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2022

Personalizing Text-to-Image Generation via Aesthetic Gradients

Gallego, Victor

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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

Personalizing Text-to-Image Generation via Aesthetic Gradients · Around

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Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, and Andrew Gordon Wilson · 2019
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
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Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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