Methods › Computer Vision › Image Generation Models › DALL·E 2
DALL·E 2
Introduced by Aditya Ramesh et al. in Hierarchical Text-Conditional Image Generation with CLIP Latents
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
DALL·E 2 is a generative text-to-image model made up of two main components: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT 7 Mar 2023 · 1 repository · arXiv:2303.04226
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Hierarchical Text-Conditional Image Generation with CLIP Latents 13 Apr 2022 · 8 repositories · arXiv:2204.06125Syntology ran 29 of 38 samples · 9 unverified · 1 pointer-only (licence)
Tasks archive 2025-07-28
7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Conditional Image Generation | 1 |
| Decoder | 1 |
| Diversity | 1 |
| Image Generation | 1 |
| Text-to-Image Generation | 1 |
| Zero-Shot Text-to-Image Generation | 1 |
| multimodal interaction | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections