StyleAdapter: A Unified Stylized Image Generation Model

In recent years, advancements in text-to-image (T2I) generation and deep learning models have significantly driven progress in artificial intelligence for image generation. However, integrating specific styles from reference images into high-quality images generated from textual prompts remains challenging. To address this issue, Zhouxia Wang and colleagues proposed a unified stylized image generation model called StyleAdapter. This paper, published in the International Journal of Computer Vision, was collaboratively authored by researchers from The University of Hong Kong, Tencent ARC Lab, the University of Macau, and Shanghai AI Laboratory.

Research Background and Significance

Current leading stylized image generation methods, such as DreamBooth and LoRA, refine original diffusion models or utilize additional small networks to adapt to specific styles. These methods effectively generate images with detailed style characteristics. However, each style requires individual fine-tuning or retraining, leading to high computational costs and inefficiency. Moreover, many methods use textual descriptions for style information, which are often limited in expressiveness, resulting in generated images with coarse style features.

In this context, a unified model that does not require per-style fine-tuning is highly desirable. StyleAdapter was developed to generate high-quality images that match both the given textual content and the style of reference images, improving efficiency and flexibility.

Paper Source and Publication Information

This paper was authored by Zhouxia Wang, Ping Luo, and Wenping Wang from The University of Hong Kong, Xintao Wang, Zhongang Qi, and Ying Shan from Tencent ARC Lab, and Liangbin Xie from the University of Macau. It was published in 2024 in the International Journal of Computer Vision (DOI: 10.1007/s11263-024-02253-x).

Core Methodology and Workflow of StyleAdapter

Key Innovations

StyleAdapter introduces the following key innovations: 1. Two-Path Cross-Attention Module (TPCA): Separately processes style information and textual prompts to ensure control over generated content. 2. Semantic Suppressing Vision Model (SSVM): Suppresses semantic information from style reference images to avoid interference with content generation. 3. Compatibility and Extensibility: Integrates seamlessly with existing control methods, such as T2I-Adapter and ControlNet, enabling more stable and controllable image generation.

Research Workflow

1. Research Subjects and Dataset:
   StyleAdapter was trained on 600k image-text pairs from the LAION-Aesthetics dataset. The test set comprises 50 text prompts, 50 content images, and 8 groups of style reference images.

2. Model Architecture:
   StyleAdapter is based on the Stable Diffusion (SD) model and the CLIP vision model. Its main components include:

   • Textual features extracted by CLIP’s text model.
   • Style features extracted from reference images using SSVM and processed via a Style Embedding module.
   • TPCA module that independently injects textual and style features and fuses them using learnable weights, ensuring controllability over generated content.

3. Experimental Design and Metrics:
   To evaluate performance, the study employed both subjective and objective metrics, including Text Similarity (Text-Sim), Style Similarity (Style-Sim), and Frechet Inception Distance (FID). Additionally, expert evaluations were conducted through user studies.

Data Processing and Experimental Results

Experiments demonstrated that StyleAdapter outperforms existing methods like LoRA and DreamBooth in terms of text consistency, style fidelity, and image quality. Without requiring per-style fine-tuning, StyleAdapter showed excellent generalization capability. Moreover, the TPCA and SSVM modules significantly improved the model’s control over textual content while preserving style details.

Main Conclusions and Contributions

Conclusions

1. StyleAdapter maintains control over generated content by separately processing textual and style features.
2. SSVM effectively suppresses semantic information in style references, mitigating its interference with content generation.
3. The unified model design eliminates the need for per-style fine-tuning, greatly enhancing efficiency and flexibility.

Academic and Practical Contributions

• Scientific Value: The modular design and innovative attention mechanisms of StyleAdapter offer new directions for image generation research.
• Practical Applications: StyleAdapter has broad applicability in fields such as art creation, advertising, and game development, lowering technical barriers and costs.

Highlights and Future Directions

Highlights

1. Innovative Methods: The combination of TPCA and SSVM improves generation quality while maintaining efficiency.
2. Generalization Capability: StyleAdapter can generate various stylized images without requiring style-specific fine-tuning, reducing costs significantly.
3. Enhanced Controllability: The model balances text and style control, meeting diverse generation needs.

Limitations and Future Work

While StyleAdapter performs well, it struggles with processing complex styles such as transparency due to limited data in its training set. Future efforts will focus on constructing more comprehensive datasets and optimizing algorithms to improve generalization further.

Summary

The introduction of StyleAdapter marks significant progress in stylized image generation research. Its innovative design principles and exceptional experimental performance pave the way for new paths in related fields. With the growing demand for diverse style generation, StyleAdapter provides an effective solution to the challenges of efficiency and scalability in existing methods.