Optical illusion hidden picture is an interesting visual perceptual phenomenon where an image is cleverly integrated into another picture in a way that is not immediately obvious to the viewer. Established on the off-the-shelf text-to-image (T2I) diffusion model, we propose a novel training-free text-guided image-to-image (I2I) translation framework dubbed as \textbf{P}hase-\textbf{T}ransferred \textbf{Diffusion} Model (PTDiffusion) for hidden art syntheses. PTDiffusion harmoniously embeds an input reference image into arbitrary scenes described by the text prompts, producing illusion images exhibiting hidden visual cues of the reference image. At the heart of our method is a plug-and-play phase transfer mechanism that dynamically and progressively transplants diffusion features' phase spectrum from the denoising process to reconstruct the reference image into the one to sample the generated illusion image, realizing deep fusion of the reference structural information and the textual semantic information in the diffusion model latent space. Furthermore, we propose asynchronous phase transfer to enable flexible control to the degree of hidden content discernability. Our method bypasses any model training and fine-tuning process, all while substantially outperforming related text-guided I2I methods in image generation quality, text fidelity, visual discernibility, and contextual naturalness for illusion picture synthesis, as demonstrated by extensive qualitative and quantitative experiments. Our project is publically available at \href{https://xianggao1102.github.io/PTDiffusion_webpage/}{this web page}.

翻译：光学错觉隐藏图像是一种有趣的视觉感知现象，其中一幅图像被巧妙地融入另一幅图片中，使观察者无法立即察觉。基于现成的文本到图像（T2I）扩散模型，我们提出了一种新颖的免训练文本引导图像到图像（I2I）转换框架，称为**相位迁移扩散模型**（PTDiffusion），用于隐藏艺术合成。PTDiffusion 将输入的参考图像和谐地嵌入到由文本提示描述的任意场景中，生成呈现参考图像隐藏视觉线索的错觉图像。我们方法的核心是一个即插即用的相位迁移机制，该机制动态且渐进地将扩散模型特征在去噪过程中重建参考图像时的相位谱，移植到生成错觉图像的采样过程中，从而在扩散模型的潜在空间内实现参考图像结构信息与文本语义信息的深度融合。此外，我们提出了异步相位迁移，以实现对隐藏内容可辨识程度的灵活控制。我们的方法绕过了任何模型训练和微调过程，同时在错觉图像合成的图像生成质量、文本保真度、视觉可辨识性和上下文自然度方面，显著优于相关的文本引导 I2I 方法，这已通过广泛的定性和定量实验得到验证。我们的项目公开于此网页：\href{https://xianggao1102.github.io/PTDiffusion_webpage/}{此网页}。