# GradCheck: Analyzing classifier guidance gradients for conditional diffusion sampling

arXiv: https://arxiv.org/abs/2406.17399 · HTML: https://mlsanity.com/paper/2406.17399/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 8 of 8)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [Protein Design with Guided Discrete Diffusion](https://mlsanity.com/paper/2305.20009/) (2023)
- [Diffusion Visual Counterfactual Explanations](https://mlsanity.com/paper/2210.11841/) (2022)

## Built on by (most cited first, 7 of 7)

- [Rethinking Visual Counterfactual Explanations Through Region Constraint](https://mlsanity.com/paper/2410.12591/) (2024)
- [C2FG: Control Classifier-Free Guidance via Score Discrepancy Analysis](https://mlsanity.com/paper/2603.08155/) (2026)
- [NatADiff: Adversarial Boundary Guidance for Natural Adversarial Diffusion](https://mlsanity.com/paper/2505.20934/) (2025)
- [Diffusion Classifier Guidance for Non-robust Classifiers](https://mlsanity.com/paper/2507.00687/) (2025)
- [Controllable Stylistic Text Generation with Train-Time Attribute-Regularized Diffusion](https://mlsanity.com/paper/2510.06386/) (2025)
- [Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability](https://mlsanity.com/paper/2410.20890/) (2024)
- [Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects](https://mlsanity.com/paper/2602.19153/) (2026)

## Abstract (arXiv)

To sample from an unconditionally trained Denoising Diffusion Probabilistic Model (DDPM), classifier guidance adds conditional information during sampling, but the gradients from classifiers, especially those not trained on noisy images, are often unstable. This study conducts a gradient analysis comparing robust and non-robust classifiers, as well as multiple gradient stabilization techniques. Experimental results demonstrate that these techniques significantly improve the quality of class-conditional samples for non-robust classifiers by providing more stable and informative classifier guidance gradients. The findings highlight the importance of gradient stability in enhancing the performance of classifier guidance, especially on non-robust classifiers.