{"id":"https://openalex.org/W7143430308","doi":"https://doi.org/10.48550/arxiv.2603.25872","title":"DRiffusion: Draft-and-Refine Process Parallelizes Diffusion Models with Ease","display_name":"DRiffusion: Draft-and-Refine Process Parallelizes Diffusion Models with Ease","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7143430308","doi":"https://doi.org/10.48550/arxiv.2603.25872"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25872","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25872","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":null,"license_id":null,"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.2603.25872","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130976725","display_name":"Runsheng Bai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bai, Runsheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100722691","display_name":"Chengyu Zhang","orcid":"https://orcid.org/0000-0002-7285-289X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chengyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130947840","display_name":"Yangdong Deng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deng, Yangdong","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":0.7688000202178955,"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":0.7688000202178955,"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":0.031099999323487282,"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":0.026399999856948853,"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/acceleration","display_name":"Acceleration","score":0.6129999756813049},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.608299970626831},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.512499988079071},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.45969998836517334},{"id":"https://openalex.org/keywords/diffusion-process","display_name":"Diffusion process","score":0.41679999232292175},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.38670000433921814},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.3774000108242035},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.36959999799728394}],"concepts":[{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.6129999756813049},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.608299970626831},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5630000233650208},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5273000001907349},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.512499988079071},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.45969998836517334},{"id":"https://openalex.org/C68710425","wikidata":"https://www.wikidata.org/wiki/Q5275442","display_name":"Diffusion process","level":3,"score":0.41679999232292175},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.38670000433921814},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.36959999799728394},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3075999915599823},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.30379998683929443},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30079999566078186},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2685000002384186},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2624000012874603},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25872","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25872","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.25872","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25872","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":null,"license_id":null,"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":[1,110],"have":[2],"achieved":[3],"remarkable":[4],"success":[5],"in":[6,17,24,59,65,115],"generating":[7],"high-fidelity":[8],"content":[9],"but":[10],"suffer":[11],"from":[12],"slow,":[13],"iterative":[14],"sampling,":[15],"resulting":[16],"high":[18],"latency":[19],"that":[20,34,152],"limits":[21],"their":[22,56],"use":[23],"interactive":[25],"applications.":[26],"We":[27],"introduce":[28],"DRiffusion,":[29],"a":[30,39],"parallel":[31],"sampling":[32],"framework":[33],"parallelizes":[35],"diffusion":[36,109],"inference":[37],"through":[38],"draft-and-refine":[40],"process.":[41],"DRiffusion":[42,103,153],"employs":[43],"skip":[44],"transitions":[45],"to":[46,70],"generate":[47],"multiple":[48,108],"draft":[49],"states":[50],"for":[51],"future":[52],"timesteps":[53],"and":[54,123,137,146,157],"computes":[55],"corresponding":[57],"noises":[58],"parallel,":[60],"which":[61],"are":[62],"then":[63],"used":[64],"the":[66,88,98,132],"standard":[67],"denoising":[68],"process":[69],"produce":[71],"refined":[72],"results.":[73],"Theoretically,":[74],"our":[75],"method":[76],"achieves":[77],"an":[78],"acceleration":[79,156],"rate":[80],"of":[81,100,131,144],"$\\tfrac{1}{n}$":[82],"or":[83,90],"$\\tfrac{2}{n+1}$,":[84],"depending":[85],"on":[86,118,127],"whether":[87],"conservative":[89],"aggressive":[91],"mode":[92],"is":[93],"used,":[94],"where":[95],"$n$":[96],"denotes":[97],"number":[99],"devices.":[101],"Empirically,":[102],"attains":[104],"1.4$\\times$-3.7$\\times$":[105],"speedup":[106],"across":[107],"while":[111,135],"incur":[112],"minimal":[113],"degradation":[114],"generation":[116],"quality:":[117],"MS-COCO":[119],"dataset,":[120],"both":[121],"FID":[122],"CLIP":[124],"remain":[125],"largely":[126],"par":[128],"with":[129],"those":[130],"original":[133],"model,":[134],"PickScore":[136],"HPSv2.1":[138],"show":[139],"only":[140],"minor":[141],"average":[142],"drops":[143],"0.17":[145],"0.43,":[147],"respectively.":[148],"These":[149],"results":[150],"verify":[151],"delivers":[154],"substantial":[155],"preserves":[158],"perceptual":[159],"quality.":[160]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-31T00:00:00"}
