Effective and Efficient Masked Image Generation Models
Authors: Zebin You
Jingyang Ou
Xiaolu Zhang
Jun Hu
JUN ZHOU
Chongxuan Li
Venue: ICML 2025 poster (2025)
Keywords: masked diffusion model
mask image modeling
generative model
efficient
effective
Primary area: deep_learning->generative_models_and_autoencoders
TL;DR: We introduce eMIGM, a powerful generative model that significantly accelerates the sampling speed while maintaining high image quality.

Abstract: Although masked image generation models and masked diffusion models are designed with different motivations and objectives, we observe that they can be unified within a single framework. Building upon this insight, we carefully explore the design space of training and sampling, identifying key factors that contribute to both performance and efficiency. Based on the improvements observed during this exploration, we develop our model, referred to as \textbf{eMIGM}. Empirically, eMIGM demonstrates strong performance on ImageNet generation, as measured by Fréchet Inception Distance (FID). In particular, on ImageNet $256\times256$, with similar number of function evaluations (NFEs) and model parameters, eMIGM outperforms the seminal VAR. Moreover, as NFE and model parameters increase, eMIGM achieves performance comparable to the state-of-the-art continuous diffusion model REPA while requiring less than 45\% of the NFE. Additionally, on ImageNet $512\times512$, eMIGM outperforms the strong continuous diffusion model EDM2. Code is available at \url{https://github.com/ML-GSAI/eMIGM}.

Effective and Efficient Masked Image Generation Models
Zebin You 123
Jingyang Ou 123
Xiaolu Zhang*
Jun Hu
JUN ZHOU Chongxuan Li Li3
Abstract
Shi al., 2024; Lou al., 2024a). In image generation, MaskGIT (Chang al., 2022) introduced masked image generation, offering efficiency and quality improvements over autoregressive models but still lagging behind diffu- sion models (Ho et al., 2020; Sohl-Dickstein et al., 2015; Song et al., 2020) due to information loss from discrete tokenization (Esser et al., 2021; Van Den Oord et al., 2017). MAR (Li et al., 2024) eliminated this bottleneck via diffu- sion loss, achieving strong results, yet key factors (e.g.. masking schedule, loss function) remain underexplored. Moreover, with limited sampling steps (e.g., 16), its per- formance falls short of coarse-to-fine next-scale prediction model VAR (Tian et al., 2024).
Although masked image generation models and masked diffusion models are designed with dif- observe ferent motivations and objectives,
that they can be unified within single frame- work. Building upon this insight, we carefully explore the design space of training and sam- pling, identifying key factors that contribute to both performance and efficiency. Based on the improvements observed during this exploration, we develop model, referred to e.IIGM. Empirically, eMIGM demonstrates strong per- formance on ImageNet generation, as measured by Fréchet Inception Distance (FID). In particu- lar, on ImageNet 256 x 256, with similar num- ber of function evaluations (NFEs) and model parameters, eMIGM outperforms the seminal VAR. Moreover, as NFE and model parameters increase, eMIGM achieves performance compa- rable to the state-of-the-art continuous diffusion model REPA while requiring less than 45% of the NFE. Additionally, on ImageNet 512 x 512, eMIGM outperforms the strong continuous diffu- sion model EDM2. Code available at https: / //github.....//// com BM00mmMMMGGSSA GAALLLGGSAAAAAA CAA-CCAAAIMMMIOOA GM.
In parallel, masked diffusion models (Sahoo et al., 2024; Shi al., 2024; Lou et al., 2024a; Ou al., 2024) have shown promise in text generation, demonstrating scaling properties (Nie et al., 2024) similar to ARMs and offering principled probabilistic framework for training and in- ference. However, their applicability to image generation remains an open question.
We propose unified framework integrating masked image modeling (Chang al., 2022: Li al., 2024; Bai al., 2024) and masked diffusion models (Lou et al., 2024a; Sa- hoo et al., 2024; Shi et al., 2024), leveraging the strengths of both paradigms. This enables a systematic exploration of training and sampling strategies to optimize performance. For training, we find that images, due to their high redun- dancy, benefit from a higher masking ratio, a simple weight- ing function inspired by MaskGIT and MAE (He al., 2022a) tricks, improving generation quality. We also present CFG with Mask, replacing the fake class token with mask token for unconditional generation, further enhancing per- formance. For sampling, predicting fewer tokens in early stages improves results. However, early-stage guidance de- creases variance, raising FID. To counter this, we propose a time interval strategy for classifier- ffree guidance in masked image generation, applying guidance only in later stages. This maintains strong performance while significantly ac- celerating sampling by reducing NFEs.
1. Introduction
Masked modeling has proven effective across various do- mains, including self-supervised learning (He et al., 2022a; Bao et al., 2021; Devlin, 2018), label to image genera- tion (Li et al., 2023; Chang et al., 2022; Li al., 2024:
Ni et al., 2024), text to image generation (Bai et al., 2024;
Shao et al., 2024) and text generation (Sahoo et al., 2024:
!ork done during an internship at Ant Group Gaoolin School of Artificial Intelligence, Renmin University of China, Beijing, China. BBejjing Key Laboratory of Research on Large Models and Intelligent Governance. 'Engineering Research Cen- ter of next-Generation Intelligent Search and Recommendation, MOE. 'Ant Group. Correspondence to: Chongxuan <chongxu- anli @ ruc. edc.edu.cn.>.
Building on our training and sampling improvements, we develop eMIGM and evaluate it on ImageNet (Deng et al., 2009) at 256 256 and 512 x 512 resolutions. As model pa- rameters scale, eMIGM achieves progressively higher sam-
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

---

Effective and Efficient Masked Image Generation Models
Figure I. Generated samples from eMIGM trained ImageNct 512 512.
ple quality in a predictable manner (Fig. 4(a)). Larger mod- els further enhance efficiency, maintaining superior quality with similar training FLOPs and sampling time (Fig.4(b), Fig. 4(c)). Notably, eMIGM delivers high-quality samples with few sampling steps. On ImageNet 256 256, with similar NFEs and model parameters, it consistently out- performs VAR (Tian et al., 2024). Increasing NFE and model size, our best- pessfrerformin eMIGM-H becomes com- parable to state-of-the-art diffusion models like REPA (Yu al.. 2024) (FID 1.57 vs. 1.42) 1.42)-will without
In summary, our key contributions are as follows:
We propose a unified formulation to systematically ex- plore the design space of masked image generation mod- els, uncovering the role of each component.
We introduce the time interval strategy for classifier-free guidance, maintaining high performance while signifi- cantly reducing sampling time.
(4it)tttthuu requiring self- supervised features. On ImageNet 512 512, eMIGM-L. surpasses EDM2 (Karras et al., 2024) while using lower parameter count, demonstrating efficiency and scalability. Qualitatively, eMIGM generates realistic and diverse images (Fig. 1).
We surpass the seminar diffusion models on ImageNet 512 512.
We demonstrate that eMIGM benefits from scaling, with larger eMIGM models achieving greater efficiency.
2

---

Effective and Efficient Masked Image Generation Models
2. Preliminaries
the forward process of MDM is defined as adding noise independently in each dimension:
2.1. Masked Image Generation
N-1
Let x = [>=]]==1 represent the discrete tokens of an image obtained via a VQ encoder (Esser et al., 2021; Van Den Oord al., 2017), and let [M] denote the special mask token. We consider two seminal masked image generation methods.
(,
II
q. (=)(<:)))))))
(2)
where
MaskGIT (Chang et al., 2022) first extends the concept of masked language modeling from BERT (Devlin, 2018) (i.e., predicting masked tokens based on unmasked tokens) to image generation, achieving excellent performance with low sampling cost (approximately 10 sampling steps) on ImageNet (Deng et al., 2009). However, its performance degrades when the number of sampling steps increases under its default mask schedule.
1 1-7
%b-
x. n/
(3)
Yt: x
[M].
The training objective of MDM is to optimize the upper bound of the negative log-likelihood of the masked tokens, which defined as:
During training, MaskGIT optimizes the cross entropy loss as follows. A ratio r is sampled from [0, 1]. and based on the mask scheduling function Tr. masked image x'M sampled from masking distribution 9-e (# (x)) that randomly masks [ tokens of x as [M].
((xo) ('' Le B Bo(ass/ro) 7t
Σ
- log pe (xg1)T)
dt.
[1|m] (M])
(4)
Interestingly, the explicit time input of MDM is theoretically redundant 1 (Ou et al., 2024), and has also been empirically validated in image generation (Hu & Ommer, 2024).
The loss function is then defined as:
[(x) = Er-U(0.11 Bay (anglar)
Σ (
log po (o' am)
During sampling, given two noise levels 8 and I, where 0 8 <t 1, the reverse process is characterized as:
N
During sampling, MaskGIT starts with an image where all tokens are masked, Vy. For each iteration t E (1,2..... T), the number of masked tokens is n, = 4 N1. and the model receives input ا=1. The model predicts the proba- bilities for all tokens, and the â4 = π - n, tokens with the highest confidence are unmasked, updating to x..
(x11(20(00)) = II guje (x)
(5)
where
1,
x = x o # [M].
x = [M], क [M],
Goje (aje(tr)/a
T
MAR (Li et al., 2024) proposes using diffusion model (Sohl-Dickstein et al., 2015) to model the per-token distribution, which eliminates the need for discrete tokeniz- ers. By avoiding the information loss of discrete tokenizers, MAR achieves excellent image generation performance.
2i -
2010
of A [M], .: [M].
0,
otherwise.
(6)
3. Unifying Masked Image Generation
During training, MAR samples the masking ratio m, from truncated Gaussian distribution with mean 1.0, standard deviation 0.25, truncated [0.7, 1.0]. For sampling, MAR adopts decoding strategy similar to that of MaskGIT.
After removing the explicit time input from MDM, we ob- serve that the MaskGIT objective (Eq. 1) can be expressed in terms of the general MDM loss formulation (Eq. 4). Specif- ically, the Monte Carlo expectation over r in Eq. is equiv- alent to integrating over from 0 to I, where r can be interpreted as a scaled time variable corresponding to the masking schedule. In this reinterpretation, the masked im- age zy in MaskGIT can be understood as E; in the general
2.2. Masked Diffusion Models
Let æ |,>] represent the discrete text tokens of sentence, [M] denote the special mask token, and 7 rep- resent the mask schedule. MDMs (Lou et al., 2024b; Shi et al., 2024; Sahoo et al., 2024) gradually add masks to the data in the forward process and remove them during the reverse process. Here, we focus on the parameterized form of RADD (Ou et al., 2024). Given noise level E [0, 1),
Unlike continuous state diffusion which require both x; and as inputs to the model input to denoise, the mask discrete diffu- sion operates by using Po (#) (z;) instead of po l f). That's because the timestep dependence can he extracted as weight coefficient outside of the cross-entropy loss.
3

