Generating Multi-Image Synthetic Data for Text-to-Image Customization (2502.01720v1)

Abstract: Customization of text-to-image models enables users to insert custom concepts and generate the concepts in unseen settings. Existing methods either rely on costly test-time optimization or train encoders on single-image training datasets without multi-image supervision, leading to worse image quality. We propose a simple approach that addresses both limitations. We first leverage existing text-to-image models and 3D datasets to create a high-quality Synthetic Customization Dataset (SynCD) consisting of multiple images of the same object in different lighting, backgrounds, and poses. We then propose a new encoder architecture based on shared attention mechanisms that better incorporate fine-grained visual details from input images. Finally, we propose a new inference technique that mitigates overexposure issues during inference by normalizing the text and image guidance vectors. Through extensive experiments, we show that our model, trained on the synthetic dataset with the proposed encoder and inference algorithm, outperforms existing tuning-free methods on standard customization benchmarks.

Insightful Overview of "Generating Multi-Image Synthetic Data for Text-to-Image Customization"

The paper "Generating Multi-Image Synthetic Data for Text-to-Image Customization" addresses the need for efficient model customization in text-to-image generation frameworks. The authors propose an innovative approach for synthesizing high-quality, multi-image datasets to enhance the customization process by overcoming limitations associated with single-image datasets.

Text-to-image models have traditionally faced difficulties in maintaining visual consistency when tasked with generating images featuring novel compositions of objects in varied contexts. This paper introduces a Synthetic Customization Dataset (SynCD) that contains multiple images of the same object in different scenarios, such as variations in lighting, backgrounds, and poses. By leveraging existing 3D datasets alongside an efficient text-to-image model framework, the SynCD facilitates improved object consistency and context diversity at an unprecedented scale.

The proposed method comprises a two-pronged approach. Firstly, the dataset generation pipeline utilizes Masked Shared Attention (MSA) mechanisms to enhance visual consistency. Secondly, the model incorporates 3D priors, sourced from the Objaverse dataset, to ensure multi-view consistency, an advantage particularly useful for rigid objects. This combination ensures that synthetic images maintain object identity while allowing background and pose variability.

The authors also introduce a novel model architecture reliant on shared attention mechanisms to facilitate fine-grained detail integration from reference images. Notably, a new inference technique is developed to mitigate overexposure issues by normalizing the text and image guidance vectors, thereby achieving a harmonious balance between text alignment and visual fidelity.

The robustness of the method is reinforced through extensive experiments, demonstrating its superiority over existing tuning-free methods across standard customization benchmarks. Strong numerical results include an improvement on image alignment and text fidelity, exhibiting better performance compared to current state-of-the-art methods such as JeDi, Emu-2, and IP-Adapter.

The implications of this research are significant for practical and theoretical advancements in AI model customization. Practically, it reduces the computational expense associated with model tuning for each new context, presenting a scalable alternative that maintains high image fidelity. Theoretically, it challenges traditional approaches by showcasing the effectiveness of synthetic datasets incorporating MSA, paving the way for future exploration into synthetic data generation and attention-based models.

Notably, while the paper advances the customization capabilities in text-to-image models, it also opens avenues for refining synthetic data generation techniques, especially in handling intricate textures and diverse pose variations. Integrating recent developments in text-to-3D and video generative models could address current limitations and further enhance dataset quality.

Future investigations could focus on refining dataset quality, incorporating more complex object textures, and extending the methodology to dynamic video data. The exploitation of advanced attention mechanisms in generative models underscores the potential to redefine benchmarks within the model customization paradigm, thereby broadening the landscape of applications in AI-driven generative tasks.