QVGen: Pushing the Limit of Quantized Video Generative Models
Authors: Yushi Huang
Ruihao Gong
Jing Liu
Yifu Ding
Chengtao Lv
Haotong Qin
Jun Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: quantization-aware training
video diffusion models
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A quantization-aware training framework that enhances the performance and efficiency of video diffusion models under low-bit quantization, achieving high-quality video synthesis comparable to full-precision models.

Abstract: Video diffusion models (DMs) have enabled high-quality video synthesis. Yet, their substantial computational and memory demands pose serious challenges to real-world deployment, even on high-end GPUs. As a commonly adopted solution, quantization has proven notable success in reducing cost for image DMs, while its direct application to video DMs remains ineffective.  In this paper, we present *QVGen*, a novel quantization-aware training (QAT) framework tailored for high-performance and inference-efficient video DMs under extremely low-bit quantization (*e.g.*, $4$-bit or below). We begin with a theoretical analysis demonstrating that reducing the gradient norm is essential to facilitate convergence for QAT. To this end, we introduce auxiliary modules ($\Phi$) to mitigate large quantization errors, leading to significantly enhanced convergence. To eliminate the inference overhead of $\Phi$, we propose a *rank-decay* strategy that progressively eliminates $\Phi$. Specifically, we repeatedly employ singular value decomposition (SVD) and a proposed rank-based regularization $\mathbf{\gamma}$ to identify and decay low-contributing components. This strategy retains performance while zeroing out additional inference overhead. Extensive experiments across $4$ state-of-the-art (SOTA) video DMs, with parameter sizes ranging from $1.3\text{B}\sim14\text{B}$, show that QVGen is *the first* to reach full-precision comparable quality under $4$-bit settings. Moreover, it significantly outperforms existing methods. For instance, our $3$-bit CogVideoX-2B achieves improvements of $+25.28$ in Dynamic Degree and $+8.43$ in Scene Consistency on VBench. Code and models are available at https://github.com/ModelTC/QVGen.

Published as a conference paper at ICLR 2026
QVGEN: PUSHING THE LIMIT OF QUANTIZED VIDEO GENERATIVE MODELS
Yushi Huangl.3+ Ruihao Gong2.31 Jing Liu" Haotong Qin Jun Zhang't
Yifu Ding
Chengtao Lv 5x
'Hong Kong University of Science and Technology BBehhang University "SenseTime Research
"Monash University $Nanyang Technological University 'ETH Zürich
[huangyushil, gongruihao, tvchengtaa/@ssensetie com Saujing-90@outt Look. yi Fuding@buaa. seriing@buaa.edu.co edu. on haotong baotong..gin@pll. htt.pp...eeeeee........o ch eejzhang@ust.hir .kk
ABSTRACT
Video diffusion models (DMs) have enabled high-quality video synthesis. Yet, their substantial computational and memory demands pose serious challenges to real-world deployment, even on high-end GPUs. As a commonly adopted solu- tion, quantization has achieved notable successes in reducing cost for image DMs, while its direct application video DMs remains ineffective. In this paper, we present QVGen, novel quantization-awwan training (QAT) framework tailored for high-performance and inference-efficien video DMs under extremely low-bit quan- tization (i.e., 4-bit below). We begin with theoretical analysis demonstrating that reducing the gradient norm is essential to facilitate convergence for QAT. To this end, introduce auxiliary modules (Φ) to mitigate large quantization errors, leading to significantly enhanced convergence. To eliminate the inference overhead of Φ, propose rank-decay strategy that progressively eliminates क. Specifi- cally, we repeatedly employ singular value decomposition (SVD) and proposed rank-based regularization Y identify and decay low-contributing components. This strategy retains performance while zeroing out additional inference overhead. Extensive experiments across state-of- of-the-art art (SOTA) video DMs, with parameter sizes ranging from 1.3B~14B, show that QVGen is the first to reach full -ull-precision comparable quality under 4-bit settings. Moreover, significantly outperforms existing methods. For instance, our 3-bit Cog VigeoXe0222B achieves improvements +25.28 in Dynamic Degree and +8.43 in Scene Consistency on VBench. Code and models are available at https: / gi tittt thub. com commModee Mte//Modeel1 Mww.eecc///eeeeee QVGen.
INTRODUCTION
Recently, advancements in artificial intelligence- geeeiigencceggeertteed content (AIGC) have led to significant breakthroughs in text (Touvron et al., 2023; DeepSeek-Al et al., 2025), image (Xie et al., 2025; Labs, 2024), and video synthesis (Wan'eea et al., 2025; Kong et al., 2025). The development of video generative models, driven by the powerful diffusion transformer (DiT) (Peebles & Xie, 2023) architecture, has been particularly notable. Leading video diffusion models (DMs), such as closed-source OpenAl Sora (OpenAI, 2024) and Kling (Kuaishou, 2024), and open-source Wan TWa et al., 2025) and Cog VideoX (Yang et al., 2025), can successfully model motion dynamics, semantic scenes, etc. Despite their impressive performance, these models demand high computational resources and substantial peak memory, especially when generating long videos at high resolution. For example, Wan 14B requires more than 30 minutes and 50GB of GPU memory to generate a 10-second 720p resolution video clip is infeasible on most customer- grade PCs, let alone eesource-constraiigg edge devices, As a result, their practical applications across various platforms face considerable challenges.
single H100 GPU. Even worse, deploying such models
In light of these problems, model quantization, which maps high-precision (e.g., FP1 6/BF1 6) data to low- preciiiioonnn (e.g., INT8/INT4) formats, stands out as compelling solution. For instance,
Work done during internships at SenseTime Research. "ooresponniig authors.

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Published as a conference paper at ICLR 2026
6P16
17444 QGGn (Ours)
(c) #444 EllicienDM (Hie et el. 2024)
(d) w4a4 Q-DM (Li et al. 20236)
W4A- LSQ (Esser al., 2020b)
(1) #436 SVDQuant (Li al., 2025)
Test prompt: "In hauniigg backdrp of is muudged with azh, a stient restument the choos ground het. Her eyes giiitening with mix of sorrow and resillence, capouring the row emotion of world that has lost intocence the rasgees confice."
and crambled walls
desanation, polgnast close-up young Her
Figure Comparison of samples generated by CogVin VidcoX-222 -2B (Yang al., 2025) with fixed random seed. "WarAy" denotes "x" per-channel weight and "y" -bit per-token activation quantization. Our approach far outperforms previous PTQ (i.e., (I) and QAT (i.e., (c)-(e)) methods. To be noted, methods (c)-(f) have achieved noticeable performance for 4-bit image DMs. More visual results can be found in Sec. P.
employing 4-bit models with fast kernel implementation can achieve significant 3x speedup ratio with about 4x model size reduction compared with floating- point models on NVIDIA RTX4090 GPUs (Li et al., 2025). However, quantizing video DMs is more challenging than quantizing image DMs, and it has not received adequate attention. As shown in Fig. applying prior nigh-performing approaches to quantize a video DM into ultra-low bits (< 4 bits) is ineffective. In contrast to post- training quantization (PTQ), quantization aware training (QAT) can obtain superior performance through training quantized weights. Nevertheless, it still leads to severe video quality degradation, as demonstrated by Fig. (a) vs. (c)-(e). This highlights the need for an improved QAT framework to preserve video DMs exceptional performance under 4-bit lower quantization.
In this work, we present a novel QAT framework, termed QVGen. It aims to improve the convergence without additional inference costs of low-bit Quantized DMs for Video Generation.
Specifically, we first provide theoretical analysis showing that minimizing the gradient norm l| the key to improving the convergence of QAT for video DMs. Motivated by this finding, we introduce auxiliary modules & for the quantized video DM to mitigate quantization errors. These modules effectively help narrow the discrepancy between the discrete quantized and full-precision models, leading to stable optimization and largely reduced ||ge|12. The quantized DM thus achieves better convergence. Our observation also implies that the significant performance drops (Fig. 1) of the existing SOTA QAT method (Li al., 2023b) may result from its high ||ge||2 (Fig. 3).
Moreover, to adopt + for improving QAT while avoiding its substantial inference overhead, we progressively remove & during training. Upon further analysis, have found that the amount of small singular values in W4 (the weight of ④) increases throughout the training process. This indicates that the quantity of low-contributing components in Wa. which are related small singular values (Zhang et al., 2015; Yang et al., 2020), grows during QAT. As result, an increasing number of these components can be removed with minimal impact on training. Leveraging this insight, we introduce rank-decay strategy to progressively shrink W.. To be more specific, singular value decomposition (SVD) first applied to recognize V''s low-impact components. Then, rank-based regularization is utilized to gradually decay these components to Ø. Such processes (i.e., decompose and then decay) are repeated until Wa is fully eliminated, which also means that removed. In terms of results, this strategy incurs minimal performance impact while getting rid of the extra inference overhead.
To summarize, contributions
follows:
We introduce a general-purge purpose QAT paradigm, called QVGen. To our knowledge, this is the first QAT method for video generation and achieves effective 3-bit and 4-bit quantization.
To optimize extremely low-bit QAT, we enhance a quantized DM with auxiliary modules (#) to reduce the gradient norm. Our theoretical and empirical analysis validates the effectiveness of this
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method in improving convergence.
To eliminate the significant inference overhead introduced by Q, we propose a rank-decay strategy that progressively shrinks Φ. It iteratively performs SVD and applies rank-based regularization y to obtain and decay low-impact components of Φ, respectively. As result, this method incurs minimal impact performance.
Extensive experiments across advancing CogVideoX and Wan families demonstrate the SOTA performance of QVGen. Notably, our W4A4 model is the first show full-precision comparable performance. In addition, we apply QVGen to Wan 14B, one of the largest open-source SOTA models, and observe negligible performance drops on VBench-2.0.
2 PRELIMINARIES
Video diffusion modeling. The video DM (Ho et al., 2022; Zheng et al., 2024) extends image diffusion frameworks (Li et al., 2023b; Song et al., 2021a) into the temporal domain by learning dynamic inter-frame dependencies. Let To E R /xxxxxx be latent video variable, where f denotes the count of video frames, each of size h x w with channels. DMs are trained to denoise samples generated by adding random Gaussian noise c~N(0, I) to Zo:
Φ 30 + O,E,
(1)
where (ky, > are scalar values that collectively control the signal-to-noise ratio (SNR) according
to a given noise schedule (Song et al., 2021b) at timestep t E [I....,.]] .V]. One typical training objective (i.e., predict the noise (Ho et al., 2020b)) of a denoiser Eg with parameter can be formulated as follows:
() = Exp,,,,,-llle
C,
where C' represents conditional guidance. like texts or images, and || || denotes the Frobenius norm. Additionally, v-prediction (Salimans & Ho, 2022) (i.e., predict d== ) is also a prevailing option (Yang et al., 2025; WanTeam et al., 2025; Kong et al., 2025) the target. During inference, we can employ Eg with various sampling methods (Lu et al., 2022; Zheng et al., 2023) progressively denoising from random Gaussian noise xN I) to a clean video variable. The raw video is obtained by decoding the variable via a video variational auto-encoder (VAE) (Yang et al., 2025).
dr
Quantization.
The current video DM based the diffusion transformer (DiT) (Peebles & Xie, 2023) architecture primarily consists of linear layers. Given an input X E Rm*x a full-precision linear layer with weight W _uxx and the layer's 's quantized version can be formulated as:
WX, Y Qo (X),
where Y E Raxk and Ý Bank represent the outputs of the full-precision (e.g., FP1 6/BF16) and quantized linear layers, respectively. (-)-) denotes the function of b-bit quantization. In this paper, we adopt asymmetric uniform quantization. For example, Qx can be represented as:
2 X = (clip(1 x 2, 0, - 1) - 2) x s, where
max m))min(X)
min X)
25-1
Here, guantization parameters and z denote the scaler and zero shift, respectively. clip(-, bounds the integer values into [0, 2 1]. To ensure the differentiability of the rounding function [-] for QAT, ttroight-through estimator (STE) (Bengio et al., 2013) is widely applied as:
#0.(X) ax
Logi Tosl*1100 x1+=521-1-
(5)
Similar to existing works (Li et al., 2023b; Zheng et al., 2025b), we employ the full- precision model as the teacher to guide the training of the quantized model in a knowledge distillation-basee (KD-based) manner. Therefore, the training loss can be defined as:
L = Ex,, C.r [|| C, r) - C,
(6)
where ég denotes the quantized denoiser video DM.
N denotes maximum timestep.
Here, denotes the output channel, m signifies the token dimension, and A is the token number. We omit the hatch dimension for clarity.
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3 QVGEN
Considering the substantial vidco-quality drops (see Fig. 1) observed in existing QAT methods (Li et al., 2023b; He et al., 2024; Esser al., 2020b), we believe that the quantized video DM suffers from poor convergence. In the following subsections, we propose QVGen (see Fig. 2) to address this issue while maintaining inference efficiency.
(a) Improving Convergence with Auxiliary Modules A
(b) Progressively Shrinking via Rank-Decay
Loue londicsspe
SVD 2i= 9
2. (X)
We
KD
Bor 0
(2OL)R2, (X)
Oups
A)r
Teaning frme
Blxx Are
Lisour Lover
RCF
AnE
(instol)
W
pantes
Elimioule the lou-
2. (W)
2x(W22 (X)
[1] [1]
(runcate)
Depoct
RI (1 13
(x)
Cvest oner
(1: X)r]
(K0)
Wa
R
W L'R'
Figure 2: Overview of the proposed QVGen. (a) This framework integrates auxiliary modules T to improve training convergence (Sec. 3.1). (h) To maintain performance while eliminating inference overhead induced by 2. we design rank-decay schedule that progressively shrinks the entire
through iteratively applying the following two strategies (Sec. 3.2): (i) SVD identify the low-impact components in (ii) A rank-based regularization 7 to decay the identified components to Ø. A detailed procedure can be found in Sec. A.
3.1 IMPROVING CONVERGENCE WITH AUXILIARY MODULES +
To begin with, we analyze the convergence of a quantized video DM using the regret, which is widely used analyses of deep learning optimizers (Kingma & Ba, 2017; Luo al., 2019). is defined as:
R(T) Σ (o.) f(()))
where T signifies the total number of training iterations and fe(.) is the unknown cost function at iteration t. Here, O, represents the parameters of the quantized video DM at training step t, constrained within convex compact set Sd, while 0 arg minges# f f((0) is the optimal parameters. In QAT, 0, is updated by gradient descent, with the learning rate 17: and gradient gt, as:
oc+1 0: 7/:9t-
(8)
Theorem 3.1. Assume that fa is convex" and v0;, 0, E Sd, - 10:-0;loo S Doo. Then the average MM
regret is upper-bounded as: T <
R(T)
dD²
ΣT 2 11..1
2TT7"
A smaller value of R(T) "T" implies a closer convergence to the optimum. Thm. 3.1 (with proof becomes negligible), minimizing l|gella is critical for improving convergence behavior of QAT. lower ||g:|12 is typically observed more stable training processes (Takase et al., 2024; Xie et al., 2024). Therefore, to reduce ||g: ||2. we aim to stabilize the QAT process by mitigating aggressive training losses (e.g., loss spikes) (Kumar et al., 2025; Li et al., 2024b). Specifically, quantized linear layer of a video DM. This trainable module aims to mitigate severe quantization- induced errors during QAT, thereby preventing aggressive training losses. The forward computation of such a decquipped layer becomes:
dD2
provided in Sec. B) suggests that for large T (i.e., 2T
introduce a Iearnable auxiliary module & to enhance each
Ý = L (x) (X)())),
where Φ (X)) W. Q6 (X). Here, W is initialized before QAT by the weight quantization error, defined as W Qo (W). More initialization approaches for W. a can be found in Sec. L.2.
"This may not hold for deep networks. Therefore, we also provide a nonconvex convergence analysis in Sec. C. mh. and 17." are the maximum and minimum values of nhe respectively.
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C (Yang et al., 2025) (b) Kan 1.3B (WanTeam ot al., 2025)
To validate the effectiveness of o we con- duct experiments for CogVideoX 2B (Yang et al., 2025) and Wan 1.3B (WanTeam et al., 2025). Compared with the previ- ous SOTA QAT method Q-DM (Li et al., 2023b), the proposed approach exhibits consistently lower ||g: and reduced train- ing loss, as depicted in Fig. 3. This aligns well with both the theoretical and the em- pirical analysis discussed earlier. There- fore, incorporating & in QAT effectively reduces the gradient norm and leads to bet- ter convergence for QAT. In addition, as evidenced by Fig. 3, the substantial perfor- mance degradation of Q-DM (e.g., depicted in Fig. 1) for the video generation task could be attributed its relatively large 11 ll2l- Besides, provide further analyses of [9 222 in video generation QAT in Sec. H.
Lalb
500
1000
1500
2000
500
1000
1500
2000
TTriiing Sieps
I' /Ttaining Steps
0.4
Giss DM
0.1
WANe G G-IN
222
02-
0.0
500
L000 1500
0.0
1000
2000
ATraining Steps
ATraining Strps
Figure 3: (Upper) l1 2|a vs. #steps and (Lower) training loss
(i.e., Eq. (6)) vs. #steps across different video DMs and 4-bit QAT methods." " denotes our approach in Sec. 3.1.
3.2 PROGRESSIVELY SHRINKING & VIA Rank-Decay
However, during inference, the auxiliary module & introduces non-negligible overhead. Concretely, & incurs additional matrix multiplications between b-bit activations Qs (X) and full-precision weights W.. This is inapplicable to low-bit multiplication kernels and thus hinders inference acceleration. In addition, the storage of full-precision Wa a for each & leads to significant memory overhead, exceeding that of the quantized diffusion model by several fold.
To improve QAT while eliminating the inference overhead, we propose to progressively remove throughout the training process. This allows the model to benefit from & during QAT, while ultimately yielding standard quantized model (Li et al., 2023b; Esser et al., 2020b) with extra inference cost. To achieve this goal, a straightforward solution is to decay all parameters of & directly. However, we have noticed that it is ineffective and suboptimal (see Tab. 4). This observation calls for a fine-grained decay strategy.
(a) Cog VideoX 2B (Yang al., 2025)
(b) Nan 1.3B (WanTeam el al., 2025)
Attention
FFN
Attention
FFN
stop
0 stop
D
IK sup
step
LK stop
1K step
Value
2K stop
Vilue
dkp
sirp
0.50
step
0.2
8:25
00
0.5
DAM/
256
1024
1024
1024
256 1024
Figure 4: Singular value variation in W across training iterations for 4-bit video DMs. We visualize the average of the singular values {ax)... 2.2. ot U {aa) across layers of all Attention blocks (Vaswani et al., 2023) and feed-forward networks (FFNs), respectively. "O step" denotes the initialization state before QAT.
Therefore, we begin to investigate the contribution of fine-grained components in t. Specifically, we apply singular value decomposition (SVD) (Li et al., 2025; 2023c) to W. at various training steps:
W. +
Σ - U,u.
(10)
where d min(n, n, m) and 01 2 02 2 ... Ta are the singular values. The vectors u, and v, denote the left and right singular vectors associated with a., respectively. By tracking the evolution of the average J,, we observe two key findings (exemplified by Fig. 4 (a) Attention): (i) Wa a contains substantial number of small singular values. For example. approximately 73% (0-th step) of the average o, are 144 smaller than the largest one σ (i) The presence of these small a, becomes increasingly pronounced as QAT progresses, with the proportion rising to 99% (2K-th step).
These findings suggest that an increasing number of orthonormal directions {u,, V.} contribute little, as their associated singular values o, are small (Zhang et al., 2015; Yang et al., 2020). We posit that the main weight W gradually learns to absorb quantization errors. As training proceeds, W. collapses to low rank and plays an ever-diminishing role in error compensation. This, in turn implies that only an increasingly low-rank portion of & (ie, 1 U, V. where d' < d) is needed, and the
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remaining components can be decayed without noticeably affecting performance. Motivated by this, we propose a novel rank-decay schedule that progressively shrinks by repeatedly identifying and eliminating the computation of + as:
low- www-imact parts. First, to attain these parts, reformulate the
(11) where L = [Voru...... Vo, E R and R IVo V. R**" for given rank r. In practice, we set r < d to reduce training costs, as W. already exhibits a non- neglnggli number of small singular values before QAT. Consequently, Vogu, and Vo,u, the components in We represent the s-th level of contribution. Then, with a rank-based regularization y applied, the forward computation of quantized linear layer during training is modified as:
+ (x)( LX),
Y = 26 (x) + (7 @ L))),
(12)
where y is defined as:
Y con (x)r))) E Rnxr.
(13)
Here, u follows cosine annealing schedule that decays from to 0, À (0, 1] represents the shrinking ratio, and @ denotes element-wise multiplication. Eq. (12) and Eq. (13) allow us to progressively eliminate the low-impact components of & (i.e. (1 121412(11100 (((1-)))+1s--. Vo, and [1 -)r+ vo Once u reaches O, we truncate (L, R to {L', R'}, and
rewrite W a as:
L' = L:: X)r] R'= R|: (1 A)r
We = L'R'.
(14)
In Eq. (14), the rank Wa a is shrunk from r to (1 A)r. During the subsequent training phase, the above procedures (i.e., both decomposition and decay) are iteratively applied. Ultimately, we fully eliminate & by reducing r to 0, which incurs negligible impact on model performance (see Tab. 3). is worth noting that set À for Eq. (13) in this work, based on its effectiveness demonstrated in Tab. 4. Overall, the overview of the rank-decay schedule is exhibited in Fig. 2 (b).
4 EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
Models. We conduct experiments on open-source SOTA video DMs, including Cog VideoX22B and 1.5-5B (Yang al., 2025), and Wan 1.3B and 14B (WanTeam et al., 2025). Classifier-free guidance (CFG) (Ho & Salimans, 2022) is used for all models, and the frame number of generated videos is fixed to 49 for Cog VideoX-X222 22 and 81 for the others.
Baselines. We adopt previous powerful PTQ and QAT methods as baselines: ViDIT-Q (Zhao et al., 2025a), SVDQuant (Li et al., 2025), LSQ (Esser et al., 2020b), Q-DM (Li et al., 2023b), and EfficientDM (He et al., 2024). Since these methods were designed for image DMs convolutional neural networks (CNNs), we adapt these works to video DMs using their open-source code (if available) the implementation details provided in the corresponding papers. Without specific clarification, static per-channel weight quantization with dynamic per-token activation quantization, a common practice the community (Zhao et al., 2025a; Liu et al., 2023), used for all linear layers.
Training. We employ 16K captioned videos from OpenVidHO 0. 4M (Nan et al., 2025) as the training dataset. The AdamW (Loshchilov & Hutter, 2019) optimizer is utilized with a weight decay of 10-4. We employ a cosine annealing schedule to adjust the learning rate over training. During QAT, we train Wan 14B (WanTeam et al., 2025) and CogVideoXI epochs on H100 GPUs and 110H100 GPUs, respectively. For the other DMs, we employ 8 training epochs on 8xH100 GPUs. Additionally, we allocate the same training iterations for each decay phase (i.e., shrinking the remaining to (1 - X)r). The same settings are applied to all QAT basclines.
(Yang et al., 2025) for 16
Evaluation. We select 8 dimensions in VBench (Huang et al., 2024c) with unaugmented prompts to comprehensively evaluate the performance following previous studies (Zhao et al., 2025a; Ren et al., 2024). Moreover, for huge (2 5B parameters) DMs, we additionally report the results VBench-2.0 (Zheng et al., 2025a) with augmented prompts to measure the adherence of videos to physical laws, reasoning, etc. More detailed experimental setups can be found in Sec. D.

