{"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/generative-diffusions-in-augmented-spaces-a","title":"A Complete Recipe for Diffusion Generative Models","arxiv_id":"2303.01748","date":"2023-03-03","proceeding":"ICCV 2023 1","authors":["Kushagra Pandey","Stephan Mandt"],"abstract":"Score-based Generative Models (SGMs) have demonstrated exceptional synthesis outcomes across various tasks. However, the current design landscape of the forward diffusion process remains largely untapped and often relies on physical heuristics or simplifying assumptions. Utilizing insights from the development of scalable Bayesian posterior samplers, we present a complete recipe for formulating forward processes in SGMs, ensuring convergence to the desired target distribution. Our approach reveals that several existing SGMs can be seen as specific manifestations of our framework. Building upon this method, we introduce Phase Space Langevin Diffusion (PSLD), which relies on score-based modeling within an augmented space enriched by auxiliary variables akin to physical phase space. Empirical results exhibit the superior sample quality and improved speed-quality trade-off of PSLD compared to various competing approaches on established image synthesis benchmarks. Remarkably, PSLD achieves sample quality akin to state-of-the-art SGMs (FID: 2.10 for unconditional CIFAR-10 generation). Lastly, we demonstrate the applicability of PSLD in conditional synthesis using pre-trained score networks, offering an appealing alternative as an SGM backbone for future advancements. Code and model checkpoints can be accessed at \\url{https://github.com/mandt-lab/PSLD}.","url_abs":"https://arxiv.org/abs/2303.01748v2","url_pdf":"https://arxiv.org/pdf/2303.01748v2.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":"generative-diffusions-in-augmented-spaces-a","repo_url":"https://github.com/mandt-lab/PSLD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.01748","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.01748"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/mandt-lab/PSLD","reach":null}],"summary":{"ran":1,"ran_draft_wrong":1,"ran_violates":1,"unverified":1},"by_repo_kind":{"official":{"samples":4,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"fa622c9f4a07bab5","entry":"PSLD","repo":"mandt-lab/PSLD","repo_kind":"official","path":"main/models/sde/psld.py","file_url":"https://github.com/mandt-lab/PSLD/blob/HEAD/main/models/sde/psld.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"fa622c9f4a07bab5"}},{"code_sha256_prefix":"0f7dbb625f311002","entry":"register_module","repo":"mandt-lab/PSLD","repo_kind":"official","path":"main/models/sde/psld.py","file_url":"https://github.com/mandt-lab/PSLD/blob/HEAD/main/models/sde/psld.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0f7dbb625f311002"}},{"code_sha256_prefix":"62c4665c28db4ae0","entry":"reshape","repo":"mandt-lab/PSLD","repo_kind":"official","path":"main/models/sde/psld.py","file_url":"https://github.com/mandt-lab/PSLD/blob/HEAD/main/models/sde/psld.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"62c4665c28db4ae0"}},{"code_sha256_prefix":"b73524af8fa13564","entry":"SDE","repo":"mandt-lab/PSLD","repo_kind":"official","path":"main/models/sde/psld.py","file_url":"https://github.com/mandt-lab/PSLD/blob/HEAD/main/models/sde/psld.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b73524af8fa13564"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}