Beyond Uniformity: Sample and Frequency Meta Weighting for Post-Training Quantization of Diffusion Models
Authors: Cuong Pham
Dung Anh Hoang
Cuong C. Nguyen
Trung Le
Dinh Phung
Gustavo Carneiro
Thanh-Toan Do
Venue: ICLR 2026 Poster (2026)
Keywords: Post-training Quantization
LLMs
Primary area: other topics in machine learning (i.e., none of the above)

Abstract: Post-training quantization (PTQ) is an attractive approach for compressing diffusion models to speed up the sampling process and reduce memory footprint. Most existing PTQ methods uniformly sample data from various time steps in denoising process to construct a calibration set for quantization and consider calibration samples equally important during the quantization process. However, treating all calibration samples equally may not be optimal. One notable property in the denoising process of diffusion models is that low-frequency features are primarily recovered in early stages, while high-frequency features are recovered in later stages of the denoising process. However, none of the previous works on quantization for diffusion models consider this property to enhance the effectiveness of quantized models. In this paper, we propose a novel meta-learning approach for PTQ of diffusion models that jointly optimizes the contributions of calibration samples and the weighting of frequency components at each time step for quantizing noise estimation networks. Specifically, our approach automatically learns to assign optimal weights to calibration samples while selectively focusing on mimicking specific frequency components of data generated by the full-precision noise estimation network at each denoising time step. Extensive experiments on CIFAR-10, LSUN-Bedrooms, FFHQ, and ImageNet datasets demonstrate that our approach consistently outperforms the compared PTQ methods for diffusion models.

Published as a conference paper at ICLR 2026
BEYOND UNIFORMITY: SAMPLE AND FREQUENCY META WEIGHTING FOR POST-TRAINIM QUANTIZA-
TION OF DIFFUSION MODELS
Cuong Pham'
Hoang Anh Dung'
Cuong C. Nguyen Thanh-Toan Do
Trung Le
Dinh Phung!
Gustavo Carneiro
"Department of Data Science and AI, Monash University, Australia
Centre for Vision, Speech and Signal Processing, University of Surrey, United Kingdom cuong. pham1, hoang. dung, trunglm, dinh.phung toan. do @menash. edu (c. nguuen, g. carneirio @surrey. ac.uk
ABSTRACT
Post-training quantization (PTQ) is an attractive approach for compressing diffu- sion models speed up the sampling process and reduce memory footprint. Most existing PTQ methods uniformly sample data from various time steps in denoising process to construct calibration samples equally important during the quantization process. However, treating all calibration samples equally may not be optimal. One notable property the denoising process of diffusion models is that low-frequency features are primarily recovered in early stages, while high-frequency features are recovered later stages of the denoising process. However, none of the previous works quan- tization for diffusion models consider this property to enhance the effectiveness of quantized models. In this paper, we propose novel meta- weea-lea approach for PTQ of diffusion models that jointly optimizes the contributions of calibration samples and the weighting of frequency components each time step for quan- tizing noise estimation networks. Specifically, our approach automatically learns assign optimal weights calibration samples while selectively focusing on mimicking specific frequency components of data generated by the full- full-precision noise estimation network at each denoising time step. Extensive experiments on CIFAR-10 10, our approach consistently outperforms the compared PTQ methods for diffusion models.
for quantization and consider calibration
FFHQ, and ImageNet datasets demonstrate that
INTRODUCTION
Recently, diffusion models (Ho et al. 2020 Dhariwal & Nichol 2021 Rombach al. 2022 have attracted significant attention due to their ability to generate high-quality images. However, the sampling process diffusion models is computationally expensive, requiring hundreds of denoising steps to generate high-quality image. Additionally, the noise estimation networks in diffusion models are often complex and have large number of parameters, which limits diffusion models' practical applications on resource-coe conotrcc-onntr devices. To address these challenges. an attractive approach is to quantize diffusion models. Neural network quantization (Han al.) 2016 Courbariaux et al. 2015 Nagel et al. 2019 2020 Cai et al.) 2020) is a popular approach for model compression that can significantly reduce computational cost and memory usage. Post-training quantization (PTQ) is particularly effective quantization approach due to its ability quantize deep neural networks without relying on a large amount of training data or necessitating model retraining.
Calibration data plays crucial role in PTQ for diffusion models and is typically generated from various time steps of the denoising process. There are several works that use heuristics to select
calibration data for PTQ on diffusion models. For example, in PTQ4DM (Shang et al.) (023). the authors sample denoising time steps from distribution (p,, 0.57) where μ < 0.57, and use images generated these sampled time steps calibration data. In Q-Diffusion (Li et al. 2023 the

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authors select generated images at fixed step intervals across all denoising time steps calibration data. In TFMQ-DN (Huang et al. 2024). the authors adopt the Q-Diffusion method to construct the calibration data and propose temporal feature maintenance quantization framework to improve the performance of the PTQ for diffusion models. is worth noting that in previous works (Shang et al., 2023 Li et al., 2023 Huang al. 2024), calibration samples are treated equally during the quantization process. Different from previous works (Shang et al. 2023 Li et al., 2023 Huang al. 2024 we hypothesize that each calibration sample could have different contributions the performance of the quantized model. To validate this, we conduct an empirical study by comparing uniform sample weighting against multiple random weighting schemes on the CIFAR-10 dataset. As shown in Figure [ among 50 different weighting schemes, 18 outperform uniform weighting in terms of FID score, demonstrating that uniform weighting suboptimal and that better weighting solutions exist. Therefore, unlike previous methods that treat calibration samples equally, we propose a principled approach inspired by (Ren et al. 2018) to automatically weight the contribution of each calibration sample for quantizing the noise estimation network. Specifically, we propose sample-weighting mechanism that leverages meta-learning to automatically learn a weight for each calibration sample, with the objective that the quantized model trained with the calibration samples and their corresponding weights can achieve good performance on the validation set.
