One step further with Monte-Carlo sampler to guide diffusion better

2026-03-04

AI Summary

This work addresses the susceptibility of existing stochastic differential equation (SDE)-based guided diffusion methods to estimation errors during posterior sampling, which often leads to gradient bias and inconsistent generation. To mitigate these issues, the authors propose a plug-and-play backward denoising strategy that incorporates an additional denoising step combined with adaptive Monte Carlo sampling (ABMS). This approach effectively reduces cross-condition interference, thereby enhancing both the accuracy and stability of guidance. Notably, the method is compatible with high-order diffusion samplers and requires no modifications to the underlying model architecture. Experimental results across diverse tasks—including handwritten trajectory generation, image inpainting, and molecular inverse design—demonstrate consistent and significant improvements in generation quality over current state-of-the-art approaches.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Stochastic differential equation (SDE)-based generative models have achieved substantial progress in conditional generation via training-free differentiable loss-guided approaches. However, existing methodologies utilizing posterior sam- pling typically confront a substantial estimation error, which results in inaccu- rate gradients for guidance and leading to inconsistent generation results. To mitigate this issue, we propose that performing an additional backward denois- ing step and Monte-Carlo sampling (ABMS) can achieve better guided diffu- sion, which is a plug-and-play adjustment strategy. To verify the effectiveness of our method, we provide theoretical analysis and propose the adoption of a dual-focus evaluation framework, which further serves to highlight the critical problem of cross-condition interference prevalent in existing approaches. We conduct experiments across various task settings and data types, mainly includ- ing conditional online handwritten trajectory generation, image inverse problems (inpainting, super resolution and gaussian deblurring) molecular inverse design and so on. Experimental results demonstrate that our approach can be effec- tively used with higher order samplers and consistently improves the quality of generation samples across all the different scenarios.

Problem

• posterior sampling
• estimation error
• gradient guidance
• inconsistent generation
• conditional generation

Innovation

• Monte-Carlo sampling
• diffusion guidance
• posterior sampling
• plug-and-play
• conditional generation