One step further with Monte-Carlo sampler to guide diffusion better
Minsi Ren, Wenhao Deng, Ruiqi Feng, Tailin Wu
TL;DR
This paper introduces a Monte-Carlo sampling method with an additional backward denoising step to improve guidance in SDE-based generative models, reducing estimation errors and enhancing sample quality across various tasks.
Contribution
It proposes ABMS, a plug-and-play adjustment strategy that mitigates posterior sampling errors in diffusion models, supported by theoretical analysis and a new evaluation framework.
Findings
Improves sample quality across multiple tasks
Effectively reduces guidance estimation errors
Compatible with higher order samplers
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…
Peer Reviews
Decision·ICLR 2026 Poster
1. The paper tackles a fundamental challenge in guided diffusion. The "dual-focus evaluation" paradigm, which explicitly calls for balancing task-specific performance (e.g., reconstruction error) with general sample quality (e.g., FID), is an excellent and necessary framing for this problem area. The authors clearly articulate why strong guidance often leads to poor results, providing strong motivation for their work. 2. The proposed ABMS method is simple, well-motivated, and elegant. The core i
1. The primary drawback of ABMS is the increased computational cost, which scales with the number of Monte-Carlo samples, `M`. The paper demonstrates the effectiveness of `M=3` and `M=5` but never explicitly analyzes or reports the trade-off between performance and inference time/FLOPs. For a sampling method, this performance-cost analysis is crucial for researchers and practitioners to assess its viability. While Figure 3 implicitly shows the performance gain for different `M`, the associated c
The problem—bias in loss-guided diffusion—is relevant and well-motivated. The proposed method (MC sampling one step earlier) is simple, plug-and-play, and compatible with existing samplers. Also parallelization of MC sampler justifies the added computations. Evaluation across multiple domains, with clear quantitative metrics.
The “unbiased” claim is overstated: ABMS still produces a lower-bias approximation but not a provably unbiased gradient of the tilted posterior. No formal unbiasedness proof or convergence result is provided. The theoretical bound (Sec. 4.2) only compares error upper bounds under strong assumptions (Lipschitz fff, monotone denoiser accuracy). Variance of the stochastic gradient is unaddressed. Scope limited to DPS. Extensions to other plug-and-play or variational samplers (e.g., RED-Diff, flow
- **Well-Motivated and Simple Solution**: The proposed ABMS method is intuitive, well-motivated by the law of total expectation, and directly targets the identified source of bias (single-point estimation). It seems to be easily adapted to the existing codebase due to its simplicity and can be widely utilized as it does not require any additional conditions. - **Comprehensive Experiments**: The method's effectiveness is demonstrated across three different domains. The consistent improvements ac
- **Computational Overhead**: The most significant weakness is the increased computational cost. ABMS requires $M$ denoising network evaluations in addition to the original denoising steps. While it can be parallelized, the memory consumption can grow rapidly as it also requires additional gradient calculations. For a more comprehensive analysis, the additional computational time and memory consumption for ABMS should be reported. - **Novelty in Context**: The idea of using Monte Carlo sampling
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Model Reduction and Neural Networks · Gaussian Processes and Bayesian Inference