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title

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Encoder for Fast Personalization of Text-to-Image Models

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author

updated

2026-03-02 11:44:33

raw text

Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models Anonymous Authors Paper Code (Coming soon) TL;DR: We use an encoder to personalize a text-to-image model to new concepts with a single image and 5-15 tuning steps. Image credits: James Boyes , Venus the cat Abstract Text-to-image personalization aims to teach a pre-trained diffusion model to reason about novel, user provided concepts, embedding them into new scenes guided by natural language prompts. However, current personalization approaches struggle with lengthy training times, high storage requirements or loss of identity. To overcome these limitations, we propose an encoder-based domain-tuning approach. Our key insight is that by underfitting on a large set of concepts from a given domain, we can improve generalization and create a model that is more amenable...

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