JET is a conditional flow matching framework that generates EEG as continuous raw sequences with added constraints for spectral and temporal properties, achieving over 40% lower TS-FID than prior discrete denoising methods on three benchmarks.
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Back to Basics: Let Denoising Generative Models Denoise
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Today's denoising diffusion models do not "denoise" in the classical sense, i.e., they do not directly predict clean images. Rather, the neural networks predict noise or a noised quantity. In this paper, we suggest that predicting clean data and predicting noised quantities are fundamentally different. According to the manifold assumption, natural data should lie on a low-dimensional manifold, whereas noised quantities do not. With this assumption, we advocate for models that directly predict clean data, which allows apparently under-capacity networks to operate effectively in very high-dimensional spaces. We show that simple, large-patch Transformers on pixels can be strong generative models: using no tokenizer, no pre-training, and no extra loss. Our approach is conceptually nothing more than "Just image Transformers", or JiT, as we call it. We report competitive results using JiT with large patch sizes of 16 and 32 on ImageNet at resolutions of 256 and 512, where predicting high-dimensional noised quantities can fail catastrophically. With our networks mapping back to the basics of the manifold, our research goes back to basics and pursues a self-contained paradigm for Transformer-based diffusion on raw natural data.
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- abstract Today's denoising diffusion models do not "denoise" in the classical sense, i.e., they do not directly predict clean images. Rather, the neural networks predict noise or a noised quantity. In this paper, we suggest that predicting clean data and predicting noised quantities are fundamentally different. According to the manifold assumption, natural data should lie on a low-dimensional manifold, whereas noised quantities do not. With this assumption, we advocate for models that directly predict clean data, which allows apparently under-capacity networks to operate effectively in very high-dimens
co-cited works
representative citing papers
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.
Proposes discretized Matérn process noise for triangulation-agnostic flow matching on meshes with PoissonNet denoiser, tested on elastic states and humanoid poses for meshes exceeding one million triangles.
Binomial flows close the gap between continuous flow matching and discrete ordinal data by using binomial distributions to enable unified denoising, sampling, and exact likelihoods in diffusion models.
A sparse voxel-space diffusion method with structure-adaptive modulation achieves up to 10x training speedup and state-of-the-art results for 3D medical image denoising and super-resolution.
Diffusion models show grokking on modular addition by composing periodic operand representations in simple data regimes or by separating arithmetic computation from visual denoising across timesteps in varied regimes.
CoReDi coevolves semantic representations with the diffusion model via a jointly learned linear projection stabilized by stop-gradient, normalization, and regularization, yielding faster convergence and higher sample quality than fixed-representation baselines.
FARM is a foundation model combining masked autoencoders and diffusion decoders to estimate high-resolution aerial radio maps from a new multi-band low-altitude dataset, claiming superior accuracy and generalization over prior methods.
Free-Range Gaussians uses flow matching over Gaussian parameters to predict non-grid-aligned 3D Gaussians from multi-view images, enabling synthesis of plausible content in unobserved regions with fewer primitives than grid-aligned methods.
LGS pretrained on 2.5M trajectories across 16 systems matches deterministic baselines at one step and halves 20-step error while using far less compute and adapting to held-out higher-resolution flows.
PiD is a pixel diffusion decoder that performs latent-to-pixel conversion and 4-8x upsampling in one generative step, enabling early stopping of latent diffusion and achieving sub-second 2048x2048 decoding with claimed better fidelity than cascaded baselines.
A vanilla Diffusion Transformer trained via x-prediction on frozen DINOv2 features reaches FID 1.14 on ImageNet 256x256 with fewer parameters and faster sampling than prior DiT variants.
Spatial Gram Alignment aligns internal self-similarities of LDM features with foundation priors to reconcile global structure and fine details in ultra-high-resolution text-to-image synthesis.
HDFM adds a continuous heat-dissipation (blur) process to flow matching, aligns an interpolated path to fix ill-posed inverse heat dissipation, and uses x-prediction to ease high-dimensional regression, yielding better performance than most baselines on image datasets.
WavFlow performs direct waveform audio generation via flow matching on 2D token grids from raw patches plus amplitude lifting, matching latent-based methods on VGGSound and AudioCaps without intermediate compression.
RAE v2 reaches gFID 1.06 on ImageNet-256 in 80 epochs by combining multi-layer encoder sums, complementary REPA targets, and free guidance via output reparameterization.
