UniFlow: A Unified Pixel Flow Tokenizer for Visual Understanding and Generation
Authors: Zhengrong Yue
Haiyu Zhang
Xiangyu Zeng
Boyu Chen
Chenting Wang
Shaobin Zhuang
Lu Dong
Yi Wang
Limin Wang
Yali Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Unified Understanding and Generation;Vision Tokenizer; Visual Representation Learning;
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: UniFlow tokenizes images into a unified latent space by distilling a pre-trained vision model’s semantics via self-distillation, then reconstructs pixels with a flow-based decoder, simultaneously enabling representation and generation.

Abstract: Tokenizer is a crucial component for both visual understanding and generation. To advance toward the ultimate goal of universal modeling, recent research has focused on developing a unified tokenizer. However, existing tokenizers face a significant performance trade-off between understanding and generation, stemming from the inherent conflict between high-level semantic abstraction and low-level pixel reconstruction. To tackle this challenge, we propose a generic and unified tokenizer, namely $\textbf{UniFlow}$, by flexibly adapting any visual encoder with a concise reconstruction decoder. 
Specifically, we introduce $\textit{layer-wise adaptive self-distillation}$ applied to the well-pretrained visual encoders, which enables UniFlow to simultaneously inherit the strong semantic features for visual understanding and flexibly adapt to model fine-grained details for visual generation.
Moreover, we propose a lightweight $\textit{patch-wise pixel flow decoder}$, which efficiently achieves high-fidelity pixel reconstruction by modeling a conditional flow from the noisy state back to the patch-wise pixel domain. 
By leveraging the semantic features as visual conditions for the decoder, we effectively alleviate the training conflicts between understanding and generation. Furthermore, the patch-wise learning strategy simplifies the data distribution, thereby improving training efficiency.
For instance, our 7B UniFlow-XL not only surpasses the 14B TokenFlow-XL by 6.05\% on average understanding benchmarks, but also achieves a competitive results in both visual reconstruction and generation, surpassing UniTok by 0.15 in rFID and 0.09 in gFID (without guidance), respectively.

UNIFLOW: A UNIFIED PIXEL FLOW TOKENIZER FOR VISUAL UNDERSTANDING AND GENERATION
Zhengrong Yue! 1.
Haiyu Zhang?? Xiangyu Zeng Boyu Chen Chenting Wang
Shaobin Zhuang!
Lu Dong Yi Wang? Limin Wang
Yali Wang
Shanghai Jiao Tong University Shanghai AI Laboratrry 3 "eehhang University
Shenzhen Key Lab of Computer Vision and Pattern Recognition, Shenzhen Institutes of
Advanced Technology, Chinese Academy of Sciences
5 Nanjing University UUniversity of Science and Technology China
7 School of Artificial Intelligence, University of Chinese Academy of Sciences
ABSTRACT
Tokenizer a crucial component for both visual understanding and generation. To advance toward the ultimate goal of universal modeling, recent research has focused on developing a unified tokenizer. However, existing tokenizers face a significant performance trade-off between understanding and generation, stemming from the inherent conflict between high-level semantic abstraction and low-level pixel re- construction. To tackle this challenge, we propose a generic and unified tokenizer, namely UniFlow, by flexibly adapting any visual encoder with concise recon- struction decoder. Specifically, we introduce layer-wise adaptive self-distillation applied to the well-pretrained visual encoders, which enables UniFlow to simulta- neously inherit the strong semantic features for visual understanding and flexibly adapt to model fie-grained details for visual generation. Moreover, we propose lightweight patch-wil wiss pixel flow decoder, which efficiently achieves high-fidelity pixel reconstruction by modeling conditional flow from the noisy state back to the patch-wise pixel domain. By leveraging the semantic features as visual conditions for the decoder, we effectively alleviate the training conflicts between understand- ing and generation. Furthermore, the patch-wise learning strategy simplifies the data distribution, thereby improving training efficiency. Extensive experiments across 13 challenging benchmarks spanning widely studied visual understanding and generation tasks demonstrate that UniFlow achieves a win-win instance, our 7B UniFlow-XL not only surpasses the 14B TokenFlow-2 on average understanding benchmarks, but also achieves a competitive results both visual reconstruction and generation, surpassing UniToo by 0.15 in rFID and 0.09 in gFID (without guidance), respectively.
win outcome. For
by 6.05%
INTRODUCTION
The field of computer vision has witnessed a remarkable evolution, with aarge-scale models achieving
significant success both visual understanding and generation (Chen et al. [2024b) Peebles & Xie
2023 Rombach et al. 2022 Batifol et al. 2025: Wang et al. 2025b; Chen al. 2024a] 2025d b
Yue et 2025). Vision foundation models (VFMs) (Oquab et al. 2023 Radford et al. 202 1 He
et 2022 Tschannen et al., 2025 Yu et al.
2022:
Chen al. (022a)) have emerged powerful
backbones, offering discriminative semantic representations for a wide range of understanding tasks.
Meanwhile, generative models (Kingma al.
2019
Yu al., 2021 Peebles & Xie 2023 Rombach
ct al. 2022 Sun et al. 2024a) have achieved fideliiyy visual synthesis by distribution modeling approaches. To build more generalist models, researchers attempt integrate understanding and
generation within single framework (Team] 2024 Wu et al, 2025b; Wang et al., 2202cc) Xie et al. 2024b [eng et al., (205)). However, they depend on different tokenizers for understanding and generation, resulting in divergent optimisation objectives that hinder achieving excellent performance in both tasks. Consequently, recent studies have focused on designing unified tokenizers (Wu et al.
2024b Ma et al. 2025 Zhao et al. 2025b
Qu et al. 2025 Song et al. 2025).
" "Corresponding author.

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Comparison with SOTA S5IM
Dual Encoder
Semantic Encoder (b)
Unified Encoder
(c)
(d)
TOA
Fixel Encoder
Semantic Encoder
Semantic Encoder
Unified Encoder
Unified
PSNR
Encoder
Contrastive
Dccillattoon
MNV
Pixel
Lacent Diffusion Decoder
Patchwwise Pixel Flow Decoder
Decoder
WIE
MMB
(e.g. TokenFlow, MUSE-VL) Understanding
(e.g 8, FMil)))
QLI., Unfrok]
(UniFlow)
Understanding
Understanding
Understanding
14
GGA
Reconstruction
Reconstruction
Reconstruction
Reconstruction
POPE
MME
Training Efficiency
Training Efficioncy
Training Efficlency
Training Ffficiency
Figure 1: Comparison of different training paradigms for unified tokenizers. All multimodal large language models are trained on LLaVA-v data with Vicuna-7B, except that TokenFlow uses Vicuna- 13B. UniFlow simultaneously improves performance and training efficiency.
As shown in Fig. T (a), the pioneering methods (Qu et al. 2025 Xie et al., 2024c) employ pixel and semantic encoders to address generation and understanding tasks, respectively. However, this dual-encoder paradigm not only introduces substantial model redundancy but also causes training inefficiency due to the presence of separate embedding spaces. To alleviate this problem, researchers try to design a unified encoder architecture. Some methods (Sun et al. 2024b Chen et al.) 2025t)) utilize frozen, well-pretrained vision foundation models as visual encoders and incorporate latent diffusion decoder for pixel reconstruction, as shown in Fig. [b). Although they inherit strong understanding capabilities from vision foundation models, the features extracted by the semantic encoder fail to model fine-graine grained details, limiting high-fidelity reconstruction. Moreover, the reliance on the pretrained Variational Auto-Encoder (VAE) imposes a ceiling on reconstruction performance. Alternatively, as shown in Fig. [c). (Wu et al. 2024b Ma et al. 2025; Zhao et al. 2025b) initialize visual encoders using pretrained foundation models and finetune them with a pixel decoder by directly mapping semantic tokens to pixel targets for unification. However, this approach may degrade the understanding capacity of visual encoders, as high-level features are simultancously optimized for low-level reconstruction. Although vision-text contrastive learning is introduced to mitigate this, it is computationally expensive and still struggles to achieve strong understanding capabilities. Hence, this leads to the question: How can we efficiently unify visual representations within single tokenizer to achieve both powerful semantic understanding and high-fidelity reconstruction?
To fill this gap, we propose generic and unified tokenizer, named UniFlow, which efficiently resolves this long-standing trade-off problem via a novel patch-wise pixel flow decoder seamlessly compatible with any semantic encoders. As shown in Fig. (d), UniFlow synergistically integrates these two key components to achieve a optimal balance. Specifically, we leverage a well-pretrained vision foundation model as the encoder. To preserve strong understanding capabilities, we design a layer-wise adaptive self-distillation method that aligns our unified encoder with a frozen encoder, thus preserving hierarchical semantic knowledge, while flexibly complementing its fine grained representations. Additionally, we propose novel patch-wise pixel flow decoder to efficiently transform high-level semantic features into the pixel space via Flow Matching (Liu et al. 2022). By modeling conditional flow directly in the pixel space, achieve superior reconstruction performance without being constrained by the pre-trained VAE's limitations. The patch-wise learning strategy further reduces the learning burden, thereby improving training efficiency. As result, UniFlow effectively alleviates the optimization conflict, enabling the encoder to concentrate on discriminative representation learning, while the decoder excels at high-fidelity reconstruction guided by high-level semantic features. Thanks to the well-pretrained encoder and lightweight decoder, UniFlow serves as general unified adaptation paradigm that fits any pretrained encoder, whether standalone VFMs or visual backbone of MLLMs, in only 30 ImageNet training epochs.
We conduct extensive experiments on challenging benchmarks across 7 mainstream tasks, including understanding tasks (i.e., visual question answering, image classification, semantic segmentation, depth estimation, object detection) and generation tasks (i.e., image generation, image reconstruction), to demonstrate UniFlow's 's effectiveness. For example, our 7B UniFlow-XI, trained with 40% less data, surpasses the 14B TokenFlow-XX XL by 6.05% on overall average understanding benchmarks. Furthermore, UniFlow demonstrates superior performance in visual reconstruction and generation, achieving a new state of the art in reconstruction by outperforming UniTok by 0.15 and SD-VA VAE by 0.41 in rFID, and competitive results in generation (gFID better than UniTok by 0.09 without guidance). These results demonstrate that UniFlow achieves a win outcome, confirming its versatility in both visual understanding and generation.
2

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2 RELATED WORK
Visual Tokenizer for Generative Modeling.
Visual tokenizers are widely used by modern genera-
tive models (Rombach et al. 2022 Labs 2024) to obtain compact latent representations, process that greatly reduces computational complexity. Some methods improve reconstruction quality via KL constraints (Kingma et al.
2019) enhancing codebook utilization (Luo et al.) [024] Mentzer
et al. 2023; Yang et al. (021). while yielding suboptimal semantic representations for multimodal understanding. Others attempt enrich latents with semantic information by aligning features from powerful pre-trained models (ao et al., 2025 Li et al. 2024c; Chen et al. (025c). However, their weak alignment fails to preserve the semantic integrity of the original models. Tokenizers based on diffusion or flow matching decoders (Yang et al. 2025b Shaulov et al. 2025 Wang et al. 2025a)
are constrained by a frozen VAE latent space, hindering high-fidelity reconstruction. While these methods preserve local details, they often struggle to capture rich high-level semantic context.
Unified Tokenizer for Understanding and Generation. Recent approaches (Wu et al. 2024b Ma et al. [025] Wu et al. 2025d) explored unified vision encoders aligning features for both tasks, yet their single-flow architecture rigidly constrains high-level semantic and low-level pixel repre- sentations, causing inherent objective conflicts that limit performance. To address this, others used dual encoders or multi-layer representations from a single encoder to handle semantic understanding and pixel reconstruction separately (Qu et al. 2025 Lin al. 2025), ,, but this introduced inefficient inference and token redundancy. Additionalyy ly. emerging models (Sun et al.) 2024b] Chen et al.) 2025f) aligned pretrained diffusion models with frozen encoders. However, the frozen encoders struggle to capture fine-grained details, which hinders high-fidelity reconstruction under diffusion frameworks. In contrast, UniFlow addresses these limitations via layer- wise deff-disilllatinn coupled with pixel-level flow decoder.
3 METHOD
Our Unified Pixel Flow Tokenizer (UniFlow) is a novel autoencoder architecture designed to resolve the inherent trade-off between semantic understanding and tigh-fidelity pixel reconstruction. As illustrated in Fig. 2 UniFlow consists of a unified encoder Ey and a lightweight flow-based decoder Daow The encoder preserves the hierarchical semantic knowledge of a pre-trained encoder via Layer- wise Adaptive Self-Distillation (Sec. B.I.. Unlike classical autoencoders, we adopt a lightweight Patch-wise Pixel Flow Decoder to reconstruct high-fidelity pixel in a patch-wist wise manner conditioned on semantic features (Sec. 3.2.
3.1 LAYER-WISK WIVER-WISEAA ADAPTIVE
robust unified encoder must possess dual capability: low-level pixel details for high-fidelity reconstruction and high-level representations for semantic understanding. These competing demands create an inherent conflict for the encoder (Song et al. (025), making it difficult to fulfill them. For instance, approaches that distill only the final layer (Tang et al., 2025) impose weak constraints, risking semantic degradation and requiring complex multi-stage training. Meanwhile, large-scale contrastive learning methods (Zhao et al.) 2025b; Ma et al. 2025) face inherent conflicts between global features and local details, remaining prone to distribution shifts even with high training costs.
To overcome these limitations, propose layer-wise adaptive self-distillation method inspired by prior observations Song et al. (2025): ) Lin et al. (2025) that deeper layers specialize in semantic EoservationsSoongeeee
disambiguation, whereas shallow layers excel at capturing fine-grained details. We posit that distil- lation should respect this specialization: deeper layers require stronger preservation for semantic capabilities, while shallow layers need flexibility for iiteigraneed reconstruction. Our method follows this principle by dynamically adjusting distillation strength across layers, bridging semantic stability and reconstruction fidelity. In this way, not only preserves the powerful and hierarchical semantic representations but also allows the encoder to flexibly complement fine- nnee-raaied details.
Specifically, we use student encoder En and frozen teacher encoder & for distillation. For an input image I E #xxx3 x3 both encoders produce feature maps ( E S at each layer I. S denotes the number of spatial tokens and D denotes the channel dimension. Our method fuses two key factors to compute the adaptive layer-wise weights wr. First, hierarchical prior whess T
3

