LSGQuant: Layer-Sensitivity Guided Quantization for One-Step Diffusion Real-World Video Super-Resolution

arXiv:2602.03182 · cs.CV · Submitted 2026-02-03 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Hi, I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: We're looking at the paper: "LSGQuant: Layer-Sensitivity Guided Quantization for One-Step Diffusion Real-World Video Super-Resolution".

Jane: Here's the gist of the paper. The gist The method introduces LSGQuant, a layer-sensitivity guided quantization approach for one-step diffusion-based real-world video super-resolution, which achieves nearly performance to origin model with full-precision and significantly exceeds existing quantization techniques.

Tom: The paper addresses the challenge that low-bit quantization for diffusion models in real-world video super-resolution is difficult due to the high dynamic range of input latent and diverse layer behaviors.

Jane: Aggressive low-bit quantization often leads to severe performance degradation and noticeable loss in generation quality, particularly in video settings.

Tom: Furthermore, minimizing the quantization error during optimization often leads to overfitting the low-bit representation instead of optimal VSR performance.

Paper summary: Tom: We just covered the high-level idea behind LSGQuant, which is using a layer-sensitivity guided quantization approach for one-step diffusion based real world video super resolution. To recap, the paper argues that low bit quantization struggles with the high dynamic range of input latents and diverse layer behaviors in real video models.

Jane: And they propose LSGQuant to solve this by incorporating a Dynamic Range Adaptive Quantizer called DRAQ to fit those activations, plus they estimate layer sensitivity and use a Variance-Oriented Layer Training Strategy called VOLTS based on calibration statistics.

Lu: What’s important is that they also introduce Quantization-Aware Optimization, or QAO, which jointly refines the quantized branch and a retained high precision branch during training to ensure good results.

Meng: So, it sounds like the focus is on creating a robust quantization pipeline where you aren't just blindly quantizing everything uniformly across the model layers. It’s more surgical.

Tom: Exactly. They are collecting per channel layer statistics during a single inference process to calculate layer sensitivity and then allocating training iterations based on those estimated sensitivities before starting the actual training step.

Jane: That calibration step is key because it tells the system how important each layer actually is to the final video output, which is what drives that variance oriented strategy.

Lu: They select WAN2 point 1 as their full precision backbone and use DOVE settings for one-step diffusion VSR tasks, which grounds their work in specific state of the art models and training configurations <ref:2602.03182#pg1>.

Meng: When we look at the numbers they give, they show that under four bit quantization, LSGQuant has only negligible performance degradation and surpasses recent state of the art quantization settings <ref:2602.03182#pg2>. That's a concrete result for us to look at.

Tom: And when competing with the bfloat16 model, four bit LSGQuant achieves reductions of seventy point four nine percent in parameters and seventy-one point ninety two percent in operations. That shows a real efficiency gain when we compare it against the bfloat16 baseline.

Jane: So, for someone who just listens, this means we’re talking about a way to shrink these heavy diffusion models down significantly while keeping the video quality pretty good and getting much faster inference on less powerful hardware.

Lu: The paper's contribution is introducing LSGQuant as an approach for one step diffusion based real world VSR in model quantization, specifically investigating low bit scenarios like four bit and designing DRAQ to address the quantization error in the quantized inference process <ref:2602.03182#pg2>.

Meng: And they show that their approach has advantageous behavior over existing quantization settings when evaluated on both real world and synthetic datasets.

Tom: It’s about practical value for deployment on edge devices or platforms with limited resources, which is what this paper is aiming for by showing these performance metrics.

Conclusion: Jane: So, looking at the title "LSGQuant: Layer-Sensitivity Guided Quantization for One-Step Diffusion Real-World Video Super-Resolution," what does that really boil down to in simple terms?

Tom: It means they’ve developed a method that uses information about how sensitive different layers are to changes in precision during quantization to guide the process, which results in better performance than just applying a uniform low bit quantization.