---

Effective and Efficient Masked Image Generation Models
Table Comparison of different masked image modeling approaches through a unified framework. The differences among these approaches are defined by the choice of masking distribution (<x, (zo). weighting function w(t), and conditional distribution pa wt).
METHOD
MASKING DISTRIBUTION
WEIGHTING FUNCTION w(1)
CONDITIONAL DISTRIBUTION
g(22: (20)
pa (xx)
MASKGIT UNIFORMLY MASK [Wey]] TOKENS w/O REPLACEMENT
((t) =
CATEGORICAL DISTRIBUTION
MAR
UNIFORMLY MASK [ TOKENS W/O REPLACEMENT
w(()=
DIFFUSION MODEL
MDM
MASK N TOKENS INDEPENDENTLY WITH RATIO
n(()) = I CATEGORICAL DISTRIBUTION
framework, representing the noisy or partially masked im- age at time t. That is, the masking distribution 9y, (x))()) can be mapped to a specific instance of ( charac- terized by the chosen mask scheduling function "/-. See the equivalence between these two masking distributions in Appendix A. After aligning these two masking distribu- tions, MaskGIT, MAR, and MDM can be expressed within a unified loss function, defined as:
et al., 2017; Esser et al., 2021), we use a diffusion model to model the conditional distribution pe (zf] Our ex- ploration begins with the standard MDM, which utilizes a single encoder transformer architecture and linear mask schedule, in addition using the diffusion model model the conditional distribution pe (xr) 2z).
Mask schedule. The first critical aspect of our exploration is the choice of "ts which determines the probability of masking each token during the forward process (See Ap- pendix for details). In this section, we use the weighting function of wt)) = 7 which is mainly used in MDM. We T
[(xo) = (((
Σ [M
log po (rd
xx)) dt.
consider three mask schedules: (1) Linear: 7/2 t; (2) Co-
sine: 7/- = cos ( (1 (1- - f)); (3) Exp: "/- = 1 - exp(-5t). The first two mask schedules are also mentioned in Shi et al. (2024), while the last one is our design to achieve a higher masking ratio during training. As shown in Fig. 2(a), the cosine schedule outperforms the linear schedule. We hypothesize that, due to the high information redundancy in images, the cosine schedule achieves a higher mask ra- tio during training, providing stronger learning signals and leading to improved performance. The exp schedule fur- ther increases the mask ratio but destabilizes MDM training, likely due to the persistently large weighting function wtt), even at high mask ratios (see Fig. 5 for visualization of w(t) and y)).
In this unified formulation, the key differences between the models primarily lie in the three components outlined in Table 1. We explain these components as follows:
Masking distribution q<x, (2)) For MaskGIT and MAR, [N tokens are uniformly masked without replacement as [M]. For MDM, each of the N tokens is masked with probability "f independently.
Weighting function wt)). The weight function w(t) de- termines the importance of the loss at each time step. For MaskGIT and MAR, wt(() 1: for MDM, w(t) Ti. Conditional distribution pe (z/ æ2). For MaskGIT and MDM, the conditional distribution pe (zú æex) is modeled as a categorical distribution. In contrast, for MAR, we employ a diffusion model assisted by a latent variable z, leading to the following formulation:
Weighting function. We consider two choices for w()). (1) wo(t) = 1í as used in MDM; (2) wn(t) 1, as used in MaskGIT. Notably, the weighting function significantly affects the choice of mask schedule. For instance, using w(t) = % led to unstable training, particularly with the exp schedule. In contrast, as shown in Fig. 2(b), setting w(() = 1 stabilized the training process and improved performance, similar to the phenomenon observed in DDPM (Ho et al., 2020); under this setting, the exp schedule yielded the best results. Therefore, we adopted this combination (wit) = 1 and the exp schedule) as our default.
/ deg (=*12z [xx:) diff (=)) 22 dzz"
=
7h
Here, 60, (=/|az) represents the output of the mask pre- diction model with input æ,, and gdif (ar) donated the output of diffusion model conditioned on z
4. Investigating the Design Space of Training
Building upon the unified framework, we now explore vari- ous design choices within this formulation. Given the equiv- alence of masking distributions, we adopt MDM's as the default setting. Furthermore, to mitigate the information loss introduced by the discrete tokenizer (Van Den Oord
Model Architecture. We consider two model architectures: (1) A single-encoder transformer; (2) The MAE (He et al., 2022a) architecture, which decomposes the transformer into an encoder-decoder structure, where the encoder processes
4

---

Effective and Efficient Masked Image Generation Models
50
50
50-
Linear
Cosine with w(! 1i
Exp without MAE
FID (10K somples)
40
Cosine
FID (10K samples)
hn
FID (10K somples)
40
Exp with MAE
Cosine with w(1 w(0-1
Exp with w(t)=
30
30-
30
20
20
10
10
10
100
200
300
400
100
200
300
400
100
200
300
400
Training Epochs
Training Epochs
Training Epochs
(a) Choices of mask schedule
(b) Choices of weighting function
(c) Use the MAE trick
30
30
Exp with Liin - 0.4
Standard CFG
(10K samples)
Exp with Lai
FID (10K sumples)
CFG with mask
Exp with Faia - 0.2
20
20.
10
10
100
200
300
400
100
200
300
400
Training Epochs
Training Epochs
(d) Use the time truncation
(c) Use CFG with mask
Figure Exploring the design space of training, Orange solid lines indicate the preferred choices in each subfigure.
only unmasked tokens. The primary difference between these architectures is whether the encoder receives masked tokens as input. As shown in Fig. 2(c), under the exp sched- ule, the MAE architecture outperforms the single-encoder transformer. Interestingly, despite being originally designed for self-supervised learning, MAE retains its advantages in image generation. Therefore, unless otherwise specified, we adopt the MAE architecture as the default setting.
able fake class token as input. Unsupervised classifier-free guidance was initially developed for text generation (Nie et al., 2024), a process involving the unconditional model receiving special mask token as input. Inspired by this method, our paper adapts it for image generation. We term this adapted approach CFG with Mask to emphasize its fo- cus on masked image generation. As shown in Fig. 2(e), CFG with mask improves generation performance compared to standard CFG. Notably, here we use only simple condi- tional generation without guidance, our results suggest that using fake class token negatively impacts the conditional generation performance of MDM. Thus, we adopt CFG with mask as the default setting.
Time Truncation. To achieve a higher mask ratio during training, in addition to selecting a more concave function for 7t,
also use time truncation, which restricts the minimum value of to min- We consider three choices: (1) tmin 0, the original design; (2) mmn = 0.2; (3) min 0.4. As shown in Fig. 2(d), we observed that an appropriate time truncation (fmin 0.2) can be beneficial and acceler- ates training convergence. However, excessive truncation (fmin 0.4, where over 80% of image tokens are masked during training) provides no benefit and may even degrade performance compared to no time truncation. Unless other- wise noted, we adopt minn 0.2 as the default setting.
5. Investigating the Design Space of Sampling
In the previous section, we carefully explore the training design space. In the following sections, we investigate the sampling design space. On one hand, we expect the model's performance to improve as the number of mask prediction steps increases. On the other hand, we aim to maintain strong performance even with a low number of mask prediction steps (e.g., 16).
CFG with Mask. Classifier-Free Guidance (CFG) (Ho & Salimans, 2022) is widely used for guiding continuous dif- fusion models and masked image generation. It combines outputs of a conditional model (with class information) and an unconditional model (without class information) to im- prove alignment with the conditional output. In standard CFG, the unconditional model typically receives a learn-
5.1. Mask Schedule during Sampling
During training, we observe that the exp schedule achieves the best performance. However, during sampling, different
5

---

Effective and Efficient Masked Image Generation Models
Linear
10
Exp
4.0
DPMS
FID (1OK samples)
35
Cosine
Exp with DPMS
DPMS with time interval
Exp
3.5
25
8
(0ok cul
3.0
15w
6
2.5
0
50
100
150
200
250
0
50
100
150
200
250
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Steps
Steps
Avg sec per image
(a) Choices of sample mask schedule
(b) Use the DPM-Solver
(c) Use the time interval
Figure 3. Exploring the design space of sampling, For each plot, points from left to right correspond to an increasing number of mask prediction steps: 8, 16, 32, and up to 256. each subfigure, DPM-Solver is donated as DPMS. (a) The exp schedule outperforms others by predicting fewer tokens early. (b) OPo-Srlver performs better with fewer prediction steps. (c) The time interval maintains performance while reducing sampling cost for each mask prediction step, particularly for high mask prediction steps.
schedules may be employed. We are interested in identify- ing which mask schedule can achieve both of our goals.
For example, with 8 mask prediction steps, DPM-Solver achieves an FID 6.6, while DDPM, with a temperature of 1.0, achieves an FID of 10.6. We hypothesize that for low mask prediction steps, DDPM requires careful tempera- ture tuning, whereas DPM-Solver, being an ODE sampler, does not require such adjustments. Moreover, DPM-Solver achieves good performance with fewer than 15 diffusion steps, while DDPM requires 100 diffusion steps. Therefore, unless specified, we default to DPM-Solver.
To this end, we first conduct a simulation experiment (see details in Appendix B.2) compare the number of tokens predicted during each mask prediction step across differ- ent mask schedules. We observe that the linear schedule predicts a nearly constant number of tokens per step, while the cosine schedule predicts fewer tokens early in the pro- cess and progressively more later. This observation aligns with the findings reported in Shi et al. (2024). Besides, the exp schedule predicts even fewer tokens initially, with more gradual increase the process continues. As shown in Fig. 3(a), we observe that each mask schedule benefits more prediction steps. Moreover, for low mask prediction steps (e.g., 8 or 16). the exp schedule outperforms the co- sine schedule, which in turn outperforms the linear schedule. This suggests that, in the early stages of sampling, predict- ing fewer tokens may contribute improved performance at lower mask prediction steps. Thus, we adopt the exp sched- ule our default for sampling unless otherwise specified.
5.3. Time Interval for Classifier Free Guidance
Previously, we adopt a linear CFG schedule following MAR (Li et al., 2024), where the CFG value gradually increased from to the target value during the mask predic- tion process. With a constant CFG schedule, we find that the generation performance is highly sensitive to the CFG value, as shown in Fig. 7. We hypothesize that, for MDM, token generation is irreversible- once token is generated, it cannot be modified. Therefore, a strong guide in the early stages may reduce the variation in the results, leading to a higher FID. This is similar to our earlier observation with the linear mask schedule, where generating too many incor- rect tokens early can cause error accumulation and degrade the performance. We conduct an experiment with total of 256 sample tokens and 16 mask prediction steps (see details in Appendix C) to validate our hypothesis. Let s, and t; denote the endpoint and start of the i-th step in the mask prediction process. We apply CFG if s, E [cfg..... cfg.....:: otherwise, we use simple conditional generation. As shown in Fig. 8(a), when cfg.... < cfgr... S 0.5, we achieve a relatively low FID, supporting our hypothesis. In particular, the best performance is achieved when efg..... 0.1 and cfg..... = 0.3, using only 60% of the NFE (the number of function evaluations) compared to standard CFG. Specifi- cally, for standard CFG, NFE = 16 x 2, while for the time interval, NFE 16 + 16 x (0.3 0.1).
5.2. The Sampling Method of Diffusion Loss
We use the diffusion loss to model the distribution of pe (x) az)). Previously, we follow MAR (Li et al., 2024) and use DDPM (Ho et al., 2020) sampling method with 100 diffusion steps. Additionally, MAR employs the tempera- ture 7 sampling method from ADM (Dhariwal & Nichol, 2021) to scale the noise by r, which requires careful tuning for optimal performance.
In contrast, DPM-Solver (Lu al., 2022abb) is training- free, fast ODE sampler that accelerates the diffusion sam- pling process and converges faster with fewer steps. Inter- estingly, although DPM-Solver is designed for accelerating the diffusion process, we observe that, with low mask pre- diction steps, it outperforms DDPM, as shown in Fig. 3(b).
6

---

Effective and Efficient Masked Image Generation Models
20
25
4.5
eMIGM-XS
eMIGM XS
eMIGM-XS
16
eMIGM-S
20
eMIGM-S
eMIGM-S
FID- FID-10K
eMIGM-B
FID FID-100 10K
eMIGM-D B
3.5
<MIGM-B
eMIGM-L
eMIGM-L
eMIGM-L
12
cMIGM-H
eMIGM-11
eMIGM-I1
2.5
10
10th
10
5
10
10
30
10
1.5
0.00
0.25
0.50
0.75
1.00
FLOPs
FLOPs
Avg per image
(a) FLOPs vs. FID across model scales.
(b) FLOPs vs. FID under different budgets.
(c) Inference speed vs. FID.
Figure Scalability eMIGM. (a) negative correlation demonstrates eMIGM henefits from scaling. (h) Larger models are more beining-efficienn (i.e., achieving better sample quality with the same training FLOPs). (c) Larger models sampling sampling-efficient (i.e.. achieving better sample quality with the same inference time).
Table 2. Image generation results ImageNet 256 256. denotes results taken from MaskGIT (Chang et al., 2022), and indicates results that require assistance from self-supervised model. With 42% of function evaluations (NFE), eMIGM-H achieves performance comparable to the nate-of-the-ars diffusion model REPA (Yu al., 2024). We bold the best result under each method and underline the second- best result.
METHOD
NFE (
FID (1)
#Params
METHOD
NFE ())
FID ())
#Params
Diffusion models
GANs
ADM-G (Dhariwal & Nichol, 2021)
250x 2
4.59
554M
BigGAN (Brock, 2018)
6.95
ADM-G-U (Dhariwal & Nichol, 2021)
750
3.94
S54M
StyleGAN XL. (Sauer et al., 2022)
1x2
2.30
LDM-4G (Rombach et al., 2022)
250x 2
3.60
400M
Masked models
VDM++ (Kingma & Gao, 2024)
512x2
2,40
2B
SimDiff (Hoogeboom al., 2023)
512x2
2.44
2B
MaskGIT (Chang et al., 2022y
6.18
227M
U-VIT-H/2 (Bao et al., 2023)
50x2
50IM
MAR-B (Li et 2024)
256x2
2.31
208M
DIT-XL/2 (Peebles& Xie, 2023)
250x2
2.27
675M
MAR-L (Li al., 2024)
256xx
1.78
479M
Lirge-DiT (Alpha-VI.I.)
2024)
38
MAR-H (Li et al., 2024)
256x2
1.55
943M
Large-DiT (Alpha-VLLM, 2024)
250x2
TB
Ours
STT-XL et al., 2024)
250x2
2.06
675M
<MIGM-XS
16x1.2
4.23
69M
SS---S-----IG (Yan al., 2024)
250x2
2.28
660M
DifiT (Hatamizadeh et al.. 2025)
250x2
1.73
561M
eMIGM-S
16x1.2
3.44
97M
eMIGM-B
16x1.2
2.79
208M
REPA (Yu et al., 2024)*
250x 1.7
1.42
675M
eMIGM-L
2.22
478M
ARs
eMIGM-H
16x1.2
2.02
942M
VQGAN (Esser 2021)*
256
18.65
227M
eMIGM-XS
1.8
3.62
VAR-216 (Tian al., 2024)
10x2
3.30
310M
eMIGM-S
1.8
2.87
97M
VAR-420 (Tian et al. 2024)
10x2
2.57
600M
cMIGM-B
128x1.35
2.32
208M
VAR-424 (Tian et al., 2024)
10x2
2.09
1B
eMIGM-I
1.8x1.4
1.72
478M
VAR-330 (Tian et al., 2024)
10x2
1.92
2B
eMIGM-H
128x1.4
1.57
942M
As shown in Fig. 3(c), we observe that the time interval maintains performance at each mask prediction step while reducing sampling time. This demonstrates its efficiency and effectivvneess. Therefore, we adopt the time interval for all subsequent experiments in this paper.
6.1. Larger Models Are Training and Sampling Efficient
First, to demonstrate the scaling properties of eMIGM, we plot the FID-IOK at 400 training epochs for different model sizes of eMIGM against training FLOPs. As shown in Fig. 4(a), we observe a negative correlation between train- ing FLOPs and FID-10K, indicating that eMIGM benefits from scaling. Second, for different model sizes of eMIGM, scale the FLOPs and analyze the FID-10K in relation to training FLOPs. As shown in Fig. 4(b), for each model size of eMIGM, training epochs and training FLOPs increase, performance also improves. Additionally, we observe that for the same training FLOPs, larger eMIGM models achieve better performance. For instance, eMIGM-L outperforms
6. Experiments
By fully considering the design space mentioned above, we evaluate eMIGM on ImageNet 256 x 256 and ImageNet 512 x 512 (Deng et al., 2009), benchmarking the sample quality using Fréchet Inception Distance (FID) (Heusel al., 2017). See experiment settings in Appendix D.