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Layer-wise Adaptive Self-Distillation
Patch-wits Pixel Flow Decoder
A Hierarchical Prior
Patch-arto Flow Matching Loss
Letent Space
I/L
Target Velocity
(2)+))
GTB
MRSA
Pixel Space
PPN
Prediction Velocity
Syeer
Alignment Penalty (os,
dc -
Pretrained Encoder
Unified Encoder
gfoal
cunditin dhul
segar
Loyerwise Dssiiltttinn
Pixel Flow
Decoder
Flow
Lotiett Space
clean glf3))
My U
Softanax(Bar
timosapp
Space
Visual Encoder
Efficient Adapatation
Flow Decoder
The framework of UniFlow. Our UniFlow model trained end-to-end to endow a powerful VFM with both semantic understanding capabilities and high-fidelity pixel reconstruction.
Figure 2:
ensures that deeper layers receive higher coefficient, where is the total number of layers. Second, we introduce an alignment penalty a,, which measures the average cosine distance between the student tokens and teacher tokens in layer 1. The adaptive weight is normalized combination of these two factors, prioritizing poorly aligned layers by assigning greater weight to those with higher alignment penalty:
whee cxp(3 a4)
W1 Ek
w hase exp(3 -a))
where temperature hyperparameter 3 controls the weight of poorly aligned layers. The self-distillation loss is then the weighted sum of per-layer cosine distances between features:
(...2))
1 11-5
ddstt
1i
1
(2)
1=1
where (i,j) indexes the 2D spatial token location. Finally, the last-layer features of the student encoder
H are projected to compact latent space via linear projection z Paowa (H(()) E IR xd for subsequent generative modeling.
3.2 PATCH-WISE PIXEL FLOW DECODER
Prior diffusion-basee tokenizers (Shaulov al. 2025: Wang al. 2025a) achieve image reconstruc- tion by modeling conditional distribution in latent space, but often rely on pretrained VAE decoders. This dependency sets an implicit ceiling on reconstruction fidelity and increases inference-time cost via redundant components. In contrast, lightweight flow decoder Dnow directly learns velocity field in pixel space, which not only bypasses the limitations of pretrained VAE decoders, but also simplifies the learning burden and significantly improves training efficiency via patch-wise modeling.
Due to the lack long-range interactions among individual patches in localized decoding process, patch-wise flow decoder may suffer from "grid artifacts". To address this, introduce global transformer blocks GT GTB(() of depth K. We first lift the latent code z from the encoder to a higher- dimension space via linear projection (.)()), yielding a set of initial conditional latents. The 2D position embeddings PE are added to the initial conditional latents being fed into the global transformer blocks,
C = CTG + PE).
Each global transformer block consists of self-attention and FFN, enabling all tokens to exchange information and perceive a global context. The resulting condition tokens C E IR & globally coherent, serving a powerful condition for the flow decoder.
are
The flow decoder up( (x, f, c), parameterized by 8, is a light- MLP network that learns a continuous velocity field in pixel space. This network models the transition between patch-wise data and Gaussian noise, following the principles of Rectified Flow (Liu et al. 2022). The conditional latents с E Eexpx provide a compact representation of the desired visual content. Specifically, given pixel patch (+.) RPxpx3 we define linear interpolation between the ground truth patch
4

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Visual Reconstruction
Input
15
50-VACXL XL
TokenFlow
D
QLIP
UniF low (Ours)
Visual Understanding
Visual Generation
What can be inferred about the traffic
there people
flow pedestrian wearing hots image? this image?
M "No."
When was Phil's Market established!
Phil's MARKET
8 "g."
"The image two buses, incicating buyy street.
and
1984
(8) 1994
Pedestrions crass sufely at cresswalks, traffic ensuring smooth
(C) 2004
Visual-Centric Tasks
Image Classification
Depth Estimation
-Semantic Segmentation
Object Detection
Welker
ostrich
idogsled
Indigo bunding
Figure 3: Various downstream tasks demonstrate UniFlow's robust visual representation.
(i.f)
Pdota and gaussian noise sample c(55)) I), a random timestep pc:
(i-)) (1 - ()x((()) + t E [0,
x
(4)
the instantancous velocity of this trajectory is constant and defined as ( (b-))
(i.)) The flow decoder is trained to predict the velocity based on the diffuse time and the noisy pixel patch (i.f)
along with its corresponding patch-wist wise conditional latent (+-))
Xi
The training objective is to minimize the mean squared eтror loss to predict the velocity field. The loss applies each patch, and is formally defined as:
Liow
Eg (t.)) Samnt trupe
(x( (b.5) c(t.)) - (c(i.) ((x))
(5)
By relying solely on an intuitive flow matching loss, we avoid the complexity of combining multiple losses (e.g., GAN, L1, L2, LPIPS), which leads to more stable training and focus on pixel-leval fidelity. The total training objective of UniFlow a weighted combination the Eq. and Eq. 5
Lisal
Ad + Xy Lnow,
where As and Ny are hyperparameters.
EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
Datasets. For the unified tokenizer, we utilize the 1.2M ImageNet-IK (Russakovsky et al. 2014) training set for efficient adaptation training. To enable fair comparison, subsequently evaluate
5

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Table Comparison of reconstruction quality on the 256 x 256 ImageNet-IK and MS-COCO 2017 validation sets. "Ratio" denotes downsampling ratio: "Type" indicates tokenizer traits (VQ usage and decoder type). UniFlow achieves state-of-the-art (SOTA) performance in unified tokenizers while also being competitive with the best generative tokenizers. See Appendix
B. for data details.
Method
Type
Training Data
Ratio
ImageNet-II
MS-COCO 2017
PSNR SSIM 7 rFID 1 PSNR SSIM rFID
Generative Only Tokemizer
Casmas- DI
(Agarwal et al.
2025
Discrete- Diserete-Pixel
16
19.98
0.54
4.40
19.22
0.48
11.97
LlamaGon Son
40
Discrete-Pixel
MS+IN-IK
16
20.65
0.54
2.47
20.28
0.55
8.40
Open Den.MAGNTIZ
2024
Dixel
Mixed100M
16
22,70
0.64
1.67
22.31
0.65
6.76
BSQ-VIT Yang ef
Discrete-Proe Pixel
IN-IK
28.14
0.45
SD-VAE Rombach
2022
Continuous Pixel
Olmg
23.54
0.68
1.22
23.21
0.69
5.94
SD-VAE 2xx
Kombach ot
2122
Continuaus-P Pixel
Olmg+LAne
23.54
0.68
1.22
26.62
0.77
4,26
OmniTokenizer [Wang
Continuous-Pie
IN-IK+K600
26.74
0.82
0.83
SD.VAE XI.
Poleel di A721
Continuous Pixel
Ofmg-I.Ancs+
27.37
0.78
0.67
27.08
0.80
3.93
Qwen-Image Wa ot 21255
Continoous-
32.18
0.90
1,459
32.01
0.91
SD-VAE3 Easer
2024
Continuous Pixcll
31.29
0.87
0.20
31,18
0.89
1.67
Wan2. (Wan et al 2025a
Continuous- Pixel
31.34
0.89
0.95
31.19
0.90
3.45
FLUX-VAE
Continuous-
32.74
0,92
Cosmos-C1 Cl Aginwal or
2025
Continnous-Pian
16
25.07
0.70
0.96
24.74
0.71
5.06
VA-VAB Yo al. 2025
Continuous-Piss
IN-IK
16
27.96
0.79
0.28
27.50
0.81
2.71
Wan2.2 Wan 20256
Continuous Pixel
16
31.25
0.88
0.749
31.10
0.89
3.28
SelfTok
Lao et al 2024
Diserete- Diffuusooo
IN-IK
24.14
0.71
0.70
FlowMo-HH
(Sbadiv 01
2025
Discrete- Diittt--iilisiin
DeDeook
Yang e 11.53
Continuous Diffusion
IN IK
16
0.68
Unified Tokenizer
Show-o
Xie al.
2024b
Biserete-Pisel
16
21.34
0.59
3.50
20.90
0.59
9.26
QLIP-B
Zhau et al
20158
Discrete Pixel
DC-IB
16
23.16
0.63
3.21
VII.A-U
Wa
Discrete-Pisel
WI--IOR-CY-IB
16
1,80
Tokenllow
21253
Discrete Pixel
LA+CY
16
21.41
0.69
1.37
DualViTok
Tunnn
Pixel
Mixed
22.53
DualToken Song al 2025
Discrete-Pine Pixel
CC12M
16
23,56
0.74
0.54
MUSE-VI VL. Xie el.
A17AG
Discrete Pixel
IN-IK-CCI2N
16
20.14
0.646
2.26
SemHiTok Chen
Discrete-Pise
CY-50M
16
1,16
UniTok (Ma et
4125
Discrete Pixel
DC-1B
16
27.28
0.77
0.41
Wa
11754
Discrete-Piae
IN-IK+Olmg
2.07
UniLIP Tang èt
2015
Continnous- Pixel
BP-32M
32
22.99
0.747
0.79
EMU12 Sum el
20245
Continunus- Diffusion
LA-COsLAAa
14
13.49
0.42
3.27
BLIP3-0
20256
Continuous- Diffussio
BP-32M
16
14.71
0.58
3.18
UniFlow(CL7P
Continucus- Diftusion
IN-1K
14
28.66
0.91
0.67
29.61
0.92
3.69
UniFlow(SigL//P))
Continnous- Cotimmous-Diffusion
29.38
0.93
0.62
UniFlow(D/NOv2)
Continuous- Continoos-Diiffuiioo
IN-IK
14
31.01
0.94
0.54
30.66
0.94
2.81
UniFlow((ateraak))
Continuous- Diffusson
IN-IK
14
33.23
0.96
0.26
32.48
0.95
1.88
UniFlow's performance on the ImageNet-50KK validation set and the MS-COCO 2017 (Lin al. 2014) validation set. For multimodal understanding, we employ the Pretrain-558K and Instruction-666 datasets as (Liu et al. 2023) for training. For the UniFlow-XL variant, we utilize the approximately 6M subset from LLa VnsOnnn (Li al. 2024a). We evaluate our models on comprehensive
suite of vision-languee
benchmarks, including MMVet (Yu et al. 2023). POPE (Li et al. 2023).
VQAv2 (Goyal et al. MMBench (Liu et al. 2024c), and MME (Fu et 2023). For visual generation, we train UniFlow downstream vision tasks, perform linear probing experiments for classification, object detection, depth estimation, and semantic segmen- tation, with models evaluated ImageNet-1K: MS-COCO 2017, NYU-Depth-v2 (Nathan Silberman
2017). GQA (Hudson & Manning 2019). TextVQA (Singh et al. 2019).
on ImageNet- K. To further verify UniFloo''s 's performance
& Fergus 2012). and ADE20K (Zhou et al. 2019).
Settings. In our experiments, we employ four variants the UniFlow Tokenizer, initialized with different semantic teacher models and encoders: DFN-CLIP ViT-L/14-224 (Fang et al.) (023).. SigLIP2 ViT-L/16-256 (Tschannen al. 2025). DINOv2 ViT-L/14-378 (Oquab al. 2023). and Intern NTT-300M/144444 (Chen et al. 2024b) derived from the pretrained Intern VI.3 2--2B-Innttrue
(Zhu et al. 2025)- The distillation default use 2, while the latent space dimension is set to
= 64. For lightweight flow decoder, we adopt global transformer blocks layer followed with an MLP head. All models are trained for 30 epochs with global batch size 512 and fixed learning rate 2c-4. All reported reconstruction performance is based on one-step euler inference. All experiments conducted on A800 GPUs with PyTorch. More details in the Appendix

