Native-Resolution Image Synthesis
Authors: ZiDong Wang
LEI BAI
Xiangyu Yue
Wanli Ouyang
Yiyuan Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Native-resolution
Image Synthesis
Diffusion Models
Primary area: deep_learning
TL;DR: Native-Resolution Image Synthesis

Abstract: We introduce native-resolution image synthesis, a novel paradigm in generative modeling capable of synthesizing images at arbitrary resolutions and aspect ratios. This approach overcomes the limitations of standard fixed-resolution, square-image methods by inherently handling variable-length visual tokens—a core challenge for conventional techniques. To this end, we propose the Native-resolution diffusion Transformer (NiT), an architecture that explicitly models varying resolutions and aspect ratios within its denoising process. Unconstrained by fixed formats, NiT learns intrinsic visual distributions from images encompassing a wide range of resolutions and aspect ratios. Notably, a single NiT model simultaneously achieves the state-of-the-art performance on both ImageNet-256x256 and 512x512 benchmarks. Surprisingly, akin to the robust zero-shot capabilities seen in advanced Large Language Models, NiT, pretrained solely on ImageNet, demonstrates excellent zero-shot generalization performance. It successfully generates high-fidelity images at previously unseen high resolutions (e.g., 1024x1024, 1536x1536) and diverse aspect ratios (e.g., 16:9,3:1, 4:3), as shown in Figure 1. These findings indicate the significant potential of native-resolution modeling as a bridge between visual generative modeling and advanced LLM methodologies.

Native-Resolution Image Synthesis
Zidong Wang',
Lei Bai Xiangyu Yue', Wanli Ouyang" Yiyuan Zhang 1.2
CUHK MMLab Shanggai Al Lab
vangzd20220gmail. com, xyyueRcuhk. edu edu hk, ouyangvanliepjiab org.cn cn
Project Page: detps://wzdthus wwzzthu.ggithb.. io/NiT
416
He ghc 9900
Herpht 1334
Height GoO
Width: 1536
Wid 3024
Widdh 480
Henel 312
Heiohh 320
952
W Widhh:3
256
Width: 320
Figure 1: Nasivioresolution image synthesis on ImageNet. A single Native-resolution diffusion Transformer (NiT) model, trained on ImageNet, generates images across diverse, arbitrary resolutions and aspect ratios (examples shown from 256 256 to 2048 2048, and aspect ratios from 1 5 to 3 3: 1). This capability extends far beyond conventional fixed-resolution, square-image generation (e.g., 256 256), demonstrating strong generalization.
Abstract
We introduce native-resolution image synthesis, a novel generative modeling paradigm that enables the synthesis of images at arbitrary resolutions and aspect ratios. This approach overcomes the limitations of conventional fixed-resolution. image methods by natively handling variable-length visual tokens a core
Corresponding authors: yiyuan @ lin..cchk.edle bailei@ppjab.orrg.com
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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challenge for traditional techniques. To this end, we introduce the Vative-resolution diffusion Transformer (NiT), an architecture designed to explicitly model varying resolutions and aspect ratios within its denoising process. Free from the constraints of fixed formats, NiT' learns intrinsic visual distributions from images spanning a broad range of resolutions and aspect ratios. Notably, single NiT model simul- taneously achieves the state-of-the-art performance on both ImageNet-256. x 256 and 512 x 512 benchmarks. Surprisingly, akin to the robust zero-shot capabilities seen in advanced large language models, NiT, trained solely on ImageNet, demon- strates excellent zero-shot generalization performance. It successfully generates high-fidelity images at previously unseen high resolutions (e.g.. 1536 x 1536) and diverse aspect ratios (e.g., 16: 3: 1. 4: 3), shown in Figure I These findings indicate the significant potential of native-resolution modeling as a bridge between visual generative modeling and advanced LLM methodologies.
Introduction
The emergence of Large Language Models (LLMs) 22 43 49
74 77 78 84 represents
transformative development in the Al-Generated Content (AIGC) area. Their success largely attributed to two key characteristics; exceptional scalability and remarkable zero-shot generalization. Scalability is empirically validated by established scaling laws [ 34,, which demonstrate predictable performance gains with increased model size and dataset scale. Mcanwhile, zero-shot generalization evidenced by their capability to perform tasks for which they were not explicitly trained, such as seamlessly handling unseen questions of variable lengths, affording exceptional flexibility.
Concurrently, diffusion models 23 30 35
have risen to prominence in visual generative modeling, lauded for their capacity to synthesize high-fidelity data. typically standardize images fixed, often square, dimensions during training. This preprocessing step, while simplifying model architecture, inherently discards crucial native resolution and aspect ratio information. Such a practice curtails the models' ability to learn visual features across diverse scales and orientations [ 46 57 80 85), thereby limiting their intrinsic flexibility and generalization capabilities concerning ipput variability.
36
42
47
55 57
66
83
85
88
89
However, prevailing diffusion transformers 23 47 55
Large Language Models effectively process variable-length text by training directly on native data 78 84]. This inherent adaptability inspires critical question for image synthesis: Can diffusion models achieve similar flexibility, learning to generate images directly at their diverse, native resolutions and aspect ratios? Conventional diffusion models face considerable challenges in generalizing across resolutions beyond their training regime. This limitation stems from three core difficulties: 1) Strong coupling between fixed receptive fields in convolational architectures and learned feature scales [8] 35| 36 [1]. These models internalize visual concepts at a resolution- specific scale. hindering effective feature extraction when resolution changes. 2) Fragility of positional
formats 16 22
71
72
encoding and spatial coordinate dependencies in transformer architectures [25] 47 [55. Hardcoded
or learned positional encoding for specific grid size, leads distorted spatial reasoning and object coherence at novel resolutions. 3) Inefficient and unstable training dynamics from variable inputs.
Padding variable inputs 46 causes waste and artifacts; while aspect ratio bucketing []] 57
increases training complexity. Both methods harm efficiency and the diffusion process's 's sensitivity to cosolution-dependeee image statistics. Addressing these interconnected challenges is crucial for developing truly native-resolution diffusion models.
In this work, we overcome these limitations by proposing novel architecture for diffusion transform- ers that directly models native-resolution image data for generation. Drawing inspiration from the we reformulate image generative modeling
variable-sequence nature of Vision Transformers [6 21I. within diffusion transformers as "native-resolutic generation". And we present the Native-resolution diffusion Transformer (NiT), which demonstrates the capability to generate images across wide spectrum of resolutions and aspect ratios. By exclusively training on images at their original reso- lutions, without resolution conittioo-moiiiinngggg augmentations, our model inherently captures robust spatial relationships. NiT is developed based on the DiT [35] architecture and incorporates several key architectural innovations: 1) Dynamic Tokenization. Images native resolution are converted into variable-length token sequences and the corresponding height and width tuples. Without requiring input padding, it avoids substantial computational overhead. 2) Variable-Lenge Sequence Process- ing. We use Flash Attention [15] to natively process heterogencous, unpadded token sequences by

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Resolution
Aspect Ratio
(a) Data Distribution of ImageNet
(b) Resolution Generalization
(c) Aspect Ratio Generalization
Figure 2: NiT's Superior Generalization Beyond ImageNet's Typical Resolution Distribution. (a) ImageNet resolutions are mainly concentrated between 200 to 600 pixels (width/height), with sparse data beyond 800 pixels. Despite this, (b) shows our NiT model's 's superior generalization to unseen high resolutions (e.g.. 1024, 1536). evidenced by significantly lower FID scores. (c) further confirms NiT also exhibits the strongest generalization across various aspect ratios.
cumulative sequence lengths using the memory tiling strategy. 3) 2D Structural Prior Injection. We introduce the axial 2D Rotary Positional Embedding [8] (2D RoPE) to factorize the height and width impact and maximize the 2D) structural prior by relative positional encoding.
Extensive experiments in class class-guided image generation validate NiT significant advancement due to its native-resolution modeling. With a single model, NiT firstly attains state-of-the-ar (SOTA) results on both 256 x 256 (2.03 FID, Fréchet inception distance) and 512 x 512 (1.45 FID) benchmarks in class-guide ImageNet generation [17. Impressively, NiT highlights its strong zero-shot generalization ability. For instance, as shown Fig. 2 achieves an FID of 4.52 on unseen 1024 x 1024 resolution and an FID of 4.11 on novel 9 16 aspect ratio images (i.e., 416 x 768), excelling in its flexibility and transferability to unfamiliar resolutions and respective ratios.
2 Related Work
2.1 Explorations of Variable-length Generalization
78 are trained using text text sequences with native, original length, which enables them to take flexiblelenngt texts as input and generate arbitrary-length outputs. Seminal works, such as RoPE (68). NoPE (37), Alibi (58), and KERPLE (14), study the impact of positional encoding on the length generalization in language models. RoPE is then utilized in a wide range of L.LMs as it unifies the relative position encoding with the absolute positional encoding. Beyond the valuable properties of RoPE, series of works have been proposed to study the extreme context-length generalization of LLMs. NTK-RoPE 45). YaRN [56), LongRoPE [20). LM-Infinite [27]. PoSE [91], and CLEX 9 explore the extreme length generalization of LLMs, enabling LLMs with the capability of a very long context length (> 128K) generation.
Large language models (LLMs)
49
In the realm of computer vision, NaVIT 16.. DINO-v2 (54), and RADIO-v2.5 28 have explored multi-resolution training to obtain more robust vision representation. Recently, advanced vision language models (VLMs), including Qwen2-VL [4) Geminil.5 .5 (72). Intern-V--2.. [13), and Seed VL 770], have explored the native-resolution training their vision encoders. However, the power of autive-resolution training in visual content generation appears to be somewhat locked. In this work, we bridge the gap by exploring the native-resolution training diffusion transformer models.
2.2 Explorations of Resolution Generalization in Visual Content Generation
Current visual generative models, whether autoregressive or diffusion-based, typically do not directly process visual content at native resolution. Existing strategies accommodate variable resolutions can be broadly categorized into three main approaches:
3

