A Hybrid Wavelet-Fourier Method for Next-Generation Conditional Diffusion Models
This work addresses the challenge of generating high-fidelity images with better global coherence and fine textures for applications in generative modeling, though it appears incremental as it builds on existing diffusion methods.
The paper tackled the problem of synthesizing high-quality images by proposing a hybrid wavelet-Fourier diffusion model that improves spatial localization and feature capture, achieving competitive or superior performance on benchmarks like CIFAR-10 and CelebA-HQ as measured by FID and IS scores.
We present a novel generative modeling framework,Wavelet-Fourier-Diffusion, which adapts the diffusion paradigm to hybrid frequency representations in order to synthesize high-quality, high-fidelity images with improved spatial localization. In contrast to conventional diffusion models that rely exclusively on additive noise in pixel space, our approach leverages a multi-transform that combines wavelet sub-band decomposition with partial Fourier steps. This strategy progressively degrades and then reconstructs images in a hybrid spectral domain during the forward and reverse diffusion processes. By supplementing traditional Fourier-based analysis with the spatial localization capabilities of wavelets, our model can capture both global structures and fine-grained features more effectively. We further extend the approach to conditional image generation by integrating embeddings or conditional features via cross-attention. Experimental evaluations on CIFAR-10, CelebA-HQ, and a conditional ImageNet subset illustrate that our method achieves competitive or superior performance relative to baseline diffusion models and state-of-the-art GANs, as measured by Fréchet Inception Distance (FID) and Inception Score (IS). We also show how the hybrid frequency-based representation improves control over global coherence and fine texture synthesis, paving the way for new directions in multi-scale generative modeling.