Concept-centric Personalization with Large-scale Diffusion Priors

• URL: http://arxiv.org/abs/2312.08195v1
• Date: Wed, 13 Dec 2023 14:59:49 GMT
• Title: Concept-centric Personalization with Large-scale Diffusion Priors
• Authors: Pu Cao, Lu Yang, Feng Zhou, Tianrui Huang, Qing Song
• Abstract summary: We present the task of customizing large-scale diffusion priors for specific concepts as conceptcentric personalization. Our goal is to generate high-quality concept-centric images while maintaining the versatile controllability inherent to openworld models.
• Score: 7.684688573874212
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite large-scale diffusion models being highly capable of generating diverse open-world content, they still struggle to match the photorealism and fidelity of concept-specific generators. In this work, we present the task of customizing large-scale diffusion priors for specific concepts as concept-centric personalization. Our goal is to generate high-quality concept-centric images while maintaining the versatile controllability inherent to open-world models, enabling applications in diverse tasks such as concept-centric stylization and image translation. To tackle these challenges, we identify catastrophic forgetting of guidance prediction from diffusion priors as the fundamental issue. Consequently, we develop a guidance-decoupled personalization framework specifically designed to address this task. We propose Generalized Classifier-free Guidance (GCFG) as the foundational theory for our framework. This approach extends Classifier-free Guidance (CFG) to accommodate an arbitrary number of guidances, sourced from a variety of conditions and models. Employing GCFG enables us to separate conditional guidance into two distinct components: concept guidance for fidelity and control guidance for controllability. This division makes it feasible to train a specialized model for concept guidance, while ensuring both control and unconditional guidance remain intact. We then present a null-text Concept-centric Diffusion Model as a concept-specific generator to learn concept guidance without the need for text annotations. Code will be available at https://github.com/PRIV-Creation/Concept-centric-Personalization.

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