Zero-Painter：面向文本到图像合成的免训练布局控制
Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis
AI总结：
Zero-Painter提出免训练的布局条件文本到图像合成框架，通过PACA和ReGCA两阶段交叉注意力机制，实现对象与文本提示及掩码形状的精确对齐，在文本细节保留和掩码形状遵循上超越现有方法。
摘要：
我们提出Zero-Painter，一个新颖的免训练框架，用于布局条件文本到图像合成，有助于从文本提示创建详细且可控的图像。我们的方法利用对象掩码和单独描述，结合全局文本提示，生成高保真图像。Zero-Painter采用两阶段过程，包含我们新颖的Prompt-Adjusted Cross-Attention（PACA）和Region-Grouped Cross-Attention（ReGCA）模块，确保生成对象与文本提示及掩码形状精确对齐。大量实验表明，Zero-Painter在保留文本细节和遵循掩码形状方面超越了当前最先进的方法。
英文摘要：
We present Zero-Painter, a novel training-free framework for layout-conditional text-to-image synthesis that facilitates the creation of detailed and controlled imagery from textual prompts. Our method utilizes object masks and individual descriptions, coupled with a global text prompt, to generate images with high fidelity. Zero-Painter employs a two-stage process involving our novel Prompt-Adjusted Cross-Attention (PACA) and Region-Grouped Cross-Attention (ReGCA) blocks, ensuring precise alignment of generated objects with textual prompts and mask shapes. Our extensive experiments demonstrate that Zero-Painter surpasses current state-of-the-art methods in preserving textual details and adhering to mask shapes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
