{"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/regularization-by-texts-for-latent-diffusion","title":"Regularization by Texts for Latent Diffusion Inverse Solvers","arxiv_id":"2311.15658","date":"2023-11-27","proceeding":null,"authors":["Jeongsol Kim","Geon Yeong Park","Hyungjin Chung","Jong Chul Ye"],"abstract":"The recent advent of diffusion models has led to significant progress in solving inverse problems, leveraging these models as effective generative priors. Nonetheless, there remain challenges related to the ill-posed nature of such problems, often due to inherent ambiguities in measurements or intrinsic system symmetries. To address this, drawing inspiration from the human ability to resolve visual ambiguities through perceptual biases, here we introduce a novel latent diffusion inverse solver by regularization by texts (TReg). Specifically, TReg applies the textual description of the preconception of the solution during the reverse diffusion sampling, of which the description is dynamically reinforced through null-text optimization for adaptive negation. Our comprehensive experimental results demonstrate that TReg successfully mitigates ambiguity in the inverse problems, enhancing their effectiveness and accuracy.","url_abs":"https://arxiv.org/abs/2311.15658v2","url_pdf":"https://arxiv.org/pdf/2311.15658v2.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":"regularization-by-texts-for-latent-diffusion","repo_url":"https://github.com/treg-inverse/treg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"negation","task_name":"Negation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.15658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.15658"}},"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":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/treg-inverse/treg","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"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":1,"samples":[{"code_sha256_prefix":"7146e40644c908e7","entry":"reshape_y","repo":"treg-inverse/treg","repo_kind":"official","path":"solve.py","file_url":"https://github.com/treg-inverse/treg/blob/HEAD/solve.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7146e40644c908e7"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}