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Bucket Sampling [ 57 82
facilitates dynamic resolution handling across different training batches by grouping samples into pre-defined "buckets". While the resolution and aspect ratio are fixed within each batch, they can vary between batches. The primary limitation is its reliance on these pre-defined buckets, restricting true flexibility to discrete set of image dimensions.
Padding and Masking [46 81 establishes a maximum sequence length, padding all image tokens to this length, and employing mask exclude padded regions from the loss calculation. This allows for dynamic resolution processing within a single batch. However, the approach often leads to sigmificant computational and memory inefficiencies due the processing of extensive padded areas, especially for images considerably smaller than the maximum resolution.
Progressive Multi-Resolution 10 85 methods split the training process into several stages and progressively increase the image resolutions at each stage. While this method can effectively achieve high-resolution generation, it often exhibits suboptimal performance on smaller resolutions earlier stages. Besides, it cannot generalize to higher resolutions beyond the last training stage.
The success of LLMs with variable-length inputs underscores gap in visual content generation, where true native-resolution flexibility still clusive. Existing methods to handle varied resolutions often introduce trade-off's in efficiency generalization. To bridge this, we explore native-resolution training for diffusion transformer models, seeking a more fundamental solution to these persistent challenges. Our proposed methodology is detailed next.
3 Native-Resolution Diffusion Transformer
3.1 Preliminaries
Diffusion Foundation. Given the noise distribution e NN I) and the data distribution (x)a()), the time-dependent forward diffusion process is defined as: 2t = 0rT + oye, where a, is decreasing function of / and dN is an increasing function of I. There are different strategies to
train a diffusion model, including DDPM [30 52), score matching [32 64 65
EDM [35] 36), and
flow matching 2 3 42 44). We adopt flow matching with linear path in NiT. which restricts the forward and reverse process on E [0, 1] and set a = 1 - t, o = t. interpolating between the two distributions with velocity target v
The Logit-Normal time distribution & T+o from EDM
introduced, where In(a) ~ ( mean: Pla) with manually selected coefficients Piman and Paa-
Conventional Fixed-Resolution Modeling The prevalent training strategy of diffusion models involves pre-setting batch-wide image resolution, denoted Htarget W Wargett often with Hzorget = Wtorget for benchmarks like ImageNet-22 256 x 256. Images Lorig of native dimensions Horig x W Wrrig are then subjected resizing and cropping operations to conform. While simplifying model design, this fixed-resolution approach introduces three significant issues:
Spatial Structure and Semantic Degradation. As discussed in previous works 33 53 resizing an image to a certain size via an interpolation function finterp(.) with scaling factors 8H, sw can be detrimental. Upsampling (if sH > 1 or sw 1) often introduces blurriness, diminishing sharpness. Conversely, downsampling (if 3и < 1 or sw < 1) leads to an irrecoverable loss of high-frequency details. Subsequent cropping Htargez W Wtergets
67
particularly when aspect ratios to semantic incompleteness or contextual loss, an effect observed to influence generated samples in models, as revealed in SDXL [57]
W Worig * Wrargst discards image regions, potentially leading Htarget
Horiu
Inhibited Resolution Generalization. Models trained exclusively at fixed reso- lution (Hearget, W Wtarget) exhibit poor generative performance at novel resolutions (Hinfers Winfer) # (Htarget, W Wtarget) This limitation is particularly acute for Transformer-basse diffusion models, like DiT [55] and SiT [47]. Their reliance on ab- solute positional embeddings lacks 2D structural modeling, which does not readily adapt to changes in the number spatial arrangement of patches resulting from differing resolutions.
Inefficient Data Utilization and Computational Overhead. Natural image datasets contain rich visual information encoded in their diverse native resolutions. Standardizing all inputs to Htarget x W Warrget discards this inherent scale diversity [57] Consequently, achieving high
We provide detailed comparison on this generalization ability in Table and

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x
Native-rcsolution diffusion Transformer
Velocity Prediction
Scal
413 iotene
St tcken
361 sikees Image
Irage
Linear & Reshape
Adding Nois
Faes-Porand
Image AutoEnncder
Layer Norm
Scal, Shitt
Narm
NiT Blocks
7.4
Scali
Faroat
Patcify
Embed
Scally Stit.
High
Norm
Adda N
Timestap
&
Cand
Ixnge:
Noley
Class
Figure 3: Architecture Design of Native Resolution Diffusion Transformer (NiT). NiT takes noisy latent representations, tokenizes them into variable-length sequences based the original image resolution. Each NīT block utilizes Packed Multi-Head-Sele Self-AAtention (MHSA) with 2D RoPE and incorporates timestep and class conditioning via adaptive layer normalization.
performance across multiple resolutions necessitates training distinct model instances for each specific resolution. So. for different resolutions, current approaches incur cumulative computational training cost
3.2 Native-Resolution Modeling
Reformulation. As illustrated in Fig. 3 given sequence of images in arbitrary resolutions, we use the image autoencoder to compress the image sequence latent sequence
{I1, I2, T3, ... )n)- Similar LLMs q we pre-define a maximum sequence length L and pack the latents I; Rexx xw, together, where c, h; and w, are the dimension, height and width of the i-th latent, and is the patch size. Please that in the packing algorithm, to maintain the maximum sequence length, image instance number n is also dynamic in different iterations.
3i Bexh,
TR
(x(Pp)
(eew))((pp)))
12 22, T3.
R
where the packing operation is to concatenate the variable-length latents to the maximum sequence length and improve computation efficiency.
After packing, we add noise and patchify the packed latent sequence æ. For each latent Hs, we sample
Arrn )x(ep)) and
each Gaussian noise in a variable-length independently, where the noise e; E R time t; E [0, 1] are added to the real data Z;:-
1 (1 1,) 44
Then we use the patch embedding layers to project the noisy data to the visual tokens z:
Zo PatthhEbed((2) IR (xd )
where d is the hidden size of the diffusion transformer. Thus, sequence of netivuoresolution images formulated into a packed sequence of visual tokens, and it satisfies: (y E,h; - w.) < L.
Use axial 2D-RoPE to inject 2D structural priors. The advantages 2D Rotary Position Em- bedding (RoPE) for modeling inputs at their native resolutions are two-fold: 1) 2D RoPE explicitly
88 achieving strong results ImageNet-2566 256, separate model, roughly 2x training compute, are required for 512 512 resolution. highlighting an inefficient use resources compared models capable handling variable resolutions.
For example, despite SiT-REPA
We adopt efficient longest-pack pacc-paccksssss first histogram packing algorithu 39
5

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models 2D structural relationships within the image plane, offering better adaptability to various resolutions compared to learnable positional embeddings. 2) The axial nature of 2D RoPE decouples height and width modeling, independently generating 1D rotational frequency components based on each token's 's patchfied height 1ig and width w/. Let 2y: be the tokens associated with height-widthh coordinates ( w()) . d is the hidden size of query (qi) and key (kg) vectors derived from 2g.- The dimensionality of the rotary angle space is dg = d/2. Base angular frequencies wj are defined as:
W 0 25/d, for j
.......d/// 10-11 1},
(4)
where 0 a hyperparameter Fl The composite angle vector Png."n E IRd. for the token at position (Kig wx)) is formed by concatenating angles derived from its height and width:
L PA m Concat (thle w
dd./2-1 Hww wy)l-=011
(5)
In the self-attention mechanism. the query qı R' and key kj IR are transformed using these rotary embeddings. For any such vector v Rd (representing either qu or k;), its transformed version v' is obtained by rotating its feature pairs. Specifically, for each / {0, (O,....d, de 1}:
sin (Dn
12g U2
COs
U2!
(6)
sin
( (Kk,o()))
COS
U2l+1
This axial 2D RoPE effectively injects 2D structural priors suitable for variable heights and widths, while inherently preserving RoPE's capacity to encode relative positions within the self-attention.
Processing packed sequences, which concatenate multiple variable- length visual token sequences from distinct data instances, necessitates restricting self-attention computations to operate only within the tokens of each original instance. While standard attention masking can enforce this, its application to the highly sparse structure of packed sequences incurs prohibitive computational overhead. Drawing inspiration from the efficient native-resolution processing in advanced Vision Language Models (VLMs) [4] 70, 72, 75], we employ FlashAttention-2 [15] to achieve this efficiently. Specifically, for a packed batch comprising n data instances, where the i-th instance contributes N; = (hę wr)/p² tokens (derived from its latent dimensions hs, wi and patch size p). define the individual token sequence lengths and their cumulative sums:
Packed Full-Attention.
n-1
T
CuSeqLens = [0, N1, N₁ N2.
N
NzJ-
This leverages FlashAttention-225 ability to handle packed inputs with variable sequence lengths (specified by CuseqLens). thereby enabling efficient full-attention within each data instance without explicit masking or padding. A detailed algorithm is demonstrated in Algorithm !
Packed Adaptive Layer Normalization. Conventional Adaptive Layer Normalization (AdaLN) methods are not directly suited for packed sequences due to the heterogeneity in sequence lengths and the need for instance-specific conditioning. To address this, we introduce Packed Adaptive Layer Normalization. For each k-th data instance within the packed sequence, its unique conditional embedding Cı R (where d is the token feature dimension) is utilized modulate its corresponding visual tokens. Specifically, (B&² E Idd) parameters. These parameters are then broadcast across all N₂- = (hg- wa) w tokens originating from the k-th data instance. If 2μ.j is the j-th token of the k-th instance after standard Layer Normalization (applied to the entire packed sequence z to produce 2), the AdaLN operation is:
is first projected to produce instance instance-specific scale (?x: E R2)) and shift
Adal.N(2x.j c&) = 7 ÊR. + 3k:
(8)
where @ denotes glement-wise multiplication. This ensures that adaptive normalization is applied consistently and specifically to each sub-sequence of tokens based on its original data instance, maintaining fidelity of conditioning within the computationally efficient packed representation.
Conclusion. NYT's architecture design of native-resolution generative modeling fundamentally enhances image synthesis. By preserving the complete spatial hierarchy and detail of original inputs, NřT intrinsically learns scale-independent visual distributions. This leads to superior fidelity and adaptability in zero-shot generalization across diverse resolutions and aspect ratios.
$ Following the common practice [4,
46
68
70
74
85
we use 0 = 10000 to ensure distinct positional
signals over typical sequence lengths.

