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Personalizing Text-to-Image Generation via Aesthetic Gradients

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arxiv 2209.12330 v1 pith:FSVTADTF submitted 2022-09-25 cs.CV cs.LG

Victor Gallego This is my paper · ORCID
classification cs.CVcs.LG
keywords aestheticdiffusiongradientsmodelaesthetically-filteredaestheticsapproachclip-conditioned
verification ladder T0 review T1 audit T2 compute T3 formal
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This work proposes aesthetic gradients, a method to personalize a CLIP-conditioned diffusion model by guiding the generative process towards custom aesthetics defined by the user from a set of images. The approach is validated with qualitative and quantitative experiments, using the recent stable diffusion model and several aesthetically-filtered datasets. Code is released at https://github.com/vicgalle/stable-diffusion-aesthetic-gradients

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hierarchical Anti-Aesthetics: Protecting Facial Privacy against Customized Diffusion Models

    cs.CV 2026-07 unverdicted novelty 6.0 of 10

    Hierarchical anti-aesthetic adversarial noise, guided by global and face-local preference reward models, degrades customized diffusion outputs and reduces facial identity leakage more than prior cloaking methods.

  2. Dialogue with the Machine and Dialogue with the Art World: Evaluating Generative AI for Culturally-Situated Creativity

    cs.CY 2024-12 conditional novelty 6.0 of 10

    A qualitative evaluation method pairing artist-to-expert dialogue with hands-on generative AI experimentation yields culturally situated critiques and design recommendations.

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