Historical context & research account
Parti explores an autoregressive approach alongside diffusion, generating image tokens as another kind of language. It shows that the expansion of text-to-image generation in 2022 did not follow a single technical route. Token-based modelling remains a parallel way to organize the relation between descriptions and visual outputs.
Dates & version record
Date displayed for this node：2022-06 · Date recorded in the research master：2022-06
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Original sources & further reading
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Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
https://sites.research.google/parti/
Current cited URL
Provenance, translation & verification
Archive node #084 · Historical node
Research masters & supplement references · 3
AI Art Genealogy Research Master · p. 25 · 084
Date as recorded: 2022-06
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation ↗
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
GenAI Visual Release Chronology Research Master · p. 6 · R023
Date as recorded: 2022-06
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation ↗
Google Parti
GenAI Visual Release Chronology Research Master · p. 6 · R025
Date as recorded: 2022-06
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation ↗
Parti
Archive account adapted from the masters, not a full translation of the linked work. AI-assisted translation; human review pending.
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Cite this node
ANTI-AI ARCHIVE. Scaling Autoregressive Models for Content-Rich Text-to-Image Generation. Art-history node #084. https://salondesrefuses.cn/en/art-history/084
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