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arxiv: 2606.07079 · v1 · pith:7KOXAEU5new · submitted 2026-06-05 · 💻 cs.CV

AsyncPatch Diffusion: spatially-flexible image generation

Samuele Papa , Valentin De Bortoli , Guillaume Couairon , Daniel S\'ykora , Romuald Elie , Klaus Greff This is my paper

Pith reviewed 2026-06-27 22:31 UTC · model grok-4.3

classification 💻 cs.CV
keywords diffusion modelsimage generationinpaintingspatially adaptive generationELBOnoise schedulingjoint diffusion
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The pith

AsyncPatch Diffusion assigns independent noise levels to different image regions in a single joint-diffusion model.

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

Standard diffusion models apply one shared noise level across an entire image, so every region follows the same denoising path. AsyncPatch Diffusion instead lets each pixel or token receive its own noise level, creating a valid generative process that still admits an evidence lower bound. The method proves this asynchronous corruption works and introduces a controlled sampler that keeps training from over-focusing on extreme heterogeneity. One resulting model can then generate images with spatially varying denoising rates, perform inpainting, and apply input guidance without any task-specific retraining. Experiments on ImageNet 256 and LSUN show quality comparable to conventional diffusion while enabling new adaptive strategies such as uncertainty-guided acceleration.

Core claim

AsyncPatch Diffusion is a joint-diffusion framework that assigns distinct noise levels to separate input dimensions such as pixels or latent tokens. This asynchronous corruption defines a valid generative process and supports a richer family of spatially heterogeneous denoising trajectories; the paper proves the first valid ELBO for the process. A controlled noise-level sampler regulates both average corruption and spatial variability so that homogeneous configurations remain well represented during training.

What carries the argument

The asynchronous corruption mechanism that assigns independent noise levels to different spatial dimensions while preserving a valid joint generative process.

If this is right

  • A single pretrained model can perform inpainting by denoising unknown regions while holding known regions at low or zero noise.
  • Input guidance from clean or partially corrupted regions improves texture matching and local consistency in generated areas.
  • Uncertainty-guided acceleration and autoregressive sampling become native capabilities of the same model.
  • Spatially adaptive generation works without any task-specific fine-tuning on standard benchmarks.

Where Pith is reading between the lines

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

  • The framework may reduce the number of specialized models needed for conditional or region-specific image tasks.
  • Extending the per-dimension noise idea to video or 3D data could allow independent temporal or depth denoising schedules.
  • If the sampler generalizes, similar asynchronous corruption might apply to other generative processes such as score-based or flow models.

Load-bearing premise

The controlled noise-level sampler can balance homogeneous and heterogeneous configurations during training without degrading performance on standard uniform-noise trajectories.

What would settle it

Train the AsyncPatch model on ImageNet 256 with the controlled sampler and check whether FID scores remain within a few points of a matched standard diffusion baseline under identical architecture and compute.

Figures

Figures reproduced from arXiv: 2606.07079 by Daniel S\'ykora, Guillaume Couairon, Klaus Greff, Romuald Elie, Samuele Papa, Valentin De Bortoli.

Figure 1
Figure 1. Figure 1: Comparison between different approaches to image generation with AsyncPatch, which [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Perlin sampling produces an inpainting-like clean/noisy partition, patchwise sampling draws [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Sampled training timesteps and distribution of the mean timestep per image. From left to [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative performance of the models on ImageNet 256 and LSUN bedroom. Shown are [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effect of input guidance (0.0 for top vs 2.0 for bottom) in the inpainting of the right part of [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: On the left: autoregressive sampling from full noise to sample. In the middle and right: [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Additional details on timestep sampling used during training. [PITH_FULL_IMAGE:figures/full_fig_p028_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Effect of input guidance and classifier-free guidance (CFG) on both LPIPS and FID [PITH_FULL_IMAGE:figures/full_fig_p032_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of timestep sampling methods on four ImageNet-64 classes. The same seed [PITH_FULL_IMAGE:figures/full_fig_p033_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Generated ImageNet 256 samples using AsyncPatch latent diffusion. [PITH_FULL_IMAGE:figures/full_fig_p034_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Generated ImageNet 256 samples using AsyncPatch latent diffusion. [PITH_FULL_IMAGE:figures/full_fig_p035_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Generated ImageNet 256 samples using AsyncPatch latent diffusion. [PITH_FULL_IMAGE:figures/full_fig_p036_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Qualitative comparison of texture synthesis. Images are original and un-altered, zoom-in [PITH_FULL_IMAGE:figures/full_fig_p037_14.png] view at source ↗
read the original abstract

