UltraHR-100K: Enhancing UHR Image Synthesis with A Large-Scale High-Quality Dataset
Authors: Chen Zhao
En Ci
Yunzhe Xu
Tiehan Fan
Shanyan Guan
Yanhao Ge
Jian Yang
Ying Tai
Venue: NeurIPS 2025 poster ()
Keywords: Ultra-High-Resolution Image Synthesis
Primary area: applications

Abstract: Ultra-high-resolution (UHR) text-to-image (T2I) generation has seen notable progress. However, two key challenges remain : 1) the absence of a large-scale high-quality UHR T2I dataset, and (2) the neglect of tailored training strategies for fine-grained detail synthesis in UHR scenarios. To tackle the first challenge, we introduce \textbf{UltraHR-100K}, a high-quality dataset of 100K UHR images with rich captions, offering diverse content and strong visual fidelity. Each image exceeds 3K resolution and is rigorously curated based on detail richness, content complexity, and aesthetic quality. To tackle the second challenge, we propose a frequency-aware post-training method that enhances fine-detail generation in T2I diffusion models. Specifically, we design (i) \textit{Detail-Oriented Timestep Sampling (DOTS)} to focus learning on detail-critical denoising steps, and (ii) \textit{Soft-Weighting Frequency Regularization (SWFR)}, which leverages Discrete Fourier Transform (DFT) to softly constrain frequency components, encouraging high-frequency detail preservation. Extensive experiments on our proposed UltraHR-eval4K benchmarks demonstrate that our approach significantly improves the fine-grained detail quality and overall fidelity of UHR image generation. The code  is available at \href{https://github.com/NJU-PCALab/UltraHR-100k}{here}.

UIrrHHR-100K:
Enhancing UHR Image Synthesis with A Large-Scale High-Quality Dataset
Chen Zhao', En Cil," Yunzhe Xu Tiehan Fan', Shanyan Guan',
Yanhao Ge Jian Yang', Ying Tai
State Key Laboratory of Novel Software Technology, Nanjing University, China vivo Mobile Communication Co., Ltd., China
AltraHR-1O0
Figure Our UltraHR-100K (left) a large-scale high-quality dataset for ultra-high-resolutie (UHR) image synthesis, featuring diverse range of categories. Utilizing this dataset enables the generation of high-fidelity UHR images (right).
Abstract
Utra-biihh-resolution (UHR) text-to-image (T2I) generation has seen notable progress. However, two key challenges remain 1) the absence of a large-scale high-quality UHR T2I dataset, and (2) the neglect of tailored training strategies for fine-grained detail synthesis in UHR scenarios. To tackle the first challenge, we introduce UlrraHR-100K, a high-quality dataset of 100K UHR images with rich captions, offering diverse content and strong visual fidelity. Each image ex- ceeds 3K resolution and is rigorously curated based on detail richness, content complexity, and aesthetic quality. To tackle the second challenge, we propose a frequency-aware post-training method that enhances fine-detail generation in T21 diffusion models. Specifically, we design (i) Detail-Oriented Timestep Sampling (DOTS) to focus learning on detail-critical denoising steps, and (ii) Seff-Weightigg Frequency Regularization (SWFR), which leverages Discrete Fourier Transform (DFT) to softly constrain frequency components, encouraging high-frequency detail preservation. Extensive experiments our proposed UltraHR-eval4K benchmarks demonstrate that our approach significantly improves the fine mne-grained detail quality and overall fidelity of UHR image generation. The code is available at here)
'Equal Contribution. Cooresponnence to: Ying Tai.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Recent advances in text text-to-image (22)) diffusion models have greatly improved image quality and
controllability [ 2 4 6 7 8 9 TO However, most existing models are constrained to fixed
resolutions (typically 1024x1024), and exhibit noticeable quality degradation and structural artifacts when directly scaled to ultra-hin setra-high-resouttin
(UHR) image generation [12 13 15
16 17
[8] 19].
This limitation poses a significant barrier for real-world applications that demand inneggained detail and high visual fidelity, such as digital art, virtual content creation, and commercial design.
