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Generative Semantic Nursing
2 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28
Generative Semantic Nursing is a task of intervening the generative process on the fly during inference time to improve the faithfulness of the generated images. It works by carefully manipulating of latents during the denoising process of a pre-trained text-to-image diffusion model.
Source: Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models
Description from the archive archive 2025-07-28; Papers-with-Code links inside it are rewritten to this site.
Benchmarks archive 2025-07-28
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Datasets archive 2025-07-28
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Most implemented papers archive 2025-07-28
2 shown of 2 papers with code (2 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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31 Jan 2023 2 repositories listed Syntology ran 2 of 7 samples · 5 unverifiedRecent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt.
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20 Jul 2023 1 repository listed Syntology ran 6 of 8 samples · 2 unverified · 8 pointer-only (licence)To address the challenges posed by complex prompts or scenarios involving multiple entities and to achieve improved attribute binding, we propose Divide & Bind.
Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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