DVD-Quant: Data-free Video Diffusion Transformers Quantization
Authors: Zhiteng Li
Hanxuan Li
Junyi Wu
Kai Liu
Haotong Qin
Linghe Kong
Guihai Chen
Yulun Zhang
Xiaokang Yang
Venue: ICLR 2026 Poster (2026)
Keywords: video generation models; post-training quantization
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: DVD-Quant is a novel Data-free quantization framework for Video DiTs

Abstract: Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical deployment. While post-training quantization (PTQ) presents a promising approach to accelerate Video DiT models, existing methods suffer from two critical limitations: (1) dependence on computation-heavy and inflexible calibration procedures, and (2) considerable performance deterioration after quantization.
To address these challenges, we propose DVD-Quant, a novel Data-free quantization framework for Video DiTs. Our approach integrates three key innovations:
(1) Bounded-init Grid Refinement (BGR) and 
(2) Auto-scaling Rotated Quantization (ARQ) for calibration data-free quantization error reduction, as well as
(3) $\delta$-Guided Bit Switching ($\delta$-GBS) for adaptive bit-width allocation.
Extensive experiments across multiple video generation benchmarks demonstrate that DVD-Quant achieves an approximately 2$\times$ speedup over full-precision baselines on  advanced DiT models while maintaining visual fidelity. Notably, DVD-Quant is the first to enable W4A4 PTQ for Video DiTs without compromising video quality. Code and models will be released to facilitate future research.

Published as a conference paper at ICLR 2026
DVDDQUANT: DATA-FREE
VIDEO DIFFUSION TRANSFORMERS QUANTIZATION
Zhiteng Li Hanxuan -i Junyi Wu', Kai Liu', Haotong Qin', Linghe Kong',
Guihai Chen', Yulun Zhang', Xiaokang Yang"
Shanghai Jiao Tong University, "Zhejiang University, 'ETH Zürich
ABSTRACT
Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical de- ployment. While post-training quantization (PTQ) presents promising approach to accelerate Video DiT models, existing methods suffer from two critical limita- tions: (I) dependence on computation-Ne heavy and inflexible calibration procedures, and (2) considerable performance deterioration after quantization. To address these challenges, we propose DVDD-uuant, a novel Data-free quantization framework for Video DiTs. Our approach integrates three key innovations: (1) Bounded-init Grid Refinement (BGR) and (2) Auto-scaling Rotated Quantization (ARQ) for calibration data-free quantization
reduction, well (3) 5-Guided Bit Switching (5-GBS) for adaptive bit-width allocation. Extensive experiments across multiple video generation benchmarks demonstrate that DVD-Quant achieves approximately 2x speedup over full-precision baselines on advanced DiT mod- els while maintaining visual fidelity. Notably, DVD--uant is the first to enable W4A4 PTQ for Video DīTs without compromising video quality, Code and models will be released to facilitate future research.
BF16
Aug. Imaging Quallty = 80.62
Avg. Imaging Quality = 59.72
Avg. Imaging Quality 64.22
W4A
Avg. Imaging Quallty = 24.78
Avg. Imaging Quality 40.10
Avg. Imaging Quality 61. 82882
Figure DVD-Quant generates fighffideitty videos under both W4A6 DVD-Quant W4A4 settings, while baseline methods fail under low-bit activation quantization. DDVD-Quant remains effective even in such extreme scenarios.
MinMax
VIDIT-Q
and
INTRODUCTION
Recent advances in diffusion transformers (DiT's (Peebles & Xie, 2023) have revolutionized video generation (Singer al., 2023), enabling high-fidelity synthesis through iterative denoising processes. Innovations such as Sora's unified framework (OpenAI, 2024) and SkyReels-V V2's infinite-length generation (Chen al., 2025a) demonstrate enhanced controllability, while efficiency gains emerge from quantization (Zhao et al., 2025), sparse attention mechanisms (Xi et al., 2025; Zhang et al.,
Equaa contribution
Work done during interssip Shanghai Jiao University Corresponding Xiaokang Yang, xkyang @rsjaneejjm.ooo edu..cn

---

Published as a conference paper at ICLR 2026
2025) and cache techniques (Wu et al., 2025; Liu et al., 2024a). The emergence of large-scale models like Hunyuan Video (Kong et al., 2024) has further improved video generation quality with its causal 3D VAE architecture and full-attention mechanisms, achieving film-grade quality and physics-aware generation. These developments collectively demonstrate enhanced controllability in temporal coherence and resolution, though challenges persist in computational efficiency.
Prior work in diffusion model quantization has approached these challenges through two distinct paradigms. Quantization-Aware Training (QAT) requires full model fine-tuning but achieves supe- rior low-bit performance. Ter-DiT (Lu et al., 2024) introduces RMSNorm-enhanced adaLN modules for stable ternary training of Diffusion Transformers. In contrast, Post- Dost-Training Quantization (PTQ) methods (Li* et al., 2025; Chen et al., 2025b: Zhao et al., 2025) offer deployment-friend solutions without retraining. SVDQuant (Li et al., 2025) employs low-rank SVD decomposition to absorb outliers into 16-bit branches for 4-bit weight/activation quantization on FLUX.1 models (Labs, 2024). ViDiT-Q (Zhao et al., 2025) introduces a set of techniques for DiT-based generative models and achieves negligible performance drop under W8A8 quantization. These approaches demonstrate the trade-off between QAT's higher accuracy and PTQ's 's faster deployment in DiT compression.
Although PTQ offers a plug-and-play alternative, existing PTQ methods still face two critical limitations. First, the calibration balderation-based pre-scaling adopted by most video-specific quantization techniques (Zhao et al., 2025; Wu et al., 2024; Chen al., 2025b) not only demands extensive calibration time but also struggles to adapt to timestep-dependeee aggressive W4A4 quantization results in significant performance degradation (Fig. 1), with VBench metrics dropping by 27.5% % to 61.3%. Our analysis reveals three key insights to overcome these limitations: (i) Weights exhibit Gaussian-like distributions (Fig. 3), making fixed quantization ranges suboptimal for preserving critical parameters. (ii) Activation scales vary significantly across denoising timesteps, necessitating dynamic rather than static quantization strategies. (iii) Latent feature variations exist across diffenrent denoising timesteps, enabling the possibility of adaptive bit-width allocation during online inference.
scale variations in DiTs. Second,
Building on these insights, we propose DVD-Quant, a comprehensive quantization framework tailored for DiT's To approximate the Gaussian-like weight distribution, we propose an iterative grid refinement strategy, which progressively adjusts the quantization scale and zero-point starting from their bounded-searde initialization. For handling the timestep-varian activations scales, we combined online scaling with Hadamard rotation. Compared to calibration based pre-scaling, it can achieve notably lower quantization error. Furthermore, to adapt to the latent feature variations across denoising steps, we propose mechanism that automatically allocate appropriate bit-widths for activations at different timesteps. Our contributions can be summarized as follows:
We conduct systematic analysis of quantization challenges in large-scale Video DiTs, iden- tifying three key characteristics: Gaussian-like weight distributions, substantial activation scale discrepancics, and latent feature variations across denoising timesteps.
We propose Bounded-init Grid Refinement (BGR), novel weight quantization scheme for Gaussian-like distributions. By iteratively refining quantization grid upon tightening bounds, BGR significantly reduces the quantization error compared to fixed-range methods.
We propose Auto-scaling Rotated Quantization (ARQ), a calibration- free activation quantization method designed to address timestep-dependent scale variations. Leveraging online scaling with Hadamard rotation, ARQ maintains high model accuracy without requiring extensive calibration procedures.
We propose 5-Guided Bit Switching, an adaptive temporal wise mixed- anism that allocates activation bit-widths by leveraging timestep feature variations. This mechanism optimizes bit allocation while incurring negligible additional inference overhead.
mixed-precision precision mech-
2 RELATED WORKS
2.1 DIFFUSION TRANSFORMERS (DIT)
Diffusion Transformers (DiT') (Peebles & Xie, 2023) represent paradigm shift in generative modeling, replacing the traditional U-Net (Chitwan Saharia, 2022; Andreas Blattmann, 2023; Aditya Ramesh & Chen, 2022) backbone with transformer architectures. This breakthrough was attributed to the self-attention mechanism's ability to model long-range dependencies (Vaswani et al., 2023), proving especially beneficial for high-resolution synthesis.
2