Existing solutions to face this challenge can be grouped into two main paradigms: training-free
13 14
20
21
22
24
25
26 and training-based methods 12 15
27]. Training-free
methods attempt to generate UHR images by modifying network architectures (20 22 23
or by adjusting inference schemes [14] 21). However, these techniques exhibit excessive smoothing.
produce implausible details, and incur prolonged inference linitttiin that severely hinder their practical deployment [1]. Fundamentally, training-five methods depend on pre-trained T2I models 2
3 5
that were not exposed to UHR data during training, and consequently lack the
inherent capacity to render the fine- grained photorealistic details essential that world UHR image synthesis requires.
Recently, thining-based models for UHR image generation have shown promising re- sults 13 16, 17]. However, they still face two critical challenges: 1) The absence of open-source, large-scale high-quality UHR T21 dataset. llih-fidelity UHR image col- lection is burdensome due to the scarcity of suitable data. Although Aesthetic-4K (16) introduced the first open-source UHR '21 dataset, it remains limited in both scale (ap- proximately 10K images) and quality (the
Dotail Ridiaes
KASTT
1:796
100
Content Complesits
k212
2200
lack of rigorous selection criterion), con-
Aedbelle Quality
straining its generalizability and high-quality generation capabilities in real-world sce- narios. Consequently, constructing a open-source, large-scall nigh-quulity UHR T21 dataset represents both significant chal- lenge and critical necessity. 2) The ne- glect of tailored training strategies for UHR fine- grained detail synthesis. Existing mod- els primarily focus on training efficiency to fine-tune pre-trained T2I models 15 [77. overlooking the high-fidelity detail synthesis. Large-scal pre- vre-training equips T21 models with strong semantic planning abilities, but they struggle to synthesize fine- ine-grained de- tails in the UHR setting 2 3 5 77. Thus, a
excluded data
UltralIR-IOOK
Figure 2: We perform rigorous selection of UltraHR- 100K by evaluating all collected images across three key dimensions: detail richness, content complexity, and aesthetic quality. Left: We present representative www-quality (bad case) examples for each dimension along with their corresponding scores, highlighting the necessity of such filtering. Right: In contrast, our UltraHR- 100K exhibit superior texture details, seman- tic complexity, and aesthetic appeal.
detail-oriented training strategy is essential for achieving high-quality UHR image synthesis.
Large-Scale High-Qualits Dataset for Tackling Challenge 1: We construct URraHRR-100K, large-scale high-quality UHR '21 dataset consisting of 100K UHR images paired with rich textual descriptions. As illustrated in Figure I UlrraHR-100K offers the following key advantages: I) Scale and Diversity: Compared to recent publicly available Aesthetic-4K [16), our UltraHR- 100K approximately 10x larger, featuring 100K images spanning a broad spectrum of categories and visual concepts. 2) Higher Quality: All images in UltraHR-1000 are rigorously selected from three key dimensions: detail richness, content complexity, and aesthetic quality. Notably, the minimum resolution across the proposed dataset exceeds 3K (average of width and height), ensuring high- resolution content, as shown Figure 3 3) Fine-Grained Captions: To provide detailed textual annotations for each image, we leverage Gemini 2.0 [28) a powerful commercial vision- model (VLM), to generate high-quality captions. As shown in Figure our captions are significantly more detailed and semantically rich compared to those in the Aesthetic-4K (16).

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800
3000
Figure 3: Left: Resolution distribution of our HR-aHHR-OOO.. All images have minimum resolution of 3K, defined as the average of height and width exceeding 3000 pixels. Middle: Image categories across our dataset. The proportion of each category mirrors its distribution in our dataset. Right: Caption length distribution. Compared to the recent Acsthetic-4K[16] our captions are significantly longer, providing richer semantic supervision.
Table I: Overview of our data processing pipeline. The first stage involves large-scale data collection and preliminary filtering to ensure a baseline level of visual quality. The second stage performs three parallel filtering procedures. The final high-quality dataset is obtained by taking the intersection of these subsets. We further employ a strong VLM (Gemini 2.0) to annotate each image.
