Bayesian diffusion models memorize training data when mutual information between restricted observations and training data exceeds log dataset size, and generalize otherwise.
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Denoising Diffusion Implicit Models
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abstract
Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples $10 \times$ to $50 \times$ faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.
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- abstract Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose revers
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representative citing papers
Lip Forcing distills a 14B bidirectional video diffusion teacher into autoregressive students that achieve real-time lip synchronization at 31 FPS using two denoising steps without CFG.
TAKO demonstrates real-time adversarial takeover of robotic diffusion policies via reusable universal patches on visual inputs, achieving 100% success in steering attacker-chosen trajectories across multiple tasks, encoders, and diffusion methods.
ActivityForensics is the first large-scale benchmark for temporally localizing activity-level forgeries in videos, paired with a diffusion-based baseline called TADiff.
Flow-GRPO is the first online RL method for flow matching models, raising GenEval accuracy from 63% to 95% and text-rendering accuracy from 59% to 92% with little reward hacking.
Consistency models achieve fast one-step generation with SOTA FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 by directly mapping noise to data, outperforming prior distillation techniques.
GB-FESO backpropagates a KL-divergence loss through a frozen conditional diffusion model's sampling trajectory to optimize system parameters so the generated ensemble matches a target free-energy surface.
Counterfactually fair image classifiers need not satisfy group fairness when a latent attribute G correlates with the sensitive attribute; reducing reliance on G (via CKD) restores both.
Flow-Map GRPO uses anchored stochastic flow map composition to enable GRPO-based RL alignment of deterministic few-step flow-map generators while preserving their marginal paths.
Introduces a Bridge latent interface that maps mismatched student latents into teacher space, enabling distillation from modern diffusion teachers to compact one-step students and raising SD 1.5 HPSv3 from 5.4 to 9.4 while keeping one-step speed.
MUSE shows that the native timestep embedding in diffusion models acts as a parameter-free steering signal for multi-task monocular depth and normal estimation via manifold decoupling in latent space.
Introduces the ASTAD task and training-free ASTModel framework for semantically consistent asymmetric style transfer using labeled synthetic content and unlabeled real references.
SDS extracts stable spectral signatures from diffusion model denoisers via frequency-controlled perturbations, achieving 99.9% attribution accuracy across eight models and 96.2% under prompt shift.
Synthetic minority augmentation improves threshold-integrated and optimized classification metrics only under model misspecification by correcting ranking errors, while providing no fundamental benefit beyond possible variance reduction under well-specified score models.
Sparse Context achieves 2-4x faster inference in reference-conditioned diffusion models by fine-tuning with random token dropping and applying task-aware selection at inference time, without loss of visual quality.
MeshFlow applies equivariant optimal-transport flow matching to generate triangle meshes as soups, matching autoregressive quality with an 18x inference speedup.
Introduces the first autonomous whole-body vision control system for soft vine robots via an end-to-end visuomotor policy trained on demonstrations.
A method that treats 3D box pairs as exact transformation specs, adds a depth-aware floor reference, and trains an image generator on synthetic scenes plus Objectron videos to perform large 3D edits on real photographs.
Timage generates text query overlays on images via Constrained Schrödinger Bridge to boost fine-grained spatial reasoning in vision-language models, outperforming larger systems on VMCBench with a 7B backbone.
A weakly-supervised image quality transfer method generates synthetic distorted DWI images from quality labels to train improved distortion correction models for prostate MRI.
Introduces Robust-TOOC benchmark for corrupted images and Dual-TTT test-time training that updates only a text-guided denoising module to boost robustness in open-vocabulary counting.
Flow Reversal Steering steers flow matching generalist policies by reversing suboptimal actions to nearby better modes, enabling improved zero-shot control, quick distillation, and RL bootstrapping in robotic manipulation.
DCIC uses dual constraints on a diffusion decoder to realize adjustable RDP operating points in neural image compression without extra rate cost.