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Published as a conference paper at ICLR 2026
Table 1: Performance comparison across different quantization methods VBench (Huang al., 2024c). "4" indicates PTQ methods and "*" signifies QAT methods. "Full Prec." denotes the BF16 6 model. " " represents that we apply fine-grained per-group weight-actin activvaiii quantization with a group size of 64 and keep some linear layers unquantized, which is the same as the official settings of SVDQuant (Li ct al., 2025) (details can be found in Sec. D). "Full Fine-tuning" denotes fine-tune the model with the same data QVGen. Best and sccond-bes best results are highlighted bold and underline formats, respectively.
Method
(WAA) Quality
#Bits Imaging, Assibetic,
Quality
Motion Samoodianess
Dynamic Degrze
Background. Consisiency
Subject Consistency
Scene Consistency
Overall Consittacy
Cog Vigeeooo (CPG 6.D, (Slp. Epa
Full Prec.
16/16
59.15
54.49
97.43
67.78
94.79
92.82
36,24
25 06
Fine-tuning
16/16
56.53
65.30
93.43
34.99
25.50
VIDITQQ (Zhao et al., 2025ut
4/6
51.72
13.01
92.18
43:22
90.76
81.02
26.25
20.41
SVDQqant (Li et ol., 2025)
416
58.27
47.06
95.28
40.83
92.41
87.45
27.69
21:34
SVDQuant
2025)
51.60
94.03
91.78
22.89
1.QQ (Esser et ol., 20200)
414
58.23
54.20
97:57
45.00
92.97
92.41
24.06
23.17
Q-DM (Li et 202363"
4/4
54,96
52.71
98.00
48.61
93.82
91.86
28.02
23.87
RMlicienDM (Ho al., 2014)
55.96
$1.97
28.03
46.67
04. 11)
91.70
27.76
24.78
QVGen (Ours)
4/4
60.16
54.61
96.06
67.22
94.38
93.01
31.42
24.61
LSQ (Esser 20220g"
56.46
40.35
97.98
0.56
94.08
89.18
4,80
13,80
Q-DM (1. et al., 20258g
3/3
30.88
40.41
98.03
5.56
93.93
K7.75
7.33
15.98
FMficenDM (He al., 2024y
3/3
52.86
44.18
97.13
28.61
93.15
88.26
15:42
20.42
QVGen (Ouns)
22.92
War: 1,38
(CPG 5.0, 480p, Epa
16)
Full Prec.
16/16
64.30
58.21
97.37
70.23 28
95.94
93.84
28.05
24.67
Fine-tuning
16/16
64.59
58.85
97.46
83.61
94. 34.30
93.68
27.55
24. 36
VIDITQQ (Zhao et al., 2025
4/6
56.24
50.18
94,81
52.43
59.67
82.53
13.45
19.58
SVDQuant ol., 2025)
58.16
51.27
97.05
93.74
91.71
14.18
23.26
SVDQuant (Li ct al., 2025)
4/4
57.57
46.30
94,21
72.22
93.16
77.96
12.73
21.91
LSQ (Baser 2020ay"
414
39.11
49.09
96.35
71.11
92.66
91.57
10.38
18.83
Q-DM al., 20236)
97.22
76.67
93.37
13.28
21.63
BfficiemLM (He al., 2024)"
414
60:20
53.52
96.18
56.39
93.24
91.20
11.77
21.19
QVGen (Oun)"
4/4
63.06
54.67
98.25
77.78
94.08
92.57
15,32
23.01
L.0Q (Esser ol. 2000u"
58.80
98.22
86
89.43
0.89
15.51
Q-DM (Li et ol., 20236g
3/3
56.19
44.95
95.13
76.94
92. 01
K3.82
1.79
16.89
MM (He 2004y"
10
74.79
11.38
QVGen (Ouns)
3/3
67.35
49.71
98.93
84.14
93.62
92.25
5.71
20.11
4.2 PERFORMANCE ANALYSIS
Comparison with baselines. We report VBench score comparisons in Tab. 1. In W4A4 quantization, recent QAT methods (Esser al., 2020b; He et al., 2024; Li et al., 2023b) show non-negligible performance degradation. With W3A3, performance drops become more pronounced. In contrast, the proposed QVGen achieves substantial performance recovery in 3-bit models and comparable results to full-precision models in 4-bit quantization. Specifically, it shows higher scores less than a 2% decrease in all metrics for W444 CogVideoX XB (Yang et al., 2025), except Scene Consistency. For PTQ baselines (Zhao et al., 2025a; Li et al., 2025), all fail to generate meaningful content in M4A4 per-channel and per-token settings. Therefore, we apply W4A6 quantization for these methods and also utilize fine-grained per-group W4A4 quantization for SVDQuant (Li et al., 2025). In these cases, W4A4 QVGen outperforms them by a large margin, particularly with 8.37 and 14.61 higher Aesthetic Quality and Subject Consistency for Wan 1.3B compared to W4A4 SVDQuant. In addition to these findings, we observe that for Wan 1.3B. Dynamic Degree recovers easily during QAT, even surpassing that of the BF16 model. However, for Cog VideoX-2B, this metric significantly drops. Moreover, Scene Consistency is the most challenging metric to maintain across models and methods. Detailed training loss curves across QAT methods and trial of combining SVDQuant (Li et al., 2025) and QVGen can be found in Sec. G and Sec. I, respectively.
Beyond the quantitative results, we provide qualitative results in Fig. and Sec. P, where QVGen markedly improves visual quality over prior methods. Although clear gap remains between 3-bit and 4-bit outputs, our approach shifts the Pareto frontier toward higher accuracy at a smaller model size than existing techniques (e.g., 3-bit QVGen for VogVid achieves superior Aesthetic Quality than previous 4-bit methods in Tab. 1). We view this initial exploration as setting a direction and providing valuable insight for future work on 2-bit quantization.
Besides, we provide more comparisons with additional metrics in Sec. J and comparisons under relatively higher bit-width in Sec. E. We also apply our approach to image generation in Sec. M.
Results for huge DMs. To demonstrate the scalability of our method, we further test two huge video DMs, including Cog VideoX1. 155BBB and Wan 14B, at 720p resolution. As illustrated in Tab. 2,
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Table 2: Performance for huge video DMs on VBench, Comparison with baselines can be found in Sec. K.
Method
[WAA] Quality
#Bin Imaging, Aesthsic, Quality
Motion Smontness
Dynamic,
Degree Consistency
1
Background Subject Consistancy
Scene Consistency
Overall Consistency
Cog VngVideoX 5-58 (CPG - 6.0. 720p, Eps 16)
Full Prec.
|6/16]
66.25
59.49
98.12
59.72
96.57
95.28
39.14
26.18
QVenn (Ours)
95.83
94.88
28,47
24.45
QVEin (Churs)
3/3
54.44
35.85
97.23
58,89
96.48
90.17
13.27
17:15
14B
(CFG
5.0,
720p
16)
Full Proc.
16/16]
67.89
61.54
97.32
70.56
96.31
94.08
33.91
26.17
QVGen (Ours))
4/4
66.87
59.41
97.71
76.11
96.50
91.45
19.81
25.70
QVGen (Curs))
99.05
95.35
94.71
2,81
13.97
our 3-bit and bit models follow the same pattern seen in smaller models (Tab. 1). However, 3-bit quantization incurs much larger drops on demanding metries such Scene and Overall Consistency, underscoring the challenge of
pushing these larger models to 3 bits. In Fig. 5, we fur- ther assess the models with VBench-2.0; the W4A4 DMs incur only negligible overall performance loss.
(a) CogVideo 1.5-5B (Yang et al., 2025)
(b) Nan 14B (WanTeam al., 2025)
Dynasic Spridl
Dymuriic Altiiaa
Relationsiz
Dynamic
Diveaity
Fivesity
Mosic Ordker
Marion Orider
Uiikestreding
Cletbas
Understandng
Ciethor
Buras Identty
Huras
Eatsu
Compiec
Eassa: Asatiryy
Carpllx
Harza Anowmy
Tanticay
Leshicap
4.3 ABLATION STUDIES
Canpls
Cvrrpies
Mete-New Coesionncy
Consionry
Canera Mlsin
Maxcial
Canera
Material
To demonstrate the effect of each design, we em- ploy W4A4 Man 1.3B with VBench (Huang et al., 2024c) for ablation studies. More ab- lations can be found in Sec. I..
Remonaity
Matics
Themrsiic
10166
W4644
Menez
Remeratiy
Matox
Themmoke
W4M
B016
W3K3
Instance
WSAS
Figure 5: Performance for huge video DMs on VBench-2.0 (Zheng et al., 2025a). Our 4-bit models exhibit a minimal drop of 11% in total score.
Effect of different components. We evaluate the contribution of each component in Tab. 3. The auxiliary module & (Sec. 3.1) yields substantial performance improvements across all metrics. Fur- ther, the rank-decay schedule (Sec. 3.2) effectively eliminates extra inference overhead, while induc- ing less than a 0.6% drop in most metrics. It even leads to improvement in Overall Consistency.
Table 3: Ablation results of each component. "Naive" denotes naive QAT in a KD- based manner. "Rank" signifies our rank-decay schedule.
Method
Imaging, Aestettic, Dynamic, Quality
Quality
Degree
Scene Consistency
Overall Consistency
Naive
60.40
$2.50
76.67
13.28
21.63
+
63,41
54.75
77.89
15.51
22.98
Rank
63.08
54.67
77.78
15,32
23.01
Choice of the shrinking ratio X. To determine a proper shrinking ratio X in Eq. (13), we conduct experiments in Tab. 4. By maintaining the same training iterations for each decay phase 5, a small ratio results in an excessively rapid descent of u in Eq. (13) from 1 to O, potentially destabilizing the training process. On the other hand, a larger ratio may cause the premature removal of high- contributing components during each phase. An extreme scenario would involve ratio 100% (i.e.,
= 1), in which all We is removed in a single decay phase, leading to huge performance drops.
Table 4: Results of different shrinking ratios X in Table 5: Results of different initial ranks for Eq. (14). Eq. (13) for each decay phase, decaying the entire Wa. x = 2 is used in this work. in this work.
means directly r Orepresents "Naive" in Tab. We employ r 32
A Quality Quality
Imaging,, Aesthetic, Dynamic, Socne Consistency
Degree
Overall Consistency'
Imaging, Aesthetic, Dynamic, Quality Quality
Degrce
Scene Consistency
Overall Comsistency
60.40
$2.50
76.67
13.28
1/1
63.02
54,23
76.84
15.18
22,85
62.71
54.47
74.62
14.42
22.81
1/2
63.08
84.67
77.78
15.32
23.01
62.99
54.62
76.58
14.84
23,00
3/4
62.89
$4.62
77.91
15.04
22.89
63,08
54,67
77.78
15.32
23.01
1
61.05
52.48
76.48
13.82
21.81
64
63.06
54.30
26.74
15.40
22.92
Choice of the initial rank We further present the results for different initial ranks of W a in
Tab. 5. As r increases, the performance gains diminish and eventually deteriorate (ie., at = 64).
We attribute this trend to the same issue associated with small shrinking ratio discussed earlier. Specifically, increasing r to 2r introduces an additional decay phase, which shortens the training time allocated to each phase and may lead to overly rapid decay (e.g., at r 64).
We also employ fixed total number of training epochs.
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Analysis of different fine-grained decay strategies. To further demonstrate the effectiveness of our rank-decay strategy, we evaluate alternative decay strategies in Tab. 6. "Linear" in the table denotes linearly reducing the magnitude of the entire full-rank Wa to by set À = 1 with linear schedule as u for Eq. (13). Inspired by network pruning (Han et al., 2015; 2016), we introduce a "Sparse" strategy that progressively prunes the largest values in W o during training. Additionally, motivated by residual quantization (Li et al., 2017), we design a "Res. Q." strategy, which first quantizes W.p into 4-bib tensors with the same shape and then progressively removes them one by one. Among all these methods, the "Rank" strategy outperforms others across all the metrics by a large margin. Additionally, both "Sparse" and "Res. Q." strategies require at least 1.8x training hours at the same setups compared with our "Rank" approach.
In addition, we introduce two stronger baselines: "Sparse w/ Wanda" and "Sparse w/ MaskLLM". Rather than pruning the smallest magnitudes in "Sparse", the former employs Wanda (Sun al., 2024) to prune We using 128 randomly selected train- ing samples in each decay phase (see "Sparse" in Sec. F). Following MaskLLM (Fang et al., 2024a), the lat- ter applies learned 2:4 structured prun- ing masks to Ws in each phase. All other settings match those of "Sparse". "Sparse w/ Wanda" yields a small improvement over "Sparse", while "Sparse w/ MaskLLM" provides a larger gain; however, both remain below "Rank" on all metrics. It also worth noting that "Sparse w/ Wanda" requires 1.8x the training time of "Rank", similar to "Sparse", and "Sparse w/ MaskLLM" requires x.1x x due to mask learning.
Table 6: Results of different decay strategies. Details of these methods can he found Sec. F. "Rank" denotes the rank-decay strategy in this work.
Decay
Imaging Aesthetic, Dynamic, Quality
Overall Consistoncy
Stratogy
Quality
Degroe
Consistency
Linear
60.82
52.81
73.19
13.34
21.87
Sparse
61.15
54.06
74.24
13.86
Sparse w/ Wanda
61.43
54.08
74.36
13.94
22,48
Sparse w/ MaskLLM
61.36
54.12
74.82
14.15
22.57
Res. Q
61.72
54.01
72.41
14.17
22.31
Rank
63.08
54.67
77.78
15.32
23.01
4.4 EFFICIENCY DISCUSSION
Inference efficiency. We report the per-step latency of W4A4 DiT components on one A800 GPU in Fig. 6 (b). Adapted from the CUDA kernel implementation by Ashkboos et al. (2024), W4A4 QVGen achieves 1.21x and 1.44x speedups for Wan 1.3B and 14B, respectively.
Besides, it exhibits ~4x mem- ory savings compared to the
(a) Model Size (GB)
(b) Inference Lateacy (6)
(c) Inference Latency (s)
BF16 format, as shown in Fig. 6 (a). Nevertheless, we be-
3.9X
55.00
L44x
L 1,08s
255
lieve that the acceleration ratio could be further improved with advanced kernel-fusion tech- niques and optimization for specific tensor shapes in these models, which we leave to future work (More discussion can be found in Secs. N-O). In addition, it is worth noting that our QVGen adheres standard uniform quantization, enabling drop-in in deployment with existing W4A4 kernels across various devices.
BF16 WAN B6 WdAd
BF16
WAM
BF16
W4M
SVG
-WSA4
SV0
SVD
SVG
Figure 6: Evaluation of memory compression and inference acceleration. Blue color and yellow color denote Wan 1.3B and 14B, respectively.
Although previous research (Zhang al., 2025b) mentioned that the current 3D full-attention occupies significant computation in video generation, our QVGen is orthogonal to works that focus on accelerating 3/ attention and can deliver notable speedups to models that already employ those techniques. For example, when SVG (Xi et al., 2025) is paired with W4A4 QVGen, the model runs 1.70x and 2.63x faster, compared with 1.31x and 1.68x for SVG alone, shown in Fig. 6 (c).
Training efficiency. Moreover, we conduct a comparative train- ing efficiency analysis across dif- ferent models and QAT base- lines, demonstrated in Tab. 7. LSQ (Esser et al., 2020b) does not distillation-bann based QAT (i.e., teacher forward pass) and is therefore the fastest to train. Effi- cientDM (He et al., 2024) updates only LoRA parameters, which substantially reduces optimizer-statt memory GPUs. We believe such strategy in EfficientDM can be combined with method to
Table 7: Training costs across different methods and models.
Train. Time (GPU days)
Train. Mem. (GB/GPU))
Model
[SQ Q.DM EfficienDMM QVGen LSQ Q-DM EfficienDM QVGen
Cog
8.64
9.30
8.97
9.44
62.78
67.26
44.27
67.93
1.38
9.92
10.92
10.68
11.11
66.15
42.74
66.67
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further improve training efficiency, and plan to explore this in future work. Relative to distillation- based QAT (i.e., Q-DM (Li et al., 2023b)), our method with low-rank & adds only ~1.02x GPU-days and ~1.01 x peak GPU memory for Wan 1.3B model. To be noted, all baselines greatly fall short of our method in final performance, as illustrated in Sec. 4.2. In future work, we will delve into improving QVGen's 's training efficiency while maintaining its strong performance.
5 RELATED WORK
Video diffusion models. Building upon the remarkable success of diffusion models (DMs) (Ho et al., 2020a; Song et al., 2021a; Chen et al., 2024a) in image generation (Labs, 2024; Xie et al., 2025), the exploration in the field of video generation (Yang et al., 2025; WanTeam et al., 2025; Kong et al., 2025) is also becoming popular. In contrast to convolution-
diffusion models (Ho et al., 2022: Blattmann et al., 2023), the success of OpenAI Sora (OpenAI, 2024) has spurred researchers to adopt the diffusion transformer (DiT) (Peebles & Xie, 2023) architecture and scale it up for high-quality video generation. However, advanced video DiTs (WanTeam et al., 2025; Yang et al., 2025; Kong et al., 2025) often involve billions of parameters, lengthy multi-step denoising, and intensive computation over long frame sequences. This results in substantial time and memory overhead, which limits their practical deployment. To enable faster video generation, some works have introduced step-distillation (Yin et al., 2025; Lin et al., 2025) on pre-trained models to shorten the denoising trajectory. Others focus efficient attention (Zhang et al., 2025a; Xi et al., 2025; Huang et al., 2025a), feature caching (Lv et al., 2025; Zou et al., 2025; Huang et al., 2024b), or parallel inference (Fang et al., b024c;b) to accelerate per-step computations. Moreover, achieve memory-efficient inference, existing research has explored efficient architecture design (Wu et al., 2024b; Liu et al., 2025), structure pruning (Ben Yahia et al., 2024; Zhao et al., 2025b), and model quantization (Zhao et al., 2025a; Chen et al., 2024b; Tian et al., 2024). These methods aim to achieve both model size and computational cost reduction.
Model quantization. Quantization (Jacob et al., 2017) is predominant technique for minimizing storage and accelerating inference. It can be categorized into post-training quantization (PTQ) (Nagel et al., 2020) and quantization-awart training (QAT) (van Baalen et al., 2020). PTQ compresses models without re-training, making it fast and data-efficient. Nevertheless, it may result in suboptimal per- formance, especially under ultra-low bit-width (e.g.. 3/4-bit). Conversely, QAT applies quantization during training or finetuning and typically achieves higher compression rates with less performance degradation. For DMs, previous quantization research (Li et al., 2023a; He et al., 2023; Huang et al., 2024a; 2025b; So et al., 2023; Wu et al., 2024a; Shang et al., 2023; Wang et al., 2024) primarily focuses image generation. Video DMs, which incorporate complex temporal and spatial modeling, are still challenging for low-bit quantization. QVD (Tian et al., 2024) and Q-DiT (Chen et al., 2024b) first apply PTQ to convolution-basee (Xu et al., 2023b; Guo et al., 2024) and DiT-based (Zheng et al., 2024) video DMs, respectively. Furthermore, ViDiT-Q (Zhao et al., 2025a) employs mixed-precision and fine-grained PTQ improve performance. However, experiences video quality loss with 8-bit activation quantization and has not been extended to more advanced models (Yang et al., 2025; WanTeam et al., 2025). Consequently, QAT for advanced video DMs is urgently needed.
In this work, we identify and address the ineffective low-bit QAT for the video DM. Our proposed method significantly enhances model performance and incurs zero inference overhead in 3/4-bit settings. Moreover, our framework is orthogonal to existing QAT methods, which target gradient estimation (Gong et al., 2019), oscillation reduction (Nagel et al., 2022), etc.
6 CONCLUSIONS AND LIMITATIONS
In this work, we are the first to explore the application of quantization-awar training (QAT) in video DMs. Specifically, we provide theoretical analysis that identifies that lowering the gradient norm is essential to improve convergence. Then, we propose an auxiliary module (Φ) to achieve this. Additionally, we design rank-decay schedule to progressively eliminate & for zero inference overhead with minimal impact on performance. Extensive experiments for 3-bit and 4-bit quantization validate the effectiveness of our framework, QVGen. In terms of limitations, we focus video generation in this work. However, we believe that our methods can be generalized to more tasks, e.g., natural language processing (NLP), which we will explore in the future.
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ACKNOWLEDGEMENT
This work was supported by the National Natural Science Foundation of China (Nos. 62476018), and the Postdoctoral Fellowship Program of CPSF (No. BX20250487). This work was also supported by the Hong Kong Research Grants Council under the Areas of Excellence scheme grant AoE/E-601/22- and NSFC/RGC Collaborative Research Scheme grant CRS. HKUST603/22.
REFERENCES
Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman. Quarot: Outlier-free 4-bit inference in rotated Ims,, 2024. URL https: /ttpappp://axxi...///a
wttpaaiivvvvvoo..aaoaaaa www./vvoorrraoooooo abs//bss//22 2404. 00456.
Haitam Ben Yahia, Denis Korzhenkhov, Ioannis Lelckas, Amir Ghodrati, and Amirhossein Habibian. Mobile video diffusion. arXiv, 2024. URL httph:tpps..cc //tp://aaavvvv//aa///aa/ee.alaaa org/ abs///////// 20lp../221.....0 10
Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating propagating gradients through stochastic neurons for conditional computation, 2013. URL https::/arxii..ttt/// org.abb..rrg//......./g 1308. 3432. 3, 22
Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, Varun Jampani, and Robin Rombach. Stable video diffusion: Scaling latent video diffusion models to large datasets, 2023. URL
https: http///axvvvvvvv org/abs////ooooooooooo........rt httaaa//////111.
10
Léon Bottou, Frank E. Curtis, and Jorge Nocedal. Optimization methods for large-scale machine learning, 2018 URL httph:ttt/////tt /twpivixvvvv//./////v//.../aaa.a org/ /0ob///116.... 04838. 21
Junsong Chen. Jincheng YU, Chongjian GE, Lewei Yao, Enze Xie, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart-S aalpha- Fast training of diffusion transformer for photorealistic text-to-image synthesis. In The Twelfth International Conference on Learning Representations, 2024a. URL httttss://ppeeeeettttttt http///ppeeeeeeeeeeeeeeeeeeeeeee net/ forum? ? dd=eAKmOPeee.e e 3m1. 10
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, 2024b. https:
// //arxivv.rrg abs 2406. 17343. 10
Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. Flashattention: Fast and memory-efficient exact attention with io-awareness, 2022. URL https: /ttp:i///aavv..///eeeeeeaemaa org. org.oorg/aa..... /abs/ 2205. 14135. 22
DeepSeek-AI, Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangho Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Haowei Zhang. Honghui Ding, Huajian Xin, Huazuo Gao. Hui Li, Hui Qu, J. L. Cai, Jian Liang, Jianzhong Guo, Jiaqi Ni, Jiashi Li, Jiawei Wang. Jin Chen, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, Junxiao Song, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang. Lecong Zhang, Lei Xu, Leyi Xia, Liang Zhao, Litong Wang, Liyue Zhang, Meng Li, Miaojun Wang, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingming Li, Ning Tian, Panpan Huang, Peiyi Wang, Peng Zhang, Qiancheng Wang, Qihao Zhu, Qinyu Chen, Qiushi Du, R. J. Chen, R. L. Jin, Ruiqi Ge. Ruisong Zhang, Ruizhe Pan, Runji Wang. Runxin Xu, Ruoyu Zhang, Ruyi Chen. S. S. Li, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shaoqing Wu, Shengfeng Ye, Shengfeng Ye, Shirong Ma, Shiyu Wang. Shuang Zhou, Shuiping Yu, Shunfeng Zhou, Shuting Pan, T. Wang, Tao Yun, Tian Pei, Tianyu Sun, W. L. Xiao, Wangding Zeng. Wanjia Zhao, Wei An, Wen Liu, Wenfeng Liang, Wenjun Gao, Wenqin Yu, Wentao Zhang, X. Q. Li, Xiangyue Jin, Xianzu Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang. Xiaojin Shen, Xiaokang Chen, Xiaokang Zhang, Xiaosha Chen, Xiaotao Nie, Xiaowen Sun, Xiaoxiang Wang, Xin Cheng. Xin Liu, Xin Xie, Xingchao Liu, Xingkai Yu, Xinnan Song, Xinxia Shan, Xinyi Zhou, Xinyu Yang, Xinyuan Li, Xuecheng Su, Xuheng Lin, Y. K. Li, Y. Q. Wang, Y. X. Wei, Y. X. Zhu, Yang Zhang, Yanhong Xu, Yanhong Xu, Yanping Huang, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Li, Yaohui Wang, Yi Yu, Yi Zheng,
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---

