{"id":115753,"date":"2025-12-29T13:40:10","date_gmt":"2025-12-29T13:40:10","guid":{"rendered":"https:\/\/ekamu.net\/?p=115753"},"modified":"2025-12-29T13:40:10","modified_gmt":"2025-12-29T13:40:10","slug":"cinli-arastirmacilardan-yapay-zekada-cigir-acan-video-uretim-teknigi","status":"publish","type":"post","link":"https:\/\/ekamu.net\/index.php\/2025\/12\/29\/cinli-arastirmacilardan-yapay-zekada-cigir-acan-video-uretim-teknigi\/","title":{"rendered":"\u00c7inli ara\u015ft\u0131rmac\u0131lardan yapay zekada \u00e7\u0131\u011f\u0131r a\u00e7an video \u00fcretim tekni\u011fi"},"content":{"rendered":"

<\/span> \u00c7inli ara\u015ft\u0131rmac\u0131lar, yapay zeka destekli video \u00fcretiminde h\u0131z ve maliyet dengesini k\u00f6kten de\u011fi\u015ftirmesi beklenen TurboDiffusion<\/strong> adl\u0131 yeni bir teknik geli\u015ftirdi. K\u0131sa s\u00fcre \u00f6nce yay\u0131mlanan akademik \u00e7al\u0131\u015fmaya g\u00f6re TurboDiffusion, g\u00f6r\u00fcnt\u00fc kalitesinden \u00f6d\u00fcn vermeden video \u00fcretim<\/strong> s\u00fcrecini \u00f6l\u00e7ekli kullan\u0131mda 200 kata kadar h\u0131zland\u0131rabiliyor<\/strong>. <\/figure>\n

Ara\u015ft\u0131rma, Pekin\u2019deki Tsinghua \u00dcniversitesi, yapay zek\u00e2 modeli geli\u015ftiricisi Shengshu Technology ve Kaliforniya \u00dcniversitesi Berkeley b\u00fcnyesindeki uzmanlar\u0131n ortak \u00e7al\u0131\u015fmas\u0131yla haz\u0131rland\u0131. \u00c7al\u0131\u015fmada, TurboDiffusion\u2019\u0131n performans\u0131 t\u00fcketici s\u0131n\u0131f\u0131 bir sistem \u00fczerinde test edildi. Nvidia\u2019n\u0131n RTX 5090\u2019\u0131n kullan\u0131ld\u0131\u011f\u0131 bu testlerde, standart \u00e7\u00f6z\u00fcn\u00fcrl\u00fckte be\u015f saniyelik bir video klibin \u00fcretim s\u00fcresi 3 dakikadan 1,9 saniyeye<\/strong> d\u00fc\u015f\u00fcr\u00fcld\u00fc. Bu sonu\u00e7, yakla\u015f\u0131k 100 katl\u0131k bir h\u0131z art\u0131\u015f\u0131na kar\u015f\u0131l\u0131k geliyor.<\/p>\n

Video \u00fcretiminde 200 kata varan h\u0131zlanma<\/b><\/p>\n

Ayn\u0131 donan\u0131m \u00fczerinde ger\u00e7ekle\u015ftirilen bir ba\u015fka deneyde ise y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc, be\u015f saniyelik bir videonun \u00fcretim s\u00fcresi neredeyse 80 dakikadan 24 saniyeye<\/strong> indirildi. Bu da TurboDiffusion\u2019\u0131n baz\u0131 senaryolarda 200 kata yakla\u015fan bir h\u0131z kazanc\u0131<\/strong> sa\u011flayabildi\u011fini ortaya koyuyor. OpenAI\u2019\u0131n metinden videoya d\u00f6n\u00fc\u015ft\u00fcrme modeli Sora, k\u0131sa klipler olu\u015fturmak i\u00e7in birka\u00e7 dakikaya ihtiya\u00e7 duyuyor. Di\u011fer benzer platformlarda da video \u00fcretimi \u00fc\u00e7-be\u015f dakika s\u00fcrebiliyor.<\/p>\n