---

Published as a conference paper at ICLR 2026
Bounded-init Grid Refinement
Auto-scaliin Rotated Quanfization
Delta-Guided Switching
min(W)
max(W)
T
T-49
LL+2
Daige
8
6
300
E1+3
25
Ec
E--
Time-Varing Activation Distribution
Bounded-init Gaussian-ike Dist.
E F-2
Quatoate Siee
Mo bis ibulos lons Coss@Ohisi Offiion finstas
Y
A 1.
W
A W
A, diag(s "" St, ...)
Refinement
Time-Dopendant Channel Scales
Bit Width by Indicator
Figure 2: Overview of DVD-Quant. Bounded-ini Grid Refinement and Auto-scaling Rotated Quantization data-free methods designed to reduce quantization errors for weights and activations, respectively. 5-Guided Bit Switching adaptively assigns bit-widths to different time steps.
The success of DiTs in image generation quickly extended to video synthesis. Latte (Ma al., 2025) pioneered this transition by adapting the DiT framework for temporal modeling, achieving unprece- dented coherence text-to-video generation. This was followed by Sora (OpenAI, 2024), which scaled DiTs to massive parameter counts and dataset sizes, demonstrating remarkable capabilities in long video generation with complex dynamics. Open-source implementations like Hunyuan- Video (Kong et al., 2024), Open-Sora (Zheng et al., 2024; Peng et al., 2025) and CogvideoX (Yang et al., 2024) further democratized these advancements, making them widely accessible.
Despite their impressive results, DiTs inherit fundamental challenges from both transformer architec- tures and diffusion processes. Attention's 's quadratic complexity is particularly problematic for video generation, where sequence length scales with spatial resolution and frame count. Moreover, the iterative nature of diffusion models requires multiple forward passes through the entire architecture (typically 50- 100 steps), with each step processing increasingly refined features. These limitations have driven efforts to boost DiTs' efficiency, such as caching (Wu et al., 2025; Liu et al., 2024a), and quantization (Li* al., 2025; Chen al., 2025b; Zhao al., 2025).
2.2 MODEL QUANTIZATION
Model quantization (Xiao et al., 2023; Li* al., 2025; Liu et al., 2024b; et al., 2023a) has emerged as a fundamental technique for compressing deep neural networks (Li et al., 2025b;e;d;a; 2023d; 2025c; 2023c;b) by converting full full-precision parameters into low-bit representations, significantly reducing memory footprint and computational costs. Post-Training Quantization (PTQ) (Li et al., 2025f; Frantar et al., 2023; Yan et al., 2025) has proven particularly effective as it compresses pre-trained models without requiring extensive retraining.
Significant progress has been made to address quantization challenges in transformers (Vaswani al., 2017). SmoothQuant (Xiao et al., 2023) introduces channel-wise scaling balance weight and activation quantization difficulty, while Quarot (Ashkboos et al., 2024) employs orthogonal matrix rotations to distribute values evenly across channels. For diffusion models, quantization presents unique challenges due to their time-dependent nature. Q-Diffusion (Li et al., 2023a) and PTQ4DM (Shang al., 2023) address this by collecting timestep-wise activation statistics to determine optimal quantization parameters. Q-DiT (Chen et al., 2025b) tackles channel-wise imbalance via customized quantization parameters. Subsequent work such as SVDQuant (Li* et al., 2025) introduces low-rank branches to handle activation outliers, enabling 4-bit quantization without performance degradation. For video generation, ViDiT-Q (Zhao et al., 2025) achieves lossless W8A8 quantization.
Despite these advances, text-to-video generation with quantized DiTs still suffers from two persistent limitations. First, offline calibration for activation pre-scaling in existing video quantization meth- ods (Zhao et al., 2025; Wu et al., 2024; Chen et al., 2025b) imposes heavy computational burdens and struggles to adapt to large activation vatiants. Second, pushing quantization precision below 8 bits for activations (W4A4) triggers significant quality degradation. Our framework overcomes these obstacles through three key innovations presented in Sec. 3.
3

---

Published as a conference paper at ICLR 2026
Algorithm Bounded-init Grid Refinement
Input: W E R n E N, c E I
Output: A R", 2" Z"
61 8, (max W) (o-mii(W))//
2: m- = 20, A = z 0
3: for = 1,2, ...: n do
4:
W clamp( W, min W) + 61, max (W) - 5.)
5:
A (0) (max W c) min W (2 - 1),
z
-mmin W ( 0
W clamp( W @ [ z 26 -1)
6:
7:
g(O) = I|W A
④ (W 2 ) =
8:
repeat
AU) (w 0 q
2(-1) W now 0 (W! (W)j-1)
z (-1 w(i-1) (111)) ))so
9:
z g() clamp W (j (--1) W Ø (1) 00,"
10:
w = clamp( W 2 A + z()) 2,2),,2²* 0,2" 1)
11:
gG) = W A6) (Wl)) 2 = +1
12:
13:
until g(- 1) E(i-2) <E
14:
if < -" then
15:
8x 80-1), A A0-1), 2" - 2(1-1)
16:
end if
end for
18:
return 2²
3 METHOD
Overview. As shown in Fig. Bounded-init Grid Refinement (BGR) iteratively refines quantization grid upon progressive tightening bounds, which preserves Gaussian-distributed DiT weights with minimal error (Sec. 3.1). Auto-scaling Rotated Quantization (ARQ) eliminates calibration overhead by jointly optimizing rotation and online scaling for activation outliers, which also offers greater flexibility for deeeste--deenddett scale variants (Sec. 3.2). Additionally, 8-Guided Bit Switching further allocates bit-widths adaptively across timesteps by tracking feature evolution (Sec. 3.3). The synergy of these techniques achieves lower bit- bit-ws (e.g., W4A4 and W4A6) with negligible quality degradation, overcoming limitations of prior quantization approaches.
3.1 BOUNDED-INIT GRID REFINEMENT (BGR)
For weight matrix W E R vanilla MinMax (per-channel) quantization (Jacob et al., 2018) directly adopts the extreme values for range calculation through Quan//Deuant processes:
Quant: W, clamp( W 2 A] + 2, 1),
W = A ④ ( - 2), where A = (max WW)minnWee (uin(W))/((2 - 1) E R", z - [mmin W) E -d denote the step-size and zero-point for each channel respectively.
DeQuant:
(2)
This approach is particularly problematic for muuusiann-disributeen weights, as fixed ranges amplify quantization errors in two ways: (i) by allocating excessive bins to outlier regions (only 0.3% of parameters), and (ii) by creating suboptimal interval spacing around the zero-mean concentration. To address this, we revisit the optimization objective for low-bit quantization, which is
A' z
arg migminn.. W A (W, =)]
(3)
Notably, the heuristic solution from MinMax algorithm provides an initialization for this optimization problem. As post-training quantization aims avoid computationally expensive process (e.g.. gradient descent), we seek a closed-form solution for the quantization objective. Assuming we have obtained the initial state of A, and W g. goal is refine quantization error. Refining them simultancously highly challenging, instead, we can refine one parameter while fixing the other two. For instance, first fix and W. then derive better solution
all of them to reduce
4

