Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance Scale

• URL: http://arxiv.org/abs/2312.07586v5
• Date: Mon, 3 Jun 2024 04:17:49 GMT
• Title: Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance Scale
• Authors: Candi Zheng, Yuan Lan,
• Abstract summary: We propose characteristic guidance, a guidance method that provides first-principle non-linear correction for classifier-free guidance. Experiments show that characteristic guidance enhances semantic characteristics of prompts and irregularities in image generation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Popular guidance for denoising diffusion probabilistic model (DDPM) linearly combines distinct conditional models together to provide enhanced control over samples. However, this approach overlooks nonlinear effects that become significant when guidance scale is large. To address this issue, we propose characteristic guidance, a guidance method that provides first-principle non-linear correction for classifier-free guidance. Such correction forces the guided DDPMs to respect the Fokker-Planck (FP) equation of diffusion process, in a way that is training-free and compatible with existing sampling methods. Experiments show that characteristic guidance enhances semantic characteristics of prompts and mitigate irregularities in image generation, proving effective in diverse applications ranging from simulating magnet phase transitions to latent space sampling.

Related papers

• Enhancing Diffusion Model Guidance through Calibration and Regularization [9.22066257345387]
  This paper introduces two complementary contributions to address this issue.<n>First, we propose a differentiable calibration objective based on the Smooth Expected Error (Smooth ECE)<n>Second, we develop enhanced sampling guidance methods that operate on off-the-shelf classifiers without requiring retraining.
  arXiv  Detail & Related papers  (2025-11-08T04:23:42Z)
• TAG:Tangential Amplifying Guidance for Hallucination-Resistant Diffusion Sampling [53.61290359948953]
  Tangential Amplifying Guidance (TAG) operates solely on trajectory signals without modifying the underlying diffusion model.<n>We formalize this guidance process by leveraging a first-order Taylor expansion.<n> TAG is a plug-and-play, architecture-agnostic module that improves diffusion sampling fidelity with minimal computational addition.
  arXiv  Detail & Related papers  (2025-10-06T06:53:29Z)
• Test-Time Anchoring for Discrete Diffusion Posterior Sampling [38.507644561076894]
  Posterior sampling is a challenging problem for pretrained discrete diffusion foundation models.<n>We introduce Anchored Posterior Sampling (APS) for masked diffusion foundation models.<n>Our approach achieves state-of-the-art performance among discrete diffusion samplers across linear and nonlinear inverse problems.
  arXiv  Detail & Related papers  (2025-10-02T17:58:37Z)
• G$^2$RPO: Granular GRPO for Precise Reward in Flow Models [74.21206048155669]
  We propose a novel Granular-GRPO (G$2$RPO) framework that achieves precise and comprehensive reward assessments of sampling directions.<n>We introduce a Multi-Granularity Advantage Integration module that aggregates advantages computed at multiple diffusion scales.<n>Our G$2$RPO significantly outperforms existing flow-based GRPO baselines.
  arXiv  Detail & Related papers  (2025-10-02T12:57:12Z)
• Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance [52.705112811734566]
  A novel diffusion-based framework is introduced for solving inverse problems using a piecewise guidance scheme.<n>The proposed method is problem-agnostic and readily adaptable to a variety of inverse problems.<n>The framework achieves a reduction in inference time of (25%) for inpainting with both random and center masks, and (23%) and (24%) for (4times) and (8times) super-resolution tasks.
  arXiv  Detail & Related papers  (2025-07-22T19:35:14Z)
• Navigating Sparse Molecular Data with Stein Diffusion Guidance [48.21071466968102]
  optimal control (SOC) has emerged as a principled framework for fine-tuning diffusion models.<n>A class of training-free approaches has been developed that guides diffusion models using off-the-shelf classifiers on predicted clean samples.<n>We propose a novel training-free guidance framework based on a surrogate optimal control objective.
  arXiv  Detail & Related papers  (2025-07-07T21:14:27Z)
• Solving Inverse Problems with FLAIR [59.02385492199431]
  Flow-based latent generative models are able to generate images with remarkable quality, even enabling text-to-image generation.<n>We present FLAIR, a novel training free variational framework that leverages flow-based generative models as a prior for inverse problems.<n>Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.
  arXiv  Detail & Related papers  (2025-06-03T09:29:47Z)
• ADT: Tuning Diffusion Models with Adversarial Supervision [16.974169058917443]
  Diffusion models have achieved outstanding image generation by reversing a forward noising process to approximate true data distributions. We propose Adrial Diffusion Tuning (ADT) to stimulate the inference process during optimization and align the final outputs with training data. ADT features a siamese-network discriminator with a fixed pre-trained backbone and lightweight trainable parameters.
  arXiv  Detail & Related papers  (2025-04-15T17:37:50Z)
• Variational Control for Guidance in Diffusion Models [19.51536406897083]
  We introduce Diffusion Trajectory Matching (DTM) that enables guiding pretrained diffusion trajectories to satisfy a terminal cost. DTM unifies a broad class of guidance methods and enables novel instantiations. For instance, in ImageNet non-linear deblurring, our model achieves an FID score of 34.31, significantly improving over the best pretrained-method baseline (FID 78.07)
  arXiv  Detail & Related papers  (2025-02-06T00:24:39Z)
• Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking [50.325021634589596]