---

Effective and Efficient Masked Image Generation Models
Table 3. Image generation results on ImageNet 512 x 512. denotes results taken from MaskGIT (Chang et al., 2022). & denotes
results obtained using Guidance Interval (Kynkäänniemi et al., 2024). With function evaluations (NFE), eMIGM-L outperforms strong visual autoregressive models VAR (Tian et al., 2024). When the NFE increases to 80, eMIGM-L surpasses the strong diffusiio model EDM2 (Karras et al., 2024). We bold the best result under each method and underline the second-best result.
METHOD
NFE (1)
FID
#Params
METHOD
NFE (1)
FID (L)
#Params
Diffusion models
ARs
ADM-G (Dhariwal & Nichol, 2021)
250x 2
7.72
559M
VQGAN (Esser et al., 2021)*
1024 10x2
26.52
227M
ADM-G-U (Dhariwal & Nichol, 2021)
750
3.85
559M
VAR-d36-s (Tian et al., 2024)
2.63
2.3B
VDM++ (Kingma& & Gao, 2024)
512x2
2.65
2B
SunDiff (Huogebuom al., 2023)
512x2
2B
Masked models
V/4 (Ban et al., 2023)
50x2
4.05
501M
MaskGIT (Chang et al., 2022y
12
7.32
227M
DIT-XI.2 / (Pechles& & Xie, 2023)
250x2
3.04
675M
MAR (Li et al., 2024)
256x2
1.73
481M
Large-DiT AAlph--VLLM, 2024)
250x2
2.52
3B
SIT-XL (Ma al., 2024)
250x2
2.62
675M
Ours
EDM2-XXL (Karrus 2024)
32x2
1.11
1.5B
EDM2-XXL (Kynkäānniemi et al., 20244
3.2
1,40
1.5B
eMIGM-XS
16x
4.63
104M
eMIGM-S
16x 1.2
3.65
132M
Consistency models
cMIGM-B
16x
2.78
244M
sCT-XXL. (Lu&& & Song. 2024)
2
3.76
1.5B
eMIGM-L
16x 16x
2.19
478M
sCD XXI.XXI. (I.u & Song, 2024)
2
1,88
1,5B
eMIGM-XS XS
64x1.25
4.45
104M
GANs
eMIGM-S
64x1.25
3.29
132M
BigGAN (Brock, 2018)
1 8.43
eMIGM-B
64x1.25
2.31
244M
StyleGAN XL. (Sauer et al., 2022)
1x2
2.41
cMIGM-L
1421.25
1.77
478M
eMIGM-B with approximately 2020 FLOPs. Third, we ob- served the inference-time scaling behavior of eMIGM. As shown in Fig. 4(c), we plot the performance of different eMIGM model sizes across various mask prediction steps (ranging from 16 to 256). The speed is measured using a single A A100 GPU with a batch size of 256. We observe that as the number of prediction steps increases, each model size of eMIGM achieves better performance, particularly for smaller models (i.e., eMIGM-XS and eMIGM-S). For larger model sizes, similar best performance is reached with just 64 steps. Additionally, we also find that larger eMIGM models achieve better performance while maintain- ing similar inference speeds. For example, at a speed of about 0.2 seconds per image, e.IIGM-L achieves a strong FID of 1.8, outperforming eMIGM-B with an FID of 2.3.
310M parameters. Notably, as we increase the NFE, all of our models consistently show significant improvements in generation performance. For instance, eMIGM-L achieves FID of 1.72 with 180 NFEs, compared to FID of 2.22 with 20 NFEs. By increasing the NFE, eMIGM-L, despite having only 478M parameters, outperforms the best VAR-d30, which achieves an FID of 1.92 with 2B param- eters. Lastly, our more powerful eMIGM-H achieves an FID of 1.57 with 180 NFEs, outperforming strong diffu- sion models such Large- (Alpha-VI.LMM, VL.LM, 2024) and DiffiT (Hatamizadeh et al., 2025). cMIGM-H is also com- parable to the best diffusion models REPA (Yu et al., 2024), which require 425 sequential steps and the assistance of the self-supervised model. Furthermore, compared to the state- of-the-art GAN model StyleGAN-XL (Sauer et al., 2022), eMIGM-B B achieves superior performance. We also present more evaluation metrics on Tab. 8 in the appendix.
6.2. Image Generation on ImageNet
In Tab. 2, we compare eMIGM with state-of-the-art gener- ative models on ImageNet 256 x 256. Notably, in Tab. 2 and Tab. 3, we list only the NFE of eMIGM's 's transformer component. When measured on a single A 100 GPU with batch size of 256, we found that the MLP diffusion block introduces approximately 14% additional computational overhead beyond the NFE of the main transformer. How- ever, since the transformer component remains the primary computational bottleneck, NFE continues to be a valid effi ciency metric. By exploring the design space of sampling, eMIGM with few NFEs (approximately 20) outperforms VAR (Tian al., 2024) with a similar model size. Specifi- cally, eMIGM-B achieves an FID of 2.79 with only 208M parameters, while VAR-d16 achieves an FID of 3.30 with
We also evaluate eMIGM on higher resolution images (i.e., 512 x 512) in Tab. 3. Specifically, with similar NFEs, cMIGM-L (with only 478M parameters) achieves an FID of 2.19, outperforming the strong generative model VAR (Tian et al., 2024) (with 2.3B parameters), which achieves an FID of 2.63. Furthermore, compared the strong diffusion model EDM2 (Karras et al., 2024), eMIGM-L achieves an FID of 1.77, outperforming EDM2's ' FID of 1.81. These quantitative results demonstrate that eMIGM achieves excel- lent generation performance and high sampling efficiency across diverse resolutions. However, when using the guid- ance interval (Kynkäänniemi et al., 2024), EDM2-XXL achieves superior performance while needing more param- eters. A comparison of the sampling speeds for eMIGM and EDM2 (Karras et al., 2024) is also presented in Tab. 10.
8

---

Effective and Efficient Masked Image Generation Models
Furthermore, when compared to MAR, eMIGM-L achieves competitive performance while using NFE of less than 20%.
models with fewer NFEs. We believe that eMIGM will inspire future research in masked image generation.
Acknowledgements
7. Related Work
This work was sponsered by the Beijing Nova Program (No. 20230484416); National Natural Science Foundation of
Visual generation. Modern visual generation models pri- marily fall into four categories: GANs (Goodfellow al., 2014; Brock, 2018; Sauer al., 2022), diffusion mod- els (Song et al., 2020; Sohl-Dickstein et al., 2015; Ho et al., 2020), masked prediction models (Chang et al., 2022; Li et al.,, 2023; 2024; Bai et al., 2024; Shao et al., 2024; Ni et al., 2024), and autoregressive models (Esser et al., 2021; Tian et al., 2024; Sun et al., 2024; Tang al., 2024). The most related works to our study are MaskGIT (Chang al., 2022) and MAR (Li al., 2024). We provide unified framework that integrates both approaches and systematically explore the impact of each component. Additionally, guidance in- terval (Kynkäänniemi et al., 2024) and CADS (Sadat et al., 2023) also observed that strong guidance early in the pro- cess negatively affects diversity. Therefore, they proposed sampling strategies to adjust the guidance application dur- ing sampling. Besides, Wang al. (2024) also analyses the schedule of classifier-free guidance in continuous diffusion models. However, unlike our proposed time interval, which applies guidance at the token level, their methods operate at different noise levels of the entire image. Besides, our proposed time interval is motivated by MDM's unique ir- reversible token generation constraint, Furthermore, Shao al. (2024) proposed an enhanced inference technique to improve the speed and performance of masked image gen- erative models such MaskGIT (Chang et al., 2022) and Meissonic (Bai et al., 2024). Their technique is orthogonal to our method and can also be applied to our work.
China (No. 924701188 18); Beijing Natural Science Foundation (No. L.247030); the ant Group Research Fund.
Impact Statement
We introduce eMIGM, a powerful generative model that sig- nificantly accelerates the sampling speed while maintaining high image quality. However, this increased efficiency may increase the potential for misuse of generated images. To mitigate this, watermarks can be embedded into the gener- ated images without affecting the generation quality, helping to prevent misuse and verify if an image is generated.
References
Appa-VLLMM Large-dit-imagenet, https://github. com/Alpher WWWM / main/Large-Dixe Iimaaagge,,,,,---atetttttee 2024.
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and van den Berg, R. Structured denoising diffusion models in dis- crete state-spaces. In Advances in Neural Information Prucessing Systems, 2021.
Bai, J., Ye, T., Chow, W., Song, E., Chen, Q.-G,, Li, X., Dong, Z., Zhu, L., and Yan, S. Meissonic: Revitaliz- ing masked generative transformers for efficient high- resolution text-to-image synthesis. In The Thirteenth International Conference on Learning Representations, 2024.
Masked diserete diffusion models. Recently, masked dis- crete diffusion models (Austin et al., 2021; Campbell et al., 2022), a special case of discrete diffusion models (Sohl- Dickstein et al., 2015; Hoogeboom et al., 2021), have achieved remarkable progress in various domains, including text generation (He et al., 2022b; Lou et al., 2024a; Shi et al., 2024; Sahoo et al., 2024; Ou et al., 2024; Zheng et al., 2023; Chen et al., 2023; Gat et al., 2024; Nie et al., 2024), mu- sic generation (Sun al., 2023), protein design (Camphell al., 2024), and image generation (Hu & Ommer, 2024).
Bao, F., Nie, S., Xue, K., Cao, Y., C., Su, H., and Zhu, J. All are worth words: A vit backbone for diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.
2023,
Bao, H., Dong, L., Piao, S., and Wei, Beit: Bert pre-training of image transformers. arXiv preprint 2rXiv:21660082. 2021.
Brock, A. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809. (eti::1809.1100000 11096, 2018.
8. Conclusion
In this paper, we present a single framework to unify masked image generation models and masked diffusion models and carefully examine each component of design space to achieve efficient and high-quality image generation. Empiri- cally, we demonstrate that eMIGM can achieve comparable performance with the state-of-fe of-t-o---te-arr continuous diffusion
Campbell, A., Benton, J., Bortoli, V. D., Rainforth, T. Deligiannidis, G., and Doucet, A. A continuous time framework for discrete denoising models. In Advances in Neural Information Processing Systems, 2022.
Campbell, A., Yim, J., Barzilay, R., Rainforth, T., and Jaakkola, Generative flows on discrete state-spaces:

