  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.
