Papers › Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis
Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis
Marianna Ohanyan, Hayk Manukyan, Zhangyang Wang, Shant Navasardyan, Humphrey Shi
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.
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