Register tokens enhance pixel-space DiT training and output quality via cleaner high-noise feature maps, and a dual-stream design adds further gains with little overhead.
L2P repurposes pre-trained LDMs for direct pixel generation via large-patch tokenization and shallow-layer training on synthetic data, matching source performance with 8-GPU training and enabling native 4K output.
BitLM replaces per-token softmax with bitwise continuous diffusion inside causal blocks to generate multiple tokens in parallel while preserving autoregressive structure.
A multivariate diffusion generative downscaling method preserves inter-variable correlations in climate data under large resolution increases, enabling more accurate compound risk assessment.
ELF is a continuous embedding-space flow matching model for language that stays continuous until the last step and outperforms prior discrete and continuous diffusion language models with fewer sampling steps.
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Smaller end-to-end autonomous driving models achieve optimal 3-second trajectory prediction accuracy at lower or intermediate temporal sampling frequencies, whereas larger VLA-style models perform best at the highest frequencies across Waymo, nuScenes, and PAVE datasets.
FREPix achieves competitive FID scores on ImageNet by decomposing image generation into separate low- and high-frequency paths within a flow matching framework.
citing papers explorer
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Let EEG Models Learn EEG
JET is a conditional flow matching framework that generates EEG as continuous raw sequences with added constraints for spectral and temporal properties, achieving over 40% lower TS-FID than prior discrete denoising methods on three benchmarks.
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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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Mat\'ern Noise for Triangulation-Agnostic Flow Matching on Meshes
Proposes discretized Matérn process noise for triangulation-agnostic flow matching on meshes with PoissonNet denoiser, tested on elastic states and humanoid poses for meshes exceeding one million triangles.
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Binomial flows: Denoising and flow matching for discrete ordinal data
Binomial flows close the gap between continuous flow matching and discrete ordinal data by using binomial distributions to enable unified denoising, sampling, and exact likelihoods in diffusion models.
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Structure-Adaptive Sparse Diffusion in Voxel Space for 3D Medical Image Enhancement
A sparse voxel-space diffusion method with structure-adaptive modulation achieves up to 10x training speedup and state-of-the-art results for 3D medical image denoising and super-resolution.
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Grokking of Diffusion Models: Case Study on Modular Addition
Diffusion models show grokking on modular addition by composing periodic operand representations in simple data regimes or by separating arithmetic computation from visual denoising across timesteps in varied regimes.
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Coevolving Representations in Joint Image-Feature Diffusion
CoReDi coevolves semantic representations with the diffusion model via a jointly learned linear projection stabilized by stop-gradient, normalization, and regularization, yielding faster convergence and higher sample quality than fixed-representation baselines.
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FARM: Foundational Aerial Radio Map for Intelligent Low-Altitude Networking
FARM is a foundation model combining masked autoencoders and diffusion decoders to estimate high-resolution aerial radio maps from a new multi-band low-altitude dataset, claiming superior accuracy and generalization over prior methods.
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Free-Range Gaussians: Non-Grid-Aligned Generative 3D Gaussian Reconstruction
Free-Range Gaussians uses flow matching over Gaussian parameters to predict non-grid-aligned 3D Gaussians from multi-view images, enabling synthesis of plausible content in unobserved regions with fewer primitives than grid-aligned methods.
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Latent Generative Solvers for Generalizable Long-Term Physics Simulation
LGS pretrained on 2.5M trajectories across 16 systems matches deterministic baselines at one step and halves 20-step error while using far less compute and adapting to held-out higher-resolution flows.
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PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion
PiD is a pixel diffusion decoder that performs latent-to-pixel conversion and 4-8x upsampling in one generative step, enabling early stopping of latent diffusion and achieving sub-second 2048x2048 decoding with claimed better fidelity than cascaded baselines.
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RiT: Vanilla Diffusion Transformers Suffice in Representation Space
A vanilla Diffusion Transformer trained via x-prediction on frozen DINOv2 features reaches FID 1.14 on ImageNet 256x256 with fewer parameters and faster sampling than prior DiT variants.
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Spatial Gram Alignment for Ultra-High-Resolution Image Synthesis
Spatial Gram Alignment aligns internal self-similarities of LDM features with foundation priors to reconcile global structure and fine details in ultra-high-resolution text-to-image synthesis.
-
Multi-Scale Generative Modeling with Heat Dissipation Flow Matching
HDFM adds a continuous heat-dissipation (blur) process to flow matching, aligns an interpolated path to fix ill-posed inverse heat dissipation, and uses x-prediction to ease high-dimensional regression, yielding better performance than most baselines on image datasets.
