REG: Rectified Gradient Guidance for Conditional Diffusion Models

• URL: http://arxiv.org/abs/2501.18865v1
• Date: Fri, 31 Jan 2025 03:16:18 GMT
• Title: REG: Rectified Gradient Guidance for Conditional Diffusion Models
• Authors: Zhengqi Gao, Kaiwen Zha, Tianyuan Zhang, Zihui Xue, Duane S. Boning,
• Abstract summary: We propose rectified gradient guidance (REG) to boost the performance of existing guidance methods.<n>REG provides a better approximation to the optimal solution than prior guidance techniques.<n>In experiments on class-conditional ImageNet and text-to-image generation tasks, REG consistently improves FID and Inception/CLIP scores.
• Score: 16.275782069986253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Guidance techniques are simple yet effective for improving conditional generation in diffusion models. Albeit their empirical success, the practical implementation of guidance diverges significantly from its theoretical motivation. In this paper, we reconcile this discrepancy by replacing the scaled marginal distribution target, which we prove theoretically invalid, with a valid scaled joint distribution objective. Additionally, we show that the established guidance implementations are approximations to the intractable optimal solution under no future foresight constraint. Building on these theoretical insights, we propose rectified gradient guidance (REG), a versatile enhancement designed to boost the performance of existing guidance methods. Experiments on 1D and 2D demonstrate that REG provides a better approximation to the optimal solution than prior guidance techniques, validating the proposed theoretical framework. Extensive experiments on class-conditional ImageNet and text-to-image generation tasks show that incorporating REG consistently improves FID and Inception/CLIP scores across various settings compared to its absence.

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