---

Published as a conference paper at ICLR 2026
single monduultoooltti
Adw
single, linesr
18-5
single blocks.linear2
BGR (Ang: 2.72c-00)
DOR (Avg: 3.58
BGR (Avg: 1. 11e--05
Quant Brroy
Minitax (Arg: 2.446-00 05)
Error Reduction: 88, .555%
Quertt Errror
MinHaax (Avg: 2.22e-05)
Error Reduction: 02%
Brree
2.3
MinMax (Avg: 1.41=005)
Raduction: 91.54%
1.5
11.0
200
220
240
260
280
500
320
200
220
240
250
280
300
320
200
220
240
250
230
500
320
Layer
Layer
Layer
double blocks. bng mip Ic2
double blocks, Ext mip fcl
double blocks. bxt attn glov
1.2
BGR (Avg: 7.46e-d6)
aon (Avg: 5.13c-08)
1.0
MinNax (Avg: 9.80
MinMax (Avg: 3.33
0.8
Errer Reduction:
troor Reduction: 86.04%
0.6
Quart
Quant
0.4
0.2
100 Layer Indax
Index
Index
Figure 3: Quantization enror comparison: MinMax (Jacob et al., 2018) vs. BGR across layers on
Hunyuan Video (Kong et al., 2024).
for A by solving the least squares problem for Eq. 3:
A = (Wa- z, W)row Ø (W₂- z, W z/ro
(4)
where now denotes the row-wise inner product, with details provided in the supplementary file.
Once A' is refined, we further refine z with A' and W, fixed. However, z restricted to integers and this discreteness precludes a closed-form derivative solution. We thus first relax the range of z and then apply rounding:
z' clamp( W W 2 A' 0, - 1).
(5)
After refining A' and zz, W, is also updated under the new quantization grid:
W clamp(L [ Ø A' +z', 0, 2 -)),,,
(6)
notably, this refinement of W, aligns with its definition in Eq. 1.
This sequential refinement procedure can be further extended to multiple rounds and combined with various initialization algorithms. Beyond MinMax, search bound methods (Liu et al., 2024b; Shao et al., 2023) also serve as valid initial strategies, which exclude outliers by progressively clipping full-precision weights:
W = clamp( W, min W) + 5t, max W) - 8m):
A = (max W.) - min W (2' - 1), 2, = - (min (W.) Ø Acl,
(8)
where 6, and on denote the shrink step sizes for lower and upper bounds, respectively. Since search bound algorithm provides better initial state for quantization step-size and zero-point, our grid refinement algorithm is built on this initialization (see Alg. 1).
Our experiments in Fig. 3 quantitatively show a substantial 86% reduction in quantization error, effec- tively preserving the critical parameters within high-density regions. These gains incur no additional inference-time computational overhead, making BGR more accurate than previous approaches.
3.2 AUTO-SCALING ROTATED QUANTIZATION (ARQ)
Activation quantization in Diffuusin Transformers (DiTs) poses two fundamental challenges: First, the dynamic variation of activations across denoising timesteps renders offline scaling factor calibra- tion ineffective. Second, traditional rotation- based methods incur substantial online computational overhead while risking the amplification of quantization errors via value redistribution.Naaall existing approaches fail to address these two challenges comprehensively.
Pre-scaling methods (Xiao et al., 2023; Zhao et al., 2025; Wu et al., 2024) introduce a channel-wise mask RC to alleviate quantization difficulty by transferring it from activations to weights:
Y = (Xiiag()))) ((diag(s)W)) = X - s; max( (x,,) / (= (9)
where is a hyperparameter that controls the degree of difficulty mitigation. Moreover, these methods typically require a calibration dataset to compute scaling factors offline. When applied to DiT models (which involve 50 denoising steps (Xiao et al., 2023; Zhao et al., 2025)), the calibration set often fails to capture the full dynamic range of activation distributions across all timesteps, introducing substantial quantization errors and potential bias.
5

---

Published as a conference paper at ICLR 2026
double_blon bllocks...txtmmlpp.....
single_blocks. bbc_blloocc222 27. linear2
15
Figure 4: Visualization of activation distribution before and after rotation.
Rotation-based methods (Ashkboos et al., 2024; Liu et al., 2025; Zhao et al., 2025) employ an orthogonal rotation matrix Q satisfying QQ" = -I and |Q] I. While multiplying by Q can effectively suppress large outliers, the rotation operation may inadvertently amplify certain activation values. This risks introducing new quantization errors in the transformed space, as shown in Fig. 4.
To address these challenges, we propose Auto-scaling Rotated Quantization (ARQ) for activation quantization. Our approach combines the strengths of both rotation-based and scaling- based methods while overcoming their individual limitations. We first incorporate Hadamard matrix multiplica- tion (Tseng et al., 2024) on both sides of activations and weights to preserve computational invariance: Y XW (H W). Hadamard rotation is computed by fast Hadamard transform, which introduces marginal overhead the inference latency. Subsequently, per-channel scaling factors computed online and applied directly to activations (rather than being transferred to weights):
X -(XHA-1) W BBGR(WH). Y XAWW
(10)
where X = XH, 8, || = max([ I), and diag (81)...,5e)) sc).
We analyze how ARQ addresses the massive outliers and channel-wist outliers inherent in DiT activations. For massive outliers, rotation redistributes these extreme values across multiple channels. For channel-wise outliers, the post-rotation online scaling reinforces channel-wis -wise consistency and mitigates the limitation of rotation shown in Fig. 4. Furthermore, our online scaling strategy eliminates reliance on offline calibration datasets. For DiT models, where activation distributions vary drastically across denoising steps, this runtime adaptation ensures optimal quantization parameters at every timestep, sustaining generation quality throughout the entire diffusion process.
In practice, ARQ is implemented under hardware-aligned constraint that ensures full compatibility with low-bit Tensor Cores. While eq. (10) presents a per-channel scaling formulation for theoretical completeness, our deployed kernels adopt block-wise scaling variant aligned with Tensor Core's GEMM granularity (more details in supplementary file). All latency and accuracy results in Sec. derived under this hardware-alli aligned,, block-wise ARQ configuration.
3.3 8-GUIDED BIT SWITCHING (5-GBS)
The denoising process in video DiTs exhibits non-uniform feature evolution across timesteps. Uni- form quantization wastes computational resources on these redundant timesteps while potentially compromising quality during critical transformation phases. Existing adaptive quantization methods either depend on expensive calibration analysis (Zhao et al., 2025) or adopt static timestep segmenta- tion (Wu al., 2024). Neither of them capture dynamic feature changes with respect different input prompts in video generation.
To further enhance video generation quality, we propose 8-Guided Bit Switching, tailored input characteristics. Prior work (Kahatapitiya et al., 2024; Liu et al., 2024a; Wu et al., 2025; Junhyuk So & Park, 2024; Ma al., 2024) found that denoising process of DiTs contains redundant timesteps, where latent feature changes are marginal. For these redundant timesteps, we can apply lower bit-width quantization across the entire model. In contrast, we use higher precision for critical timesteps with significant feature transformations. Specifically, our mixed-precision mechanism operates follows:
blow Σ (F. 5
[ Failla
B₁, bhigh
Ci(F,t)
(11)
ES ((F,))
||,-1||1
where tp denotes the most recent timestep at which cumulative error tracking is reset, and F represents the model's output features at timestep f. Our algorithm works by continuously monitoring normalized LI distances between successive outputs. When cumulative feature changes remain below the threshold 6, we apply bit quantization, indicating marginal feature evolution lower precision. Once the accumulated enror exceeds 8, we switch to brigh -bt bit quantization to preserve critical details, while simultaneously resetting the cumulative error counter to zero. This adaptive
tolerate