Ara\u015ft\u0131rmac\u0131lar, elde edilen bu \u00e7arp\u0131c\u0131 h\u0131zlanmay\u0131 model e\u011fitimi alan\u0131ndaki yeniliklere ba\u011fl\u0131yor. TurboDiffusion\u2019da kullan\u0131lan \u2018seyrek do\u011frusal dikkat mekanizmas\u0131\u2019<\/strong> (sparse linear attention) yakla\u015f\u0131m\u0131, yapay zeka modelinin t\u00fcm veriyi ayn\u0131 anda i\u015flemek yerine yaln\u0131zca en kritik b\u00f6l\u00fcmlere odaklanmas\u0131n\u0131 sa\u011fl\u0131yor. Bu y\u00f6ntem, hem i\u015flem s\u00fcresini hem de hesaplama maliyetlerini ciddi \u00f6l\u00e7\u00fcde azalt\u0131yor.<\/p>\n

Ger\u00e7ek zamanl\u0131 video \u00fcretimine ge\u00e7i\u015f<\/b><\/p>\n

<\/span> Sekt\u00f6r analisti Kyon Xu, video \u00fcretimindeki bu h\u0131zlanman\u0131n b\u00fcy\u00fck bir paradigma de\u011fi\u015fimine i\u015faret etti\u011fini belirterek, yapay zeka video modellerinin art\u0131k \u00fcretim s\u00fcrecinde bir darbo\u011faz olmaktan \u00e7\u0131kaca\u011f\u0131n\u0131 vurguluyor. <\/figure>\n

Yapay zekan\u0131n farkl\u0131 sekt\u00f6rlerde adil ve kapsay\u0131c\u0131 bi\u00e7imde yayg\u0131nla\u015fmas\u0131n\u0131 hedefleyen AI Native Foundation da TurboDiffusion\u2019a dikkat \u00e7ekti. Vak\u0131f, X platformunda yapt\u0131\u011f\u0131 payla\u015f\u0131mda bu tekni\u011fin \u201can\u0131nda \u00fcretim\u201d noktas\u0131na ge\u00e7i\u015fi simgeledi\u011fini ve ger\u00e7ek zamanl\u0131 yapay zeka video uygulamalar\u0131n\u0131n \u00f6n\u00fcn\u00fc a\u00e7t\u0131\u011f\u0131n\u0131 ifade etti. A\u00e7\u0131klamaya g\u00f6re TurboDiffusion, \u015firketlerin daha d\u00fc\u015f\u00fck maliyetle, daha h\u0131zl\u0131 iterasyonlarla ticari \u00f6l\u00e7ekte video \u00fcretmesini m\u00fcmk\u00fcn k\u0131lacak. Daha da g\u00fczel olan ise TurboDiffusion\u2019\u0131n tamamen a\u00e7\u0131k kaynak<\/strong> olarak GitHub<\/strong> \u00fczerinden payla\u015f\u0131lmas\u0131.<\/p>\n\n

<\/span><\/p>\n

Kaynak :\u00a0https:\/\/www.donanimhaber.com\/cinlilerden-yapay-zekada-cigir-acan-video-uretim-teknigi–200257<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

\u00c7inli ara\u015ft\u0131rmac\u0131lar, yapay zeka destekli video \u00fcretiminde h\u0131z ve maliyet dengesini k\u00f6kten de\u011fi\u015ftirmesi beklenen TurboDiffusion adl\u0131 yeni bir teknik geli\u015ftirdi. K\u0131sa s\u00fcre \u00f6nce yay\u0131mlanan akademik \u00e7al\u0131\u015fmaya g\u00f6re TurboDiffusion, g\u00f6r\u00fcnt\u00fc …<\/p>\n","protected":false},"author":1,"featured_media":115754,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[745,452,326,26,6929],"class_list":["post-115753","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-teknoloji","tag-hiz","tag-model","tag-sure","tag-video","tag-video-uretim"],"_links":{"self":[{"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/posts\/115753","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/comments?post=115753"}],"version-history":[{"count":1,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/posts\/115753\/revisions"}],"predecessor-version":[{"id":115757,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/posts\/115753\/revisions\/115757"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/media\/115754"}],"wp:attachment":[{"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/media?parent=115753"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/categories?post=115753"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/tags?post=115753"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}