LogoSticker: Inserting Logos into Diffusion Models for Customized Generation

TL;DR

LogoSticker is a novel method for inserting logos into diffusion models, enabling accurate and harmonious logo generation in varied contexts, addressing the challenge of customizing logos unlike common objects.

Contribution

The paper introduces a two-phase pipeline with novel algorithms for logo insertion into diffusion models, a task previously underexplored due to logos' unique complexity.

Findings

LogoSticker outperforms existing customization methods.

It achieves precise localization and identity extraction of logos.

Effective in diverse visual contexts.

Abstract

Recent advances in text-to-image model customization have underscored the importance of integrating new concepts with a few examples. Yet, these progresses are largely confined to widely recognized subjects, which can be learned with relative ease through models' adequate shared prior knowledge. In contrast, logos, characterized by unique patterns and textual elements, are hard to establish shared knowledge within diffusion models, thus presenting a unique challenge. To bridge this gap, we introduce the task of logo insertion. Our goal is to insert logo identities into diffusion models and enable their seamless synthesis in varied contexts. We present a novel two-phase pipeline LogoSticker to tackle this task. First, we propose the actor-critic relation pre-training algorithm, which addresses the nontrivial gaps in models' understanding of the potential spatial positioning of logos and interactions with other objects. Second, we propose a decoupled identity learning algorithm, which enables precise localization and identity extraction of logos. LogoSticker can generate logos accurately and harmoniously in diverse contexts. We comprehensively validate the effectiveness of LogoSticker over customization methods and large models such as DALLE~3. \href{https://mingkangz.github.io/logosticker}{Project page}.