Dual-Path Condition Alignment for Diffusion Transformers
Authors: Changhao Peng
Yuqi Ye
Shuangjun Du
Wenxu Gao
Wei Gao
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Transformer
Self-Supervised Learning
Representation Learning.
Primary area: generative models

Abstract: Denoising-based generative models have been significantly advanced by representation-alignment (REPA) loss, which leverages pre-trained visual encoders to guide intermediate network features. However, REPA's reliance on external visual encoders introduces two critical challenges: potential \textit{distribution mismatches} between the encoder's training data and the generation target, and the high \textit{computational costs} of pre-training. Inspired by the observation that REPA primarily aids early layers in capturing robust semantics, we propose an unsupervised alternative that avoids external visual encoder and the assumption of consistent data distribution. We introduce \textit{\textbf{DU}al-\textbf{P}ath condition \textbf{A}lignment} (\textbf{DUPA}), a novel self-alignment framework, which independently noises an image multiple times and processes these noisy latents through decoupled diffusion transformer, then aligns the derived conditions\textemdash low-frequency semantic features extracted from each path. Experiments demonstrate that DUPA achieves FID$=$1.46 on ImageNet 256$\times$256 with only 400 training epochs, outperforming all methods that do not rely on external supervision. DUPA is also model-agnostic and can be readily applied to any denoising-based generative model, showcasing its excellent scalability and generalizability. Code is available at https://github.com/PCH-gg/DUPA, https://openi.pcl.ac.cn/OpenAIDriving/DUPA.

Published as a conference paper ICLR 2026
DUAL-PATH CONDITION ALIGNMENT FOR DIFFUSION
TRANSFORMERS
Changhao Peng', Yuqi Ye', Shuangjuu Du², Wenxu Gao', Wei Gao School of Electronic and Computer Engineering, Peking University Institute of Automation, Chinese Academy of Sciences
"Corresponding author.
ABSTRACT
Denoising-based generative models have been significantly advanced by representation- alignment (REPA) Joss, which leverages pre-trained visual encoders to guide intermediate network features. However, REPA's reliance on external visual encoders introduces two critical challenges: potential distribution mismatches between the encoder's training data and the generation target, and the high computational costs of pre-training. Inspired by the observation that REPA primarily aids early layers capturing robust semantics, we pro- pose an unsupervised alternative that avoids external visual encoder and the assumption of consistent data distribution. We introduce DUal-Path condition Alignment (DUPA), novel self-alignment framework, which independently noises image multiple times and processes these noisy latents through decoupled diffusion transformer, then aligns the derived conditions --w--loo-frlll Now-frequency semantic features extracted from each path. Ex- periments demonstrate that DUPA achieves FID=1.46 on ImageNet 256x 256 with only 400 training epochs, outperforming all methods that do not rely external supervision. DUPA is also madel-agnostic and can be readily applied to any denoising-basse genera- tive model, showcasing its excellent scalability and generalizability. Code is available at https//ggghubb.om////cgg////
NU
Independent Noisnn
FIo SON on Caceeeeen 1350033
STRLUO
Visul Encoder
SEAAULO
Condition
Maight?
Condittan
DUPAige
REPA
F
14
Velecity
Shared Wuigh?
Velocity becoder
Decoder
DUN-XUU
6539-31/2
Ldkt
Entennal
200
400
642
920
Epocha
3530
1200
1000 3844
Unsupervised representation alignment can efficiently train diffusion transformer as REPA does. By aligning the representations of different noised images, DUPA achieves FID performance compa- rable to that of REPA with only 400 training epochs, which means 2 3x faster convergence than current state-of-the-aa methods that do not rely on supervision from an external visual encoder. The radius the circles in the right figure denotes model size while the gray ring surrounding REPA represents the auxiliary visual encoder.
Figure 1:

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Published as a conference paper ICLR 2026
1 INTRODUCTION
In recent years, denoising- generative models (Peebles & Xie 2023; Ma et al. 2024) have achieved remarkable progress in modeling complex data distributions. Such models are typically composed of stack- ing transformer blocks. REPA (Yu et al.) 2025] points out that aligning the intermediate representations of transformer blocks with the features extracted by high-performance visual encoders (e.g., CLIP (Radford et al. 202Th, DINOv2 (Oquab et al. 2024), etc.) can significantly enhance the performance of generative models. Since the proposal of REPA, most methods in class-to-image generation tasks have been built upon this approach.
However, applying REPA to specific application scenarios may face the following challenges from our per- spective:
Out of distribution. If there is a significant discrepancy between the data distribution modeled by the generative model and the pre-training distribution of the large visual encoder, the features extracted by the visual encoder may not only fail to facilitate the training of the generative model but could also potentially "mislead" it, resulting in performance degradation.
Additional computational costs, Both pre-training and fine-tuning large visual encoders for specific ap- plication scenarios incur additional computational costs. For instance, pre-training DINOv2 requires 1.1 billion model parameters, I, 1,500 training epochs, and 142 million images- far exceeding the computational resources needed train DiT (Yao et al.) 2024) SiT et al. 2024). Moreover, if the data distribution in specific domain differs from the pre pre-training distribution, further fine-tuning of the visual encoder is necessary, which further increases the computational costs.
