Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis
<|reference_start|>Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis: 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.<|reference_end|>
@article{ohanyan2024zero-painter:,
    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},
    journal={arXiv preprint arXiv:2406.04032},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04032},
    primaryClass={cs.CV}
}