---

Effective and Efficient Masked Image Generation Models
Enabling multimodal flows with applications protein co-design, 2024.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. Gans trained by two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.
Chang, H., Z,ang, H., Jiang, I.,, Liu, C., and Freeman, W. T. Maskgit: Masked generative image transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11315-11325, 2022.
Ho, and Salimans, T. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022.
Chen, J., Cai, H., Chen. J., Xie, E., Yang, S., Tang, H., Li, M., Lu, Y., and Han, S. Deep compression autoen- coder for efficient high-resolution diffusion models, arXiv preprint 2410: 10733, 2024.
Ho, J., Jain, A., and Abbeel, P. Denoising diffusion proba- bilistic models. Advances in neural information process- ing systems, 33:6840-6851, 2020.
Hoogeboom, E.,, Nielsen, D., Jaini, P., Forré, P., and Welling, M. Argmax flows and multinomial diffusion: Learn- ing categorical distributions. NeurIPS, 34:12454-12465, 2021.
Chen, Z., Yuan, H., Li, Y., Kou, Y., Zhang. J., and Gu, Q- Fast sampling via de-randomization for discrete diffusion models. arXiv preprins arXiv:2312.09194 2023.
Deng, J., Dong, W., Socher, R., Li, L.-J,, Li, K., and Fei-Fei, L. Imagenet: A scale hierarchical image database. In 2009 IEEE conference on computer vision and paltern recognition, pp. 248-255. Iece, 2009.
Hoogeboom, E., Heek, J., and Salimans, T. simple diffu- sion: End-to-end diffusion for high resolution images. In International Conference on Machine Learning, pp. 13213-13232. PMLR, 2023.
Devlin, J. Bert: Pre-training of deep bidirectional trans- formers for language understanding. arXiv preprint 1rX1v:1810.04805. 2018.
Hu, V. and Ommer, B. [mask] all you need, 2024. URL https://aziv..org http://aa.ee2....5.. 06787.
Karras, T., Aittala, M., Lehtinen, J., Hellsten, J., Aila, T., and Laine, S. Analyzing and improving the training dynamics of diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 24174-24184, 2024.
Dhariwal, P. and Nichol, A. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780-878 2021.
Esser, P., Rombach, R., and Ommer, B. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, pp. 12873-12883, 2021.
Kingma, D. and Gao, R. Understanding diffusion objectives as the elbo with simple data augmentation. Advances in Neural Information Processing Systems, 36, 2024.
Gat, I., Remez, T., Shaul, N., Kreuk, F., Chen, R. T., Syn- naeve, G., Adi, Y., and Lipman, Y. Discrete flow match- ing. NeurlPS, 2024.
Kynkäääniemi, T., Aittala, M., Karras. T., Laine, S., Aila, T.,
and Lehtinen, J. Applying guidance in limited interval improves sample and distribution quality in diffusion models. arXiv preprint 2xXiv:2404.07224 2024.
Goodfellow, I., Pouget-Abatee J. Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. Generative adversarial nets. Advances in neural informa- tion processing systems, 27, 2014.
Li, T., Chang, H., Mishra, S., Zhang, H., Katabi, D., and Krishnan, D. Mage: Masked generative encoder to unify representation learning and image synthesis. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2142-2152, 2023.
Hatamizadeh, A., Song, J., Liu, G., Kautz, J., and Vahdat, A. Diffit: Diffusion vision transformers for image generation. In European Conference on Computer Vision, pp. 37-55. Springer, 2025.
Li, T., Tian, Y., Li, H., Deng, M., and He, Autoregres- sive image generation without vector quantization. arXiv preprint arXiv:2406. /838, 2024.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R. Masked autoencoders are scalable vision learners. Proceedings of the IEEF/CV conference on computer vision and pattern recognition, pp. 16000-16009, 2022a.
Lou, A., Meng, C., and Ermon, Discrete diffusion mod- eling by estimating the ratios of the data distribution, 2024a.
He, Z., Sun, T., Wang, K., Huang, X., and Qiu, X. Diffu- sionbert: Improving generative masked language models with diffusion models. arXiv preprint arXiv:2211.15029, 2022b.
Lou, A., Meng, C., and Ermon, S. Discrete diffusion mod- eling by estimating the ratios of the data distribution. In Forty-first International Conference on Machine Learn- ing, 2024b.
10

---

Effective and Efficient Masked Image Generation Models
Lu, C. and Song, Y. Simplifying, stabilizing and scal- ing continuous-time consistency models. arXiv preprint arXiv:2410. 11081, 2024.
Shao, S., Zhou, Z.,, Ye, T., Bai, L., Xu, Z., and Xie, Z. Bag of design choices for inference of high-resolution masked generative transformer. arXiv preprint arXiv:2411.10781 2024.
Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., and Zhu, Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in Neural Information Processing Systems, 35:5775-57877 2022a.
Shi, J., Han, K., Wang, Z., Doucet, A., and Titsias, M. K. Simplified and generalized masked diffuusion for discrete data. arXiv preprint arXiv:2406. 04440000429, 2024.
Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C.,, and Zhu, J. Dpm- solver++: Fast solver for guided sampling of diffusion probabilistic models. arXiv preprint 1rXXi:2211.01095, 2022b.
J., Weiss, E., Maheswaranathan, N., and Ganguli, S. Deep unsupervised learning using nonequi- librium thermodynamics. In International conference on machine learning, pp. 2256-2265. PMLR, 2015.
Ma, N., Goldstein, M., Albergo, M. S., Boffi, N. M., Vanden- Eijnden, E., and Xie, S. Sit: Exploring flow and diffusion- based generative models with scalable interpolant trans- formers. arXiv preprint 4rXhv2241.088740, 2024.
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Er- mon, S., and Poole, B. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:201 13.,156,,, 2020.
Ni, Z., Wang, Y., Zhou, R., Lu, R., Guo, J., Hu, J., Liu, Z., Yao, Y., and Huang, G. Adanat: Exploring adaptive policy for token-based image generation. In European Conference Computer Vision, pp. 302-319. Springer, 2024.
Sun, H., Yu, L., Dai, B., Schuurmans, D., and Dai, H. Score- based continuous-time discrete diffusion models. In The Eleventh International Conference on Learning Repre- sentations, 2023.
Sun, P., Jiang, Y., Chen, S., Zhang, S., Peng, B., Luo, P., and Yuan, Z. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.00 044000065255 2024.
Nie, S., Zhu, F., Du, C., Pang. T., Liu, Q., Zeng, G., Lin, M., and Li, C. Scaling up masked diffusion models on text. arXiv preprint 240.x:2400.885 (4,1111440 2024.
Ou, J., Nie, S., Xue, K., Zhu, F., Sun, J., Li, Z., and Li, C. Your absorbing discrete diffusion secretly models the conditional distributions of clean data, arXiv preprint arXiv:2406. 03736 2024.
Tang, H., Wu, Y., Yang, S., Xie, E., Chen, J., Chen, J., Zhang, Z., Cai, H., Lu, Y., and Han, S. Hart: Efficient visual generation with hybrid autoregressive transformer. arXiv preprint arXiv:2410. 10812, 2024.
Peebles, W. and Xie, S. Scalable diffusion models with transformers. Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pp. 4195-4205 2023.
Tian, K., Jiang, Y., Yuan, Z., Peng, B., and Wang, L. Visual autoregressive modeling: Scalable image generation via next-scale prediction. arXiv preprint arXiv:2404.02905. 2024.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition, pp. 10684-10695. 2022.
Van Den Oord, A., Vinyals, O., et al. Neural discrete rep- resentation learning. Advances in neural information processing systems, 30, 2017.
Wang. X., Dufour, N., Andreou, N., Cani, M.-P., Abre- vaya, V. F., Picard, D., and Kalogeiton, V. Analysis of classifier-free guidance weight schedulers. arXiv preprint MwXiv:2404.13044 2024.
Sadat, S., Buhmann, J., Bradley, D., Hilliges, O., and Weber, R. M. Cads: Unleashing the diversity of diffusion models andition-annealed sampling. arXiv preprint arXiv:2310.1733 17347, 2023.
through condition annealed
Yan, J. N., Gu, J., and Rush, A. M. Diffusion models without attention. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 8239- 8249, 2024.
Sahoo, S. S., Arriola, M., Schiff, Y., Gokaslan, A., Marro- quin, E., Chiu, T., Rush, A., and Kuleshov, V. Simple and effective masked diffusion language models, arXiv preprint arXiv:2406.077 075544, 2024.
Yu, S., Kwak, S., Jang, H., Jeong, J., Huang, J., Shin, J., and Xie, S. Representation alignment for generation: Training diffusion transformers is easier than you think. arXiv preprint arXiv:24 10. 06,06940 2024.
Sauer, A., Schwarz, K., and Geiger, A. Stylegan-xl: Scaling stylegan to large diverse datasets. In ACM SIGGRAPH 2022 conference proceedings, pp. 1-10, 2022.

---

Effective and Efficient Masked Image Generation Models
Zheng, L.,, Yuan, J., Yu, L., and Kong, L. A reparameter- ized discrete diffusion model for text generation. ArXiv, abs/2302.055377, 2023.
12

---

Effective and Efficient Masked Image Generation Models
Table
Mask schedule formulations.
Mask schedule
2
Linear
Cosine
008 (7 1))
tan (1 1))
Exp
1 - exp(-5t)
5 expp 5t)
expp
A. Equivalence of the masking strategies of MaskGIT and MDM
In this section, we demonstrate that the masking strategies of MaskGIT and MDM are equivalent expectation. MaskGIT first samples a ratio r from [0, and then uniformly masks [N tokens of a as [M]. In contrast, for MDM, each token independently masked [M] with probability "/--
First, for MDM, the entsopyn loss in Equation (4) has multiple equivalent forms (Ou et al., 2024). To facilitate better understanding, we reformulate Equation (4) as an expectation over t:
((xo) = (0, 1y (mm)
7
Σ
- log po (2/
(9)
Vo
(fl=f-(M)
As an example, we consider the linear mask schedule, where 7t this formulation, the forward process involves independently masking each token based uniformly sampled Under this setting, the loss simplifies
((ro)) [0 (w()
Σ [M])
log po (arg (2x))
(10)
For MaskGIT, the number of masked tokens is sampled from uniform distribution [11, after which I tokens in æo randomly masked as [M]. Under this scheme, the loss function can be rewritten as:
1 I Σ
((xo) = B,- (aa
- log pe (2)
(1I)
[M]}
As shown in Ou et al. (2024), Equation (11) and Equation (10) are equivalent in expectation. In this paper, we adopt the formulation of Equation (4) with an exponential mask schedule as the default setting.
B. Mask schedules
B.1. Formulations and Illustrations of Mask Schedules
We present different choices of mask schedules in Fig. 5 and Tab. 4. The linear schedule achieves the best empirical performance in text generation, as demonstrated in previous work (Lou et al., 2024b; Sahoo et al., 2024; Shi et al., 2024). In comparison to the linear schedule, the cosine and exp schedules mask more tokens during the forward process of MDM.
B.2. Sampling Simulator Experiment
During sampling, we conducted a simulation experiment with total of 256 sample tokens and 16 sampling steps. Therefore, the temporal interval [0, 1] is discretized into 16 equally sized segments for sampling purposes. Let 8; and t; represent the endpoint and starting point of the i-th segment, respectively, where E 21, 16). The indexing is defined such that f corresponds to the start of the first segment. Specifically, the endpoints are defined as 8; = and the starting points as 16-(+1 In each step the prediction for each token is made with probability of 24. 74, given by Equation (6).
74,
13

---

Effective and Efficient Masked Image Generation Models
-1,f
0.0
1.0
-25
0.8
5.0
7.5
0,6
100.0
0.4
Linear
Lincar
15.0
Cosine
Cosine
Exp
-17.5
Exp
0.0
-20.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
Figure 5. Different choices of mask schedules. Left: ?t. (i.c., the probability that cach token is masked during the forward process). Right: Weight of the loss MDM.
70
Cosine
60
Exp
Average Remaved Tokeess
Linear
50
40
30
20
10
6
8
10
12
14
16
Step
Figure 6. Comparison of mask removal for different sample mask schedule.
We simulated the process 10,000 times and calculated the average number of tokens predicted in each step. The experimental results are shown in Fig. 6.
We observed the following trends: For the linear schedule, the model predicts almost the same number of tokens in each step. In contrast, for the cosine schedule, the model predicts fewer tokens in the earlier steps and more tokens in the later steps. Compared the cosine schedule, the exp schedule predicts even fewer tokens in the earlier steps and progressively more tokens in the later steps.
C. Time Interval for Classifier Free Guidance
To validate our hypothesis that an excessively strong guide the early stages may drastically reduce the variation in generated samples, leading higher FID, we conducted an experiment with a total of 256 sample tokens and 16 sampling steps. A more detailed description of the sampling procedure can be found in Appendix B.2. Let s; and represent the endpoint and starting point of the i-th sampling step, respectively. We define fmin and taax for CFG. If s, [min tmax] apply CFG to guide the sampling; otherwise, we do not use CFG and rely solely on simple conditional generation. As shown in Fig. 8, we observe that when Imin = and 1max = 1, the FID value is 22.48, demonstrating low variation in the generated samples. Additionally, in the top left corner of Fig. 8(a) (i.e., when Emin < mmax S 0.5), we achieve a relatively low FID (indicating higher variation), which supports our hypothesis and encourages the application CFG guidance only during the later stages of sampling.
14

