What is recorded here?
Documented encoder-decoder modeling of discrete image tokens and released a broad compositional-prompt evaluation resource to characterize scaling, content fidelity and failure modes in text-to-image synthesis.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
ViT-VQGAN and sequence-to-sequence modeling are inherited. Acquire integrated research/evaluation resource, not an invention for every model scale. Prompt success and benchmark scores do not imply complete visual world knowledge or dependable text rendering.
Read the original work
- Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
- Yu et al.: Parti autoregressive image synthesis and compositional prompt resource — author/institutional project
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Jiahui Yu; Yuanzhong Xu; Jing Yu Koh; Thang Luong; Gunjan Baid; Zirui Wang; Vijay Vasudevan; Alexander Ku; Yinfei Yang; Burcu Karagol Ayan; Ben Hutchinson; Wei Han; Zarana Parekh; Xin Li; Han Zhang; Jason Baldridge; Yonghui Wu
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
Yu et al.: Parti autoregressive image synthesis and compositional prompt resource
Documented encoder-decoder modeling of discrete image tokens and released a broad compositional-prompt evaluation resource to characterize scaling, content fidelity and failure modes in text-to-image synthesis.
Scientific scope
ViT-VQGAN and sequence-to-sequence modeling are inherited. Acquire integrated research/evaluation resource, not an invention for every model scale. Prompt success and benchmark scores do not imply complete visual world knowledge or dependable text rendering.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Jiahui Yu; Yuanzhong Xu; Jing Yu Koh; Thang Luong; Gunjan Baid; Zirui Wang; Vijay Vasudevan; Alexander Ku; Yinfei Yang; Burcu Karagol Ayan; Ben Hutchinson; Wei Han; Zarana Parekh; Xin Li; Han Zhang; Jason Baldridge; Yonghui Wu
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.