Prompt-Softbox-Prompt: A Free-Text Embedding Control for Image Editing

While text-driven diffusion models demonstrate remarkable performance in image editing, the critical components of their text embeddings remain underexplored. The ambiguity and entanglement of these embeddings pose challenges for precise editing. In this paper, we provide a comprehensive analysis of text embeddings in Stable Diffusion XL, offering three key insights: (1) \textit{aug embedding}~\footnote{\textit{aug embedding} is obtained by combining the pooled output of the final text encoder with the timestep embeddings.this https URL} retains complete textual semantics but contributes minimally to image generation as it is only fused via the ResBlocks. More text information weakens its local semantics while preserving most global semantics. (2) \textit{BOS} and \textit{padding embedding} do not contain any semantic information. (3) \textit{EOS} holds the semantic information of all words and stylistic information. Each word embedding is important and does not interfere with the semantic injection of other embeddings. Based on these insights, we propose PSP (\textbf{P}rompt-\textbf{S}oftbox-\textbf{P}rompt), a training-free image editing method that leverages free-text embedding. PSP enables precise image editing by modifying text embeddings within the cross-attention layers and using Softbox to control the specific area for semantic injection. This technique enables the addition and replacement of objects without affecting other areas of the image. Additionally, PSP can achieve style transfer by simply replacing text embeddings. Extensive experiments show that PSP performs remarkably well in tasks such as object replacement, object addition, and style transfer. Our code is available atthis https URL.