---

Published as a conference paper at ICLR 2026
Table 1: Performance comparison of various quantization methods on VBench (Huang et al., 2024).
Imaging Quality
Subject. Consist.
Dynamic Degree
Motion Smooth.
Method
(W/))
Quality
Consist.
Consist.
Cousist
Hunyuan Video (Kong al., 2024)
16/16
62.53
64.78
25.86
42.81
97.01
96.05
51.39
99.30
MinMax (Jacob et al., 2018)
4/8
59.44
60.62
25.78
36.41
97.61
95.83
52.78
98.89
SmoothQuam (Niao al., 2023)
60.50
64.47
25.56
28.85
97.72
96.29
51.39
99.05
Quarot (Astabboos al., 2024)
58.80
56.86
25.33
34.30
98.10
95.72
55.56
99.03
VIDiT-Q (Zhao al., 2025)
4/8
57.01
59.74
24.77
27.11
91.37
95.16
48.61
99.06
DVVD-quamn
MinMax UJcob al., 2018)
4/4
24.20
24.78
4.27
0.00
98.05
96.27
0.00
99.03
SmoothQuam (Niao 2023)
Quarot (Ashkboos et al., 2024)
4/4
44.85
54.30
17.33
0.94
97.69
92.64
87.5
92.22
ViDiT-Q (Zhan 2025)
414
45.36
40.10
19.66
7.85
97.19
97.29
0.00
99.43
2VD-Qquant
61.96
61.82
25.68
29.94
97.82
96.61
56.94
99.15
BF16
W4A*
W4A4
MinMax
Smotth Quant
Quarot
DVD-Quant VIDIT-Q
Figure 5: Visual comparisons between DVD--uant and BF16 baseline (Kong al., 2024), along- side with quantization methods: MinMax (Jacob et al., 2018), SmoothQuant (Xiao et al., 2023), Quarot (Ashkboos et al., 2024) and ViDiT-Q (Zhao et al., 2025) on Hunyuan Video. indicates 8 for baselines (W4A8) and 6 for DVD-Quant (W4A6, mixed-pre preedd-recision....
approach optimizes bit allocation while incurring negligible additional inference overhead, with threshold ê acting hyperparameter to balance performance and efficiency.
EXPERIMENTS
4.1 SETUP
Video Generation Evaluation Settings: We apply DVD--uant to Hunyuan Video (Kong et al., 2024) and generate videos using 50-step flow matching scheduler (Lipman et al., 2023) with em- bedded CFG (Ho Salimans, 2022) scale 6.0 and flow shift factor 7.0. We also apply DVD--uant to 1 (Wan et al., 2025), detailed configurations and results shown in the supplementary file. For evaluation, we adpot the benchmark suite provided by VBench (Huang et al., 2024) to comprehensively assess the quality of the generated videos. Specifically, across eight major dimensions (Ren et al., 2024) that reflect key aspects of video generation. These metrics designed align closely with human perception, ensuring reliable and standardized
evaluate the model
7

---

Published as a conference paper at ICLR 2026
Table 3: Ablation studies of BGR and ARQ under W4A6 and W4A4 quantizations.
Methods
Bit Width
Acsthetic Quality
Imaging Quality
Subject Consist.
Motion Smooth.
BG.
(W/A)
Consist.
BGR
ARQ
W4A6
58.15
58.68
98.04
98.49
98.21
W4A6
$7.85
57.72
98.23
97.86
98.10
W4A6
60.46
61.93
98.91
98.95
98.40
W4A4
53.95
$2.67
97.92
98.71
97.89
W4A4
43.26
58.31
95.36
96.08
97.35
W4A4
59.57
58.93
98.67
99.00
98.47
assessment of model performance. We compare DVD-Quant with MinMax (Jacob et al., 2018), SmoothQuant (Xiao et al., 2023), Quarot (Ashkboos et al., 2024), and the current SOTA video PTQ method ViDiT-Q (Zhao et al., 2025). All experiments are conducted on a single RTX4090 GPU.
4.2 MAIN RESULTS
VBench Quantitative Comparison. As shown in Tab. 1, DVD--uant achieves significant improvements over several state-of-the-an quantization methods under two configurations. (I) High Precision Regime: When weights are set to 4 bits, existing methods require 8-bit activations to maintain basic functionality, while our configuration demonstrates superior performance with 25% lower activation precision. Notably, our W4A6 mixee-precision configuration nearly matches the 6F16 Hunyuan Video model while significantly outperforming all W4A8 baselines across most metrics. (2) Low Precision Challenge: In the extremely challenging W4A4 setting where all baseline methods either fail completely
suffer severe degradation, DVD--uant maintains remarkable stability. For example, we achieve 58.94 Aesthetic Quality and 60.38 Imaging Quality, outperforming the best W4A4 baseline by + 10.53 in Aesthetic Quality. Besides, our method preserves temporal dynamics while maintaining high motion smoothness, whereas prior works either degrade visually or fail to generate coherent motion.
Qualitative Comparison. In W4A8 settings, while baseline methods preserve only coarse outlines, they still suffer from significant loss of fine details. As illustrated in Fig. 5, ViDiT-Q's W4A8 outputs show washed-out textures in elements like launch towers. In contrast, our method consistently recovers these subtle features, demonstrating robust performance. The advantage becomes more evident in more challenging W4A4 settings, where conventional methods fail completely, generating either incoherent noise patterns or severely distorted outputs. Our method maintains remarkable visual coherence with marginal quality degradation compared to the BF16 baseline.
4.3
ABLATION STUDY
BGR and ARQ. We validate the effectiveness of BGR and ARQ in Tab. 3. The full model with both BGR and ARQ achieves optimal performance across all metrics, demonstrating their synergistic effects. Removing only BGR leads to significant drops in VBench scores, indicating the critical role of progressively narrowing the weight quantization bound to preserve perceptual fidelity. Conversely, disabling ARQ components results in comparable declines, highlighting the importance of dynamically adjusting quantization scales during inference. Both BGR and ARQ maintain their effectiveness irrespective of 5-GBS.
Comparison with other mixed-precision strategies. In the experiment, set brow 4 bit and bnigh = 8 bit in Eq. 11. Our method achieves average bit width of across 50 timesteps. We compare the proposed 6-Guided Bit Switching with other fixed-patter mixed- precision strategies. Specifically, we compare with four representative approaches: (1) Static Temporal Partitioning (STP): allocate 4-bit precision for the first 25 timesteps and 8-bit precision for the subsequent 25 timesteps; (2) Inverse Temporal Partitioning (ITP): reverse the bitwidth allocation order of STP with 8-bit initially and 4-bit later; (3) Alternating Bitwidth Switching (ABS): dynamically alternate between 4-bit and 8-bit precision at each timestep; (4) Stochastic Bitwidth Allocation (SBA): randomly assign 4-bit or 8-bit precision to each timestep while maintaing the average bit number 25. As shown in Tab. 2, 5-Guided Bit Switching surpasses other static mixed- precision strategies in imaging quality. This error-driven dynamic decision mechanism effectively resolves the challenge of bit allocation in conventional mixed-preciste strategies.
Table 2: Performance comparison of Different mixed -xeedssrecision Strategies.
Method
5-GBS
STP
ITP
ABS
SBA
Imaging Quality
61.93
61.33
61.40
61.03
61.26
8

