SubZero: Composing Subject, Style, and Action via Zero-Shot Personalization
Shubhankar Borse, Kartikeya Bhardwaj, Mohammad Reza Karimi Dastjerdi,, Hyojin Park, Shreya Kadambi, Shobitha Shivakumar, Prathamesh Mandke, Ankita, Nayak, Harris Teague, Munawar Hayat, Fatih Porikli

TL;DR
SubZero introduces a tuning-free framework for personalized subject, style, and action generation in diffusion models, achieving high flexibility and quality without fine-tuning, suitable for mobile devices.
Contribution
The paper presents a novel, tuning-free method with constraints and orthogonalized temporal aggregation for improved personalized composition in diffusion models.
Findings
Significant improvement over state-of-the-art methods in subject, style, and action composition.
Effective reduction of content and style leakage artifacts.
Compatible with edge devices for real-time personalized generation.
Abstract
Diffusion models are increasingly popular for generative tasks, including personalized composition of subjects and styles. While diffusion models can generate user-specified subjects performing text-guided actions in custom styles, they require fine-tuning and are not feasible for personalization on mobile devices. Hence, tuning-free personalization methods such as IP-Adapters have progressively gained traction. However, for the composition of subjects and styles, these works are less flexible due to their reliance on ControlNet, or show content and style leakage artifacts. To tackle these, we present SubZero, a novel framework to generate any subject in any style, performing any action without the need for fine-tuning. We propose a novel set of constraints to enhance subject and style similarity, while reducing leakage. Additionally, we propose an orthogonalized temporal aggregation…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Artificial Intelligence in Games · Human Motion and Animation
MethodsDiffusion · Sparse Evolutionary Training
