HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling

• URL: http://arxiv.org/abs/2506.20452v1
• Date: Wed, 25 Jun 2025 13:58:37 GMT
• Title: HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling
• Authors: Tobias Vontobel, Seyedmorteza Sadat, Farnood Salehi, Romann M. Weber,
• Abstract summary: HiWave is a training-free, zero-shot approach that substantially enhances visual fidelity and structural coherence in ultra-high-resolution image synthesis.<n>A user study confirmed HiWave's performance, where it was preferred over the state-of-the-art alternative in more than 80% of comparisons.
• Score: 1.9474278832087901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have emerged as the leading approach for image synthesis, demonstrating exceptional photorealism and diversity. However, training diffusion models at high resolutions remains computationally prohibitive, and existing zero-shot generation techniques for synthesizing images beyond training resolutions often produce artifacts, including object duplication and spatial incoherence. In this paper, we introduce HiWave, a training-free, zero-shot approach that substantially enhances visual fidelity and structural coherence in ultra-high-resolution image synthesis using pretrained diffusion models. Our method employs a two-stage pipeline: generating a base image from the pretrained model followed by a patch-wise DDIM inversion step and a novel wavelet-based detail enhancer module. Specifically, we first utilize inversion methods to derive initial noise vectors that preserve global coherence from the base image. Subsequently, during sampling, our wavelet-domain detail enhancer retains low-frequency components from the base image to ensure structural consistency, while selectively guiding high-frequency components to enrich fine details and textures. Extensive evaluations using Stable Diffusion XL demonstrate that HiWave effectively mitigates common visual artifacts seen in prior methods, achieving superior perceptual quality. A user study confirmed HiWave's performance, where it was preferred over the state-of-the-art alternative in more than 80% of comparisons, highlighting its effectiveness for high-quality, ultra-high-resolution image synthesis without requiring retraining or architectural modifications.

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