Xie et al. point out in REPA: performance. We hypothesize that this enables the remaining layers to concentrate on capturing high- frequency details. building on a strong representation." Similarly, Wang et al. note in Decoupled Diffusion Transformer (Wang et al., 2025); "Current diffusion transformers are fundamentally constrained by their low-frequency semantic encoding cupacity." Therefore, we posit that the primary contribution of REPA lies providing accurate and invariant representations derived from pure images to the first few transformer blocks when they extract semantic features from noisy images. As illustrated on the left of Figure 2 REPA acts like a "data annotator" during training, supplying "labels" (i.e., effective representations) obtained from "ground truth"((.e. (i.e., pure images) for noisy images, which is similar supervised learning. However, dis- cussed above, this "supervised learning" approach in REPA faces two challenges compared to unsupervised learning: "costliness of labeling" and "inaccurate labeling" issues. Consequently, aim to utilize un- supervised learning to provide effective representation guidance for generative model training, much like REPA does but without the assumption of consistent data distribution and expensive additional compu- tational costs.
"Limiting regularization to the first few layers further enhances generation
Recently, several works have incorporated unsupervised learning into generative model training to improve performance. Broadly, we categorize these works into two types: introducing masked image modeling into the denoising process to enhance the contextual reasoning ability of generative models, such as MaskDiT (Zheng et al., 2024) and SD-DIT (Zhu et al.) 2024];; and utilizing intermediate representations of generative models for contrastive learning (lypically treating them as negative pairs) to improve training efficiency, such as Contrastive Flow Matching (Stoica et al. (025) and Dispersive Loss (Wang & He 2025). However, neither of these unsupervised approaches can provide accurate representation guidance for each image in the way REPA does, making it difficult for their performance to match that of REPA.
Based on the above insights, we propose DUal-Path condition Alignment (DUPA). As shown on the right of Figure 2 an image is independently noised multiple times during training, and use Decoupled Diffusion Transformer to predict different denoising paths. In this way. the condition encoder can extract different conditions, which are low- frequeqqecy semantic features from different noisy images. Since these conditions
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Published as a conference paper ICLR 2026
originate from the same pure image, they should be similar, much like the representations obtained by large visual encoders in REPA. We propose to align these different conditions derived from independently noised versions of a single image to furnish effective representation guidance for model training. In summary, our contributions can be outlined as follows:
We point out that REPA may face issues of out of distribution and high computational costs, and hypothe- size that internal alignment of noisy images can also provide effective representation guidance for training of diffusion transformer without external supervision.
We introduce DUPA, a simple alignment for two noisy views of single image without external supervi- sion, which can be easily applied to other denoising- boadeinng--aeddd generative models.
Our proposed DUPA achieves remarkable FID of 1.46 after only 400 training epochs, surpassing all evaluated methods that do not rely on external supervision. It also significantly narrows the performance gap with REPA (FID=1,42) 1,42), a model trained for 800 epochs under the guidance of external visual encoders. Furthermore, compared DUPA's base model, DUPA accelerates training by 5x and inference by 10x.
2
RELATED WORKS
2.1 DIFFUSION TRANSFORMERS WITH REPRESENTATION LEARNING
Diffusion transformers (Peebles & Xie 2023 present an innovative architecture for diffusion models which
integrates transformers /Vaswani et al. 2023) into the diffusion framework, effectively replacing the conven- tional U-Net structure. Studies demonstrate that this architecture can surpass traditional methods particularly when sufficiently trained. SiT (Ma et al.) 2024) further validates the effectiveness of transformers and ex- tends their application to challenging tasks such as text-to-image generation (Chen et al. [023]] 2024). Fur- thermore, diffusion transformers have achieved remarkable progress in the text-to-video domain, exhibiting outstanding visual and motion quality (Hong et al. 2022 Kong et al. 2025).
2.2 REPRESENTATION LEARNING DIFFUSION MODELS
In image generation research, REPA leverages auxiliary representation learning to optimize generative mod- els by aligning their intermediate representations with those of high-capacity pretrained encoders trained on external data. Building on this foundation, SARA (Chen et al. 2025) innovates by incorporating structured and adversarial alignment strategies. SoftREPA (Lee et al.] 2025) extends this approach to the multimodal domain by aligning noisy image representations with soft semantic embeddings. While these approaches demonstrate strong performance practice, they exhibit a high dependency on additional pretraining and external data.
2.3 UNSUPERVISED LEARNING IN DIFFUSION MODELS
The integration of masked image modeling(Xit et al. 2022) into diffusion transformers significantly en- hances training efficiency and semantic representation. By masking image tokens during training, masked image modeling forces the model to learn contextual reasoning within the diffusion process, often using an asymmetric encoder-decoder structure that reduces computational cost. This approach accelerates training, improves generation quality, and enables zero-shot image editing capabilities like inpainting. Models such as MaskDiT (Zheng et al. 2024) and MDTv2(Gao et al. 2023b) demonstrate its effectiveness in producing high-quality images with better structural coherence.