Furthermore, previous works (Yang al. 2023 Qian et al.] 2024) show that each time step in the diffuusion process learns distinct features and serves unique role in the diffusion model. From frequency perspective, diffusion mod- els recover low- low-frequency features at early de- noising stages and gradually add high-frequency features at the later denoising stages (Qian et al. 2024). Therefore, the quantization of the noise estimation network should focus dif- ferent frequency components at different time steps during the quantization process. To this end, we propose a novel approach for PTQ for diffusion models by utilizing these frequency characteristics. Specifically, we propose a fre- quency weighting method that leverages meta- learning to automatically assign weights to the frequency loss components, derived from the frequencies of features extracted by the full- precision and quantized noise estimation net- works at each time step, such that the learned frequency weights lead to the minimization of the quantized model's validation loss. Addi- tionally, we propose a regularization term on frequency weights to encourage the quantized model to focus more on mimicking high-frequency components and pay less attention to low- frequency components of samples generated by the full-precision model the time step decreases during the denoising process.
6.5
Stochaste Sample Weighting
Uniform Sample Weighing
SFID Score
5.5
4.5
3.5
4
4.5
5
5.5
6
6.5
FID Score
Figure 1: Comparison of FID and sFID scores of 50 sample weighting sets including the uniform weighting in the quantized noise estimation net- work DDPM (Ho et al. 2020) with the W4A32 setting on the CIFAR-10 dataset. All experiments use the same calibration dataset generated from the full-precision model and the same approach with TFMQ (Huang et al. 2024).
We form the optimization of the sample weights and frequency weights bi-level optimization problem. The aim of the optimization is to learn sample weights and frequency weights such that the quantized model obtained from the training using calibration samples with those weights achieve a good performance on the validation set, i.c., minimizing the validation loss. We validate our proposed approach on the widely used CIFAR- 10 (Krizhevsky et al. 2010)). LSUN-Bedrooms (Yu et al.) 2011)). FFHQ (Karras et al., 2219), and ImageNet (Deng et al. 2009) datasets with various noise estimation network architectures under different bit-width settings. The extensive experiments demonstrate that our method outperforms compared PTQ methods for diffusion models. To summarize, the contributions of this paper are outlined as follows:
We propose a novel PTQ method that leverages meta-learning to automatically learn to weight the contribution of each calibration sample in PTQ training for diffusion models.
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Such a weighting mechanism prioritizes important samples, improving the performance of the quantized model.
We propose a meta-learning based method to automatically learn to weight components of the frequency loss. We also propose regularization term to encourage the quantized model to focus more on mimicking high-frequency components and pay less attention to low-frequency components of the data generated from the full-precision counterpart as the time step decreases during the denoising process.
We extensively validate our proposed approach on the CIFAR-10. 10, -UUN-Bedrooms, FFHQ, and ImageNet datasets. The experimental results show that our method consistently outper- forms compared PTQ methods for diffusion models in terms of the FID score.
RELATED WORKS
Post-training quantization of diffusion models. Diffusion models (Ho et al. 2020 Song et al. 20216) can generate high-quality images through an iterative denoising process. However, the excessive cost of a large number of time steps the denoising process could limit the practical applications of diffusion models. Although several works significantly reduce sampling time (Lu ctt al., 2022] Song et al. 2021a Zhao et al., 2023), they still face challenges in computational cost and memory usage due to complex noise estimation networks. Model compression, especially model quantization (Han et al.) 2016 Courbariaux et al. 2013] Nagel et al.
2019: 2020 Cai et al., 2020
Xu et al.) 2020), is effective approach to reduce the computational cost and memory usage of these networks. Post-training quantization (Nagel et al., 2020 Li et al. 2021 Liu et al., 2023 Wei
2023 is an effective approach quantize diffusion models. This family of et al. 2022 Jeon al.
Techniques requires constructing appropriate calibration data and a quantization scheme for the model quantization. Existing PTQ methods for diffusion models mainly focus on obtaining calibration samples. To construct the calibration data, PTQ4DM (Shang et al. 2023) shows that generated samples in the denoising process are better than those from the forward process for PTQ for diffusion models. Q-Diffusion (Li et al. 2023) improves upon this by selecting generated images at fixed step intervals across all denoising time steps and introducing shorteut-splitinng quantization, achieving enhanced performance across a broader dataset range. In APQ-DM (Wang et al.) (024)) the authors propose using the structural risk minimization principle to find optimal time steps for generating calibration data. However, these works (Shang et al. 2023 Li et al. 2023 Wang al., 2024 treat all calibration samples with equal importance during the quantization process, ignoring the fact that certain samples may contribute more critically model performance than others.
Frequency in diffusion models. Frequency information has been widely adopted in conventional generative models, such as GANs (Fu et al. 2021 I Yang et al. 2022 Zhang et al., 2022). Recently, improve the performance of diffusion models (Yang et al., 2023) [Phung et al. 20233 Qian et al., 2024). In (Phung et al. [202)). the authors propose frequency awwre architectures for diffusion models to reduce the inference time while maintaining high quality of generated samples. From a temporal perspective, different time steps in the diffusion process exhibit distinct frequency characteristics (Yang al. 2023). The
several works have leveraged the frequency domain information
denoising process typically recovers low-frequency features in early time steps before gradually incorporating high-frequency details in later stages (Yang al., 2023). Spectral Diffusion (Yang et al., 2023) exploits this frequency evolution through wavelet gating for spectrum-aware distillation. In (Qian et al.] 2024), the authors propose a training-free approach that leverages frequency domain information to enhance the stability of the denoising process and improve the performance of diffusion models. While there are previous works exploiting the frequency domain information to improve the performance of full-precision diffusion models, research on leveraging frequency domain information for the quantization of diffusion models to improve quantized diffusion model performance remains limited.