Published as a conference paper at ICLR 2026
Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Ying Tang, Yishi Piao, Yisong Wang, Yixuan Tan, Yiyang Ma. Yiyuan Liu, Yongqiang Guo, Yu Wu, Yuan Ou, Yuchen Zhu, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yukun Zha, Yunfan Xiong, Yunxian Ma, Yuting Yan, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Z. F. Wu, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhen Huang, Zhen Zhang, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhibin Gou, Zhicheng Ma, Zhigang Yan, Zhihong Shao, Zhipeng Xu, Zhiyu Wu, Zhongyu Zhang, Zhuoshu Li, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Ziyi Gao, and Zizheng Pan. Deepseek-v3 technical report, 2025. URL https:
pstttp::// : //tpiiivvvvv...v///aaaa/aaavvvva org/ /tt///b////// 2412.41222. 19437.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, and Robin Rombach. Scaling rectified flow transformers for high-resolution im- age synthesis. In Forty-first International Conference on Machine Learning, 2024a. URL https: http///ppeeeeeeeeeeeeeeeeeeeee.e net/ / wwt.m/iddorrruuiiiieaa ddFFnUhsQJJS. 24
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederie Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach. Scaling rectified flow trans- formers for high-resolution image synthesis, 2024b. URL hwtps::/taaaaee..e .rrg abs /4003 03206. 29
Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dhar- mendra S. Modha. Learned step size quantization. In International Conference on Learning Representations, 2020a. URL http:::tp///ss httpss///pppeeeee.eeee.. ftrum? wiw.orrummndrkgg66666r00.
Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dhar- mendra S. Modha. Learned step size quantization, 2020b. URL wttph::tt//atiit//att/t 1902.08155 08153. 2, 4, 5, 6, 7, 9, 22, 23, 24, 26, 29, 31, 32, 33
/bs/
Gongfan Fang, Hongxu Yin, Saurav Muralidharan, Greg Heinrich, Jeff Pool, Jan Kautz, Pavlo Molchanov, and Xinchao Wang. Maskllm: Learnable semi-structured sparsity for large language models, 2024a. URL https:/ http://axxvvvv//// org/ abss///b//aa 2409. 17481. 9
Jiarui Fang, Jinzhe Pan, Xibo Sun, Aoyu Li, and Jiannan Wang. xdit: an inference engine for diffusion transformers (dits) with massive parallelism. arXiv preprint arXiv:2411.01738, 2024b. 10
Jiarui Fang, Jinzhe Pan, Jiannan Wang, Aoyu Li, and Xibo Sun. Pipefusion: Patch-level pipeline parallelism for diffusion transformers inference. arXiv preprint arX1v:2405.14440, 2024c. 10
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. Gptq: Accurate post-training
quantization for generative pre-trained transformers, 2023, URL
22
Saeed Ghadimi and Guanghui Lan. Stochastic first- and zeroth-order methods for nonconvex stochastic programming, 2013. URL /ttps::/axxiv. org/ ab/////ss333 5549. 21
Dhruba Ghosh, Hanna Hajishirzi, and Ludwig Schmidt. Geneval: An focused framework for evaluating cext-to-image alignment, 2023. URL https: http:///wwww....///////1../.// 29
Ruihao Gong, Xianglong Liu, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, and Junjie Yan. Differentiable soft quantization: Bridging full-precision and low-bit neural networks. 2019. URL https:://// //axxv arxiv.. org/ abs /wt.:/99...000 05033. 10
Yuwei Guo, Ceyuan Yang. Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. International Conference on Learning Representationss 2024. 10
Song Han, Jeff Pool, John Tran, and William J. Dally. Learning both weights and connections for efficient neural networks, 2015. URL https: / aariiv.iivv org/
atggabs/////eee/eeee///ee//ee abs /00.. 02 626. 9, 23
Song Han, Huizi Mao, and William J. Dally. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, 2016. URL https: / /arxiv.. /rg/ abs/15k 0 001 4 9. 9, 23
12