Pipeline
Tool
Remark
Data collecting
Python
Get 400K high-resolution images
Preliminary data filtering
Laplacian and Sobel
Obtain subset 5 with basic visual quality
Detail richness
GLCM
Obtain the set So with rich fine-grained details
Content complexity
Shannon entropy
Ohtain the Sy with complex and diverse content
Aesthetic score
LAION aesthetic predictor
Get high aesthetic score set S
The final dataset
Intersection
Obtain intersection: UltrHHR-1000 S, O Sy O Se Obtain long and fine-grained descriptions for the images
UHR image caption
Gemini 2.0
Detail-Oriented Training Strategy for Tackling Challenge 2: To enhance UHR detail synthesis, we propose frequency-aware post-training method, which consists of detail-oriented timestep sampling (DOTS) and soft-weighting frequency regularization (SWFR). DOTS improves detail synthesis in UHR image generation by directing more training focus to timesteps associated with fine- grained details. Unlike the discrete and block-based decomposition approach used in Diffusion4K [66. which relies on DWT for frequency separation, our SWFR utilizes the continuous spectrum provided by the Discrete Fourier Transform (DFT) to enable more precise frequency control. By applying a seithweighted constraint across frequency bands, SWFR encourages the model to better reconstruct high-frequency details, without compromising low-frequency structural integrity.
Through the proposed dataset and training strategy, we can enhance the synthesis capability of existing pre-trained T2I models 22 B. 15 7 in UHR image generation, with a particular focus on improving fine-gu ine-grained detail representation. Furthermore, we construct large 4K T2I benchmarks, UltraHR-eval4k (4096 x 4096), to comprehensively evaluate existing UHR generation models. Extensive experimental results demonstrate the effectiveness of our method.
2 Related Work
2.1 Text-to-Image Synthesis
Text-to-image (T2I) generation
8 29 30
33
has made notable
progress owing to the emergence of diffusion based frameworks [6
37
38
39
40
42
43
45]
which exhibit impressive ability synthesizing visually compelling content from textual descriptions. Early methods such as Denoising Diffusion Probabilistic Models (DDPM) [46] and Denoising
Diffusion Implicit Models (DDIM) (47 revealed the strength of iterative denoising procedures for producing realistic images. Subsequently, the attention to latent space diffusion [88 brought major breakthrough, significantly lowering training complexity and enhancing scalability 2 More
recently, incorporating transformer 3, [5 49 50 into diffusion models has further boosted image
3

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generation quality. In this paper, we aim to enhance the generative capability of T2I models in UHR scenarios.
2.2 Ultra-High-Resolttiin Image Synthesis
UHR image generation plays crucial role in practical domains such as industry and entertainment
16 51 52]. Due to computational constraints, current advanced latent diffusion models typically
operate at maximum resolution of 1024 1024 [2 3 6
7
g 53 54
However, scaling to 4K
resolution significantly increases computational demands, with cost growing quadratically with image size. Several training-free approaches have extended existing latent diffusion models for 4K generation by modifying the inference strategies of diffusion models. [3] 14 [55] 20 211 [22]
23 56]. HiffuseHigh [3] enhances the base-resolution generation by upscaling and subsequently
re-denoising it, guided by structural information from the DWT. HiFlow [13] adopts a cascaded generation paradigm to effectively capture and utilize low-resolution flow characteristics. However, these techniques exhibit excessive smoothing, produce implausible details, and incur prolonged inference time [2]. Pixart-o [17] takes a pioneering step by approaching direct 4K image generation through efficient token compression in DiT. Similarly, Sana [15] introduces a cost-effective 4K generation pipeline. Despite these advancements, existing models primarily focus training efficiency, overlooking the high-fidelity detail synthesis.
Constructing UltraHR-100K
To face the challenge of the lack of high- quality text-image pairs UHR image gen- eration, we construct a large-scale high- quality dataset named UlrKHHR-100K. We be- gin by collecting approximately 400K high- resolution images (with a minimum resolution of 3840x2161 160) using a custom Python crawler built with Scrapy, sourcing images from the web and various high-resolution imaging de- vices. However; high resolution alone does not guarantee high quality. We pose a central question: What constitutes a high-quality image for UHR image generation? We argue that beyond resolution, such images should exhibit rich content complexity, fine-grained visual details, and aesthetic appeal. Accordingly, we conduct rigorous filter- ing process based on three criteria- content complexity, detail richness, and aesthetic quality to curate a 100K-level T2I dataset that meet these standards. The proposed UltraHR- 100K provides reliable foundation for training and evaluating models high-fidelity UHR image generation. The data processing pipeline is provided in Table
Table 2: Dataset statistical comparisons.