Meta-learning for post-training quantization. Meta-learning has been explored for convolutional neural network quantization (Chen et al.] 2019; Wang et al., 2020 Youn et al.) 2022 Kim et al. 2024). For instance, MEBQAT (Youn et al. (022)) leverages meta-learmin precision quantization strategy that swiftly adapts to diverse bit-width configurations while preserving model accuracy. In another approach, MetaMix (Kim et al. 2024) addresses the prevalent issue of
optimize a mixed-
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FFNALL
Backward Pass
VALL
(3) Quanlize model using optimized
0
θQ
ADAM
(2) Optimize
(1) Optimize A
X
w
Frequency Weight
Sample Weight
Callbration Data
Validation Data
Figure 2: In general, the proposed method consists of three main optimization steps: (1) updating frequency weight x, (2) updating sample weight w using the validation loss L in Eq. ( and (3) leveraging both sample and frequency weights to quantize the model by minimizing the final loss LFINAL. in Eq. (12).
activation instability in mixed-precision quantization models and utilizes meta-learning mitigate this instability and improve robustness. On the other hand, MetaQuantNer (Wang et al. 2020) presents meta-learning framework that autonomously identifies optimal quantization policies before employing these policies to enhance network quantization. However, the use of meta-learning in diffusion quantization settings remains largely unexplored. To the best of our knowledge, our work is the first to leverage meta-learning techniques specifically to post-training quantization within the context of diffusion models.
PROPOSED METHOD
3.1 PRELIMINARY DEFINITIONS
Our goal is to optimize set of sample weights and frequency weights that guide the quantization process to better preserve the full-precision model's behavior. The details of the algorithm are illustrated in Figure 2 We first obtain the calibration set following the approach outlined in the O-Diffusion method (Li et al. 2023), by selecting generated samples at fixed step intervals across all denoising time steps. Each calibration sample is denoted as (xi, where a; is the generated sample with the corresponding time step 4;
In our method, each calibration sample (c;,4) is assigned a learnable weight w;, representing its impact the quantized model's performance. The set of N weights corresponding to N calibration samples is denoted as w p {w
Besides the sample weighting, we propose to weight individual frequency components for each time step to better align with the evolution dynamics of different frequency components. Specifically, the Discrete Wavelet Transform (DWT) (Graps) 1995) is a well- frequency analysis method. In this paper, we utilize DWT and leverage the frequency properties diffusion models for quanti- zation, thereby enhancing the effectiveness of quantized models. In practice, any tensor input is
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decomposed into four wavelet subbands by applying DWT (Graps 1995) as follows:
DWT(a) (a., Oth ani anh).
(1)
Note that here we implement DWT as the classical Haar wavelet (Stankovic Falkowski 2003) for simplicity. Among the four wavelet subbands, au refers to the low-frequency component that reflects the basic object structure, while a (Ik,) represent high-freog fegh-freuuncyy components that capture texture details. When quantizing the t layer of the network, we assign all calibration samples of time step with set of learnable weights { {Ar,0: Ar,, 24, 3), denoting the weights corresponding to au, aah. ant. OAh at time step ł;. Let us denote À as learnable frequency weight matrix of size x 4, where the 1/th row λ {Ar,.o- A,, Ae, ),,3) is a vector of length 4. We normalize the total weight of all frequency components at each time step I; equal to (i.e. Σk Ata = 1).
3.2 THE JOINT OPTIMIZATION OF SAMPLE AND FREQUENCY WEIGHTTS
Both the sample weight and the frequency weight are optimized to maximize the model's performance on the validation set. Given full-precision model fpp and quantized model &q. the joint optimization objective of À and is formed bi-level optimization problem follows:
w", x argmin
Σ
Lval 2).2.
(2)
S
TyESO
s.t: Qq argmin
Σ
(LQ((qq xi, + Cr vi, 21,11)
(3)
8G
where S and S are the calibration dataset and validation dataset, respectively; |S| denotes the cardinality of the set S: the index of the layer/block that we want calibrate, and y hyperparameter.
Regarding the loss Lo in Eq. (3) The loss Lo is used to update the h block of the model 0g to obtain the model lg. which is defined follows:
(o 1)
(2) (1),
1 eQ (zri t:)l"
where (T,, 4)) and EQ (z,, 4:) are the outputs of the 1th block of the full-precision model 0pr and the quantized model OQ for sample (æ,, 4;), respectively.
Regarding the loss LF in Eq. 33. During the quantization process, we encourage the frequency components of images generated by the quantized model to match their counterparts from the full-precision model. Specifically, for any two tensors a. of the same size, we employ DWT to decompose two tensors a, b into four wavelet subbands as DWT(a) (au, am. ant, aah) and DbT(b) (bu. bi byg b. Given frequency weight vector X (No. X2, Aa), their weighted frequency difference will be defined as:
Ca, b, 2) = Roolan an - bull2 + À laan - 11 + Rallan buil2 + Rallann bnall 1.
(5)
Then the frequency loss LF can be defined as follows:
(1) Lx(0Q I,, X,, C, (e0 (1 4), Epp (2 (144))))) A.).
(6)
Regarding the loss Lval Eq. Our goal is to maximize the performance of model 0g on the validation set S". Therefore, at the validation step, we validate the quantized model 0g on the validation set S² The validation loss Lesl presented as below:
(7) where e with subscript is the final output of the corresponding model of interest; 3 is a hyper- parameter. The first term in the validation loss, Lval, is the reconstruction loss between the outputs of the full- precision model and the quantized model for each sample z, in the validation dataset S while the second term represents the regularization loss frequency weights.
(ra (0 A) ((pp(I,,,t)
t,)1 + BL (2),
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Regarding the regularization LReg in Eq. (7). As the full-precision model gradually recovers the fwwfrrquency components at the early stages of the denoising process and gradually recovers the high-frequency components at the later stages, the quantized model is encouraged to follow this pattern. Specifically, the weight of the low-frequency components (At,0) is regularized to be decreased and the weights of the high-frequency components (Ar,1 Az, At,5) in frequency loss Lp is regularized to be increased as the time step decreases, To this end, we define the regularization loss Ceeg
T-2
Re (A)
Σ (O, rt To41),
where r A,o 2 (A.,1 A.2 + A,,,), and 2 denotes element-wise division. As the time step decreases, the regularization loss LReg( (A) will encourage λς to decrease while λτ,1 + X,,2 + A to increase.
When optimizing the sample weights {wr we keep the frequency weight À fixed throughout this step, and conversely. The model (w:), when optimizing sample weights w, is approximated by solving Eq. (3) using a single step of gradient-based methods (e.g., SGD or Adam) as follows:
00=00 00
Σ
V Vogui [Lo(0q, 21, + ~ 24, x,,,
ES
where no denotes the learning rate of the quantized model.
