Gwara reVisual AI

GLIDE Diffusion Model

GLIDE yanga iri yepakutanga OpenAI yemhando yekusiyanisa mavara-kune-mufananidzo yairatidza kukurumidza uye 'classifier-yemahara nhungamiro' yaigona kukunda zvekare GAN-based masisitimu.

Teerera kugwaro iriAI-yakarondedzerwa · ~2 min

Tanga kuverengaTora mubvunzo
Pa peji ino2 min verenga
  1. Pfupiso
  2. Kudzika Kwakadzika
  3. Kuitwa muNyika yeChokwadi
  4. Ramangwana reGLIDE Diffusion Model
  5. Kuzvishandisa
  6. Ramba Uchiongorora
  7. Mibvunzo inowanzo bvunzwa

Pfupiso

It was a key stepping stone on the path to DALL-E 2.

Kudzika Kwakadzika

Released by OpenAI in late 2021, GLIDE (Guided Language to Image Diffusion for Generation and Editing) demonstrated that diffusion models guided by text could produce photorealistic, prompt-faithful images. Its biggest contribution was comparing two ways to steer generation: CLIP guidance versus classifier-free guidance. The team found classifier-free guidance produced more realistic and better-aligned images, a result that shaped nearly every text-to-image model since. GLIDE also supported text-driven inpainting, letting users edit part of an image with a new prompt. It used a 3.5-billion-parameter diffusion model plus an upsampler. OpenAI released a smaller, filtered version publicly while withholding the full model over misuse concerns, and the DALL-E 2 research paper describes an unCLIP decoder that modifies the GLIDE architecture to accept CLIP-image conditioning.

Kuitwa muNyika yeChokwadi

Kugadzira mufananidzo kubva mumutsara wakaita seyakatsanangurwa chiitiko, ichiratidza kukurumidza kukurumidza-kutendeka synthesis

Text-driven inpainting: masking chikamu chemufananidzo uye uchizadza nechinhu chitsva chinotsanangurwa mumashoko

Kugadzirisa mufananidzo uripo nekuwedzera kana kutsiva zvinhu kuburikidza nekutevera-up kukurumidza

Kushanda sehwaro hwekutsvagisa kwakaratidza kutungamira-yemahara kutungamira kunokunda CLIP gwara rekugadzirisa

Ramangwana reGLIDE Diffusion Model

GLIDE pachayo inhoroondo, inotsiviwa neDALL-E 2, Imagen, uye Yakagadzikana Diffusion, asi pfungwa dzayo dzinoramba dziri kwese kwese. Classifier-yemahara kutungamira inoramba iri iyo yakasarudzika knob yekutengesa kubva pakuvimbika uye kusiyana, uye mavara-anotyairwa pendi ikozvino yakajairika. Masisitimu emangwana anoramba achinatsa masheji ekutungamira, achidzikisa maartifact akasimba ekutungamira zvinokonzeresa, uye nekuwedzera iwo masimusi kuvhidhiyo uye 3D diffusion, saka pesvedzero yeGLIDE inodarika iyo modhi.

Kuzvishandisa

Nei zvichikosha

  • Kumhanya uye chiyero. Visual AI inogona kuita otomatiki yekuongorora, yekuona, uye yekumaka mabasa pachiyero.
  • Vaka sarudzo. Zvikwata zvekugadzira zvinogona prototype pfungwa nekukurumidza nekudzokororwa kwemaoko mashoma.
  • Team uye workflow. Mashandisirwo anogona kushandisa masaini emifananidzo nemavhidhiyo ayo aimbove akaoma kugadzirisa.

Chenjera

  • Kodzero dzemifananidzo uye kubvumirwa kunogona kuve njodzi dzepamutemo kana provenance isina kujeka.
  • Kuita kwemuenzaniso kunogona kusiyanisa kupenya, huwandu hwevanhu, uye nharaunda.
  • Manyepo enhema anogona kusacherechedzwa kunze kwekunge zvikumbaridzo zvekuvimba zvikatariswa.

Matanho anotevera

  1. Tsanangura maitiro ekugamuchirwa echokwadi, kurangarira, uye mutengo wekukanganisa.
  2. Edzai nedata rinoenderana nemamiriro chaiwo ekugadzira.
  3. Wedzera ongororo yemunhu kune yakaderera-kusavimbika kana yakakwirira-inokanganisa kufanotaura.
  4. Tevera modhi kudonha uye simbisa mushure mekuchinja kwekamera kana dataset.

Ramba Uchiongorora

Kuona kweKomputa

Nzwisisa masisitimu epasi anopa simba kuona AI.Verenga Guide

AI Kugadzira mifananidzo

Ongorora kugadzira mafambiro uye modhi tradeoffs.Verenga Guide

Tsamba yemahara

Tora iyo yemazuva ese AI pfupiso

Nhatu dzakasimbiswa dzeAI nyaya mangwanani ega ega pakati pevhiki, dzakanyorwa neChirungu chakajeka. Yemahara zvachose, hapana ads.

Imwe email zuva rega rega revhiki. Bvisa kunyoresa kamwe chete. Hatimbotengese kana kugovera kero yako.

Zviedze iwe pachako

Take the GLIDE Diffusion Model quiz

Mhinduro yepakarepo pamhinduro yega yega, uye chitupa chinogovaniswa chine ID inobatika kana wapedza kosi.

Tanga mibvunzo

Tsigira dzidzo yemahara yeAI. AI Understanding ndeye 501(c)(3) isingabatsiri - hapana ads, hapana paywall, zvachose. Ita chipo

Mibvunzo inowanzo bvunzwa

What is GLIDE Diffusion Model?

GLIDE yanga iri yepakutanga OpenAI yemhando yekusiyanisa mavara-kune-mufananidzo yairatidza kukurumidza uye 'classifier-yemahara nhungamiro' yaigona kukunda zvekare GAN-based masisitimu. Yakanga iri kiyi yekutsika munzira inoenda kuDALL-E 2.

Ndeipi nzira yekutungamira iyo GLIDE yakawana yakagadzira zvirinani, zvakanyanya kukurumidza-yakavimbika mifananidzo?

GLIDE yakaratidza classifier-yemahara nhungamiro yakapa zvinonyatsoitika uye zvakamisikidzwa zvirinani pane kutungamira kweCLIP.

What size did the published full GLIDE base diffusion model have, before counting its upsampler?

The full research model used a 3.5-billion-parameter base diffusion model with a separate upsampling stage.

Kutungamira-yemahara kutungamira kunoshanda sei panguva yekudzidziswa?

Nekudzidziswa pane ese ari maviri echokwadi uye asina chinhu kukurudzira, iyo modhi inodzidza ine mamiriro uye isina magadzirirwo chizvarwa, yobva yawedzera pakati pavo pasampling.

Which later system describes its diffusion decoder as modifying the GLIDE architecture in the DALL-E 2 research paper?

The paper describes adding CLIP-image conditioning to a modified GLIDE architecture for its unCLIP decoder; this is a concrete architectural relationship.

Sei OpenAI yangoburitsa kavhezheni diki, akasefa eGLIDE pachena?

OpenAI yanyima modhi izere pamusoro pekunetsekana kwekushandisa zvisirizvo uye ndokuburitsa yakasefa, diki musiyano.

Ramba uchidzidza

Mazano anoenderana

Mamwe madhairekitori akasarudzirwa nyaya iyi

Wana chitupa cheAI chemaharaKosi pfupi yemahara nebvunzo. Hapana mutengo, hapana kadhi. Inoda kusvika maawa 2.5.Tanga mahara →