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arxiv: 2511.13720 · v2 · submitted 2025-11-17 · 💻 cs.CV

Back to Basics: Let Denoising Generative Models Denoise

Tianhong Li , Kaiming He This is my paper

Pith reviewed 2026-05-11 22:10 UTC · model grok-4.3

classification 💻 cs.CV
keywords denoising diffusiongenerative modelstransformersimage generationmanifold assumptionImageNetpixel-level predictionclean data prediction
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The pith

Predicting clean images directly with simple Transformers on raw pixels produces competitive generative models for ImageNet at 256 and 512 resolutions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that current denoising diffusion models avoid directly predicting clean images and instead forecast noise or other noised quantities. It argues that this choice ignores the manifold structure of natural data, where clean images occupy a low-dimensional surface while noisy versions fill the full high-dimensional space. By instead training models to map noisy inputs straight back to clean pixels, apparently limited networks can still succeed in pixel space without tokenizers, pre-training, or extra losses. The resulting JiT models, which are plain large-patch Transformers, reach competitive generation quality on ImageNet at both 256 and 512 resolution.

Core claim

Directly predicting the clean data from noised inputs, rather than predicting noise or a noised quantity, lets simple large-patch Transformers operate effectively as generative models on raw pixels. These JiT networks require no tokenizer, no pre-training, and no auxiliary loss yet produce competitive samples on ImageNet at 256 and 512 resolution, where high-dimensional noise prediction tends to fail.

What carries the argument

JiT, or Just image Transformers: large-patch Transformers applied directly to pixels that predict clean data from noised versions by exploiting the manifold structure of natural images.

If this is right

  • Networks with limited capacity can still generate high-resolution images when trained to recover points on the data manifold.
  • Generative performance remains competitive without tokenizers or pre-training when the prediction target is the clean image.
  • Large patch sizes of 16 and 32 become viable for Transformer-based diffusion on raw pixels.
  • A self-contained training paradigm for diffusion models on natural images is possible without auxiliary components.
  • Direct clean-image prediction avoids catastrophic failure modes observed when predicting high-dimensional noised quantities.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same direct-prediction strategy could reduce architectural complexity in generative models for other high-dimensional data such as audio or video.
  • Training dynamics might change when the network is explicitly encouraged to map back onto the manifold rather than into the ambient noise space.
  • Model-size requirements for high-resolution generation could be re-examined under the clean-prediction objective.
  • Classical signal-processing denoising ideas may map more directly onto modern diffusion training once the target is restored to clean data.

Load-bearing premise

Natural data lies on a low-dimensional manifold while noised data does not.

What would settle it

A clean-data-predicting large-patch Transformer that produces visibly worse or incoherent samples than a noise-predicting baseline at 512 resolution on ImageNet would falsify the central claim.

read the original 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-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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that standard denoising diffusion models predict noise or noised quantities rather than clean data, and that directly predicting clean images is fundamentally different because natural data lies on a low-dimensional manifold while noised quantities do not. This allows simple, under-capacity networks to operate in high-dimensional pixel space. The authors introduce JiT (Just image Transformers): large-patch pixel Transformers trained with no tokenizer, no pre-training, and no extra loss, and report competitive ImageNet results at 256 and 512 resolutions with patch sizes 16 and 32, where noise-prediction baselines fail catastrophically.

Significance. If the results hold, the work demonstrates that a back-to-basics clean-data prediction target can enable competitive generative performance with minimal architectural complexity on raw pixels. This provides an empirical existence proof for simple large-patch Transformers as generative models and highlights the modeling choice of prediction target as potentially more important than tokenization or pre-training in high-dimensional settings.

