  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.
