# Diffusion Models

Stochastic diffusion samplers have lagged deterministic ones, and raising approximation order does not necessarily improve their strong convergence order. F-DDPM evaluates the data-prediction model at a cleaner lookahead point built from the previous prediction and the previously injected noise, still with one model evaluation per iteration and a first-order strong convergence guarantee. On CIFAR-10, ImageNet, FFHQ and LSUN it reaches lower FID than the evaluated stochastic baselines at matched NFE budgets, and with 1.5 times the NFE it approaches or beats deterministic baselines in many settings.

Stochastic diffusion samplers have lagged deterministic ones, and raising approximation order does not necessarily improve their strong convergence order. F-DDPM evaluates the data-prediction model at a cleaner lookahead point built from the previous prediction and the previously injected noise, still with one model evaluation per iteration and a first-order strong convergence guarantee. On CIFAR-10, ImageNet, FFHQ and LSUN it reaches lower FID than the evaluated stochastic baselines at matched NFE budgets, and with 1.5 times the NFE it approaches or beats deterministic baselines in many settings.

Diffusion sampling accumulates the model's own prediction errors, and a corrector trained on the base model's predictions against the same clean target lowers DiT-XL/2 FID on ImageNet class-conditional generation from 9.58 to 2.07, approaching CFG without guidance.

Diffusion sampling accumulates the model's own prediction errors, and a corrector trained on the base model's predictions against the same clean target lowers DiT-XL/2 FID on ImageNet class-conditional generation from 9.58 to 2.07, approaching CFG without guidance.

The work proposes Retain-aware Activation Steering (RASteer), a training-free method that first builds a retain subspace from the concepts to preserve, then uses Retain-Orthogonal Steering (ROS) to remove components aligned with this subspace from the erasure direction, and further introduces Overlap-Adaptive Calibration (OAC) to control, at each layer and denoising step, how much of each shared component is removed based on the overlap between the erasure direction and the retain subspace, thereby better balancing target erasure and concept preservation; experiments on unsafe-content, instance, and artistic-style erasure across multiple backbones and benchmarks show RASteer matches or outperforms the activation-steering and weight-editing baselines evaluated.

The work proposes Retain-aware Activation Steering (RASteer), a training-free method that first builds a retain subspace from the concepts to preserve, then uses Retain-Orthogonal Steering (ROS) to remove components aligned with this subspace from the erasure direction, and further introduces Overlap-Adaptive Calibration (OAC) to control, at each layer and denoising step, how much of each shared component is removed based on the overlap between the erasure direction and the retain subspace, thereby better balancing target erasure and concept preservation; experiments on unsafe-content, instance, and artistic-style erasure across multiple backbones and benchmarks show RASteer matches or outperforms the activation-steering and weight-editing baselines evaluated.

The work proposes BiasRecon, a bias-calibrated adaptation framework grounded in a minimal-intervention principle, which alternates frequency-guided prior calibration (learnable scalars α and β modulating low- and high-frequency components at each U-Net skip layer, adding only 2L parameters), score-based denoising, and adaptive regularization that uses Stein's Unbiased Risk Estimator (SURE) to tune the regularization parameter γ; trained on FastMRI knee (973 volumes) and evaluated without retraining on FastMRI-Brain (cross-anatomy), Stanford Knee (cross-center) and CMRxRecon (combined shift) under 8× Gaussian 1D undersampling, it improves PSNR over DDS by +1.19 dB, +2.06 dB and +1.60 dB respectively, and is best in 10 of 12 sampling/acceleration settings.

The work proposes BiasRecon, a bias-calibrated adaptation framework grounded in a minimal-intervention principle, which alternates frequency-guided prior calibration (learnable scalars α and β modulating low- and high-frequency components at each U-Net skip layer, adding only 2L parameters), score-based denoising, and adaptive regularization that uses Stein's Unbiased Risk Estimator (SURE) to tune the regularization parameter γ; trained on FastMRI knee (973 volumes) and evaluated without retraining on FastMRI-Brain (cross-anatomy), Stanford Knee (cross-center) and CMRxRecon (combined shift) under 8× Gaussian 1D undersampling, it improves PSNR over DDS by +1.19 dB, +2.06 dB and +1.60 dB respectively, and is best in 10 of 12 sampling/acceleration settings.