Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis

Marianna Ohanyan, Hayk Manukyan, Zhangyang Wang, Shant Navasardyan, Humphrey Shi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 8764-8774

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.

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[bibtex]
@InProceedings{Ohanyan_2024_CVPR, author = {Ohanyan, Marianna and Manukyan, Hayk and Wang, Zhangyang and Navasardyan, Shant and Shi, Humphrey}, title = {Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {8764-8774} }