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Table 1: Benchmarking class-conditional image generation standard ImageNet 256 x 256 and 512 x 512 benchmarks. Notably, a single NiT model can compete on both two benchmarks. "L" "1" indicate lower higher values better. "# Res" and "# Token" respectively represent the resolution, total training token budget. "mFID" denotes the average Fréchet inception distance (FID) value of two benchmarks. "t": independent model is required for each benchmark, leading cumulattve computational cost, as reflected by the huge "# Token". All the results are reported with the utilization of classificr- cussifier-free-guicee free-uuddaece (CFG).
Method
Parnon
Res
# Token
FID
sFID
236 256
181
Prec.1
Rec.
FID,
sFID.
512 x 512
187
Prec.1
Rec:1
mFID!
Auto-rogresstre for Specific Resolutious
MaskGiT
256
6.18
182.1
0.80
0.51
JamaCien-BB
3B
384
221
2.18
5.96
263.33
0.82
0:58
ST-AR-XL
775M
256
2.37
6.05
270.59
0.82
0.58
VAR 2B'
2B
256
1.73
350.2
0.82
0.60
2.63
303.2
2.18
Diffasion Models for Specific Resolutions
DIT-XL/2
675M
2564:512
2.27
4.60
278.24
3.01
0.54
2.66
STT-XL2!
675M
2566:512
1428H
2.06
4.50
270.3
01.82
0.57
2.62
4.18
252.2
0.84
0:55
2.34
PlowDCN
675M
25646512
158.7
2,00
4.37
263.16
0.82
0.58
2.44
4.53
252.8
0.84
0.54
2.22
HTV2-XL/
671M
2568-512
23788
2.36
4.44
293.83
0.80
0.62
2.62
6.63
307.54
0.80
0.57
2.44
SITREEAT
675M
2564-512
525/1
1.42
4.70
305.7
0180
0.65
2.08
4.19
274.6
0.83
0.58
1.75
PixNerd!
2568:512
4.58
297.00
0.79
2.84
0.59
2.49
EDM2-L
777M
512
472B
1.88
4.27
258.21
0.81
0.58
EDM2-XXL
1.58
512
172.
1.81
Generalist Diffusion Models for Arbitrary Resolution
NIT-XL
675M
Native
131B
2.16
6.34
253.44
0.79
1.57
260.69
0.63
NIT-XI.
675M
Narive
1978
2.03
6.28
265.26
0.80
0.62
1.45
4.04
272.77
0.81
0.62
1.74
Experiments
4.1 Setup
Native-Resolution Generation Evaluation.
To comprehensively evaluate the resolution generalization, we conduct a wide range of resolution spectra.
We evaluate NiT standard 256 256 and 512 512 benchmarks with a single model, differing from the previous implementations with two distinct models.
For high-resolution generalization evaluation, experiments are conducted on four resolutions: {768 x 768, 1024 x 1024, 1536 x 1536, 2048 x 2048).
For aspect ratio generalization analysis, experiments are conducted on six aspect ratios: {1 3,9 16,3 4,4 : 3, 16 9,3 1}. The corresponding resolutions are: {320 x 960, 416 x 768, 480 x 640, 640 x 480, 768 x 416, 960 x 320).
Implementation Details. We use DC-AE 12 with a 32x down-sampling scale and 32 latent dimensions as our image encoder. Therefore, an image with the shape of H x W encoded into a latent token vector z E IR 2/ W x32, For class -aised image generation experiments, the model architecture follows DiT [55] except for using a patch size of 1. Our model trained with native- resolution images, so the batch size is unsuitable in our setting. Therefore, similar to LLMs [22 84 we use token budget (i.e., the summation of total tokens in all training iterations) to represent the training compute. For class-guided image generation, we use 131, 072 tokens in one iteration. Unless otherwise stated, all results in Tabs. I are evaluated with the NiT model trained for 1000
steps (corresponds to 131B token budgets). We report FID [29], sFID [50], Inception Score (IS) 62
Precision and Recall [40] using ADM evaluation suite 18.. Text-to-image generation experiments detailed in Appendix
4.2
State-of-the-Art
Image Generation
Standard Benchmarks
We first demonstrate the effectiveness of NiT standard ImageNet 256 x 256 and 512 512 benchmarks. We compare NiT with state-of-the-art autoregressive models: MaskGit (8) Llam-
'It holds: 131,072 072 256 x 512 = 1024 x 128. For 256 x 256 resolution (256 tokens each in DiT (55) this corresponds to larger batch of 512 images. Conversely, for higher-resolution 512 x 512 images (1024 tokens each), it corresponds smaller batch of 128 images is used to maintain the same total token count.
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Table 2: Benchmarking resolution generalization capabilities on ImageNet. As FiTv2 and SiT- REPA have failed to generalize to 1024 x 1024 resolution, we use "X" to represent their inabilities to generalize to higher resolutions.
768 x 768
1024 x 1024
1536 1536
2048 x 2048
Method
FID
sFID.
1SI
FID
sFID)
1S1
FID
sFID!
IST
FID
sFID)
IS"
EDM2-L
9.02
18.57
248.15 15
40.74
47.29
119.41
105.57
69.31
40.05
172.30
89.18
16.82
FlowDCN
9.817
24.52
202.86
18.64
42.36
206.66
41.170
61.75
150.22
69.88
68.15
81,33
FiTv2-XI.
190.69
143.78
8.56
281.55
209.20
4.55
X
X
X
X
SIT-REPA
274.63
215.25
3.58
286.79
235.07
2.643
x
X
X
x
NIT-XL
4.05
8.77
262.31
4.52
7.99
286.87
6.51
9.97
230.10
24.76
18.01
131,36
Table 3: Benchmarking aspect ratio generalization capabilities on ImageNet. " SiT-REPA is evaluated with 160 480, 216 x 384, 240 320, 320 240, 384 x 216 and 480 160, because only the model trained on 256-resolution image data is open- sourced For other models, the exact
resolutions are: 320 x 960, 416 x 768, 480 640, 640 x 480, 768 x 416 and 960 320.
1:3
9:
16
3:4
4:
16:9
3:1
Method
FID.
1ST
FID
1ST
FID.
1S1
FID.
18'
FID
181
FID]
ISI
EDM2-L
32.48
68.45
8.19
170.06
5.00
183.78
5,97
170.06
11,58
144.65
39,94
59.69
FlowDCN
34.97
73.30
8.25
178.16
5,311
197.01
5.74
185.67
10.31
154.92
40.721
64.11
FiTv2-XL
67.57
37.95
50.58
60.52
49,96
59,40
62.18
46.06
71.79
42.89
94.15
35.74
SYT-REPA
147.61
14.44
34.42
94.13
3.87
242.61
4.03
242.42
37.77
88.46
114.01
18.56
NIT-XL
16.85
189.18
4.11
254.71
3.72
284.94
3,41
259.06
5.27
218.78
255.05
as well state-of-the-art diffusion models: DiT 48), SiT 47. DCN 80). PixelNerd 79 FiTv2 81], SiT-REPA [88], and EDM2 (36). All these are resolution-exper methods, independently training two models for the two benchmarks.
aGen 699 VAR 76 and ST-AR
90].
Performance Analysis. As demonstrated in Tab. I NiT-XL achieves the best FID 1.45 on the 512 x 512 benchmark, outperforming the previous SOTA EDM2-XXL with half of the model size. On the 256 x 256 benchmark, our model surpasses the SiT-XL, DīT-XL, and FiTv2-XL. models on FID with the same model size well as outperforms the LlamaGen-3B model with much smaller parameters. Compared with all the baseline models, our model demonstrates significant training efficiency, it avoids the cumulative computes train distinct models. To the best of our knowledge, this is the first time a single model can compete on these two benchmarks simultaneously. For mFID metric, NiT-XL largely outperforms DiT-XL and SiT-XL with 9.17% of the training costs. And it can surpass SiT-REPA, with only 37 52% of the token budget.
Beyond standard benchmarks 256 x 256 and 512 resolutions, we comprehensively conduct high- resolution generalization in Tab. 2 and aspect ratio generalization evaluations in Tab. We compare method with 4 videly-recognized baselines: 1) EDM2 [3). SOTA CNN-based diffusion model; 2) FlowDCN 80) CNN-based diffusion model designed for multi- restiiresooltion image generation;
768x768 class-88 class-207
1024x1024
2048:2048 768x416 640x480 1536x1536
480x640
416x768
class-980
class-985
class-402 class-250
class-270
class-117
NIT-XL
(Ours)
EDM2-L
FlowDCN
Figure 4: Qualitative Comparison of Resolution and Aspect Ratio Generalization. We provide the visualization of NiT, EDM2 and FlowDCN, because F2Tv2 and SiT-REPA demonstrate inferior generalization capability revealed by quantitative results.

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Table 4: Ablation study on resolution generalization. We explore different data mixtures to realize why NiT can generalize to unseen resolutions. All these models are trained for 100K Steps.
256 x 256
512 x 512
768 x 768
Data Mixture
FID1
sFID,
1St
FID,
sFID,
IS
FID
sFID.
IST
(a) Below 256
21.08
13.47
68.32
180.27
72.65
10.16
241.09
129.49
5,46
(b) Below 512
36.10
14.95
35.77
28.06
14.23
58.22
195.99
83.87
10.00
(c) Native Resolution
31.95
16.69
41.31
24.52
12.84
65.89
25.68
13.89
70.56
(d) Native Resolution 256 + 512
24.54
8.39
53.30
17.62
5.77
81.64
21.03
11.75
86.25
(c) 256 512
28.22
8.44
46.71
20.21
5.98
72.17
50.28
91.45
43.86
(f) 512
127.47
53.72
10.60
24.64
6.18
59.14
86.36
124.91
19.43
Table 5: Ablation study resolution and aspect ratio generalization. We further evaluate the data mixture on different aspect ratios. All these models are trained for 500K Steps.
1024 x 1024
1536 x 1536
2048 x 2048
320
960
416 x 768
480 x
640
Data
FIDJ
18
FIDJ
1ST
FID
1S1
FID]
181
FID.
1ST
FID.
IS1
(c)
13.15
159.52
18.11
132.53
41.95
71.19
40.14
53.41
13.45
137.31
10.63
160.91
(d)
9.73
180.29
14.29
152.37
37.79
78.66
43.86
50.25
12.14
131.96
8.55
169.24
3) FiTv2
81
diffusion transformer model designed for multi-resolution image generation; 4) current SOTA diffusion transformer model.
SiT- SIT-REPA 88
Generalization Analysis As demonstrated in Tab. 2 NiT-XL significantly surpasses all the baselines on resolution generalization. Remarkably, NiT-XI. achieves FID 4.07, 4.52, and 6.51 on 768, 1024 1024, and 1536 1536, respectively, demonstrating almost no performance degradation when scaling to unseen higher resolutions. EDM2-L and FlowDCN can generalize to 768 x 768 resolution, but they fail generalize higher resolutions beyond 1024 x 1024 resolution. FfTv2 and SiT-REPA demonstrate very inferior resolution generalization capability beyond their training resolutions. As shown in Tab. 3 NiT-XL can also generalize to arbitrary aspect ratios, greatly outperforming all the baselines. Although EDM2-L, SiT-REPA and FlowDCN can generalize to 4 3 and 3 aspect ratios, they fail to generalize more extreme aspect ratios, like 9 : 16 and 1 3. NiT-XL can generalize up to 9 16 and 16 9 aspect ratios with negligible performance loss, and perform best 1 3 and 3 aspect ratios. These results indicate NIT is initially equipped with the resolution-free generation capability, bridging the gap between vision generation and the sequence- free generation 84].
in LLMs
The qualitative comparison in Fig. 4 is consistent with the aforementioned quantitative results. NiT demonstrates superior generalization quality to EDM2-L and FlowDCN, producing reasonable generated samples. When beyond 768 resolution, EDM2-L in particular generates images dominated by non-informative textures, while FlowDCN-XI. tends to replicate objects multiple times a single image. Regarding aspect ratio generalization, models like DM2-L and FlowDCN-XL XXL exhibit distinct cropping-induced bias. This strongly suggests the models have internalized a truncation bias due to their training predominantly on square or tightly cropped image samples. Consequently, they tend to generate unnaturally framed outputs with truncated object boundaries, especially when encountering extreme aspect ratios. This aligns with findings from SDXL [57], revealing that image cropping during data preparation can propagate biases into generated samples, leading to adverse effects, particularly with extreme aspect ratios. More visualizations are provided in Appendix [
4.3 Ablation Study
Set up. We find the surprising generalization ability of NiT in unseen resolutions and aspect ratios. In this part, we explore what enables the impressive generalization ability. We conduct groups for ablation: (a): we keep the aspect ratio of image data and resize its height and width smaller than 256:(b): we keep the aspect ratio of image data and resize its height and width smaller than 512; (c): we only use native-resolution image data for training: (d): besides
we add 256 x 256-resolution, and 512 x 512-resolution image data for training; (e): without native-resolution images, we only use 256 x 256 and 512 x 512 images; (f): we only use fixed 512 resolution images. All the experiments are conducted using a NiT-B model with 131 M parameters, and all the results evaluated with the usage of CFG (CFG scale is set as 1.5).