---

Effective and Efficient Masked Image Generation Models
22.5
20.0
Inception Score
17.5
15.0
200
10.0
7.5
150
5.0
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
CFG
CFG
(a) CFG vs. FID
(b) CFG vs. IS
Figure Generation performance sensitive to the CFG value when using the constant schedule.
4.0, efg conssuule
Inception Score (cfg-4.0, efg scheuule-coonstann))
0.0
5.91
5.51
5.98
7.76
8.94
11.53
14.12
15.48
18.85
22.48
172.8
219:2
239.7
273.3
288.5
311.0 323.0 327.2. 334.4 336.9
0.1
5.47
5.33
7.49
12.55
13.98
17.44
21.16
0.1
185.6
211.5 254.0
270.5 298.2 313.4 318.4 326.8 330.0
0.2
6.30
0.2
295.6
302.8
0.3
5.40
5.51
7.17
9.54
10.97
14.52
18:57
0.3
192.9
217.9
258.7
283.4
292.4
306.2
312.9
min
mimi
0.4
6.27
5.79
7.58
9.01
12:45
16.46
0.4
162.2
217.0
251.9
264.5
286.6
295.7
0.5
5.64
6.70
7.97
11.50
15.36
0.5
188.2
234.7
248.3
274.2
284.1
0.6
5.93
6.43
9.38
13.41
0.6
183.8
206.8
240.0
260.5
0.7
6.60
7.63
11.31
0.7
156.0
207.2
231.0
0.8
6.93
10.18
0.8
187.3
216.9
0.9
0.9
0.1
0.2
0.3
0.4 0.5
0.6
0.7
0.8
0.9
1.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
cfg_ tigtmma max
cfg
max
(a) FID vs. Time interval
(b)
IS Time interval
Figure 8, Performance across different time intervals. Subplots show (a) FID and (b) Inception Score(IS).
Table 5. The code links and licenses.
Method
Link
License
MAR
https://gitthub.coo//Zit
MIT License
DPM-Solver
MIT License
DC-AE
Apache-2.0 2.0 license
D. Experiment settings and results
We implement eMIGM upon the official code of MAR (Li et al., 2024), DC-AE (Chen et al., 2024), DPM-Solver (Lu et al., 2022a;b), whose code links and licenses are presented in Tab. 5.
Image Tokenizer. For ImageNet 256 x 256, we use the same KL-16 image tokenizer as in MAR (Li et al., 2024), which has stride of 16. That is, for an image of size 256 256, outputs an image token sequence of length 16 16, with each
15

---

Effective and Efficient Masked Image Generation Models
Table Training configurations of models on ImageNet 256x256.
Model Size
XS
S
B
L
H
Architecture Configurations
Transformer blocks
20
24
24
32
40
Transformer width
448
512
768
1024
1280
MLP blocks
12
MLP width
1024
1024
1024
1280
1536
Params (M)
69
97
208
478
942
Training Hyperparameters
Epochs
800
800
800
800
800
Learning rate
4.0e-4
4.0e-4
8.0e-4
8.0e-4
8.0e-4
Batch size
1024
1024
2048
2048
2048
Adam $1
0.9
0.9
0.9
0.9
0.9
Adam B2
0.95
0.95
0.95
0.95
0.95
Table 7. Training configurations of models on ImageNet 512x512.
Model Size
XS
Architecture Configurations
Transformer blocks
Transformer width
448
512
768
1024
MLP blocks
MLP width
1280
1280
1280
1280
Params (M)
104
132
244
478
Training Hyperparameters
Epochs
800
800
800
800
Learning rate
4.0e-4
4.0e-4
8.0e-4
8.0e-4
Batch size
1024
1024
2048
2048
Adam B1
0.9
Adam B2
0.95
0.95
0.95
0.95
token having a dimensionality of 16. For ImageNet 512 512, we use the DC-AE-132 tokenizer (Chen et al., 2024) for efficiency, which has a stride of 32, and each token has dimensionality of 32.
Classifier-Free Guidance (CFG). In the original CFG, during training, the class condition is replaced with a fake class token with probability of 10%. During sampling, the prediction model takes both the class token and the fake class token as input, generating outputs z, and zu. Conceptually, CFG encourages the generated image to align more closely with the result conditioned on 2e while deviating from the result conditioned on 2 For CFG with Mask, we replace the fake class token with masked token the input for unconditional generation. We use constant CFG schedule and the time interval strategy in our main results presented in Tab. 2 and Tab. 3, achieving excellent performance while significantly reducing the sampling cost. Moreover, we observed that with the time interval strategy, we can use a consistently high CFG value to guide generation at each prediction step, eliminating the need for CFG value sweeping.
Training Settings. The detailed training settings for ImageNet 256 x 256 and ImageNet 512 x 512 are provided Tab. and Tab. 7, respectively.
16

---

Effective and Efficient Masked Image Generation Models
Table Image generation results on ImageNet 256 256.
Method
NFE
FID4
sFID.
1S
Precision
Recall
VAR-d30 (Tian al., 2024)
10x2
1.92
323.1
0.82
0.59
REPA (Yu et al., 2024)
2.0
1.42
4.70
305.7
0.80
0.65
eMIGM-XS
16x1.2
4.23
5.74
218.63
0.79
0.50
eMIGM-S
1.x
3.44
5.31
244.16
0.80
0.53
cMIGM-B
1.21.2
2.79
5,20
284.62
0,82
0.54
eMIGM-L
16x 1.2
2.22
4.80
291.62
0.80
0.59
cMIGM-H
16x 1.2
2.02
4.66
299.36
0.80
0.60
cMIGM-XS
128 1.4
3.62
5,47
224.91
0,80
0.51
eMIGM--
128 1.4
2.87
5.53
254.48
0.80
0.54
eMIGM-B
128 x 1.35
2.32
4.63
278.97
0.81
0.57
cMIGM-L
128x 1.4
1.72
4.63
304.16
0.80
0.60
cMIGM-H
1.8x1.4
1.57
4.68
305.99
0.80
0.63
Table 9. Ablation study on different mask schedules, reporting FID scores.
Epoch
Linear
Cosine
Exp
Log-Exp
100
38.66
24.99
28.63
25.38
200
30.55
16.70
17.97
11.81
300
24.55
15.00
11.57
12.48
400
24.96
12.39
11.90
9.91
Table 10. Comparison of sampling speed.
Model
Avg sec per imagel.
FID.
eMIGM-L
0.165
1.77
EDM2-XXL (Karras et al., 2024)
0.552
1.81
EDM2-XXL with guidance interval (Kynkäänniemi et al., 2024)
0.481
1.40
More Evaluation Metrics. We present additional evaluation metrics ImageNet 256 256 in Tab.
More Mask Schedules. In this paper. explored three mask schedules: (1) Linear: Yt t; (2) Cosine: Yt cos [g - -))); and (3) Exp: 7r = 1 exp(-5t). All these schedules are designed to satisfy the approximate boundary conditions To A 0 and 71 ss 1. We observed that the exp mask schedule, when used in conjunction with w(t) 1, achieves superior performance compared to other settings.
Tog( Tog(1 (e[(+((e-1)44) which aims to balance mask ratios by reducing 5
Furthermore, we developed a log-exp schedule, = extremes in both high and low masking. Following the experimental setup detailed in Fig. 2(b), we present the FID results Tab. 9. observed that the log-exp schedule demonstrates improved convergence and performance, thereby validating the benefit of exploring new masking schedules. We leave further investigation of more mask schedules for future work.
Sampling Speed Comparison with EDM2. Compared with EDM2's generation network, EDM2's guidance network is relatively small. We therefore conducted additional experiments compare sampling speeds single A100 GPU (batch size 256), with the results presented in Tab. 10. eMIGM-L achieves faster sampling than EDM2-XXI,, primarily due to its lower parameter count. Despite requiring a higher NFE, it still maintains competitive performance.
17