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Table Evaluation on multimodal understanding benchmarks. UniFlow-LV indicates training in the LLaVA-v1.5 setting, marked by f. Our UniFlow-LV achieves SOTA in unified tokenizers. MME is divided by 20 for the Avg.
Method
VisEac.
#1LMM Parans.
Ros. POPE GQA
TQA MMY MMB MME-S MME-P Avg.
Understanddig Only ML..M.
InstreetBLPP Dai al. 2023
CLIP-G
Vicuna-7B 7B
224
49.2
50.7
26.2
MiniGPT-4 Zhirer
CLIP-G
Vicuna-13B
1158.7
86666
InstractBL (DareF 20zz3
CLIP.G
Vicuma 13B
224
78.9
49.5
50.7
25.6
36,0
1212.8
IDFFICS Liireggr 2114
CIJP-H
LLAMA-7B
224
38.4
25.9
48.2
mPLUG--OWLZ (Ye al. 2024
CLIP-L
LLaMA-2-7B
448
86.2
56.1
58.2
36.5
64.5
InternVL Chat (Caya
LLaVA-1.5 of A021b
InteraViT-6BB
Vicuna-78 7B
224
85.2
57.7
1298.5
CLIP-L
Vicuna-7B 7B
85.9
62.0
46.1
1510.7
Qwen-VL-Chin Want 2024b
CLIP-G
Owen: 7B
448
57.5
1848.3
1457.5
LLLVA4-Oneisn Vision
SigLIP-S040IM
Qwen-2-78
384
46.1
57.5
80.8
1998.0
1580.0
Unified MLLM
DreanI.LM (ong al
2025
CLIF-L
Vicuna-7B
224
41.8
22.6
Lavrr
CLIPG
LLaMA-2-7B
Unified-00
1022
Lacee 2023
VQ-GAN
6.8B from scratich
384
87.7
59.1
31.3
71.5
1338,0
Janus
Wu al M0AS0
SigLIP-
DeepSack-LLL--1
384
87.0
59.1
34,3
69.4
1338.0
VO-GAN
LLaMA-2-7B
75.2
18.8
SEED
Gc cl. 3074
Qwen-VL-Vi
LLaMA-2-136
448
84.2
47.9
1435.7
Show-a
MAGVIT-2
Phi-1.5-1.38
1007.2
MetaMoraa
Uipla 4.
2023
SigLIP-SOHOMM
LLaMA-3.1-SB
384
60.5
75.2
Orthus
Koo ct
A14
VAE
Chameloon-78
256
79.6
52.8
1265.8
SynerGen
SBER-MOYO-GAN
Intern M2-Mo-222
512
85.3
1381.0
Liquid Ww ct al. 202460
VQ-GAN
Gemuna-78 78
512
81.1
58.4
42.4
1119
VII
SigL SP
1LMMA-2-78
384
85.8
60.8
1401.8
Jamas-Pr
Chnn 01
(025)
SigLIP-L
Dospood-LLM-7P
384
87.4
62.0
50.0
79.2
1567.1
Show-o2
S16 df
X025
Wan2. 1- L-R----AR
Qwen2.5-70 5-7B
432
63.1
79.3
162055
MIL.M with Unifted Tokenier
VILA-U
Wu al 2024b
Vicuna-77 7B
256
81.6
1311.5
UniTok
Ma ar. 2025
Vitamin-L.
Vicuna-7B
256
81.7
1448,0
SemHiTTo
Chen el
SigLIP-L
Vicuna-7B
1400.6
QUIIP
Zhaxer
CLIP4
Viaana77B
392
86.1
61.8
55.2
33.3
1498.3
TakenF
Qu
2015
CLIP-B
Vicuma-3BB
224
84.0
59.3
49.8
22.4
55.3
1660.4
1353.6 60.21
TokenFlon-1.
0u al
0025
VTTain-XL
Vicuma- 133ma-133BB
256
85.0
60.3
54.1
27.7
60.3
1622.9
1365,4 62.40
UniTok Ma et ml. 2175
Vitamin. L
LLaMa-2-7B
256
83.2
61.1
51.6
33.9
1448.0
VO-GAN+VIT-SOOON
Qwen2.5:70
1448.4
TokenFaoR-X1
[0l e0 2025
Sigl PSS.PM
Qwen2 148
384
87.8
62.5
62.3
48.2
76.8
1922.2
1551.1
73.04
UniFlow-LV
DEN-CLIP-L
Vizuna-78 7B
224
86.56
61.38
53.40
30.2
63.83
1748,0
1446.9
65.02
UniFlow-LY
Vicuna-78 7B
255
87.94
63.29
58.0
32.1
68.38
1823.0
1477.9
67.87
UniFlow-LV
DINOv2-L
Vicuna- 7B
378
88.04
59.37
45.53
25.6
51.48
1590.5
1257.7
58.92
UniFlow-LY
InteraViT-300M
Vicuna-78
88.97
1505.1
69.01
UniFlow-XI.
InternViT:30MM.
Qwen2 5-7B
448
89.81
65.86
81.59
54.0
83.50
2063.0
1513.7
79.09
4.2
COMPARISON WITH STATE-OF-THE-ARE METHODS
Visual Reconstruction. As shown in Tab. our UniFlow method only requires training on ImageNet achieve state-of-the-art reconstruction performance among unified tokenizers on 256 x 256 ImageNet- I and MS-COCO 2017 datasets. Notably, UniFlow is also competitive with the best generative-only tokenizers. Specifically, UniFlow(IntermViT) achieves 0.26 rFID, surpassing UniTok by 0.15 on the ImageNet-IK. These results validate the effectiveness of our pixel-level flow decoder design in preserving fine- ane-grained visual details. Notably, we achieve single-step decoding through our patch-wise decoder design, significantly improving inference speed with high- reconstruction. Furthermore, as demonstrated in Tab. 4al UniFlow exhibits strong reconstruction capabilities at the original resolutions of its respective teacher models.
Multimodal Understanding. As shown in Tab. 2 our UniFlow tokenizer consistently demonstrates SOTA performance across comprehensive suite of multimodal understanding benchmarks. We first evaluate our UniFlow-IX which consists of four distinct variants trained under the standard LA- VA-
v1.5 setting (Liu et al. 2023), each with different semantic teacher. Using Vicuna-7B as the language backbone, our UniFlow-Li variants consistently outperform prior unified tokenizers such as VILA-U, QLIP, and UniTok across all VQA benchmarks. Notably, the variant using UniFlow(Intern VIT) achieves the highest performance within this group, with POPE score of 88.97 and an MME-P score of 1505. surpassing all others. For the more advanced UniFlow-XL XL, we train the model under ALavA-One Vision setting (Li et al.] (2024) but with Qwen2.5-7B (Yang et al. 2024) as the language backbone. UniFlow-Xi achieves a new state-of-the--ar,, which is competitive with or superior to leading approaches that employ larger models and more extensive training data, such TokenFlow, showcasing the powerful understanding capabilities of our UniFlow tokenizer.
Text-to-Im Image Generation. As shown in Tab. 3 we trained a Multimodal Diffusion Trans- former (MMDiT) model to verify UniFlow's generative capability. For the training, we first train
7

---

Table Evaluation of text-to-image generation ability on GenEval Ghosh al. 2023) and DPG-
Bench (Hu et al. 2024) benchmark.
Methods
Model Size
Type
Res.
GenEval
DPG-Bench 1
SD v1.5
Rombach et al
2022
860M
Diffusion
512
0.43
63.18
SD v2.1
Rombach ct
2022
866M
Diffusion
512
0.50
64,20
PixArt-o
Chen
2023
610M
Diffusion
512
0.48
71.11
SANA Xie et al. 2024a
0.6B
Diffusion
512
0.64
84.30
SD XL
Podell et al.
2023
2.6B
Diffusion
1024
0.55
74.65
Show-o
Xie et al. 2024b
1.5B
AR+Diffusion
256
0.53
67.27
TokenFlow (Qu al. 2025
7B
AR
256
0.55
73.38
EMU3-Gen (Wang al.
2024c
8B
AR
512
0.54
80.6
UniFlow
0.6B
Diffusion
256
0.65
84.76
Figure 4: Text-to-image generation results with UniFlow 256 256.
a UniFlow(SigLL/P2)) tokenizer with its latent space aligned using DINOv2, and adopted the two-stage training strategy proposed by DC-Gen (He et al. 2025) to initialize the model from SANA-0.6B. This approach allows UniFlow to seamlessly adapt to the T2I task with limited data, outperforming both the strong baseline SANA-0.6D and the larger TokenFlow-7B5 Fig. 4 further illustrates the open-domain generation quality achieved by UniFlow. More details can be Tound in Appendix B.3
Visual Generation. evaluate UniFlow's visual generation, we train MAR-L (Li al. 2024b) on ImageNet-1 K with the at 448 resolution and resized to 256 for evaluation. As shown Tab, 4bb UniFlow achieve lower FID than those using the MAR-VAE without CFG, demonstrating that the incorporation of high-level semantics enhances guidance- free generation performance. Fig. 3 displays the diverse and realistic
variant as image tokenizer. Images were generated
image generation results. This observation aligns with recent studies (Yu et al. 2025 More details can be found in Appendix B.4
Ma et al.) 2025
Visual-Centric Tasks. We conduct comprehensive evaluations of UniFlow's transfer learning capabilities across four visual-centric tasks, with comparative results summarized in Tab. 5 On ImageNet-IK, UniFlow achieves competitive results for linear probing with 82.6% top-1 accuracy on a ViT-L backbone, surpassing strong baselines like MoCo v3 (Chen et al. 2021) (+4.5%) and MAE (He al. 2022) (+6.8%) while keeping the encoder frozen. For object detection COCO,
8

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Table 4: Image reconstruction (left) and class-conditional generation (right).
(a) Image reconstruction performance on Ima-
(b) Class-conditional image generation results on Ima- geNet 256x256. "CFG"
geNet at pre-training resolutions of VFMs.
Tokenizer
Res. PSNR
Res.
SSIM rFID
Tokenizer
Generator Type #Parums w/o CFG
1st
gEID w/ CPG
SD-VAE-XL
224
25.72
0.75
0.90
MauFlo(CCLPP
224
29.01
0.91
0.36
VOGAN
LlamaGen
AR
1.4.
14.65
86,3
2.34
253.9 227.5
UniTok
LlamaGen
AR
1.4
2.51
216.7
2.77
DD- XL
256
27.37
0.78
0.67
UniFlow(Sigt.P2))
256
29.62
0.93
0.62
SD-VAE
SiT
LDM
675 M
8.61
131.7
2.06
270.3
SD-VAE
REPA
LDM
675
5,90
1.42
305.7
DD-VAE--XL
376
26.73
0.76
0.73
VOGAN
MAGE
MGM
230 M
6.93
15.8
(DDNOo)))
378
30.38
0.92
0.58
TTlokLL
MaskGIT MGM 177 M
3.15
173.0
2.77
199.8
SD- DD-VA--XL
448
27. 49
0.7747
0.51
MAR-VAE
LEQ
MAGVITv2 MGM
MAR
MGM
479 M
2.60
213.1
221.4
1.78
296.0
UmFloo((ntermVi))
448
32.48
0.95
0.28
UniFlow
MAR
MGM 479 M
2.45
228.0
1.85
290.0
Table 5: Comparison on various visual-centric tasks.
(a) ImageNet- IK classifica-
(b) Object detection results
(c) Monocular depth esti-
(d) Semantic segmenta-
tion linear probing results.
MS-COCO 2017 val.
mation NYUv2-Depth. tion mloU on ADE20K.
Methods
Size
ACC1p
Methods
Size
Methods
RMSE,+
Methods
Size
mIOU,,
VEMTok
VIL-
69.4
Supervised
DORN
Supervised
VIT.I
49.9
MAE
REIT
VIT-I.
I.
75.8
73.5
MoCo v3
WT-L
54.1
VNL
0.416
MoCo v3
VIT-L
49.1
78.9
BETT MAE
WTH
56.2
BTS
0.392
BETT MAE
VIT..
53.3
MoCo
WTT-H
78.1
WT-L 57.5
DPT-Hy
0.357
VIT-L
53.6
UniFlow
VII-L
82.6
UniFlow
UniFlow
UniFlow
VIT-
Table 6: Ablation studies of UniFlow training. We highlight the default setting. (b) Loss balance
Distillation strategy
(c) Decoder design
Distillation strategy PSNR1 IFID MME-P1
Au by MME-PI PSNKI (ID)
Decoder Design
PSNRT
SSIM
rF]1)
Final-layer
33.41
0.25
1435.6
1478.6
pixct
25.12
0.7245
1.89
Uniform
30.77
0.45
1518.2
10'11
1513.7
1521.4
26.57
32.48
0.62
Dizen flow
26.48
0.7362
0.72
Progressive (3=0)
31.91
0.38
1495.3
1:10P
1453.0
32.88
0.22
D Dase
30.15
0.9124
0.51
Adaptive (8=2)
33.23
0.26
1513.7
0:1
817.2
33.69
0.19
Drodt how w/ GTB
33.23
0.9636
0.26
1500
188
32.8
0.6
may
Uniit New
HME-
PANR
MME-R
32.0
0.5
31.2
rove 60k
30.4
0.3
1420
0 Value
GTB Layers
PO5E
GOA
YOA
WMD
WWV
NHE-SHM24
(w) UniFlow(Internl WTT') vs. Intern
(c) GTB Layers Figure 5: Ablation studies on training comparison and hyperparameters.
(b) Sensitivity
our method achieves 59.2 AP with a VIT-L backbone, outperforming MAE and BEiT (Bao et al. 202I) by +1.7 and +3.0 points respectively. The flow matching objective's explicit preservation of spatial coherence yields superior fine-grained localization. On depth estimation. UniFlow achieves an RMSE of 0.324 on NYU Depth v2, outperforming DPT-Hybrrd (Ranfil al. 2021) by +10.2% demonstrating the ability to learn dense features. Finally, for semantic segmentation on ADE20K, we achieve 55.4 4 mloU, surpassing MAE and BEiT by +1.8 and +2.1 points respectively. This significant improvement highlights UniFlow'ss capability to capture both semantic meanings and precise spatial relationships. More experimental details and analysis in Appendix B.5
ABLATION STUDY
Impact of Distillation Strategy. As shown Tab, ba] our ablation study validates the superiority of the adaptive distillation strategy. While final-layer distillation as (Tang et al. 2025) excels at reconstruction and uniform distillation across all layers prioritizes understanding. progressive baseline (8 0) shows significant gains over both by by linearly increasing distillation weights with depth. Ultimately, our layer-wise adaptive distill (5 = 2) achieves the best overall performance by dynamically balancing the objectives.