Ambient Diffusion Policy enables better imitation learning from suboptimal robot data by leveraging spectral properties to restrict data usage to specific diffusion times.
citing papers explorer
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An exact information theory of generalization phase transitions in Bayesian diffusion models
Bayesian diffusion models memorize training data when mutual information between restricted observations and training data exceeds log dataset size, and generalize otherwise.
-
Lip Forcing: Few-Step Autoregressive Diffusion for Real-time Lip Synchronization
Lip Forcing distills a 14B bidirectional video diffusion teacher into autoregressive students that achieve real-time lip synchronization at 31 FPS using two denoising steps without CFG.
-
Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policies
TAKO demonstrates real-time adversarial takeover of robotic diffusion policies via reusable universal patches on visual inputs, achieving 100% success in steering attacker-chosen trajectories across multiple tasks, encoders, and diffusion methods.
-
ActivityForensics: A Comprehensive Benchmark for Localizing Manipulated Activity in Videos
ActivityForensics is the first large-scale benchmark for temporally localizing activity-level forgeries in videos, paired with a diffusion-based baseline called TADiff.
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Flow-GRPO: Training Flow Matching Models via Online RL
Flow-GRPO is the first online RL method for flow matching models, raising GenEval accuracy from 63% to 95% and text-rendering accuracy from 59% to 92% with little reward hacking.
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Consistency Models
Consistency models achieve fast one-step generation with SOTA FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 by directly mapping noise to data, outperforming prior distillation techniques.
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Gradient-Based Inverse Design of Free-Energy Landscapes with Diffusion Models
GB-FESO backpropagates a KL-divergence loss through a frozen conditional diffusion model's sampling trajectory to optimize system parameters so the generated ensemble matches a target free-energy surface.
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Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? -- A Theoretical and Empirical Study
Counterfactually fair image classifiers need not satisfy group fairness when a latent attribute G correlates with the sensitive attribute; reducing reliance on G (via CKD) restores both.
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Flow-Map GRPO: Reinforcement Learning for Few-Step Flow-Map Generators via Anchored Stochastic Composition
Flow-Map GRPO uses anchored stochastic flow map composition to enable GRPO-based RL alignment of deterministic few-step flow-map generators while preserving their marginal paths.
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Cross-Space Distillation: Teaching One-Step Students with Modern Diffusion Teachers
Introduces a Bridge latent interface that maps mismatched student latents into teacher space, enabling distillation from modern diffusion teachers to compact one-step students and raising SD 1.5 HPSv3 from 5.4 to 9.4 while keeping one-step speed.
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MUSE: Unlocking Timestep as Native Task Steering for One-Step Dense Prediction
MUSE shows that the native timestep embedding in diffusion models acts as a parameter-free steering signal for multi-task monocular depth and normal estimation via manifold decoupling in latent space.
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ASTAD: Asymmetric Style Transfer for Synthetic-to-Real Adaptation in Autonomous Driving
Introduces the ASTAD task and training-free ASTModel framework for semantically consistent asymmetric style transfer using labeled synthetic content and unlabeled real references.
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Diffusion Model Attribution via Spectral Coupling of Denoiser Responses
SDS extracts stable spectral signatures from diffusion model denoisers via frequency-controlled perturbations, achieving 99.9% attribution accuracy across eight models and 96.2% under prompt shift.
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When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?
Synthetic minority augmentation improves threshold-integrated and optimized classification metrics only under model misspecification by correcting ranking errors, while providing no fundamental benefit beyond possible variance reduction under well-specified score models.
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Keep The Essentials: Efficient Reference Conditioned Generation via Token Dropping
Sparse Context achieves 2-4x faster inference in reference-conditioned diffusion models by fine-tuning with random token dropping and applying task-aware selection at inference time, without loss of visual quality.
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MeshFlow: Mesh Generation with Equivariant Flow Matching
MeshFlow applies equivariant optimal-transport flow matching to generate triangle meshes as soups, matching autoregressive quality with an 18x inference speedup.
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PanoVine: Whole-Body Visuomotor Control for Soft Growing Vine Robot
Introduces the first autonomous whole-body vision control system for soft vine robots via an end-to-end visuomotor policy trained on demonstrations.
