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Though impressive progress has been recently made, diverse semantic synthesis that can efficiently produce semantic-level multimodal results, still remains a challenge. In this paper, we propose a novel diverse semantic image synthesis framework from the perspective of semantic class distributions, which naturally supports diverse generation at semantic or even instance level. We achieve this by modeling class-level conditional modulation parameters as continuous probability distributions instead of discrete values, and sampling per-instance modulation parameters through instance-adaptive stochastic sampling that is consistent across the network. Moreover, we propose prior noise remapping, through linear perturbation parameters encoded from paired references, to facilitate supervised training and exemplar-based instance style control at test time. Extensive experiments on multiple datasets show that our method can achieve superior diversity and comparable quality compared to state-of-the-art methods. Code will be available at \\url{https://github.com/tzt101/INADE.git}","url_abs":"https://arxiv.org/abs/2103.06878v1","url_pdf":"https://arxiv.org/pdf/2103.06878v1.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":"diverse-semantic-image-synthesis-via","repo_url":"https://github.com/tzt101/INADE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-to-image-translation-on-ade20k-labels","task":"Image-to-Image Translation","dataset":"ADE20K Labels-to-Photos","model":"INADE","rank_in_archive_order":16,"of":16,"metrics":{"LPIPS":"0.400"},"uses_additional_data":false},{"leaderboard":"/sota/image-to-image-translation-on-cityscapes","task":"Image-to-Image Translation","dataset":"Cityscapes Labels-to-Photo","model":"INADE","rank_in_archive_order":21,"of":21,"metrics":{"LPIPS":"0.248"},"uses_additional_data":false},{"leaderboard":"/sota/image-to-image-translation-on-deep-fashion-1","task":"Image-to-Image Translation","dataset":"Deep-Fashion","model":"INADE","rank_in_archive_order":1,"of":2,"metrics":{"FID":"9.97"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.06878","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.06878"}},"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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