui Zhao","Lingmin Ran","YuChao Gu","Difei Gao","Mike Zheng Shou"],"abstract":"Significant advancements have been achieved in the realm of large-scale pre-trained text-to-video Diffusion Models (VDMs). However, previous methods either rely solely on pixel-based VDMs, which come with high computational costs, or on latent-based VDMs, which often struggle with precise text-video alignment. In this paper, we are the first to propose a hybrid model, dubbed as Show-1, which marries pixel-based and latent-based VDMs for text-to-video generation. Our model first uses pixel-based VDMs to produce a low-resolution video of strong text-video correlation. After that, we propose a novel expert translation method that employs the latent-based VDMs to further upsample the low-resolution video to high resolution. Compared to latent VDMs, Show-1 can produce high-quality videos of precise text-video alignment; Compared to pixel VDMs, Show-1 is much more efficient (GPU memory usage during inference is 15G vs 72G). We also validate our model on standard video generation benchmarks. Our code and model weights are publicly available at https://github.com/showlab/Show-1.","url_abs":"https://arxiv.org/abs/2309.15818v2","url_pdf":"https://arxiv.org/pdf/2309.15818v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"show-1-marrying-pixel-and-latent-diffusion","repo_url":"https://github.com/showlab/show-1","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"text-to-video-generation","task_name":"Text-to-Video Generation"},{"task_slug":"video-alignment","task_name":"Video Alignment"},{"task_slug":"video-generation","task_name":"Video Generation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-to-video-generation-on-evalcrafter-text","task":"Text-to-Video Generation","dataset":"EvalCrafter Text-to-Video (ECTV) Dataset","model":"Show-1","rank_in_archive_order":2,"of":5,"metrics":{"Motion Quality":"52.19","Temporal Consistency":"60.83","Text-to-Video Alignment":"62.07","Total Score":"229","Visual Quality":"53.74"},"uses_additional_data":true},{"leaderboard":"/sota/text-to-video-generation-on-msr-vtt","task":"Text-to-Video Generation","dataset":"MSR-VTT","model":"Show-1","rank_in_archive_order":8,"of":18,"metrics":{"CLIPSIM":"0.3072","FID":"13.08","FVD":"538"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2309.15818","atlas_url":"https://app.syntology.ai/?focus=2309.15818","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.15818"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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