Compared to masked image modeling, contrastive learning (Khosla et al. 2020 has recently been demon- strated to be simpler yet also effective unsupervised method for improving diffusion transformer training. These methods primarily work by constructing negative samples to separate distinct representations. Con- trastive Flow Matching (Stoica al. 2025) proposes significantly reduce the number of sampling steps
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Published as a conference paper ICLR 2026
T=1
T=
O
T=1
REPA
DUPA
REFAige Loo
f
Visual Encoder
Figure 2: Comparison between REPA and DUPA. REPA needs an external visual encoder to generate
effective representations, whereas DUPA can get effective representations through internal alignment.
required during inference by maximizing the dissimilarities between the predicted velocity and the ground- truth velocity of an image from another category. Dispersive Loss (Wang & He (025) suggests that maxi- mizing pairwise distances among different intermediate representations within the same batch can enhance the generative capability of diffusion transformers without considering whether these representations belong the same category.
3 PRELIMINARIES
3.1 FLOW AND CRRPUSION-BASER MODELS
Based the unified framework of stochastic interpolants, flow and diffusion- models are characterized by a continuous interpolation process between data and noise X; Q,X, + dre, where x, ~ p(x) is data and (O I) Gaussian noise, with a decreasing and o, increasing in time t. The dynamics are governed by probability flow ODE X, v(x,tt, enabling deterministic sampling, and an equivalent reverse SDE
dx, vxxe, ldlt
2 wxss(xe l)dt + widde..
enabling stochastic sampling. The velocity field
v(x, () dr,/xx/Xx x] à, x]
(2)
is trained by minimizing the objective
Evelocity (0) Ex..ett (x,, àxx 0xc|171
unifying both ODE and SDE-based generation approaches.
3.2 DECOUPLED DIFFUSION TRANSFORMER
Decoupled Diffusion Transformer (DDT) (Wang et al., 2025) introduces a novel incoder-decoder archi- tecture resolve the optimization dilemma in traditional diffusion transformers between low-frequency semantic encoding and high-freyuency detail decoding.
Specifically, DDT uses dedicated condition encoder to extract semantic condition features 2; Encoder(xe, (x,, f, y) and a velocity decoder to predict the velocity field v, Decoder(xx, 2.). This encoder- decoder architecture significantly improves training efficiency while reducing FID (Deng et al. 2009).
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4 DUPA: DUAL-PATH CONDITION ALIGNMENT
4.1 DUAL-PATH SAMPLING
For an imput image and its class label y. we conduct multiple samplings to get different noises e and timestamps 42 thereby generating distinct noisy latents Xt, =a, + oo, Ed, S k S K to be denoised, where K represents the number of independent sampling times.
Then we use DDT to estimate the velocity for X
74, Encoder(xx,, 4), Vo, Decoder(xx, Lk: 7t.)
(4)
Considering the overall performance and computational cost trade-off (refer Figure 3a we set K Multiple independent noise sampling of a single pure image are performed for two main reasons.
2.
Training efficiency. It enables the training of different noised states of image through single training step. As will be discussed in Section 5.4, this approach is more efficient compared to applying only single noising operation.
Different conditions to align. Multiple independent noise sampling can obtain different velocity conditions for decoding velocities of distinct paths with the same "end point" via DDT. By aligning these conditions, DDT can encode more accurate low-frequency semantic information, which will be discussed in detail in Section
4.2 CONDITION ALIGNMENT
In REPA and DDT, the features extracted from pure images by state-of-the-art visual encoders are used to align the conditional features learned by DiT blocks from noisy latents, which has been shown signifi- cantly enhance the model's performance:
CREPA (0, = Exx.ee
Σ
sim(y [0] (z. (m]
(5)
N 1==1
where y- denotes the output of the visual encoder, 2, represents the conditions extracted by DDT, and zs a trainable MLP used to align the data dimensions of y. and z,. 0 and ø are the parameters of DDT and 2g. respectively. N is the patch number and sim(., is a pre-defined similarity function.
However, large visual encoders introduce additional training data and model parameters. We posit that the features output by the visual encoder provide consistent and accurate conditioning for different noisy latents derived from the same pure image during training. The fact that different condition features of the same image converge toward the representation extracted by the visual encoder during training resembles clustering in unsupervised learning. This inspires sample multiple condition features in a single training step and align them towards the cluster center-whie -wich corresponds to the representation extracted by the visual encoder in REPA as intuitively illustrated in 2
Similarly, We align any two conditions of {Zt,} } in the manner of REPA:
CDUPA (0, = -F (c K(K
2
Σ
Σ sim(z0(2)))
(6)
N
ISKJSK
On the other hand, modify the original diffusion model's loss to the average of diffusion losses over K-times samplings:
K
Evelocity (8) := Ex deu
Vee(x t4) - x, - 6x12
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Algorithm 1 Dual-Path Condition Alignment Batch Step
1: Input: DDT de, batch of B flow examples F' = (1x.88)..... (X8, un]). projector learning rute
8. sampling times K 2 and hyperparameter À 0.5.
2: Output: Updated model parameters B.
3: L10.0)
4: for range(B) do
5: for j in range(K) do
6:
fj ~ (U,, 1)..).... Gj ~ N 1], x1, a,x o..
7:
v,. ( Ej. M6). V; = Ci 0,,
8:
2; 2;= =g(2,)
9:
L(0, () - 1|V; vill
for À range(j)
[(0,0) () K( sim(ss.
