{"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/diffusion-sampling-correction-via","title":"Diffusion Sampling Correction via Approximately 10 Parameters","arxiv_id":"2411.06503","date":"2024-11-10","proceeding":null,"authors":["Guangyi Wang","Wei Peng","Lijiang Li","Wenyu Chen","Yuren Cai","Songzhi Su"],"abstract":"Diffusion Probabilistic Models (DPMs) have demonstrated exceptional performance in generative tasks, but this comes at the expense of sampling efficiency. To enhance sampling speed without sacrificing quality, various distillation-based accelerated sampling algorithms have been recently proposed. However, they typically require significant additional training costs and model parameter storage, which limit their practical application. In this work, we propose PCA-based Adaptive Search (PAS), which optimizes existing solvers for DPMs with minimal learnable parameters and training costs. Specifically, we first employ PCA to obtain a few orthogonal unit basis vectors to span the high-dimensional sampling space, which enables us to learn just a set of coordinates to correct the sampling direction; furthermore, based on the observation that the cumulative truncation error exhibits an ``S''-shape, we design an adaptive search strategy that further enhances the sampling efficiency and reduces the number of stored parameters to approximately 10. Extensive experiments demonstrate that PAS can significantly enhance existing fast solvers in a plug-and-play manner with negligible costs. For instance, on CIFAR10, PAS requires only 12 parameters and less than 1 minute of training on a single NVIDIA A100 GPU to optimize the DDIM from 15.69 FID (NFE=10) to 4.37.","url_abs":"https://arxiv.org/abs/2411.06503v2","url_pdf":"https://arxiv.org/pdf/2411.06503v2.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":"diffusion-sampling-correction-via","repo_url":"https://github.com/onefly123/PAS","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"}],"methods":[{"method_slug":"pca","method_name":"PCA"},{"method_slug":"set","method_name":"SET"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2411.06503","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.06503"}},"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/onefly123/PAS","reach":null}],"summary":{"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"official":{"samples":2,"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":0,"samples":[{"code_sha256_prefix":"3eed7631aeebeabc","entry":"get_denoised","repo":"onefly123/pas","repo_kind":"official","path":"pas_solvers.py","file_url":"https://github.com/onefly123/pas/blob/HEAD/pas_solvers.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3eed7631aeebeabc"}},{"code_sha256_prefix":"7a95cc8476c56eff","entry":"PCA_process","repo":"onefly123/PAS","repo_kind":"official","path":"pca_utils/utils.py","file_url":"https://github.com/onefly123/PAS/blob/HEAD/pca_utils/utils.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":"7a95cc8476c56eff"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}