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ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models

Authors:

Oliver Deussen,

Changsheng XuAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 42, Issue 6

Article No.: 244, Pages 1 - 14

https://doi.org/10.1145/3618342

Published: 05 December 2023 Publication History

Abstract

Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textual conditioning space and compose new natural sentences for text-to-image diffusion models. However, representing and editing specific visual attributes such as material, style, and layout remains a challenge, leading to a lack of disentanglement and editability. To address this problem, we propose a novel approach that leverages the step-by-step generation process of diffusion models, which generate images from low to high frequency information, providing a new perspective on representing, generating, and editing images. We develop the Prompt Spectrum Space P*, an expanded textual conditioning space, and a new image representation method called ProSpect. ProSpect represents an image as a collection of inverted textual token embeddings encoded from per-stage prompts, where each prompt corresponds to a specific generation stage (i.e., a group of consecutive steps) of the diffusion model. Experimental results demonstrate that P* and ProSpect offer better disentanglement and controllability compared to existing methods. We apply ProSpect in various personalized attribute-aware image generation applications, such as image-guided or text-driven manipulations of materials, style, and layout, achieving previously unattainable results from a single image input without fine-tuning the diffusion models. Our source code is available at https://github.com/zyxElsa/ProSpect.

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Index Terms

ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 42, Issue 6

December 2023

1565 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3632123

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Copyright © 2023 Owner/Author.

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Association for Computing Machinery

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Published: 05 December 2023

Published in TOG Volume 42, Issue 6

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