Translation of Text Embedding via Delta Vector to Suppress Strongly Entangled Content in Text-to-Image Diffusion Models

TL;DR

This paper introduces a delta vector technique to modify text embeddings, effectively suppressing strongly entangled content in text-to-image diffusion models, improving image generation quality and control.

Contribution

The paper presents a novel delta vector method for suppressing entangled content in diffusion models, including a zero-shot approach and a region-specific suppression mechanism called SSDV.

Findings

Significantly reduces undesired content in generated images

Outperforms existing suppression methods quantitatively and qualitatively

Enables precise suppression in personalized text-to-image models

Abstract

Text-to-Image (T2I) diffusion models have made significant progress in generating diverse high-quality images from textual prompts. However, these models still face challenges in suppressing content that is strongly entangled with specific words. For example, when generating an image of "Charlie Chaplin", a "mustache" consistently appears even if explicitly instructed not to include it, as the concept of "mustache" is strongly entangled with "Charlie Chaplin". To address this issue, we propose a novel approach to directly suppress such entangled content within the text embedding space of diffusion models. Our method introduces a delta vector that modifies the text embedding to weaken the influence of undesired content in the generated image, and we further demonstrate that this delta vector can be easily obtained through a zero-shot approach. Furthermore, we propose a Selective Suppression with Delta Vector (SSDV) method to adapt delta vector into the cross-attention mechanism, enabling more effective suppression of unwanted content in regions where it would otherwise be generated. Additionally, we enabled more precise suppression in personalized T2I models by optimizing delta vector, which previous baselines were unable to achieve. Extensive experimental results demonstrate that our approach significantly outperforms existing methods, both in terms of quantitative and qualitative metrics.