Dataset
Number
Height
Width
PixArt-30 [7
30,000
1,615
1,801
Aesthetic-4K [16]
12,015
4,128
4,640
UltralIR- 100K
104,117
3,648
5,119
Resthetic-Fyal@eaee
195
4,912
6,449
UlraHHR-eval4K
2,000
4,912
7,175
Preliminary Data Filtering. High-resolution images scraped from the web often suffer from blur, noise, or lack of texture, which can significantly degrade image quality. To eliminate such artifacts, we apply a two-stage low-level quality filter. First, we compute the Laplacian variance to assess image sharpness and discard samples below a blur threshold. Second, we apply the Sobel operator measure edge density, removing overly flat or textureless images. This process yields a cleaned subset 5 with sufficient basic visual quality.
Detail Richness. Fine-grained details are essential for training generative models to preserve high- frequency content. To quantify the aspect, we compute Gray-Level Co-occurrence Matrix (GLCM) score, including contrast, entropy, and correlation across multiple directions. These metrics capture spatial pixel relationships indicative of texture complexity. We then select the top 50% of images from S with the highest aggregated GLCM scores, resulting in subset Sq-
Content Complexity. Visually complex images and diverse spatial structures are more valuable for guiding generation models to achieve rich content. We use Shannon entropy proxy measure the content complexity of each image. Images with higher entropy tend to contain more varied pixel intensities. From subset S, retain the top 50% highest-entropy images to construct subset Sg.

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rock foarmations the The setling goldes contrasting with the and blom the creating servee and straasphem
Aestetic-IK
UltrHR--100
Comparison between our UltrHR--100K and Aesthetic44K [6]. Captions our UltraHR- 100K provide more expressive descriptions, encompassing not only global summaries of the image content but also rich details that enhance semantic alignment.
Figure 4:
Aesthetic Quality. Aesthetic appeal is an important factor in image realism and human preference. To incorporate this dimension, we adopt the LAION Aesthetic Predictor [57]. neural network trained estimate perceptual quality. outputs scalar score reflecting visual composition, color harmony, and overall appeal. We rank all images in S' by their aesthetic scores and retain the top 50% to form subset SA, consisting the most visually pleasing samples.
To ensure that the final dataset consists of high-quality UHR images with diverse content, rich textures, and strong aesthetic appeal, we take the intersection of the three selected subsets. Specifically, the final dataset is defined as:
UltraHR-100K.
UltrHR--100K So SE A
This intersection guarantees that each image in UltraHR- HOOraHR--OOK simultancously meets high standards in detail richness, content complexity, and aesthetic quality, as shown Figure 2 In addition, we construct evaluation subset from our dataset-Ultrafli Hevva4KK -onttinnnnn 2,000 images. Table compares our UltraHR- 100K with Aesthetic-4K [16] and PixArt-30K (17). These statistics highlight that litraHR-100K not only improves dataset scale, but also provides more extensive spatial content.
UHR Image Caption. UHR images typ- ically contain significantly more visual in- formation than stanutod-resolutionn images, making them inherently more semantically complex. However, existing datasets [16] 57 often provide only short captions, limit- ing the semantic expressiveness of genera- tive models. To address this issue, we lever- age Gemini 2.0 (28) state-of-the-art com- mercial vision-languaa
Beta Diztr bution Sampiing Petterns
pw4, 2v6,202
2x2,
c-1,1-1
model (VLM), to generate rich and detailed captions for our dataset. As illustrated in Figures [and]]] our captions are not only substantially longer but also encompass both global summaries and fine-grained descriptions, enhancing align- ment with complex image content.
MernaDed
Figure 5: Relation between weighting ratio and timesteps with beta sampling strategy.
5

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Frequency-Aware Post-Training
Pretrained T21 models, trained large-scale datasets, exhibit strong capabilities in semantic and content planning. However, they often struggle to synthesize fine-grained details when extended to UHR scenarios 12 In this work, we focus enhancing the detail synthesis ability of pretrained T2I models through tailored post-training strategies. To this end, we propose a frequency-awan post-training method (FAPT). Specifically, FAPT consists of two parts: detail-oriented timestep sampling (DOTS) and soft-weighting frequency regularization (SWFR). DOTS improves detail synthesis in UHR image generation by directing more training focus to timesteps associated with fine-grained details. Mcanwhile, SWFR imposes a soft-weighted constraint across the frequency spectrum, guiding the model to better preserve and reconstruct high-frequency details.
