{"id":"https://openalex.org/W7133355783","doi":"https://doi.org/10.48550/arxiv.2603.01623","title":"Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration","display_name":"Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133355783","doi":"https://doi.org/10.48550/arxiv.2603.01623"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01623","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01623","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.01623","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123670768","display_name":"Jiaqi Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Jiaqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127963979","display_name":"Juntong Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Juntong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032045258","display_name":"Puheng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Puheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127971544","display_name":"Haotian Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Haotian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127906459","display_name":"Qiushan Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Qiushan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127998393","display_name":"Stefano Ermon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ermon, Stefano","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.3610999882221222,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.3610999882221222,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.15459999442100525,"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":0.054999999701976776,"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/speedup","display_name":"Speedup","score":0.7878999710083008},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6837999820709229},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.6266999840736389},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.4731000065803528},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4641999900341034},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4293000102043152},{"id":"https://openalex.org/keywords/sharpening","display_name":"Sharpening","score":0.39250001311302185},{"id":"https://openalex.org/keywords/cache","display_name":"Cache","score":0.3864000141620636},{"id":"https://openalex.org/keywords/chebyshev-filter","display_name":"Chebyshev filter","score":0.3788999915122986}],"concepts":[{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.7878999710083008},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6837999820709229},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6833000183105469},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.6266999840736389},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.4731000065803528},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4641999900341034},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4629000127315521},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4293000102043152},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4115000069141388},{"id":"https://openalex.org/C2781137444","wikidata":"https://www.wikidata.org/wiki/Q237105","display_name":"Sharpening","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C21424316","wikidata":"https://www.wikidata.org/wiki/Q718621","display_name":"Chebyshev filter","level":2,"score":0.3788999915122986},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.36309999227523804},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.3596999943256378},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.349700003862381},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3472999930381775},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.3273000121116638},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.30649998784065247},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C55128770","wikidata":"https://www.wikidata.org/wiki/Q5275440","display_name":"Diffusion map","level":4,"score":0.29429998993873596},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2646999955177307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01623","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01623","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.01623","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01623","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":"article"},"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":[0,28],"models":[1,185],"have":[2],"become":[3],"the":[4,23,32,39,113,117,132,173,188,215],"dominant":[5],"tool":[6],"for":[7,134],"high-fidelity":[8],"image":[9,181],"and":[10,42,77,123,163,182,202],"video":[11,183],"generation,":[12],"yet":[13],"are":[14],"critically":[15],"bottlenecked":[16],"by":[17,53],"their":[18,61],"inference":[19],"speed":[20],"due":[21],"to":[22,38,71,79,144,197],"numerous":[24],"iterative":[25],"passes":[26],"of":[27,116,190],"Transformers.":[29],"To":[30],"reduce":[31],"exhaustive":[33],"compute,":[34],"recent":[35],"works":[36],"resort":[37],"feature":[40,93,103],"caching":[41],"reusing":[43],"scheme":[44],"that":[45,99,155,168],"skips":[46,76],"network":[47],"evaluations":[48],"at":[49,83,147],"selected":[50],"diffusion":[51,92,150,184],"steps":[52],"using":[54],"cached":[55],"features":[56,115,146],"in":[57],"previous":[58],"steps.":[59,151],"However,":[60],"preliminary":[62],"design":[63],"solely":[64],"relies":[65],"on":[66,178,200,205],"local":[67],"approximation,":[68],"causing":[69],"errors":[70],"grow":[72],"rapidly":[73],"with":[74,105,126,172,214],"large":[75],"leading":[78],"degraded":[80],"sample":[81,211],"quality":[82,212],"high":[84],"speedups.":[85],"In":[86,109],"this":[87],"work,":[88],"we":[89,111,130,194],"propose":[90],"spectral":[91],"forecaster":[94],"(Spectrum),":[95],"a":[96],"training-free":[97],"approach":[98,157],"enables":[100],"global,":[101],"long-range":[102],"reuse":[104],"tightly":[106],"controlled":[107],"error.":[108],"particular,":[110],"view":[112],"latent":[114],"denoiser":[118],"as":[119],"functions":[120],"over":[121],"time":[122],"approximate":[124],"them":[125],"Chebyshev":[127],"polynomials.":[128],"Specifically,":[129],"fit":[131],"coefficient":[133],"each":[135],"basis":[136],"via":[137],"ridge":[138],"regression,":[139],"which":[140],"is":[141],"then":[142],"leveraged":[143],"forecast":[145],"multiple":[148],"future":[149],"We":[152],"theoretically":[153],"reveal":[154],"our":[156,191],"admits":[158],"more":[159],"favorable":[160],"long-horizon":[161],"behavior":[162],"yields":[164],"an":[165],"error":[166],"bound":[167],"does":[169],"not":[170],"compound":[171],"step":[174],"size.":[175],"Extensive":[176],"experiments":[177],"various":[179],"state-of-the-art":[180],"consistently":[186],"verify":[187],"superiority":[189],"approach.":[192],"Notably,":[193],"achieve":[195],"up":[196],"4.79$\\times$":[198],"speedup":[199,204],"FLUX.1":[201],"4.67$\\times$":[203],"Wan2.1-14B,":[206],"while":[207],"maintaining":[208],"much":[209],"higher":[210],"compared":[213],"baselines.":[216]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-04T00:00:00"}
