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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.04032 |
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| _version_ | 1866907928602083328 |
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| author | Ohanyan, Marianna Manukyan, Hayk Wang, Zhangyang Navasardyan, Shant Shi, Humphrey |
| author_facet | Ohanyan, Marianna Manukyan, Hayk Wang, Zhangyang Navasardyan, Shant Shi, Humphrey |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04032 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis Ohanyan, Marianna Manukyan, Hayk Wang, Zhangyang Navasardyan, Shant Shi, Humphrey Computer Vision and Pattern Recognition 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. |
| title | Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.04032 |