Picasso: Analyzing Prompt Design for Text-to-Image Generative Diffusion Models from a Temporal-Spatial Perspective

Visual Concept Implantation (VCI) is essential in text-to-image fields. While VCI methods in diffusion models have matured, Visual Concept Disentanglement (VCD) remains an unexplored area. VCD involves analyzing Prompt Spaces trained in VCI to produce disentangled SubPrompts or SubCones for exploring interpretability. However, challenges arise due to Prompt Space design complexity and feature information extraction. We propose Picasso, a unified framework for VCD in diffusion models (DM). Our contributions include: for performance evaluation: Transforming VCD in DM into a regular clustering task by Visualization based on SubCones (VbSC); for unified framework design: Picasso processes diverse Prompt Spaces using spatially clusterable features; for Prompt Space exploration: Introducing a temporal-spatial SOTA Prompt Design subset based on temporal features. Our method provides a feasible mechanism for VCD in DM. Through functional and interpretability validation methods, we will comprehensively evaluate the effectiveness of our proposed method in visual concept implantation tasks and verify the correctness of the parameter space design principles. Picasso will be released at https://haoyu-cai.github.io/our_picasso .