12: end for
end for end
1. -- VVL(0,00)
Then we sum the condition alignment loss and diffusion loss to construct the loss function for model training:
= Lvelacity + DDPUAA
(8)
where À is a hyperparameter that controls the tradeoff between condition alignment and denoising. Algo- rithm Iillustrates the implementation of arbitrary batch step in training DUPA.
5 EXPERIMENTS
We conduct extensive experiments to evaluate DUPA's performance and effectiveness, focusing on three key aspects:
Performance comparison between DUPA and current state-of-the-art methods. (Section 5.2
Effectiveness and necessity of DUPA's components and settings. (Section 5.3 5.4
Time and computational costs of DUPA during training and inference. (Section 5.5
5.1 EXPERIMENTAL SETUP
Implementation details. Our experimental setup aligns with DiT, SiT, REPA, and DDT. DUPA is trained on 256x256 ImageNet datasets with a batch size of 256. Images are processed through the off-shelf Stable Diffusion VAE to obtain latents E 32x322x4 Adam optimizer with a learning rate of 0.0001 is employed throughout the entire training process. DUPA's model configuration is shown Appendix B which main- tains the same model size with SiT. We set hyperparameter À = 0.5 and independent noise sampling times K 2, choose cosine similarity as sim(:.) and do not use classifier-free guidance (CFG) unless otherwise specified. Our default training infrastructure consisted of 8xA100 GPUs. For more experimental details, please refer to Appendix D
Initialization of projector. It is crucial to avoid setting both the weights and biases to 0 when initializing projector Zg. Otherwise, the condition used to align with will remain 0, leading to shortcut learning. In our experiments, employ Kaiming initialization (He et al. (015) for the first layer of projector 24 to preserve variance during forward propagation, while utilizing a reduced-gain Xavier initialization (Glorot & Bengio 2010) for subsequent layers to prevent gradient explosion overfitting.
Evaluation. We report following five quantitative metrics to evaluate model's performance: Fréchet incep- tion distance (FID; (Heusel al. 2017)) sFID (Nash et al., 2021). inception score (IS; (Salimans al.
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Table 1:
System-Level Performance on ImageNet 256 x 256. Our results are bolded to indicate that
DUPA performs better than methods without external supervision of large visual encoders, while high- lighted to indicate that DUPA performs the best among all methods. + indicates a lower value is better and f indicates higher value is better.
Method
Training
F Fpuchs
*params
External
Imagie
Exturnal
Parsin
FID:
aFID)
Generation wib CRG
181
Prec.
Rec."
FID,
sFID:
Generation of CPG
IS:
Prec.
Rec.
No Auxiliary Troc
2.27
278,2
2006
270.3
0.59
FaserbXT
0.90
673M
251.7
0.59
Masked Imsage Musleling
MaskATT
555
227M
0
6.18
182:
0
0.51
300
3.18
0
9.38
9.24
112.9
0.69
0.67
2.18
5.97
263.3
0.81
0.54
VAR
350
2088
0
1,80
365.4
0.83
0.57
VTT22
1080
307M
0
3.65
200.5
1.78
319.4
MAR
800
945M
0
1.35
227.8
0.79
0.62
1.55
303.7
0.31
0.62
MiskDIT
1600
673M
0
0
5.09
10.34
177.9
0.74
0.50
2.25
5.67
276.6
0.30
0.61
MDT
1300
673M
0
6.23
5.23
143.0
0.71
0.65
1.79
4.57
283.0
0.81
0.61
MDTY2
920
673M
0
1.58
4.52
314.7
0.79
0.65
Contraedivv Learning
APM
8:0
673M
0
1.97
4.53
268.4
0.65
Diep-1ae Los
1300
673M
1.97
4.61
275.2
0.61
Supervisel Representation Aliiuunent
0.70
673M
142M
1.18
1.96
264.0
0.60
5,73
157.8
0.70
0.63
Uncuperissd Representation Alligmment
80
8.71
4.65
1146
0.70
0.65
2.28
4.48
237.2
0.83
0.99
DUPA (Ours)
200
673M
0
6.57
4.63
196.5
0.70
0.68
1.70
4.45
265.3
0.83
0.61
400
599
4.63
1496
0.7
0.69
1.46
4.45
206.2
0.84
0.62
2016)), precision (Prec.) and recall (Rec.) (Kynkäänniemi et al. 2019). We sample 50,000 images calculate the above quantitative metrics.
Sampler. We use the SDE Euler-Marune Maruyaaa sampler (for SDE with w, o₂) and set the number of function
evaluations (NFE) as 250 which follows SiT unless otherwise specified.
Baselines. We select state-of- the-art generative models in recent years as our baselines. Unlike other works, we do not distinguish DUPA and baselines based on model architecture, but rather based on the types of auxiliary tasks used for generation: (a) No auxiliary task: Dit (Peebles & Xie (023). SiT (Ma al. 2024).
FasterDiT (Yao et (024) and DDT (Wang et al. (2225). (b) Masked Image Modeling: MaskGIT, (Chang
eet al.) 2022), LlamaGen (Sun et al. 2024). VAR (Tian et al., 2024), MagViT-ve (Yu et al.
