{"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/implicit-diffusion-models-for-continuous","title":"Implicit Diffusion Models for Continuous Super-Resolution","arxiv_id":"2303.16491","date":"2023-03-29","proceeding":"CVPR 2023 1","authors":["Sicheng Gao","Xuhui Liu","Bohan Zeng","Sheng Xu","Yanjing Li","Xiaoyan Luo","Jianzhuang Liu","XianTong Zhen","Baochang Zhang"],"abstract":"Image super-resolution (SR) has attracted increasing attention due to its wide applications. However, current SR methods generally suffer from over-smoothing and artifacts, and most work only with fixed magnifications. This paper introduces an Implicit Diffusion Model (IDM) for high-fidelity continuous image super-resolution. IDM integrates an implicit neural representation and a denoising diffusion model in a unified end-to-end framework, where the implicit neural representation is adopted in the decoding process to learn continuous-resolution representation. Furthermore, we design a scale-controllable conditioning mechanism that consists of a low-resolution (LR) conditioning network and a scaling factor. The scaling factor regulates the resolution and accordingly modulates the proportion of the LR information and generated features in the final output, which enables the model to accommodate the continuous-resolution requirement. Extensive experiments validate the effectiveness of our IDM and demonstrate its superior performance over prior arts.","url_abs":"https://arxiv.org/abs/2303.16491v1","url_pdf":"https://arxiv.org/pdf/2303.16491v1.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":"implicit-diffusion-models-for-continuous","repo_url":"https://github.com/ree1s/idm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-super-resolution-on-celeba-hq-128x128","task":"Image Super-Resolution","dataset":"CelebA-HQ 128x128","model":"IDM","rank_in_archive_order":2,"of":4,"metrics":{"Consistency":"2.14","PSNR":"24.01","SSIM":"0.71"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-div2k-val-4x","task":"Image Super-Resolution","dataset":"DIV2K val - 4x upscaling","model":"IDM","rank_in_archive_order":15,"of":21,"metrics":{"PSNR":"27.59","SSIM":"0.78"},"uses_additional_data":true},{"leaderboard":"/sota/image-super-resolution-on-div2k-val-4x","task":"Image Super-Resolution","dataset":"DIV2K val - 4x upscaling","model":"LAR-SR","rank_in_archive_order":16,"of":21,"metrics":{"PSNR":"27.03","SSIM":"0.77"},"uses_additional_data":true},{"leaderboard":"/sota/image-super-resolution-on-div2k-val-4x","task":"Image Super-Resolution","dataset":"DIV2K val - 4x upscaling","model":"HCFlow","rank_in_archive_order":17,"of":21,"metrics":{"PSNR":"27.02","SSIM":"0.76"},"uses_additional_data":true},{"leaderboard":"/sota/image-super-resolution-on-div2k-val-4x","task":"Image Super-Resolution","dataset":"DIV2K val - 4x upscaling","model":"Bicubic","rank_in_archive_order":18,"of":21,"metrics":{"PSNR":"26.7","SSIM":"0.77"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-div2k-val-4x","task":"Image Super-Resolution","dataset":"DIV2K val - 4x upscaling","model":"HCFlow++","rank_in_archive_order":19,"of":21,"metrics":{"PSNR":"26.61","SSIM":"0.74"},"uses_additional_data":true},{"leaderboard":"/sota/image-super-resolution-on-div2k-val-4x","task":"Image Super-Resolution","dataset":"DIV2K val - 4x upscaling","model":"RankSRGAN","rank_in_archive_order":20,"of":21,"metrics":{"PSNR":"26.55","SSIM":"0.75"},"uses_additional_data":true},{"leaderboard":"/sota/image-super-resolution-on-div2k-val-4x","task":"Image Super-Resolution","dataset":"DIV2K val - 4x upscaling","model":"ESRGAN","rank_in_archive_order":21,"of":21,"metrics":{"PSNR":"26.22","SSIM":"0.75"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.16491","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16491"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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