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Table 6: Ablation study on data and positional embeddding. 1 corresponds to SiT [47]. 1 corresponds to NiT. All the models trained for 100K Steps.
256 x 256
512 sFID)
512
768
768
Data Positional Embedding
FID.
sFIDI
1St
FID4
1S1
FID
sFID)
IST
(d)t Sin-Cos PE
35,66
8.76
35.65
29.41
6.60
51.26
31.06
12.69
59.15
(d)
RoPE
24.54
8.39
53,30
17.62
5.77
81.64
21.03
11.75
86.25
(f)
Sin-Cos PE
125.40
57.82
9.26
29.40
6.61
53.73
124.51
126.33
12.01
(f)
RoPE
127.47
53.72
10.60
24.64
6.18
59.14
86.36
124.91
19.43
As demonstrated in Tab. 4 (c) consistently beat (a) and (b) resolution generalization. When training images are capped at 256 pixels on the long side (a). the model excels at 256 but degrades sharply at 512 x 512 and 768 x 768. Raising the cap to 512 pixels (b) postpones, but does not climinate the drop-off: once the test resolution exceeds the highest resolution encountered in training, FID climbs rapidly. By contrast, training at native image resolution with no upper bound (c) yields consistently strong performance across the full range of resolutions we evaluated. Further, (d) and (e) consistently beat (c) on 256 x 256 and 512 x 512 benchmarks. This is because the model is not optimized for these two resolutions in (c), thus demonstrating inferior performance. The performance on 256 256 and 512 x 512 of (d) and (e) is comparable; however, (e) extremely lags (d) 768 x 768 resolution generation. We think that the training of (e) is solely on two resolutions, significantly restricting the generalization capabilities of models. Meanwhile, in Tab. 3 as we scale up training steps to 500k, we further compare the performance of (c) and (d) on resolution generalization and aspect ratio generalization. We find that (d) demonstrates stronger generalization capability than (c).
Positional Embedding Comparison. Tab. 6 demonstrates the effects of data and positional embed- ding resolution generalization. When using native-resolution data (f), both using Sin-Cos PE and using RoPE can demonstrate strong generalization capability across multiple resolutions. For both fixed-resluution settings and native-resolunee settings, RoPE can improve model performance across multiple resolutions, indicating its effectiveness on 2D structural prior injection.
Insights. The ablation study reveals that NiT's strong generalization to unseen resolutions and aspect ratios is primarily enabled by training in native-resolution to learn resolution- and aspect- ratio invariant visual distribution. While adding fixed resolutions like 256 x 256 and 512 x 512 improves performance on those specific sizes and aspect ratios of 1 1, 768 x 768, omitting native- resolution data severely hinders generalization to other aspect ratios. Therefore, a combination of varied resolution and aspect ratios, with native resolution playing a key role, is essential for robust generalization, rather than just training on a limited set of fixed scales.
Efficiency Analysis. We compare the training and inference efficiency on the ImageNet-2566 bench- mark using a single NVIDIA A100 GPU, revealing that NiT demonstrates better training and inference efficiency compared to DIT. Analysis is conducted with the NiT-B and DłT-B model, both with 131 M parameters. We set the token number in one iteration as 65536. Specifically, NiT achieves faster training speed of 1.28 sterations/eecond (iter/s) compared to DiT's 1.08 iter/s. Furthermore, NiT ex- hibits lower inference latency at 0.246 seconds, while DiT has latency of 0.322 seconds, highlighting NiT's greater computational efficiency.
5 Conclusion & Limitation
In conclusion, this work introduces the native-resolution image synthesis paradigm for visual content generation. We reformulate the resolution modeling as "native-resolution generation" and propose Native-resolution diffusion Transformer (NiT). To the best of our knowledge, NIT is the first model that can achieve dual SOTA performance on 256 x 256 and 512 x 512 benchmarks in ImageNet generation with a single model. We demonstrate NiT's robust generalization capabilities to unseen image resolutions and aspect ratios, significantly outperforming previous methods, While NIT shows strong performance, its generalization ability on extremely high-resolution and aspect ratios is still not satisfactory. Future research could explore optimal strategies for balancing diverse training resolutions for improved efficiency and further investigate the model's generalization limits across a wider spectrum of data domains (e.g., video generation) and highly disparate aspect ratios. Additionally, the computational resources required for comprehensive multi-scale training remain a practical consideration for broader application.
10

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Acknowledgements
This work was supported by the JC STEM Lab of Al for Science and Engineering, funded by The Hong Kong Jockey Club Charities Trust, the Research Grants Council of Hong Kong (Project No. CUHK14213224).
This work is partially supported by the National Natural Science Foundation of China (Grant No. 62306261), and the SHIAE Grant (No. 8115074). This study was supported in part by the Centre for Perceptual and Interactive Intelligence, a CUHK-led InnoCentre under the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government. This work is also partially supported by Hong Kong RGC Strategic Topics Grant 4TG//E.-43/24-N.
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Appendices
A Text-to-lmage Generation
A.1 Streamlined NIT Architecture for Texa-to-lmage Generation
X N
A magpie perched on wooden post. The bird has glossy black head, wings, and tail, with striking white belly und underparts. The bird stunding post that part of'a wire fence.
NI--22 Block
Feed Forward
V3.
LLM Decoder
Packed MHCA
Y2. P2
Packed MHSA
ai
LoRA
Vr.B1
AdaL.N
Adding Noise
Image AE
Patcify
Time Embeuding
Figure 5: Illustration NiT blocks used for text-to-image generation.
We introduce a streamlined architecture to incorporate textual information. As in Fig. 5 we insert cross-attention block between the self-attention block and the feed-forward network. As the adaptive LayerNorm (AdaLN) module only conditions on the time embedding, we thus reduce its parameters through LoRA [31 design. Given transformer hidden size d, the AdaLN layer predicts tuple of all scale and shift parameters S = [33, ,3, 33, 72, 73, on (22 03), where 8, " represents the shift and scale parameters for block, and the subscript 1, 2, denote self-attention, cross-attention, and feed-forward respectively. Based on the embedding for time step t E TRdd S for the l-th NiT-121 block computed as:
s' [B B/. h, 7/2
1/. ai.a2, an) W R
(9)
where W E (9xd)xrr W/ E Rrxd and the bias parameters are omitted for simplicity. We can adjust the LoRA rank r align the block parameters with the NiT-C2I blocks.
A.2 Advanced Text-to-Image Generation
Implementation details. For eext-to-image generation, and adopt Gemma3-1B-ir [3] text encoder. We use LoRA rank r = 192 in AdaLN, which leads to a total 673M model parameters, matching the model parameters of the XL model in the C2I (class celass-to-ine to--tt--mmmn to-image) setting. We use the REPA [88] strategy in the training, where a RADIO-v2.52
[28) model serves as the visual encoder. We conduct text-to-image generation experiments the SAM 38] dataset with captions generated by MiniCPM-V [86]. We use token number in each iteration as 786, 416 and train the model for benchmark.
400K steps and evaluate image quality using c0C0-val-2014
41
Compared baselines and Results. We report the zero-shot text-to-image performance on COCO- 2014 benchmark in Tab. 7 competing with DALL-E [60], View [99] Parti [87]. Make-A- Scene 24 Muse (7) GLIDE (11) DALL-E (59, LDM 61], eDiff-1 5 SD-v1.5 61), and SDXL- Turbo 63 NIT-T2I demonstrates competitive performance with the baseline models. Notably,
NFT-T2I achieves the best CLIP score of 0.345, and surpasses SD-v1.5 and SDXIo-Turbo on both
FID and CLIP scores with much smaller model size and training costs.
23

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Table 7: Text-to-image generation of NIT. We compare models by the zero-shot generation task on the COCO-2014 [4]] dataset.
Method
Param.
Res.
FID (4)
CLIP
DALL-E
12B
256
27.5
CogView2
6B
256
24.0
Parti-750M
750M
256
10.71
Parti-3B
3B
256
8.10
Parti-20B
20B
256
7.23
Make-A-Scene
256
11.84
Muse
3B
256
7.88
0.32
GLIDE
5B
256
12.24
DALL-E
5.5B
256
10.39
LDM
1,45B
256
12.63
Imagen
3B
256
7.27
eDiff-I
9B
256
6,95
SD-v1.5
0.9B
512
9.78
0.318
SDXL-Turbo
3B
1024
23,19
0.334
NiT-T21
673M
1024
9.18
0.345
B Detailed Quantitative Results
In this section, we report all the metrics of generalization experiments in Tab. addition, we provide the CFG cclasifier-ftee-uuiaan hyperparameters of NiT-XL.
Table 8: Detailed Quantitative Results of NIT-LL. We further provide the CFG scale and interval for each experiment and report all the metric values generalization experiments.
Resolution
CFG-scale
CFG-interval
FID
sFID
1St
Prec.
Rec.
256 256
2.25
[0.0, 0.7]
2.16
6.34
253.44
0.79
0.62
512 512
2.05
[0.0, 0.7]
1.57
4.13
260.69
0.81
0.63
768 x 768
3.0
[0.0, 0.7]
4.05
8.77
262.31
0.83
0.52
1024 x 1024
3.0
[0.0, 0.8]
4.52
7.99
286.87
0.82
0.50
1536 x 1536
1.5
[0.0, 1.0)
6.51
9.97
230.10
0.83
0.42
2048 x 2048
1.5
[0.0, 1.0)
24.76
18.02
131.36
0.67
0.46
320 960
4.0
[..9.
16.85
17.79
189.18
0.71
0.38
416 768
2.75
0.70
4.11
10.30
254.71
0.83
0.55
480 640
2.75
0.70
3.72
8.23
284.94
0.83
0.54
640 480
2.5
0..0
3.41
8.07
259.06
0.83
0.56
768 416
2.85
0.70
5.27
9.92
218.78
0.80
0.55
960
[0.0, 0.9]
9.90
25.78
255.95
0.74
0.40
C Detailed Implementation of NiT
This section demonstrates the detailed implementation of Packed Full-Attentinn and Packed Adaptive Layer Normalization (Adal.) .N) in an NiT block. Different from traditional attention implementation for batched data, we use FlashAttention2 [15] for packed data, Ieading to enhanced efficiency, as in Algorithm [ Besides, we use broadcast mechanism conditional vector for Packed-AdaLN, detailed in Algorithm
D Qualitative Results of NiT
D.1 Generalization Comparison with Baseline Models
We provide the qualitative results of NiT-XI., EDM2-L (6), and FlowDCN-XI. 80 on resolu- tion generalization and aspect ratio generalization. The qualitative results of RTv2-XL 8 and

---

Algorithm Packed Full-Attention with FlashAttention for flexible-length sequence processing.
import torch
import torch. from flash_attn import alash on_ func
def rotate_halI(x):
... r', 2) x torch. stack x1), din -1)
rearrange
x1, x2 x.unbind -1)
rearrange ....
(d x)
11
class Attention (nn. :oddull)):
13
def ..init_..et (self,
15
nun_heads: int gkv_bias: bool False, qk_norm: bool Palse. attn_drop: float 0., droj.drop: float
16
18
20
norn_layer: an. Module an Layertiorm None:
22
super in.itt)
23
assert din nun_heads 0, "din should be divisible by nun_heads"
self nalffnun_heads hea_heads
25
self.hoad.din dim // nun_heada
hesd self. an Linerr self.q.norn aore_layer fttelffhaddddim. head_dim) gk_norn Identity relf.k_norm nora_ (self. (self.head_dip if qk_norn else Identity attn_drro an. Dropout (attn_dropp) salf.proj Ln.ein (din, din) self.proj.drop an Dropout (proj.drop)
27
din 3, blas-gky_biase
30
32
34
def fsrlf
35
Tensor,
36
cu_seqlens:
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fraqs_cos: torch.Tenses
freqs_sin: torch. Tensor
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-> torch. Tensor:
x: packed sequence
shape [8, D]
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cu_seglens: [0, h_1+vl, 1i4o__th__2x.22 ...), the comulated sequence length
freqs_cos, freqa_ain:
frequences
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X, c xhepe gkv self self.gkr(x).
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N, self-aus_heade bead.bead.din
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) pereutee (1, 0, 2, 3)
ori_dtype gkv. dtype
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k, qkv. unbind
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9, k self.
self.k_
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Use axial 20-RoPE to inject 20 atructural priors freqe_cos rotate_halfee k =k freqq_cos retate_half (k) freqs_sin
freqs_ein
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9. q.to(orri_tyype) k.to(ori_dtypoo)
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_ax_seqlen (cu_seqlens [1:] su_seglens [:-1]) max iten()
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apply flash-atta for efficient implementation
flaeb_ k, cu_seglens, cu_seqlens, max_seglen, seqlee ).reshape -1)
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self, =eelf.rro (x)
helf.proj_drop retura x
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25

