{"id":"https://openalex.org/W4392487724","doi":"https://doi.org/10.48550/arxiv.2403.01329","title":"Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models","display_name":"Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models","publication_year":2024,"publication_date":"2024-03-02","ids":{"openalex":"https://openalex.org/W4392487724","doi":"https://doi.org/10.48550/arxiv.2403.01329"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2403.01329","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.01329","pdf_url":"https://arxiv.org/pdf/2403.01329","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2403.01329","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092160807","display_name":"Neta Shaul","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shaul, Neta","raw_affiliation_strings":["Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta "],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta ","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079470269","display_name":"Uriel Singer","orcid":"https://orcid.org/0000-0001-8451-8533","observed_orcids":["https://orcid.org/0000-0001-8451-8533"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Singer, Uriel","raw_affiliation_strings":["Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta "],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta ","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029262768","display_name":"Ricky T. Q. Chen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Ricky T. Q.","raw_affiliation_strings":["Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta "],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta ","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063364645","display_name":"Matthew Le","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Matthew","raw_affiliation_strings":["Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta "],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta ","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026143507","display_name":"Ali Thabet","orcid":"https://orcid.org/0000-0001-7513-0748","observed_orcids":["https://orcid.org/0000-0001-7513-0748"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thabet, Ali","raw_affiliation_strings":["Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta "],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta ","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023922146","display_name":"Albert Pumarola","orcid":"https://orcid.org/0000-0003-4185-6991","observed_orcids":["https://orcid.org/0000-0003-4185-6991"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pumarola, Albert","raw_affiliation_strings":["Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta "],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta ","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039995559","display_name":"Yaron Lipman","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lipman, Yaron","raw_affiliation_strings":["Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta "],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Weizmann Institute of Science 2 GenAI , Meta 3 FAIR , Meta ","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.003100000089034438,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.001500000013038516,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/diffusion-models","display_name":"diffusion models","score":0.7179999947547913},{"id":"https://openalex.org/keywords/flow-model","display_name":"flow model","score":0.6940000057220459},{"id":"https://openalex.org/keywords/sample-efficiency","display_name":"sample efficiency","score":0.7139999866485596},{"id":"https://openalex.org/keywords/ode-solvers","display_name":"ODE solvers","score":0.6990000009536743},{"id":"https://openalex.org/keywords/progressive-distillation","display_name":"progressive distillation","score":0.7070000171661377},{"id":"https://openalex.org/keywords/conditional-image-generation","display_name":"conditional image generation","score":0.890999972820282},{"id":"https://openalex.org/keywords/text-to-image-generation","display_name":"text-to-image generation","score":0.996999979019165},{"id":"https://openalex.org/keywords/text-to-audio-generation","display_name":"text-to-audio generation","score":0.9449999928474426}],"concepts":[{"id":"https://openalex.org/C44210515","wikidata":"https://www.wikidata.org/wiki/Q16968978","display_name":"Bespoke","level":2,"score":0.9598641395568848},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5798830389976501},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.557039737701416},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.5204087495803833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5187404751777649},{"id":"https://openalex.org/C57879066","wikidata":"https://www.wikidata.org/wiki/Q41217","display_name":"Mechanics","level":1,"score":0.23179027438163757},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.20032688975334167},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1365993618965149},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13409829139709473},{"id":"https://openalex.org/C112698675","wikidata":"https://www.wikidata.org/wiki/Q37038","display_name":"Advertising","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2403.01329","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.01329","pdf_url":"https://arxiv.org/pdf/2403.01329","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2403.01329","doi":"https://doi.org/10.48550/arxiv.2403.01329","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2403.01329","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2403.01329","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.01329","pdf_url":"https://arxiv.org/pdf/2403.01329","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337393","display_name":"Division of Chemistry","ror":"https://ror.org/01ar8dr59"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392487724.pdf","grobid_xml":"https://content.openalex.org/works/W4392487724.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W2741430497","https://openalex.org/W3015870721","https://openalex.org/W3110257065","https://openalex.org/W3119800657","https://openalex.org/W4221159371","https://openalex.org/W4225319995","https://openalex.org/W4288099666","https://openalex.org/W4297676498","https://openalex.org/W4303647933","https://openalex.org/W4385823130","https://openalex.org/W4385825212","https://openalex.org/W4387582985","https://openalex.org/W4388963769","https://openalex.org/W4411528087"],"related_works":["https://openalex.org/W4388093137","https://openalex.org/W7133795686","https://openalex.org/W7155058324","https://openalex.org/W7129014093","https://openalex.org/W4416960648","https://openalex.org/W4389712518","https://openalex.org/W4404406012","https://openalex.org/W7138300466","https://openalex.org/W7133189877","https://openalex.org/W4415036863"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"Bespoke":[3],"Non-Stationary":[4],"(BNS)":[5],"Solvers,":[6],"a":[7,25,57],"solver":[8,79,102],"distillation":[9,80,88],"approach":[10],"to":[11,50,77],"improve":[12],"sample":[13,43,133],"efficiency":[14],"of":[15,27,67,72],"Diffusion":[16],"and":[17,37,74,126],"Flow":[18],"models.":[19],"BNS":[20,53,101,118],"solvers":[21,29,36,54,119],"are":[22],"based":[23],"on":[24],"family":[26],"non-stationary":[28],"that":[30],"provably":[31],"subsumes":[32],"existing":[33],"numerical":[34],"ODE":[35],"consequently":[38],"demonstrate":[39],"considerable":[40],"improvement":[41,131],"in":[42,75,94,112,132,136],"approximation":[44,134],"(PSNR)":[45,135],"over":[46],"these":[47],"baselines.":[48],"Compared":[49],"model":[51],"distillation,":[52],"benefit":[55],"from":[56,86],"tiny":[58],"parameter":[59],"space":[60],"($<$200":[61],"parameters),":[62],"fast":[63],"optimization":[64],"(two":[65],"orders":[66],"magnitude":[68],"faster),":[69],"maintain":[70],"diversity":[71],"samples,":[73],"contrast":[76],"previous":[78],"approaches":[81],"nearly":[82],"close":[83],"the":[84,95],"gap":[85],"standard":[87],"methods":[89],"such":[90],"as":[91],"Progressive":[92],"Distillation":[93],"low-medium":[96],"NFE":[97,111],"regime.":[98],"For":[99],"example,":[100],"achieves":[103],"45":[104],"PSNR":[105],"/":[106],"1.76":[107],"FID":[108],"using":[109],"16":[110],"class-conditional":[113],"ImageNet-64.":[114],"We":[115],"experimented":[116],"with":[117],"for":[120],"conditional":[121],"image":[122],"generation,":[123,125],"text-to-image":[124],"text-2-audio":[127],"generation":[128],"showing":[129],"significant":[130],"all.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2024-03-06T00:00:00"}
