Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis
Marianna Ohanyan, Hayk Manukyan, Zhangyang Wang, Shant Navasardyan, Humphrey Shi
Abstract
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. We will make the codes and the models publicly available.
BibTeX
@inproceedings{cvpr2024_zeropaintertrain,
title = {Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis},
author = {Marianna Ohanyan and Hayk Manukyan and Zhangyang Wang and Shant Navasardyan and Humphrey Shi},
booktitle = {CVPR 2024},
year = {2024}
}