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arxiv: 2604.05524 · v1 · submitted 2026-04-07 · 💻 cs.CV

Cross-Resolution Diffusion Models via Network Pruning

Jiaxuan Ren , Junhan Zhu , Huan Wang This is my paper

Pith reviewed 2026-05-10 19:57 UTC · model grok-4.3

classification 💻 cs.CV
keywords diffusion modelsimage synthesisnetwork pruningcross-resolution generationUNet architectureperceptual fidelitysemantic coherenceprompt refinement
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The pith

Pruning certain weights in diffusion models restores image quality at resolutions not seen during training.

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

Diffusion models trained at fixed resolutions produce lower quality images when asked to generate at different sizes. The paper identifies that some network parameters helpful at the trained size turn harmful at other sizes, disrupting the model's internal structure. CR-Diff removes these problematic weights using a block-wise pruning process and then boosts the remaining output to clean up the result. This leads to better images across different scales on multiple model types, with little loss at the original resolution, and allows users to refine results for specific text prompts. The finding matters for applications where flexible image sizes are needed without retraining the entire system.

Core claim

The core discovery is that resolution shifts cause certain weights in the UNet of diffusion models to become adverse, weakening semantic alignment and causing instability. By selectively pruning these adverse weights in a block-wise manner and amplifying the pruned predictions, CR-Diff achieves improved perceptual fidelity and semantic coherence at unseen resolutions across various backbones while preserving default performance and enabling prompt-specific refinements.

What carries the argument

Block-wise pruning of resolution-dependent adverse weights in the diffusion UNet, followed by pruned output amplification to purify predictions.

Load-bearing premise

That the degradation at different resolutions stems mainly from identifiable adverse weights removable by block-wise pruning without causing new problems in the model.

What would settle it

Running the unpruned model and the pruned model on the same set of prompts at a shifted resolution and checking whether the pruned version shows measurably higher perceptual quality and fewer structural artifacts; failure to improve would challenge the claim.

Figures

Figures reproduced from arXiv: 2604.05524 by Huan Wang, Jiaxuan Ren, Junhan Zhu.

Figure 1
Figure 1. Figure 1: This paper presents CR-Diff, a method to improve the cross-resolution visual consistency of UNet–based diffusion models by masking out some parameters in the model, i.e., network pruning – a technique that has been widely used for reducing model size; while here, we novelly repurpose it for generalizing diffusion models to unseen resolutions. The samples above compare the original SDXL [33] model with its … view at source ↗
Figure 2
Figure 2. Figure 2: Effects of magnitude-based unstructured pruning on [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of CR-Diff. Most UNet–based diffusion models exhibit resolution-dependent degradation when generating at unseen scales. CR-Diff addresses this issue through a two-stage pruning and optimizing process, consisting of a block-wise (B-W) pruning ratio strategy and a pruned output amplification (POA) mechanism. As shown in [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Simulated annealing (SA) search process for deter [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Block-wise (B-W) pruning applies differentiated [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Visual comparison across three generation settings. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Ablation results of block-wise pruning. (a) Performance comparison under uniform and block-wise pruning strategies [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Radar comparison across pruning strategies on [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Additional cross-resolution comparisons between SDXL [ [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Additional cross-resolution comparisons between SDXL [ [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Additional cross-resolution comparison on a subset of 5K prompts from the MS-COCO 2014 validation set [ [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Additional cross-resolution comparison on a subset of 5K prompts from the MS-COCO 2014 validation set [ [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Additional cross-resolution comparison on a subset of 5K prompts from the MS-COCO 2014 validation set [ [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Visual comparison across two generation settings. [PITH_FULL_IMAGE:figures/full_fig_p017_14.png] view at source ↗
read the original abstract

Diffusion models have demonstrated impressive image synthesis performance, yet many UNet-based models are trained at certain fixed resolutions. Their quality tends to degrade when generating images at out-of-training resolutions. We trace this issue to resolution-dependent parameter behaviors, where weights that function well at the default resolution can become adverse when spatial scales shift, weakening semantic alignment and causing structural instability in the UNet architecture. Based on this analysis, this paper introduces CR-Diff, a novel method that improves the cross-resolution visual consistency by pruning some parameters of the diffusion model. Specifically, CR-Diff has two stages. It first performs block-wise pruning to selectively eliminate adverse weights. Then, a pruned output amplification is conducted to further purify the pruned predictions. Empirically, extensive experiments suggest that CR-Diff can improve perceptual fidelity and semantic coherence across various diffusion backbones and unseen resolutions, while largely preserving the performance at default resolutions. Additionally, CR-Diff supports prompt-specific refinement, enabling quality enhancement on demand.

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 manuscript proposes CR-Diff, a two-stage post-training procedure for UNet-based diffusion models. It first performs block-wise pruning to remove weights that exhibit adverse behavior at out-of-training resolutions (identified via analysis of resolution-dependent parameter effects), then applies pruned output amplification to refine the predictions. The central claim is that this selectively improves perceptual fidelity and semantic coherence at unseen resolutions across multiple backbones while largely preserving performance at the default training resolution, and that it additionally supports prompt-specific refinement.