2023), MAR
(Li et al. 2024), MaskDiT (Zheng al. 2024). MDT (Gao et al.) (223a) and MDTv2(Gao et al. (023b).
(c) Contrastive learning: AFM (Stoica et al. 2025) and Disp-Loss (Wang & He 2025). (d) Supervised
representation alignment: REPA Yuu et al. (025). (e) Unsupervised representation alignment: DUPA. We
categorize all autoregressive models as (b). The original DDT introduces architectural improvements, such
SwiGLU (Touvron et al. 2023). RoPE (Su al. 2024), and RMSNorm (Touvron al. 2023), well
supervision from external visual encoders. Our approach solely focuses on its core contribution- encoder-decoder architecture. Therefore, the following results regarding DDT are all reproduced based on
SiT.
5.2 SYSTRM-LEVEL COMPARISON
Table shows the performance of our method compared to different sizes of base models. It can be seen that DUPA has improved all sizes of base models in various generation metrics.
Table I presents a comparative analysis of DUPA-XL/2 against current state-of-the-ar methods on the Im- ageNet 256 256. In terms of sFID, DUPA outperforms all other listed methods, both with and without
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Table 2: Component-wiie analysis. All models are DUPA-L/2 trained for 400K iterations with different set- tings. "Resampling" column indicates whether to inde- pendently resample timestamp t noisc c.
Table Model performance across different sizes with 400K training steps.
Model
FID
sFID
IS1
Prec.
Rec.t
SiT-B/2
33.0
6.46
43.7
0.53
0.63
Resampling Depth Objective
FID
DDT--/2
29.5
6.23
51.7
0.57
0.63
Vanilla SIT-1/2
18.8
DUPA-B/2
25.2
67.4
0.63
t
8
Cos. sim.
0.5
13.2
SiT-L/2
18.8
5.29
72.0
0.64
0.64
c
8
Cos. sim.
0.5
12.4
DDT-L/2
14,9
5.17
87.8
0.65
0.64
DUPA-L/2
11.1
4.91
104.8
0.69
0.65
f
4
Cos. sim.
0.5
11.8
SiT-XL/2
17.2
5.07
76.5
0.65
0.63
c
6
Cos. sim.
0.5
11.3
DDT-XL/2
12.8
4.98
91.3
0.67
0.63
10
Cos. sim. 0.5
11.2
DUPA-XL/2
8.71
4.65
114.6
0.70
0.65
1,6
12
Cos. sim. 0.5
11.6
1,6
14
Cos. sim. 0.5
11.9
t,
16
Cos. sim. 0.5
12.1
Table 4: Ablation study of proposed improvements.
6
8
NT-Xent 0.5
11.6
f
8
Cos. sim.
0.25
11.2
Method
FID sFID
ISt
Prec.
Rec.
8
Cos. sim. 0.75
11.1
DDT-L/2
14.9
5.17
87.8
0.65
0.64
8
Cos. sim.
11.1
Sampling
12.5 5.02
96.6
0.68
0.65
c
8
Cos. sim. 0.5
11.1
Condition Alignment 11.1 4.91
104.8
0.69
0.65
CFG. Furthermore, achieves the hest recall score in the non-CFG setting and the best precision score when CFG is applied.
Notably, for FID, DUPA surpasses all methods that do not rely on external supervision after only 400 training epochs. Even when compared REPA-a model trained for full 800 epochs with the aid of large visual encoders' representation DiignmenttDUPA'S performance is within narrow 3% margin. This achievement, despite the shorter training schedule (we train DUPA-XL/2 only for 400 epochs due to resource and time limits), strongly demonstrates the superior efficiency of DUPA.
5.3
COMPONENT- WISE ANALYSIS
The resampling strategy. recoder architecture, condition alignment method and hyperparameter settings of DUPA significantly impact the model's performance. Results of the impact of these components are shown in Table
Resampling strategy. Experiments show that independently resampling of both timestamp / and noise performs the best. We believe this provides more diverse noisy images, thereby enhancing the reliability of cluster centers of extracted condition representations.
Condition encoder depth. We investigate the impact of the number of layers in the condition encoder on DUPA-L/2. Similar to the conclusion in REPA, aligning the representations output by the first few layers can help the subsequent network predict high-frequency details. In the remaining experiments, we perform condition alignment at the 8th layer.
Alignment objective. We compare the effects of two similarity functions which are commonly used in con- trastive learning: Normalized Temperatuue-scaled Cross Entropy (NTT-Xent) and negative cosine similarity (cos. sim.), and we choose cos. sim. in other experiments.
Effect of tradeoff parameter, As shown in Table 2 DUPA is robust to the tradeoff parameter X.
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Mem. FID
STE-I2
400K 256
0.97
22.6
188
DDTLL22
400K 512
1.87
35.5
13.9
DITE-L22
8OOK 256
101
23.3
13.4
DUPA
DUPA-L/2
400K
256
1.26
32.5
10.8
DUPA L/2 800K 256
1.4
38.2
10.7
a6c Restion
"ca Denstin Staal
(a) "BS" indicates batch size, "K" indicates noising times, "TS" indicates (b) Image sampling is performed training speed (sec/step) and "Mem." indicates memory usage a single DUPA-XL/2 and DDT-XL/2 trained for 400K iterations.