---

Published as a conference paper at ICLR 2026
Yefei He, Luping Liu, Jing Liu, Weijia Wu, Hong Zhou, and Bohan Zhuang. Ptqd: Accurate post- training quantization for diffusion models, 2023. URL https::/arxiv.orggg /305. 10657. 10
Yefei He, Jing Liu, Weijia Wu, Hong Zhou, and Bohan Zhuang. EfficientDM: Efficient quantization- aware fine-tuning low-bit diffusion models. In The Twelfth International Conference on Learning Representations, 2024. URL https: http:://pppeeeeeeeeeee n forum? 3 4, 6, 7, 23, 24, 26, 29, 31, 32,
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Bras, and Yejin Choi. Clipscore: A reference- free evaluation metric for image captioning, 2022. URL https: wawpivivii...///a//...///aaaa http//aawvv...aaaaaaaa/a wwt./bs//////oooooo..oo.. /2104. 08718. 29
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by two time-scale update rule converge local nash equilibrium, 2018. URL https: arxii.iiivv//..a org/ abs/ 08500. 29
Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance, 2022. URL httthttss:///rxx.
org/abs /ba/ 2207.22722222 12598. 6
Jonathan Ho. Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (eds.), Advances in Nearal Information Processing Systems, volume 33, pp. 6840-6851. Curran Asso-
neurips ciates, Inc., 2020a. URL httpttt://ppccceeeeiieeeee mee.rroeeednnnnnnnnrrirsss.. Ac5bcfec3584arte SBC.CCCCBBBEa0GTTTT..0000-0----0 http:/sssn..pppppppppaaaeee/ee/e cctpppper//2/////ccceopocp
10
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models, 2020b. URL
https: wawiiv.vvv. orgg alss/ /000 11239. 3,
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J.
Fleet. Video diffusion models, 2022. 3, 10
Yushi Huang, Ruihao Gong, Jing Liu, Tianlong Chen, and Xianglong Liu. Tfmq-dm: Temporal feature maintenance quantization for diffusion models, 2024a. URL httpt:://ta.ii.....t/ abs/2311. 16503. 10
Yushi Huang, Zining Wang, Ruihao Gong, Jing Liu, Xinjie Zhang, Jinyang Guo, Xianglong Liu, and Jun Zhang. Harmonica: Harmonizing training and inference for better feature caching in diffusion transformer acceleration. arXiv preprint arXiv:2410.01725. 2024b. 10
Yushi Huang, Xingtong Ge, Ruihao Gong, Chengtao Lv, and Jun Zhang. Linvideo: A post-training framework towards (n) attention in efficient video generation. arXiv preprint arXiv:2510.08318, 2025a. 10
Yushi Huang, Ruihao Gong, Xianglong Liu, Jing Liu, Yuhang Li, Jiwen Lu, and Dacheng Tao. Temporal feature matters: A framework for diffusion model quantization, 2025b. URL https:
//aaxiivogg/aaaall......a..oo..a www//bs///bb....o.oo.o.o........ 2407. . 9547. 10
Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, al. Vbench: Comprehensive benchmark suite for video generative models. In Proceedings of the JEE//VV Conference on Computer Vision and Pattern Recognition, pp. 21807-21818, 2024c. 6, 7, 8, 22, 25, 26
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-arithmetiionnly inference, 2017. URL https: /ttp:///aiiivii/ii wttp////////../////// 05877. 10
Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization, 2017. URL
https: //axxiv wwtp/oo////oo/ooo //o//112..... 6980. 4
13

---

Published as a conference paper at ICLR 2026
Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, Kathrina Wu, Qin Lin, Junkun Yuan, Yanxin Long, Aladdin Wang. Andong Wang. Changlin Li, Duojun Huang, Fang Yang. Hao Tan, Hongmei Wang, Jacob Song, Jiawang Bai, Jianbing Wu, Jinbao Xue, Joey Wang, Kai Wang, Mengyang Liu, Pengyu Li, Shuai Li, Weiyan Wang, Wenqing Yu, Xinchi Deng, Yang Li, Yi Chen, Yutao Cui, Yuanbo Peng. Zhentao Yu, Zhiyu He, Zhiyong Xu, Zixiang Zhou, Zunnan Xu, Yangyu Tao, Qinglin Lu, Songtao Liu, Dax Zhou, Hongfa Wang, Yong Yang, Di Wang, Yuhong Liu, Jie Jiang, and Caesar Zhong. Hunyuanvideo: A systematic framework for large video generative models, 2025. URL https:/ /wwwvv.rxivvvvava//////.......v org/ wtwp////////oooooo/o 2412. 03603. 3, 10
Kuaishou. Kling ai. https: petps://kkingee kuaiskuukkihou com/, 6 2024. Accessed: 2024-06-30.
Abhay Kumar, Louis Owen, Nilabhra Roy Chowdhury, and Fabian Güra. Zelip: Adaptive spike mitigation for Ilm pre-training, 2025. URL
205. 02507.4 4
Black Forest Labs. Flux. https: attps://githee /ttp://ggthb//t/////
forbllcc-foreett-ab//uuuuru 2024. 1, 10
Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024a. URL
https: / /arxivv.vvv. org/ abs/ 2 2402 1 17445. 29
Muyang Li, Yujun Lin, Zhekai Zhang, Tianle Cai, Junxian Guo, Xiuyu Li, Enze Xie, Chenlin Meng, Jun- Zhu, and Song Han. SVDQuant: Absorbing outliers by low-rank component for 4-bit diffusion models. In The Thirteenth International Conference on Learning Representations, 2025. net/ forum? ? i ddv
URL https::/p//.......t....t http///pppeeeeeeeeeeeeeeeeeeeeee /
Qur. 2, 5, 6, 7, 22, 23, 25, 26
Xiaolong Li, Zhi-Qin John Xu, and Zhongwang Zhang. Loss spike in training neural networks,
2024b. URL https::/ //appi:////xvv..///////:as// arxiv. org/ abs/ /abs/2300 2305. 12133.
Xiuyu Li, Yijiang Liu, Long Lian, Huanrui Yang, Zhen Dong, Daniel Kang, Shanghang Zhang, and Kurt Keutzer. Q-diffusion: Quantizing diffusion models, 2023a. URL https: http//arxvvvv/o////e.e org//
abs/ 22//2332.000 0404. 10
Yanjing Li, Sheng Xu, Xianbin Cao, Xiao Sun, and Baochang Zhang. Q-DM: An efficient low- bit quantized diffusion model. In Thirty-sevnnth Conference on Neural Information Processing Systems, 2023b. URL https: / openreviee. net Eorum? 1e=sFckL5BsPP 2,3, 4, 5, 6, 7, 10, 22, 23, 24, 25, 26, 29, 31, 32, 33
Yixiao Li, Yifan Yu, Chen Liang, Pengeheng He, Nikos Karampatziakis, Weizhu Chen, and Tuo Zhao. Loftq: Lora-fieetuuing-avee quantization for large language models, 2023c. URL https: http:://axvvvvvo
abs/
5
Zefan Li, Bingbing Ni, Wenjun Zhang, Xiaokang Yang, and Wen Gao. Performance guaranteed network acceleration via high-order residual quantization, 2017. URL httpss///axxii org/
abs/1708. 086 9, 23
Shanchuan Lin, Xin Xia, Yuxi Ren, Ceyuan Yang, Xuefeng Xiao, and Lu Jiang. Diffusion adversarial post-training for one-step video generation. arXiv preprint arXiv:2501.08316, 2025. 10
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling, 2023, URL https:
/ http///axvvvv.o www.ooo////bb/oooo.o//... 2210 02747. 22
Dongyang Liu, Shicheng Li,, Yutong Liu, Zhen Li, Kai Wang, Xinyue Li, Qi Qin, Yufei Liu, Yi Xin, Zhongyu Li, Bin Fu, Chenyang Si, Yuewen Cao, Conghui Hc, Ziwei Liu, Yu Qiao, Qibin Hou, Hongsheng Li, and Peng Gao. Lumina-video: Efficient and flexible video generation with multi- scale next-dit, 2025. URL
abs 2502. 06782. 10
Zechun Liu, Barlas Oguz, Changsheng Zhao, Emie Chang, Pierre Stock, Yashar Mchdad, Yangyang Shi, Raghuraman Krishnamoorthi, and Vikas Chandra. Llm-qat: Data-fice quantization aware training for large language models, 2023. URL http:: httpss//axxvvvvoo wwtpg/////////./
ab3////bb///3..//./ 2200 17888.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, 2019. URL https:
// //aaxivvv. wwtpxiiw.wo.aaa///oo /atp///bb///11a////// /t1.////b//111111111111 05101.
14

---

Published as a conference paper at ICLR 2026
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dom-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps, 2022. URL https: //tpxxi..rg///a/.eel..././/.ollt abs/ 2206. 00927.0
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models, 2023. URL https://arxiv org/abs/ 2211 015. 22
Liangchen Luo. Yuanhao Xiong, Yan Liu, and Xu Sun. Adaptive gradient methods with dynamic bound of learning rate, 2019. URL https: /arxiv.. org/ abs/ 1902. 09843.
Zhengyao Lv. Chenyang Si, Junhao Song, Zhenyu Yang, Yu Qiao, Ziwei Liu, and Kwan- Yee K. Wong. Fastercache: Training-fin video diffusion model acceleration with high quality. In The Thirteenth International Conference Learning Representations, 2025. URL https:
//openreviee ew net/ wtw.eeewwnneeooteiieeeeeeee
i d= 90jepGxx. 10
Markus Nagel, Rana Ali Amjad, Mart van Baalen, Christos Louizos, and Tijmen Blankevoort. Up down? adaptive rounding for post-training quantization, 2020. URL htttp:://arxiv.. abs/2004 105.105666 10568. 10
Markus Nagel, Marios Fournarakis, Rana Ali Amjad, Yelysei Bondarenko, Mart van Baalen, and Tijmen Blankevoort. A white paper neural network quantization, 2021. URL https://
arxiv.org/looooo. abs www.a//........./e...eeeeeeeee.e 02215...829.. 28
Markus Nagel, Marios Fournarakis, Yelysei Bondarenko, and Tijmen Blankevoort. Overcoming
oscillations in quantization-aware training, 2022. URL 11086. 10
Kepan Nan, Rui Xie, Penghao Zhou, Tichan Fan, Zhenheng Yang, Zhijic Chen, Xiang Li, Jian Yang, and Ying Tai. Openvid-Im: A large-scale high-quality dataset for text-to-video generation, 2025. URL
org/ abs/ 2407 02371.
OpenAL. Video generation models world simulators. https: http:///ppeaa....co wwwpwidw////n///t/co/oootccccc vi
www.:ooommmmmoaass--ooooo------- www.oorddddsmmmatrraooooo 2024. Accessed: 2025-04-09.
1, 10
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of
the IEEE/CVF international conference computer vision, pp. 4195 4195-4205, 2023, 1, 10
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. Zero: Memory optimizations toward training trillion parameter models, 2020. URL https: httpps://ariivvii watpv.iviivv////a///a../aaaaavla org wttg/aa/////ee 02054. 22
Weiming Ren, Huan Yang, Ge Zhang, Cong Wei, Xinrun Du, Wenhao Huang, and Wenhu Chen. Consisti2y: Enhancing visual consistency for image-to-video generation, 2024. URL https: //arxiv. abs////b//// 2402 04324.
Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling diffusion models, 2022.
URL pettts://azziie www.//orrroooo abs/ /2002. 00512.
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski. Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev. Laion-5b: An open large-scale dataset for training next generation image-text models, 2022. URL https: /aaxiiiv orgg alss 221 0 08402. 24
Yuzhang Shang, Zhihang Yuan, Bin Xie, Bingzhe Wu. and Yan Yan. Post-training quantization diffusion models, 2023. URL https:/ arxiv www...rg/abao 2212 15736. 10
Junhyuk So, Jungwon Lee, Dachyun Ahn, Hyungjun Kim, and Eunhyeok Park. Temporal dynamic quantization for diffusion models, 2023. URL https: wttp:/vv..r///////////0/ 02316. 10
15

---

Published as a conference paper at ICLR 2026
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. ArXiv, abs/2010.02502, 202 3, 10, 22
Yang Song, Jascha Sohl-Dickstein, Diederik Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations, 2021b. URL
https: /arxiv.///aavv///aa org/ hta//bbs//21110 13456.
Mingjie Sun, Zhuang Liu, Anna Bair, and J. Zico Kolter. simple and effective pruning approach for large language models, 2024. URL https: / arwii. www.i.wgw..a...a.............o wwwp///ass....o.oo..ee/e.////see
9
Sho Takase, Shun Kiyono, Sosuke Kobayashi, and Jun Suzuki. Spike no more: Stabilizing the pre- training of large language models, 2024. URL https:
Shilong Tian, Hong Chen, Chengtao Lv, Yu Liu, Jinyang Guo, Xianglong Liu, Shengxi Li, Hao Yang, and Tao Xie. Qvd: Post-training quantization for video diffusion models, 2024. URL
https:: /ttp:://aaivvii/iii org/ atcp//bs//2// 111.4.1........ 10, 28
Hugo Touvron, Thibaut Lavril, Gautier Izacard. Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix. Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models, 2023. URL https::tt//..mc arxiv. org/ abs 22002 13971. 1
Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang. Tijmen Blankevoort, and Max Welling. Bayesian bits: Unifying quantization and pruning, 2020. URL https: // //rxxiv.oom www.orr//abaa.. aba//ba/// 2005 07093. 10
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need, 2023. URL https: /ttaas///arxiii //t.iv.rrg// abs/1706. 03 67.
Changyuan Wang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie Zhou, and Jiwen Lu, Towards accurate post-training quantization for diffusion models, 2024. URLhttps: / http//axvvvvv////////////e org/ abs/2305. 18723. 10
Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004 doi: 10.1109//1P.220338110000
25
WanTeam, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang. Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang. Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng. Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang. Tianyi Gui, Tingyu Weng. Tong Shen, Wei Lin, Wei Wang. Wei Wang. Wenmeng Zhou, Wente Wang, Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang, Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu, and Ziyu Liu. Wan: Open and advanced large-scale video generative models, 2025. URL https: /aaxivvvvvv. org/ abs/ 2503 20314. 1, 3, 5, 6, 8, 10, 22, 24, 25, 28, 31, 32, 33, 34
Junyi Wu, Haoxuan Wang, Yuzhang Shang, Mubarak Shah, and Yan Yan. Ptg4dit: Post-training quantization for diffusion transformers, 2024a. URL https: 16005. 10
/205 2405.
Yushu Wu, Zhixing Zhang, Yanyu Li, Yanwu Xu, Anil Kag, Yang Sui, Huseyin Coskun, Ke Ma, Aleksei Lebedev, Ju Hu, Dimitris Metaxas, Yanzhi Wang, Sergey Tulyakov, and Jian Ren. Snapgen- v: Generating five-second video within five seconds a mobile device, 2024b. URL https:
//gg/aiv.rr//bb/// 2tt.b/////..... 104494.099......9 10494.
10
Haocheng Xi, Shuo Yang. Yilong Zhao, Chenfeng Xu, Muyang Li, Xiuyu Li, Yujun Lin, Han Cai, Jintao Zhang, Dacheng Li, Jianfei Chen, Ion Stoica, Kurt Keutzer, and Song Han. Sparse videogen: Accelerating video diffusion transformers with spatial-temporal sparsity, 2025. URL https://arre wwwiiiioorgggaaee abs 2200. 01 776. 10, 29, 30
16