Significance. If the hypothesized causal mechanism is isolated and the empirical gains are reproducible, the work would provide a lightweight, training-free adaptation strategy for increasing resolution flexibility in pre-trained diffusion models. This addresses a practical limitation in current generative pipelines without the cost of full retraining or architectural changes.

major comments (2)
  1. [Method (two-stage procedure)] The central claim requires that resolution-dependent adverse weights can be reliably identified and that their removal (plus amplification) produces gains beyond generic pruning effects. No ablation is described that compares the block-wise adverse-weight criterion against random pruning, magnitude-based pruning, or pruning without the subsequent amplification stage; without such controls, improvements could be explained by sparsity-induced regularization rather than the proposed mechanism.
  2. [Abstract and Experiments] The abstract asserts that 'extensive experiments suggest' improvements in perceptual fidelity and semantic coherence, yet supplies no quantitative metrics, tables of FID/LPIPS scores, ablation tables, or error bars at specific unseen resolutions. This absence makes it impossible to assess effect sizes or consistency of the cross-resolution gains.
minor comments (2)
  1. [Method] The term 'pruned output amplification' is introduced without a formal definition, equation, or pseudocode; a precise formulation of the amplification operator would improve reproducibility.
  2. [Method] The manuscript should clarify whether the block-wise pruning decisions are made once per backbone or recomputed per prompt, as the prompt-specific refinement claim implies the latter but the description is ambiguous.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and will revise the manuscript to incorporate the requested controls and quantitative results.

read point-by-point responses

  1. Referee: [Method (two-stage procedure)] The central claim requires that resolution-dependent adverse weights can be reliably identified and that their removal (plus amplification) produces gains beyond generic pruning effects. No ablation is described that compares the block-wise adverse-weight criterion against random pruning, magnitude-based pruning, or pruning without the subsequent amplification stage; without such controls, improvements could be explained by sparsity-induced regularization rather than the proposed mechanism.

    Authors: We agree that the manuscript would be strengthened by explicit ablations isolating the block-wise adverse-weight criterion. In the revised version we will add comparisons to random pruning, magnitude-based pruning, and the pruning stage without amplification. These controls will be reported with the same evaluation protocol to demonstrate that the observed gains exceed generic sparsity effects and arise from the resolution-dependent analysis. revision: yes

  2. Referee: [Abstract and Experiments] The abstract asserts that 'extensive experiments suggest' improvements in perceptual fidelity and semantic coherence, yet supplies no quantitative metrics, tables of FID/LPIPS scores, ablation tables, or error bars at specific unseen resolutions. This absence makes it impossible to assess effect sizes or consistency of the cross-resolution gains.

    Authors: We acknowledge that the abstract and main text currently lack the requested quantitative tables and error bars. The revised manuscript will expand the abstract to reference key metrics and will include new tables reporting FID, LPIPS, and other scores with standard deviations across multiple unseen resolutions and backbones. This will allow direct assessment of effect sizes and reproducibility. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper traces degradation to resolution-dependent adverse weights via empirical analysis, then applies block-wise pruning followed by output amplification as a two-stage procedure. This chain does not reduce any central claim to a self-defined quantity, a fitted parameter renamed as prediction, or a load-bearing self-citation; the method is presented as an external, experimentally validated intervention whose effectiveness is tested on multiple backbones and unseen resolutions rather than derived tautologically from its inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract alone does not specify any free parameters, axioms, or invented entities; pruning thresholds and amplification factors are likely implicit but unstated.

pith-pipeline@v0.9.0 · 5464 in / 1021 out tokens · 21465 ms · 2026-05-10T19:57:07.150872+00:00 · methodology

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    Simulated Annealing (SA) Algorithm Algorithm 1 summarizes the simulated annealing (SA) routine used to search for the optimal pruning ratio con- figurationr=r down, rmid, rup. The hyperparameters in- clude the initial temperatureT init, cooling rateα, it- eration budgetN iter, a set of candidate seedsS seeds, and a restart limitR max. Starting from the be...

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    SDXL, natively trained at 1024×1024with a resampler and high-resolution cross- attention, effectively internalizes dense object struc- tures and sharp boundaries

    Analyses on Unseen Resolutions Beyond the detailed analysis in Section 4.2, which demonstrates consistent improvements under CR-Diff at unseen resolutions, we provide additional analyses at higher resolutions for SDXL. SDXL, natively trained at 1024×1024with a resampler and high-resolution cross- attention, effectively internalizes dense object struc- tur...

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    Expanded Qualitative Analyses Representative Teaser Results.In Figures 9 and 10, we present additional representative teaser examples fol- lowing the style of Figure 1, further illustrating the ef- fectiveness of CR-Diff in enhancing cross-resolution vi- sual consistency over the dense SDXL [33]. Results on the 5K Dataset.In Figures 11, 12, and 13, we pre...

    work page 2014