major comments (2)
  1. [Abstract and §1] Abstract and §1: The explanatory link between direct clean-data prediction and success in high-dimensional space rests on the untested manifold assumption (natural images occupy a low-dimensional manifold while noised quantities do not). No intrinsic-dimension estimates (PCA, MLE, or correlation dimension), ablation on manifold properties, or comparison of effective dimensionality at training noise levels are provided to ground this premise.
  2. [Experiments] Experiments section: The claim that noise/noised-quantity prediction 'fails catastrophically' at large patch sizes while clean prediction succeeds is load-bearing for the central argument, yet the manuscript does not report controlled ablations isolating the prediction target from other factors such as loss geometry, optimization dynamics, or network capacity. Without these, the reported competitive FID or other metrics cannot be confidently attributed to the manifold-based rationale.
minor comments (2)
  1. [§2] §2: The precise mathematical formulation of the clean-data prediction objective (e.g., the training loss and how it differs from standard noise-prediction diffusion) should be stated explicitly with an equation for reproducibility.
  2. [Tables and figures] Tables and figures: Ensure quantitative tables report both patch size and resolution explicitly and include error bars or multiple seeds for the ImageNet 256/512 results to allow direct comparison with noise-prediction baselines.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address the major comments below, providing clarifications and indicating where revisions will be made to strengthen the paper.

read point-by-point responses

  1. Referee: [Abstract and §1] Abstract and §1: The explanatory link between direct clean-data prediction and success in high-dimensional space rests on the untested manifold assumption (natural images occupy a low-dimensional manifold while noised quantities do not). No intrinsic-dimension estimates (PCA, MLE, or correlation dimension), ablation on manifold properties, or comparison of effective dimensionality at training noise levels are provided to ground this premise.

    Authors: We appreciate this observation. The manifold hypothesis for natural images is a standard assumption in the field, with substantial supporting evidence from prior studies on the low-dimensional structure of image data. Our work builds on this by demonstrating that direct prediction of clean data enables effective modeling in high-dimensional pixel space with simple architectures, in contrast to noise prediction. While we do not provide new intrinsic dimension calculations, the empirical results—particularly the failure of noise prediction at large patch sizes—serve as indirect validation. In the revised manuscript, we will expand the discussion in Section 1 to include references to key literature on image manifolds and clarify the role of this assumption. revision: partial

  2. Referee: [Experiments] Experiments section: The claim that noise/noised-quantity prediction 'fails catastrophically' at large patch sizes while clean prediction succeeds is load-bearing for the central argument, yet the manuscript does not report controlled ablations isolating the prediction target from other factors such as loss geometry, optimization dynamics, or network capacity. Without these, the reported competitive FID or other metrics cannot be confidently attributed to the manifold-based rationale.

    Authors: We agree that careful isolation of variables strengthens the argument. Our experiments compare clean-data prediction (JiT) against noise-prediction baselines using the exact same Transformer architecture, patch sizes, and training protocol on raw pixels, with the only difference being the prediction target. This setup controls for network capacity and largely for optimization dynamics, as the training procedure is identical. The loss geometry is inherently tied to the choice of target, which is the central modeling decision under investigation. We believe this provides sufficient evidence for the importance of the prediction target. However, we will add a note in the experiments section acknowledging potential confounding factors and discussing why the target choice is the primary variable. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical demonstration remains self-contained without reductions to fitted inputs or self-citations.

full rationale

The manuscript advances an empirical claim that direct clean-image prediction with large-patch pixel Transformers yields competitive ImageNet results at 256/512 resolution, without tokenizers or pre-training. The manifold assumption is invoked as an explanatory premise for why this modeling choice succeeds where noise prediction fails, but the paper presents no equations, derivations, or parameter fits that reduce the reported performance to the assumption by construction. No self-citation chains, uniqueness theorems, or ansatzes are used to justify core choices; results are benchmark numbers rather than forced predictions. The derivation chain is therefore independent of its inputs and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on one domain assumption about data manifolds; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Natural data lies on a low-dimensional manifold, whereas noised quantities do not.
    Invoked in the abstract to justify why predicting clean data is fundamentally different and advantageous.

pith-pipeline@v0.9.0 · 5504 in / 1122 out tokens · 41294 ms · 2026-05-11T22:10:03.888395+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

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  • Foundation.LawOfExistence defect_zero_iff_one echoes
    ?
    echoes

    ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

    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.

  • Foundation.JCostCoshIdentity jcost_exp_cosh_form echoes
    ?
    echoes

    ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

    Predicting clean data is fundamentally different from predicting noise or a noised quantity.

  • Foundation.DiscretenessForcing continuous_no_isolated_zero_defect echoes
    ?
    echoes

    ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

    simple, large-patch Transformers on pixels can be strong generative models: using no tokenizer, no pre-training, and no extra loss.

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supports
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Reference graph

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