---

Published as a conference paper at ICLR 2026
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant: Accurate and efficient post-training quantization for large language models, 2024. URL https: //aaxivv org/ abs 2211.22111.1 10438. 22
Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, and Song Han. SANA: Efficient high-resolution text-to-image synthesis with linear diffusion transformers. In The Thirleenth International Conference on Learning Representations, 2025. URL https:
pettps://ppenee http:///eeeeeeeeeeeet.ee.. ott.::/ttt.ooomiii-oo0o1111001
10
Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, and Masashi Sugiyama. On the overlooked pitfalls of weight decay and how to mitigate them: A gradient-norm perspective, 2024. URL
https: /tte:///xxiiv wwwpxvv.oogr/ee. aaaaa///aaa /2011 11152. 4, 24
Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, and Andreas Geiger. Unifying flow, stereo and depth estimation, 2023a. URL httts::/aaxxiy org abs ab2/2211. /2211. 05783. 24
Zhongcong Xu, Jianfeng Zhang, Jun Hao Liew, Hanshu Yan, Jia- Liu, Chenxu Zhang, Jiashi Feng, and Mike Zheng Shou. Magicanimate: Temporally consistent human image animation using diffusion model, 2023b. URL /tap::://axxv..../ org/ abs 2311. 68.8. 10
Huanrui Yang. Minxue Tang, Wei Wen, Feng Yan, Daniel Hu, Ang Li, Hai Li, and Yiran Chen. Learning low-rank deep neural networks via singular vector orthogonality regularization and singular value sparsification, 2020. URL pettps::/arxee org/ abs /ass/20000 2000000000000 09031. 2, 5, 27
Zhuoyi Yang, Jiayan Teng. Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Yuxuan Zhang, Weihan Wang, Yean Cheng, Bin Xu, Xiaotao Gu, Yuxiao Dong, and Jie Tang. Cogvideox: Text-to-video diffusion models with expert transformer, 2025. URL https: /tapps://axiivii www//og///aa...oooo... 2408 06072. 1, 3, 3, 6, 7, 8, 10, 22, 24, 25, 26, 28, 31, 32,
Tianwei Yin, Qiang Zhang, Richard Zhang, William T Freeman, Fredo Durand, Eli Shechtman, and Xun Huang. From slow bidirectional to fast autoregressive video diffusion models. In CVPR, 2025. 10
Jintao Zhang, Jia wei, Pengle Zhang, Jun Zhu, and Jianfci Chen. Sageattention: Accurate 8-bit atten- tion for blug-and-play inference acceleration. In The Thirteenth International Conference on Learn- ing Representations, 2025a. URL httpt::tt///ot /ttp///eeeee.neeeeeeeeee / 10
Peiyuan Zhang, Yongqi Chen, Runlong Su, Hangliang Ding, Ion Stoica, Zhenghong Liu, and Hao Zhang. Fast video generation with sliding tile attention, 2025b. Btlhttsss///xxivv 22s22 04507.
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as perceptual metric, 2018. URL. http:://: http:://///vvvvo/oo abs/ 1801 03924. 25
Xiangyu Zhang, Jianhua Zou, Kaiming He, and Jian Sun. Accelerating very deep convolutional networks for classification and detection, 2015. URL https://arxiv.. org. abs/////////////// /1005. 06798. 2, 2,
Tianchen Zhao, Tongcheng Fang, Haofeng Huang, Rui Wan. Widyadewi Soedarmadji, Enshu Liu, 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 gen- cration. In The Thirteenth International Conference on Learning Representations, 2025a. URL https:/ wpwpneeveewww.cc/////p/p. nnt wttpleewwnet///ouruueeee forummidddooooo www.ddd-EN11xdexx1 6, 7, 10, 22, 28
Wangbo Zhao, Yizeng Han, Jiasheng Tang, Kai Wang, Yibing Song, Gao Huang, Fan Wang, and Yang You. Dynamic diffusion transformer. In The Thirteenth International Conference on Learning Representations, 2025b. http:// http::////ppeevveeeeeeeeee net/ forum d= a=ta 10
17

---

Published as a conference paper at ICLR 2026
Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, and Jiwen Lu. Unipe: A unified predictor- corrector framework for fast sampling of diffusion models. NeurIPS, 2023a. 22
Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, Alban Desmaison, Can Balioglu, Pritam Damania, Bernard Nguyen, Geeta Chauhan, Yuchen Hao, Ajit Mathews, and Shen Li. Pytorch fsdp: Experiences on scaling fully sharded data parallel, 2023b. URL https: /ttpavaxivv/v.///////////////avl orq/ wwwpg////////ooo///o///ooeeeee// 2t.bs/2334.11. 112777. 22
Dian Zheng, Ziqi Huang, Hongbo Liu, Kai Zou, Yinan He, Fan Zhang, Yuanhan Zhang, Jingwen He, Wei-Shi Zheng, Yu Qiao, and Ziwei Liu, Vbench-2.0: Advancing video generation benchmark suite for intrinsic faithfulness, 2025a. URL https: wwwaa.iiivvvo..... abs/ 21755. 6, 8, 23
Kaiwen Zheng, Cheng Lu, Jianfei Chen, and Jun Zhu. Dpm-solver-v3: Improved diffusion ode solver with empirical model statistics, 2023. URL httpss////a/ http://wwwwww.o...aaa/// wtt.://///////oooo//////// 13268.
Xingyu Zheng, Xianglong Liu, Haotong Qin, Xudong Ma, Mingyuan Zhang. Haojie Hao, Jiakai Wang, Zixiang Zhao, Jinyang Guo, and Michele Magno. Binarydm: Accurate weight binarization for efficient diffusion models, 2025b. URL https: /ttp::///aa/ii//i/ http:///////////////// 05662.
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 arXiv:24/2.20404 2024. 3, 10
Chang Zou, Xuyang Liu, Ting Liu, Siteng Huang, and Linfeng Zhang. Accelerating diffusion trans- formers with token-wise feature caching. In The Thirteenth International Conference on Learning Representations, 2025, URL https: //openreview....m net htt./oorrmmmmde wtl.orrmm..dyyyybcoo....... 10
18