---

InterniIT
SD-WAF
Imput image
Question: there thres dogs n the trage? Please
arswerr yes or ras.
InternVIT: Yes
hamster
steel drum
freight car Jack lanttrr
UniFlow: No
ciff dwelling
parallel
SDXL-VAL
UniFlew
(a) Fine-Grrined VQA
(b) SSE Visualization
(c) Visualization
Figure 6: Qualitative analysis of representations. (a) VQA: demonstrates UniFlow's 's superior understanding of detailed concepts. (b) t-SNE: UniFlow generates more semantically coherent clusters than Intern VTT and SI> XI. (c) PCA: UniFlow maintains richer spatial information with clearer object contours.
Effect on Loss Balance, Tab. 6b show a clear trade-off between semantic alignment and recon- struction objectives. High Aa prioritizes understanding the cost of reconstruction, while high X5 yields the opposite. With a balanced loss (dd = Xy). our unified model achieves a near-perfect reconstruction while gaining 35.1 MME-P points over the distillation-only baseline.
Ablation of Decoder Design. As shown in Tab. 6c we progressively improve the decoder design. simple pixel loss (iizzet) results the worst performance. Utilizing flow model in the latent space (Diatent flow) offers better results, but it remains constrained by the frozen VAE (Podell et al. 2023). By employing a flow model directly in the pixel space (D (Ppizel flow), we achieve significant performance leap. Finally, the introduction of the Global Transformer Block GTB B eliminates the 'grdd effect' observed in the early stages of training and achieves the best overall performance.
What Does UniFlow Learn? To understand what UniFlow learns, conducted qualitative analyses comparing our UniFlow (InternnIIV to a semantic encoder (Intern and a generative one (SD-VAE XL). As seen in Fig. o (a), in a Fine-Grained VQA example, LLaVA- 1.5mt.... fails to correctly identify the cat in the upper-right corner, mistaking it for a dog. This lack of detail captures these details better and gives
results in misunderstanding while LLaVA-v1.5( correct answers. In Fig. (b),, 1-SNE plots show that while the semantic encoder (Intern ViT) has clear class clusters and the generative tokenizer (SD-VAE XI.) does not, UniFlow's feature space exhibits semantic clustering comparable to Intern This demonstrates UniFlow's 's ability to inherit robust understanding capabilities. Furthermore, in Fig. 6 (c). PCA feature visualizations highlight that UniFlow's features are not only semantically rich but also preserve fine-grained spatial information.
Comparison with the Teacher Encoder. We conducted an ablation study under the standard LLLVA-v1.5 setting, comparing UniFlow([nternVT] with its baseline vision encoder, Intern As shown in Table (a). by integrating our UniFlow framework, the model not only extended its task range image reconstruction and generation but also achieved significant performance improvements on core understanding tasks. On benchmarks like GQA and MMB, UniFlow consistently outperforms the original Intern ViT. We attribute this improvement to our unique layer-wise adaptive distillation and patch-wise pixel flow decoder designs. Specifically, layer-wise adaptive distillation dynamically preserves the powerful hierarchical semantic representations of the pre-trained encoder, while allowing the flow decoder to supplement fine iinengranedd features, thus enhancing the model's 's visual capabilities without sacrificing understanding performance.
More Ablation Studies. See Appendix for more ablation analysis about Fig.
CONCLUSION
In this paper, we proposed UniFlow, unified pixel flow tokenizer designed address the per- formance trade-off between visual understanding and visual generation. Our approach integrates layer-wise adaptive self-distillation strategy for robust semantic preservation and lightweight patch-wise pixel flow decoder for superior pixel reconstruction. Extensive experiments demonstrate that UniFlow achieves win-win outcome, proving its effectiveness and versatility in unifying visual representations.
10

---

ACKNOWLEDGMENTS
This work was supported by Guangdong Science and No.2024TQ08X365).
Technology
Program
(Grant
ETHICS STATEMENT
Our UniFlow method developed for advancing representation learnin of generative and understand- ing, with focus supporting creative and beneficial applications. To ensure ethical compliance, our training data carefully selected from public sources and take deliberate measures to minimize potential biases, fully aligning with universal ethical guidelines. We explicitly emphasize that this framework is not intended for misuse in achieving harmful purposes: downstream users are encour- aged adhere ethical principles when applying the technology. Additionally, all authors declare no conflicts of interest related to this work.
REPEATABILITY STATEMENT
To ensure full reproducibility, we will publicly release all code and data necessary to replicate our experiments upon paper acceptance. Comprehensive implementation details, including model architecture. hyperparameters, and training methodology. are provided in this paper and its appendix. We are committed to open-soureing all essential resources to ensure that our findings can be fully verified and built upon by the research community.
REFERENCES
Nvidia Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chattopadhyay, Yongxin Chen, Yin Cui, Yifan Ding, Daniel Dworakowski, Jiaojiao Fan, Michele Fenzi, Francesco Ferroni, Sanja Fidler, Dieter Fox, Songwei Ge, Yunhao Ge, Jinwei Gu, Siddharth Gururani, Ethan He, Jiahui Huang, Jacob Samuel Huffman, Pooya Jannaty, Jingyi Jin, Seung Wook Kim, Gergely KI ar, Grace Lam. Shiyi Lan, Laura Leal-Taixé Anqi Li, Zhaoshuo Li. Chen-Hsuan Lin, Tsung- Yi Lin, Huan Ling. Ming- Yu Liu, Xian Liu, Alice Luo, Qianli Ma, Hanzi Mao, Kaichun Mo, Arsalan Mousavian, Seungjun Nah, Sriharsha Niverty, David Page, Despoina Paschalidou, Zeeshan Patel, Lindsey Pavao, Morteza Ramezanali, Fitsum A. Reda, Xiao-Shuai Ren, Vasanth Rao Naik Sabavat, Ed Schmerling, Stella Shi, Bartosz Stefaniak, Shitao Tang, Lyne P. Tchapmi, Przemek Tredak, Wei-Cheng Tseng, Jibin Rajan Varghese, Hao Wang, Haoxiang Wang, Hengyi Wang, Tingwei Wang, Fangyin Wei, Xinyue Wei, Jay Zhangjie Wu, Jiashu Xu, Wei Yang, Lin Yen-Chen, Xiaohui Zeng, Yuan Zeng, Jing Zhang, Qinsheng Zhang, Yuxuan Zhang, Qingqing Zhao, and Artur Zolkowski. Cosmos world foundation model platform for physical ai. ArXiv, abs/2501 00t/2500.03575, 2025.
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: Bert pre-training of image transformers. arXiv preprint arXhv22106.08254 2021.
Stephen Batifol, Andreas Blattmann. Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, Sumith Kulal, al. Flux. kontext: Flow matching for in-context image generation and editing in latent space. arXiv e-prints, pp. arXiv-2506, 2025.
Boyu Chen, Siran Chen, Kunchang Li, Qinglin Xu, Yu Qiao, and Yali Wang. Percept, chat, and then adapt: Multimodal knowledge transfer of foundation models for open-world video recognition. arXiv preprint arXiv:2402.1885 18951, 2024a.
Boyu Chen, Siran Chen, Kunchang Li, Qinglin Xu, Yu Qiao, and Yali Wang. Super encoding network: Recursive association of multi-modal encoders for video understanding. arXiv preprint arXiv:2500600576, 2025a.
Boyu Chen, Siran Chen, Zhengrong Yue, Kainan Yan. Chenyun Yu, Beibei Kong, Cheng Lei, Chengxiang Zhuo, Zang Li, and Yali Wang. G-ubs: Towards robust understanding of implicit feedback via gwoup-aware behavior simulation. arXiv preprint arXiv:2508.05709, 2025b.
11

---

Boyu Chen, Zikang Wang. Zhengrong Yue, Kainan Yan, Chenyun Yu, Yi Huang. Zijun Liu, Yafei Wen, Xiaoxin Chen, Yang Liu, et al. Videochat-ml: Collaborative policy planning for video understanding via multi-agent reinforcement learning. arXiv preprint arXhv:2511.19524 2025c.
Boyu Chen, Zhengrong Yue, Siran Chen, Zikang Wang, Yang Liu, Peng Li, and Yali Wang. Lvagent: Long video understanding by multi-round dynamical collaboration of mllm agents. arXiv preprint arXiv:2503. 10200 2025d.
Hao Chen, Yujin Han, Fangyi Chen, Xiang Li, Yidong Wang, Jindong Wang, Ze Wang, Zicheng Liu, Difan Zou, and Bhiksha Raj. Masked autoencoders are effective tokenizers for diffusion models. In Ferty-second International Conference Machine Learning. 2025e.
Jiuhai Chen, Zhiyang Xu, Xichen Pan, Yushi Hu, Can Qin, Tom Goldstein, Lifu Huang, Tianyi Zhou, Saining Xie, Silvio Savarese, et al. Blip3-o: A family of fully open unified multimodal aoabels-rccittectuee, training and dataset. arXiv preprint WrXiv:2505.09566 2025f.
Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart-a: Fast training of diffusion transformer for photorealistic text-to-image synthesis, 2023. URL https::////////c /ttp//aavvvvv/// htttss//arrvv.orrr/aa/////333 00426
Siran Chen, Boyu Chen, Chenyun Yu, Yuxiao Luo, Ouyang Yi, Lei Cheng, Chengxiang Zhuo, Zang Li, and Yali Wang. Vragent-rl: Boosting video recommendation with milm-based agents via reinforcement learning. arXiv preprint wrxiv22507.02626, 2025g.
Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan. Janus-pro: Unified multimodal understanding and generation with data and model scaling, 2025h. /tthttss::/ //axivv org/ abs/ 2tbp//251..11 17811
Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self self-supervised vision transformers. Proceedings of the IEEE/CVF international conference on computer vision, pp. 9640-9649, 2021.
Zhe Chen, Jiannan Wu, Wenhai Wang. Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation models and aligning for generic risual-linguistic tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 24185 24198, 2024b.
Zisheng Chen, Chunwei Wang, Xiuwei Chen, Hongbin Xu, Runhui Huang, Jun Zhou, Jianhua Han, Hang Xu, and Xiaodan Liang. Semhitok: A unified image tokenizer via semantic-guided hierar- chical codebook for multimodal understanding and generation. arXiv preprint wxXi:2503.00666. 2025i.
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. Vicuna: An open-source chatbot impressing gpt-4 with 90%* * chatgpt quality, March 2023. URL httpp/
//lmsys. 202 3000
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi. Instructblip: Towards general- purpose lasion-language models with instruction tuning. Advances in neural information processing systems. 36:49250-49267, 2023.
Google DeepMind. Gemini 2.5 flash, 2025. URL wwt.:httpp//mmmmie www.gemmiiiioogo...oo... googleooeeeeooooooooooooo..o. comm
Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, et al. Emerging properties in unified multimodal pretraining. arXiv preprint arXiv:2505. 0rtee25555..88333 2025.
Runpei Dong, Chunrui Han, Yuang Peng, Zekun Qi, Zheng Ge, Jinrong Yang, Liang Zhao, Jianjian Sun, Hongyu Zhou, Haoran Wei, et al. Dreamllm: Synergistic multimodal comprehension and creation. arXiv preprint arXiv:2309. 1499, 2023.
12

---

Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In International Conference on Machine Learning, 2024.
Alex Fang, Albin Madappally Jose, Amit Jain, Ludwig Schmidt, Alexander Toshev, and Vaishaal Shankar. Data filtering networks. arXiv preprini arXiv:2309. 17425, 2023.
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang. Xiawu Zheng, Ke Li, Xing Sun, et al. Mme: A comprehensive evaluation benchmark for multimodal large language models. arXiv preprint arXiv:2306. 13394, 2023.
Yuying Ge, Sijie Zhao, Jinguo Zhu, Yixiao Ge, Kun Yi, Lin Song. Chen Li, Xiaohan Ding, and Ying Shan. Seed-x: Multimodal models with unified multi-granularity comprehension and generation. arXiv preprint arXiv:2404. 14396, 2024.
Dhruba Ghosh, Hanna Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-inee to-image alignment, 2023. URL httppp///aaxxi..og///a.o/
11513
Yash Goyal, Tejas Khot, Douglas Summers- Stay, Dhruv Batra, and Devi Parikh. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In Proceedings of the IEEE conference computer vision and patlern recognition, pp. 6904-6913, 2017.
Agrim Gupta, Linxi Fan, Surya Ganguli, and Li Fei-Fei. Metamorph: Learning universal controllers with transformers. arXiv preprint arXiv:2203. 1931, 2022.
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969, 2017.
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16000-16009, 2022.
Wenkun He, Yuchao Gu, Junyu Chen, Dongyun Zou, Yujun Lin, Zhekai Zhang, Haocheng Xi, Muyang Li, Ligeng Zhu, Jincheng Yu, Junsong Chen, Enze Xie, Song Han, and Han Cai. De-gen: Post- tost-training diffusion acceleration with deeply compressed latent space, 2025. URL https
7/arxiv. org/
25180
Xiwei Hu, Rui Wang, Yixiao Fang, Bin Fu, Pei Cheng, and Gang Yu. Ella: Equip diffusion models with I1m for enhanced semantic alignment, 2024.
Runhui Huang. Chunwei Wang, Junwei Yang. Guansong Lu, Yunlong Yuan, Jianhua Han, Lu Hou, Wei Zhang, Lanqing Hong, Hengshuang Zhao, et al. Illume+: Illuminating unified milm with dual visual tokenization and diffusion refinement. arXiv preprint mxXiv2254.00922. 2025.
Drew Hudson and Christopher Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6700-6709, 2019.
Diederik P Kingma, Max Welling, et al. An introduction to variational autoencoders. Foundations and Trends in Machine Learning, 12(4):307-392, 2019.
Siqi Kou, Jiachun Jin, Zhihong Liu, Chang Liu, Ye Ma, Jian Jia, Quan Chen, Peng Jiang, and Zhijie Deng. Orthus: Autoregressive interleaved image-text generation with modality-specifie heads. arXiv preprint arX0v:2412.00117,, 2024.
Black Forest Labs. Flux, 2024. htt.hhttsss//gthhb..oo////// forestsst-- law.-aabbbbsee flux
Hugo Laurençon, Léo Tronchon, Matthieu Cord, and Victor Sanh. What matters when building laniun-language models?, 2024.
Bo Li, Yuanhan Zhang. Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408. 03326, 2024a.
13

