FreSCo: Joint Frequency-Aware and Spatial Control for Image Zero-Shot Style Transfer

Latent Diffusion Models (LDMs) have become a cornerstone for zero-shot style transfer in multimedia content creation, but they frequently struggle with a critical trade-off between artistic stylization fidelity and semantic structural preservation. A key oversight in existing methods is the neglect of frequency-domain distinctions in visual signals, which leads to prevalent issues like content drift and style leakage. To address these limitations, we propose FreSCo, a novel training-free framework that explicitly decouples content and style through dual-domain control mechanisms. First, the Dynamic Wavelet Latent Fusion (DWLF) module decomposes latent features via Discrete Wavelet Transform (DWT), injecting style exclusively into high-frequency texture sub-bands while boosting spectral energy to counteract VAE-induced smoothing. Second, the VAE-Compressed Masking strategy encodes edge maps directly into the latent space, resolving pixel-latent misalignment for precise spatial control. We construct a comprehensive benchmark with 1,280 content-style pairs to rigorously evaluate performance. Extensive experiments demonstrate that FreSCo achieves state-of-the-art results, generating high-fidelity artistic textures while maintaining superior structural consistency across diverse multimedia content creation scenarios compared to existing baselines.