In the second stage, we optimize the sample weights {wi and the frequency weight matrix À with
respect the quantized model lq. We employ alternating optimization scheme in which of parameters held fixed while the other is updated. Concretely, the sample weights are updated as:
va
(o(w),
W/ = Wi
Ma
Vi = 1,2,...,
(10)
S
Owy
E,E8"
followed by an update of the frequency weights:
OLval (A5(A),
1
de,b A 7A
Vt (1....., T), k (,,,,
(11)
S
0de,
where 7/ denotes the learning rate of the frequency weight matrix X, 7o denotes the learning rate of the sample weights. In the sample weight optimization step, since the frequency weight matrix À is fixed, Legg (A) in Eq. (7) ignored.
3.3 FINAL OPTIMIZATION OBJECTIVE
For the th layer/block, once we have obtained the sample weights {wi) and the frequency weight matrix corresponding to that layer/block, the model will be quantized over the training set with combined loss, defined as:
N
FPINAL Σ wilLq(Oa, I.,1) + xLr (fq, Iz, A,, X,])
(12)
The overall algorithm of our proposed method is presented in Algorithm I
4 EXPERIMENTS
4.1 EXPERIMENTAL. SETUP
Models and datasets. We evaluate the performance of our proposed method on common diffusion
models, including the pixel-space diffusion model DDPM (Ho al. 2020) for unconditional image

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Algorithm 1 Sample and frequency meta weighting for post-training quantization of diffusion models
1:
procedure TRAIN(OFP, S S*)
2:
Opp: full-precision model
3:
lo: quantized model
4:
L: number of blocks the full-precision model
5:
Sh: calibration dataset
6:
D S": validation dataset
7:
Ny: number of iterations for updating frequency weight X
8:
N₂: number of iterations for updating sample weights {wi Jim1
9:
D No: number of iterations for model weight quantization
10:
Uniformly initialize sample weights w = [wi],= and frequency weight Initialize the quantized model BQ for / = 1 to L do
11:
12:
13:
while converged do b> Fix {
14:
1=1 and update frequency weight X
for n = 1 to N do
16:
Compute (A) using Eq. 9
17:
Compute L (0 (A), S", A) using Eq. 7
18:
Update A: A t (Cal(Ca (A), S", A)) > Fix and update sample weights {wi I.
19:
20:
for n, to .N. do
21:
Compute (q(w) using Eq. (9)
22:
Compute Lval ( (W), S", A) using Eq. 7
23:
Update { [wi] & (vAM(Cal(@Q(W)) > Optimize parameters of the quantized model
24:
25:
for mg = 1 to No do
26:
Optimize the quantizer parameters of the 1hh block in model fq by minimizing IN LFINAL. from Eq. (12 over S using weights { and A.
27:
return quantized model OQ
generation, and the latent-space diffusion model LDM (Rombach al. 2022) for both unconditional
and class- cass-conniiional image generation. We extensively evaluate the proposed method on various
datasets, including CIFAR-10 10 32 32 (Krizhevsky al. 2010). --N-Bedrooms 256 x 256 (Yu
et al. 2015), FFHQ 256 x 256 (Karras al. 2019), and ImageNet 256 x
256 (Deng et al. 2009).
Implementation details. We follow state-of-the-ar post-training quantization (PTQ) methods for both weights and activations in diffusion models (Shang et al.) 2023) Huang et al., 2024). Specifically, weights and activations in PTQ for DM are typically quantized separately. We first keep the activations in full precision while quantizing the weights. For weight quantization, we learn the rounding function using AdaRound (Nagel al. 2020) and use block-wise reconstruction (Li et 202T) to quantize the noise estimation networks. On the other hand, applying a similar approach to optimize activation quantizers may introduce additional training overhead while only yielding minimal performance gains, as outlined in TFMQ-DM (Huang et al. (002)). Therefore, we adopt the simpler activation quantization approach used TFMQ-DM. This approach estimates activation ranges using EMA (Jacob et al. 2018) with mini-batch size of 16. The quantized model 0e is initialized from the full-precision model using LAPQ (Nahshan et al.) 2021). following previous works (Shang et al.) 2023 Li et al. 2023 Huang et al. (024). The calibration data is generated through the full-precision diffusion models as described in D-Diffusion (Li ct al. 2023) and is
identical the calibration set used in TFMQ-DM (Huang et al. 2024). We also adopt the temporal feature maintenance quantization technique in the TFMQ-DM (Huang et al. 2024) method. The number of iterations No for optimizing each block of the quantized model
10 iterations following previous works (Shang et al. 2023 Li et al. 2023 Huang et al. 2024). Meanwhile, we set N2 100 and N, 200 for updating frequency weight and sample weight, respectively. We employ the Adam optimizer (Kingma & Ba 2015) with learning rate of 4 10-5 to update the
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Table Quantization results for unconditional image generation with DDIM on CIFAR-IO 32 32.
CIFAR-10 32 x 32
Methods
W/A
FIDL
sFID,
W/A
FIDJ
sFID)
FID,
sFID
PTQ4DM (Shang et
2023
5.65
5.14
5.69
O-Difiusion Li A023
5.08
4.98
4.98
5.68
4.78
4.75
APQ-DM Wang al. 2024
9.96
7.63
12.2
7.66
6.34
4.44
4/32
4/8
8/8
TFMQ-DM [Hoing
2024
4.73
4.78
4.24
TCAQ DM [uung et al
2025
4.28
4.59
4.09
Ours
4.21
4.47
4.25
4.46
4.15
4.36
sample weight and frequency weight A. Gradients in Eq. (10) and Eq. (1I calculated using the
higher library The typer-parameter y is set to 0.1 in Eq. (9 and Eq. (12). When optimizing the
frequency weight X, set the 0.05 for the Lvval in Eq. Regarding the validation set S
use a subset of the generated data as the validation set.
