{"id":"https://openalex.org/W4404037204","doi":"https://doi.org/10.1109/mlsp58920.2024.10734817","title":"FQ4DM: Full Quantization for Diffusion Model","display_name":"FQ4DM: Full Quantization for Diffusion Model","publication_year":2024,"publication_date":"2024-09-22","ids":{"openalex":"https://openalex.org/W4404037204","doi":"https://doi.org/10.1109/mlsp58920.2024.10734817"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp58920.2024.10734817","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp58920.2024.10734817","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Chieh-En Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chieh-En Wang","raw_affiliation_strings":["Graduate Institute of Electrical Engineering, National Taiwan University,Taipei,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate Institute of Electrical Engineering, National Taiwan University,Taipei,Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009835161","display_name":"Yu-Shan Tai","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu-Shan Tai","raw_affiliation_strings":["Graduate Institute of Electrical Engineering, National Taiwan University,Taipei,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate Institute of Electrical Engineering, National Taiwan University,Taipei,Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109465340","display_name":"An-Yeu Wu","orcid":"https://orcid.org/0000-0003-4731-8633"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"An-Yeu Wu","raw_affiliation_strings":["Graduate Institute of Electrical Engineering, National Taiwan University,Taipei,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate Institute of Electrical Engineering, National Taiwan University,Taipei,Taiwan","institution_ids":["https://openalex.org/I16733864"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16733864"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.26969999074935913,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.26969999074935913,"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/quantization","display_name":"Quantization (signal processing)","score":0.5695047378540039},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5634694695472717},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.48001497983932495},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.15210047364234924},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.13097912073135376}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5695047378540039},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5634694695472717},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.48001497983932495},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.15210047364234924},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.13097912073135376},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp58920.2024.10734817","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp58920.2024.10734817","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2916051212","display_name":null,"funder_award_id":"NSTC 113-2218-E-002-036 -MBK","funder_id":"https://openalex.org/F4320322795","funder_display_name":"Ministry of Science and Technology, Taiwan"}],"funders":[{"id":"https://openalex.org/F4320322795","display_name":"Ministry of Science and Technology, Taiwan","ror":"https://ror.org/02kv4zf79"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W967544008","https://openalex.org/W2019115117","https://openalex.org/W2108598243","https://openalex.org/W2765811365","https://openalex.org/W2963122961","https://openalex.org/W3036167779","https://openalex.org/W3041956526","https://openalex.org/W3121370741","https://openalex.org/W4221139906","https://openalex.org/W4221159371","https://openalex.org/W4226014430","https://openalex.org/W4226125322","https://openalex.org/W4226148126","https://openalex.org/W4281661987","https://openalex.org/W4285601701","https://openalex.org/W4301206121","https://openalex.org/W4303440777","https://openalex.org/W4312497550","https://openalex.org/W4312756164","https://openalex.org/W4312933868","https://openalex.org/W4313069943","https://openalex.org/W4375868854","https://openalex.org/W4377130726","https://openalex.org/W4377191398","https://openalex.org/W4379539434","https://openalex.org/W4386065704","https://openalex.org/W4386076368","https://openalex.org/W4390874074","https://openalex.org/W4394625844","https://openalex.org/W6625168331","https://openalex.org/W6765779288","https://openalex.org/W6779823529","https://openalex.org/W6780593937","https://openalex.org/W6783713337","https://openalex.org/W6788990321","https://openalex.org/W6809884996","https://openalex.org/W6810093873","https://openalex.org/W6810940779","https://openalex.org/W6811424244","https://openalex.org/W6838327568","https://openalex.org/W6845281891","https://openalex.org/W6852874946","https://openalex.org/W6852954323","https://openalex.org/W6853011858"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Diffusion":[0],"models":[1],"(DMs)":[2],"have":[3],"recently":[4],"gained":[5],"acclaim":[6],"for":[7,111],"their":[8,13],"superior":[9],"imaging":[10],"capabilities.":[11],"However,":[12],"extensive":[14],"computational":[15],"and":[16,37],"memory":[17],"demands":[18],"often":[19],"limit":[20],"the":[21,49,65,100,119,124],"practical":[22],"application":[23],"on":[24],"portable":[25],"devices.":[26],"Post-training":[27],"quantization":[28,80,89],"(PTQ)":[29],"offers":[30],"a":[31,58,87,105],"solution":[32],"that":[33,72,91,118],"enables":[34],"model":[35,109],"compression":[36],"reduces":[38],"runtime":[39],"without":[40],"retraining.":[41],"Nonetheless,":[42],"traditional":[43],"PTQ":[44,61],"methods":[45],"struggle":[46],"to":[47,63,78],"handle":[48],"unique":[50],"time-variant":[51],"distribution":[52],"in":[53],"DMs.":[54],"Accordingly,":[55],"we":[56,103],"propose":[57],"novel":[59],"timestep-grouping":[60],"approach":[62],"address":[64],"multiple":[66],"timestep":[67],"issue.":[68],"We":[69,82],"also":[70],"identify":[71],"non-uniform":[73],"post-SiLU":[74],"activations":[75],"may":[76],"lead":[77],"significant":[79],"loss.":[81],"tackle":[83],"this":[84],"issue":[85],"with":[86,99],"region-specific":[88],"strategy":[90],"better":[92],"represents":[93],"extreme":[94],"values":[95],"after":[96,127],"quantization.":[97,129],"Combined":[98],"above":[101],"methods,":[102],"achieve":[104],"fully":[106],"quantized":[107],"diffusion":[108],"feasible":[110],"hardware":[112],"implementation.":[113],"Our":[114],"experimental":[115],"results":[116],"show":[117],"proposed":[120],"method":[121],"successfully":[122],"maintains":[123],"FID":[125],"score":[126],"8-bit":[128]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