Lu: The authors investigated four bit quantization scenarios specifically and designed DRAQ to handle the dynamic range issue, combined with VOLTS for training budget allocation and QAO for jointly refining both branches <ref:2602.03182#pg2>.

Meng: For someone looking at this from a production side, the implication is that we can deploy one step diffusion models more efficiently because they get near full precision quality at much smaller sizes and with much lower operational costs.

Jane: It’s about taking these large diffusion models that are promising for real world video super resolution and making them viable for deployment on devices where resources are limited, by intelligently managing the quantization trade off.

Tom: The paper's main contribution is introducing LSGQuant, which investigates low bit scenarios like four bit, designing DRAQ to minimize error during inference, and using VOLTS and QAO in calibration to provide reliable layer sensitivity estimation <ref:2602.03182#pg2>.

Lu: The evaluation on both real world and synthetic datasets demonstrated the advantageous behavior of this approach over existing quantization settings.

Meng: Ultimately, LSGQuant provides a practical value for deployment on edge devices or platforms with limited resources because it delivers performance that significantly exceeds prior quantization techniques while achieving substantial parameter and operation reductions.

Tianxing Wu, Zheng Chen, Cirou Xu, Bowen Chai, Yong Guo, Yutong Liu, Linghe Kong, Yulun Zhang

Shanghai Jiao Tong University 2024 Shanghai Jiao Tong University 2024 Huawei

cs.CV

Submitted: 2026-02-03

Updated: 2026-10-20

Comments: Accepted at ICML 2026. Code is available at: https://github.com/zhengchen1999/LSGQuant

Code: https://github.com/zhengchen1999/LSGQuant

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 91/100

The gist: The gist The method introduces LSGQuant, a layer-sensitivity guided quantization approach for one-step diffusion-based real-world video super-resolution, which achieves nearly performance to origin

Key concepts

Dynamic Range Adaptive Quantizer (DRAQ): This component is designed to minimize quantization error by robustly estimating the per-channel dynamic range of video token activations. It normalizes these activations, ensuring that the quantization process accurately captures the full range of input data, which is crucial for maintaining high generation quality in diffusion models.
Variance-Oriented Layer Training Strategy (VOLTS): VOLTS dynamically allocates training resources by re-estimating layer sensitivity based on activation statistics. It categorizes layers into three groups—low, medium, and high variance—to decide whether to freeze, lightly adapt, or fully optimize them during training. This tailored approach optimizes performance based on how much each layer contributes to the overall output.
Quantization-Aware Alternating Optimization (QAO): QAO is an algorithm that jointly refines both the quantized branch and a retained high-precision branch during optimization. It iteratively computes the residual difference between these two branches, ensuring that the final quantized model achieves optimal performance by balancing accuracy with computational efficiency.

Terminology

Quantization — 4× in this paper · explained in 14 episodes
Calibration — 1× in this paper · explained in 9 episodes
Diffusion Model — 1× in this paper · explained in 10 episodes
Diffusion Models — 1× in this paper · explained in 9 episodes
Model Compression — 1× in this paper · explained in 2 episodes
Transformer — 1× in this paper · explained in 2 episodes
Transformers — 1× in this paper · explained in 2 episodes

Summary

The gist The method introduces LSGQuant, a layer-sensitivity guided quantization approach for one-step diffusion-based real-world video super-resolution, which achieves nearly performance to origin model with full-precision and significantly exceeds existing quantization techniques.

Motivation

The paper addresses the challenge that low-bit quantization for diffusion models in real-world video super-resolution is difficult due to the high dynamic range of input latent and diverse layer behaviors. Aggressive low-bit quantization often leads to severe performance degradation and noticeable loss in generation quality, particularly in video settings. Furthermore, minimizing the quantization error during optimization often leads to overfitting the low-bit representation instead of optimal VSR performance.