GPU (GB).
Figure Time and computational cost analysis. (b)Training efficiency and inference speed comparison.
(a) Time and computational costs comparison.
5.4 ABLATION STUDY
Compared to the baseline model DDT, our primary improvements lie in dual- sampling and condition alignment. Since condition alignment relies dual-path sampling, we conduct the following three sets of ablation experiments on DUPA-L/2: DUPA without dual-path sampling and condition alignment (which degenerates DDT), DUPA without condition alignment and the vanilla DUPA. The results of the ablation experiments are shown in Table 4
Dual-path sampling offers more precise gradient guidance for model parameter optimization in a training step, enhancing training efficiency, while conditional alignment enables the condition encoder to capture more accurate semantic representations from noisy images, further boosting model performance.
5.5 TIME AND COMPUTATIONAL. COSTS
Since training and sampling of generative models require significant time and computational resources, we emphasize evaluating the model's computational cost in addition to its performance. During training, multiple independent sampling of noises and velocity prediction for single image represent the primary extra computational overhead introduced by our method. For the sampling phase, we also conduct experiments to explore whether DUPA can accelerate the sampling procedure through aligned condition feature.
Noise sampling times. We compare the impact of different noise sampling times on training speed, GPU memory usage, and model performance in Figure 3al To illustrate the difference between multiple sampling and batch size enlargement, we additionally train DDT with batch size of 2 x 256 512.
Neither doubling the batch size nor the training steps of DDT can achieve the performance DUPA. More- over, the former approach leads to nearly doubled training cost. On the other hand, increasing K signif- icantly raises GPU memory usage and slows down training speed, without significant FID gains. We thus select K in other experiments.
Improved training efficiency and inference speed. accelerate experiments, we compare the training efficiency of DUPA-L/2 and DDT-L//. As shown in Figure 3b DUPA achieved 5x and 10x speedups over DDT in training and inference stages respectively without external supervision, which highlights DUPA's remarkable efficiency.
6 CONCLUSION AND FUTURE WORK
Inspired by REPA, propose DUPA, which provides efficient semantic information for denoising basedd generative models' training by aligning the representations different noisy views from the same image, which is similar REPA. DUPA achieve performance comparable to that of REPA without any external supervision of large visual encoder, which can easily applied to any denoising- based models. Furthermore, intend to conduct further testing and improvement of DUPA text-to-image tasks in the future.
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ACKNOWLEDGMENT
This work was supported by National Science and Technology Major Project (2024ZD01NL00101), Nat- ural Science Foundation of China (62271013), Guangdong Provincial Key Laboratory of Ultra High Def- inition Immersive Media Technology (2024B12) 2010000)))) Guangdong Province Pearl River Talent Pro- gram (2021QN020708), Guangdong Basic and Applied Basic Research Foundation (2024A1515010155
Shenzhen Science and Technology Program JCCY1202408131102000000 JCYI20220800110800000
SYSPC20242111330004))
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A USE OF LARGE LANGUAGE MODELS
We acknowledge the use of Large Language Models (LLMs), specifically OpenAl's GPT-5 and Google's Gemini 2.5 Pro, to assist in the preparation this manuscript. The specific applications were follows:
Information Gathering: assist in consulting background information and identifying potential litera- ture related the research field.
Language and Readability: To improve the grammar, clarity, and overall readability of the manuscript
through language polishing.
Format Checking: To assist in checking the paper's layout and citation style for general compliance with conference requirements.
We emphasize that all scientific claims, cited works, experimental results, and final conclusions were inde- pendently reviewed and verified by the human authors. The authors take full and final responsibility for the entire content of this submission, including any potential errors or inaccuracies, in accordance with ICLR policy.
MODEL CONFIGURATION
Table Model configuration details.
Config
# Layeers Hidden dim #Heads Enc depth Patch size
DUPA-S/2
12
384
4
DUPA-B/2
12
768
12
4
2
DUPA-L/2
24
1024
DUPA-XL/2
28
1152
C CLASSIFIER FREE GUIDANCE
Considering that classifier-fres free guidance significantly affect the generation quality, adopt interval guidance with interval [0, 0.7] following REPA, which apply classifier-fres guidance only to the phase of generating high-frequency details, thereby ensuring the diversity of the generation results. The results of classifier-free guidance scale w are shown Table 6
Table 6: Detailed evaluation results of DUPA-XL/? at 2M iteration with different classifier-free guidance scale w.
Model
#Params
Iter.
FID4
sFID
IST
Prec.
Rec.
DUPA-XL/2
675M
2M
1.56
1.51
4.47
274.6
0.82
0.63
DUPA-XL/2
675M
2M
1.58
1.47
4.45
286.8
0.83
0.62
DUPA-XI./2
675M
2M
1.60
1.46
4.45
296.2
0.84
0.62
DUPA-XI//2
675M
2M
1.62
1.49
4.44
304.7
0.84
0.61
DUPA-XL/2
675M
2M
1.64
1.53
4.43
309.5
0.84
0.60
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D IMPLEMENTATION DETAILS
Table 7: Experimental setup.