---

Algorithm 2 Packed Adaptive Layer Normalization and NiT block.
import torch
import torch. from time www.eeaaaiiiioo..aaatttooooo import MIp
acale): return x (1 scale) + shift
(x
10
class NiTBlock (on, Nodule);
11
adaptive layer norn (adaLN -Zero) conditioning,
13
..init__. bidden_size, nun_heads, nlp.ratio=4.0, stblock,kvarga):
15
super ini_.in
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self.norm: mn. (ww.eyyylorraaiidde...... self Attention(
dlaeettieee.alllaeaaeee eps-le-6)
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bidden_size, gk_nora-block_kwarg ("ak_aorn")
gkv_bias-True,
20
norm2 Layarraroorr
alanettolae..fffheeeaaaa apa=1a-6)
22
dip int aize ulp.ratio)
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approx_gels lambda: an GELU( taan"" self mlpp.mpp- Mlp(
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27
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self adaLX modulattion a an SiLU(),
an. nn. Sequentaa
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an Linein (hidden_rize, bidden_size, bias-True)
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foreard x, by_list, freqs_cos, freqs_ein):
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x: packed saquence with ahape [N1 D]
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6: conditional vector with shape [a, D], (a represetns number of instances) hv_liet: [(h_., v_1l, [b2, w_2],
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[b_n, v_a]]
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freqs_cos, freqs_sin: 2D-RoPE frequences
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seqlens hu_liat 0] hu_liat[:, 1] cu_seqlens torch.cat(1 Corch.tennoor [[0], din-0, dtype-torch. torch, cumsun(eeqlees
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(n, D) >> (I, D) for Packed AdaLM })
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([c[t]]unaquueza unaqueeze reeeatt (saqlana[L) [i], 1) for in range(()], dim=0) torch.eat ([c[i]
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predict
acale paranatera _acked
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19
shift_nes, scale_nss, gate_nes, shift_mlp, scale_ulp, gate_ulp self adaiX_nodulation(en (6, dim--1)
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gate_nsa self.attn(
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modulate nermf (x), ehift_nea, scale_nea), cu_saglena, treqa_coa, freqa_ain
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gate_nlp self.mlp (modulate (self norn2 (x), shift_mlp, scale_mlp))
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return x
26

---

REPA-XL [8 are not provided because these two methods demonstrate very weak generalization capabilities. The resolution generalization visualizations are shown in Figs. to 9 while the aspect
ratio generalization visualizations are demonstrated in Figs. TO to 15
Based on these visualization results, we find that NiT-XL. achieves the best qualitative performance on both resolution generalization and aspect ratio generalization, consistent with its best FIDs and IS scores in the manuscript. We then provide more analysis on the qualitative results.
Analysis of resolution generalization visualization. As demonstrated in Figs. to NiT-XL demonstrates almost quality degradation from 768 x 768 to 1536 x 1536 resolution. It can also generate reasonable content on 2048 x 2048 resolution. However, EDM2-L and FlowDCN-X demonstrate inferior visual quality in resolution generalization. Although EDM2-L and FlowDCN- 768 resolution, they fail to generalize to higher
XL can generate some plausible samples on 768 x resolutions (1024 1024 2048 2048). The key limitations are:
1. Lack of Semantic Coherence. Both EDM2-L and FlowDCN-XX XL frequently fail to generate identifiable, realistic instances of the target classes, especially for high resolution (see Fig. 2
2. Repetitive Textures. EDM2-L in particular generates images dominated by repetitive, non- informative textures, lacking structural variation or clear object boundaries.
3. Object Duplication and Spatial Disruption. FlowDCN-XL tends to replicate objects multiple times in a single image, resulting in cluttered and spatially implausible compositions.
4. Color and Lighting Artifacts. EDM2-L often outputs grayscale or dull images, while FlowDCN-XL introduces unnatural color schemes and poor lighting consistency.
These limitations reveal that neither model effectively integrates objects into realistic backgrounds, with EDM2-L omitting context and FlowDCN-XL producing jumbled scenes. There is an evident reliance on local textures at the expense of global object structure and semantics.
Analysis of aspect ratio generalization visualization. As demonstrated in Figs. TO to 15 NIT-XL demonstrate superior aspect ratio generalization performance than EDM2-L and FlowDCN-X -LL. Compared to the NiT-XL, both EDM2-L and FlowDCN-XL exhibit several notable shortcomings in image generation quality:
1. Cropping-Induced Bias. Long or wide objects, such as guitars (i.c., class-402) or parrots (i.c., class-88,89 88), are often truncated improperly framed. This suggests form of information leakage or truncation bias introduced by over-reliance on square or tightly cropped training samples, leading to unnaturally framed and truncated object boundaries. This corresponds to the finding in SDXL [57]: cropping image data can leak into the generated samples, causing malicious effects, especially in extreme aspect ratios (see Figs. 10 and [T].
2. Blurring and Lack of Detail. Many generated images, such as the sea turtle (class-33) in FlowDCN-XL. or the flowers (class-985) in EDM2-L., lack the sharpness and textural richness seen in the NiT-XL outputs. This indicates poor high-frequency detail modeling, which compromises the realism and clarity of the outputs.
3. Object Repetition and Spatial Artifacts. There is frequent duplication of objects and struc- turally incoherent arrangements, breaking spatial consistency. Some entities appear anatom- ically incorrect or exhibit unnatural part arrangements, especially in animal classes.
4. Color Artifacts. Inconsistent coloring and unnatural saturation further diminish the realism of the generated images.
The visualization highlights critical limitation of conventional training pipelines that rely on center cropping and resizing to square resolutions. Models like EDM2-L and FlowDCN- XL, which were trained under such regimes, often fail to generalize to objects with other aspect ratios. This is particularly evident examples such as guitars (class-402), parrots (class-88, 89), and volcanoes (class-980) that are naturally elongated either horizontally or vertically. In contrast, NIT-XL demonstrates robust aspect ratio generalization, preserving the spatial integrity and composition of clongated objects without distortion, demonstrating the effectiveness of its native resolution modeling.
27

---

chees250
chee-1170
clse-279 chas-360
NIT-XL
EDM2 L FID-4.02 02
FID+9.82
Figure 6: Qualitative comparison of resolution generalization on 768 x 768 resolution.
class-85 88
class-250
clase-270
claas2299
cloos-600
class-985
NIT-XL (Ows)
FID-0.52
EDMO-L
FID-40.74
Hlo
FID-18664
Figure 7: Qualitative comparison of resolution generalization on 1024 x 1024 resolution.
D.2 More Qualitative Results of NiT
More gualititative results of NiT-XL are demonstrated in Figs. 16 to 25
28

---

class 2i
class 250
class 270
class 279
class 360
class 995
NIT-XI (Ours)
FID+6.51
EDMO-L FID-105:557
FlowDON-XL FID-41.17
Figure 8: Qualitative comparison of resolution generalization 1536 x 1536 resolution.
class-88
class-270
class-279
(Ous)) FID 24.76
FID-172.30
FlswDXN--XL FID 69.88
Figure 9: Qualitative comparison of resolution generalization on 2048 x 2048 resolution.
29

---

88
class-89
class-250
class-387
class-388
class: 402
class-980
NiT-XL (Ours)
FID-9.90
EDM2-L FID-39.94
FlowDCN-XL. FID-40.72
Figure 10: Qualitative comparison of aspect ratio generalization on 960 x 320 resolution (correspond- ing aspect ratio).
class-33
class-207
class-291
class-985
NIT-XL (Ours) FID=16.85
EDM2-1. FID-32.48
FlowDCN-XL FID-34.97
Figure 11: Qualitative comparison of aspect ratio generalization on 320 x 960 resolution (correspond- ing to 3 aspect ratio).
30

---

class-88
class-89
class-250
class-388
class-402
class-980
NIT (Ouns)
FID-5.27
EDM2-L FID-11.58
FlowDCN-XL FID-10.31
Figure 12: Qualitative comparison of aspect ratio generalization on 768 x 416 resolution (correspond- ing to 16 9 aspect ratio).
class-33
class-207
class-291
class-387
class-985
NIT-XL (Ous) FID-4.11
EDM2-L FID-8.19
FlowDCN-XL FID-8.25
Figure 13: Qualitative comparison of aspect ratio generalization on 416 x 768 resolution (correspond- ing to 9: 16 aspect ratio).
31

---

class-88
class-89
class-250
class-402
class-980
NIT-XL (Our))
HID-3.41
EDM2-L FID=5,97
FlowDCN-XI. FID-5.74
Figure 14: Qualitative comparison of aspect ratio generalization 640 x 480 resolution (correspond- ing to 3 aspect ratio).
class-33
class-207
class-291
class-387
class-388
class-985
NiT-XI (Ours)
FID-3.72
EDM2-L FID-5.00
FlowDCN-XL FID=S.31
Figure 15: Qualitative comparison of aspect ratio generalization 480 x 640 resolution (correspond- ing to aspect ratio).
32

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Figure 16: Uncurated generation results of NiT-XL, We use the class label as 33.
Trate 1233
Figure 17: Uncurated generation results of NIT-XL, We use the class label as 88.
33

---

Nav
Figure 18: Uncurated generation results of NiT-XL. We use the class label as 250.
Nete
Watcs 212
Traah: 1235
Widh
Figure 19: Uncurated generation results of NiT-XL, We use the class label as 279.
34

---

Wor. 212
Watx 21h
Kete 1255
Figure 20: Uncurated generation results of NiT-XL. We use the class label 388.
WARK
MN
10th
Figure 21: Uncurated generation results of NiT-XL. We use the class label as 417.
35

---

Wall
Figure 22: Uncurated generation results of NiT-XL. We use the class label 437.
MIRA
With
Figure 23: Uncurated generation results of NiT-XL, We use the class label as 812.
36

---

Figure 24: Uncurated generation results of NiT-XL. We use the class label as 980.
Figure 25: Uncurated generation results of NiT-XL, We use the class label as 985.
37


