{"id":"https://openalex.org/W7162514802","doi":"https://doi.org/10.48550/arxiv.2605.27102","title":"JLT: Clean-Latent Prediction in Latent Diffusion Transformers","display_name":"JLT: Clean-Latent Prediction in Latent Diffusion Transformers","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162514802","doi":"https://doi.org/10.48550/arxiv.2605.27102"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27102","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27102","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.2605.27102","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137091799","display_name":"Funing Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Funing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137188381","display_name":"Tenghui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Tenghui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137086907","display_name":"Guanyu Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Guanyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137145393","display_name":"Junyong Cen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cen, Junyong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137159671","display_name":"Qichao Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Qichao","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":0.9035000205039978,"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":0.9035000205039978,"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":0.020600000396370888,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.018200000748038292,"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/gaussian","display_name":"Gaussian","score":0.45910000801086426},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4334000051021576},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.366100013256073},{"id":"https://openalex.org/keywords/isotropy","display_name":"Isotropy","score":0.36579999327659607},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3330000042915344},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.3111000061035156},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.30469998717308044},{"id":"https://openalex.org/keywords/conditional-expectation","display_name":"Conditional expectation","score":0.2937000095844269}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5289000272750854},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.45910000801086426},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4334000051021576},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39430001378059387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38370001316070557},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3686000108718872},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.366100013256073},{"id":"https://openalex.org/C184050105","wikidata":"https://www.wikidata.org/wiki/Q273163","display_name":"Isotropy","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3330000042915344},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.30469998717308044},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C2776116443","wikidata":"https://www.wikidata.org/wiki/Q55989","display_name":"Convertible","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27102","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27102","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.2605.27102","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27102","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":[{"score":0.8237074613571167,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Flow":[0],"matching":[1],"with":[2,68,129,132],"clean-data":[3],"prediction":[4,67,117,144],"has":[5,42],"shown":[6],"that":[7,102,143],"regressing":[8],"the":[9,47,74,82],"clean":[10,116],"point":[11],"can":[12],"exploit":[13],"low-dimensional":[14],"structure":[15],"more":[16],"effectively":[17],"than":[18,154],"predicting":[19],"an":[20,106],"ambient":[21],"noised":[22],"quantity.":[23],"We":[24,51],"ask":[25],"whether":[26],"this":[27],"principle":[28],"remains":[29],"useful":[30],"after":[31],"images":[32],"are":[33,89,149],"mapped":[34],"into":[35],"a":[36,54,69,93,97,133],"learned":[37],"latent":[38,56,113,147],"space,":[39],"where":[40],"compression":[41],"already":[43],"removed":[44],"much":[45],"of":[46],"raw":[48],"pixel":[49],"variability.":[50],"introduce":[52],"JLT,":[53],"130M":[55],"diffusion":[57,148],"Transformer":[58],"over":[59,137],"frozen":[60],"FLUX.2":[61],"VAE":[62],"codes,":[63],"and":[64,78,87,110],"compare":[65],"clean-latent":[66],"matched":[70],"velocity-prediction":[71],"DiT":[72],"under":[73],"same":[75],"representation,":[76],"backbone,":[77],"training":[79],"settings.":[80],"Although":[81],"three":[83],"variables":[84],"x,":[85],"epsilon,":[86],"v":[88],"linearly":[90],"convertible":[91],"for":[92],"fixed":[94],"corruption":[95],"time,":[96],"local":[98],"Gaussian":[99],"analysis":[100],"shows":[101],"velocity":[103,138],"regression":[104],"inherits":[105],"isotropic":[107],"target-covariance":[108],"floor":[109],"amplifies":[111],"low-variance":[112],"directions,":[114],"while":[115],"damps":[118],"them.":[119],"On":[120],"ImageNet":[121],"256":[122],"x":[123],"256,":[124],"JLT-B/1":[125],"obtains":[126],"FID-50K":[127],"2.50":[128],"classifier-free":[130],"guidance,":[131],"large":[134],"matched-target":[135],"gap":[136],"prediction.":[139],"These":[140],"results":[141],"suggest":[142],"targets":[145],"in":[146],"representation-dependent":[150],"geometric":[151],"choices,":[152],"rather":[153],"interchangeable":[155],"algebraic":[156],"parameterizations.":[157]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-28T00:00:00"}