Latent Wavelet Diffusion For Ultra-High-Resolution Image Synthesis

• URL: http://arxiv.org/abs/2506.00433v3
• Date: Wed, 24 Sep 2025 15:22:22 GMT
• Title: Latent Wavelet Diffusion For Ultra-High-Resolution Image Synthesis
• Authors: Luigi Sigillo, Shengfeng He, Danilo Comminiello,
• Abstract summary: We present Latent Wavelet Diffusion (LWD), a lightweight training framework that significantly improves detail and texture fidelity in ultra-high-resolution (2K-4K) image synthesis.<n>LWD introduces a novel, frequency-aware masking strategy derived from wavelet energy maps, which dynamically focuses the training process on detail-rich regions of the latent space.
• Score: 56.311477476580926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-resolution image synthesis remains a core challenge in generative modeling, particularly in balancing computational efficiency with the preservation of fine-grained visual detail. We present Latent Wavelet Diffusion (LWD), a lightweight training framework that significantly improves detail and texture fidelity in ultra-high-resolution (2K-4K) image synthesis. LWD introduces a novel, frequency-aware masking strategy derived from wavelet energy maps, which dynamically focuses the training process on detail-rich regions of the latent space. This is complemented by a scale-consistent VAE objective to ensure high spectral fidelity. The primary advantage of our approach is its efficiency: LWD requires no architectural modifications and adds zero additional cost during inference, making it a practical solution for scaling existing models. Across multiple strong baselines, LWD consistently improves perceptual quality and FID scores, demonstrating the power of signal-driven supervision as a principled and efficient path toward high-resolution generative modeling.

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