Reviews:
[{"review_id": "msQ1FpPif4", "reviewer": "Reviewer_S4nj", "summary": "This paper proposes Native-resolution Image Synthesis, a new approach to image generation. It introduces the Native-resolution diffusion Transformer (NiT), which handles images of any resolution or aspect ratio. NiT uses variable-length visual tokens to overcome the limitations of fixed-size models. Trained only on ImageNet, it achieves state-of-the-art results on both 256×256 and 512×512 benchmarks. It also generalizes well to unseen sizes like 1024×1024 and unusual aspect ratios.", "questions": "See weakness", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n1. This paper effectively addresses the challenge of using images with diverse resolutions during training. Unlike previous methods that rely on fixed-size inputs, the proposed NiT model learns directly from native-resolution images. As a result, it achieves strong and stable performance not only on standard benchmarks like 256×256 and 512×512, but also on unseen higher resolutions and aspect ratios.\n\n2. The methodology is clearly presented, with well-structured explanations of each component. \n\n\n**Weakness**\n1.The paper briefly mentions multi-resolution training in related work, which has been proven effective and practical in large-scale settings. Its approach is especially similar to the frame packing strategy used in CogVideoX. However, whether the proposed method retains its efficiency and effectiveness at large scale remains an open question, as the paper does not provide empirical validation under such conditions.\n\n2. The paper mentions training with images of diverse resolutions, but lacks concrete details on the experimental setup. For example, it does not specify how image resolutions are mixed within a batch or across GPUs during training. Compared to bucket sampling, it's unclear whether the proposed method can truly support more diverse and continuous resolution ranges in practice. Additionally, since the model relies on a high compression ratio DC-AE with a downsampling factor of 32, all image resolutions must be multiples of 32. This constraint may limit the flexibility and granularity of supported resolutions in real-world applications.\n\n3. The paper emphasizes its ability to resolution extrapolate. However, combining multi-resolution training with RoPE-style positional encoding already enables some level of resolution extrapolation. While the paper presents strong results in this area, it would be helpful to clarify how much of the performance gain comes from the proposed architectural design versus the known benefits of RoPE and resolution diversity in training.", "was_revised": true, "final_justification": "weak accept", "rebuttal": "We sincerely appreciate your constructive comments and efforts in helping us enhance the quality of our paper. Below, we provide detailed point-to-point responses addressing each of your valuable suggestions:\n\n**1. Effectiveness in large-scale settings**\n\nNiT remains both efficient and effective, even in large-scale text-to-image scenarios. As mentioned in line 222 of our manuscript, we have included detailed setup information and experimental results for the text-to-image generation task in the supplementary materials. Specifically, we trained a model containing $673M$ parameters on the SAM [1] dataset (comprising $10M$ images), utilizing descriptive captions generated by MiniCPM-V. Importantly, NiT-T2I achieves a strong CLIP score of $0.345$ and notably outperforms SD-v1.5 and SDXL-Turbo in both FID and CLIP scores, despite having a significantly smaller model size and lower training costs.\n\n**2. Training with diverse resolutions**\n\n**2.1 Concrete details on experimental setup.** \nWe provide the details on multi-GPU parallel in our native-resolution training setting. \n* Preparing a packed distributed sampler. We first construct the metafile, restoring the index and information of each image instance. Given a preset maximum length, we pack different image instances into one sequence, using the longest-pack-first histogram packing algorithm [2]. We record the index of each image instance in a sequence. Then, we use these indices to load the corresponding image instance and pack them into a sequence for each training iteration. \nFor example, we first obtain the meta file of each image file with a piece as follows:\n    ```\n    {\"index\": 0, \"image_file\": \"n01601694/n01601694_11629.JPEG\", \"image_h\": 384, \"image_w\": 576,}\n    {\"index\": 1, \"image_file\": \"n01601694/n01601694_11799.JPEG\", \"image_h\": 320, \"image_w\": 480}\n      ......\n    {\"index\": 1281166, \"image_file\":......}\n    ```\nAfter setting the index of each image instance, we build a packed distributed sampler:\n    ```\n    [ \n        [846047, 838285, 837472, 836430, 828275, 807897],\n        [816704, 796202, 793598, 792986, 781934, 769695], \n        .......,\n    ]\n    ```\nEach list records the indices of image instances to be packed into one sequence. Note that the list may have different numbers of image instances, as we adhere to the native token number of each image instance. \n* Given $N$ GPUs, with the above packed distributed sampler, we load the processed image latent codes according to the index provided by the sampler. Then we pack these codes into one sequence independently on each device. On the local devices, the model uses these packed sequences to calculate and then gather the loss to calculate the gradient. Then the gradient back-propagates to update the model parameters.\n\n\n**2.2 Comparison with bucket sampling.** \nThe comparison of bucket sampling and native-resolution strategy can be summarized as follows:\n| Aspect                  | Bucket Sampling                          | Native-Resolution Strategy               |\n|-------------------------|------------------------------------------|------------------------------------------|\n| Resolution Handling     | Predefines fixed resolution buckets      | Utilizes dataset's inherent resolution diversity |\n| Image Processing        | Resizes and crops images to fit buckets  | Resizes images to the nearest multiple of 32 |\n| Flexibility             | Limited to discrete set of resolutions   | Integer-continuous resolution range in latent space |\n| Performance Beyond Range| Significant drop in generation quality   | Maintains performance across diverse resolutions |\n| Latent Code Resolution  | Discrete steps based on predefined buckets | Continuous resolution scaling (multiple of 32) |\n| Dataset Utilization     | Constrained by bucket definitions        | Leverages full resolution diversity of training data |\n\nTo further validate our claims, we use the same dataset (SAM [1] and JourneyDB [3]) as PixArt-$\\alpha$ [4] and train our NiT-XL model to compare the effectiveness of our native-resolution strategy. PixArt-$\\alpha$ divides the image size into $40$ buckets with different aspect ratios, each with varying aspect ratios ranging from $0.25$ to $4$. In contrast, our NiT directly learns the native-resolution of these image datasets. We use FID and CLIP Score on the COCO-30K [5] benchmark to evaluate the models. The results are summarized as follows:\n\n| Method | Params | 512x512 FID$\\downarrow$ | 512x512 CLIP$\\uparrow$ | 768x768 FID$\\downarrow$ | 768x768 CLIP$\\uparrow$ | 1024x1024 FID$\\downarrow$ | 1024x1024 CLIP$\\uparrow$ |\n|--------------|--------|---------------|---------------|---------------|---------------|---------------|---------------|\n| PixArt-$\\alpha$ | 0.6B   | 315.66  | 0.0461  | 22.42  | 0.3462  | 20.11  | 0.3581  |\n| NiT (Ours)     | 0.67B  | 19.13  | 0.3746  | 5.81  | 0.3592  | 11.95  | 0.3721  |\n\nNiT demonstrates more stable performance on a diverse resolution range than PixArt-$\\alpha$. On $512\\times512$ resolution, PixArt-$\\alpha$ fails to generate meaningful samples, but NiT can generalize to this resolution. Besides, NiT demonstrates superior FID and CLIP Score on other resolutions, validating the effectiveness of our native-resolution training strategy. \n\n**2.3 Reliance on VAE.** We follow the dominant latent diffusion model (LDM) paradigm to train our models, necessitating the usage of an image VAE. We use DC-AE with a $32$ downsampling scale to support super high-resolution (up to $2048$ resolution) image synthesis. For $2048\\times2048$ resolution, it leads to $4096$ tokens with DC-AE and $65536$ tokens with SD-VAE [6], which has an $8$ downsampling ratio. In the real-world scenario, the former setting is more practical as it produces fewer tokens, facilitating the device requirement. We recognize that a $32$ downsampling scale may limit the granularity of resolutions, but it is compatible with most normal resolutions (such as $256, 512, 1024, 2048$, etc.) and is more feasible in super high-resolution generation scenarios.  \n\n\n**3. Performance gain** \n\nWe rigorously conduct experiments to validate the effects of each component. We conduct four groups for this component analysis: (a) we use fixed $512\\times512$-resolution data and absolute positional embedding (sin-cos PE) as our positional embedding, corresponding to the SiT [7] baseline. (b) We use fixed $512\\times512$-resolution data and RoPE. (c) We use native-resolution data and absolute positional embedding. (d) We use native-resolution data and RoPE, corresponding to our NiT. The following table shows the FID score of each setting and resolution.\n| setting | Resolution | PosEmbed | 256x256 | 512x512 | 768x768 |\n| ------- | ---------- | -------- | ------- | ------- | ------- |\n| (a)     | fxied-512  | AbsPE    | 125.40 | 29.40  | 124.51 |\n| (b)     | fxied-512  | rope     | 127.47 | 24.64  | 86.36  |\n| (c)     | native     | AbsPE    | 35.66  | 29.41 | 31.06 |\n| (d)     | native     | rope     | 24.54  | 17.62 | 21.03 |\n\nThe above table demonstrates the performance gain of each component. (1) NiT' s strong generalization to unseen resolutions and aspect ratios is primarily enabled by the training resolution diversity. (a) and (b) learn to overfit the fixed-resolution $512\\times512$, thus losing the capability to generalize to other resolutions.  When trained with native-resolution data, both using absolute PE (Config (c)) and using RoPE (Config (d)) can demonstrate strong generalization capability across multiple resolutions. (2) RoPE can accelerate the convergence speed compared to absolute PE. For both fixed-resolution settings and native-resolution settings, RoPE can improve model performance across multiple resolutions.\n\n\n\n*Reference*\n\n[1] Kirillov, Alexander, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao et al. \"Segment anything.\" In Proceedings of the IEEE/CVF international conference on computer vision, pp. 4015-4026. 2023.\n\n[2] Krell, Mario Michael, Matej Kosec, Sergio P. Perez, and Andrew Fitzgibbon. \"Efficient sequence packing without cross-contamination: Accelerating large language models without impacting performance.\" arXiv preprint arXiv:2107.02027 (2021).\n\n[3] Sun, Keqiang, Junting Pan, Yuying Ge, Hao Li, Haodong Duan, Xiaoshi Wu, Renrui Zhang et al. \"Journeydb: A benchmark for generative image understanding.\" Advances in neural information processing systems 36 (2023): 49659-49678.\n\n[4] Chen, Junsong, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang et al. \"Pixart-$\\alpha $: Fast training of diffusion transformer for photorealistic text-to-image synthesis.\" arXiv preprint arXiv:2310.00426 (2023).\n\n[5] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014.\n\n[6] Rombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. \"High-resolution image synthesis with latent diffusion models.\" In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10684-10695. 2022.\n\n[7] anye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie. Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers. arXiv preprint arXiv:2401.08740, 2024."}, {"review_id": "Dfl9mFnRAa", "reviewer": "Reviewer_dH7i", "summary": "This paper introduces \"native-resolution image synthesis,\" a new paradigm for generative modeling that handles images of arbitrary resolutions and aspect ratios. The authors propose the Native-resolution diffusion Transformer (NiT), which is inspired by Large Language Models and processes variable-length visual tokens without resizing or cropping inputs. The proposed model is shown to achieve state-of-the-art results on standard benchmarks and demonstrates impressive zero-shot generalization to unseen resolutions and aspect ratios.", "questions": "I like this paper which would benefit to the community in my opinion.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. The paper addresses a significant and practical limitation of current generative models, which are typically constrained to fixed, square resolutions. The proposed native-resolution approach is a novel and impactful contribution to the field.\n2. The technical design of the NiT architecture is sound and well-motivated, cleverly adapting concepts like variable-length sequence processing and rotary positional embeddings (2D RoPE) from LLMs to the vision domain.\n\nWeaknesses:\n1. The evaluation relies heavily on quantitative metrics. The paper would benefit from a more extensive qualitative analysis in the main text, particularly showing failure cases to better illustrate where the model struggles with object coherence or detail at unseen scales.\n2. While the generalization is strong, the model's performance degrades noticeably at extremely high resolutions like 2048x2048 and on extreme aspect ratios. A deeper analysis of these failure points would provide a more complete understanding of the method's limitations.\n3. The ablation study confirms the importance of native-resolution training data but could further explore how the specific distribution of resolutions and aspect ratios within the training set impacts generalization performance.", "was_revised": true, "final_justification": "Thanks for the authors' reply. Most of my concerns are solved. Considering the comments from other reviewers, I would keep my rating.", "rebuttal": "Thank you very much for your insightful feedback and careful review, which has greatly helped improve the manuscript. In response to your comments, we have carefully prepared point-to-point clarifications as follows:\n\n**1. Qualitative analysis**\n* We thank the reviewer for suggesting more qualitative results in the main text, and we will add a detailed qualitative analysis in the following revision. Currently, we have provided the qualitative results of our model and the comparison with baselines EDM and FlowDCN in the supplementary material. \n* We regret that our submission did not include visual examples of failure cases. In follow-up tests at very high resolutions (e.g., 2048 × 2048), we indeed observed spatial artefacts: certain objects are rendered with anatomically implausible features or with parts arranged unnaturally, compromising spatial coherence. Because rebuttal guidelines prohibit anonymous external links, we cannot attach the images here; however, we will include representative visualizations, together with an analysis of their causes and possible mitigations, in the revised manuscript.\n\n\n**2. The limitation of this method**\n\n**2.1 Resolution scalability**\nTo further evaluate the resolution scalability of NiT, we study the resolution extrapolation law of NiT. First, we counted the average sequence length of the whole training ImageNet dataset $L_\\text{avg}$. We find that NiT takes an average sequence length of $L_{\\text{avg}}=171$ during training. Then we calculate the sequence length and the FID score of these extrapolated resolutions, detailed in the following table:\n| resolution          | 512x512 | 768x768 | 1024x1024 | 1280x1280 | 1536x1536 |\n| ------------------- | ------- | ------- | --------- | --------- | --------- |\n| Sequence length $L$   | 256     | 576     | 1024      | 1600      | 2304      |\n| Extrapolation ratio $r$ ($L$/$L_{\\text{avg}}$}  ) | 1.497   | 3.368   | 5.988     | 9.356     | 13.473    |\n| FID                 | 1.57    | 4.05    | 5.49      | 5.91      | 6.51      |\n\nThus, we can quantify the resolution scalability of NiT by an extrapolation ratio $r = L/L_{\\text{avg}}$, where $L$ is the longer side of the test image and $L_{\\text{avg}}$ is the average longer side in the training set. Empirically, we observe a remarkably tight logarithmic relationship between this ratio \\$r$ and FID metrics, captured by $\\text{FID}(r) = 2.234\\cdot\\ln r + 1.0224$ with an $R^{2}=0.9641$, indicating that the formula explains more than 96 % of the variance across resolutions. The slope 2.234, therefore, quantifies the resolution scalability of NiT, effectively describing the high-resolution extrapolation quality of NiT. We will incorporate these findings and discuss potential strategies to flatten this slope and further improve high-resolution fidelity in the revised manuscript.\n\n**2.2 Aspect ratio scalability.**  NiT-XL is trained on native-resolution images and therefore transfers smoothly to the common photographic aspect ratios—9 : 16, 3 : 4, 4 : 3, and 16 : 9—without any special handling. The vast majority of ImageNet training images fall within the continuous band [9/16, 16/9], and within this range, the model preserves object shapes and spatial coherence. Once the aspect ratio drifts outside that interval, however, objects start to appear unnaturally stretched or compressed. The most pronounced degradation occurs at the extremes of 1 : 3 and 3 : 1, ratios that are virtually absent from the training distribution. As a result, we treat [1/3, 3] as the current practical upper bound for NiT’s aspect-ratio generalization; supporting ratios beyond this would likely require targeted data augmentation or architectural adjustments.\n\n**2.3 Rationale for the current limitation and future work** \n* The chief bottleneck is the narrow resolution distribution in ImageNet itself. As shown in Fig. 2(a) of the manuscript, most ImageNet images fall between 200 and 600 pixels on their longer side, with very few exceeding 800 pixels. Consequently, the average sequence length is only 171 tokens, providing limited signal for learning truly scale-invariant representations.\n* Looking ahead, we plan to (i) design architectures and training schemes that explicitly target resolution and aspect-ratio robustness, and (ii) study a dedicated “aspect-ratio scaling law” for NiT, which we expect to be more nuanced than the resolution law reported here. These steps should extend the model’s effective operating range beyond the present [1/3, 3] aspect-ratio band and improve high-resolution fidelity.