Reviews:
[{"review_id": "S8ab4AZRYk", "reviewer": "Reviewer_7cuF", "summary": "This work explores masked diffusion and image modeling through a unified framework, systematically analyzing several key design choices in this domain. Within this framework, the authors ablate masking schedules, loss weighting, and sampling strategies, leading to improvements over existing standards in each area. Building on these insights, they propose eMIGM, an improved masked diffusion modeling method that rivals state-of-the-art continuous diffusion models like EDM2. Experimental results demonstrate that eMIGM achieves competitive FID scores on ImageNet at 256$\\times$256 and 512$\\times$512 resolutions while requiring fewer NFEs than most diffusion models.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer 7cuF for the interest and acknowledgement of our contributions and the valuable comments. We respond below to your questions and concerns.\n\n> **Claim1:** using a weight schedule for guidance\n\nCompared to existing work, our approach is motivated by MDMs' unique irreversible token generation constraint. We found that early strong guidance restricts generation diversity and increases FID scores. We will revise our paper to properly acknowledge prior work and clarify our specific contributions to guidance strategies for MDMs.\n\n> **Claim2 and Weak1**: unsupervised guidance method\n\nWe will clarify that we adapt the unsupervised CFG from text generation [4] to image generation. For clarity, we rename it to \"CFG with Mask\" to better reflect our focus on masked image generation. Besides, we will add a discussion of [5] in the final version.\n\n> **Weak2:** number of NFEs remains relatively high\n\nOur work focuses on improving masked image modeling efficiency. For example, in 512x512 generation, eMIGM-L matches MAR's performance with fewer NFEs (64x1.25 vs 256x2). We believe future work applying distillation could further improve efficiency.\n\n> **Weak3:** Tables 2 and 3 are not entirely fair\n\nWhile we previously cited results from the original EDM2 paper, we will update our comparison to include EDM2 with Guidance Interval [2].\n\n> **Weak4:** NFE comparison with EDM2\n\nWe conducted additional experiments comparing sampling speeds on a single A100 GPU (batch size 256). As shown below, eMIGM achieves faster sampling than EDM2 while maintaining competitive FID.\n\nModel|Avg sec per image↓|FID↓\n-|-|-\neMIGM-L|0.165|1.77\nEDM2-XXL|0.552|1.81\nEDM2-XXL with interval|0.481|1.40\n\n> **Sugg1:** More discuss about weighting strategy and masking schedule\n\nOur experiments showed that weighting functions significantly affect noise schedule performance. Using $w(t)=\\frac{\\gamma_t'}{\\gamma_t}$ led to unstable training, especially with the exp schedule. Switching to $w(t)=1$ stabilized training across all schedules and improved performance, with the exp schedule yielding the best results. We adopted this combination as our default and will clarify this relationship.\n\n> **Sugg2:** About additional sampling cost\n\nIn our 512x512 image generation experiments with eMIGM-L (NFE=64x1.25), the diffusion model need 14 sampling steps using DPM-Solver, introducing approximately 14% additional sampling speed on a single A100 GPU (batch size 256). This is more efficient than MAR, which requires 100 diffusion steps. As shown in Response to W4, eMIGM achieves faster sampling (0.165s vs 0.552s per image) while maintaining competitive FID scores compared to EDM2. We will include a efficiency comparison table in our final version.\n\n> **Sugg3:** Fig.4 is misplaced\n\nWe will relocate Fig.4 to be positioned near Section 6.1.\n\n> **Q1:** Experimented with other weighting and masking functions\n\nRegarding to the mask schedules, we explored three mask schedules (linear, cosine, and exponential) constrained by $\\gamma_0 \\approx 0$ and $\\gamma_1 \\approx 1$. In response to your question, we introduce a log-exp schedule that balances mask ratios by reducing extreme cases of both high and low masking in exp schedule. The schedule is defined as: $\\gamma_t=\\frac{\\log\\left(1 + (e^5 - 1) \\cdot t\\right)}{5}$. Following the experimental setup in Figure 2(b) with $w(t)=1$, we present comparative FID in the table below.\nEpoch|Linear|Cosine|Exp|Log-Exp\n-|-|-|-|-\n100|38.66|24.99|28.63|25.38\n200|30.55|16.70|17.97|11.81\n300|24.55|15.00|11.57|12.48\n400|24.96|12.39|11.90|9.91\n\nThe log-exp schedule shows better convergence and performance, validating the benefits of exploring new masking schedule.\n\nRegarding weighting functions, we explored two primary options: $w(t) = \\frac{\\gamma_t^\\prime}{\\gamma_t}$ and $w(t) = 1$. The log-normal weighting from EDM isn't directly transferable to our framework. Exploring alternative weighting functions remains promising for future work.\n\n> **Q2:** Performance with <16 sampling steps\n\nWe evaluated eMIGM's performance with fewer sampling steps on ImageNet 256×256, with results shown in the table below:\nMethod|NFE|FID\n-|-|-\neMIGM-XS|8x1.2|5.19\neMIGM-S|8x1.2|4.69\neMIGM-B|8x1.2|3.77\neMIGM-L|8x1.2|3.30\neMIGM-H|8x1.2|3.07\n\n> **Q3:** Could diffusion component benefit from cfg\n\nOur diffusion component already implements classifier-free guidance. During sampling, our model processes both class and mask token inputs to generate $z_c$ and $z_u$ outputs. CFG guides generation toward class-conditioned results using the formula $\\epsilon=\\epsilon_\\theta(x_t|t,z_u)+\\omega\\cdot(\\epsilon_\\theta(x_t|t,z_c)-\\epsilon_\\theta(x_t|t,z_u))$, where $\\omega$ is the guidance scale. We will clarify this in our final paper.\n\n> **Q4:** model receive the class condition as input\n\nThe model still receives class conditions during training, but we randomly replace the true label with a mask token at a fixed probability. We will clarify this in our revised paper.", "claims_and_evidence": "The paper’s main claims regarding performance and efficiency are well-supported by the experimental results. However, some claims should be rephrased to better align with common practices in prior works. Specifically, using a weight schedule for guidance is a well-established technique for enhancing the diversity of CFG, as proposed in several recent works [1, 2, 3]. Additionally, the unsupervised guidance method was introduced in [4] and is closely related to [5]. To ensure clarity and accuracy, the authors should revise certain sections to avoid presenting these established methods as novel contributions of this work.\n\n[1] Sadat S, Buhmann J, Bradley D, Hilliges O, Weber RM. CADS: Unleashing the diversity of diffusion models through condition-annealed sampling. arXiv preprint arXiv:2310.17347. 2023 Oct 26.\n\n[2] Kynkäänniemi T, Aittala M, Karras T, Laine S, Aila T, Lehtinen J. Applying guidance in a limited interval improves sample and distribution quality in diffusion models. arXiv preprint arXiv:2404.07724. 2024 Apr 11.\n\n[3] Wang X, Dufour N, Andreou N, Cani MP, Abrevaya VF, Picard D, Kalogeiton V. Analysis of classifier-free guidance weight schedulers. arXiv preprint arXiv:2404.13040. 2024 Apr 19.\n\n[4] Nie S, Zhu F, Du C, Pang T, Liu Q, Zeng G, Lin M, Li C. Scaling up Masked Diffusion Models on Text. arXiv preprint arXiv:2410.18514. 2024 Oct 24.\n\n[5] Karras T, Aittala M, Kynkäänniemi T, Lehtinen J, Aila T, Laine S. Guiding a diffusion model with a bad version of itself. Advances in Neural Information Processing Systems. 2024 Dec 16;37:52996-3021.", "methods_and_evaluation_criteria": "The authors use well-established benchmarks to evaluate the performance of eMIGM.", "theoretical_claims": "There is no major theoretical claim in the paper.", "experimental_designs_or_analyses": "The experiments are well-designed and clearly show how different components affect the performance of eMIGM.", "relation_to_broader_scientific_literature": "The authors discuss most of the relevant prior work in the main text. Masked diffusion modeling is a novel technique with significant potential for generative modeling, yet it remains underexplored. Since most existing methods in this domain have been developed for language modeling, the authors' contribution to advancing performant MDMs for image generation is noteworthy. As such, the paper is well-positioned within the current literature on masked diffusion models. Moreover, the proposed unified framework can facilitate more systematic research into the performance of these models. That said, some claims presented as new contributions require greater clarity. Please refer to the Claims and Evidence section for further details.", "essential_references_not_discussed": "Please refer to the Claims and Evidence section for further details.", "other_strengths_and_weaknesses": "### **Strengths**  \n- The paper is well-written, with clear and easy-to-follow explanations.  \n- The experiments are thorough, with a structured, step-by-step introduction that enhances readability and understanding.  \n- Improving the performance of discrete diffusion models for image generation is an important and timely research direction.  \n\n### **Weaknesses**  \n- Some prior works are not adequately discussed, which affects the accuracy of certain novelty claims.\n- The number of NFEs used by the model remains relatively high (e.g., exceeding 64), which slightly contradicts the paper's efficiency claims., although the method still performs well with lower NFEs.\n- The comparisons in Tables 2 and 3 are not entirely fair, as most reported FIDs are calculated using constant CFG. For instance, EDM2 with Guidance Interval achieves an FID of 1.40, whereas the reported value is 1.81. This discrepancy should be addressed for a more accurate comparison.\n- The NFE comparison with EDM2 is not entirely representative, as EDM2's guidance network is relatively small. Consequently, the NFEs for the conditional and unconditional parts do not have the same computational cost. A more appropriate metric for comparing the sampling speed of eMIGM with EDM2 would be generation throughput (images/sec).", "other_comments_or_suggestions": "- The discussion on the mask schedule suggests that the cosine schedule is optimal, yet the authors ultimately use the exponential schedule. This should be clarified—one way to address this is to discuss the weighting strategy and masking schedule together to explain why the exponential schedule with $w(t) = 1$ is the final choice.  \n- If the diffusion model is used for clean data prediction similar to MAR, it is unclear how much additional sampling cost is introduced by the diffusion model beyond the NFEs required for the main transformer. \n- Figure 4 is misplaced.", "questions_for_authors": "1) Have you experimented with other weighting and masking functions, such as sigmoid masking or the log-normal weighting from EDM?\n\n2) How does the method perform when using fewer than 16 sampling steps?\n\n3) Could the diffusion component for data prediction benefit from classifier-free guidance instead of temperature sampling?\n\n4) Does the model still receive the class condition as input when using the CFG mask token for training?", "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": "jtOLYs5M0T", "reviewer": "Reviewer_Bt5L", "summary": "The paper presents eMIGM, a novel model for effective and efficient masked image generation. It unifies masked image generation models and masked diffusion models within a single framework, exploring the design space of training and sampling to identify key factors impacting performance and efficiency. The model demonstrates strong performance on ImageNet generation, outperforming seminal models like VAR and achieving results comparable to state-of-the-art continuous diffusion models with significantly fewer NFEs. Key contributions include a unified formulation for exploring the design space, a time interval strategy for classifier-free guidance, surpassing state-of-the-art diffusion models on ImageNet 512×512 with fewer NFEs, and demonstrating scalability benefits.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> **Claims:** Some claims could be further strengthened by additional analyses. \n\nWe appreciate your feedback regarding our efficiency claims. \n\nOur analysis of training efficiency examined the relationship between training FLOPs and FID scores. As shown in Fig.4(b), larger eMIGM models achieve better FID scores at equivalent FLOP budgets—for example, eMIGM-L outperforms eMIGM-B when both consume approximately 10^20 FLOPs, confirming superior training efficiency of larger models.\n\nRegarding sampling efficiency, we evaluated the inference speed-FID trade-off (Fig.4(c)), with measurements conducted on an A100 GPU using batch size 256. Results demonstrate that larger eMIGM consistently deliver better FID scores than smaller models at comparable inference speeds.\n\nWe will follow your suggestion to enhance the clarity of these findings in our revised paper.\n\n> **References:** Some references are not discussed.\n\nThank you for suggesting these reference. We will incorporate a discussion of them into the revised paper.\n\n> **Weak1:** The generalizability to other datasets and domains could be further explored.\n\nWe appreciate your feedback regarding the generalizability of eMIGM. eMIGM performs effectively with both VAE (ImageNet 256x256) and DC-AE (ImageNet 512x512) tokenizers despite their distinct latent spaces. This adaptability highlights the transferability of our design principles across different data representations. We plan to explore eMIGM's application across diverse datasets and domains in future work.\n\n> **Weak2:** The paper could benefit from a more detailed discussion of the practical implications of using eMIGM\n\nWe appreciate this valuable suggestion. The practical implications of eMIGM are significant and multifaceted. Our model achieves higher-quality sample generation with fewer sampling steps (NFE), offering substantial advantages in computational resource utilization. Based on these efficiency gains, we believe eMIGM has strong potential for effective application across diverse domains currently served by diffusion models, including text to image and video generation. Besides, we systematically identify better design choices in masked generative modeling, offering practical guidance for developing efficient models. Our framework demonstrates how empirical analysis can effectively guide architectural decisions in this domain.\n\n> **Q1:** Are there any limitations or adjustments needed when applying the model to higher or lower resolution images?\n\nThank you for this question. When applying eMIGM to different resolutions, we find that the model architecture is inherently resolution-agnostic. The transformer backbone can process different sequence lengths without architectural modifications, allowing for flexible adaptation to various image resolutions. Our experiments on both 256×256 and 512×512 resolutions demonstrate this capability. The primary adjustment needed is simply retraining the model on the target resolution data, as is standard practice with most generative models.\n\n> **Q2:** What are the main computational bottlenecks when scaling up eMIGM models\n\nThank you for this important question. In our current implementation and experiments with eMIGM, we have not yet encountered significant computational bottlenecks that would require specialized optimization techniques. The model scales effectively with our available computational resources. \n\n> **Q3:** are there any plans to explore more efficient architectural designs?\n\nWe appreciate this valuable question. For future efficient architectural designs, we are exploring two promising directions: (1) U-ViT architecture, which achieves comparable performance to DiT on ImageNet 256x256 while using only ~30% of training FLOPs through its U-shaped design; and (2) REPA-inspired alignment between eMIGM's intermediate Transformer outputs and pretrained DINOv2 encoder features. Our preliminary experiments with the latter approach showed slight improvements when aligning earlier layers (4th), while later layers (18th) degraded performance. Though current benefits are marginal, more systematic investigation could yield substantial efficiency gains in future work.\n\n> **Q4:** Could you discuss the potential applications of eMIGM beyond image generation?