Prompt-Softbox-Prompt: A free-text Embedding Control for Image Editing

2024-08-24

arXiv.org

AI Summary

In text-to-image diffusion models, semantic entanglement and imprecise spatial localization of text embeddings hinder fine-grained controllable editing. To address this, we propose the Prompt-Softbox-Prompt (PSP) paradigm: leveraging semantic role parsing, PSP decouples SDXL’s text embeddings—including BOS, EOS, augmentation, and padding tokens—at cross-attention layers and applies cross-layer interpolation or token replacement, integrated with Softbox spatial masks for precise regional semantic injection. We systematically characterize the distinct semantic versus stylistic roles of individual token positions in SDXL’s text encoder—establishing the first interpretable, editable mapping between embedding positions and visual attributes. Evaluated on object addition/replacement and style transfer, PSP significantly outperforms baselines while preserving background consistency, enabling zero-shot style switching and localized fine-grained editing.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Text-driven diffusion models have achieved remarkable success in image editing, but a crucial component in these models-text embeddings-has not been fully explored. The entanglement and opacity of text embeddings present significant challenges to achieving precise image editing. In this paper, we provide a comprehensive and in-depth analysis of text embeddings in Stable Diffusion XL, offering three key insights. First, while the 'aug_embedding' captures the full semantic content of the text, its contribution to the final image generation is relatively minor. Second, 'BOS' and 'Padding_embedding' do not contain any semantic information. Lastly, the 'EOS' holds the semantic information of all words and contains the most style features. Each word embedding plays a unique role without interfering with one another. Based on these insights, we propose a novel approach for controllable image editing using a free-text embedding control method called PSP (Prompt-Softbox-Prompt). PSP enables precise image editing by inserting or adding text embeddings within the cross-attention layers and using Softbox to define and control the specific area for semantic injection. This technique allows for obejct additions and replacements while preserving other areas of the image. Additionally, PSP can achieve style transfer by simply replacing text embeddings. Extensive experimental results show that PSP achieves significant results in tasks such as object replacement, object addition, and style transfer.

Problem

• Analyzes text embeddings in Stable Diffusion XL for image editing
• Addresses ambiguity and entanglement in text embeddings for precise editing
• Proposes PSP method for training-free, precise image and style editing

Innovation

• Modifies text embeddings in cross-attention layers
• Uses Softbox for precise semantic injection control
• Enables object addition and style transfer