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Autoregressive image models generate a sequence of visual tokens one at a time, conditioning each next token on earlier tokens and any text prompt.
Déglul tegtal biiIA-nettali · ~2 min
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Résumé
This sequential procedure differs from masked parallel decoding.
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PixelRNN/PixelCNN model conditional distributions over pixels in an ordered raster sequence. The original DALL·E work instead uses a discrete VAE to represent images as tokens and trains an autoregressive Transformer on text and image tokens; its paper reports a 250-million image-text-pair dataset. Such models sample tokens sequentially. MaskGIT uses a different masked-token iterative decoding approach to predict multiple tokens in parallel.
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PixelCNN and PixelRNN model image pixels autoregressively in their published image-generation studies.
The original DALL·E paper tokenizes images with a discrete variational autoencoder before sequence modeling.
A causal attention mask prevents a Transformer decoder from using future image tokens during autoregressive prediction.
MaskGIT is a separate masked-token method that iteratively predicts many image tokens in parallel.
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Compare autoregressive and parallel masked-image methods using matched image quality, compute and sampling conditions. Evaluate prompt alignment and artifacts on the intended domain. The cited papers establish their own architectures and experiments, not a universal winner or future deployment trend.
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Take the Autoregressive Image Generation quiz
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Luy defar nataal yuy dellu ginaaw?
Autoregressive image models generate a sequence of visual tokens one at a time, conditioning each next token on earlier tokens and any text prompt. This sequential procedure differs from masked parallel decoding.
Luy 'autoregressive' ci wàllu defar nataal?
Modèle autorégresif yi dañuy factoriser nataal bi ci anam wu ñuy toppalante, di wax luy waaja am ci token bu nekk wala pixel bu sukkandiko ci élément yi ko jiitu.
Ak naka la xeetu nataali autoregressif yu bees yu melni DALL-E 1 di wane nataal balaa ñu koy wax luy am?
Sistem yu bees yi dañuy tënk nataal bi ci ay token yu wuute (ñuy faral di jaar ci encodeur bu nuroo ak VQ-VAE), ba noppi ñu wax luy toppalante token yi ak Transformer.
Yan model yu njëkk ñoo defar nataal benn pixel bu ñor benn yoon?
PixelRNN ak PixelCNN ñoo njëkka amal xeetu otorégressif yuy skane ak seetlu nataal yi pixel par pixel.
Ban mekaniism mooy tax Transformer bi di toppatoo jeton yu njëkk yi ci jamonoy defar autorégresif?
maskeer causal dafay tere position bu nekk dem ci token yi ci kanam, di jaay doole factorisation cammo-ci-ndijoor.
Lan mooy njariñ li gëna mag ci sampling nataal autorégressif?
Ndax token bu nekk mingi aju ci yi ko jiitu, inference dafa wara daw token par token, suko defee generation bi di yeex.
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