---

Hao Li, Changyao Tian, Jie Shao, Xizhou Zhu, Zhaokai Wang, Jinguo Zhu, Wenhan Dou, Xiaogang Wang, Hongsheng Li, Lewei Lu, et al. Synergen-vl: Towards synergistic image understanding and generation with vision experts and token folding. In Proceedings of the Computer Vision and Puttern Recognition Conference, pp. 29767-29779, 2025.
Tianhong Li. Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. Advances in Neural Information Processing Systems, 37: 2024h.
Xiang Li, Kai Qiu, Hao Chen, Jason Kuen, Jiuxiang Gu, Bhiksha Raj, and Zhe Lin. Imagefolder: Autoregressive image generation with folded tokens. arXiv preprint arXiv:2410.01756, 2024c.
Yanghao Li, Saining Xie, Xinlei Chen, Piotr Dollar, Kaiming He, and Ross Girshick. Benchmarking detection transfer learning with vision transformers. arXiv preprint arXiv:2111.11429) 2021.
Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang. Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. arXiv preprint arXiv:2305. 10355, 2023.
Haokun Lin, Teng Wang. Yixiao Ge, Yuying Ge, Zhichao Lu, Ying Wei, Qingfu Zhang, Zhenan Sun, and Ying Shan. Toklip: Marry visual tokens to clip for multimodal comprehension and generation. arXiv preprint arXiv:2505 05422,, 2025.
Lin, Hongxu Yin, Wei Ping, Pavlo Molchanov, Mohammad Shoeybi, and Song Han. Vila: On pre-training for visual language models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 26689-26699, 2024.
Tsung- Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In European Conference on Computer Vision, 2014. URL https: www.etiiscco............. org/
(ooppuSDD:4413361
Hao Liu, Wilson Yan, Matei Zaharia, and Pieter Abbeel. World model on million-length video and language with ringattention. arXiv preprint, 2024a.
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning, 2023.
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 26296-26306, 2024b.
Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow, 2022. URL
Yuan Liu, Haodong Duan, Yuanhan Zhang. Bo Li, Songyang Zhang. Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. Mmbench: Is your multi-modal model an all-around player? In European conference on computer vision, pp. 216-233. Springer, 2024c.
Jiasen Lu, Christopher Clark, Sangho Lee, Zichen Zhang, Savya Khosla, Ryan Marten, Derek Hoiem, and Aniruddha Kembhavi. Unified-io 2: Scaling autoregressive multimodal models with vision, language, audio, and action, 2023. URL
org/
Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, and Ying Shan. Open- magvit2: An open-source project toward democratizing auto-regressive visual generation. ArXiv, abs/2400.044100 2024.
Chuofan Ma. Yi Jiang, Junfeng Wu, Jihan Yang. Xin Yu, Zehuan Yuan, Bingyue Peng, and Xiao- juan Qi. Unitok: A unified tokenizer for visual generation and understanding. arXiv preprint 20022:25002.0331,, 2025.
Fabian Mentzer, David Minnen, Eirikur Agustsson, and Michael Tschannen. Finite scalar quantization: Vg- made simple. arXiv preprint arXiv:2309. 15505, 2023.
14

---

Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, 2012.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fermandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pp- 4205, 2023.
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint 2:XX1:2307..11922, 2023.
Liao Qu, Huichao Zhang, Yiheng Liu, Xu Wang. Yi Jiang, Yiming Gao, Hu Ye, Daniel K Du, Zehuan Yuan, and Xinglong Wu. Tokenflow: Unified image tokenizer for multimodal understanding and generation. In Proceedings of the Computer Vision and Patlern Recognition Conference, pp.
2555, 2025.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp.
5748-8763. PmLR, 2021.
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction.
In Proceedings of the IEEE/CVF international conference on computer vision, pp. 12179-12188 2021.
René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3), 2022.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björ Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma. Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Li Fei-Fei. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115: 211 - 252, 2014.
Ariel Shaulov, Itay Hazan, Lior Wolf, and Hila Chefer. Flowmo: Variance-based flow guidance for coherent motion in video generation. arXiv preprint arXiv:2506.01 /44, 2025.
Minglei Shi, Haolin Wang, Wenzhao Zheng, Ziyang Yuan, Xiaoshi Wu, Xintao Wang, Pengfei Wan, Jie Zhou, and Jiwen Lu. Latent diffusion model without variational autoencoder, 2025. URL
hhttss://arxinn wtwp://.www....////0..... 15301
Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. Towards vqa models that can read. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 8317-8326, 2019.
Wei Song, Yuran Wang, Zijia Song, Yadong Li, Haoze Sun, Weipeng Chen, Zenan Zhou, Jianhua Xu, Jiaqi Wang, and Kaicheng Yu. Dualtoken: Towards unifying visual understanding and generation with dual visual vocabularies. arXiv preprint arXiv:2503. 2025.
Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. ArXiv, abs/2406.06525, 2024a.
Quan Sun, Yufeng Cui, Xiaosong Zhang, Fan Zhang, Qiying Yu, Yueze Wang, Yongming Rao, Jingjing Liu, Tiejun Huang, and Xinlong Wang. Generative multimodal models are in-context learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14398-1444 14409, 2024b.
15

---

Hao Tang, Chenwei Xie, Xiaoyi Bao, Tingyu Weng, Pandeng Li, Yun Zheng, and Liwei Wang. Unilip: Adapting clip for unified multimodal understanding, generation and editing. arXiv preprint arXiv:2507.23277. 2025.
Chameleon Team. Chameleon: Mixed-modae early-fusion foundation models. arXiv preprint arXiv:2405.098//, 2024.
Michael Tschannen, Alexey Gritsenko, Xiao Wang. Muhammad Ferjad Naeem, Ibrahim Alabdul- mohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, et al. Siglip 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features. arXiv preprini arXiv:2502. 14786, 2025.
Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang. Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang. Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng. Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang, Tianyi Gui, Tingyu Weng. Tong Shen, Wei Lin, Wei Wang. Wei Wang. Wenmeng Zhou, Wente Wang. Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang. Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng. Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu, and Ziyu Liu. Wan: Open and advanced large-scale video generative models, 2025a. httpttss::/ www.a.aivv..... org/ wtw./bs////ooooooooo/.../o...eee 2503. 20314
Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang. Jianyuan Zeng, Jiayu Wang. Jingfeng Zhang, Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng, Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang, Tianyi Gui, Tingyu Weng. Tong Shen, Wei Lin, Wei Wang. Wei Wang. Wemmeng Zhou, Wente Wang, Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang. Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu, and Ziyu Liu. Wan: Open and advanced large-scale video generative models. arXiv preprint 2r03v:2503.20333 20314, 2025b.
Bohan Wang, Zhongqi Yue, Fengda Zhang, Shuo Chen, Li'an Bi, Junzhe Zhang, Xue Song, Ken- nard Yanting Chan, Jiachun Pan, Weijia Wu, et al. Selftok: Discrete visual tokens of autoregression, by diffusion, and for reasoning. arXiv preprint 2055v:2505.07538, 2025a.
Junke Wang, Yi Jiang, Zehuan Yuan, Bingyue Peng, Zuxuan Wu, and Yu-Gang Jiang. Omnitokenizer:
A joint imdge-video tokenizer for visual generation. Advances in Neural Information Processing Systems, 37:28281-28295 2024a.
Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint 2xXVv22409.12191,, 2024b.
Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang. Zhen Li. Qiying Yu, al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409. 18869, 2024c.
Zikang Wang, Boyu Chen, Zhengrong Yue, Yi Wang, Yu Qiao, Limin Wang. and Yali Wang. Videochat-al: Thinking with long videos by cfain-of-shot reasoning. arXiv preprint arXiv:2506.06099 2025b.
Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng-ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, et al. Qwen-image technical report. arXiv preprint 250.02208.. 2025a.
Chengyue WU, Xiaokang Chen, Zhiyu Wu, Yiyang Ma, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, Chong Ruan, et al. Janus: Decoupling visual encoding for unified multimodal understanding and generation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 29966-12977 2025b.
16

---

Junfeng Wu, Yi Jiang, Chuofan Ma, Yuliang Liu, Hengshuang Zhao, Zehuan Yuan, Song Bai, and Xiang Bai. Liquid: Language models are scalable and unified multi multi-modal generators. arXiv preprint ar arXiv:2412.04332,, 2024a.
Junfeng Wu, Dongliang Luo, Weizhi Zhao, Zhihao Xie, Yuanhao Wang, Junyi Li, Xudong Xie, Yuliang Liu, and Xiang Bai. Tokbench: Evaluating your visual tokenizer before visual generation, 2025c. URL WtR.:ttts://// http::::/aaxxvvoooo www.vvooorggass.... 2505. 18142
Shengqiong Wu, Hao Fei, Xiangtai Li, Jiayi Ji, Hanwang Zhang, Tat-Seng Chua, and Shuicheng Yan. Towards semantic equivalence of tokenization in multimodal Ilm, 2025d. URL https
05127
Yecheng Wu, Zhuoyang Zhang, Junyu Chen, Haotian Tang, Dacheng Li, Yunhao Fang, Ligeng Zhu, Enze Xie, Hongxu Yin, Li Yi, et al. Vila-u: a unified foundation model integrating visual understanding and generation. arXiv preprini arXiv:2449.0442 2024b.
Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun. Unified perceptual parsing for scene understanding. In Proceedings of the European conference on computer vision (ECCV), pp. 418-434, 2018.
Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, and Song Han. Sana: Efficient high-resolution image synthesis with linear diffusion transformers, 2024a. URL httt/t:s/////xiiii org/ aa/p///ss//2/// 10629
Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin,
Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408, 12528, 2024b.
Jinheng Xie, Zhenheng Yang. and Mike Zheng Shou. Show-o2: Improved native unified multimodal models, 2025. URL htttps::/ http:://axvvvvvo wtg.ivvoorr//aoo 2506 15564
Rongchang Xie, Chen Du, Ping Song, and Chang Liu. Muse-vl: Modeling unified vlm through
semantic discrete encoding. arXiv preprint arXiv:241 177762 2024c.
An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang. Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li. Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report. arXiv preprint Xrx 2024.
Huanrui Yang, Lin Duan, Yiran Chen, and Hai Li. Bsq: Exploring bit-level sparsity for mixed- precision neural network quantization. arXiv preprint xxXiv22102.1046) 2021.
Jiawei Yang. Tianhong Li, Lijie Fan, Yonglong Tian, and Yue Wang. Latent denoising makes good visual tokenizers. arXiv preprint 20XV:22507.15856 2025a.
Jiawei Yang, Tianhong Li, Lijie Fan, Yonglong Tian, and Yue Wang. Latent denoising makes good visual tokenizers. arXiv preprint arXiv:2507.15854 2025b.
Jingfeng Yao, Bin Yang, and Xinggang Wang. Reconstruction vs. generation: Taming optimization dilemma in latent diffusion models. In Proceedings of the Computer Vision and Puttern Recognition Conference, pp. 15703-15712. 2025.
Qinghao Ye, Haiyang Xu, Jiabo Ye, Ming Yan, Anwen Hu, Haowei Liu, Qi Qian, Ji Zhang, and Fei Huang. ow12: Revolutionizing multi-modal large language model with modality collaboration. In Proceedings the conferencce on computer vision and pattern recognition. pp. 13040-13051, 2024.
Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu. Vector-quantized image modeling with improved vggan. arXiv preprint 11Xiv:2110.04627. 2021.
17

