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LoRaQ: Optimized Low Rank Approximated Quantization Error for 4-bit Quantization

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Post-Training QuantizationTransformersDiffusionImage Generation
Abstract:

Post-training quantization (PTQ) is essential for deploying large diffusion-based transformers on resource-constrained hardware. However, aggressive 4-bit quantization introduces significant degradation in generative performance. While existing solutions mitigate quantization error through outlier smoothing or rotation techniques, low-rank approximation methods that add auxiliary linear branches to each quantized layer represent a promising new paradigm. Yet, these approaches suffer from computational overhead due to the data movement required by full-precision (W16A16) branches, limiting practical deployment. In addition, data-based calibration contributes to the computational complexity of the quantization process, especially because search policies must evaluate many parameter configurations using a small calibration subset. We propose LoRaQ (low-rank approximated quantization), a data-free calibration approach to optimize quantization error compensation. This method can be used in composition with other PTQ models. LoRaQ further enables mixed-precision configurations by quantizing the low-rank branch itself, overcoming the limitations of prior work. While LoRaQ achieves superior quantization performance than state-of-the-art methods in their native W4A4 setting on PixArt-Sigma and SANA, it also allows for configurations such as W8A8, W6A6 and W4A8 for low-rank branch alongside a W4 main layer. This reduces data movement overhead and enables a fully quantized, hardware-efficient solution.

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Overview

Taxonomy

Core-task Taxonomy Papers
46
3
Claimed Contributions
29
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: Low-rank approximation for post-training quantization of diffusion transformers. The field has organized itself around several complementary strategies for compressing diffusion models without extensive retraining. At the highest level, researchers pursue diverse bit-width strategies—ranging from moderate 8-bit schemes to extreme 2–4 bit quantization—while simultaneously addressing activation outliers that destabilize low-precision inference. A substantial body of work explores temporal and timestep-aware methods that adapt quantization parameters across the denoising trajectory, recognizing that different diffusion steps exhibit distinct numerical characteristics. Low-rank decomposition techniques offer an orthogonal avenue for reducing parameter counts, and many recent efforts combine quantization with such factorizations to achieve greater compression. Meanwhile, some branches focus on unified compression strategies that merge pruning, distillation, and quantization, or specialize in non-image modalities such as audio and video generation, reflecting the broadening scope of diffusion applications. Within the extreme low-bit quantization branch, a particularly active line of work tackles the challenge of maintaining generation quality at 2–4 bits. Methods such as Mpq-dm[4], Svdquant[8], and Mpq-dmv2[9] demonstrate that careful calibration and mixed-precision allocation can preserve fidelity even under severe bit constraints, while Terdit[11] explores ternary representations. LoRaQ[0] situates itself in this cluster by integrating low-rank approximation directly into the post-training quantization pipeline, aiming to recover representational capacity lost during aggressive bit reduction. Compared to neighbors like Svdquant[8], which emphasizes singular value decomposition for weight matrices, LoRaQ[0] leverages rank-constrained updates to compensate for quantization error without requiring full fine-tuning. This approach contrasts with Mpq-dm[4] and Mpq-dmv2[9], which rely more heavily on mixed-precision search and calibration heuristics, highlighting an ongoing exploration of whether structural decomposition or adaptive precision offers a more scalable path to ultra-low-bit diffusion inference.

Claimed Contributions

LoRaQ: Data-free calibration approach for optimizing quantization error compensation

The authors introduce LoRaQ, a post-training quantization method that optimizes quantization error by directly approximating the error using low-rank matrices through gradient descent, eliminating the need for data-dependent calibration. This approach can be composed with other PTQ models and simplifies the quantization process.

10 retrieved papers
Can Refute
Mixed-precision quantization strategy with quantized low-rank branches

The method enables flexible mixed-precision configurations (such as W8A8, W6A6, and W4A8) for the low-rank branch alongside a W4 main layer, reducing data movement overhead and enabling a fully quantized, hardware-efficient solution without requiring full-precision operations.

9 retrieved papers
Can Refute
Open-source hardware-agnostic PTQ library for transformer blocks

The authors provide an open-source library that enables systematic benchmarking of post-training quantization methods across different configurations and supports scalable quantization of large models in multi-GPU environments, facilitating reproducible research and practical deployment.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

LoRaQ: Data-free calibration approach for optimizing quantization error compensation

The authors introduce LoRaQ, a post-training quantization method that optimizes quantization error by directly approximating the error using low-rank matrices through gradient descent, eliminating the need for data-dependent calibration. This approach can be composed with other PTQ models and simplifies the quantization process.

Contribution

Mixed-precision quantization strategy with quantized low-rank branches

The method enables flexible mixed-precision configurations (such as W8A8, W6A6, and W4A8) for the low-rank branch alongside a W4 main layer, reducing data movement overhead and enabling a fully quantized, hardware-efficient solution without requiring full-precision operations.

Contribution

Open-source hardware-agnostic PTQ library for transformer blocks

The authors provide an open-source library that enables systematic benchmarking of post-training quantization methods across different configurations and supports scalable quantization of large models in multi-GPU environments, facilitating reproducible research and practical deployment.

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