---

Published as a conference paper at ICLR 2026
Table 4: Performance and speedup of DVDD-uaat + TeaCache (Liu et al., 2024a).
Bit-width (WIA)
Aesthetic Quality
Imaging Quality
BG. Consist.
Subject. Consist.
Motion Smooth.
Method
Speedup
W4A8
61.39 58.78
62.96
98.24 98.43
98.73 98.68
98.75 98.92
4.01
W4A4
58.20
4.85x
Threshold of d-Guided Bit Switching. In our mixed- precision strategy, the threshold determines when to switch bit-width during inference. Empirically. lower 5 leads to more time steps using high-bit quantization, im- proving visual quality the cost of increased bit-width. This presents a trade-off between performance and re- source usage. Thus, conduct series of experiments with different thresholds. As shown in Fig. 6, our exper- iments demonstrate that 8 increases, the average bit- width decreases, leading to a gradual reduction in imaging quality. Notably, key advantage of 5-GBS over existing methods is its smooth and continuous bit-width adaptation: when 8 -> O, the model consistently uses W4A8; when 5 + oo, it reduces W4A4. Critically, for intermediate values, 8-GBS dynamically interpolates between 4-bit and 8-bit at the activation level, ensuring continuous precision transition based on input characteristics. This smooth adaptation is in stark contrast to conventional ap- proaches, which typically switch abruptly between discrete bit-widths, causing instability in output quality.
0.06
Avg. Imaging Quality =
6 0.08
Avg. Imaging Quality 61.93
6
0.12
Avg. Imaging Quality 61.43
6
Avg. Imaging Quality
Avg. Imaging Quality 61.00
Figure 6: Comparison of different 6.
4.4 MEMORY AND LATENCY
DVD-Quant achieves significant improvements in both memory efficiency and inference speed. As shown in Tab. 5, compared to the BF16 baseline (16/16 bit-width), our W4A8 configuration reduces memory usage by 3.68x while accelerating latency by 1.75x. More aggressive quantization (W4A4) maintains the same memory savings but further boosts latency speedup to 2.12x. No- tably, W4A6 means roughly half the denoising steps use W4A8 and the other half use W4A4. This allows direct use of the widely adopted W4A4 and W4A8 GEMM kernels, eliminating the need to design dedicated sexeedppecision kernels.
Table Memory saving and la- tency speedup of DVD -uaant on ManyuanVideo
Bit-width (W/A)
Memory Opt.
Latency Opt.
16/16
1.00x
1.00x
4/8 (ours)
3.68
1.75x
4/6 (ours)
3.68x
1.93x
4/4 (ours)
3.68x
2.12x
4.5 INTEGRATE WITH CACHE MECHANISM
To demonstrate the orthogonal compatibility of DVD DuDntuaant with other compression paradigms, we integrate it with one of the SOTA cache methods TeaCache (Liu al., 2024a). As illustrated Tab. DYD-Quant maintains its compression efficiency alongside TeaCache (Liu et al., 2024a), achieving additive speedup (up to 4.85x) without degrading key video generation metrics. This validates the practicality of DVD-Quant in resource constrained scenarios where multiple optimization dimensions could be jointly addressed.
CONCLUSION
In this work, we propose DVD--uant, a comprehensive quantization framework including three main innovations. The Bounded-init Grid Refinement (BGR) automatically adapts to weight dis- tributions through iterative grid refinement upon bound tightening, while Auto-scaling Rotated Quantization (ARQ) eliminates calibration dependencies through online rotation and scaling. The 5-Guided Bit Switching further optimizes computational efficiency through content-awarn precision allocation. Extensive experiments demonstrate that DVD-Quant achieves 2x speedup on advanced DiT models while maintaining visual quality, becoming the first framework to successfully enable W4A4 post-training quantization for video generation tasks.

---

Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This work is supported by the National Natural Science Foundation of China (62501386), Shanghai Municipal Science and Technology Major Project 2021SHZDZX0102), and the Fundamental Re- search Funds for the Central Universities. This work is also sponsored by CCF-Tencent Rhino-Bir Open Research Fund.
ETHICS STATEMENT
The research conducted in the paper conforms, in every respect, with the ICLR Code of Ethics.
REPRODUCIBILITY STATEMENT
We have provided implementation details in Sec. 4. We will also release all the code and models.
LLM USAGE STATEMENT
Large Language Models (LLMs) were used solely for polishing writing. They did not contribute to the research content or scientific findings this work.
REFERENCES
Alex Nichol Casey Chu Aditya Ramesh, Prafulla Dhariwal and Mark Chen. Hierarchical text- conditional image generation with clip latents. arXiv preprint 2rXiv:2204.06125, 2022.
Sumith Kulal Daniel Mendelevitch Maciej Kilian Dominik Lorenz Yam Levi Zion English Vikram Voleti Adam Letts et al Andreas Blattmann, Tim Dockhorn. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15122 2023.
Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman. Quarot: Outlier-free 4-bit inference in rotated llms. In NeurlPS, 2024.
Guibin Chen, Dixuan Lin, Jiangping Yang, Chunze Lin, Juncheng Zhu, Mingyuan Fan, Hao Zhang, Sheng Chen, Zheng Chen, Chengchen Ma, et al. Skyreels-v2: Infinite-length film generative model. arXiv preprint arXiv:2504. 0025504.33000 /3774, 2025a.
Lei Chen, Yuan Meng, Chen Tang, Xinzhu Ma, Jingyan Jiang, Xin Wang, Zhi Wang, and Wenwu Zhu. Q-dit: Accurate post-training quantization for diffusion transformers. In CVPR, 2025b.
Saurabh Saxena Lala Li Jay Whang Emily L Denton Kamyar Ghasemipour Raphael Gontijo Lopes Chitwan Saharia, William Chan. Photorealistic text-to-
Burcu Karagol Ayan Tim Salimans image diffusion models with deep language understanding. In NIPS, 2022.
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. GPTQ: Accurate post-training compression for generative pretrained transformers. In ICLR, 2023.
Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 2022.
Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, Yaohui Wang, Xinyuan Chen, Limin Wang. Dahua Lin, Yu Qiao, and Ziwei Liu. VBench: Comprehensive benchmark suite for video generative models. In CVPR, 2024.
Benoit Jacob, Skinmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for efficient integer-arithmetic-nlly inference. In CVPR, 2018.
10