---

Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini. and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917, 2022.
Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie. Representation alignment for generation: Training diffusion transformers is easier than you think, 2025. URL kttps: //axivv org/ awwaaa/2211 06940
Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang. and Lijuan Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308. 02490, 2023.
Zhengrong Yue, Shaobin Zhuang, Kunchang Li, Yanbo Ding, and Yali Wang. V-stylist: Video stylization via collaboration and reflection of mllm agents. In Proceedings of the Computer Vision and Puttern Recognition Conference, pp. 3195-3205, 2025.
Qinyu Zhao, Jaskirat Singh, Ming Xu, Akshay Asthana, Stephen Gould, and Liang Zheng. Disa: Diffusion step annealing in autoregressive image generation, 2025a. URL https://arxix:
heg/abs/2505. 20297
Yue Zhao, Fuzhao Xue, Scott Reed, Linxi Fan, Yuke Zhu, Jan Kautz, Zhiding Yu, Philipp Krähenbühl, and De-An Huang. Qlip: Text-aligned visual tokenization unifies auto-regressive multimodal understanding and generation. arXiv preprint arXiv:2502.06 250.00512755 1788 2025b.
Anlin Zheng, Xin Wen, Xuanyang Zhang, Chuofan Ma, Tiancai Wang, Gang Yu, Xiangyu Zhang, and Xiaojuan Qi. Vision foundation models as effective visual tokenizers for autoregressive image generation. arXiv preprint vrXiv:2507.0844) 2025a.
Boyang Zheng, Nanye Ma, Shengbang Tong, and Saining Xie. Diffusion transformers with represen- tation autoencoders, 2025b.
11690
Bolei Zhou, Hang Zhao, Xavier Puig. Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 322-333 2019.
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: En- hancing languaee understanding with advanced large language models. arXiv preprint arXiv:2304. 10592. 2023.
Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, Zhangwei Gao. Erfei Cui, Xuehui Wang, Yue Cao, Yangzhou Liu, Xingguang Wei, Hongjie Zhang, Haomin Wang, Weiye Xu, Hao Li, Jiahao Wang, Nianchen Deng, Songze Li. Yinan He, Tan Jiang, Jiapeng Luo, Yi Wang, Conghui He, Botian Shi, Xingcheng Zhang, Wenqi Shao, Junjun He, Yingtong Xiong, Wenwen Qu, Peng Sun, Penglong Jiao, Han Lv, Lijun Wu, Kaipeng Zhang, Huipeng Deng. Jiaye Ge, Kai Chen, Limin Wang, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, and Wenhai Wang. Internv13: Exploring advanced training and test-time recipes for open-source multimodal models, 2025, URL https: /tttss://arivve org/abs/////eeeoooooooo.ppo.occr abs 2504 10479
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A DIFFERENCE WITH RELATED WORKS
Prior to UniFlow, unified tokenizers for visual understanding and generation were dominated by three mainstream approaches:
()) Unified Encoder with Single-Flow Architecture. Represented by models such as VILA-U (Lin et al. 2024), QLIP (Zhao al. 20256 and UniTok (Ma al. 2025). these approaches utilize single encoder to align features for both high-level understanding and low-level reconstruction. These VQ-based solutions typically serve discrete autoregressive (AR) or masked diffusion- based unified models (Team, 2024, Xie et al. 2024b). However, this single-stream design creates an inherent objective conflict that compromises performance. single network is forced to learn two competing objectives: discarding fine-grained detail for semantic understanding while retaining it for reconstruction.
(2) Dual-Encoder or Multi-Layer Architectures. Represented by models like TokenFlow (Qu ct al. 2025). SemhiTok (Chen et al. 20251), DualToken (Song et al., 2025), and Toklip (Lin et al. 2025), these methods address the objective conflict by using separate encoders different layers of a single encoder to handle understanding and reconstruction tasks independently. While this strategy can successfully separate the two tasks, it introduces significant inefficiencies, including model redundancy, inefficient inference, and token redundancy.
(3) Encoder-Decodeer Alignment with Pre-trained Models. Represented by models such as Emu2 and BLIP-3o (Sun et al., 2024b; Chen et al.] (025f). these approaches align a pre-trained diffusion model (Rombach al.] 2022 Xie et al. (024a) with a frozen encoder (Radford al.
2021; Tschannen et al. 2025). Although they inherit strong understanding capabilities from the encoder, the frozen encoder's ' features may lack fine-grained details, which hinders high-fidelity reconstruction. Furthermore, frozen pre-trained VAE also sets upper limit on reconstruction performance. This combination of factors leads to poor reconstruction capabilities, which directly impairs the fine-grained editing ability of unified models. The inherent diversity of diffusion models, while beneficial for generation, also works against deterministic, high-fidelity reconstruction.
Compared to these three mainstream approaches, UniFlow introduces unique solution that addresses the limitations of all of them. Our model resolves the fundamental trade-off between understanding and generation by decoupling the two objectives. We design layer- layer-wise self-distillation strategy that preserves the robust semantic features of pre-trained encoder for understanding tasks. At the same time, we introduce separate, lightweight pixel-level flow decoder to achieve high-fidelity reconstruction directly in the pixel space. This design enables our model to achieve state-of-the tfrttheart performance in both understanding and reconstruction benchmarks, while also maintaining high training efficiency. As a continuous tokenizer, UniFlow will serve the unified models of AR+Diffusion paradigm, such as BAGLE (Deng al. 2025). Show-o2 (Xie al. 2025). etc. Additionally, UniFlow presents an efficient adaptation paradigm that can effectively adapt any visual foundation model into a unified tokenizer, whether it's an independently pre-trained ViT or vision encoder already integrated with VLM..
B
MORE IMPLEMENTATION DETAILS
B. UNIFIED TOKENIZER
Dataset Abbreviations. The specific datasets and their corresponding abbreviations, as used in Table I are follows: YFCC100M (YF), OpenImages (OImg), MS-COCO 2017 (MS), ImageNet- IK (IN-1K), LAION-Aesthetics (LAae), Kinetics-600 (K600), LAION (LA), COYO-700M (CY), DataComp-1B (DC-1B), WebLI (WL). GLIPPo-Pretrain--33 (BP-32M), and LAION-COCO (LA- CO).
UniFlow Implementation Details. The provided tables 7 8 and 10 detail the training config-
urations for four UniFlow model variants, each initialized with a different vision-language teacher model: DFN- MIN- ViT-L/14-224 (Fang et al. 2023), SigLIP2 ViT-L/16- 256 (Tcchannen et al. 2025). DINOv2 VIT-L/14-378 (Oquab et al. (023), and Intern 3iT-300M/14-444
(Chen et al. 2024b). Since the typical resolution of vision foundation models (VFMs) differs from 256 x 256, and to align with their native downsampling ratios (14x 16x), we train our tokenizer directly the VFMs'
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original resolution. For evaluation, we follow the protocol of (Zheng et al., 2025a) and resize the reconstructed images to 256 x 256 to enable consistent quantitative comparison, consistent with the methodology in (Sun al. 2024a).
Table 7: UniFlow((ntera ViT) training setting.
Table 8: UniFlow(CLIP) training setting.
model
UniFlow(InternVi ViT)
model
SaiFlow(CLIP DFN-CLIP-I/14
init weight
Intern VTT-
init weight
training data
ImageNet-IK [448, 448]
training data
ImageNet-IK
image size
image size
[224, 224]
data augmentation
random crop, resize
data augmentation
random crop, resize
downsample
14 x 14
downsample
14 x 14 False
ema
False
ema
3
2
2
encoder depth
24
encoder depth
24
GTB blocks
6
GTB blocks
D (hidden size)
1024
D (hidden size)
1024
d (latent channel)
64
d (latent channel)
64
flow head depth
12 1024
flow head depth
flow head width
flow head width flow head patch size optimizer
1024
flow head patch size optimizer
14
14
AdamW
AdamW
optimizer momentum learning rate schedule learning rate warmup steps
81 009, 0.95
optimizer momentum learning rate schedule learning rate warmup steps
11 $2=0.9, 0.95
consistent
consistent
2e-4
2c-4
0
total epoch
30
total epoch
30
global batchsize
256
global batchsize
256
GPU number
32 A800
GPU number
32 A800
Table 9: UniFlow(DINO) training setting. UniFlow(DINOv2)
Table 10: UniFlow(SigLIP) training setting.
model
init weight training data
DINOv2-L/14
model
UniFlow(SigLIP2) SigLIP2-S040M//
ImageNet-1K
init weight training data
image size
[378, 378]
ImageNet-1K
data augmentation
random crop, resize
image size
[256. 256]
downsample
14 x 14
data augmentation
random crop. resize
ema
False
downsample
16 x 16 Falsc
2
ema
B encoder depth
2
encoder depth
24
27
GTB blocks
6
GTB blocks
6
D (hidden size)
1024
D (hidden size)
1152
d (latent channel)
64
(latent channel)
64
flow head depth flow head width flow head patch size optimizer
12
flow head depth
12
1024
flow head width flow head patch size optimizer
1152
14
16
Adam W
AdamW
optimizer momentum learning rate schedule learning rate warmup steps total epoch
B1, B2 3.99 0.95
optimizer momentum learning rate schedule learning rate warmup steps
$1 32 =0.9, 0.95
consistent
consistent
2c-4
2c-4
0
0
30
total epoch
30
global batchsize
256
global batchsize
256
GPU number
32 A800
GPU number
32 A800
B.2 MULTIMODAL LLMS
det/als the training configurations for our multimodal LLMs, which are built upon the UniFlow visual tokenizer and its visual features are taken from the second-to-last layer of the UniFlow
20

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Table 11: UniFlow-LV training setting.
Table 12: UniFlow-XI training setting.
model
UniFlow-LV
model
UniFlow-X XI.
training stage
2 stages
training stage training data
stages S1(558K) S1.5(4M) S2(.1M))
training data
Pretrain(558K) & SFT(665K) all UniFlow variants
vision encoder
vision encoder
UniFloww(atter Qwen2,5-7B
Ilm
Vicuna-v1.55 5-
Ilm
optimizer
AdamW
optimizer
AdamW
optimizer momentum
P1. 91.02=0.9,00. 0.95
optimizer momentum
31.92 0.95
weight decay
weight decay
warmup ratio
0.03
warmup ratio
0.03
max length
2048
max length
2048
learning rate schedule
cosine le-3 2e-5
learning rate schedule
learning rate
learning rate
le-3 & le-5 & 2c-6
total epoch
total epoch
global batchsize
256 128
global batchsize
512 & 512 % 512 32 A800
GPU number
A800
GPU number
2023) framework and uses Vicuna-v1.5-7B (Chiang al. 2023) its base LLM. It's trained in two stages with global batch size of 256 and 128, respectively, on A800 GPUs. For a more powerful variant, the
visual tokenizer. The UniFlow-I.V model based on the VA-v1.5 (Liu et al.
UniFlow-XL model leverages the LLaVA-OneVision (Li et al. 2024a setting show in Tab using the Qwen2.5-7B (Yang et al. 2024) LLM and UniFlow(InternVV/ visual encoder. This model undergoes more rigorous three- three-stage training process much larger scale, with consistent global batch size of 512 across all stages on 32 A800 GPUs. Notably, it trains on 6M subset of the full VLaOO- Vision dataset.
B.3 TEXT-TO-IMAGE GENERATION
Tokenizer. We first train a new UniFlow (SigL./P2)-
f16c32 tokenizer based on siglip2-large-pacchI 16-656 on the ImageNet dataset for 40 epochs at 256 res-
Table 13:
Quantitative Comparison Re- construction Quality at 256 x 256 Resolution
olution. We follow the training paradigm of (Yao et al., 2025). and integrate DINOv2- based latent space alignment to enhance the tokenizer's genera- tive compatibility. As shown in Tab. 13 the achieved reconstruction fidelity confirms its basic ability for high-quality generation.
Tokenizer
Resolution
rFID 1
PSNR
SSIM 1
SI-VAB v1.5
256x256
1.22
23.54
0.688
DC-AE-132c32
256x256
TokenFFlow
256xx26
1.37
21.41
0.69
UniFlow (SigL1P2)
256x256
0.57
28.73
0.86
Generator. For MMDiT training, utilize 1M high-quality synthetic image-text pairs generated by PeUU--Ker (Labs) 2024) and adopt two-stage training pipeline as DC-Gen (He et al.] 2025): Stage 1.1 focuses on patch embedder alignment with learning rate of 1.5e-4 for 15k steps, aiming to bridge the representation gap between the pretrained model and the new latent space; Stage 1.2 performs joint alignment of the patch embedder and output head with learning rates of 2e-4 and 2.5e-4 respectively for 10k steps, which ensures the consistency of latent reconstruction in the new space: Stage 2 conducts LoRA fine-tuning with a rank of 256, learning rates of 2.5e-4 for LoRA adapters and le-4 for aligned components for 100k steps, designed to adapt the MMDIT to the new latent space while preserving its inherent generative quality.
B.4 VISUAL GENERATION
We use UniFlow(IntemMVTT as the tokenizer and train the MAR-L (Li et al. 2024b) which are trained with the AdamW optimizer for 400 epochs, using a batch size of 1024 and a learning rate of le-5. Diffusion models utilize a linear learning rate warmup followed by a constant schedule, while cross-entropy models are trained with a cosine schedule. Additionally, the exponential moving average (EMA) of model parameters is maintained with momentum of 0.999. In specific implementation. The training is performed on 448x448 images, and the model subsequently resized to 256 for testing. At inference, 256 autoregressive steps are used.
21

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Input
SD-VAE
SD-VAE XL
TokenFlow
BLIP3-o
QLIP
UniFlow
Figure 7: Visualization of image reconstruction. All models are inferred on 448 x 448, except for BLIP3-o, TokenFlow, and QLIP, which are inferred on 512, 384, and 392 respectively.
Input Video
SVD
VAE
4:8:8
Wan2.1 VAE
4:8:8
UniFlow
(InterWi)
1:14:14
Figure 8: Visualization of zero shot video reconstruction. All models are inferred on 448 448.
22

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Inputs
UniFlow Figure 9: More diitrr-distiinttion face reconstruction visualization at 448 x 448.
SD-VAE
Inputs
SD-VAE
UniFlow
CocaColla
Coca-Cola FER CD
Coca-ooaa UO
OOl
REFRESH
REFACSH
REFRESH NUI
Drukkeri
Drukkeri
Drikkeet
1907
baDa
uun7
Inputs
SD-VAE
UniFlow
Inputs
SD-VAE
UniFlow
Figure 10: More out-of-distributtio text reconstruction visualization at 448 448.
B.5 VISUAL-CENTRIC TASKS
Image Classification. We follow the protocol of MAE (He et al. 2022 for image classification. Specifically, evaluate our UniFlow(IntermVi7 model on ImageNet- 1K using linear probing. During this process, the UniFlow encoder is frozen, and only the linear classifier is trained. This training is conducted for 100 epochs with batch size of 128.
23

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P
brzzz
Iuzzee's
barrie's
harvie's
Cola
WHOR
Wilte
White
White
Aack White
While
Wilt
Uniflow Figure 11: Qualitative comparison TokBench text subset. All models are inferred on 448 x 448, except for BLIP3-o, TokenFlow, and QLIP, which are inferred on 512, 384, and 392 respectively.
Input
S0-VAE
SD-VAE
TokenFlow
BLIP3-o
QUI
SWIRS PARADE GAB
SWIND PARADE TA
SUTTEE BRIAAG BA
SHIND NARADE SUAV
SWIND SWIND PARADE 1949
SWING PARADE + 1646
SWING PARADE
PARADE PARADE PARADE PARADE PARADE
PARADE
PARADE
ed
Input
1.5
SD-VAE
Tokenflow
BLIP3-o
QUIP
Uniflow
Figure 12: Qualitative comparison TokBench face subset. All models are inferred on 448 x 448, except for BLIP3-o, TokenFlow, and QLIP, which are inferred on 512, 384, and 392 respectively.
24