\n\n\n**3. Impact of specific resolution and aspect-ratio distributions**\n\nWe conduct 4 groups of experiments with different dataset distributions to explore this effect. (a) we keep the aspect ratio of image data and resize its height and width smaller than $256$; (b) we keep the aspect ratio of image data and resize its height and width smaller than $512$; (c) we keep the aspect ratio of image data and resize its height and width smaller than $768$; (d) we use native-resolution image data for training without resizing them under certain resolution. We report the FID of each setting and resolution as in the following table. \n| Config | Resolution | 256x256 | 512x512 | 768x768 | 1024x1024 |\n| ------ | ---------- | ------- | ------- | ------- | --------- |\n| (a)    | Below256   | 21.08  | 180.27 | 241.09  | 264.94    |\n| (b)    | Below512   | 36.10 | 28.06  | 195.99 | 285.89    |\n| (c)    | Below768   | 38.89  | 31.64 | 32.63  | 124.14    |\n| (d)    | Full Data  | 31.95  | 24.52  | 25.68  | 32.32    |\n\nWe find that the training resolution distribution significantly impacts the generalization ability. When training images are capped at $256$ px on the long side (Config a), the model excels at $256 \\times 256$ but degrades sharply at $512 \\times 512$ and $768 \\times 768$. Raising the cap to $512$ px (Config b) or $768$ px (Config c) postpones—but does not eliminate—the drop-off: once the test resolution exceeds the highest resolution encountered in training, FID climbs rapidly. By contrast, training at native image resolution with no upper bound (Config d) yields consistently strong performance across the full range of resolutions we evaluated. These results underline a simple principle: the broader the resolution spectrum in the training set, the better NiT generalizes, highlighting the necessity of resolution diversity."}, {"review_id": "dUSbpzqoTT", "reviewer": "Reviewer_5tpv", "summary": "This paper proposes a Native-resolution diffusion Transformer (NiT) model for image synthesis at arbitrary resolutions and aspect ratios. The key contributions are:\n1. Introducing the concept of native-resolution image synthesis, which overcomes the limitations of fixed-resolution, square-image methods.\n2. Developing the NiT architecture that inherently integrates dynamic tokenization, Variable-Length Sequence Processing and 2D Structural Prior Injection into a single framework.", "questions": "1. Please describe the hyperparameters, data preprocessing steps, and exact training procedures in more detail. This will help ensure the reproducibility of your results.\n2. Please provide a more in-depth discussion of the limitations of the NiT model, especially regarding its performance on extremely high resolutions and aspect ratios.\n3. Please discuss both potential positive and negative societal impacts of your work. A comprehensive discussion of limitations and potential avenues for future improvement.\n4. Please include comparisons with more state-of-the-art baseline methods, especially those that are recent and relevant to your work.", "limitations": "Providing a more comprehensive discussion of the model's limitations, especially in extreme conditions. On the other hand, while the experiments look very impressive, I need more arguments on the idea of the proposed method, since it is built on some existing techniques.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\nQuality‌: The proposed NiT model shows impressive performance on both standard and zero-shot generalization tasks, outperforming existing methods. The ablation studies provide insights into the model's ability to generalize.\nClarity‌: The paper is well-written and easy to follow. The motivation, methodology, and experimental setup are clearly explained.\nSignificance‌: The native-resolution image synthesis paradigm has the potential to bridge the gap between visual generative modeling and large language models, offering a more flexible and scalable approach.\nOriginality‌: The idea of native-resolution modeling and the NiT architecture are novel.\nWeaknesses:\nQuality‌: The performance on extremely high resolutions and aspect ratios is still not satisfactory, indicating room for improvement.\nClarity‌: Some details of the implementation, such as hyperparameters and exact training procedures, could be more thoroughly described to ensure reproducibility.\nOriginality‌: While the main idea is novel, some components of the NiT architecture build upon existing techniques. More arguments are needed.", "was_revised": false, "final_justification": "", "rebuttal": "We truly thank the reviewer for your detailed review and thoughtful comments in helping us enhance the quality of our paper. Below, we provide detailed point-to-point responses addressing each of your valuable suggestions:\n\n**1. Detailed experimental setup**\n* Hyper-parameters. As batch size is unsuitable in our packed image processing, we use the token budget (i.e., the summation of total tokens in all training iterations) to represent the training costs. For each iteration, we use $131,072$ tokens, which corresponds to a $512$ batch size on $256\\times256$ generation of DiT models. We train our NiT-XL model for $1000K$ steps, with AdamW optimizer and a constant learning rate of $1\\times{10}^{-4}$. Mixed-precision training is adopted with $\\texttt{torch.bfloat16}$. \n* Data pre-processing. We use DC-AE to convert an image with the shape of $H\\times W$ into the latent tokens with the shape of $\\frac{H}{32}\\times\\frac{W}{32}$. All the images are processed without the cropping operation to avoid its negative effects. \n* Training procedure. To support packed training, we pre-counted the number of latent codes corresponding to different images, given a preset maximum length, we flattened the different latent tokens and packed them into one sequence. We use the longest-pack-first histogram packing algorithm to pack the images into different sequences. In our setting, the maximum sequence length is similar to the batch size in normal training. The instance number among different packed sequences is dynamic because diverse image resolutions lead to diverse latent token numbers. In the training stage, we directly train NiT with these packed sequences, as our NiT architecture (packed full-attention and packed AdaLN modules) supports efficient packed processing.\n* Reproducibility. We would open-source all the code, scripts, datasets, and model checkpoints to ensure reproducibility. \n\n\n**2. Limitation**\n\n**2.1 Resolution scalability**\nTo further evaluate the resolution scalability of NiT, we study the resolution extrapolation law of NiT. First, we counted the average sequence length of the whole training ImageNet dataset $L_\\text{avg}$. We find that NiT takes an average sequence length of $L_{\\text{avg}}=171$ during training. Then we calculate the sequence length and the FID score of these extrapolated resolutions, detailed in the following table:\n| resolution          | 512x512 | 768x768 | 1024x1024 | 1280x1280 | 1536x1536 |\n| ------------------- | ------- | ------- | --------- | --------- | --------- |\n| Sequence length $L$   | 256     | 576     | 1024      | 1600      | 2304      |\n| Extrapolation ratio $r$ ($L$/$L_{\\text{avg}}$  ) | 1.497   | 3.368   | 5.988     | 9.356     | 13.473    |\n| FID                 | 1.57    | 4.05    | 5.49      | 5.91      | 6.51      |\n\nThus, we can quantify the resolution scalability of NiT by an extrapolation ratio $r = L/L_{\\text{avg}}$, where $L$ is the longer side of the test image and $L_{\\text{avg}}$ is the average longer side in the training set. Empirically, we observe a remarkably tight logarithmic relationship between this ratio \\$r$ and FID metrics, captured by $\\text{FID}(r) = 2.234\\cdot\\ln r + 1.0224$ with an $R^{2}=0.9641$, indicating that the formula explains more than 96 % of the variance across resolutions. The slope 2.234, therefore, quantifies the resolution scalability of NiT, effectively describing the high-resolution extrapolation quality of NiT. We will incorporate these findings and discuss potential strategies to flatten this slope and further improve high-resolution fidelity in the revised manuscript.\n\n**2.2 Aspect ratio scalability.**  NiT-XL is trained on native-resolution images and therefore transfers smoothly to the common photographic aspect ratios—9 : 16, 3 : 4, 4 : 3, and 16 : 9—without any special handling. The vast majority of ImageNet training images fall within the continuous band [9/16, 16/9], and within this range, the model preserves object shapes and spatial coherence. Once the aspect ratio drifts outside that interval, however, objects start to appear unnaturally stretched or compressed. The most pronounced degradation occurs at the extremes of 1 : 3 and 3 : 1, ratios that are virtually absent from the training distribution. As a result, we treat [1/3, 3] as the current practical upper bound for NiT’s aspect-ratio generalization; supporting ratios beyond this would likely require targeted data augmentation or architectural adjustments.\n\n**2.3 Rationale for the current limitation and future work** \n* The chief bottleneck is the narrow resolution distribution in ImageNet itself. As shown in Fig. 2(a) of the manuscript, most ImageNet images fall between 200 and 600 pixels on their longer side, with very few exceeding 800 pixels. Consequently, the average sequence length is only 171 tokens, providing limited signal for learning truly scale-invariant representations.\n* Looking ahead, we plan to (i) design architectures and training schemes that explicitly target resolution and aspect-ratio robustness, and (ii) study a dedicated “aspect-ratio scaling law” for NiT, which we expect to be more nuanced than the resolution law reported here. These steps should extend the model’s effective operating range beyond the present [1/3, 3] aspect-ratio band and improve high-resolution fidelity.\n\n**3. Societal impacts of this work**\n\n**3.1 Positive societal impacts**\n* Enhanced Image Generation for Broad Applications: By accommodating various resolutions and aspect ratios within a single diffusion model, this approach can streamline workflows across industries (e.g., graphic design, healthcare imaging, education) where high-fidelity images at diverse scales are crucial.\n* Reduced Computational Overhead: Training one unified model rather than multiple resolution-specific models may lower overall computational costs and carbon footprint, benefiting sustainability efforts.\n* Democratization of High-Quality Image Synthesis: The ability to handle images at unseen scales may make advanced generative capabilities more accessible to smaller organizations or researchers with limited resources, fostering innovation.\n\n**3.2 Negative societal impacts**\n* Potential for Misinformation and Deepfakes: As generative models improve in fidelity, they can be misused to create highly convincing fake images or videos, posing risks to social trust and security.\n* Impact on Creative Industries: Widespread adoption of automated image generation tools could disrupt commercial art, photography, or design sectors, raising questions about job displacement and the value of human creativity.\n\n\n**4. Comparisons with more SOTA baselines**\n\nWe compare our NiT with more recent SOTA baselines: DiG [1] (CVPR2025), DeepFlow [2], GigaTok [3] (ICCV2025), RandAR [4] (ICML2025), and UCGM [5]. The results are summarized as follows: \n| Method       | Params | 256x256 FID$\\downarrow$ | 256x256 sFID$\\downarrow$ | 256x256 IS$\\uparrow$ | 512x512 FID$\\downarrow$ | 512x512 sFID$\\downarrow$ | 512x512 IS$\\uparrow$ | mFID$\\downarrow$  |\n|--------------|--------|-------------|--------------|------------|-------------|--------------|------------|-------|\n| DiG [1]         | 675M   | 2.07        | 4.53         | 278.95     | -           | -            | -          | -     |\n| DeepFlow [2]    | 675M   | 1.77        | 4.44         | 271.3      | 1.96        | 4.28         | 260.3      | 1.87  |\n| GigaTok [3]     | 1.4B   | 2.03        | -            | -          | -           | -            | -          | -     |\n| RandAR [4]      | 1.4B   | 2.15        | -            | 321.97     | -           | -            | -          | -     |\n| UCGM [5]        | 675M   | 2.10        | -            | -          | 2.11        | -            | -          | 2.10  |\n| NiT (Ours)   | 675M   | 2.16        | 6.34         | 253.44     | 1.57        | 4.13         | 260.69     | 1.86  |\n\nOur NiT model demonstrates superior performance compared to these baselines on both ImageNet-$256\\times256$ and ImageNet-$512\\times$ benchmarks. Notably, we use a single model to compete on the two benchmarks. \n\n*Reference*\n\n[1] Zhu, Lianghui, Zilong Huang, Bencheng Liao, Jun Hao Liew, Hanshu Yan, Jiashi Feng, and Xinggang Wang. \"Dig: Scalable and efficient diffusion models with gated linear attention.\" CVPR, 2025.\n\n[2] Shin, Inkyu, Chenglin Yang, and Liang-Chieh Chen. \"Deeply supervised flow-based generative models.\" arXiv preprint arXiv:2503.14494 (2025).\n\n[3] Xiong, Tianwei, Jun Hao Liew, Zilong Huang, Jiashi Feng, and Xihui Liu. \"Gigatok: Scaling visual tokenizers to 3 billion parameters for autoregressive image generation.\" arXiv preprint arXiv:2504.08736 (2025).\n\n[4] Pang, Ziqi, Tianyuan Zhang, Fujun Luan, Yunze Man, Hao Tan, Kai Zhang, William T. Freeman, and Yu-Xiong Wang. \"Randar: Decoder-only autoregressive visual generation in random orders.\" CVPR, 2025.\n\n[5] Sun, Peng, Yi Jiang, and Tao Lin. \"Unified Continuous Generative Models.\" arXiv preprint arXiv:2505.07447 (2025)."}, {"review_id": "js1o2TmhMB", "reviewer": "Reviewer_nzEE", "summary": "This paper introduces Native-resolution image synthesis and proposes the Native-resolution diffusion Transformer (NiT), a generative model that can synthesize images across a wide range of resolutions and aspect ratios without being constrained to a fixed format. Inspired by the variable-length generalization capabilities of LLMs, the model reformulates image generation by directly modeling visual data at its native resolution using techniques like packed variable-length token processing, axial 2D Rotary Positional Embeddings, and FlashAttention. NiT achieves state-of-the-art results on both 256×256 and 512×512 ImageNet benchmarks with a single model and demonstrates strong zero-shot generalization to unseen resolutions and aspect ratios.", "questions": "+ Please explain the different between FiT and this work.\n+ I think the comparison with other method is not fair. Author should retrain NiT with same VAE as previous method or pick the best previous method (i.e FiT) and train it with DC-AE for a fair comparison.\n+ There was a long line of research in native-resolution generation with GAN like ScaleSpace-GAN [1], CREPS [2], AnyRes-GAN [3], INR-GAN [4],.. Author should include them in related work for completeness for future readers/researchers.\n\nReference:\n\n[1] Wolski et. al. Learning Images Across Scales Using Adversarial Training. SIGGRAPH 2024\n\n[2] Nguyen et. al. Efficient Scale-Invariant Generator with Column-Row Entangled Pixel Synthesis. CVPR 2023\n\n[3] Chai et. al. Any-resolution Training for High-resolution Image Synthesis. ECCV 2022\n\n[4] Skorokhodov et. al. Adversarial Generation of Continuous Images. CVPR 2021", "limitations": "No limitations besides then weaknesses I mentioned", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n+ Impressive generalization on high-resolution compared to previous work, as shown in Tab. 2 and Tab. 3.\n+ Theory-wise, this is not new as FiT already explore similar concept of using 2D Rotary Position Embedding (RoPE). **BUT** implementation-wise, this paper propose an very interesting and efficient trick (Packed Full-Attention and Packed Adaptive Layer Normalization) to train diffusion transformer on high-resolution.\n\n**Weaknesses**\n+ The different of this work against FiT is not clear, they seem similar.\n+ The comparison with other work seem unfair as previous work utilize different VAE than the one used in this work (DC-AE).", "was_revised": true, "final_justification": "The author address my concerns well but after seeing other concerns from other author, I decide to raise the score to **4** only.", "rebuttal": "Thank you sincerely for your valuable suggestions and thorough review. We have thoughtfully addressed each of your points, as detailed in the responses below:\n\n**1. Key distinctions between FiT and NiT**\n\n| Aspect | FiT | NiT |\n|---|---|---|\n| **Training paradigm** | *Padding-and-masking.* FiT only supports a small range of resolutions (i.e., from 64 to 512 on ImageNet). A global maximum token length is fixed; shorter sequences are right-padded and the padding positions are masked during loss computation. For training stability and balance, it only supports resolutions with similar token length. | *Native-resolution packing.* NiT supports training with a large resolution range (i.e., from 32 to 2048 on ImageNet). Latent tokens from multiple images are concatenated until a preset **max sequence length** is reached. Because only real tokens are processed, GPU utilisation and training throughput rise markedly. |\n| **Generalization principle** | Learns mainly at the limited resolution range; out-of-distribution sizes rely on RoPE extrapolation tricks such as NTK/YaRN. | Directly optimises on the natural resolution distribution of the dataset, preserving full spatial hierarchy and learning scale-independent priors. Hence, it natively supports arbitrary resolutions and aspect ratios without auxiliary tricks. |\n| **Architecture** | **Masked full attention**  $$\\mathrm{Attn}(Q,K,V)=\\text{softmax}\\left(\\frac{QK^\\top}{\\sqrt{d_k}}+M\\right)V,$$ where $M_{ij}=0$ for image tokens and $M_{ij}=-\\infty$ for padding tokens. | **Packed full attention + packed AdaLN.** For a packed sequence whose \\(i\\)-th image contributes $N_i=\\frac{h_i w_i}{p^{2}}$ tokens, cumulative offsets $\\texttt{CuSeqLens}=[0,N_1,\\dots,\\sum_{j=1}^{n}N_j]$ are fed to FlashAttention-2:  $$\\mathrm{Attn}(Q,K,V)=\\texttt{FlashAttn}(Q,K,V,\\texttt{CuSeqLens}).$$  Packed AdaLN uses broadcasting to align conditioning vectors with variable-length image segments. The details of packed attention and packed AdaLN are in section C of the supplementary material.  |\n\n**Summary.** NiT replaces FiT’s padding-centric regime with a packing strategy that eliminates dummy tokens, learns directly on native resolutions, and introduces packed attention and packed AdaLN modules that respect variable sequence boundaries. NiT learns the intrinsic spatial hierarchy of original inputs, capable of generating a more diverse range of resolutions and aspect ratios. This generalization is fundamentally different from FiT's reliance on RoPE extrapolation techniques for generalization. \n\n\n**2. Fair comparison between FiT and NiT**\n\nBoth using DC-AE, we train the FiT-B/1 (i.e., 131M model size with the patch size of 1) and NiT-B/1 model with the same training configurations (131,072 token budget with 100K training steps, learning rate 1e-4). We provide the FID of FiT and NiT among diverse resolutions as follows:\n| Method | 256x256 | 512x512 | 768x768 | 480x640 | 432x768 | 320x960 |\n| ------ | ------- | ------- | ------- | ------- | ------- | ------- |\n| FiT    | 41.01 | 26.69  | 152.93 | 57.20 | 84.02  | 124.25 |\n| NiT    | 24.54  | 17.62 | 21.03 | 21.99 | 33.57  | 87.73 |\n\nThe above table reveals the superiority of our NiT compared to FiT. NiT surpasses the performance of FiT on both 256x256 and 512x512 benchmarks. Besides, NiT demonstrates more stable and robust resolution generalization capability compared with FiT.\n\n\n**3. Additional related work** \n\nWe apologize for the lack of related work on GAN-based native-resolution studies. We sincerely thank the reviewer for the constructive suggestion and would add these papers to the related work in revised manuscript."}]