---

Published as a conference paper at ICLR 2026
Appendix
CONTENTS
A ALOGRITHM OF QVGEN
19
B PROOF OF THM. 3.1
20
C CONVERGENCE ANALYSIS WITHOUT REQUIRING CONVEXITY
21
MORE EXPERIMENTAL DETAILS
22
E COMPARISON WITH BASELINES UNDER RELATIVELY HIGHER BIT- WIDTH
23
F ADDITIONAL FINE-GRAINED DECAY STRATEGIES
23
G TRAINING LOSS CURVES
24
H FURTHER ANALYSES OF GRADIENT NORM IN VIDEO GENERATION
QAT
24
H.1 VIDEO GENERATION QAT VS. IMAGE GENERATION QAT
24
H.2 IMPACT OF MOTION DYNAMICS ON GRADIENT NORM
24
H.3 DIRECTLY REGULATE THE GRADIENT NORM
25
I COMBINATION WITH SVDQUANT
25
J COMPARISON WITH BASELINES ON ADDITIONAL METRICS
25
K COMPARISON WITH BASELINES FOR HUGE DMS
26
L MORE ABLATION STUDIES
26
L.I ROBUSTNESS OF RANK-DECAY SCHEDULE ACROSS DIFFERENT ANNEALING FUNCTIONS
26
L.2 INITIALIZATION METHODS FOR AUXILIARY MODULES +
26
L.3
COMPLETE RESULTS OF TABLES IN ABLATION STUDIES
26
L.4
ADDITIONAL
REGULARIZATION
27
L.5 DURATION OF EACH DECAY FOR y
28
L.6 WEIGHT-ONLY QUANTIZATION VS. ACTIVATION- QUANTI-
ZATION
28
M RESULTS FOR IMAGE GENERATION
28
N PROFILING AND PROJECTED GAINS FROM KERNEL FUSION
29
O BREAKDOWN LATENCY ANALYSIS
29
QUALITATIVE RESULTS
30
THE USE OF LARGE LANGUAGE MODELS
30
A ALOGRITHM OF QVGEN
We summarize proposed QVGen in Alg. 1. (see Eq. (13)) in this work follows cosine annealing schedule. 2o and denote a clean video clip and its corresponding condition. N is the maximum timestep for training, which is always 1000 (Ho al., 2020b).
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Algorithm 1 Procedure of QVGen framework
FUNC QVGEN(D, i.-per.decca-phase..r.. A.
Require: D training dataset
<o (-)) fuel-prrcision DiT model
t www.eeeddeayypttaeeeee
amount of training iterations per decay phase
initial rank of introduced auxiliary module
shrinking ratio
6 quantization bit-width
// Preprocess
1: Standard uniform b-bit quantization for es to get éạ
D See Eq. (4)
2: Generate all W for ég
b See Eq. (9)
// Start QAT
3: while > X do:
4: Decompose
W to L,, R. with rank r by Eq.(11)
5: Generate by Eq. (13) > u in will decay from to 0 in following it st.per.decca.ppee iterations
6: for it in 0 to it.perrrrr... wew.dddccypheeee
do:
7: Get batched data pair (æo, C) from D
c~NV(0,1)
9:
~~ Un.fformmmm........ N))
10:
Generate æ, by Eq. (1)
11:
Calculate L by Eq. (6)
Eq. (12) is employed in ég Eq. (5) is applicd
12:
Update all W, 8, 2, and (L, R. through nack-propagation
13:
end for
14:
Truncate (L, R. to (L' and regenerate the corresponding We by Eq. (14)
15:
r= (1 X)r
16: end while
D in y will decay from in following 18: Train éø for wtt.per.dccayppeee iterations follow the recipe in Lines 6-13
Generate 7 = [u]nxr
secer.dccaypppare iterations
19: Shrink all We to Ø
b Since
nxr at the end of training, all We can be removed
20: return éμ
B PROOF OF THM. 3.1
Assumption B.1. ft is convex;
Assumption B.2. YO,, 02 ESt,100 0 Doo.
Theorem B.3. The average regret is upper-bounded as:
R(T)
dDa 27
+
+
ET
1g:1.
T
t=
2
Proof. Considering the update for the p-th entry of parameters in quantized video DM:
0 0.+1.p = 0t,p Tt.p
(A)
where nt. is the corresponding learning rate, we have:
0 (0+
07)² (OO.p Ttp Tt. It.p 0
(B)
= (Or.p 07)2 2(0 07)),, + Ri..
Rearrange the equation, and divide 2npp on both side as Ntp is none-zero,
gt.p OL. 07) 2
[0 07) (0.+1.z 0;; + g...
(C)
According to Assm. B.
(.)))) f(( < of (0, 0r).
(D)
Therefore, summing over d dimensions of 0 and T iterations, the regret satisfies:
R(T) <
(0 - 07) (0t+1,p 07 + T 1=1 Ene
gi.p
2T
2
2mp 11,p
0
(0T+1p 0
p=
2
E
(O,p 07)
2= p=
2
2n
DE=
1=1
ni
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Considering Assm. B.2, we can further relax the above inequality to:
D R(T)= S E 1-= 2nL,p
+
2t=2
2p=1
211-1.1
Do Ei= 2p=1
2
L..p
(F)
Denoting the maximum and minimum values for ((t.ppp-,,..... as r/el and v/(" respectively, then:
R(T) S
dD 247
Σ
7
(G)
1=1
Thus, the average regret becomes:
R(T)
dD
+ T
1
Σi=
11
(H)
T
2T
2
L
C CONVERGENCE ANALYSIS WITHOUT REQUIRING CONVEXITY
For completeness, also provide nonconvex convergence result for video DMs under standard smoothness assumptions. Here we consider a (possibly nonconvex) training objective F: + I and the gradient descent updates
0++1 0, 7/:9t
V (O.),
(I)
where t indexes the optimization iterations. Regret analysis in Sec. B follows the standard online learning setting, where the per-step loss St varies with t. In contrast, nonconvex convergence analysis (Bottou et al., 2018; Ghadimi & Lan, 2013) uses fixed deep-learning objective F across iterations, so no subscript needed, and convergence certified by vanishing minimum gradient norm.
Assumption C.1 (Smoothness). The function F is L-smooth, i.e., for all 0, 0' e Ed
[[VF(0) VF S L||0 0'112.
(J)
Equivalently, F satisfies the standard descent inequality:
F(O') s F(0) + (F) (0' -) + 4 00' - 0 12.
(K)
Assumption C.2 (Learning rate bounds). The learning rates satisfy
< 17" T S 1 1 for all 1,
(L)
where 17 and T are the minimum and maximum learning rates across iterations, respectively. Assumption C.3 (Lower-bounded bounded objective). There exists F* such that F(0) 2 F" for all E $d.
Theorem C.4 (Convergence to first-order stationary point). Under Assms. C-I-C.3, the iterates of Eq. (I) satisfy
2 (F((D))=F
+ ET
1gll
<
(M)
T77
Consequently.
minigIST 11 S 1
11
T O.
(N)
Thus, the iterates converge in the standard nonconvex sense to first-order stationary points, in the sense that there exists an iterate with arbitrarily small gradient norm when T is sufficiently large.
Proof. By I-ssmoothness (the descent inequality) and the update 0,41 02
7h9..
F(0-1)) < F(8)) (F(O.) ((0r+1 0,) + 2 0
0.11
=(.))) 11l1ga113 + !n 11. = F(O,)
Lmp 2
11g.11
2.
(0)
Since тソ 5 1/L, we obtain 1 Lnp./2 2 2 1/2, hence
F(0 .) < F(O.) 1g
(P)
We employ the learning rate for the entire quantized DM for simplicity.
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Summing over t {1...., T and using F(0r+1) 2 F* yields
El 3 11 < F(0:) - F*.
(Q)
With 77 2 n²" we obtain
2( 2 Tmm
+ E 19:1
(R)
which proves Eq. (M), Moreover, we have
2 2(F(O))FF)) T T
minggerl 119:11 S E 9
S
T+00 0.
(S)
Remark C.5 (Connection our convex analysis). Thm. C.4 shows that, for any finite T. the conver-
gence behavior (i.e., Eq. (N)) of QAT is determined by the average gradient norm + + l1 l13- Since this quantity admits an (1/TT) upper bound under smoothness and bounded learning rates, reducing ||g|l2 during training directly tightens the bound and improves the finite-step convergence of QAT. This matches our convex regret analysis, where the same average gradient norm appears as the core term controlling convergence. Thus, reducing the gradient norm is essential for improving QAT optimization in both settings.
D MORE EXPERIMENTAL DETAILS
In this section, we provide additional experimental setups.
Models. For testing, we employ DDIM (Song et al., 2021a) and DPM-Solver++ (Lu et al., 2023) for CogVideoX-28 and 1.5-5B models (Yang et al., 2025), respectively. For flow-based Wan models (WanTeam et al., 2025), we additionally apply UniPC (Zhao et al., 2023a) corrector and set Flow_shis ft (Lipman al., 2023) to 3.0 and 5.0 for generating 480p and 720p videos, respectively.
Baselines. For QAT baselines, we employ the same settings as our QVGen, e.g., training iterations, batch size, and optimizer, to make fair comparison. For PTQ baselines, we adopt bit-width in {2, ......., 88 for the mixed-precision strategy proposed in ViDiT-Q (Zhao et al., 2025a). For 4444 group-wise SVDQuant (Li et al., 2025), we retain 16-bit precision for linear layers involved in adaptive normal zal iataiion ion, embedding layers, and the key and value projections in cross-attent normalizations 2024) as pre-procssiing step, and GPTQ (Frantar al., 2023) as a proce-rocessiig step. Except as noted above, we only quantize all linear layers for a given video DM. For the attention module, adopt full-precision
For both W4A4 and W4A6 SVDQuant, we apply SmoothQuant (Xiao et al.,
aash-attentt on (Dao et al., 2022) to speed up inference, which is a common practice. It is also worth noting that, since we only quantize linear layers and employ Flash-attents ion for the attention modules, the "Naive" baseline (see Tab. K) is equivalent to Q-DM (Li et al., 2023b) in this paper.
Training. Before QAT, we resize and center-crop the input frames to match the evaluation resolution, except for Wan 14B, which uses 480p during QAT to reduce training costs. We then sample video clips containing 49 frames at equal- 2025) and Cog VideoX 1.5-5B (Yang et al., 2025) are trained using PyTorch FSDP (Zhao et al., 2023b), while other diffusion models are trained with DeepSpeed 7eRO-2 (Rajbhandari et al., 2020). Specifically,
intervals. During QAT, Wan 14B (WanTeam al.,
adopt warm-up phase spanning 1 of the total training epochs, and set the global batch size to 48 for Wan 1.3B and 64 for all other models. A cosine annealing schedule is applied to the learning rate ? initialized at 3 x 10-5 for moderate-sized models (22B), 5 x 10- for Cog VideoX1..55BB. and 10 for Wan 14B. For weight quantization parameters, we adopt LSQ (Esser et al., 2020b) with an initial learning rate of 3 10-5, 5, As described in the preliminary section of the main text, we use the straight-through estimator (STE) (Bengio et al., 2013) to ensure the differentiability of the quantization process. To be noted, when the remaining rank r < A we directly apply cosine annealing function (i.e. 7 = [u])x²) to gradually shrink the remaining W. to 2. Moreover, the training days are summarized in Tab. A.
Evaluation. For VBench (Huang et al., 2024c), we test 1~2B models on 8XH100 GPUs. For huge DMs, we evaluate Cog VideeX11.555 on 64xH100 GPUs and Wan 14B on 128xH100 GPUs for
Thhis mentioned learning rate is applied to both model weights and the introduced +
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Table A: #GPU days (H100) across different models. We present the increased time of QVGen compared with the naive QAT in KD-based manner red subscripts. QVGen, which uses r 32 this paper, incurs negligible time overhead but significantly higher performance (see Tab. K) than the naive method.
Method/Model CogVideoX-28
wan 1:3B
CogVideoXI.5-BB wan
QVGen (Ours)
9.4440 11.11 +0.38
~-18
both VBench and VBench-2.0 (Zheng et al., 2025a). The batch size is set to one per GPU, and each run completes within one day. Besides, we sample videos for each unaugmented text prompt across the 8 dimensions in VBench. For VBench-2.0 2.0 (Zheng et al., 2025a), we generate 3 videos per augmented text prompt across all dimensions, except for the Diversity dimension, where we generate 20 videos for each prompt.
E COMPARISON WITH BASELINES UNDER RELATIVELY HIGHER BIT-WIDTH
Besides 3/4-bit settings, we also con- duct experiments under 6. As shown in Tab. B, our QVGen achieves full-precision comparable or even bet- ter performance than BF models. Since these settings are not challeng- ing enough for QAT baselines, which also demonstrate satisfactory perfor- mance, our QVGen only shows mod- erate improvements.
Table B: Performance comparison across different methods on
VBench under w W6A6 quantization for Wan 1.3B.
Imaging. Quality
Acsthetic, Quality
Dynamic, Degree
Methol
Consistency'
Consistency
Pull Prec.
64.30
58.21
70.28
28.05
24.67
SVDQuast (Li al,, 2025)
62.05
54.37
71.08
23.25
24.56
20006)
63.20
24.86
24.07
Q-DM (Li al., 20236)
62.89
56.24
74.64
28.38
25.12
EfficienDDM (Hl al., 2024)
63.38
56.42
68.68
21.47
23.57
QVGen (Ours)
64.27
57.69
78.02
26.84
25.53
F ADDITIONAL FINE GRAINNDD DECAY STRATEGIES
In this section, we detail the "Sparse" and "Res. Q." decay strategies (see Tab. N), which shrink d to Ø, as follows:
"Sparse" strategy. Inspired by pruning techniques (Han et al., 2015; 2016), at the start of QAT, we sparsify W.p with sparse ratio of a% 50%. Specifically, we set the a% mmallest-magnituee values Wa to zero and freeze them in the whole training process. The quantized model then trained with the remaining non-zero values in W4. We divide the training process into 6 equal-length phases. In each subsequent phase, we set an additional / % of the remaining non-zor zerz values in Wa & to and update
The resulting cumulative sparse ratios across the phases are as follows: 50%(1-s phase) - 75% phase) > >> 96.895 96.875%65-t phase) > 100% phase)-
"Res. Q" strategy. Motivated by residual quantization (Li al., 2017), we first decompose W into a series of 4-bit quantized residuals. At the beginning of QAT, we quantize W to its 4- bit approximation (W(),), and then recursively quantize the quantization-indee
residuals. Specifically, we define E, W. Q+ and quantize it to Q₁ (E₁): ): the remaining residual Eg E, (E, is quantized to (E(E) and finally, Eg = Eg (E2) is quantized to (E This yields 4-term additive decomposition of Wa:
We = (+) + (E,) + (E,) + (E.).
These 4x4-bi quantized tensors* are jointly trained with the rest of the quantized model. During QAT, divide the training process into 4 equal-length phases, and apply cosine annealing within each phase to progressively decay these components in the following order: 2, (1-) phase)
24 (E2) phase) + Q4 (E)) (3-rdd phase) > 2+ ( +44-th phase)-
Compared with our rank-decay strategy (with r 32), both alternative methods require storing multiple tensors", each with the same shape as the corresponding linear layer's weight matrix. In particular, the "Res. Q." strategy incurs substantial memory overhead and necessitates CPU
*The use these 4x 4-hit tensors is designed to align with original BF1 6 format, as xx 4-hit data could theoretically match a 16-bit representation. "hhe "Sparse" strategy additionally requires storing binary masks enforce sparsity.
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offloading to avoid out-of-memoor (OOM) issues. Furthermore, the "Sparse" strategy introduces extra computational cost due to the matrix multiplication between the mask and W.. g. In practice, the "Sparse" and "Res. Q." strategies consume 20.12 and 28. 78 GPU days, respectively, whereas our proposed rank-decay requires only 11.11 GPU days (see Tab. A). More importantly, rank-decay also achieves significantly hetter performance than both alternatives, demonstrated in Tab. N.
G TRAINING LOss CURVES
In this section, we present training loss curves across different methods and models. As shown in Fig. A, QVGen and our method in Sec. 3.1 achieve faster and more stable convergence, which supports the effectiveness of the proposed approaches. Note that LSQ (Esser et al., 2020b) uses with I, a, + o,C C as its training objective, instead of Eq. (6)
Em. ||| co (x,, C. T)1
used by the remaining distillation-baset methods.
(a) Cog (Yang al., 2025)
(b) Man 1.3B (WanTeam al., 2025)
Q.04-Q.DM
WANALSO
W4.44 Q DM
WL44 LSQ WIM QVGez
WiAd 0
WANN QUGez
WEA4 O
EffickeeDMi
ElkienDDM
Triiing Loss
0.6
Trining Loss
0.1
1802
300
100
1807
2000
0.2
1500
2000
1000
2000
#Training Steps
#Training Steps
Figure Training loss vs. #steps across different video DMs and 4-bit QAT methods. "I" denotes our approach in Sec. 3.1
H FURTHER ANALYSES OF GRADIENT NORM IN VIDEO GENERATION QAT
H.1 VIDEO GENERATION QAT vs. IMAGE GENERATION QAT
First, we conduct experiments to compare the gradient norm between image generation QAT and video generation QAT. In Tab. C, we employ W4A4 Q-DM (Li et al., 2023b) to quantize the diffusion models under the same settings. We observe that reducing the gradient norm during QAT, ignored by previous research (Li et al., 2023b; He et al., 2024; Esser et al., 2020b), far more critical for video generation QAT than for image generation QAT. As Tab. C shows, with similar parameter count and the same QAT recipe, video diffusion reaches significantly larger gradient norm than image diffusion. This leads much more unstable optimization (Xie al., 2024) in training. We believe the phenomenon happens because video generation introduces complicated temporal modeling, which eventually makes quantization for video diffusion more challenging than image diffusion.
Table C: Average gradient norm comparison. SD3-medium (Esser et al., 2024a) is an advanced diffusion model for image generation. We train SD3-medium for 2K steps with 16K images from the LAION-5B dataset (Schuhmann et al., 2022) on 8xH100 GPUs "Avg. ||g: 22" the average the gradient norm across training steps.
Model
SD3-mnediun Cog VidexXX0
Avg. ||g]la l
0.2047
0.3283
#Params. (B) 2.0
2.0
H.2 IMPACT OF MOTION DYNAMICS ON GRADIENT NORM
Here, we study how the motion dynamics of video generation affect the gradient norm. Specifically, using UniMatch (Xu et al., 2023a), we compute an optical-flow score as motion difference score for each clip and split the training data (Sec. 4.1) into high-motion and low-motion subsets, each with 8K videos. In Tab. D, the model trained on the low-motion subset shows a much lower average gradient norm and a better Imaging Quality score, but its Dynamic Degree is lower than that of the model
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Table D: Results between different training vidcos. We employ W4&4 QVGen with the same configurations those in Tab. to quantize Cog VideoX-2B (Yang et al., 2025).
trained on the high-motion subset. This pattern suggests that a high- motion training set makes QAT harder and less stable (i.e., higher gradi- norm), lowering static quality but boosting motion quality. The reverse holds for low-motion clips. To be noted, with mixed dataset (containing low-motion + high-motion) in the 4-th row of Tab. D, the quantized model generalizes well in produeing either high-motion or low-motion content. There- fore, although low-motion dataset in training can cause slightly lower I|g+ (ie., better training convergence), it is necessary to properly add high-motion data to improve motion quality.
8K Training Videos
Avg.
[inggnngg Quality? Dynamic Degreet
high nigh-motion
0.1972
57.47
68.21
low-motion
0.1768
60.63
62.54
half high-motion half low-motion]
0.1821
60.12
67.08
H.3 DIRECTLY REGULATE THE GRADIENT NORM
In this subsection, we study the effect of directly constraining the gradient norm during QAT. We use torch. nn .n..niilll....... cl clip-gge pag.pp-radddd grad_norm. to rescale the gradients. As shown Tab. E, reducing the clipping threshold from 1.0 to 0.5 improves performance, which supports the benefit of controlling gradient norms for video generation QAT. However, a threshold of 0.1 causes clear performance degradation, likely because the quantized model is updated too weakly or aggressive gradient clipping disrupts normal QAT. This highlights the need for more principled ways to reduce the gradient norm.
Table E: W4A4 results for Wan 1.3B under different thresholds for gradient clipping. "1.0" corresponds to our baseline Q-DM (Li al., 2023b). We use a clipping threshold of 1.0 for all other experiments in the paper.
Gra..
Imaging, Aesthetic, Quality
Dynamic, Degree
Scene Consistency
Overall Consistancy
Clipping Quality
1.0
60.40
$2.50
76.67
13.28
21.63
0.5
68.58
$2.95
76.50
13.28
21.71
0.1
56.68
51.06
71.00
12.28
21.42
1 COMBINATION WITH SVDQUANT
In Tab. F, we show that our method can be combined with the current SOTA PTQ method SVDQuant (Li et al., 2025). We first apply SVDQuant to obtain a weight-modified DM, the quan- tization parameters, and the low-rank matrices, which we reuse t. We then run QVGen, which progressively removes Q. This combination yields further gains, likely due to the strong initialization from SVDQuant. We also evaluate an option that updates only the quantization parameters and & within this combination. It achieves sizable improvements over SVDQuant alone, but it still falls short of QVGen when model weights are updated. This suggests that QAT, which trains model weights, is currently important for video generation quantization. We will continue to explore more efficient ways to quantize video DMs while preserving performance.
Table F: W4A4 results the combination with SVDQuant (Li et al., 2025) for Wan 1.3B (WanTeam et al., 2025). "C" denotes we freeze the weights of the DM and only finetune the quantization parameters and the introduced & means we employ more fine- teaigrdined and performance friiimd quantization setting as in SVDQuant's paper (details can be found in Sec. D).
Method
Imaging, Aesthetic Quality
Dynamic. Degree
Scene Consistency
Overall Consistency
Quality
Full Prec.
64.30
58.21
70.28
28.05
24.67
SVDQuamt* (Li 2025)
$7.57
46.30
72.22
12.73
21.91
QVGen
63.08
54.67
77.78
15.32
23.01
QVGen w/ SVDQuant (l. er al., 2025)
63.64
56.23
77.42
17.65
23,89
QVGen w/ SVDQuant (Li et al,, 2025)
61.38
$2.76
75.85
14.12
22.47
J COMPARISON WITH BASELINES ON ADDITIONAL METRICS
We also evaluate the similarity between videos generated by different quantization methods and those generated by BF16 6 models on VBench (Huang et al., 2024c) captions. Specifically, we employ PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity) (Wang et al., 2004), and LPIPS (Learned Perceptual Image Patch Similarity) (Zhang et al., 2018). In Tab. G, QVGen substantially outperforms the baselines on these metrics.
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Table G: Additional #444 performance comparison across different quantization methods. We employ the same models those Tab. I.
CogVideoX-2B
Wan 1.3B
Method
PSNR1 SSIM1 LPIPS. PSNR
SSIM LPIPS..
SVDQuant (Li 2025)
11.06 0.3829 0.6305
10.14 0.3595 0.6907
LSQ (Esser et al., 2020b)
11.75 0,4158 0.6187
11.65
0.4743 0.6235
Q-DM (l. al,, 2023h)
12.07 0,4270 0.6240
11.22 0.4657 0.5942
EfficientDM (He et al., 2024)
11.91 0.4387 0.6220
11.29
0.3926 0.6232
QVGen (Ours)
16.74 0.6085 0.4127
15.94
0.5782 0.4887
K COMPARISON WITH BASELINES FOR HUGE DMS
We include comparison on VBench for large-scale 15 5B in Tab. H. QVGen again surpasses all baselines, just as it does on smaller models, underscoring its strength across wide range of model sizes. Limited resources currently prevent us from adding more baselines for these large-scale models, but we plan to do so in future work.
Table H: Performance comparison for huge DMs across different
methods on VBench (Huang ct al., 2024c). We employ w4A4
Cog V 5B (Yang et al., 2025) here.
Method
Imaging, Assheric, Dynamic, Quality
Quality
Degree
Scene Consistincy
Consistency
Full Prec.
61.15
74.24
13.86
22.52
(Li al., 2023b)
61.72
54,01
72.41
14.17
22.31