\n\nWe appreciate this important question about eMIGM's broader applications. The model's core mechanism of predicting masked content from surrounding context naturally extends to various image manipulation tasks including inpainting (treating missing regions as masks), conditional editing (regenerating masked objects based on specified conditions), and outpainting (predicting exterior content around a central image). Beyond images, we believe eMIGM could be adapted for text to image, video, and audio generation, though this would require carefully designed input formats and potential architectural modifications to accommodate these diverse data modalities.", "claims_and_evidence": "The claims made in the submission are generally supported by clear and convincing evidence. The authors provide extensive experimental results on ImageNet 256×256 and 512×512, comparing eMIGM with state-of-the-art generative models. They demonstrate that eMIGM achieves lower FID scores with fewer NFEs and parameters compared to models like VAR and even outperforms some diffusion models. The ablation studies on different components (mask schedule, weighting function, model architecture, time truncation, CFG with mask) provide strong evidence for the design choices made. However, some claims could be further strengthened by additional analyses. For instance, while the authors claim that larger models are more training and sampling efficient, a more detailed analysis of the computational resources required for different model sizes and the trade-offs between model size and performance would provide more comprehensive support.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria are appropriate for the problem of masked image generation. The unified framework integrating masked image modeling and masked diffusion models makes sense, as it allows for a systematic exploration of the design space. The choice of Fréchet Inception Distance (FID) as the evaluation metric is standard in the field and suitable for comparing image generation quality. The experimental designs, including comparisons with various state-of-the-art models and ablation studies, are well-structured to validate the effectiveness and efficiency of eMIGM.", "theoretical_claims": "the conceptual framework and derivations related to unifying masked image generation and diffusion models seem logically consistent based on the explanations provided", "experimental_designs_or_analyses": "The experimental designs and analyses are sound and valid. The authors conduct extensive experiments on ImageNet datasets, comparing eMIGM with a wide range of generative models, including diffusion models, autoregressive models, GANs, and other masked models.", "relation_to_broader_scientific_literature": "The key contributions of the paper are well-related to the broader scientific literature. The work builds upon and advances previous research in masked image modeling (e.g., MaskGIT, MAR) and masked diffusion models, integrating their strengths within a unified framework. It also connects to the extensive literature on diffusion models, autoregressive models, GANs, and other generative models, positioning eMIGM as a competitive alternative.", "essential_references_not_discussed": "[1] Hart: Efficient visual generation with hybrid autoregressive transformer, ICLR 2025.\n[2] Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis, ICLR 2025.\n[3] Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer\n[4] AdaNAT: Exploring Adaptive Policy for Token-Based Image Generation, ECCV 2024", "other_strengths_and_weaknesses": "Strengths:\nThe paper presents a novel unified framework that integrates masked image generation and diffusion models, offering a systematic approach to exploring the design space.\nExtensive experiments demonstrate the effectiveness and efficiency of eMIGM, showing strong performance on ImageNet with fewer computational resources compared to state-of-the-art models.\nThe introduction of the time interval strategy for classifier-free guidance is a valuable contribution that improves sampling efficiency.\nThe ablation studies provide valuable insights into the impact of different design choices, helping to guide future research in this area.\nWeaknesses:\nWhile the paper shows strong results on ImageNet, the generalizability to other datasets and domains could be further explored.\nThe paper could benefit from a more detailed discussion of the practical implications of using eMIGM", "other_comments_or_suggestions": "NAN", "questions_for_authors": "How does eMIGM handle different image resolutions beyond those tested in the paper (256×256 and 512×512)? Are there any limitations or adjustments needed when applying the model to higher or lower resolution images?\nWhat are the main computational bottlenecks when scaling up eMIGM models, and are there any plans to explore more efficient architectural designs?\nCould you discuss the potential applications of eMIGM beyond image generation?", "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": "NwmLIymTtX", "reviewer": "Reviewer_qvwy", "summary": "This paper provides a comprehensive study of masked diffusion models for visual generation, covering training, sampling, and architectural designs with extensive experiments. In other words, this paper investigates how to make a good MDM with regard to training and sampling settings through empirical evidences.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> **Experimental Design1:** The claim of \"CFG with Mask\" and \"fake class CFG\" \n\nThank you for your question. In standard Classifier-Free Guidance (CFG) used in diffusion models and MAR, training involves occasionally replacing the class label with a dedicated 'fake class' token, which is distinct from the 'mask' token used for image patches. Our \"CFG with Mask\" approach instead uses the existing 'mask' token to replace class labels during training, eliminating the need for a separate 'fake class' token. We will clarify this distinction in the final paper.\n\n> **Experimental Design2:** Network architectural details come too late and What is the baseline performance of the discrete variant of eMIGM?\n\nThank you for your valuable suggestion. We appreciate your feedback and will incorporate more detailed network architectural information in an earlier section of our paper to enhance clarity. As briefly mentioned at the beginning of Section 4, our experiments exclusively focus on continuous masked-based generative models to mitigate the information loss associated with discrete tokenizers. Consequently, we did not develop a discrete variant of eMIGM, particularly since prior work has demonstrated that discrete variants of MAR perform significantly worse than their continuous counterparts. We will ensure all experimental details are presented more clearly in the final version of our paper.\n\n> **Experimental Design3:** The model steps of eMIGM. \n\nWe sincerely appreciate your valuable feedback. In response, we conducted an experiment on 512x512 image generation using eMIGM-L with NFE=64x1.25 (as presented in Table 3 of our paper). In this configuration, the diffusion model requires 14 sampling steps. To determine the additional computational cost of the diffusion model beyond the main transformer's NFEs, we measured the sampling speed with different diffusion steps. Our measurements on a single A100 GPU with batch size 256 show that the diffusion model introduces approximately 14% additional computational overhead beyond the main transformer's NFE requirements. Thank you for raising this concern about NFE comparisons between eMIGM and diffusion models. While eMIGM does include diffusion steps within each masked step, since transformer forward passes remain the primary bottleneck, NFE continues to be a valid efficiency metric. We will address this point more thoroughly in the revised paper, ensuring clarity and precision in our presentation.\n\n\n> **Weakness1:** The effectiveness of the proposed method, or the necessity of the unified masked diffusion modeling framework.\nWe appreciate your valuable feedback.\n\nRegarding the necessity of the unified masked diffusion modeling framework, it serves two primary purposes. First, it enhances the theoretical understanding of masked generation and simplifies comparisons between different methods (e.g., MAR vs. MaskGIT). Second, it facilitates a systematic exploration of the design space and allows for the integration of techniques from related areas. For example, advancements in diffusion models can be readily incorporated into our diffusion head training, helping to advance the state-of-the-art in masked image generation.\n\nRegarding the effectiveness of eMIGM, our comprehensive experimental analysis demonstrates key advantages:\n\n1.  Through systematic exploration of the training and sampling design space, we identified critical factors influencing model performance and efficiency. These findings provide actionable insights for developing future masked generative models.\n2.  The proposed time-interval strategy for classifier-free guidance (CFG) empirically improves sampling efficiency while maintaining generation quality.\n\nThese improvements are substantiated by direct comparisons with MAR baselines. For ImageNet 512×512 generation, eMIGM achieves superior computational efficiency, requiring only 64×1.25 NFEs compared to MAR's 256×2 NFEs while maintaining competitive FID scores.\n\n> **Q1:** what makes the abbreviation \"eMIGM\"?\n\nThank you for your question. The abbreviation \"eMIGM\" stands for \"Effective and Efficient Masked Image Generation Models.\", where the prefix 'e' denotes both effectiveness and efficiency.\n\n> **Q2:** Why using continuous models like MAR instead of MaskGIT pipeline.\n\nThank you for this insightful question. We will clarify that our work focuses exclusively on continuous masked-based generative models. As briefly noted in Section 4, this choice stems from the need to avoid information loss inherent in discrete tokenizers. Previous research has consistently shown that discrete variants of MAR yield substantially inferior performance compared to continuous implementations. We will emphasize this choice more clearly in the final version of our paper and apologize for any previous lack of clarity.", "claims_and_evidence": "The claims made in the submission are supported by clear and convincing empirical evidence.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria make sense for the problem.", "theoretical_claims": "There are almost no theoretical claims in this submission except for the equivalence of MaskGIT and MDM masking strategies. I checked the correctness of proof in Appendix A.", "experimental_designs_or_analyses": "I checked the soundness and validity of experimental designs and analyses. There are some concerns.\n\n1. I don't quite understand the paragraph \"CFG with Mask\" in line 246 and the difference between Mask CFG and fake class CFG is hard to distinguish. The authors should put more details on what is the special mask token here.\n\n2. The network architectural details come too late in experiments. I wasn't aware of the design of continuous targets in eMIGM framework before Section 5.2, as the conventional masked based generative models typically use discrete targets to perform classification. So there comes the problem, what is the baseline performance of the discrete variant of eMIGM?\n\n3. I don't think some analysis, especially when compared to diffusion model steps, make sense. Since eMIGM use diffusion loss, there are multi-step diffusion steps in a single mask step.", "relation_to_broader_scientific_literature": "The key contributions of this paper is highly related to the MAR and MaskGIT, especially in terms of masking mechanism and diffusion loss head.", "essential_references_not_discussed": "There are no highly-related work missing in this paper in my view.", "other_strengths_and_weaknesses": "Overall, this is a borderline paper with both strengths and weaknesses. It includes extensive experiments that thoroughly explore the training and sampling space of masked diffusion models, which should be highly appreciated. On the other side, this paper lacks a clear goal or motivation to unify the masked diffusion models rather than performance. Additionally, despite extensive trial and error across many experiments, the largest eMIGM variant performs only on par with MAR-H, which raises concerns about the effectiveness of the proposed method, or the necessity of the unified masked diffusion modeling framework.", "other_comments_or_suggestions": "See Questions for Authors", "questions_for_authors": "1. What makes the abbreviation \"eMIGM\"? Is it something like \"EDM\" [1]? \n\n2. When discussing mask-based generative models, the authors should first clarify whether they are using discrete or continuous models. Also, the authors should justify why they are using continuous models like MAR instead of MaskGIT pipeline.\n\n[1] Elucidating the Design Space of Diffusion-Based Generative Models", "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": "xMifEf9hTw", "reviewer": "Reviewer_DbgT", "summary": "The paper proposes a unified framework that integrates masked image generation models (e.g., MaskGIT) and masked diffusion models. The authors systematically explore the design space of training and sampling strategies to improve both performance and efficiency. The proposed model, eMIGM, achieves state-of-the-art performance on ImageNet (256×256 and 512×512) while requiring significantly fewer function evaluations (NFEs) compared to continuous diffusion models. Empirical results show that eMIGM outperforms VAR and approaches state-of-the-art diffusion models like REPA, while requiring less than 40% of the NFEs.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer DbgT for the interest and acknowledgement of our contributions and the valuable comments. We respond below to your questions and concerns.\n\n> **Claims 1:** Define \"comparable\" more rigorously\n\nTo quantify our comparison: eMIGM achieves an FID of 1.57 on ImageNet 256x256 generation versus REPA's 1.40 - a 12% gap. We consider this competitive since eMIGM requires fewer function evaluations (128×1.4 NFEs vs. REPA's 250×2 NFEs). We will clarify this comparison more precisely in our revised paper.\n\n> **Experiment1:** Include addtional baselines (e.g. GANs)\n\nOur Tables 2 and 3 already include comparisons with GAN baselines (BigGAN and StyleGAN-XL), which we will emphasize more clearly in our revision.\n\n> **References** Missing Meissonic\n\nWe appreciate the reference suggestion and will include it in our final paper.\n\n> **Weakness1:** Limited discussion of failure cases: What types of images does eMIGM struggle with? \n\nWhile we have not observed distinct failure patterns, eMIGM does experience occasional generation failures, as do other methods like MAR and VAR. We will include representative failure cases in our revision.\n\n> **Weakness2:** Qualitative comparisons beyond FID\n\nWe provide qualitative visual comparisons with diffusion models and VAR at [link](https://anonymous.4open.science/r/icml-rebuttal-6388/icml_link.pdf) and quantitative evaluations using sFID, IS, Precision, and Recall (see Table below), both of which will be included in our revision.\n\nMethod|NFE|FID↓|sFID↓|IS↑|Precision↑|Recall↑\n-|-|-|-|-|-|-\nVAR-d30|10×2|1.92|-|323.1|0.82|0.59\n-|-|-|-|-|-|-\nREPA|250×2|1.42|4.70|305.7|0.80|0.65\n-|-|-|-|-|-|-\neMIGM-XS|16x1.2|4.23|5.74|218.63|0.79|0.50\neMIGM-S|16x1.2|3.44|5.31|244.16|0.80|0.53\neMIGM-B|16x1.2|2.79|5.20|284.62|0.82|0.54\neMIGM-L|16x1.2|2.22|4.80|291.62|0.80|0.59\neMIGM-H|16x1.2|2.02|4.66|299.36|0.80|0.60\n-|-|-|-|-|-|-\neMIGM-XS|128x1.4|3.62|5.47|224.91|0.80|0.51\neMIGM-S|128x1.4|2.87|5.53|254.48|0.80|0.54\neMIGM-B|128x1.35|2.32|4.63|278.97|0.81|0.57\neMIGM-L|128x1.4|1.72|4.63|304.16|0.80|0.60\neMIGM-H|128x1.4|1.57|4.68|305.99|0.80|0.63\n\n> **Q1:** How does eMIGM perform on out-of-distribution datasets and different image distributions?\n\neMIGM demonstrates strong adaptability across data representations, performing effectively with both VAE (256x256) and DC-AE (512x512) tokenizers despite their different latent spaces, showing our efficiency improvements generalize across resolutions and distributions.\n\nRegarding out-of-distribution, while important, our research focuses primarily on improving the efficiency and quality of the generative models themselves, following the established research direction of prior works like MAR and VAR. These foundational works concentrate on in-distribution generation, which remains our focus for high-quality image synthesis.\n\n> **Q2:** What limitations does eMIGM have compared to continuous diffusion models? When do diffusion models outperform eMIGM?\n\nCompared to continuous diffusion models, eMIGM has limitations in zero-shot classification [a], ultimate generation quality with sufficient NFEs [b], and applications in video/audio synthesis. However, we believe masked generative models can narrow this gap as they evolve. We will discuss these limitations comprehensively in our revision.\n\n[a]“Your Diffusion Model is Secretly a Zero-Shot Classifier.”\n[b]“Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps.” \n\n> **Q3:** Hybrid approach with diffusion models and modifications?\n\nA hybrid approach combining eMIGM with diffusion models could enhance performance. Recent works [c,d] show that integrating autoregressive and diffusion models achieves superior generation capabilities compared to pure continuous diffusion models. Additionally, simple modifications could bridge remaining performance gaps, such as adopting rectified flow [e] for the diffusion head's training objective or following REPA [f] by aligning eMIGM's intermediate Transformer outputs with pretrained DINOv2 encoder features. We will incorporate this discussion in our revised paper.