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Thinking in Boxes: 3D Editing in Real Images Made Easy
A method that treats 3D box pairs as exact transformation specs, adds a depth-aware floor reference, and trains an image generator on synthetic scenes plus Objectron videos to perform large 3D edits on real photographs.
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Timage: A Generative Text-in-Image Paradigm for Fine-Tuning Vision-Language Models
Timage generates text query overlays on images via Constrained Schrödinger Bridge to boost fine-grained spatial reasoning in vision-language models, outperforming larger systems on VMCBench with a 7B backbone.
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Learning to Distort: Weakly-Supervised Image Quality Transfer for Prostate DWI Correction
A weakly-supervised image quality transfer method generates synthetic distorted DWI images from quality labels to train improved distortion correction models for prostate MRI.
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Test-Time Training for Robust Text-Guided Open-Vocabulary Object Counting
Introduces Robust-TOOC benchmark for corrupted images and Dual-TTT test-time training that updates only a text-guided denoising module to boost robustness in open-vocabulary counting.
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Improving Robotic Generalist Policies via Flow Reversal Steering
Flow Reversal Steering steers flow matching generalist policies by reversing suboptimal actions to nearby better modes, enabling improved zero-shot control, quick distillation, and RL bootstrapping in robotic manipulation.
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Dual-Constrained Diffusion Image Compression for Operational Rate-Distortion-Perception Optimization
DCIC uses dual constraints on a diffusion decoder to realize adjustable RDP operating points in neural image compression without extra rate cost.
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Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics
Ambient Diffusion Policy enables better imitation learning from suboptimal robot data by leveraging spectral properties to restrict data usage to specific diffusion times.
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MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training
MaskAlign uses random token-subset alignment and pre-mask mixing to reduce diffusion models' reliance on complete clean-image token sets during representation alignment.
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Where the Score Lives: A Wavelet View of Diffusion
Derives optimal score functions for diffusion models as wavelet expansions in terms of data moments, enabling architecture-agnostic analysis of which distribution attributes matter for denoising.
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Consistent-Inversion: Reverse Consistency Guidance for Structure-Preserving Visual Editing
Consistent-Inversion introduces reverse consistency guidance that corrects early target denoising steps by checking reversibility toward the source inversion trajectory under the original prompt.
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Parallel Jacobi Decoding for Fast Autoregressive Image Generation
Parallel Jacobi Decoding accelerates autoregressive image models 4.8x-6.4x by using 2D spatial draft expansion and adjusted attention masks while keeping generation quality competitive.
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Reflection Separation from a Single Image via Joint Latent Diffusion
A joint latent diffusion model with cross-layer self-attention and disjoint sampling separates reflection and transmission layers from single images more effectively than prior methods on real-world benchmarks.
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Diffusing in the Right Space: A Systematic Study of Latent Diffusability
A large-scale empirical study across tokenizers and diffusion backbones identifies Velocity Irreducible Variance (VIV) as one of the most stable predictors of latent diffusion generation quality.
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Splatshot: 3D Face Avatar Generation from a Single Unconstrained Photo
SplatShot is a training-free method that inserts per-step 3DGS refitting and photometric feedback into diffusion denoising to enforce multi-view consistency for single-photo 3D face avatars.
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Decoupled Residual Denoising Diffusion Models for Unified and Data Efficient Image-to-Image Translation
DRDD decouples diffusion into independent noise and residual stages to preserve domain harmonization and enable unified data-efficient I2I translation.
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Sample-Efficient Diffusion-based Reinforcement Learning with Critic Guidance
CGPO integrates training-free critic guidance into diffusion denoising to produce high-Q actions as regression targets, yielding SOTA results on MuJoCo locomotion and successful Franka arm grasping.
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Midpoint Generative Models
Midpoint Generative Models define a midpoint divergence from flow matching symmetry and derive its variational form as a tractable objective for training competitive one-step generators.
