Inference-Time Attribute Distribution Alignment for Unconditional Diffusion

• URL: http://arxiv.org/abs/2605.07456v1
• Date: Fri, 08 May 2026 09:02:47 GMT
• Title: Inference-Time Attribute Distribution Alignment for Unconditional Diffusion
• Abstract summary: We formalize the inference-time attribute distributional alignment problem for pretrained unconditional diffusion models.<n>To address this, we cast inference-time attribute distributional alignment as an optimal control problem over the reverse diffusion process.<n>We solve for the perturbations using an optimal-control-based algorithm to optimize a differentiable distribution-matching objective.
• Score: 54.28942858023809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inference-time controllable generation is essential for real-world applications of unconditional diffusion models. However, most existing techniques focus on individual samples, struggling in applications that require the sample population to follow specific attribute distributions (e.g., demographic balance or semantic proportions). We formalize this setting as the inference-time attribute distributional alignment problem for pretrained unconditional diffusion models. To address this, we cast inference-time attribute distributional alignment as an optimal control problem over the reverse diffusion process, viewing the process as the rollout of a dynamical system and augmenting it with additive, time-dependent perturbations as control. We solve for the perturbations using an optimal-control-based algorithm to optimize a differentiable distribution-matching objective while penalizing control effort to preserve data fidelity. Experiment results in image generation demonstrate that our proposed plug-and-play approach can better align attribute distributions to diverse and flexible test-time targets compared to baselines, without retraining or finetuning the pretrained diffusion model.

Related papers

• Accelerating Speculative Diffusions via Block Verification [77.23389794645801]
  Speculative decoding speeds up LLM inference by using a draft model to generate tokens, with an acceptance-rejection scheme.<n>Existing diffusion adaptations either use inefficient sampling techniques or rely on an alternative scheme.<n>We introduce a novel scheme that efficiently implements the original speculative sampling mechanism for diffusion models.
  arXiv  Detail & Related papers  (2026-06-11T14:54:13Z)
• Inference-Time Scaling in Diffusion Models through Iterative Partial Refinement [22.349959761835468]
  Inference-time scaling has emerged as a major approach for improving reasoning capabilities.<n>We propose Iterative Partial Refinement (IPR), an inference-time scaling method for sequential diffusion.<n>IPR produces more globally consistent samples without external verification.
  arXiv  Detail & Related papers  (2026-05-19T03:53:50Z)
• Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance [17.353524034156205]
  MMD Guidance augments the reverse diffusion process with gradients of the Maximum Mean Discrepancy (MMD) between generated samples and a reference dataset.<n>Our framework naturally extends to prompt-aware adaptation in conditional generation models via product kernels.<n>Experiments on synthetic and real-world benchmarks demonstrate that MMD Guidance can achieve distributional alignment while preserving sample fidelity.
  arXiv  Detail & Related papers  (2026-01-13T09:42:57Z)
• Nonparametric Data Attribution for Diffusion Models [57.820618036556084]
  Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs.<n>We propose a nonparametric attribution method that operates entirely on data, measuring influence via patch-level similarity between generated and training images.
  arXiv  Detail & Related papers  (2025-10-16T03:37:16Z)
• DS-Diffusion: Data Style-Guided Diffusion Model for Time-Series Generation [3.7098771725459336]
  We propose a data style-guided diffusion model (DS-Diffusion) for time series generation tasks.<n>The DS-Diffusion avoids retraining the entire framework to introduce conditional guidance.<n>The generated samples can clearly indicate the data style from which they originate.
  arXiv  Detail & Related papers  (2025-09-23T03:06:39Z)
• Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers [49.97755400231656]
  We present the first performance guarantee with explicit dimensional dependencies for general score-mismatched diffusion samplers.<n>We show that score mismatches result in an distributional bias between the target and sampling distributions, proportional to the accumulated mismatch between the target and training distributions.<n>This result can be directly applied to zero-shot conditional samplers for any conditional model, irrespective of measurement noise.
  arXiv  Detail & Related papers  (2024-10-17T16:42:12Z)
• Constrained Diffusion Models via Dual Training [80.03953599062365]
  Diffusion processes are prone to generating samples that reflect biases in a training dataset. We develop constrained diffusion models by imposing diffusion constraints based on desired distributions. We show that our constrained diffusion models generate new data from a mixture data distribution that achieves the optimal trade-off among objective and constraints.
  arXiv  Detail & Related papers  (2024-08-27T14:25:42Z)
• Improved off-policy training of diffusion samplers [93.66433483772055]
  We study the problem of training diffusion models to sample from a distribution with an unnormalized density or energy function.<n>We benchmark several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods.<n>Our results shed light on the relative advantages of existing algorithms while bringing into question some claims from past work.
  arXiv  Detail & Related papers  (2024-02-07T18:51:49Z)

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.