{"id":"https://openalex.org/W7166774909","doi":"https://doi.org/10.48550/arxiv.2606.29360","title":"SAFE-DiT: Semantics-Aware Fast-path Execution for High-Resolution Diffusion Transformers","display_name":"SAFE-DiT: Semantics-Aware Fast-path Execution for High-Resolution Diffusion Transformers","publication_year":2026,"publication_date":"2026-06-28","ids":{"openalex":"https://openalex.org/W7166774909","doi":"https://doi.org/10.48550/arxiv.2606.29360"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.29360","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29360","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,"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.2606.29360","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125194282","display_name":"Xuanhua Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Xuanhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139648393","display_name":"Yuxuan Jia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Yuxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139667697","display_name":"Chuanzhi Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Chuanzhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139644790","display_name":"Weidong Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Weidong","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":0.259799987077713,"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":0.259799987077713,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.13850000500679016,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.09369999915361404,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.57669997215271},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5543000102043152},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5217999815940857},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.49059998989105225},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.450300008058548},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4480000138282776},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.40799999237060547},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.38760000467300415}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7506999969482422},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.57669997215271},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5543000102043152},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5217999815940857},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.49059998989105225},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.450300008058548},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4480000138282776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4169999957084656},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.40799999237060547},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3034999966621399},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30320000648498535},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.29120001196861267},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.28769999742507935},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2854999899864197},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C2781020372","wikidata":"https://www.wikidata.org/wiki/Q533093","display_name":"On the fly","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2653999924659729}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.29360","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29360","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,"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.2606.29360","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29360","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,"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":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.4334011673927307}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High-resolution":[0],"Diffusion":[1],"Transformer":[2],"(DiT)":[3],"inference":[4,175],"contains":[5],"substantial":[6],"spatial":[7,69,96,209],"redundancy,":[8],"but":[9],"many":[10],"spatially":[11],"adaptive":[12],"implementations":[13],"encode":[14],"regional":[15],"computation":[16],"as":[17,39,91,124],"attention":[18,26,85,136],"masks,":[19,93],"which":[20],"can":[21],"inadvertently":[22],"move":[23],"scaled":[24],"dot-product":[25],"(SDPA)":[27],"away":[28],"from":[29,67,164],"FlashAttention":[30],"fast":[31],"paths.":[32],"We":[33,53,112],"identify":[34],"this":[35,114],"avoidable":[36],"systems":[37],"bottleneck":[38],"Mask-Induced":[40],"Dispatch":[41],"Tax":[42],"(MIDT)":[43],"and":[44,94,108,118,155,169,184],"show":[45],"that":[46,62,78],"it":[47],"grows":[48],"with":[49,105],"latent":[50],"sequence":[51],"length.":[52],"introduce":[54],"SAFE-DiT,":[55],"a":[56,80,185,202],"training-free":[57],"Semantics-Aware":[58],"Fast-path":[59],"Execution":[60],"framework":[61],"separates":[63],"exact":[64],"mask":[65],"elision":[66],"approximation-based":[68],"scheduling.":[70],"SAFE-DiT":[71,147],"removes":[72],"only":[73],"provenance-certified":[74],"image":[75],"self-attention":[76,210],"masks":[77,89,133],"induce":[79],"row-wise":[81],"constant":[82],"shift":[83],"in":[84],"logits,":[86],"preserves":[87],"semantics-bearing":[88],"such":[90],"text-padding":[92],"realizes":[95],"adaptation":[97],"through":[98],"prompt-conditioned":[99],"token":[100],"partitioning,":[101],"selective":[102],"state":[103],"updates":[104],"global":[106],"context,":[107],"periodic":[109],"context":[110],"refresh.":[111],"call":[113],"acceleration-only":[115],"configuration":[116],"SAFE-Core":[117,193],"report":[119],"sensitivity-weighted":[120],"classifier-free":[121],"guidance":[122],"separately":[123],"SAFE-DiT+SW.":[125],"On":[126,145],"the":[127,142,195],"evaluated":[128],"PyTorch":[129],"SDPA":[130],"stack,":[131],"redundant":[132],"make":[134],"long-sequence":[135],"$4.1\\times$":[137],"to":[138,166,194],"$5.8\\times$":[139],"slower":[140],"than":[141],"mask-free":[143],"path.":[144],"Lumina-Next,":[146],"achieves":[148],"$2.69\\times$":[149],"end-to-end":[150],"acceleration":[151],"at":[152,157,162,215],"$1024^2$":[153],"resolution":[154],"$5.09\\times$":[156],"$2560^2$,":[158],"reduces":[159],"peak":[160],"memory":[161],"$2560^2$":[163],"94.1":[165],"27.9":[167],"GB,":[168],"enables":[170],"$3072^2$":[171],"generation":[172],"when":[173],"dense":[174,196],"runs":[176],"out":[177],"of":[178,192],"memory.":[179],"Paired":[180],"metrics,":[181],"component":[182],"ablations,":[183],"blinded":[186],"human":[187],"study":[188],"support":[189],"visual":[190],"non-inferiority":[191],"fast-path":[197],"baseline,":[198],"while":[199],"SAFE-DiT+SW":[200],"provides":[201],"separate":[203],"prompt-alignment":[204],"operating":[205],"point":[206],"without":[207],"reintroducing":[208],"masks.":[211],"Code":[212],"is":[213],"available":[214],"https://github.com/xuanhuayin/SAFE-DiT.":[216]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-01T00:00:00"}
