Coarse-Guided Visual Generation via Weighted h-Transform Sampling

• URL: http://arxiv.org/abs/2603.12057v1
• Date: Thu, 12 Mar 2026 15:26:19 GMT
• Title: Coarse-Guided Visual Generation via Weighted h-Transform Sampling
• Abstract summary: Coarse-guided visual generation is essential for various real-world applications.<n>Recent training-free works propose to leverage pretrained diffusion models and incorporate guidance during the sampling process.<n>We propose a novel guided method by using the htransform, a tool that can constrain processes (e.g., sampling process) under desired conditions.
• Score: 12.670888779100972
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coarse-guided visual generation, which synthesizes fine visual samples from degraded or low-fidelity coarse references, is essential for various real-world applications. While training-based approaches are effective, they are inherently limited by high training costs and restricted generalization due to paired data collection. Accordingly, recent training-free works propose to leverage pretrained diffusion models and incorporate guidance during the sampling process. However, these training-free methods either require knowing the forward (fine-to-coarse) transformation operator, e.g., bicubic downsampling, or are difficult to balance between guidance and synthetic quality. To address these challenges, we propose a novel guided method by using the h-transform, a tool that can constrain stochastic processes (e.g., sampling process) under desired conditions. Specifically, we modify the transition probability at each sampling timestep by adding to the original differential equation with a drift function, which approximately steers the generation toward the ideal fine sample. To address unavoidable approximation errors, we introduce a noise-level-aware schedule that gradually de-weights the term as the error increases, ensuring both guidance adherence and high-quality synthesis. Extensive experiments across diverse image and video generation tasks demonstrate the effectiveness and generalization of our method.

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