{"id":"https://openalex.org/W7165771400","doi":"https://doi.org/10.48550/arxiv.2606.24025","title":"Information-Theoretic Classifier-Free Guidance with Adaptive Schedule Optimization","display_name":"Information-Theoretic Classifier-Free Guidance with Adaptive Schedule Optimization","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7165771400","doi":"https://doi.org/10.48550/arxiv.2606.24025"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.24025","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24025","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.24025","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139235534","display_name":"Haobo Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Haobo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054043281","display_name":"Xiangxiang Xu","orcid":"https://orcid.org/0000-0002-4178-0934"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Xiangxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5007990317","display_name":"Yuheng Bu","orcid":"https://orcid.org/0000-0002-3479-4553"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bu, Yuheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"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":false,"cited_by_count":0,"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":0.4174000024795532,"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":0.4174000024795532,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.18529999256134033,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.04309999942779541,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.6844000220298767},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.642300009727478},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5217999815940857},{"id":"https://openalex.org/keywords/constant","display_name":"Constant (computer programming)","score":0.5134999752044678},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4494999945163727},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4138999879360199},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.3411000072956085},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.33309999108314514}],"concepts":[{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.6844000220298767},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.642300009727478},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6134999990463257},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5217999815940857},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.5134999752044678},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.507099986076355},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4494999945163727},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4138999879360199},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C83247935","wikidata":"https://www.wikidata.org/wiki/Q7234227","display_name":"Posterior predictive distribution","level":5,"score":0.28299999237060547},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.2687000036239624},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2612999975681305},{"id":"https://openalex.org/C2779466056","wikidata":"https://www.wikidata.org/wiki/Q107630651","display_name":"Time point","level":2,"score":0.25850000977516174}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.24025","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24025","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.24025","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24025","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diffusion":[0],"models":[1],"have":[2],"achieved":[3],"strong":[4],"performance":[5],"in":[6],"image,":[7],"text-to-image,":[8],"and":[9,37,122,133,148],"video":[10],"generation,":[11],"where":[12],"conditional":[13],"generation":[14],"is":[15,60],"often":[16],"controlled":[17,49],"by":[18,26,58,68,105],"classifier-free":[19],"guidance":[20,29,33,147,150],"(CFG).":[21],"CFG":[22,59,83],"improves":[23],"condition":[24],"consistency":[25],"increasing":[27],"a":[28,89],"weight,":[30],"but":[31],"stronger":[32],"typically":[34],"reduces":[35],"diversity":[36],"distributional":[38],"coverage.":[39],"It":[40],"remains":[41],"unclear":[42],"how":[43],"this":[44,75,110],"consistency-coverage":[45,97],"trade-off":[46],"should":[47],"be":[48],"across":[50,152],"the":[51,55,63,69,95,101,106,118,137],"reverse":[52],"trajectory,":[53],"since":[54],"distribution":[56,66,103],"induced":[57,104],"not":[61],"simply":[62],"fixed-time":[64],"tilted":[65],"given":[67],"guided":[70,107],"score":[71,123],"field.":[72],"To":[73],"address":[74],"issue,":[76],"we":[77],"propose":[78],"an":[79],"information-theoretic":[80],"framework":[81],"for":[82],"schedule":[84],"optimization.":[85],"Our":[86],"approach":[87],"uses":[88],"clean":[90],"endpoint":[91],"reference":[92],"to":[93,116],"specify":[94],"desired":[96],"trade-off,":[98],"while":[99],"optimizing":[100],"actual":[102],"sampler":[108],"toward":[109],"reference.":[111],"We":[112],"derive":[113],"trajectory-level":[114],"formulas":[115],"estimate":[117],"objective":[119],"from":[120],"samples":[121],"evaluations,":[124],"avoiding":[125],"explicit":[126],"density":[127],"estimation.":[128],"On":[129],"ImageNet-512":[130],"with":[131,135],"EDM-XXL":[132],"COCO":[134],"SD-XL,":[136],"learned":[138],"schedules":[139],"achieve":[140],"competitive":[141],"or":[142],"improved":[143],"trade-offs":[144],"over":[145],"constant":[146],"allocate":[149],"selectively":[151],"noise":[153],"levels.":[154]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-25T00:00:00"}