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Object Detection. To validate spatial grounding capabilities, conduct end-to-end fine-tuning of UniFlow(Intern ViT) on COCO using Mask R-CNN (He et al. 2017) with FPN (Li et al., (022). We partition the VIT blocks into four distinct subsets and apply convolutional operations to upsample or downsample the intermediate feature maps, thereby generating multi-scale representations. A Feature Pyramid Network (FPN) is subsequently built upon these multi-scale features and trained in an end-to-end fine-tuning manner. The ViT backbone is adaptively modified to be compatible with the FPN structure. We report the standard bounding box Average Precision (AP) metric on MS-COCO 2017 val split. AP denotes the mean Average Precision (mAP) computed at IoU thresholds of [0.5 0.05 0.95]. All methods use ViT-based backbones with Mask R-CNN architecture.
Depth Estimation. To evaluate the quality of UniFlow(IntermViT] features for monocular depth estimation, we adopt the experimental setup from DPT (Ranftl et al. 2021). Our approach involves two-stage training process. We first train the model for 60 epochs on the MIX-5 dataset (Ranftl
al. 2022). Subsequently, we fine-tune the model for 20 epochs on the NYU Depth training set. During both stages, we use constant learning rate of 1 10-4 and a batch size of 32. The encoder initialized with pre-trained (nitloo(Inter the model architecture, multi-scale features extracted from layers [4, 11, 17, 23] of the encoder. During training, input images are resized such that the longer side is 448 pixels, followed by a random square crop of size 448.
while the decoder is randomly initialized. For
Semantic Segmentation. For semantic segmentation, we fine-tune our UniFlow(Intern Vi7) end-to- end the ADE20K dataset (Zhou et al. 2019) for 100 epochs. We use UperNet head (Xiao et al.) 2018) with a batch size of 16.
C MORE QUALITATIVE RESULTS
C.1 VISUAL RECONSTRUCTION
Image Reconstruction. Fig. illustrates UniFlow's exceptional static image reconstruction ca- pabilities. Our method consistently generates reconstructions that are remarkably faithful to the original inputs, exhibiting sharp details, accurate textures, and precise color renditions, thereby outperforming other approaches. These results qualitatively validate the overall effectiveness of UniFlow in achieving high-fidelity image synthesis across diverse content. Fig. To further emphasize the performance gap between UniFlow and SD-VAE, more reconstruction visualizations provided in Fig. and Fig. 10 where UniFlow'ss 's superiority is consistently demonstrated.
Zero-Shot Video Reconstruction. In zero-shot video reconstruction, as depicted Fig. g UniFlow demonstrates remarkable proficiency in maintaining temporal consistency and visual quality across video frames without explicit video training. The reconstructed sequences display stable object appearances and fluid motion, showcasing UniFlow's strong generalization and robustness in handling dynamic content. This performance underscores UniFlow's effectiveness in generating coherent visual representations unseen video domains.
Qualitative Comparison on TokBench Text Subset. For more rigorous assessment of recon- struction fidelity, Fig. presents UniFlow's 's qualitative comparison on the challenging TokBench text subset (Wu et al. 2025c). UniFlow achieves superior preservation of intricate text details, where competitor models often struggle with blurriness or distortion. The crispness and legibility of UniFlow's 's reconstructed characters provide strong qualitative evidence for its ability capture and reproduce high-frequency information crucial for demanding visual tasks.
Qualitative Comparison on TokBench Face Subset. Fig. 12 offers a qualitative comparison on the TokBench face subset (Wu et al.) 2025c), a domain sensitive to perceptual realism. UniFlow consistently delivers reconstructions with superior facial attribute preservation and natural skin textures, surpassing other models which may introduce artifacts lose subtle expressions. These visual outcomes collectively affirm UniFlow's 's advanced capacity for high-fidelity generative modeling in perceptually sensitive areas, effectively handling complex and nuanced visual features.
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Is there yellow plate in the image? Please answer yes or no.
there a remote control on the table? Please answer yes no.
"No"
"Yes"
How does Courbet's use of color and brushwork enhance the flowers' beauty?
Which player is in the best position to gain control of the ball first, and what might be their strategy to do so?
"Courbet's bold colors and expressive brushstrokes highlight the flowers' vibrancy and fleeting beauty."
"Player 15 poised to control the ball first, leaning forward with extended foot, likely planning to pass dribble to advance play." "
What is the bus number and destination displayed on the bus in the image?
What time does the watch display in the image?
8
8
4
A) 61 to Zocodonecia Benduero
A) 8:15
B) to Zocodoncia Benduero
B) 9:05
C) 51 to Zocodonecia Benduero
Q) 2:41
D) 61 to Zocodoncia Bendiero
"B." "
D) 6:35
"C." "
Figure 13: Visualization of visual question answering.
C.2 VISUAL UNDERSTANDING
We provide more multimodal understanding examples in Fig. B UniFlow successfully answers the questions accurately. It successfully answers multiple-choice, open-ended, and yes/no questions.
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tiper
west Highland
Coral Reef
junco
llama
convertble
panda
golden retriever
alp
white terrier
Siberian hunky
jullfishh
Samoyed
piu peacook
sowan
mushroom
goldfish
panguin
Figure 14: Visualization of visual generation.
C.3 VISUAL GENERATION
We provide more image generation examples in Fig. 14 UniFlow (MAR-I.) generate high-quality images given calss.
C.4 VISUAL-CENTRIC DOWNSTREAM TASKS.
Our analysis confirms the effectiveness of UniFlow key visual tasks, namely depth estimation. semantic segmentation. and object detection. UniFlow showcases its versatility and robustness in these downstream tasks by demonstrating strong grasp of both global context and fine- fine-grained details within an image. As shown in Fig. [5] UniFlow accurately infers 3D spatial information from 2D images, producing clear and precise depth maps even for objects and scenes outside its training data. For semantic segmentation, as seen in Fig. [6] the model generates highly precise masks that correctly identify object boundaries and categories. In object detection, illustrated by Fig.
17 UniFlow is capable of accurately localizing and classifying various objects with high confidence. These results collectively confirm UniFlow's 's position as highly capable and generalized vision model.
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Diardbution
Figure 15: Visualization of in-distribution depth estimation on NYUi-depth-22 and out-of- distribution depth estimation.
Figure 16: Visualization of semantic segmentation ADE20K val.
Mos
STOP
Figure 17: Visualization of object detection on MS COCO 2017 val.
D MORE ABLATION STUDY.
verify UniFlow'ss training efficiency, we compare with TokenFlow, BLIP3-o, and UniToo across model size, training data, steps, batch size, and rFID (lower better reconstruction). as shown in Tab. 14 Note that BLIP3-o (SigLIP-SANA) released on the BLIP3-o GitHub repository, not the one used in the original paper. UniFlow uses a compact architecture (Intern VIT-300M encoder + 145.8M decoder), far less training data (1.2M vs. TokenFlow's 6.6M/BLIIP3-' 32M/UniTok's 1.28B), and only 70k training steps (vs. TokenFlow's 500k/BLIP3-0's 's 10wp:osss11111nnn 114k/nniTok's 's 80k), benefiting from its patch-wise decoder that simplifies data distribution. With 512 global batch size, UniFlow still achieves the best rFID (0.28), outperforming TokenFlow (0.63), BLIP3-o (3.09), and UniTok (0.38). This confirms UnwFlow balances efficiency and reconstruction via layer-wiee wise dif-disiillation and lightweight decoder.
Training Efficiency Comparison.
the model
Method
Encoder (Backbone)
Decoder Size
Training Data
Training Steps
Global Batch Size
rFID
TokenFlow
SigLIP- $0:00M
258.6M
6.6M
500k
256
0.63
BLIP3-o SiLLIP--S400M,
1771.6M
32M
114k
8192
3.09
TniTok
Vitamin-L
352.4M
1.28B
80k
16k
0.38
UniFlow
Intern
145.8M
1.2M
70k
512
0.28
Table 14: Comparison of Training Efficiency Across Different Unified Tokenizer Paradigms. The table presents rFID scores, with results for each model measured at its respective training resolution.
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Inputs
UniFlow (CLIP)
UniFlow
(SigLIP2)
UniFlow (DINOv2)
UniFlow
(InternViT)
Figure 18: PCA visualization under different pretrained representations. The representations of DINO exhibit the most regular distribution, which implies strong generative-frienlly properties.
Table 15: Different pretrained representations (left) and Latent Space optimization (right).
Model
rFID
1gFID
Method
Encoder
Align.
Res.
rFID 4 gFID +
SD-VAE 0.61
7.13
SD-VAE
VAE
None
256
0.61
7.13
DFN-CLIP 0.67
8.35
UniFlow
SigLIP2-SO
None
256
0.62
7.91
SigLIP2
0.62
7.91
DINOv2 0.54
5.16
VA-VAE
VAE
DINOv2
256
0.28
5.14
ViT 0.26
7.38
UniFlow SigLIP2-SO
DINOv2
256
0.63
5.02
Impact of Pretrained Representations on Visual Generation. The generative performance of different pretrained encoders and the role latent alignment are presented Tab. [5] core observation is that the quality of generative models is largely determined by the intrinsic properties (structure and distribution) of the latent space inherited from the pretrained encoder, rather than the adaptation framework itself. This is further validated by ablation study ImageNet, where models are trained for 64 epochs with LightningDTT-XL. Consistent with findings from recent works such VAVAE(Yao et al. 2025), RAE (Zheng al.) 2025b) and SVG (Shi et al.) (025)). DINO-style representations exhibit stronger generative-friendl properties compared to text-aligned counterparts (e.g., SigLIP, CLIP). Specifically, DINOv2's 's latent space enables significantly higher fidelity generation with FID of 5.16, while the text-aligned Intern ViT yields relatively higher gFID of 7.38 (left subtable of Tab. (5). This explains why the initial UniFlow (InternViT) does not show significant advantage: the limitation stems from Intern inherent representation characteristics, not UniFlow's s adaptation mechanism. UniFlow remains general framework whose effectiveness is contingent on the teacher encoder's properties. To further explore the potential of the UniFlow framework, we conducted additional experiments at 256x256 resolution. The results (right subtable of Tab. 15) confirm that UniFlow exhibits even stronger generative capabilities when integrated with latent alignment, even for text-aligned encoders. For example, UniFlow (SigLIP2) with DINOv2-bass based latent alignment achieves state-of-the- of-the-artttt---e- gFID of 5.02, outperforming both the vanilla UniFlow (SigLIP2) (gFID=7.91) and VA- VAEV (gFID=5.14) 14). This demonstrates that while pretrained representations lay the foundation for generative performance, UniFlow with latent
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grld artifact
high
GT
0.7
=
0.6
Flow Loss
0.4
0.3
Training Epochs
Figure 19: Visualization of Global Transformer Block (GTB) Impact on Flow Loss and Recon- struction Quality. The figure shows flow loss curves (left) and corresponding reconstructed images (right) for models with O, 3, and 6 GTB layers during training. As GTB layers increase, flow loss converges faster and to a lower value, with reconstructed images exhibiting reduced grid artifacts and higher visual fidelity.
alignment can effectively enhance the generative quality of even less generative-frienll (text-aligned) encoders.
Sensitivity of Temperature Parameter B. Fig. (b) presents detailed sensitivity analysis the temperature parameter 3 in our adaptive distillation strategy. The results reveal clear and intuitive trend. A smaller (e.g., 0.5) results in suboptimal performance across all metrics, with a PSNR of 31.99 and MME-P of 1496.2, as it insufficiently penalizes misaligned layers. As 8 increases, the model progressively improves by dynamically emphasizing cauder-tto-alige layers, reaching a robust performance plateau between / 2 and 3. Our default setting of 3 = 2 achieves an optimal balance, yielding the highest MME-P (1505.1) and a high PSNR (33.23). While a slightly higher PSNR (33.24) observed at / the performance remains stable. When 3 becomes excessively large (e.g.. 5), the model over-focuses on correcting misalignments, leading to training instability and performance degradation both understanding and reconstruction tasks, with PSNR dropping to 32.88 and MME-P to 1489.4. .4. This analysis confirms the necessity of B and validates our choice of cell-balanced and stable configuration.
Effect of Global Transformer Block. The Global Transformer Block plays crucial role in enhancing global consistency and accelerating convergence. As illustrated in Fig. 19 models without sufficient GTB utilization (e.g., layers) suffer from severe grid artifacts in reconstructed images and demonstrate slower convergence during the early training stages, as evidenced by the higher and more volatile flow loss curve. Increasing the number of GTB layers progressively improves both the convergence dynamics and reconstruction quality: the flow loss converges faster to lower steady state value, and reconstructed images exhibit reduced grid artifacts and higher visual fidelity. As shown in Fig. 5 (c), the final performance also confirms the block's 's necessity. Increasing the number GTB layers progressively improves all metrics, as the model better captures long-range dependencies across patches. Our default setting of 6 GTB layers achieves optimal balance, yielding a PSNR of 33.23 and an rFID 0.26. While the performance continues to slightly improve layers (PSNR of 33.31 and rFID of 0.25), the gains are marginal. This analysis confirms the necessity of the GTB for high-quality reconstruction and validates our choice of 6 layers as the optimal and stable configuration.
Impact of 3 on Layer-wise Weight Distribution. Fig. 20 presents simulated visualization of our layer-wise distillation strategy, designed to illustrate the critical role of the temperature parameter in balancing high-level semantics and low-level details. The x-axis represents the encoder layer
30