---

Published as a conference paper at ICLR 2026
Jungwon Lee Junhyuk So and Eunhyeok Park. Frdiff: Feature reuse for universal training-free acceleration diffusion models. ECCV, 2024.
Kumara Kahatapitiya, Haozhe Liu, Sen He, Ding Liu, Menglin Jia, Chenyang Zhang, Michael Ryoo, and Tian Xie. Adaptive caching for faster video generation with diffusion transformers. arXiv preprint arXiv:2411.02397, 2024.
Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXin:2412.03661, 2024.
Black Forest Labs. Flux. https://gitiiii woleee/oooocaccc-eeete-aalla////
2024.
Muyang Li Yujum Lin", Zhekai Zhang*. Tianle Cai, Xiuyu Li, Junxian Guo, Enze Xie, Chenlin Meng, Jun Zhu, and Song Han. Svdquant: Absorbing outliers by low-rank components for 4-bit diffusion models. In ICLR, 2025.
Xiuyu Li, Yijiang Liu, Long Lian, Huanrui Yang, Zhen Dong, Daniel Kang, Shanghang Zhang, and Kurt Keutzer. Q-diffusion: Quantizing diffusion models. In ICCV, 2023a.
Yuchen Li, Haoyi Xiong, Linghe Kong, Qingzhong Wang, Shuaiqiang Wang, Guihai Chen, and Dawei Yin. S2phere: Semi-supervised pre-training for web search over heterogeneous learning to rank data. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023b.
Yuchen Li, Haoyi Xiong, Linghe Kong, Rui Zhang, Fanqin Xu, Guihai Chen, and Minglu Li. Mhrr: Moocs recommender service with meta hierarchical reinforced ranking, IEEE Transactions on Services Computing, 2023c.
Yuchen Li, Haoyi Xiong, Qingzhong Wang. Linghe Kong, Hao Liu, Haifang Li, Jiang Bian, Shuaiqiang Wang, Guihai Chen, Dejing Dou, et al. Coltr: Semi supervisd learning to rank with co-training and wwerpaarameterization for web search. IEEE Transactions on Knowledge and Data Engineering, 2023d.
Yuchen Li, Hengyi Cai, Rui Kong, Xinran Chen, Jiamin Chen, Jun Yang, Haojie Zhang, Jiayi Li, Jiayi Wu, Yiqun Chen, et al. Towards ai search paradigm. arXiv preprint arXiv:2506. 2025a.
Yuchen Li, Zhonghao Lyu. Yongqi Zhang, Hao Zhang, Tianhao Peng, Haoyi Xiong, Shuaiqiang Wang, Linghe Kong, Guihai Chen, and Dawei Yin. SQ3) prank: Towards satisfaction-oriente learning rank with semi-supervised pre-training. IEEE Transactions Knowledge and Data Engineering, 2025b.
Yuchen Li, Haoyi Xiong, Yongqi Zhang, Jiang Bian, Tianhao Peng. Xuhong Li, Shuaiqiang Wang, Linghe Kong, and Dawei Yin. Rankelectra: Semi-supervised pre-training of learning-to-rani electra for web-scale search. In Proceedings of the 31st ACM SIGKDD Conference Knowledge Discovery and Data Mining 1, 2025c.
Yuchen Li, Hao Zhang, Yongqi Zhang, Hengyi Cai, Mingxin Cai, Shuaiqiang Wang, Haoyi Xiong, Linghe Kong, Dawei Yin, and Lei Chen. Rankexpert: mixture of textual-and-behan -nd-vviviioaaal experts for multi-objective learning-to-ee -torin- in web search. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2, 2025d.
Yuchen Li, Hao Zhang. Yongqi Zhang, Xinyu Ma, Wenwen Ye, Naifei Song, Shuaiqiang Wang, Haoyi Xiong, Dawei Yin, and Lei Chen. M 2 oerank: Multi-objective mixture-of-expeer enhanced ranking for satisfaction-orieen orientte web search. 2025 IEEE 41st International Conference on Data Engineering (ICDE), 2025e.
Zhiteng Li, Xianglong Yan, Tianao Zhang, Haotong Qin, Dong Xie, Jiang Tian, zhongchao shi, Linghe Kong, Yulun Zhang, and Xiaokang Yang. Arb-lIm: Alternating refined binarizations for large language models. ICLR, 2025f.
Yaron Lipman, Ricky TQ Chen, Heli Ben- Cen-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. In ICLR, 2023.
11

---

Published as a conference paper at ICLR 2026
Feng Liu, Shiwei Zhang, Xiaofeng Wang, Yujie Wei, Haonan Qiu, Yuzhong Zhao, Yingya Zhang, Qixiang Ye, and Fang Wan. Timestep embedding tells: It's time to cache for video diffusion model. arXiv preprint ar arX11:2411.1100, 2024a.
Kai Liu, Haotong Qin, Yong Guo, Xin Yuan, Linghe Kong*, Guihai Chen, and Yulun Zhang. 2dquant: Low-bit post-training quantization for image super-resolution. In NeurIPS, 2024b.
Zechun Liu, Changsheng Zhao, Igor Fedorov, Bilge Soran, Dhruv Choudhary, Raghuraman Krish- namoorthi, Vikas Chandra, Yuandong Tian, and Tijmen Blankevoort. Spinquant: Llm quantization with learned rotations. In ICLR, 2025.
Xudong Lu, Aojun Zhou, Ziyi Lin, Qi Liu, Yuhui Xu, Renrui Zhang, Yafei Wen, Shuai Ren, Peng Gao, Junchi Yan, et al. Terdit: Ternary diffusion models with transformers. arXiv preprint arXiv:2405. 2024.
Xin Ma. Yaohui Wang, Xinyuan Chen, Gengyun Jia, Ziwei Liu, Yuan-Fang Li, Cunjian Chen, and Yu Qiao. Latte: Latent diffusion transformer for video generation. TMLR, 2025.
Xinyin Ma, Gongfan Fang, and Xinehao Wang. Deepcache: Accelerating diffusion models for free. In The IEEE/CVE Conference on Computer Vision and Pattern Recognition, 2024.
OpenAl. Sora: Creating video from text. online, 2024. Accessed: 2024-02-15.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, 2023.
Xiangyu Peng. Zangwei Zheng, Chenhui Shen, Tom Young, Xinying Guo, Binluo Wang. Hang Xu, Hongxin Liu, Mingyan Jiang. Wenjun Li, Yuhui Wang, Anbang Ye, Gang Ren, Qianran Ma, Wanying Liang, Xiang Lian, Xiwen Wu, Yuting Zhong, Zhuangyan Li, Chaoyu Gong, Guojun Lei, Leijun Cheng, Limin Zhang, Minghao Li, Ruijie Zhang, Silan Hu, Shijie Huang, Xiaokang Wang, Yuanheng Zhao, Yuqi Wang, Ziang Wei, and Yang You. Open-sora 2.0: Training commercial-level video generation M model in 200k. arXiv preprint arXiv:2503.09642 2025.
Weiming Ren, Harry Yang, Ge Zhang, Cong Wei, Xinrun Du, Stephen Huang, and Wenhu Chen. Consisti2v: Enhancing visual consistency for image-to-vides generation. In 7MLRR 2024.
Yuzhang Shang, Zhihang Yuan, Bin Xie, Bingzhe Wu, and Yan Yan. Post-training quantization diffusion models. In CVPR, 2023.
Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo. Omniquant: Onnidirectionally calibrated quantization for large language models. arXiv preprint arXiv:2308.13. 13137, 2023.
Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. In ICLR, 2023.
Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, and Christopher De Sa. QulP#: Even better IIm quantization with hadamard incoherence and lattice codebooks. In ICML, 2024.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing systems, 2017.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. arXiv preprint arXiv: 17011737667.22. 2023.
Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wer Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. Wan: Open and advanced large-scale video generative models. arXiv preprint 2003:22503. 2025.
Junyi Wu, Haoxuan Wang. Yuzhang Shang, Mubarak Shah, and Yan Yan. Pty4dit: Post-training quantization for diffusion transformers. In NeurIPS, 2024.
12

