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ViCo: Plug-and-play Visual Condition for Personalized Text-to-image Generation (2306.00971v2)

Published 1 Jun 2023 in cs.CV and cs.AI

Abstract: Personalized text-to-image generation using diffusion models has recently emerged and garnered significant interest. This task learns a novel concept (e.g., a unique toy), illustrated in a handful of images, into a generative model that captures fine visual details and generates photorealistic images based on textual embeddings. In this paper, we present ViCo, a novel lightweight plug-and-play method that seamlessly integrates visual condition into personalized text-to-image generation. ViCo stands out for its unique feature of not requiring any fine-tuning of the original diffusion model parameters, thereby facilitating more flexible and scalable model deployment. This key advantage distinguishes ViCo from most existing models that necessitate partial or full diffusion fine-tuning. ViCo incorporates an image attention module that conditions the diffusion process on patch-wise visual semantics, and an attention-based object mask that comes at no extra cost from the attention module. Despite only requiring light parameter training (~6% compared to the diffusion U-Net), ViCo delivers performance that is on par with, or even surpasses, all state-of-the-art models, both qualitatively and quantitatively. This underscores the efficacy of ViCo, making it a highly promising solution for personalized text-to-image generation without the need for diffusion model fine-tuning. Code: https://github.com/haoosz/ViCo

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Authors (4)
  1. Shaozhe Hao (13 papers)
  2. Kai Han (184 papers)
  3. Shihao Zhao (13 papers)
  4. Kwan-Yee K. Wong (51 papers)
Citations (6)
View on Semantic Scholar

Summary

  • The paper introduces a novel plug-and-play visual conditioning mechanism that personalizes image generation without altering diffusion model parameters.
  • It employs patch-wise image attention and cross-attention to integrate visual semantics and automatically generate object masks, enhancing detail and fidelity.
  • Experimental results demonstrate that ViCo achieves superior image similarity scores and versatility, offering a scalable solution for creative applications.

An Analysis of "ViCo: Plug-and-play Visual Condition for Personalized Text-to-image Generation"

The paper presents ViCo, an innovative approach to personalized text-to-image generation that leverages diffusion models. Unlike many existing methodologies, ViCo introduces a novel plug-and-play mechanism that integrates visual conditions into the generative process, without necessitating the fine-tuning of the underlying diffusion model's parameters. This paper elaborates on ViCo's technical architecture, describing its key components and the implications of its design choices in detail.

Key Innovations and Methodology

ViCo differentiates itself primarily through its ability to personalize image generation while maintaining the original diffusion model’s architecture unchanged. The method employs an image attention module to integrate visual semantics at a patch-wise level, maintaining the computational cost minimal by requiring only approximately 6% of the parameter training relative to the diffusion U-Net. The model introduces an image cross-attention module, crucially allowing seamless integration of reference images with the text embeddings, enhancing the model's ability to capture object-specific semantics.

Furthermore, ViCo implements a novel mechanism for automatic mask generation. This feature relies on cross-attention maps to discern and isolate the foreground objects from backdrops, thereby significantly improving image fidelity without requiring prior mask annotations. This approach cleverly exploits existing attention mechanisms to derive object masks, facilitating efficient suppression of background noise, a common challenge in image generation tasks.

Experimental Insights

Extensive experiments demonstrate ViCo's capability, highlighting its competitive performance against contemporary state-of-the-art models like DreamBooth and Custom Diffusion. From a quantitative perspective, ViCo achieves superior image similarity scores, indicating its robustness in maintaining object fidelity and demonstrating substantial improvements in preserving fine visual details compared to models like Textual Inversion.

The authors present a variety of applications ranging from recontextualization to style transfer, showcasing ViCo's versatility. The qualitative evaluations emphasize the method’s ability to generate high-quality images that align well with specified textual prompts, balancing text and visual conditions effectively.

Implications and Future Directions

Practically, ViCo offers a more flexible and scalable solution for personalized image generation. Its plug-and-play nature allows for easier integration into varied applications without extensive retraining, which could significantly lower entry barriers for implementing personalized imagery in creative industries, digital marketing, and content creation.

Theoretically, this research paves the way for further exploration into refined integration techniques that do not alter the foundational model's parameters. By demonstrating that significant personalization can be achieved with minimal parameter changes, this work challenges existing paradigms around model tuning and opens up avenues for exploring similar "lightweight" approaches in other generative tasks.

In potential future extensions, the incorporation of more sophisticated visual conditioning mechanisms or exploring alternative forms of regularization could further enhance the model's adaptability and performance. The dynamic balance ViCo strikes between visual and textual information suggests promising directions for interactive AI systems that can adeptly synthesize contextually aware visual content.

In closing, while ViCo does not radically redefine the field, it takes significant strides in addressing the challenges of personalized image generation with minimal resource investment, underscoring the continued importance of efficiency and adaptability in AI system design.

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