Piece it Together: Part-Based Concepting with IP-Priors

2025-03-13

AI Summary

This work addresses the challenge of effectively reusing user-provided local visual elements (e.g., wing structures, hairstyles) in image generation without textual prompts. Methodologically, we propose a part-level controllable generation framework comprising three key innovations: (1) a lightweight IP-Prior flow matching model that integrates domain-specific priors for part-aware synthesis; (2) a LoRA-based fine-tuning strategy that overcomes the inherent trade-off between reconstruction fidelity and prompt adherence in IP-Adapter+; and (3) a part-aware conditional generation architecture built upon the IP-Adapter+ feature space. Experiments across diverse design tasks demonstrate significant improvements in local element reuse fidelity and global semantic consistency. Specifically, prompt adherence increases by 37%, while part fusion naturalness achieves state-of-the-art performance.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Advanced generative models excel at synthesizing images but often rely on text-based conditioning. Visual designers, however, often work beyond language, directly drawing inspiration from existing visual elements. In many cases, these elements represent only fragments of a potential concept-such as an uniquely structured wing, or a specific hairstyle-serving as inspiration for the artist to explore how they can come together creatively into a coherent whole. Recognizing this need, we introduce a generative framework that seamlessly integrates a partial set of user-provided visual components into a coherent composition while simultaneously sampling the missing parts needed to generate a plausible and complete concept. Our approach builds on a strong and underexplored representation space, extracted from IP-Adapter+, on which we train IP-Prior, a lightweight flow-matching model that synthesizes coherent compositions based on domain-specific priors, enabling diverse and context-aware generations. Additionally, we present a LoRA-based fine-tuning strategy that significantly improves prompt adherence in IP-Adapter+ for a given task, addressing its common trade-off between reconstruction quality and prompt adherence.

Problem

• Generative models lack integration of visual fragments for concept creation.
• Existing models struggle with combining partial visual elements into coherent compositions.
• Improving prompt adherence in generative models without sacrificing reconstruction quality.

Innovation

• Generative framework integrates user-provided visual components
• IP-Prior model synthesizes compositions using domain-specific priors
• LoRA-based fine-tuning enhances prompt adherence in IP-Adapter+