---

Published as a conference paper at ICLR 2026
Junyi Wu, Zhiteng Li. Zheng Hui, Yulun Zhang, Linghe Kong, and Xiaokang Yang. Quantcache: Adaptive importance- gerirdeeeegg guided quantization with hierarchical latent and layer caching for video generation. arXiv preprint mrXiv:2503.0644. 2025.
Haocheng Xi, Shuo Yang, Yilong Zhao, Chenfeng Xu, Muyang Li, Xiuyu Li, Yujun Lin, Han Cai, Jintao Zhang, Dacheng Li, et al. Sparse videogen: Accelerating video diffusion transformers with seatial-temporal sparsity, arXiv preprint arXiv:2502.01776, 2025.
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. SmoothQuant: Accurate and efficient post-training quantization for large language models. In /CML, 2023.
Xianglong Yan, Tianao Zhang, Zhiteng Li, and Yulun Zhang. Progressive binarization with semi- structured pruning for llms. arXiv preprint arXiv:2502.01 705, 2025.
Zhuoyi Yang. Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072, 2024.
Jintao Zhang, Jia Wei, Pengle Zhang, Jun Zhu, and Jianfei Chen. Sageattention: Accurate 8-bit attention for plug-and-play inference acceleration. In ICI.R, 2025.
Tranchen Zhao, Tongcheng Fang, Enshu Liu, Wan Rui, Widyadewi Soedarmadji, Shiyao Li, Zinan Lin, Guohao Dai. Shengen Yan, Huazhong Yang, Xuefei Ning, and Yu Wang. Vidit-q: Efficient and accurate quantization of diffusion transformers for image and video generation. ICLR, 2025.
Zangwei Zheng, Xiangyu Peng, Tianji Yang, Chenhui Shen, Shenggui Li, Hongxin Liu, Yukun Zhou, Tianyi Li, and Yang You. Open-sora: Democratizing efficient video production for all. arXiv preprint 2rXiv:241..204000 2024.
13