The quantization settings in our proposed method are consistent with those used in Q-Diffusion LLi iet al., 2.23), PTQD (He et al. 2023). and TFMQ-DM (Huang et al. 2024). In line with these works, utilize pre-trained diffusion models from the official implementations of DDIM (Song al.)
2021ap and Latent Diffusion [Rombach et al. 20222). For evaluating FID and sFID scores, we adopt
the torch-fidelity library Following the setting from (Li et al. 2023 Huang et al. 2024), we use 100 denoising time steps for DDIM on the CIFAR- 10 dataset. For
and FFHQ datasets, we use 200 denoising time steps. For class-conditional image generation on the ImageNet dataset, we employ the default DDIM sampler with 20 time steps and a guidance scale of 3.0. All experiments are implemented using PyTorch and conducted on single NVIDIA A100 GPU.
Evaluation metrics. We evaluate the performance of diffusion models using Fréchet Inception Distance (FID) (Heusel al. (017) and sFID (Salimans et al., (016)) across all experiments for fair comparison with previous works (Shang et al. [023] Li et al. 2023 Huang et al. (024). FID quantifies the difference between the Inception image features of synthetic and real images. On the other hand, sFID uses mid-level Inception features to better capture the spatial distribution similarity. For consistency, we compute the metries using 50, 000 generated samples, in line with the settings in
previous works (Shang et al. 2023: Li et al. 2023 Huang et al., 2024
4.2 COMPARISON WITH THE STATE-OF-THE-AN METHODS
We compare our proposed method with the state-of-the-ar approaches for PTQ on diffusion models,
including PTQ4DM (Shang et al. 2023). O-Diffusion (Li et al. 2023). PTQD (He et al. (023))
TFMQ-DM (Huang al. 2024), APQ-DM (Wang al., (024), and TCAQ-DM (Huang et al. 2025).
The results of competitors are taken from TFMQ-DM (Huang et al.) 2024) and TCAQ-DM (Huang)
cct al. 2025); the APQ-DM (Wang et al. 2024 results are reproduced from the official implementation.
We conduct experiments the CIFAR- 32 x 32, datasets for unconditional image generation, and on ImageNet 256 x 256 dataset for class-conditional
256 x 256, and FFHQ 256 x 256
image generation, following the same experimental settings (Huang et al. 2024)
Unconditional image generation. We conduct experiments including DDPM on the CIFAR- 10 32 x 32 dataset and LDM-4 on Bed--oes 256 x 256 and FFHQ 256 x 256 datasets, using the DDIM sampler (Song et al., 2021a) with 100, 200 and 200 time steps, respectively. As shown in Table I and Table 2 our method achieves the best performance Bedrooms 256 x 256 datasets across most bit- bit-width settings. The improvement is most evident in low bit widti settings. Specifically,
CIFAR- 32 x 32 and LSUN-
the CIFAR- 10 32 x 32 dataset, our method achieves FID score improvement of 0.52 and 0.53 over the TFMQ-DM DM in the W4A32 and W4A8 settings, respectively. On the LSUN--eerrom 256 x 256 dataset, our proposed method achieves FID improvements over TFMQ-DM by 0.44 and 0.40 in the W4A32 and W4A8 settings, respectively. Meanwhile, on the FFHQ 256 x 256 dataset, our proposed method significantly reduces the FID score over TFMQ-DM by 0.69 and 0.63 in the W4A32 and W4A8 settings, respectively.
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Table Quantization results for unconditional and class-conditional image generation with LDM-4 on LSUN-Bedrooms. 256 x 256, FFHQ 256 x 256, and ImageNet 256 x 256.
LSUN-Bedrooms
FFHQ
ImageNet
Methods
Bits (W/A)
FID,
sFID
FID
SFIDL
FID4
sFID
Full Prec.
32/32
2.98
7.09
9.36
8.67
10.91
7.67
PTQ4DM (Shang 2023
4.83
7.94
11.74
12.18
Q-Diffusion (Li et al.
2023
4.20
7.66
11.60
10.30
11.87
8.76
PTQD (He et al. 2023
4/32
4,42
7.88
12.01
11.12
11.65
9.06
TFMQ-DM (Huang al.,
2024
3.60
7.61
9.89
9.06
10.50
7.98
TCAQ-DM Huang et al.
2025
3.55
7.54
10.5
6.66
Ours
3.16
6.92
9.20
9.69
10.10
7.32
PTQ4DM Shang ct al. 2023
4.75
9.59
10.73
11.65
O-Diffusion (Li et al.
023
4.51
8.17
10.87
10.01
12.80
9.87
PTQD (He et al. 2023
8/8
3.75
9.89
10.69
10.97
11.94
8.03
TFMQ-DM (Huang ct al.
2024
3.14
7.26
9.46
8.73
10.79
7.65
TCAQ-DM (Hung
2025
3.11
7.34
10.58
7.54
Ours
3.08
7.18
9.16
9.59
10.75
7.63
PTQ4DM Shang
2023)
20.72
54.30
11.83
12.91
Q-Diffusion (i et al. 2023
6.40
17.93
11.45
11.15
10.68
14.85
PTQD (He et al. 2023
4/8
5.94
15.16
11.42
11.43
10.40
12.63
TFMQ-DM (Huang al.
2024
3.68
7.65
9.97
9.14
10.29
7.35
TCAQ-DM (Huang et al.
2025
3.65
7.64
9.97
7.67
Ours
3.28
7.05
9.34
9.74
10.01
7.21
Class-conditional image generation. For the ImageNet 256 256 experiments, with the DDIM sampler (Song et al. 2021a) (20 steps) to assess the performance of the quantized model. The results of the competitors are taken from TFMQ-DM (Huang et al. 2024). As shown in Table our method outperforms the compared methods across most settings. Specifically, the proposed method achieves significant improvements over TFMQ-DM (Huang al. 2024) in the W4A32 setting, with gains of 0.4 and 0.66 in FID) and sFID, respectively.
LDM-4
Visualization of the learned À and w. Figure and Figure 3blshow the visualization of the learned frequency weight and sample weight w, respectively. As shown, the weight of low-frequency component (Ap) decreases, while the weights of high-frequency components (21,d2, Ag) increase as the timestep decreases. For the learned sample weight w, as time steps decrease, the normalized weights of samples become more variable and tend increase, which indicates that the images generated at later time steps are often more important than those generated at earlier time steps for quantized diffusion models.