EfficienDM (He et al., 2024)
61.72
$4.01
72.41
14.17
22.31
QVGen (Ours)
63.08
77.78
15.32
23.01
L MORE ABLATION STUDIES
L.1 ROBUSTNESS OF Rank-Decay SCHEDULE ACROSS DIFFERENT ANNEALING FUNCTIONS
In this section. we test 5 different annealing functions for u. The results in Tab. I reveal that all the functions yield comparable performance, highlighting the robustness of our approach.
Table I: Results different annealing factors u. them decay from work.
We employ "Cosine" this
Imaging, Aesihetic, Quality
Mation Smoothness
Dynumic, Background, Degree
Subject Cresisioncy
Scune Consistency
Quality
Consistency
Consistency
Cosine
63,08
$4.67
98.25
77.78
91.08
92.57
15.32
23.01
Loganithmic
Exponential
63.04
$4.48
97.88
77,81
93.96
92.56
15.32
22.66
Square
Linear
63.10
54.63
98.31
77.44
94.06
92.58
15:24
22,91
L.2 INITIALIZATION METHODS FOR AUXILIARY MODULES Ø
Besides employing W - 2&((W W) to initialize Wa, we provide alternative initialization approach that considers both weight and activation effects. Specifically, we train each W. for 200 iterations to minimize the quantization error of its corresponding linear layer's output (i.e., arg minw, e Y
(1)))). The resulting layer- wise trained W is then used to initialize Φ. As shown in Tab. both initialization strategies yield similar performance. Therefore, we believe that QVGen is not sensitive such choices of initialization. Moreover, we also consider two deliberately suboptimal initialization approaches: zero initialization (i.e., "0"") and initialization with parameters randomly sampled from normal distribution (i.e., "Random"). The "0" scheme leads to slight performance degradation. while "Random" causes more noticeable performance drop. We attribute this to the fact that "0" does not compensate for quantization errors, whereas "Random" further injects noise into the model.
L.3 COMPLETE RESULTS OF TABLES ABLATION STUDIES
In Tabs. K to N, present the complete ablation results across all 8 dimensions VBench (Huang et al., 2024c), corresponding to the incomplete versions shown the ablation study of the main text. These results are consistent with the analyses provided in the main text.
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Table J: w444 results of different initialization strategies for Wa
Imaging, Acshetic, Dynamic, Quality
Scene Consistency
Overal Consistancy
Strategy
Quality
Degres
CogVideoX 2B (CFG
6.0, 480pp,
W ((W)
60.16
54.61
67.22
31.42
24.61
Laser-wise Train
59,97
54.84
66.71
31.14
25.02
$9.86
54.47
65.59
31.32
24
Random
49.42
37.68
26.57
6.24
11.68
Man
(CFG 5.0, 180p, 1ps
W Qx W)
63.08
54.67
77.78
15.32
23.01
Layer-wise Triin
62,80
54.59
77.69
15.28
22.98
Random
54,41
44.14
34,44
3.13
10.17
Table K: Complete ablation results of each component. "Naive" denotes naive QAT in KD-based manner.
" "-decay" denotes the setting where We LR initialized with r 32 but not climinated during QAT. comparable performance between " Decay" and " 40 validates that low-rank setting with r < (as mentioned in the main text) is sufficient, Furthermare, the negligible performance loss of 4 Rank" compared to --eaayy confirms the effectiveness of our proposed decay strategy.
Method
Imaping. Aestheiic Quality
Quality
Motion Smocchhess
Dynamic, Rackground, Degree
Crusisiency
Suhjact Consisenny'
Scene Consistency"
Ovenll Consistency
Naive
60:40
52.50
97.22
36.67
93.37
89.26
13.28
21.63
++b
63.41
54.75
98.40
77.89
94.36
93.29
13.51
22.98
Rank
63.08
51.67
98.25
27.78
91.08
92.57
15:32
23,01
Decay
63.32
51.61
98.31
77.79
91.15
92.61
15:10
23.03
Table L: Complete results of different shrinking ratios À for cach decay phase. À = 1 means directly decaying the entire W..
[tnaging, Quility
Anstetiic Quality
Motion Smootinesss
Dynamic, Degree
Rackgroand, Consistency
Subject Consistency
Scene Consistency
Ovemil Consisteney
1/4
54.23
76.8
94.02
15.18
1/2
63.08
54.67
98.25
77.78
94.00K
92.57
15.32
3/4
62.89
51.62
98.15
77.91
93.89
91.63
15.04
22.89
52.48
97.31
76.48
1382
21.81
Table M: Complete results of different initial ranks
represents "Naive" in Tab. K.
Imaging, Quality
Aesihetic, Quality
Mution Smouthness
Dynamic, Degree
Background, Consistency
Subject Consisiency
Scene Consistency
Overall Consisionuy
8
62.71
54.47
97.95
74.62
93.76
91.05
14,42
22.81
16
62.99
54.62
98.31
76.58
93.92
91.82
14.84
23.00
34.67
64
63.06
54.30 10.30
98.18
26.24
94.01
91.49
15.40
22.92
Table N: Complete results of different decay strategies. "Rank" denotes the rank-decay strategy in this work. To be noted, the "Sparse" and "Res. Q." strategies incur substantially 1.81x and 2.60x GPU days for training compared with the "Rank" approach, respectively (see Sec. F).
Decay lmmainn. Acsthetic,
Motion Smoothness
Dynumic, Background
Subject Coniitency'
Scene Consistency
Overall Consisiency
Stralegy Quality
Quality
Degroc
Consistancy
Spanse
61.15
24.24
22.52
Res. Q
61.72
$1.01
97.62
72.41
93.46
91.21
14.17
22.31
L.4 ADDITIONAL RANK-BASED REGULARIZATION Y
In this section, conduct experiments to validate the superiority of the proposed rank-based regularization compared with additional vank-based regularization. As shown in Tab. O, the set- ting "concat (concat((1)xx11-X) [1)
[w] (x] (adopted in this work) outperforms both "Randomx3" and
(concat([u)...) Ar, (1))r)) )" by a large margin. Moreover, the results in the table confirm our idea that removing by repeatedly decaying components of W associated with small singular values maintains performance. This also reflects that components associated with small singular values contribute little (Zhang et al., 2015; Yang et al., 2020) under the setting of this paper (i.e., jointly training + and the quantized video DM during QAT).
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Table O: Results of different y. "concat(ui)xxa wxxr, (1-))(1)))))" denotes the setting where components of W associated with the largest singular values are decayed each phase. "Random x3" represents performance over 3 experiments. each employing a different randomly generated per decay phase. Specifically, a random index set Su = {bi, b2, bx- is sampled such that b, E 21, } and vi o j.b, * by. We then define the complementary index set S1 {1, 2, ...., S.. The decay matrix y IR" is constructed by setting S.
average
n ur and Y = nx (1-x) A)r- The green subscripts indicate the standard deviations.
Imaging, Quality
Aeshheic, Quality
Motion
Dynamic,
Backgroond, Consistancy
Subject Consistency
Scene Comsistency
Overall Consistency
Smouthnes
Degree
concall exil-ba Kln (Maxsr))
63A88
54.67
98.25
77.78
92.57
15.32
23.01
Random x3
60.%
53 13,20
40.0
76.45.u
23.44,.0000
90.76-0300
13,77.0
21.92_uax
concant bl (Xxe) Xie]
97.28
13,46
22.24
L.5 DURATION OF EACH DECAY FOR Y
Here, we discuss the situation if 4's rank is diminished too rapidly relative to the schedule 7. In this case, we believe that keeping the redundant parts (that is, the rank-diminished components in b) during QAT does not harm performance and can even lead small improvement. This mainly because the slow change of in (1 + 0) increases the training time by making each decay phase longer. The results the following table support this intuition.
Table P: W4A4 quantization results different durations of each decay phase for Wan 1.3B. We control the duration to determine changing speed the schedule 7. When applying a long duration, we suggest O's rank is diminished (as an intrinsic behavior) rapidly relative to the schedule 7-
Dunation Imaging, (Fpoch) Quility
Dynamic, Degree
Quality
Consistency' Consistency
3/4
63.08
54.62
77.78
15.32
23:01
63.07
35,09
77.54
15.29
23.03
3/2
63,11
51,58
78.15
15.36
23.01
I.66 WEIGHT-ONLY QUANTIZATION vs. ACTIVATION-ONII QUANTIZATION
As demonstrated in Fig. B, activation-only quantization causes severe degradation in video generation quality compared with weight-only quantization under the 4-bit setting. This indicates that the activations of video generation models are much harder to quantize than the weights. Similar observations have also been reported in previous studies (Zhao et al., 2025a; Tian et al., 2024).
(a) BF16
(b) BF16
(c) N0A16
(d) 04416
(e) N1 62.4
( #1 6A.4
Figure B: Performacne for weight-only quantization activation-only quantization. We employ Min- max (Nagel et al., 2021) per-channel weight quantization and pok-token activation quantization for (Lefi) CogVideoX-28 (Yang et al., 2025) and (Right) Wan 1.3B (ea et al., 2025).
M RESULTS FOR IMAGE GENERATION
Our theoretical insight is broadly applicable to QAT. Additionally, the proposed QAT strategy is independent of model architecture and data type, so it can be transferred to other domains. However, further studies are needed to confirm whether the same strategy and insight can deliver similar significant improvements in other tasks. To be specific, the large gains report for video generation
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rely on the observed behavior of the gradient norm and the singular values during QAT for video diffusion. Here, we report the initial results for image generation in Tab. Q. which show that our approach can also achieve non- -on-nelli performance enhancement without additional inference overhead. We will explore more about this in the future.
Table Q: W4A4 quantization results for SD3-medium (Esser al., 2024b). We evaluate FID (Heusel et al., 2018) and CLIP score (Hessel et al., 2022) on the MJHQQ30K (Li et al., 2024a) dataset and employ GenEval (Ghosh ct al., 2023) to further measure text-image alignment.
Mathod
FDD CLIP Soore GenEvall"
Full Pree.
11.92
27.83
0.62
LSQ (Esser al. 20206)
14.87
27.72
EfacicniDM (He et al., 2020
15.23
27.11
0.61
Q.DM (Li al., 2023b)
13.82
27.68
QVGen (Ours)
12.24
27.85
0.61
N PROFILING AND PROJECTED GAINS FROM KERNEL FUSION
Table R: Latency breakdown (ms) and INT4 GEMM throughput on A800. For ease of analysis, adopt the shapes from Man 1.3B. DiT block for the model is composed Self-Attentin,, /gl////v/////// and "q2/k2/ve v2/022" denote projections Self- self-Attention and croossAtteetton, respectively.
and FFN.
Op.
(M,
Quant INTAGAAY DeQuant
Total
quant (%)
INT4GEMK Dequant
(%)
(%)
TOPS
q /L/kL//LLoL/e2/e2 k2/v2 up prod Jown-proo
(82760, 1536, 1536)
0.041
0.314
0.341
12.0
54.5
831.08
(512, 1536, 1536)
0.001
0.007
0.002
0.010
11.2
72.3
16.5
345.13
(32760, 8960, 1536)
0.041
1.246
0.661
1.948
2,10
64.0
33.9
721.76
(32760, 1536, 5960)
0.232
0.117
L.30
74.7
872.27
To better understand the efficiency bottlenecks in our current non-fused INT4 implementation, we profile representative transformer operators on an NVIDIA A800 (SM80) by annotating stages such as activation quantization (Quant), INT4 general matrix multiplication (INT GEMM), and dequantization (DeQuant) with NVTX ranges (torch. cuda nnvx.. wwaggeppus/pppeeee each operator in Tab. R, we report its GEMM dimensions (M, K, N) (i.e., [M, K K]{[K, NN), per-stage latency, the fraction of the total operator time, and the effective INT4 GEMM throughput computed as
For
TOPS-
2MNK 10
(U)
where t 412r4 is the measured INT4GEMS time in seconds. Across all tested shapes, the INT4
GEMM kernels achieve 345-872 TOPS, within the same order of magnitude as the A800's INT4 tensor-core peak (1248 TOPS). However, the surrounding non-GEMM stages (activation quantization and dequantization) still account for 25-45% of the operator latency, primarily due to extra global memory traffic and the absence of fused epilogues. Given the measured GEMM time fraction G and assuming kernel fusion removes a fraction of non-GEMM overhead (e.g., fusing quant/dequant and avoiding intermediate reads/writes), the achievable speedup is approximated by
p ワ-(1-クル1ー))
(V)
Using our NVTX-derived G values, r = 0.6 yields p R 1.18x (down.pro 1.20x (k2/v2), 1.28x x (up-pro5), 1.38x hg1//l/vll /ol / q2 o2); and r as 0.8 yields pmu 1.25x, 1.28x, 1.40x, and x,57x, respectively. These results indicate that a 1.2-1.6x per-layer speedup is a realistic target once fusion is introduced.
O BREAKDOWN LATENCY ANALYSIS
Because video DiT is implemented of a single DiT block to estimate its end-to-end impact (see Tabs. S-U). Within this block, attention computation (51.8%) is the dominant cost, while linear projections (24.5%) account for a large share of the remaining latency. To be noted, components other than linear projections can be accelerated by orthogonal strategies:
stack of identical blocks, report the latency breakdown
Attention: Sparse attention, such as SVG (Xi et al., 2025), achieves a 1.73x speedup for Self- Attention computation (31.48 us. 18.23).
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Other: This category is largely composed of memory-boune operations, including RoPE, norm, and reshape, etc. These operations often launch many small kernels, so techniques such as CUDA Graphs and torch. compile reduce dispatch overhead and enable more effective kernel fusion. Additionally, combined with fused and layout-aware kernels (Xi et al., 2025), the runtime of this category can be reduced by 5.59x (14.64 vs. 2.620).
With these strategies applied, linear projections occupy a non-trivial 41.34 .88% of the block runtime. Therefore, reducing the latency of the linear projections is an important step toward further end-to-end speedups. In this work, W4A4 quantization achieves a 2.52x speedup for these linear projections (15.14 vs. 5.991). addition, we plan to extend QVGen W4A4 attention quantization, which can further accelerate attention computation.
Table S: Latency breakdown (ms) for DiT block (implemented in torch) on A800 (Wan 1.3B).
Table T: Latency breakdown (ms)
for linear projections.
Table U: Latency break-
down (ms) for attention.
Time (ms)
Linear projections
Time (ms)
Share (%)
Time
Component
Attention
(ms)
gl/k1/v//ul/<////
0.977
Attention
32.08
51.8
k2/v2
0.038
Self-Artention
31.48 Cros-Atlention
Lineur Projections
24.5
up-pruj
5.283
0.597
Other
14.64
23.7
down-proj
3.913
P QUALITATIVE RESULTS
In this section, we present random samples generated by video DMs without cherry-picking as exhibited from Figs. C-J. For a detailed comparison, zoom in to closely examine the relevant frames.
3-bit quantization. shown in Figs. and D. our method QVGen far outperforms other baselines under 3-bit quantization. Although 3-bit quantization still introduces noticeable performance degra- dation, especially for huge DMs (see Figs. and F), we believe QVGen represents a promising step toward practical ultra-low-bit video DMs.
4-bit quantization. As depicted in Figs, G and H. previous QAT methods fail to deliver satisfactory results. In contrast, our method QVGen achieves video quality that closely approaches that of the full-precision model. Furthermore, for huge models (see Figs. and J), QVGen consistently maintains high visual fidelity and effectively preserves generation quality.
Q THE USE OF LARGE LANGUAGE MODELS
We acknowledge the use of large language models (LLMs), such OpenAl's GPT-5, as a writing- assistance tool in this work. Their role was strictly limited to proofreading and rephrasing sentences enhance linguistic quality, without any contribution the research ideation experimental results.
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BF16
33A3 QGn (Ours)
(c) #383 EtficienDMM (Hc al. 2024)
(d) #383 Q-DM (Li et 20236)
(e) M3A3 T.QQ (Fisee et al., 2020h)
Test procape: "A detailed wooden ship with intricatell carved and saiis gliding mootlly a plush, ble carper that muniss The ship's null is patated rich brown, with tiny windows, The curper, seft and rowwee, provides a perfect backdrop, resembllin on occunic expanse. Survondinng the ship vartous other and children's items, hinting planful envinament. journey gymbolizing endless adventures in whimnical indoor wetting"
the
cuptures insocence and imagination childhood with the toy
Figure C: Comparison of samples generated by full- full-precision and 3-bit Cog Vidcoo-2BB (Yang ct al., 2025).
BF16
w3A3 QVGin (Ours)
(c) #3A3 EflicienDMM (Ile al. 2024)
(d) w3A3 Q DM (Li et al., 20236)
93&3 LSQ (Esser al., 20206)
Tex: peompi: "The camera follows behind while vintage SUY with black roof rack speeds up a steep dirs rad surrondeed by pine wrer on a steep mountain slope, dust lickr from itr tires, the sunlight shines the SUV speedl along the dirt medl caring ware glow ever the reene. The dirt mod curver gondly into the dissunce, from the rear following the curve with eave, making seom if rugged drive through the rugged termain. The dirt road inself currounded by step hille and moantains, clear bloe sky aboue with wispy clouds"
ofher car vhiille in sight. ters rither side the are redinudd, with patcher of gremeryy wosterd thrughout The oor
Figure D: Comparison of samples generated by full- full-precision and 3-bit Wan 1.3B (WanTeam et al., 2025).
(a) BF15
(b) h 3A3 QVGen (Ours)
Text prompt: "On comllt brach, smoll, intricately detuiled somicastle stands the shoreline, its turnets and walls casting delicate chadonss golden sand. the gontle waves lop nearby, the cartee begins to transfomm growing taller and more claborate with each passing moment, Its towers mrrtch skyaadd adorned with reashells and senved, while intricate patterns
its expanding walls. The mujestic fortves, complete with most and faygs futtering haverr, testoment the magic imagination and the sen's simeiess beauy""
gion, hightightnng the caste's is evelving grandeur. Finully, ramds
Figure E: Comparison of samples generated by full-precision and 3-bit VideoX1.
(Yang et al., 2025).
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(a)
QVGen (Ours)
prosape goiden innaliy posmonnd 102 af juicy apple. dog, nasging
sonsniing interminng begins towands the upple, eicatually coming pireful nop right yoon of The backgrosud sunay backyurd with grven gruss and some flowers in the distunee. hightight
zeene
anootll, captring dog't dog's monemmnts and exprersions. The lighting bright and natoral emphorizing the textures of the dog fur and the apple."
lively
filmed donamic ardle, close ups
Figure F: Comparison of samples generated by full cilecicciioonn and 3-bit Wan 14B (WanTcam et al., 2025).
(a) BF16
94A4 QVGen (Ours)
(c) EfficienDDM (He al,, 2024)
(d) #424 Q-DM (Li et al. 20236)
(Esser al., 20206)
Text grompe: "A mujeestic Siberian Husky mands stop snowy mucad, its shorp blue eyes filled with uneeliienne, The dog's thick brack and white jur contrnts against the mwilight dky, where clouds drift poacefully, Bare sireound the standing guardiuns the fading light" K4A QVGen (Ours)
BP16
(h) k444 EfficienDMM (Ile al., 2024)
M444 Q.DM (Li at,, 2023b)
W424 (Esser al., 2020b)
prompe: graud, medieval hall, scoee Biokering canales that cast seft, invitinn light. The air is filled with souse of timelesmers and reverence, A slogie, vibrant conaie burs brightly in the foregroond, faae duncing genily atop
warm golden plon. long wouden strviches distance,
holder""
Figure G: Comparison of samples generated by full alll-precision and 4-bit Cog VideoX 2B (Yang et al., 2025).
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QGGn (Ours)
2424 EfficienDDM (He et al,, 2024)
#424 Q-DM (Li et al., 20236)
(Esser al., 20206)
Test peompt: "A mun with nasled durk hair stands in dramatic landsrape, his eyez blainn with fury surwyy the chantic some arownd him. Clad in ougged amid backdiop crambling mountalus and valley linered with abandoned Plgg. The sky is overaa adhing u somber tone the ansosphese, accentating his enotimai intenalty The cunern captures medium shot, forwsing on his tonor expression desolation surconding BP16
loather jasket, slightts reveuling determined
wottered
visuat cinomatic with ligh
the and powerful mood of KARS QVGen (Ours)
M al., 2024)
Q-DM 2023b)
ii LSQ
al., 2020b)
Text procptt:: "An ciderly gentfeman, with serene exprersion, sirs nie water 5 etge. a reaming cup of tea by his ndc. engrossed his artwar,, brush hund. he ronders oll painting loose fitting white stirr, while salty adds on intongible clement to masterpiece in progress. The scone one tranquility and insprration, with the artiet'
propped
weathored The
whispers through his siber hatc gendl billowing
captoring the vibrum hues the Figure H: Comparison of samples generated hy full-precision and 4-bit 1.3B (WanTeam al., 2025).
reflecting off the traquil wa."
(a) BE
(b) W4A4 QVGen (Ours)
Text prompt. "A playiul squirel, for of brmw and giay, manuls behind large rock, around it contiousty The rock siss the buse mill tree, mick zanning left high. quickly sourries
sumounded byy potch of wildffower. swift squirel darty out from acveus the fovest floos, muving smootlll over the rocks and fallon ioaves, continus its journey"" BP
(d) W4X4 QVGen (Ours)
prompt: "A liveyy tabby with strking dathes sunit memlow, itr gleoming the golden tight. The agile movemonts buur of orange agains the lash green grazs, as leaps over small wildflowers and nevigates arond scattred follen loares, Its floks with exciiepmeni and cars burely touching
peried up,
sound sevene enviroment, The ground. fraving trail of gentle rusting wake.
the errnnce freedom and playfiiness, with
Figure I: Comparison of samples generated by full- aall-precision and 4-bit Cog CogVidcoX1..o 1-5BB (Yang et al., 2025).
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BE
(b) K4A4 QVeen (Ours)
bustling city park, surrunaeed blooming graus. The person has a friendly mule on their face, taking mall bites of the ice cream as they zover each lick. Their casaai oftice includes a light jacket and joons, with sunglasses perchal person to capture the lively atmosphere of the park. Close up medium shot, showing the person' 's joyful expression and the meiting ice cream."
prompt: person enjoying delicious
grven
backgrousd vibrant cityraape with buildings
sigus. The puns slightly the
(c) BF16
(d) in 4A4 QVGen (Ours)
Text prompt: "A movth, swreping comora cincle arond a luck gonden, The gorden filedd with wheam fowerrs tall goven buohes, and neatly trimmeed hedger, Sunlight filters shrrugh leasez, costing dappted shadows the the brancher, The camera grddmlll mose fur from a wide that of the entire gonlen to clover vieus of indisddual plants and the intricate detniis the londvropp. The
gemily spraying lighting Wide Figure J: Comparison samples generated by full- precision and 4-bit Wan 14B (WanTeam al., 2025).
small fountuin
Birds chipp
among
overlll uimosphere
and beiting
pour smmutall uround the gardon."
34