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WavFlow: Audio Generation in Waveform Space
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Improved Baselines with Representation Autoencoders
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Registers Matter for Pixel-Space Diffusion Transformers
Register tokens enhance pixel-space DiT training and output quality via cleaner high-noise feature maps, and a dual-stream design adds further gains with little overhead.
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L2P: Unlocking Latent Potential for Pixel Generation
L2P repurposes pre-trained LDMs for direct pixel generation via large-patch tokenization and shallow-layer training on synthetic data, matching source performance with 8-GPU training and enabling native 4K output.
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BitLM: Unlocking Multi-Token Language Generation with Bitwise Continuous Diffusion
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Generative climate downscaling enables high-resolution compound risk assessment by preserving multivariate dependencies
A multivariate diffusion generative downscaling method preserves inter-variable correlations in climate data under large resolution increases, enabling more accurate compound risk assessment.
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ELF: Embedded Language Flows
ELF is a continuous embedding-space flow matching model for language that stays continuous until the last step and outperforms prior discrete and continuous diffusion language models with fewer sampling steps.
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HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer
A pixel-space Diffusion Transformer with Unified Transformer architecture unifies image generation, editing, and personalization in an end-to-end model that maps all inputs to a shared token space and scales from 8B to over 200B parameters.
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Temporal Sampling Frequency Matters: A Capacity-Aware Study of End-to-End Driving Trajectory Prediction
Smaller end-to-end autonomous driving models achieve optimal 3-second trajectory prediction accuracy at lower or intermediate temporal sampling frequencies, whereas larger VLA-style models perform best at the highest frequencies across Waymo, nuScenes, and PAVE datasets.
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FREPix: Frequency-Heterogeneous Flow Matching for Pixel-Space Image Generation
FREPix achieves competitive FID scores on ImageNet by decomposing image generation into separate low- and high-frequency paths within a flow matching framework.
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Taming Outlier Tokens in Diffusion Transformers
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A Few-Step Generative Model on Cumulative Flow Maps
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High-Dimensional Noise to Low-Dimensional Manifolds: A Manifold-Space Diffusion Framework for Degraded Hyperspectral Image Classification
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CoreFlow: Low-Rank Matrix Generative Models
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Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation
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V-GRPO: Online Reinforcement Learning for Denoising Generative Models Is Easier than You Think
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One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models
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VOLT: Volumetric Wide-Field Microscopy via 3D-Native Probabilistic Transport
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Cross-Modal Generation: From Commodity WiFi to High-Fidelity mmWave and RFID Sensing
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Generative Refinement Networks for Visual Synthesis
GRN uses hierarchical binary quantization and entropy-guided refinement to set new ImageNet records of 0.56 rFID for reconstruction and 1.81 gFID for class-conditional generation while releasing code and models.
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FeaXDrive: Feasibility-aware Trajectory-Centric Diffusion Planning for End-to-End Autonomous Driving
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CoD-Lite: Real-Time Diffusion-Based Generative Image Compression
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Continuous Adversarial Flow Models
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From Clues to Generation: Language-Guided Conditional Diffusion for Cross-Domain Recommendation
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FlowLM: Few-Step Language Modeling via Diffusion-to-Flow Adaptation
FlowLM converts diffusion LMs to flow matching via fine-tuning, achieving few-step generation that rivals or beats 2000-step diffusion and saturates faster than training flow models from scratch.
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ML-based approach to classification and generation of structured light propagation in turbulent media
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What Does Flow Matching Bring To TD Learning?
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Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
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Flow Map Language Models: One-step Language Modeling via Continuous Denoising
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ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning
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Protein Autoregressive Modeling via Multiscale Structure Generation
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Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
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PixelGen: Improving Pixel Diffusion with Perceptual Supervision
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Sampling-Free Diffusion Transformers for Low-Complexity MIMO Channel Estimation
A diffusion transformer directly maps noisy MIMO channel observations to clean estimates in a single pass by exploiting angular sparsity, achieving better accuracy and much lower complexity than iterative diffusion baselines.
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PixelDiT: Pixel Diffusion Transformers for Image Generation
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Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent
Stochastic MeanFlow Policies enable one-step generative control in off-policy mirror descent by mapping noise through a MeanFlow transform, yielding tractable entropy and improved MuJoCo performance over Gaussian and generative baselines.