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Impact of on Layer-wise Weight Distribution (w, = wpase Softmax(Ba,)
0.14
Final Weight
Input Factors
g 0.5
Base Weight (xy T
B 2.0
... Penalty [a) cossim)
5.0
0..8
Base Weight (wefharr) Penally (ax)
Normalized Final Weight (w)
0.10
0.08
-0.4
0.04
0.2
0.02
10
12
Layer Index (I)
Figure 20: Simulated Impact of in Layer-wise Distillation. The figure illustrates how the temperature parameter 3 modulates final distillation weights (w, left y-axis) across encoder layers (l), derived from base weights (w) favoring high-level semantics) and penalty terms (a; emphasizing low-level misalignment, right y-axis). Key findings: 3 = 0.5 under-amplifies penalties (flat curve, poor low-level utilization); 3 over-amplifies penalties (steep curve, instability); 2.0 optimally balances both, enabling effecttive trade-off between semantics and details.
CoSim,
index (l), while the right y-axis shows two key input factors: the linearly increasing base weight (woase)) which intrinsically favors high-level semantics, and the penalty term (o: = CosSim), which is highest in low-level layers due to their greater misalignment with the final representation. The left y-axis shows the final distillation weight (w)), product of these two factors as modulated by 3. As shown, small (e.g.. 0.5) results in flat weight curve, where the Softmax penalty has little effect. In this case, the final weight distribution closely follows the base weight, failing to adequately utilize fine-grained low-level features for reconstruction. Conversely, an excessively large (e.g.. 5.0) leads to a very steep curve, where the penalty term is overly amplified, causing the model to heavily prioritize the most misaligned bottom layers. This over-correction can lead training instability and compromise the model's 's semantic understanding. Our analysis confirms that an optimal 3 = 2.0 provides the ideal balance, yielding well-shaped curve that allocates sufficient weight to low-level features to correct deviations and supplement details, while also preserving the crucial high-level semantic information. This demonstrates that = 2.0 the most stable and effective setting for achieving trade-off between semantic preservation and detail supplementation.
Ablation Sampling Steps. We evaluate reconstruction quality MS-COCO-VE val images by sweeping Euler steps from 1 to 100. As plotted in Fig. [1 reconstruction shows a minor downward trend while latency grows nearly linearly with the number of steps, indicating that additional iterative steps do not bring meaningful gains to visual fidelity but instead introduce unnecessary computational overhead. This interesting one-step-optimal phenomenon aligns with prior observations from (Zhao et al.) 2025a; Liu et al. 2022): rectified-flow or diffusion based models rely on multi-step iteration to approximate complex target distributions, but their performance quickly saturates once the conditioning signal is sufficiently determined. For our tokenizer with Flow Matching decoder, the latent vector serves as a strong conditional prior-after sufficient tokenizer training, has encoded nearly all structural and textural information of the target image, which constrains the conditional distribution p(x z) to a highly concentrated, pear-singleton distribution. In this scenario, the Flow Matching's 's velocity field can directly model the linear mapping from noise to the target image (as highlighted Rectified Flow), making one-step sampling sufficient to reach the optimal reconstruction. Additional steps only fail refine the distribution but may accumulate
31

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numerical errors from iterative discretization (e.g., Euler method's first-order approximation bias) and cause minor degradation in details like edge sharpness or texture consistency. Consequently, UniFlow retains both minimal latency and maximal reconstruction performance, offering practical advantage over iterative alternatives in real-world deployment.
Input
1-step
4-step
30
PSNR (dB)
CLIP
20-step
50-step
100-step
10
SipLIP2
DINO<2
InternVIT
SD-VAE
10
10"
10
Sampling Steps
Figure 21: Impact of sampling steps reconstruction.
E LIMITATIONS AND FUTURE DIRECTIONS
While UniFlow introduces a new and efficient paradigm for unified visual tokenization, we acknowl- edge several limitations that also present promising avenues for future research.
First of all, as an academic-driven research model, UniFlow has been primarily validated on controlled, academic benchmarks such ImageNet due computational resource constraints. While method demonstrates strong performance on these standard datasets, there may still be a minor gap in visual quality compared commercial models trained vast, proprietary datasets. We believe that scaling our approach with more extensive and diverse data collections could further close this gap and unlock even greater potential.
Second, our framework is designed as flexible adaptation paradigm for existing Vision Foundation Models (VFMs). While this approach allows seamlessly integrate with powerful pre-trained encoders, it also means the input resolution is inherently constrained by the fixed resolution of the specific encoder chosen. Future work could focus on developing a more resolution-agnostee version of UniFlow or extending the framework to handle variable resolutions, thereby enhancing its applicability more diverse, real-world scenarios.
F DECLARATION OF USE OF LARGE LANGUAGE MODELS (LLM)
We affirm that this paper was primarily written by the authors. Large Language Models (LLMs) were utilized solely as ceneral-purpose assistive tools for language refinement, grammar correction, and stylistic improvements during the writing process. Specifically, Gemini 2.5 Flash (DeepMind) 2025) was employed for minor text polishing and rephrasing to enhance clarity and readability. No LLM was used for conceptual ideation, experimental design, data analysis, or generating any substantive content of the research.
32


Reviews:
[{"review_id": "eZChZWVBbZ", "reviewer": "Reviewer_je6u", "summary": "This paper presents UniFlow, a unified and generic tokenizer designed to bridge the trade-off between visual understanding and generation. The method adapts any pretrained visual encoder with a lightweight reconstruction decoder, enhanced through layer-wise adaptive self-distillation to balance semantic abstraction and fine-grained detail modeling. A patch-wise pixel flow decoder further improves reconstruction efficiency and fidelity by learning conditional flows in the pixel domain. Through this design, UniFlow effectively mitigates the conflict between understanding and generation, achieving consistent improvements across 13 benchmarks. Notably, the 7B UniFlow-XL surpasses larger models such as TokenFlow-XL (14B) in understanding tasks and achieves competitive performance in generation quality metrics.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is very clearly written, with a well-motivated problem statement and a logically coherent narrative that effectively highlights the trade-off between visual understanding and generation.\n\n2. The authors provide extensive and detailed experiments across a wide range of benchmarks, offering strong empirical evidence for the proposed method’s effectiveness.\n\n3. The model demonstrates impressive performance in both multimodal understanding and visual reconstruction, showing that UniFlow achieves a well-balanced trade-off without compromising either side.\n\n4. The introduction of layer-wise adaptive self-distillation and the patch-wise pixel flow decoder is both conceptually sound and practically meaningful, representing a thoughtful and generalizable design applicable to various pretrained vision encoders.", "weaknesses": "1. The paper lacks quantitative results on text-to-image generation benchmarks, which limits the evaluation of UniFlow’s generative capability in open-ended visual synthesis.\n\n2. The study does not explore how different frozen teacher encoders or alternative decoder types (e.g., conventional VQ-VAE decoder) would perform under the UniFlow framework, leaving an open question about the generality of its design choices.\n\n3. It remains unclear whether using UniFlow’s learned VQ tokenizer as a visual encoder for multimodal understanding actually surpasses or lags behind directly adopting the frozen teacher encoder; such a comparison would help clarify UniFlow’s benefits.\n\n4. The computational cost and training efficiency of the proposed flow-based decoder are not discussed in detail, making it difficult to assess the practicality of scaling UniFlow to larger models or broader datasets.", "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": "OZArDKYg3I", "reviewer": "Reviewer_2sSU", "summary": "This paper introduces UniFlow, a unified visual tokenizer designed to simultaneously support both visual understanding and generation. The key innovation lies in combining a layer-wise adaptive self-distillation strategy, which preserves hierarchical semantic knowledge from pretrained vision foundation models, with a patch-wise pixel flow decoder, which reconstructs fine-grained visual details via conditional flow matching in pixel space. Extensive experiments across 13 benchmarks and 7 vision tasks demonstrate that UniFlow achieves state-of-the-art results among unified tokenizers.", "questions": "1. As stated in the implementation detail part, UniFlow can do visual reconstruction with one-step Euler inference. It would be interesting to analyze whether using more steps will improve or hurt the reconstruction performance.\n2. With only 150M parameters, the flow-based decoder can achieve superior reconstruction performance on ImageNet. How was this model size determined? Could similar performance be achieved with a smaller model, and would scaling up the decoder further enhance reconstruction quality?\n3. It would be helpful to include more visualizations of face and text reconstructions, since vision encoders trained with contrastive losses are typically rich in semantic representation but tend to lose fine-grained visual details.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.UniFlow mitigates the representational conflict between understanding and generation by combining semantic-guided self-distillation and pixel-space flow reconstruction. The proposed idea is both technically novel and interesting.\n2.The paper benchmarks UniFlow across a diverse range of tasks (VQA, classification, segmentation, detection, depth estimation, generation, reconstruction), and the results show that it consistently outperforms or matches existing unified tokenizers like TokenFlow-XL and UniTok.\n3.Extensive ablations in Table 5 clearly demonstrate the effectiveness of adaptive distillation and flow decoder design.", "weaknesses": "1. This paper chose several different semantic teachers, which are pretrained with different resolutions and downsample ratios. It would be better to include a deeper analysis and comparison between them, in addition to the reconstruction, multimodal understanding and generation performance.\n2. As UniFlow is trained with both reconstruction and distillation (semantic) objectives, it would be valuable to further analyze the effects of them, e.g., mutual benefits or conflicts.", "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": "ScAtjFFe9l", "reviewer": "Reviewer_JQ4r", "summary": "This paper introduce a unified image tokenizer, named UniFlow, for visual understanding and generation.\nTo efficiently unify visual representations within a single tokenizer to achieve both powerful semantic understanding and high-fidelity reconstruction, this paper enhances the tokenizer's ability to extract semantic information and pixel information through layer-wise adaptive self-distillation and patch-wise pixel flow decoder, respectively. This method achieves competitive performance across multiple downstream tasks.", "questions": "1. Refer to the weaknesses\n2. Confusion about the motivation of the patch-wise pixel flow decoder. From my personal understanding, the core of representation learning work should be on the encoder part rather than the decoder. Because the encoder is the module used for multiple downstream task applications. Although an advanced pixel decoder can achieve better reconstruction and generation performance, this does not seem to match the core purpose of the paper. In previous work, such as BeiTv2, MAE, in the choice of decoder, they tend to have a lighter and simpler structure.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Competitive experimental results. UniFlow achieves good performance on multiple downstream tasks.\n2. The paper expresses ideas accurately and organizes them clearly. The two proposed strategies: layer-wise adaptive self-distillation and patch-wise pixel flow decoder, effectively improve the model's performance.", "weaknesses": "1. Sufficient comparative experiments. Many of the experiments in the paper do not provide a fair comparison with other methods. For example, in the multi-modal understanding benchmark, although comparisons are made with other methods on LLaVa's data, the initialized semantic encoder and resolution differ. In fact, these two modules are the key reasons for multimodal understanding. I understand that fair comparison experiments with large models are more expensive, but I still hope to reduce variables as much as possible to better illustrate the effectiveness of the method. In addition to this, there is a lack of comparisons with other unified tokenizers in vision-centered tasks. Concerning the ablation experiment section, I believe that merely comparing it to InternViT in multi-modal tasks is inadequate. More comprehensive experiments on downstream tasks are necessary.\n2. The paper do not discuss or compare the approach of concatenating features on the CLIP+VAE feature dimensions. It seems that simply concatenating the dimensions can also achieve both powerful semantic understanding and high-fidelity reconstruction, requiring only simple fine-tuning of the pixel decoder.\n3. Lack of text-to-image experiments. I think just deploy experiments on Imagenet is not enough. Referring to the settings of LlamaGen or TokenFlow, it is more comprehensive to deploy text-to-image experiments under a considerable data scale.\n4.  Comparative experiments with state-of-the-art methods are needed. In Tab.4, there is no comparison with classic representation models such as DINO and the existing unified tokenizer.", "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": "XlOADVqRgY", "reviewer": "Reviewer_JFND", "summary": "The paper proposes UniFlow, which fine-tunes a pre-trained visual encoder through layer-wise adaptive self-distillation. This method preserves semantics while supplementing the details required for image reconstruction. Additionally, the authors introduce a patch-wise pixel flow decoder that can map directly from noise to the pixel space, which improves the image reconstruction performance.", "questions": "1. Why is the reconstruction performance for the UniFlow (SigLIP) model different between Table 1 and Table 3, especially regarding the SSIM scores (e.g., 0.93 vs. 0.85)?\n2. Can UniFlow be applied to fine-tune the visual encoder already integrated into a pre-trained MLLM (like LLaVA), or must it be trained on a standalone VFM first?\n3. What are the benefits of UniFlow for generation? Can it achieve faster convergence?\n4. What are the benefits of pixel flow decoder for understanding performance?\n5. UniFlow currently only performs generative training on ImageNet. Can it be applied to text-to-image and image editing tasks?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. UniFlow can simultaneously achieve strong understanding performance and high-fidelity reconstruction performance.\n2. The pixel flow decoder simplifies the training loss and complexity compared to the VAE.", "weaknesses": "1. Despite using of a distillation loss to preserve semantics, a comprehensive comparison of the full understanding performance against the baseline encoder is missing, which prevents validation of the effectiveness of distillation loss. While a comparison between InternViT and UniFlow(InternViT) is provided in Figure 4, the performance gain on MME-S is smaller than on MME-P, suggesting a performance degradation issue for UniFlow on the MME-C benchmark.\n2. The lack of understanding of the performance comparison with the baseline also makes the comparison with other methods unfair. For example, in Table 4, it is not clear whether the improvement over other methods comes from better pretraining or Uniflow itself.\n3. UniFlow's generative performance does not show a significant advantage and is even weaker than a VAE when using guidance.\n4. Considering the generative results of UniFlow, the gain in reconstruction performance from the pixel flow decoder appears to have little positive impact on generation. This leads to concern that UniFlow may have over-optimized for the reconstruction task and consequently failed to learn a feature distribution conducive to generation.\n5. The evaluation of the generation is unfair. UniFlow uses a 448x448 resolution during generative training, but the other methods only use a 256x256 resolution during training.\n6. The UniFlow-LV notation is not defined within the main text.", "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)