Table The effects of sample and frequency weighting, and the regularization term CReg on LSUN- Bedrooms 256 x 256.
LSUN-Bedrooms 256x
Methods
Bits (W/A)
FID
sFID,
Full Prec.
32/32
2.98
7.09
TFMQ-DM (Huang et al. 20224) (Baseline)
3.68
7.65
TFMQ-DM Sample weighting
3.47
7.20
4/8
TFMQ-DM + Frequency weighting
3.38
7.39
Ours (without Lite)
3.41
7.18
Ours (sample and frequency weighting)
3.28
7.05

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Published as a conference paper at ICLR 2026
0.2502
Normalted wengitt camples
0,2501
Norm
0.2499
0.2498
0.2497
0.2496
150
llmestep (Denotsing)
(a) Visualization of the learned frequency weight vector À for different frequeney com- ponents over denoising timesteps.
(b) Box plot of the learned sample weights across time steps. The orange line indicates the median weights of the samples each time step, while the circles represent outlier values outside the typical value range.
Figure 3: Visualization of learned weights.
4.3 ABLATION STUDIES
In this section, conduct ablation studies to analyze the impact of each proposed component in framework and the effects of the regularization terms. The ablation studies for the hyper-parameterss in Eq. and in Eq. (12 are provided in the supplementary materials due to space constraints.
The effects the proposed sample and frequeney meta weighting. To evaluate the effectiveness of each component, we conduct an ablation study on the LSUN-Beerooms 256 x 256 dataset with the W4A8 setting, using the LDM-4 model and a DDIM sampler. Table shows that using either the sample weighting or frequency weighting component alone improves the performance of the quantized model. Additionally, combining these two strategies results additional performance improvements. These results indicate the effectiveness of our proposed approach.
The effects of the regularization term Litg in Eq. 77. validate the impact of the regularization term LReg on the quantized model performance, we conduct ablation studies on the 256 256 dataset using the W4A8 quantization setting. The experiments are performed on the LDM-4 model with DDIM sampler, with and without LReg. As shown in Table incorporating the regularization term Regg in Eq. (7) improves both the FID and sFID scores, showing effectiveness of the proposed regularization loss function.
5 CONCLUSION
In this work, we present novel approach for post-training quantization of diffusion models that incorporates sample and frequency weighting. Our method simultaneously optimizes the contributions of calibration samples and the weighting of frequency components at each time step to effectively quantize the noise estimation networks. By automatically learning optimal weights for calibration samples, approach prioritizes important samples and enhances the performance of the quantized model. Additionally. by learning to weight frequency components in the frequency loss for each time step, we encourage the quantized models to better mimic the frequency components of data generated from their full full-precision counterparts. Extensive experimental results show that our proposed method consistently outperforms the state-of-the-an PTQ approaches for diffusion models, demonstrating its effectiveness across different datasets.
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The statement the use of large language models. Large Language Models (LLMs) were used solely for grammar correction and language polishing of this manuscript. All research ideas, experimental design and data analysis were conducted entirely by the authors, and the use of LLMs does not impact the reproducibility or validity of our findings.
A APPENDIX
A.1
MORE EXPERIMENTS
Table A.I: Quantization results for unconditional image generation with DDIM
256 x 256. The notation indicates that we use the small real validation set.
LSUN- SSUN-Beerooms 256 x 256
Methods
Bits (W/A)
FID
sFID
Full Prec.
32/32
2.98
7.09
PTQ4DM (Shang al. 2023)
4.83
7.94
O-Diffusion (L et al. 2023
4.20
7.66
4/32
TFMQ-DM (Haagg 2024
3.60
7.61
Ours
3.16
6.92
Ours*
3.12
7.15
PTQ4DM (hang al. 2023)
20.72
54.30
Q-Diffusion (Li ct aL. 2023
6.40
17.93
4/8
TFMQ-DM (Hung et al. 2024
3.68
7.65
Ours
3.28
7.05
Ours*
3.26
7.19
Experiments with small real validation set. We conduct additional experiments with the small real validation set from the LSUN-Bedrooms dataset for computing Leal in Eq. 7 Specifically, instead of using the subset of generated images from the full-precision model, randomly sample 32 images from the LSUN-Bedrooms dataset. After that, we create corresponding images at different time steps by adding Gaussian noise to the sampled images form validation set. Following the forward diffusion process (Ho et al.. 2020), the noisy image at time step is defined as:
V + VI OGE, X;
(13)
where Xo is the original clean image, ā₂ = II a; represents the cumulative product of noise schedule coefficients, and 6 ~ -N(0,) I) is random Gaussian noise.
The results are shown in Table A.1 As shown, using either the generated images or the real images, our proposed method consistently outperforms TFMQ-DM (Huang al., (024). This may be because the full-precision model is trained on real images, using either real images or generated images for validation yields similar performance outcomes.
Experiments with an alternative method for frequency loss in Eq. ( We explore alternative approach that computes the frequency loss based on the approximated final samples (t = 0), which can be estimated directly from any generated sample æ; at time step t,. Following DDPM (Ho et al.,
2020), given an intermediate generated sample æ; at the time step f,, the corresponding generated sample म the time step = 0 can be estimated as follows:
[z
(14)
where āi, I - 0s and ai...., aT are predefined variance schedules. Using the above ap- proximation, in each iteration, we acquire approximations for the final generated images of the full-precision model and the quantized model øQ and Erp. The frequency loss Lr(.) is defined as:
Lr Fi, A) Ly (ārq. >t.)
(15)
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Table A.2: Quantization results for unconditional image generation with DDIM on CIFAR-IO 32.
The notation indicates that we use the alternative frequency loss in Eq. 15
CIFAR-10
32 X 32
Methods
Bits (W/A)
FID4
SFID
Full Prec.