Table I (DUPA-XL/2)
Table 2 (DUPA-L/2)
Table 4 (DUPA-L/2)
Figure 3a (DUPA-L/2)
Architecture
Input dim.
32x32x4
32x32x4
32x32x4
32x32x4
Num. layers
28
24
24
24
Hidden dim.
1,152
1,024
1,024
1,024
Num. heads
16
16
DUPA À Alignment depth
0.5
0.25m1
0.5
0.5
8
4~166
8
8 cos. sim. 2--4
s)m(
cos. sim. 2
cos. sim./NT-Xent
cos. sim.
Noising Times
Optimization
Training iteration Batch size Optimizer
2M
400K 256 Adam W
400K
400K 256 Adam W 0.0001 (0.9, 0.999)
256 AdamW
256 AdamW
Ir (81, (2)
0.0001 (0.9, 0.999)
0.0001 (0.9, 0.999)
0.0001 (0.9, 0.999)
Interpolants
Oe
1-t
1-t
1 d: v-prediction Fuler-Maruyama 250
dz
Wt Training objective Sampler
d: Y-prediction Fuler-Maruyama 250
dt v-prediction Fuler-Maruyama 250
v-prediction Fuler-Maruyama 250
Sampling steps
Guidance
1.6
E DISCRIMINATIVE SEMANTICS
Figure 4 presents comprehensive discriminative semantics analysis of the DUPA-XLAS and SiT-XL/2 mod- els, evaluated through two key metrics: linear probing validation accuracy and CKNNA score.
Linear probing. The linear probing results Figure 4a show that both DUPA-XL/2 and SiT-XL/2 models exhibit an initial increase in validation accuracy as layer depth increases, before eventually plateauing or decreasing. This trend is typical for discriminative models, where the initial layers learn basic features and the later layers learn more abstract, task-specific features.
Significantly, the DUPA-XL/2 model consistently outperforms SiT-XL/2 across all layers. At its peak per- formance, DUPA-XL/2 achieves 69% validation accuracy, while the SiT-XL/2 model peaks 53.5%. This large performance gap highlights DUPA-XL/2's superior ability to learn more discriminative, semantically rich representations.
CKNNA score. As shown in 4b DUPA-XL/2 demonstrates a much higher CKNNA score than the SiT-XL/2 across all three time steps (t=0.0, t=0225, and t=0.5). CKNNA score, which measures the complexity and discriminative power of the learned features, is consistently over 0.4 for DUPA-XL/2, whereas SiT-XL/2's score remains below 0.2.
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This result indicates that the features extracted by DUPA-XL/2 are not only more discriminative but also more complex and better structured for classification tasks compared to those of SiT-XL/2. The consistent gap in CKNNA scores across different time steps further confirms that the superior discriminative capability of DUPA-XL/2 is robust characteristic of the model's architecture.
GORL
Validation Acc. (%)
CKNNA
03
0.2
311
STE-U,2
6.3
DINOV2-9
32
24
b.0.2
Layer Index
TimeStep (t)
(a) Linear prohing.
(b) CKNNA score.
Figure 4: Discriminative semantics analysis.
ALIGNMENT Loss
Figure shows the change in cosine similarity during DUPA-XI/2 training, measured across different de- noising paths for condition alignment. Initially, most of the network's 's neurons are not activated, which leads to similar yet uninformative representations (note the initialization of the projector Zg to prevent shortcut learning). In the early stage of training, DUPA begins learn image features, but the cosine similarity rapidly decreases due to the influence of noise. After a small number of training steps (approximately 3,000 steps), DUPA begins to learn useful representations from different noisy latents of the same image. iee. the invariant semantic information from the pure image. Subsequently, the cosine similarity increases as training progresses.
Alggmment
100K
120
Training Steps
Figure DUPA alignment similarity during training.
