结构-语义协同优化的潜扩散模型用于快速视觉字谜合成
Structure-Semantic Co-optimized Latent Diffusion Model for Fast Visual Anagram Synthesis
AI总结：
提出结构-语义协同优化框架S2CO-Anagram，通过空文本结构对齐、语义增强和注意力引导噪声融合，在极低计算成本下生成高分辨率、高视觉和谐度与语义保真度的视觉字谜图像。
摘要：
视觉字谜是一种有趣的艺术创作形式，其中单个图像在翻转或旋转等变换下呈现不同的概念解释。最近的工作通过利用预训练的文本到图像（T2I）扩散模型实现了视觉字谜合成，但仍存在几个关键限制，包括计算效率低、美学质量次优以及语义保真度和表现力弱。本文专注于以最小的计算成本生成视觉质量显著提升的视觉字谜，从而推进幻觉数字艺术的智能创作。为了提高图像分辨率同时减少时间开销，我们将基于像素的T2I模型中的先进并行去噪算法适配到对抗性蒸馏的潜模型上，并相应地提出了一种结构-语义协同优化（S2CO）框架来抵消随之而来的视觉退化。作为我们方法的核心，S2CO框架包含三个关键创新：（I）空文本结构对齐优化；（II）语义增强优化；（III）注意力引导噪声融合。基于这些组件，我们的方法称为S2CO-Anagram，能够生成比相关SOTA方法具有显著更优视觉和谐性和语义保真度的高分辨率字谜图像，同时实现更快的推理速度。代码将公开。
英文摘要：
Visual anagram is an intriguing form of art creation wherein a single image presents different conceptual interpretations under transformations such as flipping or rotation. Recent work has achieved visual anagram synthesis by leveraging pretrained text-to-image (T2I) diffusion models, yet still suffers from several key limitations including computational inefficiency, suboptimal aesthetic quality, and weak semantic fidelity and expressiveness. This work focuses on generating visual anagrams with substantially improved visual quality at minimal computational cost, thereby advancing intelligent creation of illusionary digital art. To increase image resolution while reducing time overhead, we adapt the cutting-edge parallel denoising algorithm from pixel-based T2I model to the adversarially distilled latent-based one, and accordingly propose a structure-semantic co-optimization (S2CO) framework to counteract the consequent visual degradation. As the core of our approach, S2CO framework comprises three key innovations: (\romannumeral1) null-text structure alignment optimization; (\romannumeral2) semantic enhancement optimization; (\romannumeral3) attention-guided noise fusion. Building upon these components, our method dubbed \textbf{S2CO-Anagram} is able to generate higher-resolution anagram images with noticeably superior visual harmony and semantic faithfulness than related SOTA approaches, all while achieving substantially faster inference speed. Code will be publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