32/32
4.23
4.41
PTQ4DM (Shang et al. 2023)
5.65
Q-Diffusion (Li et al. 2023)
5.08
4.98
4/32
TFMQ-DM (Huang al. 2024
4.73
Ours
4.21
4.47
Ours*
4.29
4.45
PTQ4DM Shang et al. 2023
5.69
Q-Diffusion al. 2023
4.78
4.75
TFMQ-DM Huang et al.
2024
8/8
4.24
Ours
4.15
4.36
Ours*
4.09
4.34
PTQ4DM (hang al. 2023)
5.14
Q-Diffusion (Li et al. 2023
4.98
5.68
4/8
TFMQ-DM (Fuang 2024)
4.78
Ours
4.25
4.46
Ours*
4.31
4.57
We evaluate the alternative frequency loss on the CIFAR- 32 32 dataset. As shown Table A.2 this alternative approach yields comparable results. However, from our experiments we observe that it requires up to three times the computational cost compared the original frequency loss in Eq. (6). Therefore, we use the frequency loss defined in Eq. (6) for the results in the main paper and the remaining sections the supplementary materials.
A.2 PITER-PRRAMETER SETTINGS
Regarding the hyper-parameters 3 in Eq. (7) and in Eq. (12 in the main paper, 3 is applied to the regularization loss LReg, while controls the contribution of the frequency loss to the final objective for quantizing diffusion models.
Table A.3: Ablation studies for the hyper- syper-parameter 7 of the frequency loss in Eq. are on the CIFAR-10 10 dataset with the W4A32 setting.
12
The results
0.05
0.1
0.2
0.3
0.5
0.8
1.0
FIDI
4.41
4.21
4.29
4.35
4.58
4.76
4.71
sFID
4.50
4.47
4.53
4.56
4.67
4.98
4.91
We vary the value of y from 0.05 to and fix the value of / 0.05, and evaluate the performance of the model on the CIFAR-10 10 dataset with the W4A32 setting. The results are shown in Table A.3
Ablation studies for the hyper-parameter in Eq. (12).
As shown in the table, the performance is stable across different choices of The performance is slightly better with 7 = 0.1, whereas larger values (e.g., 7 = 1) may slightly degrade performance. This indicates that the proposed method is not sensitive to the choice of 7 and B.
Ablation studies for the hyper-parameter / in Eq. and fix the value of y = 0.1. The experiments are also conducted on the CIFAR-10 10 dataset with the W4A32 setting. The results are shown in Table A.4 As shown in the table, the performance is stable across different choices of 3. The performance slightly better with 3 = 0.05. This indicates that the proposed method is not sensitive the choice of 3.
We vary the value of 3 from 0.01 to 0.
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Table A.4: Ablation studies for the hyper-parameter / of the Reg in Eq. 77).
The results are on the
CIFAR-10 10 dataset with W4A32 setting.
3
0.01
0.02
0.03
0.05
0.08
0.1
FID4
4.25
4.34
4.31
4.21
4.68
4.73
SFID
4.6
4.56
4.52
4.47
4.55
4.58
Table A.5: Quantization results for unconditional image generation with LDM-4 on -SUN-Bedrooms 256 x 256.
LSUN-Bedrooms 256x256
Methods
Bits (W/A)
FID
sFID
Full Prec.
32/32
2.98
7.09
TFMQ-DM (Huang et al. 2024)
3.68
7.65
Ours (FFT)
4/8
3.45
7.20
Ours (DWT)
3.28
7.05
A.3 COMPARE WITH OTHER FREQUENCY TRANSFORMATION METHODS
DWT is a widely used frequency analysis method. It effectively separates and analyzes low and high frequencies from other frequency transforms, such as Fast Fourier Transform (FFT). We conduct additional experiments using FFT and leveraging Focal Frequency Loss for CF in Eq. (I2). The results in Table A.3 demonstrate that leveraging the frequency domain with either DWT or FFT for the PTQ of diffusion models outperforms the baseline TFMQ-DM (Huang et al. 2024 with DWT showing superior results.
Sample
Sample
Sumple
Sample
Samsle
Sample
Early Stage High Nolse
Coarse Structure
Late Stage Detalls
Figure A.1: Visualization of image evolution of calibration dataset sampled from the FFHQ 256 x 256 dataset.
A.4 VISUALIZATION CALIBRATION DATASET
We visualize the calibration dataset sampled from the full-precision diffusion model trained on the FFHQ 256 x 256 dataset. As shown in Figure A.I] eight different samples are displayed at three representative denoising stages: early stage (high noise), middle stage (coarse structure formation), and late stage (fine detail refinement).
These visualizations reveal important frequency characteristics across the denoising process. At carly timesteps, the images predominantly contain noise with minimal structural information. indicating that the diffusion model primarily works with noisy, unstructured patterns. During the middle stage, the model recovers coarse structures and overall composition. At late timesteps, the model refines details such textures, edges, and fine-grained features.
htttps://github.cooo http://///...iioooo/eeeeaeeeeeen frrerreeoo
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A.5 VISUALIZATION GENERATED IMAGES
We visualize sample images generated from the full-precision model, as well as from quantized models obtained using the Q-Diffusion (Li al. 2023) method, the TFMQ (Huang 2024) method, and our proposed method with the W4A8 setting, all initialized with fixed random seed. As shown in Figure A.2 and Figure A.3 our proposed method generates images that closely match those of the full-pre full-precision models, demonstrating the effectiveness of our approach.
(a) Full precision.
(b) Q-Diffusion (W4A8).
(c) TFMQ-DM (W4A8).
(d) Our proposed method (W4A8).
Figure A.2: Generated samples from (a) full- full-precision LDM-4, (b) Q-Diffusion (W4A8), (c) TFMQ-
DM (W4A8), and (d) our proposed method (W4A8) FFHQ 256 x 256 dataset with fixed random seed.
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(a) Full precision.
(b) Q- Q-Diffusion (W4A8).
(c) TEMQ-DM (W4A8).
(d) Our proposed method (W4A8).
Figure A.3: Generated samples from (a) full full-precision LDM-4, (b) Q-Diffusion (W4A8), (c) TFMQ- DM (W4A8), and (d) our proposed method (W4A8) on fixed random seed.
