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ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation (2302.13848v2)

Published 27 Feb 2023 in cs.CV

Abstract: In addition to the unprecedented ability in imaginary creation, large text-to-image models are expected to take customized concepts in image generation. Existing works generally learn such concepts in an optimization-based manner, yet bringing excessive computation or memory burden. In this paper, we instead propose a learning-based encoder, which consists of a global and a local mapping networks for fast and accurate customized text-to-image generation. In specific, the global mapping network projects the hierarchical features of a given image into multiple new words in the textual word embedding space, i.e., one primary word for well-editable concept and other auxiliary words to exclude irrelevant disturbances (e.g., background). In the meantime, a local mapping network injects the encoded patch features into cross attention layers to provide omitted details, without sacrificing the editability of primary concepts. We compare our method with existing optimization-based approaches on a variety of user-defined concepts, and demonstrate that our method enables high-fidelity inversion and more robust editability with a significantly faster encoding process. Our code is publicly available at https://github.com/csyxwei/ELITE.

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Authors (6)
  1. Yuxiang Wei (40 papers)
  2. Yabo Zhang (13 papers)
  3. Zhilong Ji (31 papers)
  4. Jinfeng Bai (31 papers)
  5. Lei Zhang (1691 papers)
  6. Wangmeng Zuo (279 papers)
Citations (255)

Summary

  • The paper introduces a dual-network approach that encodes hierarchical visual features into textual embeddings for dynamic text-to-image generation.
  • It leverages global and local mapping networks to separately capture primary concepts and fine-grained details, ensuring both precision and editability.
  • Quantitative results show ELITE achieves faster encoding and higher fidelity than optimization-based methods, enabling real-time applications.

ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation

The paper "ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation" introduces a novel methodology that seeks to improve the efficiency and accuracy of text-to-image generation models by addressing the computational drawbacks of optimization-based approaches. The authors present a learning-based encoder that innovatively encodes visual concepts into textual embeddings, facilitating a more rapid and dynamic generation process.

Methodology

The core contribution of the paper is the ELITE framework, featuring dual networks: a global mapping network and a local mapping network. The global mapping network translates hierarchical visual features from images into multiple textual word embeddings. This approach allows for the differentiation of primary concepts and auxiliary details, enabling more precise and editable representations. The local mapping network enhances detail fidelity by injecting patch-level features into cross-attention layers, ensuring that omitted local details are preserved without compromising the editability of the primary concept.

The architecture leverages the CLIP model for feature extraction, with the global mapping network utilizing deeper features to encapsulate primary concepts. The use of multi-layer features aids in segregating subject matter from background noise, ensuring robust editability. The local mapping network further refines this process by enhancing detail consistency through feature fusion, optimizing both the semantic and visual fidelity of generated images.

Results

Quantitative and qualitative evaluations reveal that ELITE achieves faster encoding and higher fidelity compared to existing models like Textual Inversion and DreamBooth. The proposed method allows for high-fidelity inversion with commendable editability, demonstrated across a variety of user-defined concepts with diverse textures and backgrounds. ELITE reduces the time to encode a new concept to mere seconds, a significant improvement over other optimization-based methodologies that require several minutes.

Implications and Future Developments

The introduction of ELITE signifies a potential shift in the efficiency of customized text-to-image generation applications. The reduction in computational load opens avenues for real-time applications, broadening the scope of use cases from artistic creation to data augmentation. Furthermore, the layered approach to representation may inspire future architectures to explore even deeper integration of semantic understanding and visual details.

The paper sets a foundation for future research, encouraging exploration into multi-concept encoding and more complex scene integration. By addressing current limitations such as handling textual character-based images, future iterations could tackle broader challenges inherent in text-to-image generation.

Overall, ELITE provides a meaningful advancement in the field of AI-driven text-to-image generation, offering both a practical and sophisticated tool for high-quality visual content creation while pushing the boundaries of existing methodologies.

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