Author rebuttal:

Meta-review: This work presents NiT, a Native-resolution Diffusion Transformer that explicitly models varying image resolutions and aspect ratios. Building on top of Diffusion Transformers, the authors propose several improvements, including dynamic tokenization (via longest-pack-first histogram packing algorithm [32]), variable-length sequence processing (via FlashAttention-2), and axial 2D RoPE. A single NiT demonstrates promising results on both ImageNet-256 and ImageNet-512 benchmarks, and shows zero-shot generalization to unseen resolutions and aspect ratios.

Initially, the reviewers raised several concerns, which are briefly outlined below:

* Reviewer S4nj: Transferability to large-scale settings, implementation details, ablation on performance gain.

* Reviewer dH7i: Lack of qualitative analysis, analysis on the failure case for large resolutions, performance generalizability.

* Reviewer 5tpv: Weak performance on large resolutions, implementation details, limited novelty.

* Reviewer nzEE: Similarity to prior work FiT, unfair comparisons with prior works.

The rebuttal and subsequent author-reviewer discussions effectively addressed most of the reviewers' concerns. After carefully considering the reviews, rebuttal, and discussion, the AC concurs with the reviewers’ assessment and thus recommends acceptance of the paper. 

While recognizing the engineering contributions (e.g., NiT's adoption of longest-pack-first histogram packing algorithm [23] and FlashAttention-2 [13]), the AC notes that axial-RoPE has been proposed in prior work (e.g., FiT or [A]). A proper discussion and citation of those related works would strengthen the revision. Finally, the authors are encouraged to incorporate the rebuttal experiments into the manuscript and address the reviewers’ feedback in the final revision.

[A] Rotary Position Embedding for Vision Transformer. ECCV 2024.
Decision: Accept (poster)