256 x 256 dataset with
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Reviews:
[{"review_id": "izI4XT7OaV", "reviewer": "Reviewer_gXhQ", "summary": "With identifying the property that low-frequency features are primarily recovered in the early stages in the denoising process of diffusion models, this paper proposed a meta-learning method for PTQ in diffusion models, which jointly optimizes the contributions of calibration samples and the weighting of frequency components at each time step. Experimental results reveal the effectiveness of the proposed PTQ method for diffusion models.", "questions": "See weaknesses. I would raise my score if my concerns are solved.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The proposed method well-solved the motivations claimed by authors. \n2. The formulation of equations are clear.", "weaknesses": "1. For measuring the impact of frequency on model performance, authors propose a learnable frequency weight. I'm curious about simply apply a set hyper-parameter frequency weight, for example a weight ranges from [0, 1]. Will this simple method also achieves performance improvement? Since the learnable weights may involve quantization time cost, authors should compare their methods with such a naive weights to find a better trade-off between performance and quantization efficiency. \n2. It would be better to plot some figures (which are sampled from the calibration dataset) for better explaining the frequency motivation in the section of introduction.", "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": "f1FAbp1c7C", "reviewer": "Reviewer_ruiM", "summary": "This paper proposes a meta-learning method based on sampling and frequency weighting based on the existing post-training quantization (PTQ). They propose that the diffusion model recovers different frequency features at different timesteps, so the model can be made to focus on specific frequency components of the generated data at different timesteps by introducing the Discrete Wavelet Transform (DWT) and frequency component weights. The paper analyzes the timesteps of the diffusion model from the perspective of frequency, and this idea is innovative and worthy of further research and experimentation.", "questions": "Further questions:\n\n1. I am curious about (a) and (b) in Fig. 3 of the experimental part. The trends of the three high-frequency components of the frequency weights in (a) are very similar; do these three components have their own independent significance? Can they be combined? It is noted in the text that the later timesteps in (b) have more dispersed sampling weights, but the text does not further explain this, is it possible to further explore the pattern?\n\n2. The experimental part of the article only uses the bitwidths 4/32, 8/8 and 4/8, what is the purpose of choosing them? Are they representative enough for the quantization under different bitwidths?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This article innovatively puts forward the idea of optimizing the post-training quantization process of diffusion models based on frequency characteristics, and details the design of the loss function and optimization process for different training phases, so as to provide suitable weights for sampling at different timesteps. The method has good originality and is worthy of further research and analysis.", "weaknesses": "1. Redundant description: A comprehensive set of parameters and optimization procedures are presented in the article, which is valuable for explaining the methodology theoretically. However, the detailed description of the parameter design and optimization steps in the Introduction seems a bit lengthy—content that might be more appropriately placed in the Experimental section, which may affect the clarity and impact of the paper. The focus could have been on the key decisions and results of the optimization, such as the rationale behind the chosen hyperparameters and the impact of these choices on the results. In addition, any trade-offs made during parameter tuning (e.g., computational cost vs. model performance) could be explicitly discussed and how these choices were justified.\n\n2. Insufficient experiments: the experimental part of the article is slightly insufficient as the only relatively new quantization method is TFMQ-DM, and more recent quantization methods (e.g., SVDQuant[1] or APQ-DM[2], etc.) could have been added. ** This time, I will strongly request that comparison experiments with SVDQuant and APQ-DM be included during the rebuttal period! ** You mentioned at NeurIPS Rebuttal that these would be added in the revised version, but I haven’t seen the supplementary experiments in the ICLR submission yet! Resubmitting without making revisions is a bad practice.\n\nIn addition, the authors did not list the detailed memory usage and inference speed of the quantization model compared with the full-precision model, and only compared the computational cost and hardware efficiency with TFMQ-DM, which can be considered to add more data in this area to improve the rigor.\n\n[1] Li, Muyang, et al. \"Svdqunat: Absorbing outliers by low-rank components for 4-bit diffusion models.\" arXiv preprint arXiv:2411.05007 (2024).\n\n[2] Wang, Changyuan, et al. \"Towards accurate post-training quantization for diffusion models.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.", "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": "Jaz0UjvnTA", "reviewer": "Reviewer_RZMH", "summary": "This paper revisits the common practice of using uniform sampling in diffusion model training and identifies its limitations, especially in terms of efficiency and learning dynamics. The authors propose a non-uniform sampling strategy that focuses training more on informative or harder timesteps instead of treating all noise levels equally. They introduce a principled sampling distribution that improves convergence, sample quality, and training efficiency without modifying the model architecture. Experiments across various benchmarks demonstrate consistent performance gains over uniform sampling. Overall, the paper aims to optimize how diffusion models learn, rather than modifying their network or objective.", "questions": "1: Generalization: How well does the proposed non-uniform sampling strategy extend to other modalities, such as text-to-image diffusion, video generation, or 3D diffusion models?\n\n2: Optimality of Sampling Distribution: Is there a theoretical way to derive the optimal sampling distribution instead of heuristically estimating timestep importance?\n\n3: Interplay with Noise Schedules: How does this sampling approach interact with different noise schedules (cosine, linear, VP/VE SDEs)? Could both be jointly optimized for further gains?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Strengths\n\n1: Insightful Problem Identification: Highlights a commonly overlooked assumption in diffusion model training, uniform timestep sampling, and provides evidence of its suboptimality.\n\n2: General and Model-Agnostic: The proposed sampling strategy applies to multiple diffusion architectures (DDPM, latent diffusion, etc.) with no changes to model design.\n\n3:Strong Empirical Results: Demonstrates improvements in FID scores and convergence speed across datasets, validating practical impact.", "weaknesses": "Weaknesses\n\n1: Additional Complexity in Training Setup: Non-uniform sampling introduces another hyperparameter and requires computing or estimating timestep importance, which may increase implementation difficulty.\n\n2: Limited Theoretical Guarantees: While the method is empirically effective, formal justification for optimality of the sampling distribution is not fully established.\n\n3: Focus on Image Generation Only: The experiments are mostly image-based; it remains unclear whether the approach extends well to text-to-image, video diffusion, or molecular diffusion models.", "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)