Reviews:
[{"review_id": "ynaw1d8aye", "reviewer": "Reviewer_U5JF", "summary": "This paper introduces QVGen, a quantization-aware training (QAT) framework specifically designed to enable high-performance, low-bit (≤4-bit) quantization of large-scale video diffusion models (DMs) based on the diffusion transformer (DiT) architecture. QVGen incorporates auxiliary modules ($\\Phi$) to mitigate the quantization error during training, supported by a theoretical analysis linking reduced gradient norm to improved convergence. To remove the inference burden of these modules, the paper proposes a rank-decay strategy using singular value decomposition (SVD) and a rank-based regularization for progressively pruning $\\Phi$ without significant loss in quality. Extensive evaluations across multiple state-of-the-art video DMs show QVGen achieves performance on par with full-precision models at 4-bit, and significantly outperforms competing quantization methods, with both quantitative metrics (VBench) and qualitative visualizations.", "questions": "While the use of singular value decomposition is effective (Fig. 4, Section 3.2), the alternative strategies (Sparse, Residual Quantization) examined in Table 6 are somewhat strawman/naive and do not fully explore more sophisticated structured pruning or adaptive fading that could yield competitive trade-offs. There is little discussion of possible pathological cases where the SVD approach might fail, particularly if singular spectrum decays slowly.\n\nThe key result (Theorem 3.1, Page 4) relies on convexity of $f_t$, which is non-standard for deep networks, and the analysis lacks formal proof linking reduced regret to generalization in nonconvex settings. No concrete evidence connects gradient norm changes to video-specific generative performance beyond empirical plots.\n\nWhile the main approach is clearly stated, critical aspects of $\\Phi$ require deeper explanation, such as its initialization scheme across different models, architecture, and potential sensitivity to scale or data distribution shifts. Section J.2 offers two initialization methods, but does not thoroughly evaluate edge cases or sensitivity to poor initialization. There is also no discussion of stability if $\\Phi$'s rank is diminished too rapidly relative to the learning rate schedule.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The work addresses the challenging and critical task of efficient, high-fidelity video generation under ultra-low-bit quantization, an area with clear importance for practical deployment.\n\nProvides a regret-based convergence analysis (see Theorem 3.1, Page 4) linking gradient norm to QAT performance, justifying the introduction of $\\Phi$.\n\nThe auxiliary module ($\\Phi$) is elegantly conceived and is integrated with a flexible, theoretically justified rank-decay scheme, allowing benefits during training while incurring no inference cost.\n\nExperiments cover a wide range of SOTA video DMs (from 1.3B to 14B parameters) with comprehensive ablations (Tables 3, 4, 5; Figs. 3–6). Quantitative results (Tab. 1, 2, H, K; Figs. 5, 6) convincingly show QVGen’s superiority over PTQ and QAT baselines in essentially all relevant metrics.", "weaknesses": "While the use of singular value decomposition is effective (Fig. 4, Section 3.2), the alternative strategies (Sparse, Residual Quantization) examined in Table 6 are somewhat strawman/naive and do not fully explore more sophisticated structured pruning or adaptive fading that could yield competitive trade-offs. There is little discussion of possible pathological cases where the SVD approach might fail, particularly if singular spectrum decays slowly.\n\nThe key result (Theorem 3.1, Page 4) relies on convexity of $f_t$, which is non-standard for deep networks, and the analysis lacks formal proof linking reduced regret to generalization in nonconvex settings. No concrete evidence connects gradient norm changes to video-specific generative performance beyond empirical plots.\n\nWhile the main approach is clearly stated, critical aspects of $\\Phi$ require deeper explanation, such as its initialization scheme across different models, architecture, and potential sensitivity to scale or data distribution shifts. Section J.2 offers two initialization methods, but does not thoroughly evaluate edge cases or sensitivity to poor initialization. There is also no discussion of stability if $\\Phi$'s rank is diminished too rapidly relative to the learning rate schedule.", "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": "TFCh9DsfSi", "reviewer": "Reviewer_RsTT", "summary": "This paper proposes a quantization method for video diffusion transformers. The key idea is to introduce additional modules containing full-precision lora parameters to mitigate aggressive training losses, whose effectiveness of reducing gradient norm and quantization error has been demonstrated by the authors. Then, the authors devise a rank shrinking strategy, which reduces the rank to 0 to avoid additional stroage of full-precision weights. Extensive experiments demonstrate the effectiveness.", "questions": "* Is it possible to directly regulate gradient norm by gradient normalization, clip, or direct optimization? These methods may yield inferior results, but can they outperform the original baseline introduced in Sec. 2?\nPlease refer to the weaknesses part above for other questions.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The work could be highly impactful for the community of quantized video generation models due to its state-of-the-art performance.\n2. The analysis of the importance of reducing the gradient norm is valid and motivating for the proposed method.\n3. Although the method first introduces full-precision parameters, the authors devise effective solutions to reduce the rank to even 0, which means eliminating the need for additional full-precision storage. From the results, such a two-stage pipeline is highly effective and superior to one-stage methods.\n4. The experiments are very extensive and sufficient to demonstrate the effectiveness of quantizing video diffusion models.", "weaknesses": "I don't find so many weaknesses, but would like to list some minor points below:\n1. It seems that the method is not tailored for video diffusion models and has potential for other models, like image generation and image backbone. The authors are encouraged to conduct experiments on these widely adopted benchmarks.\n2. It is encouraged to include another baseline of fine-tuning the model using the same data under full precision, which is useful to reflect the effect introduced by additional data and fine-tuning.", "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": "zSOsCwbYHN", "reviewer": "Reviewer_FHRA", "summary": "This paper introduces QVGen, a novel Quantization-Aware Training (QAT) framework designed specifically for very low-bit (≤4-bit) video quantization. The core of this method is the introduction of an auxiliary module ($\\Phi$) to stabilize the QAT process. To eliminate the inference overhead from this auxiliary module, the authors propose a \"rank-decay\" strategy, which uses Singular Value Decomposition (SVD) and rank regularization to progressively remove these modules during training. Experiments on video DMs ranging from 1.3B to 14B parameters show that QVGen achieves quality comparable to full-precision at 4-bit settings.", "questions": "Please see the weaknesses above, and:\n\n1. Can the proposed method be combined with SVDQuant? If so, could this combination be trained efficiently by updating only LoRA parameters?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The first full QAT method for video generation models I'm aware of.\n\n2. Experiments are conducted on four SOTA open-source video DMs (CogVideoX and Wan), with parameter scales from 1.3B to 14B, providing broad coverage.\n\n3. It validates practical efficiency gains and demonstrates orthogonality with other acceleration techniques like SVG.\n\n4. The provided experimental materials are comprehensive, and the ablation studies are extensive.", "weaknesses": "1. Since some other QAT methods are trained using only LoRA, a comparison of training time and memory (GPU VRAM) requirements against these methods should be provided for a comprehensive assessment of algorithm efficiency.\n\n2. Quantization-related initialization settings should be specified, such as the choice of quantizer (e.g., granularity, symmetric/asymmetric) and which layers, if any, are not quantized.\n\n3. In Fig.3, why the inital training loss of the proposed method is bettern than Q-DM? Did the proposed method use a better initialization strategy? Since the authors reproduce other QAT baselines, a detailed training loss curve comparison would greatly enchance the soundness of the paper.", "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": "xz4RNuq1D7", "reviewer": "Reviewer_eVaC", "summary": "The paper introduces QVGen, a quantization-aware training (QAT) method designed to enable high-quality video diffusion models under extremely low-bit quantization (4-bit or 3-bit). The paper begins with a theoretical analysis showing that reducing the gradient norm is key to stabilizing and improving convergence in training. Based on this insight, the paper introduces auxiliary modules $\\Phi$ during QAT to compensate for weight quantization errors and smooth optimization. To avoid extra inference overhead from these modules, the paper proposes a rank-decay mechanism, progressively eliminating $\\Phi$ by decomposing its weights via singular value decomposition (SVD) and decaying low-contributing components using a rank-based regularization schedule. Extensive experiments on state-of-the-art models demonstrate that QVGen achieves full-precision comparable performance in 4-bit settings and sets new records under 3-bit quantization, outperforming existing QAT and PTQ baselines such as Q-DM, EfficientDM, and SVDQuant. The method also improves memory efficiency and inference speed while maintaining compatibility with standard low-bit kernels.", "questions": "1. The proposed QVGen framework focuses on compensating quantization errors in the weights through the auxiliary module $\\Phi$. However, the paper does not analyze the effect of activation quantization separately. Could the authors provide results or discussion on how the model performs when only activations are quantized to low-bit precision while keeping the weights in full precision? This would help clarify whether the main difficulty in quantizing video diffusion models arises more from weights or activations.\n\n2. The paper mentions that the current acceleration is limited because kernel fusion is not applied. To better understand the efficiency aspect, could the authors provide additional profiling results—such as the achieved TFLOPs of the INT4 GEMM kernel used—and an end-to-end inference time breakdown? It would be very helpful if the authors could use a profiling tool (e.g., nsys profile) with `torch.cuda.nvtx.range_push` and `torch.cuda.nvtx.range_pop` tags to visualize where most time is spent and estimate how much of the overhead could be mitigated by kernel fusion, even without implementing it. The author is encouraged to provide a table that describe how much time the qkv projection, o projection, ffn up projection, ffn down projection, self attention, and all other memory bound modules take in a single dit block.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Well-motivated methodology**: The proposed approach is grounded in a clear motivation. The authors observe that the auxiliary module $\\Phi$ exhibits rank decay during quantization-aware training and effectively leverage this property through their rank-decay mechanism. This insight is both intuitive and novel.\n2. **Comprehensive experimental validation**: The paper provides extensive experimental results across multiple large-scale video diffusion models, supported by detailed ablation studies and qualitative visual comparisons.\n3. **Strong empirical performance**: QVGen achieves consistently superior results compared to existing QAT and PTQ baselines, showing that the proposed framework is highly effective in maintaining video generation quality under ultra-low-bit quantization while improving efficiency.", "weaknesses": "1. **Theory disconnected from the main method**: The theoretical analysis in the early part of the paper appears largely disconnected from the core contributions. Although the authors claim that the auxiliary module $\\Phi$ is motivated by this theory, the linkage is tenuous, and the theoretical result itself is rather weak. As a result, the theory does not substantially contribute to the understanding or justification of the proposed framework (although I think it is totally fine to be motivated empirically).\n\n2. **Limited acceleration gains**: The acceleration gains reported by QVGen are not particularly large, which somewhat limits its practical impact on efficiency. However, the authors explicitly acknowledge this limitation and attribute it to the absence of kernel fusion optimizations, which is reasonable.", "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": "1AiazpvTLo", "reviewer": "Reviewer_uekc", "summary": "This paper introduces QVGen, a novel Quantization-Aware Training (QAT) framework designed to enable high-quality video generation under extremely low-bit (3/4-bit) quantization, a task where previous methods fail. The key innovation is a two-stage process: first, it stabilizes training and improves convergence by adding lightweight auxiliary modules to mitigate quantization error, which is theoretically and empirically shown to reduce the gradient norm; second, it progressively eliminates these modules during training via a \"rank-decay\" strategy that uses Singular Value Decomposition (SVD) and a rank-based regularization to identify and shrink low-impact components to zero, resulting in a final quantized model with no inference overhead.", "questions": "Hyperparameter Sensitivity: How sensitive is the final performance to the key hyperparameters, such as the initial rank r=32 and the shrinking ratio λ=1/2? Was there a systematic process for selecting these values across different model architectures and sizes?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Well-Motivated Contribution: This paper addresses the challenge of ultra-low-bit QAT for large-scale video diffusion models, filling a significant gap in the literature which has primarily focused on image models.\n\n2. Strong Theoretical and Empirical Foundation: The paper provides a solid theoretical analysis linking gradient norm reduction to improved QAT convergence, and then designs a method (the auxiliary module Φ) specifically to achieve this. The empirical results consistently show lower gradient norms and training loss.\n\n3. Impressive and Extensive Experiments: The evaluation is comprehensive, testing on four state-of-the-art models ranging from 1.3B to 14B parameters. The results are compelling, showing that QVGen is the first method to achieve full-precision comparable quality with 4-bit quantization and significantly outperforms all baselines in 3-bit settings.\n\n4. Practical and Efficient Solution: The proposed \"rank-decay\" strategy is a clever way to gain the training benefits of the auxiliary modules without incurring any inference cost, making the final model directly deployable with standard low-bit kernels. The reported ~4x memory reduction and up to 1.7x speedup are substantial.", "weaknesses": "1. Computational Cost of QAT: While the final model is efficient, the QAT process itself is expensive, involving iterative SVD operations and training on up to 32 H100 GPUs for large models. The paper does not deeply discuss the trade-offs between this training cost and the resulting inference savings.\n\n2. Limited Analysis of \"Rank-Decay\": The strategy is shown to work, but the analysis of why the singular values of W_Φ evolve to have an increasing number of small components is somewhat surface-level. A deeper investigation into the dynamics between the quantized model and the auxiliary module during training would be valuable.\n\n3. Ablation on Simpler Alternatives: The paper compares against other fine-grained decay strategies (Sparse, Residual Quantization), but it would be strengthened by also ablating against a simple scheduled decay (e.g., linearly reducing the magnitude of all parameters in Φ to zero) to more clearly isolate the benefit of the SVD-based, rank-aware approach.", "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)