\n\n[c]“Efficient Visual Generation with Hybrid Autoregressive Transformer”\n[d]“Diffusion Transformer Autoregressive Modeling for Speech Generation” \n[e]“Learning to Generate and Transfer Data with Rectified Flow” \n[f]“Training Diffusion Transformers Is Easier Than You Think”\n\n> **Q4:** How does eMIGM's robustness to adversarial perturbations compare to diffusion models?\n\nWe appreciate your insightful question regarding adversarial robustness. While we acknowledge the critical importance of this aspect in the broader context of generative modeling, our current research primarily focuses on enhancing the efficiency and quality of the generative models themselves. We acknowledge that our expertise in adversarial perturbations is limited. Currently, we have not performed a assessment of eMIGM's robustness against such perturbations. We will discuss this important aspect in our revised paper.", "claims_and_evidence": "The paper makes several claims, and most are well-supported by empirical evidence:\n\n1. Unified framework for masked image modeling and diffusion models.\n\n2. eMIGM achieves better performance than VAR and is competitive with state-of-the-art diffusion models.\n\n3. Proposed sampling strategies (e.g., time interval classifier-free guidance) improve efficiency.\n\n4. Scaling eMIGM improves performance.\n\nHowever, the claim that eMIGM is \"comparable\" to continuous diffusion models could be more rigorously defined.", "methods_and_evaluation_criteria": "1. The methods used (masked image modeling, diffusion-based loss, classifier-free guidance) are appropriate for the problem.\n\n2. Evaluation is conducted using ImageNet (256×256 and 512×512), using FID scores.\n\n3. Comparisons with strong baselines (MaskGIT, MAR, diffusion models, VAR) are comprehensive.\n\n4. The choice of FID as the evaluation metric is standard for generative models.", "theoretical_claims": "1. The paper presents a mathematical unification of masked image generation and masked diffusion models.\n\n2. The equivalence between MaskGIT’s loss and MDM’s loss is derived in Appendix A.\n\n3. The mathematical formulation appears correct, though there is nothing new.", "experimental_designs_or_analyses": "1. Experiments are well-designed, with systematic exploration of training/sampling design choices.\n\n2. Ablation studies (Figures 2 & 3) effectively analyze the contributions of different components.\n\n3. The comparison against state-of-the-art methods is thorough, but it would be beneficial to include additional baselines (e.g., GANs) in more direct comparisons.", "relation_to_broader_scientific_literature": "1. The work extends prior efforts in masked image modeling (MaskGIT, MAR) and diffusion models.\n\n2. The proposed unified framework contributes to understanding the connection between masked models and diffusion models.", "essential_references_not_discussed": "Missing reference in masked generative transformer areas[1].\n\n[1] Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis, ICLR 2025.", "other_strengths_and_weaknesses": "Strengths:\n\n1. Novel unified framework for masked image modeling and diffusion models.\n\n2. Strong empirical results on ImageNet, with significant efficiency improvements.\n\n3. Comprehensive ablation studies exploring design choices in training and sampling.\n\n4. Well-written and clear methodology, with detailed appendices.\n\nWeaknesses:\n\n1. Limited discussion of failure cases: What types of images does eMIGM struggle with?\n\n2. Lack of qualitative comparisons against diffusion models and VAR beyond FID scores.", "other_comments_or_suggestions": "I will be happy to raise my score if all my concerns are resolved, and vice versa.", "questions_for_authors": "1. How does eMIGM perform on out-of-distribution datasets? Would the efficiency gains generalize to different image distributions?\n\n2. What are the limitations of eMIGM compared to continuous diffusion models beyond NFEs? Are there failure cases where continuous diffusion models outperform eMIGM?\n\n3. Would a hybrid approach combining eMIGM with diffusion models further improve performance? Could additional modifications bridge the gap to state-of-the-art diffusion performance?\n\n4. How does eMIGM compare in terms of robustness to adversarial perturbations? Could eMIGM be more or less vulnerable compared to diffusion models?", "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": "zNSpgLhhze", "reviewer": "Reviewer_5pkV", "summary": "This paper introduces eMIGM, a unified framework that integrates Masked Image Generation Models and Masked Diffusion Models into a single mathematical formulation. The authors categorize the possible design choices into training and sampling processes to optimize performance and efficiency. By leveraging a time interval strategy for Classifier-Free Guidance (CFG), replacing the fake class token with a mask token, eMIGM achieves comparable performance in image generation while significantly reducing function evaluations (NFEs). Experimental results demonstrate superior efficiency and sample quality on ImageNet 256×256 and 512×512 compared to existing models, including diffusion-based and masked modeling approaches.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer 5pkV for the interest and acknowledgement of our contributions and the valuable comments. We respond below to your questions and concerns.\n> **Minor Limit** Generalizability of improvements beyond tested configurations unclear\n\nEmpirical analysis is a well-established approach in developing complex models. Prior works [a, b] similarly employed ablation studies and targeted experiments to optimize their designs. Through systematic testing of components and hyperparameters, they demonstrated the value of rigorous empirical analysis, even when optimal settings may be context-dependent. Building on this precedent, our study validates specific configurations and provides a framework for systematically exploring and understanding design choices in masked generative models. We believe this methodological contribution offers significant value to the research community.\n\nBesides, eMIGM performs effectively with both VAE (ImageNet 256x256) and DC-AE (ImageNet 512x512) tokenizers despite their distinct latent spaces. This adaptability highlights the transferability of our design principles across different data representations. \n\n[a]“Elucidating the Design Space of Diffusion-Based Generative Models.”\n[b]“Analyzing and Improving the Training Dynamics of Diffusion Models.”\n\n> **Exp1:** More evaluation metrics\n\nWe've now evaluated our models using additional metrics (sFID, IS, Precision, and Recall). These comprehensive results are presented in our response to reviewer DbgT's Weakness2 and will be included in the revised paper.\n\n> **Sugg1:** novelty and analysis of the choices of training\n\nWe sincerely appreciate your recognition of our unified framework, well-structured experimental design and results.\n\nRegarding your concerns about novelty and analysis, we would like to clarify two key points:\n\n1. Our hyperparameter tuning follows an empirical approach, aligning with established practices in VAR and MAR. This practical methodology remains valuable as it yields effective, demonstrable results.\n\n2. Our modifications stem from careful analysis of MDM's behavior. For instance, the time interval strategy was motivated by our observation that MDM's generation process is irreversible, making early-stage guidance less effective - a finding we quantitatively validated in Appendix C. Similarly, replacing fake class tokens with mask tokens in CFG was informed by the fact that MDM has seen more mask tokens during training, making them a more natural choice than the fake class tokens used in diffusion models. We appreciate your suggestion and will expand our analysis of these design choices in the revision.\n\nOur key contribution is a unified framework integrating masked generative transformers and masked diffusion models, advancing masked generative modeling through systematic experimentation and analysis.\n\n> **Q1:** Further training of Figure 2(e)\n\nTo reponse this question, we trained standard CFG for 800 epochs. Below we compare its FID scores with mask CFG:\n\nModel|NFE|FID\n-|-|-\neMIGM-B with standard CFG|16x1.2|2.76\neMIGM-B with mask CFG|16x1.2|2.79\neMIGM-B with standard CFG|128x1.35|2.32\neMIGM-B with mask CFG|128x1.35|2.32\n\nThe results show both approaches achieve equivalent final performance, with CFG with Mask converging more rapidly as shown in Fig.2(e) of our paper.\n\n> **Q2:** eMIGM vs Consistency Models\n\nConsistency models and eMIGM employ distinct approaches to generation. Consistency models establish direct mappings from noise to data via consistency constraints, enabling single-step sampling. In contrast, eMIGM utilizes masked prediction, beginning with fully masked images and iteratively revealing content through progressive unmasking. This architectural distinction enables eMIGM to optimally balance generation quality and computational efficiency by modulating function evaluations.\n\n> **Q3:** choice of mask schedule\n\nWe explored three mask schedules (linear, cosine, and exponential) with constraints $\\gamma_0 \\approx 0$ and $\\gamma_1 \\approx 1$. While linear schedules are common in MDM text generation, we found concave functions like cosine performed better for images due to their information redundancy - higher mask ratios during training provide stronger learning signals. With $w(t)=1$, the exponential schedule slightly outperformed cosine, becoming our default.\n\nBased on your suggestion, we developed a log-exp schedule ($\\gamma_t=\\frac{\\log\\left(1 + (e^5 - 1) \\cdot t\\right)}{5}$) that balances mask ratios by reducing both high and low masking extremes. Using the setup from Figure 2(b) with $w(t)=1$, we present results of FID below.\n\nEpoch|Linear|Cosine|Exp|Log-Exp\n-|-|-|-|-\n100|38.66|24.99|28.63|25.38\n200|30.55|16.70|17.97|11.81\n300|24.55|15.00|11.57|12.48\n400|24.96|12.39|11.90|9.91\n\nThe log-exp schedule shows better convergence and performance, validating the benefits of exploring new masking approaches. We appreciate this insightful suggestion and will incorporate these findings in our revision.", "claims_and_evidence": "The claims made in the paper are mostly supported by clear and convincing evidence:\n\n1. The paper presents a mathematical framework that unifies Masked Image Generation and Masked Diffusion Models, showing that different approaches can be expressed under a generalized loss function. Empirical validation demonstrates that this framework enables systematic exploration of design choices, leading to performance and efficiency improvements.\n\n2. Instead of using a traditional fake class token, the paper proposes replacing it with a mask token in Classifier-Free Guidance (CFG). This modification improves conditional generation by preventing performance degradation caused by fake class tokens, as confirmed through ablation studies.\n\n3. The paper introduces a time interval approach to CFG, where guidance is applied selectively in later sampling steps. This method leads to better FID scores and reduces computational cost, as demonstrated in experiments.\n\n4. (Minor Limitation) Some hyperparameter choices (e.g., mask scheduling functions, weighting strategies) are selected based on empirical tuning rather than theoretical analysis, making it unclear whether the observed improvements generalize beyond the tested configurations.", "methods_and_evaluation_criteria": "The methods and evaluation criteria in the paper are generally appropriate. eMIGM is evaluated on ImageNet 256x256 and 512x512, which are widely used benchmarks for generative models. The paper primarily uses Frechet Inception Distance (FID) to assess image quality and Number of Function Evaluations (NFE) to measure computational efficiency. The proposed time interval strategy is systematically tested through ablation studies and experiments on different mask schedules, providing strong empirical validation. The use of NFE as an efficiency metric is well-justified, as it directly reflects computational cost.\n\nHowever, a limitation is that the evaluation relies solely on FID and NFE, without incorporating additional metrics such as Inception Score (IS), Recall, or Precision, which could provide a more comprehensive assessment of model performance.", "theoretical_claims": "All of the mathematical derivations are reasonable, and appropriate references and equivalences are provided, ensuring no issues. Additionally, after reviewing Section 3: Unifying Masked Image Generation and Appendix A: Equivalence of the Masking Strategies of MaskGIT and MDM, no logical errors or inconsistencies were found in the formulation and derivations. The mathematical framework presented in the paper is sound and well-supported by empirical validation.", "experimental_designs_or_analyses": "The paper effectively demonstrates its validity through a well-structured experimental design. To support the possible design choices, Figures 2 and 3 present experimental comparisons of weighting functions, mask schedules, and model architectures, clearly illustrating their impact on performance. To validate image generation performance, Tables 2 and 3 compare eMIGM against a diverse set of generation models, including Diffusion Models, Consistency Models, GANs, ARs, and Masked Models, establishing its performance superiority and scalability, further supported by Figure 4. \n\nHowever, most design choices are based solely on empirical selection of the best-performing methods, with limited analysis on why these choices lead to improvements. Additionally, while Consistency Models are included in the comparison tables, their differences from eMIGM are not explicitly discussed in the main text. It would be valuable to clarify how eMIGM differs from these models and what makes it superior.", "relation_to_broader_scientific_literature": "This paper is highly relevant to the broader literature on Masked Image Generation and Masked Diffusion Models: It extends prior work on masked generative transformers (MaskGIT) and masked Diffusion models (MDMs) by providing a unified framework​. This paper aligns with research on efficiency improvements in generative models, focusing on reducing NFEs while maintaining sample quality.", "essential_references_not_discussed": "-", "other_strengths_and_weaknesses": "-", "other_comments_or_suggestions": "I believe this paper effectively unifies two approaches into a single framework, systematically categorizing possible design choices to achieve optimal performance. However, rather than introducing entirely new design choices, the paper primarily selects options already used in existing models. While the modifications, such as replacing the fake class token with a mask token in CFG and introducing the time interval strategy, are well-supported by experimental results, the analysis of why these choices improve performance could be further elaborated. Due to this, the paper's overall contribution and novelty may look somewhat limited. I encourage the authors to address these aspects during the rebuttal period.", "questions_for_authors": "1. In Figure 2(e), the results appear unstable between epochs 300-400. Do you have experimental results for longer training epochs? While it is clear that CFG with Mask achieves a faster convergence, wouldn’t the final convergence point be the same if trained for a sufficiently long time?\n2. Although the experimental results include Consistency Models, the main context does not explore them in detail. What do you think are the differences between eMIGM and Consistency Models?\n3. In MaskGIT [1], various mask scheduling options are explored (e.g., cubic, square, square root, logarithmic). This paper considers a more limited selection—was this choice guided by empirical findings or specific constraints? Would exploring additional schedules further improve performance?", "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:

Meta-review: This work delves into masked diffusion and image modeling via a unified framework. It conducts a systematic analysis of several crucial design aspects in this field. In the framework, the authors conduct ablation studies on masking schedules, loss weighting, and sampling strategies, achieving enhancements compared to current standards in each of these aspects. Leveraging the insights gained, they introduce eMIGM, an advanced masked diffusion modeling approach that can compete with top-notch continuous diffusion models such as EDM2.

All reviewers (five) recommend acceptance of this work with all the problems sovled. Thus, the AC recommend acceptance with new masked diffusion framework and good performance.
Decision: Accept (poster)