Reviews:
[{"review_id": "L3XVBa5R40", "reviewer": "Reviewer_2o9A", "summary": "This work proposes DVD-Quant which is a deployment-friendly quantization framework for Diffusion Transformers (DiTs). The paper identifies three key properties of large-scale Video DiTs—Gaussian-like weight distributions, substantial activation scale discrepancies across denoising timesteps, and timestep-varying latent features—and introduces three corresponding techniques: Bounded-init Grid Refinement (BGR) for weights, which iteratively refines the quantization scale and zero-point with tightening bounds to better fit Gaussian-like distributions; Auto-scaling Rotated Quantization (ARQ) for activations, a calibration-free method combining online scaling with Hadamard rotation to handle timestep-variant scales and reduce quantization error; and δ-Guided Bit Switching, a temporal mixed-precision mechanism that adaptively assigns activation bit-widths per timestep with negligible inference overhead. Together, these components narrow the accuracy–deployment gap in DiT compression, enabling robust low-bit quantization without costly calibration or retraining.", "questions": "Please check Weaknesses", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- A systematic analysis reveals three key insights, motivating the following solutions to quantization. These finds are valuable for the future research.\n- Strong low-bit performance without retraining is achieved\n- Broad applicability to video DiTs: Designed around Video DiT characteristics (e.g., temporal variations), making it more suitable than generic PTQ baselines for large-scale video generation models.\n- Modular and complementary components: Each module targets a distinct bottleneck (weights, activations, temporal allocation), allowing flexible adoption and integration with existing pipelines.", "weaknesses": "Although the better accuracy–deployability trade-off is achieved, I remain to wonder\n- Generalization beyond Video DiTs (Hunyuan): The framework leverages timestep dynamics typical of Video DiTs. It is unclear how well it transfers to other generative models, like Wanx or even image generator.\n- Effect of various details: Choices like rotation block size, scaling granularity (per-channel vs per-tensor), and quantization granularity (weight group size) can materially affect outcomes; the method may need careful tuning per model size and dataset to reach reported gains. More analysis on those hyperparameters will help the audience follow.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4HpGwKlEFW", "reviewer": "Reviewer_Eyzg", "summary": "This paper focuses on post-training quantization (PTQ) for text-to-video generation models and proposes three key techniques **weight refinement**, **rotation-aware transformation**, and **big switching area** to enhance quantization performance.", "questions": "1. Could you provide additional experimental results across multiple benchmarks and different text-to-video models to verify the generality of your method?\n2. Could you offer more details about the latency measurements (e.g., batch size, sequence length, and hardware configuration)?\n3. I am curious about the runtime efficiency of the proposed online rotation and scaling operations. Could you include more details or analysis to clarify how these affect the overall speedup and computational cost?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The motivation behind the proposed BGR method is clear and intuitively appealing.\n2. The paper is well written, with clear explanations and easy-to-follow reasoning.", "weaknesses": "1. While rotation techniques have been extensively applied in LLM quantization, the paper only discusses QuaRot and lacks a broader comparison or discussion with several relevant works such as **DuQuant**, **RoSTE**, and **ResQ**.\n2. The proposed auto-scaling rotation mechanism appears similar to the approach adopted in DuQuant, which combines SmoothQuant with rotation—this overlap should be clarified.\n3. The paper lacks comparisons with several state-of-the-art baselines, such as **SVDQuant**.\n4. The evaluation is limited to a single benchmark and one model, which weakens the generalization claims.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "taHYsJEFb6", "reviewer": "Reviewer_p4Dz", "summary": "This paper proposes DVD-Quant, a novel data-free post-training quantization (PTQ) framework designed to reduce the computational and memory costs of Video Diffusion Transformers (Video DiTs). The method introduces three core techniques: 1) Bounded-init Grid Refinement (BGR) for more accurately quantizing Gaussian-like weight distributions, 2) Auto-scaling Rotated Quantization (ARQ) which uses online Hadamard rotation and scaling to handle activation outliers without a calibration dataset, and 3) δ-Guided Bit Switching (δ-GBS) which dynamically allocates higher or lower bit-widths to activations at different denoising timesteps based on feature change. The authors demonstrate that DVD-Quant achieves a significant speedup (up to ~2x, and 4.85x when combined with caching) and is the first method to successfully enable W4A4 (4-bit weights and activations) quantization for Video DiTs without catastrophic failure, maintaining performance close to the full-precision baseline.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Successfully enabling W4A4 PTQ for complex Video DiT models is a notable achievement, as this extreme quantization level typically causes existing methods to fail completely.\n\n2. Comprehensive and Synergistic Approach: The three proposed components (BGR, ARQ, δ-GBS) address distinct and well-motivated challenges (weight distribution, activation outliers, temporal redundancy) and are shown to work effectively together.\n\n3. Strong Empirical Validation: The paper includes extensive experiments on established models (HunyuanVideo) and benchmarks (VBench), showing clear quantitative and qualitative superiority over several strong baselines across multiple bit-width settings. The ablation studies effectively demonstrate the contribution of each component.", "weaknesses": "1. Hyperparameter Sensitivity: The performance of the adaptive δ-GBS mechanism depends on a threshold δ. While mentioned, the paper does not deeply explore the sensitivity of the results to this value or provide a robust method for selecting it across different models or tasks.\n\n2. Limited Model Scope: While tested on HunyuanVideo and briefly on Wan2.1, it's unclear how generalizable the method is to the wider family of DiT-based models (e.g., Latte, Sora's architecture) or other diffusion tasks (e.g., image generation). The claim of being \"the first\" is strong but might be limited to the specific models tested.\n\n3. Computational Overhead of ARQ: Although the Hadamard transform is described as having \"marginal overhead,\" this is not quantified. For a method focused on acceleration, a more detailed analysis of the latency/throughput trade-off introduced by the online rotation and scaling would be beneficial.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1Ks5KC0Opq", "reviewer": "Reviewer_ZFfE", "summary": "The paper focuses on post-training calibration-free quantization of diffusion models under low-bit width settings (w4a4, w4a6, w4a8). The paper proposes bounded-init grid refinement to minimize the **reconstruction error on weight matrices** using **asymmetric quantization**. The paper proposes auto-scale rotated quantization to deal with the outlier and high variance of the activation. The method first rotate weight and activation using hadamard matrix, and rescale activation on the fly during inference, **without fusing the diagonal scaling factor into weight matrices**. Finally, the paper proposes mixed precision quantization for activation, by using higher bitwidth when error accumulates to a certain threshold. Experiments are conducted on several video diffusion models and benchmarks, comparing the proposed method against standard PTQ baselines. The results claim improved perceptual quality and reduced quantization degradation at extremely low bitwidths, demonstrating that the proposed calibration-free approach can maintain generation fidelity.", "questions": "Already stated in weakness. I would list the questions here again for completeness.\n\n1. **Asymmetric Quantization Implementation**: How is asymmetric quantization implemented in practice? Specifically, what is the computation flow and corresponding kernel? What is the overhead compared with symmetric quantization?\n2. **Tensor Core Utilization under ARQ**: Given that the proposed ARQ introduces a diagonal scaling factor between activation and weight, how can the method leverage low-bit Tensor Cores for acceleration? Please give a detailed formulation.\n3. **Use of “Widely Adopted Kernels”**: In Section 4.4, the author claim that the method allows direct use of “widely adopted W4A4 and W4A8 GEMM kernels.” Could you specify which existing kernels (and how these kernels are modified to) support your asymmetric quantization and ARQ design?\n4. **Speedup Inconsistency**: The reported latency results in Table 5 show w4a4 being the fastest, followed by w4a6 and then w4a8. On RTX 4090 (on which all the experiment are conducted claimed by the author), which lacks 6-bit Tensor Core support, w4a6 should require additional dequantization compared with w4a8, implying slower execution. Could you explain this discrepancy and clarify how these results were obtained?\n\nIf the author can provide satisfactory explanation to the problems above, I would raise my score.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 1, "strengths": "1. **Calibration-free quantization.** The proposed method does not require any calibration data to do quantization, which reduces deployment time and makes the method more convenient to use.\n2. **Handling of activation/weight distribution.** The paper observes the distribution of weight, and tries to use optimization based quantization method instead of vannila max-scaling based method on weight quantization. For activations, the paper observes their high variance and uses online-scaling to increase quantization accuracy.\n3. **Detailed comparison on quantization error with baselines.** The paper conducts experiments on a great number of model, benchmarks and provided sufficient ablation study to support its claim.", "weaknesses": "**The proposed method is not possible to achieve real world end-to-end acceleration.** Although Table 5 in the main paper shows end-to-end acceleration on w4a4, w4a6 and w4a8 setting on RTX4090 of HuyuanVideo, **I, as an experienced kernel programmer with rich experience in quantization, doubt its soundness**. Detail is as follows:\n\n1. **BGR introduces asymmetric quantization.** This is a small problem. Compared with symmetric quantization, asymmetric quantization requires calculating the token sum of activation before GEMM, and use an epilogue to include the contribution of zero point. This will incur non-negligible overhead if not dealt carefully. The author are encouraged to clarify the detailed implmentation of asymmetric quantization and how the quantized GEMM functions under this asymmetric configuration.\n2. **ARQ makes it not possible to use low-bit Tensor Core.** This is a big problem. As demonstrated in section 3.2, the author uses\n$$\n\\widehat{\\mathbf{X}}=\\mathcal{Q}\\left(\\mathbf{X} \\mathbf{H} \\mathbf{\\Lambda}^{-\\mathbf{1}}\\right), \\quad \\widehat{\\mathbf{W}}=\\mathcal{B} \\mathcal{G} \\mathcal{R}(\\mathbf{W} \\mathbf{H}), \\quad \\mathbf{Y}=\\widehat{\\mathbf{X}} \\mathbf{\\Lambda} \\widehat{\\mathbf{W}}^{\\top}\n$$\nto calculate activation-weight GEMM, where $\\mathbf{\\Lambda}$ is the online calculated scaling factor. With a diagonal scaling factor between activation and weight, I cannot think how low bit tensor core can be used to get acceleration. As stated in SmoothQuant[1], this is **the per-channel activation quantization that is not compatible with INT GEMM kernel**. **So I am very curious how the author solves this problem** and get the latency reduction result (in particular how to leverage the low bit tensor core to accelerate GEMM).\n3. **Claim of using widely adopted kernels**. In section 4.4, the author claims \"This allows direct use of the widely adopted W4A4 and W4A8 GEMM kernels, eliminating the need to design dedicated mixed-precision kernels\". I personally do not know any open-source kernel that can realize the proposed ARQ method and asymmetric weight quantization (I personally do not think this is realizable with improved efficiency), so I would ask the author how (and which) common kernels can be directly adopted.\n4. **Weird speed result.** In section 4.4,  the author report the speed up of w4a4, w4a6, w4a8, with w4a4 being fastest, followed by w4a6 and finally w4a8. w4a6 and w4a8 should in theory leverage the INT8 tensor core (since there is not 6 bit tensor core on RTX4090 on which the author does all experiment). However, w4a6 requires dequantizing both weight and activation to 8bit, while w4a8 only needs to dequantize the weight. So **w4a6 should in principle be slower than w4a8**, and I wonder why the result presented paper shows the opposite conclusion.\n\nIn summary, while the paper presents an interesting set of ideas for data-free quantization, the core technical claims regarding efficiency and deployability are highly questionable, which significantly limits its practical value. \n\n[1] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models. Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, Song Han.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)