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Object-Driven One-Shot Fine-tuning of Text-to-Image Diffusion with Prototypical Embedding (2401.15708v1)

Published 28 Jan 2024 in cs.CV

Abstract: As large-scale text-to-image generation models have made remarkable progress in the field of text-to-image generation, many fine-tuning methods have been proposed. However, these models often struggle with novel objects, especially with one-shot scenarios. Our proposed method aims to address the challenges of generalizability and fidelity in an object-driven way, using only a single input image and the object-specific regions of interest. To improve generalizability and mitigate overfitting, in our paradigm, a prototypical embedding is initialized based on the object's appearance and its class, before fine-tuning the diffusion model. And during fine-tuning, we propose a class-characterizing regularization to preserve prior knowledge of object classes. To further improve fidelity, we introduce object-specific loss, which can also use to implant multiple objects. Overall, our proposed object-driven method for implanting new objects can integrate seamlessly with existing concepts as well as with high fidelity and generalization. Our method outperforms several existing works. The code will be released.

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Citations (4)
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Summary

  • The paper proposes an object-driven one-shot fine-tuning framework that uses prototypical embedding for improved image synthesis.
  • It employs tailored initialization and object-specific mask loss to mitigate overfitting and maintain model generalization.
  • Empirical results show enhanced text-image alignment and lower Kernel Inception Distance, promising higher-fidelity image generation.

Introduction

In the rapidly advancing field of text-to-image generation, deep learning models are increasingly capable of synthesizing high-quality images from textual descriptions. Despite these strides, the ability of these models to accurately depict novel objects, particularly under one-shot learning conditions, remains a daunting challenge. The fine-tuning of these models typically requires multiple instances of the target object, which are not always readily available. Consequently, current methods fall short when tasked with generating images featuring a specific object from a limited dataset, often leading to issues such as overfitting and reduced generalizability.

One-Shot Fine-tuning Methodology

To tackle these issues, the authors introduce an object-driven fine-tuning framework predicated on a novel deployment of prototypical embedding combined with class-characterizing regularization. This method diverges from the typical random initialization routine, opting instead for tailored initialization that aligns with the object's class characteristics and visual appearance. The fine-tuning process integrates additional attention mechanisms and employs an object-specific mask loss to enhance fidelity in the resulting images, significantly minimizing risks of overfitting by embedding the object within a broader understanding of similar object classes. The novel class-characterizing regularization technique preserves the model's generalization capabilities by ensuring the prototypical embeddings remain anchored to the object's class during the fine-tuning phase.

Synthesis Performance and Generalization

Empirical evidence points to a superior performance over existing approaches, with demonstrated aptitude in upholding both the fidelity and diversity of synthesized images. Quantitative evaluations conducted by the researchers showcase notable improvements across multiple metrics, most notably in text and image alignment and Kernel Inception Distance (KID). These outcomes suggest their methodology offers a more balanced trade-off between fidelity to the given image and generalization to new prompts. The authors also comprehensively investigated the impacts of each component of their method, including the initial use of prototypical embeddings and the effectiveness of class-characterizing regularization and object-specific loss functions.

Implications and Future Directions

The significance of this research lies in its implications for personalized content generation, where the quality and versatility of one-shot generation are paramount. It opens avenues for further enhancements in image generation tasks that demand intricate attention to detail when incorporating user-specific objects. Nonetheless, the authors acknowledge limitations in handling complex edges and smaller objects. Ongoing advancements are poised to address these constraints, enhancing the granularity and sophistication of mask images and introducing a multi-scale perception mechanism, which in turn would provide even higher fidelity in synthesized imagery.

In essence, this work stands as a testament to the importance of establishing robust methodologies for fine-tuning generative models. It represents a notable step towards more intelligent systems capable of synthesizing personalized content with impressive accuracy and flexibility.