LSGQuant Framework

LSGQuant incorporates several novel components to tackle these challenges. The framework utilizes a Dynamic Range Adaptive Quantizer (DRAQ) to fit video token activations. Additionally, the method estimates layer sensitivity and implements a Variance-Oriented Layer Training Strategy (VOLTS) by analyzing layer-wise statistics in calibration. Finally, QuantizationAware Optimization (QAO) is introduced to jointly refine the quantized branch and a retained high-precision branch.

Key Components

The paper introduces three novel components to address specific challenges in the quantization process.

Dynamic Range Adaptive Quantizer (DRAQ): This component is designed to minimize quantization error by computing a robust estimate of the per-channel dynamic range and normalizing activations.
Variance-Oriented Layer Training Strategy (VOLTS): This strategy dynamically allocates training resources by re-estimating layer sensitivity based on activation statistics. It categorizes layers into three groups: Low-variance layers (frozen), Medium-variance layers (lightly adapted), and High-variance layers (fully optimized) based on the variance of the channel mean
Quantization-Aware Alternating Optimization (QAO): This algorithm is formulated to jointly optimize the quantized and full-precision branches by iteratively computing the residual Wˆ −QW.

Experimental Results

Extensive experiments demonstrate that under 4-bit quantization, LSGQuant has only negligible performance degradation and surpasses recent state-of-the-art quantization settings. When competing with the bfloat16 model, 4-bit LSGQuant achieves reductions of 70.49% in parameters (Params) and 71.92% in operations (Ops) The results show that LSGQuant produces more faithful textures and clearer details compared to prior quantization approaches.

Contributions

The main contributions of the work are listed as follows.

**: We introduce LSGQuant, an approach for one-step diffusion-based real-world VSR in model quantization. To the best of our knowledge, our approch firstly investigates low-bit(e.g., 4-bit) scenario. To design a Dynamic Range Adaptive Quantizer (DRAQ) that effectively addresses the quantization error in the quantized inference process. In the calibration process, our Variance-Oriented Layer Training Strategy (VOLTS) and Quantization-Aware Alternating Optimization (QAO) provide reliable layer sensitivity estimation and quantization. Evaluations raised on both real-world and synthetic datasets demonstrate advantageous behavior of our approach over existing quantization settings. The paper concludes that LSGQuant has practical value for deployment on edge devices or platforms which have limited resources. >The gist> >Page 1 >Page 4 >Page 7 >Page 9 >Page 10</ref:2604.3c,Layer Sensitivity Estimation Scheme, Figure 6, Variance-Oriented Training Strategy (VOLTS), Table 3c, Quantization Aware Optimization (QAO), Table 3b >Page 1 >Page 4 >Page 7 >Page 9 >Page 10</ref:2604.3b,Table 1, Table 2, Figure 5, Figure 6, Table 3a, Table 3b >Page 1 >Page 4 >Page 7 >Page 9</ref:2604.3b,Table 1, Table 2, Figure 5, Figure 6, Table 3a, Table 3b >Page 1 >Page 4 >Page 7 >Page 9</ref:2604.3b,Table 1, Table 2, Figure 5, Figure 6, Table 3a, Table 3b >Page 1 >Page 4 >Page 7 >Page 9</ref:2604.

Improvements for AI systems

A system can achieve significant model compression by achieving reductions of 70.49% in parameters (Params) and 71.92% in operations (Ops) when moving from a full-precision bfloat16 model to LSGQuant W4A4, enabling deployment on resource-limited edge devices.
The system can maintain high video quality under aggressive quantization by incorporating the Dynamic Range Adaptive Quantizer (DRAQ) to minimize quantization error, which normalizes activations with respect to their channel-wise maximum magnitudes.
The system can ensure optimal performance during training by implementing the Variance-Oriented Layer Training Strategy (VOLTS) which reallocates training resources based on layer sensitivity, freezing low-variance layers or allocating more iterations to high-variance layers.
The system can achieve superior optimization results by employing Quantization-Aware Alternating Optimization (QAO), where the objective is formulated as minimizing a loss function that approximates the quantization-compensated residual between the full-precision and low-bit branches.