Recently, large-scale text-to-image (T2I) diffusion models have emerged as a powerful tool for image-to-image translation (I2I), allowing open-domain image translation via user-provided text prompts. This paper proposes frequency-controlled diffusion model (FCDiffusion), an end-to-end diffusion-based framework that contributes a novel solution to text-guided I2I from a frequency-domain perspective. At the heart of our framework is a feature-space frequency-domain filtering module based on Discrete Cosine Transform, which filters the latent features of the source image in the DCT domain, yielding filtered image features bearing different DCT spectral bands as different control signals to the pre-trained Latent Diffusion Model. We reveal that control signals of different DCT spectral bands bridge the source image and the T2I generated image in different correlations (e.g., style, structure, layout, contour, etc.), and thus enable versatile I2I applications emphasizing different I2I correlations, including style-guided content creation, image semantic manipulation, image scene translation, and image style translation. Different from related approaches, FCDiffusion establishes a unified text-guided I2I framework suitable for diverse image translation tasks simply by switching among different frequency control branches at inference time. The effectiveness and superiority of our method for text-guided I2I are demonstrated with extensive experiments both qualitatively and quantitatively. The code is publicly available at: https://github.com/XiangGao1102/FCDiffusion.

翻译：近年来，大规模文本到图像（T2I）扩散模型已成为图像到图像转换（I2I）的强大工具，能够通过用户提供的文本提示实现开放域图像转换。本文提出频率可控扩散模型（FCDiffusion），这是一种基于扩散的端到端框架，从频域视角为文本引导的I2I提供了新颖解决方案。我们框架的核心是一个基于离散余弦变换（DCT）的特征空间频域滤波模块，该模块在DCT域中对源图像的潜在特征进行滤波，生成承载不同DCT频谱带的滤波图像特征，并将其作为不同控制信号输入预训练的潜在扩散模型。我们发现，不同DCT频谱带的控制信号以不同的相关性（如风格、结构、布局、轮廓等）连接源图像与T2I生成图像，从而能够支持强调不同I2I相关性的多样化应用，包括风格引导的内容生成、图像语义编辑、图像场景转换和图像风格转换。与现有方法不同，FCDiffusion建立了一个统一的文本引导I2I框架，在推理时只需在不同频率控制分支间切换，即可适用于多种图像转换任务。通过大量定性与定量实验，我们证明了该方法在文本引导I2I任务中的有效性和优越性。代码已公开于：https://github.com/XiangGao1102/FCDiffusion。