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G MORE QUALITATIVE RESULTS
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AIG
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Reviews:
[{"review_id": "9ezcP3pnvq", "reviewer": "Reviewer_tRWZ", "summary": "The paper proposes DUPA (Dual-Path Condition Alignment), an unsupervised representation-alignment framework for training diffusion transformers. Building upon the decoupled diffusion transformer (DDT), DUPA introduces a condition alignment loss that aligns features of multiple noisy versions of the same image, effectively mimicking the self-supervised contrastive learning.\nExperimental results on ImageNet 256×256 demonstrate that DUPA outperforms the reproduced DDT baseline both in terms of training and sampling efficiency.", "questions": "- What's the main difference between DDT and the dual-path sampling in Table 4? Aren't they the same without DUPAlign loss? \n- Why does DUPA not integrate the architectural improvements of DDT? Would DUPA still retain its advantage if those were included?\n- How does DUPA work on higher resolution like ImageNet512?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 1, "contribution": 2, "strengths": "- The proposed DUPA not only achieves better FID than DDT (reproduced) with the same number of training steps but also reduces the number of denoising steps for sampling. \n- The design of DUPA is simple and easy to integrate.", "weaknesses": "- The experiments are limited to ImageNet 256×256.\n- While the paper is clear in structure, the prose is dense and overly formal in places, which introduces unnecessary friction. For example, in lines 192–194:\n\t- there are too many dependent clauses. \"thereby generating... to be denoised\". \n\t- Unnecessary formality. \"conduct multiple samplings to get different...\" is verbose. \"We sample multiple noises..\" will be more natural. \n\t- Grammar issues: \"independent sampling times\" -> \"independent samples\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TsZQUOHBi6", "reviewer": "Reviewer_bsUa", "summary": "The paper present DUPA, a self supervised approach for representation alignment for boosting the convergence speed of diffusion transformers. The core idea is to utilize representations from a parallel branch which has the input of the same image but at a different noise level Such a mode of training would force the model to learn noise robust features faster leading to faster convergence. Experiments show an improvement of training speed by 5x and the model achieving similar performance to REPA.", "questions": "1. Aside from the cosine similarity loss similar to REPA, would a simple MSE loss work for DUPA? In the case of REPA the cosine similarity loss may make sense. But is it the same case here, since the features of the same network are aligned?\n2. Could the authors provide a comparison between REPA and DUPA for text to image generation ? \n3. I'm rating the paper as borderline accept now, mainly because this approach seems scalable for text to image generation on the first look. ,but I'm willing to improve my rating if the authors can address the questions in a satisfactory way.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The idea of utilizing augmented versions of the same image as input and aligning their representations is a clever approach for representation alignment. Such a mode of training may scale better for text to image training when compared to a vision encoder that introduces an inductive bias\n2. The idea is novel and solid and the authors have performed extensive experiments to find the suitable layers and hyperparameters for DUPA. \n3. The paper is well written and easy to follow.", "weaknesses": "1. Is there some contraints on the value of the independently sampled timesteps needed for better performance? As an example, assume that one timestep is sampled with maximum noise and the other at minimum noise, aligning their representations might be a case where training with DUPA loss might not leading to a meaningful solution. \n2. Would performing DUPA on multiple layers at the same time lead to a better performance? \n3. I think the claim regarding 10x inference speed may be a bit misleading. Usually diffusion models are utilized to obtain a few samples. I believe with the current setup, DUPA will portray similar sampling speeds to REPA. I would advise the authors to correct this wording.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "K2mvKhmRfq", "reviewer": "Reviewer_7oUe", "summary": "This work (DUPA) first points out the internal issues brought by REPA: \n(1) Out of distribution. and (2) Huge additional computational costs. \nThen inspired by the idea of DDT, SD-DIT and Contrastive FM works, the authors propose DUPA to directly align  two noisy views of a single image without external supervision. And such self-alignment (unsupervised alignment)  can significantly accelerate the convergence of SiT.", "questions": "See weakness.\n\nI am very willing to raise my ratings if you can provide sufficient discussions mentioned in Weaknesses.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The proposed DUPA is simple but effective, without the reliance of external ViTs, like dinov2.\n2. The author’s writing is very straightforward and concise, without storytelling or beating around the bush.\n3. Clear convergence of SiT is brought by DUPA.\n4. Sufficient experiments .", "weaknesses": "1. More discussion and analysis about: why such self-supervised alignment could work for the convergence of SiT?\nfor example in SD-DiT[2], the choice of t=min (mostly close to pure image) is the most effective acceleration technique. And how about DUPA?\n2.  Recently there are some works [1][2] like DUPA, focusing on the self-alignment DiT, please claim the difference/advantage/difsussion compared with DUPA  and add the corresponding reference.\n\n[1] Jiang, Dengyang, et al. \"No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves.\" arXiv preprint arXiv:2505.02831 (2025).\n[2] Zhu, Rui, et al. \"Sd-dit: Unleashing the power of self-supervised discrimination in diffusion transformer.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Nvl6Sv5ggs", "reviewer": "Reviewer_A859", "summary": "This paper aims to accelerate the diffusion transformer training without an external pre-trained encoder by aligning latents from multiple noisy images through decoupled diffusion transformers. They show the proposed method, DUPA, can perform better than REPA without any external guidance.", "questions": "Please answer the Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-motivated and easy-to-follow.\n\n2. The proposed method, DUPA, consistently improves the baselines, and even perform better than REPA.\n\n3. Extensive analysis demonstrates the effectiveness of each suggested component.", "weaknesses": "1. The authors point out the weakness of REPA in practical scenarios (i.e., beyond the ImageNet), e.g., out-of-distribution problem can arise, and additional pretraining is required. However, the authors do not handle such scenarios in the main experiments: They only conducted ImageNet generation results, which makes it difficult to argue whether the proposed method indeed addresses the problem of REPA. I think such a problem of REPA does not arise in the image generation problem, as (1) recent pretrained visual representations (e.g., DINOv3 and SigLIPv2) are trained on extremely large-scale datasets, and (2) we can easily use open-source image encoders.\n\n2. The authors fixed the batch size at 256 for their experiments, but I think that increasing the sampling times K can have a similar effect to enlarging the batch size. In fact, a previous study [1] has shown that applying augmentations to the same batch can yield better performance with fewer iterations. Therefore, it is needed to verify how much of the improvement in the proposed method comes from this effect, e.g., by training SiT with a batch size of 512, or using sampling times K with only a flow matching loss.\n\n[1] Hoffer et al., Augment Your Batch: Improving Generalization Through Instance Repetition, CVPR 2020", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)