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Gradient-Aligned Calibration for Post-Training Quantization of Diffusion Models

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
QuantizationDiffusion
Abstract:

Diffusion models have shown remarkable performance in image synthesis by progressively estimating a smooth transition from a Gaussian distribution of noise to a real image. Unfortunately, their practical deployment is limited by slow inference speed, high memory usage, and the computational demands of the noise estimation process. Post-training quantization (PTQ) emerges as a promising solution to accelerate sampling and reduce the memory overhead of diffusion models. Existing PTQ methods for diffusion models typically apply uniform weights to calibration samples across timesteps, which is sub-optimal since data at different timesteps may contribute differently to the diffusion process. Additionally, due to varying activation distributions and gradients across timesteps, a uniform quantization approach is sub-optimal. Each timestep requires a different gradient direction for optimal quantization, and treating them equally can lead to conflicting gradients that degrade performance. In this paper, we propose a novel PTQ method that addresses these challenges by assigning appropriate weights to calibration samples. Specifically, our approach learns to assign optimal weights to calibration samples to align the quantized model’s gradients across timesteps, facilitating the quantization process. Extensive experiments on CIFAR-10, LSUN-Bedrooms, and ImageNet datasets demonstrate the superiority of our method compared to other PTQ methods for diffusion models.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes a gradient-aligned meta-learning framework for post-training quantization of diffusion models, addressing gradient conflicts arising from timestep-varying activation distributions. It resides in the 'Timestep-Aware Quantization Strategies' leaf, which contains nine papers—a moderately populated cluster within the broader 'Core PTQ Methods for Image Diffusion Models' branch. This positioning indicates the work targets a well-established research direction where timestep-adaptive quantization has become a recognized challenge, though the specific gradient-conflict framing appears less explored among siblings.

The taxonomy reveals neighboring leaves focused on 'Distribution Alignment and Calibration Optimization' (four papers) and 'Outlier and Activation Management' (four papers), suggesting the field has diversified into complementary strategies beyond pure timestep adaptation. The sibling papers in the same leaf emphasize dynamic bit-width allocation and timestep-grouping schemes, while the proposed gradient-alignment approach bridges calibration optimization concerns from adjacent leaves. The taxonomy's scope note explicitly excludes calibration-focused methods from the timestep-aware category, yet this work integrates both dimensions, potentially straddling conceptual boundaries between leaves.

Among the two contributions analyzed, the gradient-conflict identification examined zero candidates, while the meta-learning framework examined one candidate with no refutations found. This extremely limited search scope—one candidate total across both contributions—provides minimal evidence about prior work overlap. The absence of refutations may reflect either genuine novelty or insufficient coverage of the semantic search space. Given the moderately crowded leaf (nine papers) and the field's maturity (fifty papers across the taxonomy), a single-candidate examination offers weak signals about whether gradient-aligned calibration or meta-learned sample weighting has been previously explored.

The analysis suggests potential novelty in the gradient-conflict framing and meta-learning integration, but the single-candidate search scope severely limits confidence in this assessment. The taxonomy structure indicates active research in timestep-aware quantization, yet the specific gradient-alignment mechanism may occupy an underexplored niche. A more comprehensive literature search would be necessary to determine whether the gradient-conflict perspective and meta-learned weighting represent substantive advances or incremental refinements within this established research direction.

Taxonomy

Core-task Taxonomy Papers
50
2
Claimed Contributions
1
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: post-training quantization of diffusion models. The field has evolved into a rich taxonomy reflecting the diversity of diffusion architectures and application domains. At the highest level, the taxonomy distinguishes between core PTQ methods for image diffusion models—where foundational techniques such as timestep-aware quantization strategies and calibration schemes are developed—and specialized branches targeting diffusion transformers (DiTs), text-to-image models, video and temporal diffusion, audio diffusion, and even diffusion language models. Additional branches address quantization-aware training hybrids, specialized formats and platforms, sensitivity analysis, and niche application domains. Early works like Post-Training Quantization on Diffusion[1] and PTQD[2] established baseline approaches, while subsequent efforts have refined calibration and distribution-matching strategies to handle the unique temporal dynamics and activation distributions inherent in diffusion processes. Within the core PTQ methods for image diffusion, timestep-aware quantization has emerged as a particularly active line of inquiry, recognizing that diffusion models exhibit varying sensitivity across denoising steps. Papers such as Q-Diffusion[7] and Temporal Dynamic Quantization for[6] explore adaptive bit-width allocation and dynamic quantization schedules, while Gradient-Aligned Calibration for Post-Training[0] focuses on aligning calibration objectives with gradient flow to preserve generation quality. This contrasts with approaches like TFMQ-DM[31] and Breaking Static Barriers[38], which emphasize mixed-precision or layer-specific strategies. Meanwhile, the DiT branch—exemplified by Q-DiT[8], Vq4dit[5], and PTQ4DiT[28]—tackles the distinct challenges of transformer-based diffusion architectures, often requiring specialized attention quantization and outlier handling. Gradient-Aligned Calibration for Post-Training[0] sits squarely within the timestep-aware cluster, sharing thematic concerns with Towards Accurate Post-Training Quantization[4] and Post-Training Quantization for Diffusion[21] but distinguishing itself through its gradient-centric calibration mechanism, offering a complementary perspective to the distribution-matching emphasis seen in neighboring works.

Claimed Contributions

Identification of gradient conflict in diffusion model PTQ

The authors identify that calibration samples from different timesteps in diffusion models produce conflicting gradient signals during post-training quantization. This gradient conflict arises because different timesteps have distinct activation distributions and gradient dynamics, leading to optimization directions that interfere with each other and degrade quantization performance.

0 retrieved papers
Gradient-aligned meta-learning framework for sample weighting

The authors propose a novel meta-learning-based PTQ framework that dynamically assigns importance weights to calibration samples. The method learns these weights through bi-level optimization to promote gradient alignment across timesteps, thereby reducing gradient conflicts and improving the overall quantization quality of diffusion models.

1 retrieved paper

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Identification of gradient conflict in diffusion model PTQ

The authors identify that calibration samples from different timesteps in diffusion models produce conflicting gradient signals during post-training quantization. This gradient conflict arises because different timesteps have distinct activation distributions and gradient dynamics, leading to optimization directions that interfere with each other and degrade quantization performance.

Contribution

Gradient-aligned meta-learning framework for sample weighting

The authors propose a novel meta-learning-based PTQ framework that dynamically assigns importance weights to calibration samples. The method learns these weights through bi-level optimization to promote gradient alignment across timesteps, thereby reducing gradient conflicts and improving the overall quantization quality of diffusion models.

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