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Spectral Guidance for Flexible and Efficient Control of Diffusion Models
Spectral Guidance learns singular functions via self-supervised objective to project guidance signals onto diffusion sampling trajectories, enabling stable control without retraining or backpropagation and improving CIFAR-10 accuracy by 37 points with 4x faster sampling.
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Towards Anatomically Plausible Human Image Generation via Synthetic Localized Preferences
ASAP generates over 10K synthetic anatomical preference pairs via targeted degradation of high-fidelity images and applies a localized margin-bounded DPO to reduce anatomical errors in text-to-image human generation, supported by the new HAP dataset and HAF-Bench.
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DeltaCam: Differential Intrinsic Camera Modeling for Video Generation
DeltaCam models relative changes in camera intrinsics via Δ-parameterized neural adaptors in video diffusion models trained on synthetic data to enable controllable generation and real-world transfer.
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Loki: Representation over Architecture for Diffusion-Based Portrait Animation
Loki replaces RGB conditioning stacks with identity-orthogonal parametric face encodings rasterized for diffusion, achieving efficient cross-ID portrait animation without cross-ID training data.
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Point Tracking Improves World Action Models
JOPAT jointly models pixels, point tracks, and actions in a diffusion transformer and reports gains over pixel-only baselines on long-horizon robot tasks with occlusion and off-screen motion.
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DFSAttn: Dynamic Fine-grained Sparse Attention for Efficient Video Generation
DFSAttn is a training-free framework for dynamic fine-grained sparse attention in video DiTs that achieves up to 2.1x speedup while preserving generation quality via Hilbert reordering, hierarchical scoring, and adaptive caching.
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VDE: Training-Free Accelerating Rectified Flow Model via Velocity Decomposition and Estimation
VDE accelerates rectified flow models like Flux by 3.22x with LPIPS of 0.069 via velocity decomposition into parallel/orthogonal components plus periodic full-pass anchoring.
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Linear-DPO: Linear Direct Preference Optimization for Diffusion and Flow-Matching Generative Models
Linear-DPO replaces sigmoid utility with linear utility and adds EMA reference to improve preference alignment in diffusion and flow-matching text-to-image models.
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DrawMotion: Generating 3D Human Motions by Freehand Drawing
DrawMotion is a diffusion-based framework that fuses text and hand-drawn stickman conditions via a Multi-Condition Module and training-free guidance to generate 3D human motions.
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CAdam: Context-Adaptive Moment Estimation for 3D Gaussian Densification in Generative Distillation
CAdam reinterprets densification in generative 3DGS as signal verification via gradient-moment interference, quantile context, and SNR gating to achieve large reductions in primitive count with comparable quality.
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DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation
A hypernetwork generates complete task-specific visuomotor policy parameters from instructions alone to structurally eliminate observation leakage in language-conditioned robotic control.
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FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models
FlowErase-RL applies GRPO to reformulate concept erasure in flow matching models as reward optimization using a dynamic dual-path mechanism for target suppression and non-target preservation.
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BrepForge: Factorized B-rep Synthesis via Wireframe Composition and Boundary-Conditioned Surface Instantiation
BrepForge factorizes B-rep synthesis into face-aware autoregressive wireframe composition followed by boundary-conditioned surface instantiation using learning-free geometric priors.
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Inference-Time Scaling in Diffusion Models through Iterative Partial Refinement
IPR improves valid solution rates on MNIST Sudoku from 55.8% to 75.0% by iteratively refining partial regions in sequential diffusion models without external verifiers or reward models.
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PolycubeNet: A Dual-latent Diffusion Model for Polycube-Based Hexahedral Mesh Generation
PolycubeNet applies a dual-latent diffusion architecture to generate polycube point clouds from input point clouds, enabling robust hexahedral mesh creation without surface segmentation or templates.
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Functionalization via Structure Completion and Motion Rectification
Object functionalization is cast as neural graph completion over a functional graph of parts, contacts, and motions, followed by geometry realization that also rectifies erroneous motions, demonstrated on furniture with a new paired dataset.