Unified Control for Inference-Time Guidance of Denoising Diffusion Models

• URL: http://arxiv.org/abs/2512.12339v1
• Date: Sat, 13 Dec 2025 14:12:10 GMT
• Title: Unified Control for Inference-Time Guidance of Denoising Diffusion Models
• Authors: Maurya Goyal, Anuj Singh, Hadi Jamali-Rad,
• Abstract summary: We propose a universal algorithm, UniCoDe, which brings together the strengths of sampling and gradient-based guidance into a unified framework.<n>In this work, we propose a universal algorithm, UniCoDe, which brings together the strengths of sampling and gradient-based guidance into a unified framework.
• Score: 4.2566707664597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aligning diffusion model outputs with downstream objectives is essential for improving task-specific performance. Broadly, inference-time training-free approaches for aligning diffusion models can be categorized into two main strategies: sampling-based methods, which explore multiple candidate outputs and select those with higher reward signals, and gradient-guided methods, which use differentiable reward approximations to directly steer the generation process. In this work, we propose a universal algorithm, UniCoDe, which brings together the strengths of sampling and gradient-based guidance into a unified framework. UniCoDe integrates local gradient signals during sampling, thereby addressing the sampling inefficiency inherent in complex reward-based sampling approaches. By cohesively combining these two paradigms, UniCoDe enables more efficient sampling while offering better trade-offs between reward alignment and divergence from the diffusion unconditional prior. Empirical results demonstrate that UniCoDe remains competitive with state-of-the-art baselines across a range of tasks. The code is available at https://github.com/maurya-goyal10/UniCoDe

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