Papers › Hierarchical Text-Conditional Image Generation with CLIP Latents
Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.
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Code
Syntology Ran 29 of 38 code samples harvested from 4 repositories linked to this paper; 9 have no recorded run. Of those that ran: 3 ran · honoured contract; 5 ran · violated contract; 6 ran · our draft was wrong; 1 ran · fixture could not drive it; 14 ran with no contract checked.
By repository: community (archive-listed): 37 samples from 4 repositories, 28 ran; 1 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
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Code Syntology ran Syntology
38 samples harvested; 29 ran; 3 honoured the contract we drafted; 9 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 1 of the 38 samples is pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 4 repositories linked to this paper, official or community; each sample names its own and says which. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text-to-Image Generation | COCO (Common Objects in Context) | DALL-E 2 | FID | 10.39 | #26 of 69 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: DALL·E 2
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