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arxiv: 2504.17069 · v2 · submitted 2025-04-23 · 💻 cs.CV · cs.AI

Distilling Specialized Orders for Visual Generation

Rishav Pramanik , Amin Sghaier , Masih Aminbeidokhti , Juan A. Rodriguez , Antoine Poupon , David Vazquez , Christopher Pal , Zhaozheng Yin
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Marco Pedersoli
This is my paper

Pith reviewed 2026-05-22 17:47 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords autoregressive image generationany-order modelsorder distillationself-distillationinpaintingoutpaintingimage synthesisFID evaluation
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The pith

Any-order autoregressive image models improve quality by distilling and fine-tuning on specialized patch orders extracted from their own confidence scores.

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

The paper establishes that starting from a pretrained any-order autoregressive generator, one can use the model's confidence scores to identify a small set of high-quality generation orders, then fine-tune the same model on those orders alone. This redirection of capacity yields higher-fidelity images while the original any-order training still supports arbitrary patch sequences for tasks such as inpainting and outpainting without any retraining. A sympathetic reader would care because fixed-order autoregressive models are inflexible and any-order models pay a performance penalty; the proposed pipeline aims to capture the best of both. The approach requires only lightweight fine-tuning and no architectural changes or extra labels.

Core claim

Ordered Autoregressive generation first trains an any-order autoregressive model that can generate image patches in arbitrary sequences. It then extracts specialized generation orders by selecting sequences where the model assigns high confidence to its own predictions. Fine-tuning the original model on these distilled orders improves synthesis quality while the retained any-order capability continues to enable zero-shot inpainting and outpainting.

What carries the argument

The self-distillation pipeline that extracts specialized orders from the any-order model's own confidence scores and uses them for targeted fine-tuning.

If this is right

  • Generation quality improves because model capacity is redirected from all possible orderings to a few high-confidence paths.
  • Zero-shot inpainting and outpainting remain possible because the any-order pretraining is preserved.
  • The same pipeline produces consistent gains across ImageNet, Fashion Products, and CelebA-HQ without new annotations or architecture changes.
  • Human raters prefer the resulting images over the any-order baseline.

Where Pith is reading between the lines

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

  • The same confidence-based order selection could be tested on video or 3D generation where sequence order also matters.
  • If the extracted orders prove stable across random seeds, they might serve as a lightweight way to specialize large autoregressive models for specific domains.
  • The approach suggests that order selection itself can be treated as a learnable but compressible component rather than an exhaustive search over permutations.

Load-bearing premise

Confidence scores produced by the pretrained any-order model can reliably point to generation orders whose subsequent fine-tuning improves quality without eroding the model's ability to handle arbitrary orders.

What would settle it

After running the extraction and fine-tuning steps, measuring no reduction in FID on ImageNet 256 by 256 or observing degraded zero-shot inpainting performance would falsify the central claim.

Figures

Figures reproduced from arXiv: 2504.17069 by Amin Sghaier, Antoine Poupon, Christopher Pal, David Vazquez, Juan A. Rodriguez, Marco Pedersoli, Masih Aminbeidokhti, Rishav Pramanik, Zhaozheng Yin.

Figure 1
Figure 1. Figure 1: Generation with our distilled order on the Fashion Product dataset (Left) and the Multimodal CelebA-HQ dataset (Right) with the corresponding generation order produced by our Ordered Autoregressive (OAR) model. The generation order is visualized through color intensity, progressing from yellow (early patches) to violet (later patches). Our learned order typically starts with simpler regions of the image be… view at source ↗
Figure 2
Figure 2. Figure 2: Different Autoregressive (AR) models. (Top) A raster scan is the normal approach for autoregressive generation from top left to bottom-right. The input token contains the content xi and the position li . (Middle) Any-given-order learns to generate tokens at any possible location. However, the position of the next token should be given as input in an additional positional embedding. (Bottom) Our method, Ord… view at source ↗
Figure 3
Figure 3. Figure 3: Examples of generation on the Fashion Products dataset. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Examples of generation on the CelebA dataset. (Top) Generated images with raster AR [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Generation order with absolute and relative positioning encoding. [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Average distance between generated patches for normal Fashion with white background [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Generation order with different backgrounds. [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
read the original abstract

Autoregressive (AR) image generators are becoming increasingly popular due to their ability to produce high-quality images and their scalability. Typical AR models are locked onto a specific generation order, often a raster-scan from top-left to bottom-right; this prohibits multi-task flexibility (inpainting, editing, outpainting) without retraining. Any-order AR models address this by learning to generate under arbitrary patch orderings, but at the cost of increased complexity and lower performance. In this paper, we present Ordered Autoregressive (OAR) generation, a self-distillation pipeline that first trains an any-order AR model, then extracts specialized generation orders from the model's own confidence scores, and fine-tunes on these orders. This achieves two goals: 1) improved generation quality by redirecting capacity from learning all $N!$ orderings to a single specialized path, and 2) preserved flexibility of any-order models. On ImageNet $256\times 256$, OAR improves FID from 2.39 to 2.17 over the any-order baseline, with consistent gains on Fashion Products and CelebA-HQ. OAR supports zero-shot inpainting and outpainting without retraining, and human evaluation shows 64% preference over the baseline. The pipeline requires only lightweight fine-tuning on a pretrained any-order model, with no architectural changes or additional annotations.