Standard diffusion models corrupt an entire sample with a single shared noise level, forcing all spatial regions to follow the same denoising trajectory. We introduce AsyncPatch Diffusion, a joint-diffusion framework that assigns distinct noise levels to different input dimensions, such as image pixels, or latent tokens. We show how this asynchronous corruption defines a valid generative process while supporting a richer family of spatially heterogeneous denoising trajectories, and prove the first valid ELBO for this process. We show that a single pretrained model can perform spatially adaptive generation, where different regions are denoised on different schedules. A key challenge is training: naive independent noise-level sampling overemphasizes highly heterogeneous configurations and underrepresents homogeneous noise levels, that are crucial during sampling. We address this with a controlled noise-level sampler that regulates both the average corruption level and its spatial variability. AsyncPatch achieves generation quality comparable to conventional diffusion on ImageNet 256 and LSUN, while being natively suited for inpainting without task-specific fine-tuning. We further introduce input guidance, which uses clean or partially corrupted regions to guide the generation of unknown regions, improving local consistency and texture matching. Finally, we demonstrate adaptive generation strategies including uncertainty-guided acceleration and autoregressive sampling.

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 / 0 minor

Summary. The paper introduces AsyncPatch Diffusion, a joint-diffusion framework assigning distinct noise levels to different input dimensions (e.g., image pixels or latent tokens). It claims this asynchronous corruption defines a valid generative process supporting spatially heterogeneous denoising trajectories, provides the first valid ELBO for the process, introduces a controlled noise-level sampler to balance training configurations, and demonstrates a single pretrained model performing spatially adaptive generation, native inpainting, input guidance, uncertainty-guided acceleration, and autoregressive sampling, with generation quality comparable to standard diffusion on ImageNet 256 and LSUN.

Significance. If the ELBO is valid and the controlled sampler preserves coverage of homogeneous trajectories without bias, the framework would enable richer spatially adaptive generation and task-agnostic inpainting in a single model, representing a meaningful extension of diffusion models for heterogeneous denoising schedules.

major comments (2)
  1. [Abstract] Abstract: the controlled noise-level sampler is introduced to regulate average corruption level and spatial variability, addressing the issue that naive independent sampling overemphasizes heterogeneous configurations. However, no derivation, density analysis, or ablation demonstrates that the induced training distribution maintains sufficient measure on homogeneous noise levels (uniform-t trajectories) used at inference. This is load-bearing for the claim of comparable ImageNet/LSUN quality under conventional uniform sampling.
  2. [Abstract] Abstract: the paper asserts a valid ELBO for the asynchronous corruption process and a new sampler, but provides no derivation details, experimental controls, or error analysis. The soundness of the central generative-process claim cannot be assessed from the given information.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address the major comments point by point below, agreeing that additional details will strengthen the presentation, and will revise accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the controlled noise-level sampler is introduced to regulate average corruption level and spatial variability, addressing the issue that naive independent sampling overemphasizes heterogeneous configurations. However, no derivation, density analysis, or ablation demonstrates that the induced training distribution maintains sufficient measure on homogeneous noise levels (uniform-t trajectories) used at inference. This is load-bearing for the claim of comparable ImageNet/LSUN quality under conventional uniform sampling.

    Authors: We agree that explicit verification of coverage on homogeneous trajectories is important for supporting the quality claims. The controlled sampler is intended to balance average corruption level and spatial variability to avoid overemphasizing heterogeneous cases. In the revised manuscript we will add a formal derivation of the induced training distribution, density analysis quantifying measure on uniform-t trajectories, and ablations showing that generation quality remains comparable when the sampler is used versus naive sampling. This will directly address the load-bearing concern. revision: yes

  2. Referee: [Abstract] Abstract: the paper asserts a valid ELBO for the asynchronous corruption process and a new sampler, but provides no derivation details, experimental controls, or error analysis. The soundness of the central generative-process claim cannot be assessed from the given information.

    Authors: The manuscript states a proof of ELBO validity for the asynchronous process. We acknowledge that the current presentation may not provide sufficient accessible details for full assessment. In revision we will expand the derivation with additional step-by-step explanations, include experimental controls that validate the ELBO under asynchronous schedules, and add error analysis to quantify approximation quality. These additions will make the soundness of the generative-process claim easier to evaluate. revision: yes

Circularity Check

0 steps flagged

No significant circularity; ELBO derivation and sampler are independent of fitted inputs

full rationale

The paper presents an explicit derivation of a valid ELBO for asynchronous per-dimension noise corruption and introduces a controlled noise-level sampler to address training distribution issues. No equations or claims reduce a prediction to a fitted parameter by construction, no self-citation chains justify core premises, and no ansatz is smuggled via prior work. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no concrete information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5754 in / 1021 out tokens · 27872 ms · 2026-06-27T22:31:31.707144+00:00 · methodology

discussion (0)

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