  We propose a Tailored Optimization Preference (TailorPO) framework for aligning diffusion models with human preference. Our approach directly ranks intermediate noisy samples based on their step-wise reward, and effectively resolves the gradient direction issues. Experimental results demonstrate that our method significantly improves the model's ability to generate aesthetically pleasing and human-preferred images.
  arXiv  Detail & Related papers  (2025-02-01T16:08:43Z)
• Training-free Diffusion Model Alignment with Sampling Demons [15.400553977713914]
  We propose an optimization approach, dubbed Demon, to guide the denoising process at inference time without backpropagation through reward functions or model retraining. Our approach works by controlling noise distribution in denoising steps to concentrate density on regions corresponding to high rewards through optimization. To the best of our knowledge, the proposed approach is the first inference-time, backpropagation-free preference alignment method for diffusion models.
  arXiv  Detail & Related papers  (2024-10-08T07:33:49Z)
• Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models [59.331993845831946]
  Diffusion models benefit from instillation of task-specific information into the score function to steer the sample generation towards desired properties. This paper provides the first theoretical study towards understanding the influence of guidance on diffusion models in the context of Gaussian mixture models.
  arXiv  Detail & Related papers  (2024-03-03T23:15:48Z)
• Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following [21.81411085058986]
  Reward-gradient guided denoising generates trajectories that maximize both a differentiable reward function and the likelihood under the data distribution captured by a diffusion model. We propose DiffusionES, a method that combines gradient-free optimization with trajectory denoising. We show that DiffusionES achieves state-of-the-art performance on nuPlan, an established closed-loop planning benchmark for autonomous driving.
  arXiv  Detail & Related papers  (2024-02-09T17:18:33Z)
• Non-Cross Diffusion for Semantic Consistency [12.645444338043934]
  We introduce Non-Cross Diffusion', an innovative approach in generative modeling for learning ordinary differential equation (ODE) models. Our methodology strategically incorporates an ascending dimension of input to effectively connect points sampled from two distributions with uncrossed paths.
  arXiv  Detail & Related papers  (2023-11-30T05:53:39Z)
• Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models [63.1637853118899]
  We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models. We employ a shift control module that works on h-space of pre-trained diffusion models to manipulate a sample into a shifted version of itself. By jointly optimizing them, the model will spontaneously discover disentangled and interpretable directions.
  arXiv  Detail & Related papers  (2023-10-15T18:44:30Z)
• Observation-Guided Diffusion Probabilistic Models [41.749374023639156]
  We propose a novel diffusion-based image generation method called the observation-guided diffusion probabilistic model (OGDM) Our approach reestablishes the training objective by integrating the guidance of the observation process with the Markov chain. We demonstrate the effectiveness of our training algorithm using diverse inference techniques on strong diffusion model baselines.
  arXiv  Detail & Related papers  (2023-10-06T06:29:06Z)
• AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models [103.41269503488546]
  Existing customization methods require access to multiple reference examples to align pre-trained diffusion probabilistic models with user-provided concepts. This paper aims to address the challenge of DPM customization when the only available supervision is a differentiable metric defined on the generated contents. We propose a novel method AdjointDPM, which first generates new samples from diffusion models by solving the corresponding probability-flow ODEs. It then uses the adjoint sensitivity method to backpropagate the gradients of the loss to the models' parameters.
  arXiv  Detail & Related papers  (2023-07-20T09:06:21Z)
• Semi-Implicit Denoising Diffusion Models (SIDDMs) [50.30163684539586]
  Existing models such as Denoising Diffusion Probabilistic Models (DDPM) deliver high-quality, diverse samples but are slowed by an inherently high number of iterative steps. We introduce a novel approach that tackles the problem by matching implicit and explicit factors. We demonstrate that our proposed method obtains comparable generative performance to diffusion-based models and vastly superior results to models with a small number of sampling steps.
  arXiv  Detail & Related papers  (2023-06-21T18:49:22Z)
• Towards Controllable Diffusion Models via Reward-Guided Exploration [15.857464051475294]
  We propose a novel framework that guides the training-phase of diffusion models via reinforcement learning (RL) RL enables calculating policy gradients via samples from a pay-off distribution proportional to exponential scaled rewards, rather than from policies themselves. Experiments on 3D shape and molecule generation tasks show significant improvements over existing conditional diffusion models.
  arXiv  Detail & Related papers  (2023-04-14T13:51:26Z)
• GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration [64.8770356696056]
  We propose GibbsDDRM, an extension of Denoising Diffusion Restoration Models (DDRM) to a blind setting in which the linear measurement operator is unknown. The proposed method is problem-agnostic, meaning that a pre-trained diffusion model can be applied to various inverse problems without fine-tuning.
  arXiv  Detail & Related papers  (2023-01-30T06:27:48Z)
• Nonlinear Isometric Manifold Learning for Injective Normalizing Flows [58.720142291102135]
  We use isometries to separate manifold learning and density estimation. We also employ autoencoders to design embeddings with explicit inverses that do not distort the probability distribution.
  arXiv  Detail & Related papers  (2022-03-08T08:57:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.