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

3 major / 2 minor

Summary. The paper introduces Ordered Autoregressive (OAR) generation: an any-order autoregressive image model is first pretrained, specialized generation orders are then extracted from its per-token confidence scores, and the model is lightly fine-tuned on those orders. The central claims are that this yields higher-quality samples (FID 2.39 → 2.17 on ImageNet 256×256, with gains on Fashion Products and CelebA-HQ) while retaining zero-shot inpainting/outpainting capability and receiving 64 % human preference, all without architectural changes or extra annotations.

Significance. If the reported gains are shown to be robust and the any-order flexibility is quantitatively preserved, the work would offer a practical route to reconcile the quality advantage of fixed-order AR models with the multi-task flexibility of any-order models. The self-distillation framing and absence of additional supervision are strengths; the concrete FID deltas and human-study result would be noteworthy for the autoregressive visual-generation literature if properly controlled.

major comments (3)
  1. [Abstract and §4] Abstract and §4 (Experiments): the manuscript states that OAR “preserved flexibility of any-order models” and supports zero-shot inpainting/outpainting, yet reports no any-order FID, no performance on randomly sampled unseen orders, and no comparison of multi-task metrics before versus after the fine-tuning stage. Because the central claim rests on the premise that capacity is redirected without eroding the any-order regime, the absence of these controls is load-bearing.
  2. [§3.2] §3.2 (Order Extraction): the procedure that converts per-token confidence scores into a discrete set of specialized orders is described only at a high level; no threshold, top-k value, or selection criterion is specified, nor is any sensitivity analysis provided. This free parameter directly affects both the reported FID improvement and the reproducibility of the pipeline.
  3. [§4] §4 (Ablations and Statistics): the paper provides no ablations on the number of distilled orders, no error bars or multiple random seeds for the FID numbers, and no comparison of fine-tuning on high-confidence orders versus random orders of the same cardinality. These omissions leave open the possibility that gains arise from data selection rather than the distillation mechanism itself.
minor comments (2)
  1. [§2] Notation for the number of patches N and the ordering set is introduced without an explicit equation; a short definition would improve clarity.
  2. [§4.3] The human-preference study description lacks details on the number of raters, image pairs shown, and statistical significance of the 64 % figure.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments highlight important aspects for strengthening the claims on flexibility preservation and experimental rigor. We address each point below and have revised the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (Experiments): the manuscript states that OAR “preserved flexibility of any-order models” and supports zero-shot inpainting/outpainting, yet reports no any-order FID, no performance on randomly sampled unseen orders, and no comparison of multi-task metrics before versus after the fine-tuning stage. Because the central claim rests on the premise that capacity is redirected without eroding the any-order regime, the absence of these controls is load-bearing.

    Authors: We agree that quantitative validation of preserved any-order flexibility is necessary to substantiate the central claim. In the revised manuscript we report any-order FID after fine-tuning, performance on randomly sampled unseen orders, and direct before-versus-after comparisons for zero-shot inpainting and outpainting. These additions confirm that multi-task capability is retained while quality improves. revision: yes

  2. Referee: [§3.2] §3.2 (Order Extraction): the procedure that converts per-token confidence scores into a discrete set of specialized orders is described only at a high level; no threshold, top-k value, or selection criterion is specified, nor is any sensitivity analysis provided. This free parameter directly affects both the reported FID improvement and the reproducibility of the pipeline.

    Authors: We have expanded §3.2 with the exact threshold, top-k value, and selection criterion used to derive the orders. A sensitivity analysis varying these parameters and reporting resulting FID changes has also been added to demonstrate robustness and improve reproducibility. revision: yes

  3. Referee: [§4] §4 (Ablations and Statistics): the paper provides no ablations on the number of distilled orders, no error bars or multiple random seeds for the FID numbers, and no comparison of fine-tuning on high-confidence orders versus random orders of the same cardinality. These omissions leave open the possibility that gains arise from data selection rather than the distillation mechanism itself.

    Authors: We have augmented §4 with an ablation on the number of distilled orders, FID scores accompanied by error bars from multiple random seeds, and a controlled comparison of fine-tuning on high-confidence orders versus random orders of matching cardinality. The new results indicate that the observed gains arise from the specialized orders rather than generic data selection. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical self-distillation pipeline with independent experimental validation

full rationale

The paper presents an empirical method: pretrain any-order AR model, extract high-confidence orders from its outputs, then lightweight fine-tune. Performance claims (FID 2.39→2.17 on ImageNet 256×256, human preference 64%, zero-shot inpainting/outpainting) are measured on held-out test sets and external benchmarks, not derived by construction from the input data or model. No equations reduce a 'prediction' to a fitted parameter by definition, no load-bearing self-citations, and no uniqueness theorems imported from prior author work. The pipeline follows standard distillation patterns where the extracted orders are an intermediate artifact validated by downstream metrics, leaving the central claims falsifiable and non-circular.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Empirical ML method with no new mathematical axioms or invented physical entities. Relies on standard assumptions that any-order AR training is feasible and that confidence scores correlate with generation quality.

free parameters (1)
  • Order selection criteria (threshold or top-k on confidence)
    The method must choose which orders count as 'specialized'; this choice is not fixed by prior literature and directly affects the fine-tuning data.
axioms (1)
  • domain assumption Any-order autoregressive models can be trained to generate images under arbitrary patch orderings
    This is the prerequisite stated in the abstract for the first stage of the pipeline.

pith-pipeline@v0.9.0 · 5805 in / 1426 out tokens · 67348 ms · 2026-05-22T17:47:29.574364+00:00 · methodology

discussion (0)

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