4.1 Detail-Oriented Timestep Sampling
Motivation. Existing study [58] have validated the observation that the overall image structure (low-frequ llw-frequency signals) is largely reconstructed in the early denoising steps, while fine-grained details (high-frequency signals) are progressively synthesized in the later stages of the denoising process. This insight motivates to design sampling strategy that emphasizes the later stages of the denoising process, aiming to enhance the learning of fine-grained details during post-training stage.
DOTS. To achieve this target, we adopt beta sampling strategy, which provides simple yet flexible mechanism to bias the sampling distribution over denoising timesteps, as shown in Figure [ Specifically, first draw timestep t E (0, 1) from Beta distribution parameterized by shape parameters a and 3:
~ Beta(a, B).
(2)
The Beta distribution yields a rich family of unimodal or skewed distributions over the interval (0, 1). and its probability density function is given by:
beta (t; a. () = B(a, B)
1" 11 t)3-1
(3)
where B(a, B) ((a) is the Beta function. By adjusting and 8, control the bias of the sampling distribution. This sampling mechanism naturally supports adaptive emphasis training: by
T(a+8)
emphasizing later denoising timesteps, we can guide the model to focus on high- figh-frequency details.
4.2 Soft-Weightig Frequency Regularization
Motivation. Large pre-trained T2I models 2 3 7 demonstrate strong semantic planning from diverse data exposure but struggle with fine-grained detail synthesis in UHR scenarios. Existing UHR T2I models focus mainly training efficiency [7]. often neglecting high-fidelity detail. Diffusion44K [16] introduces DWT-based frequency decomposition to enable 4K training, yet DWT yields coarse and discontinuous frequency separation, limiting its effectiveness for UHR modeling. To overcome this, we adopt DFT-based decomposition, which provides finer, globally coherent frequency representations better suited for capturing fine-scale structures in high-resolution synthesis.
SWER. To enhance fine-scale fidelity in UHR image synthesis, we introduce a soft-weighting frequency regularization that complements the standard diffusion loss by explicitly supervising frequency consistency, with an emphasis on high-frequency components. Formally, consider the standard diffusion process:
a2 Ko
+ t
(4)
where æy denotes the data distribution, e is sampled from standard normal distribution, and dg d; known coefficients in the diffusion formulation. Recent T2I models 7 15] adopt rectified flows to predict velocity v, with the objective as follows:
ue(2,, () .0-
To regularize the model in the frequency domain, we compute the 2D Discrete Fourier Transforms (DFT) of both prediction æ and target y:
x F(x), F()),
(6)

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BiFlaw (FLUN)
Pisart-iigua
Ours
Figure 6: Qualitative comparisons with SOTA methods on our UltraHR-eval4K (4096x 4096). Compared with previous works, method is capable of generating visually complex images with rich semantic content. More visual examples are available in the supplementary materials.
where F()) denotes the DFT. Let and y denote the model prediction (e.g., x (z,, t)) and target (e.g., y = œD), respectively. We define frequency domann regularization term as:
Lireg E [l((r)) + w(r) "
where w(r) a frequency soft weighting function designed to boost high-frequency supervision:
exp(yr) - cxp(7) - E [0, 1],
w(r)
and r is the normalized distance from the center of the frequency plane. Hyperparameters À and control the strength and steepness of high-frequency emphasis, respectively. Finally, the overall training objective is defined as:
Liotal Laift + Afreg Lrreq
where Lai denotes the diffusion loss, which can be instantiated as velocity prediction loss (110)(2),)) - (E - 2D) 112). Afreg balancing coefficient that controls the strength of frequency- domain supervision. This regularization Lire encourages the model to maintain consistent spectral power between prediction and target, especially in high-frequency bands.
Experiments
5.1 Implementation Details
Overall Training Setting. We adopt two-stage training strategy. In the first stage, we follow the Logit-Normal Sampling scheme introduced in SD3 [6] and perform fine-tuning on our UltraHR 100K dataset, aiming to enhance the semantic planning capability UHR generation. In the second stage, we apply our proposed frequency aware post-training method, which focuses on tigh-frequency learning to further improve the fine-grained details. use the CAMEWrapper [5] optimizer with constant learning rate of 1e-4, and employ mixed-precision training with batch size of 24. The first-stage training conducted for 4K iterations, followed by 8K iterations in the second stage. Due to computational constraints, conduct training solely on SANA, and all experiments performed on four H20 GPUs
aware
To comprehensively evaluate our approach, we conduct extensive comparisons against SOTA methods for UHR image generation, which can broadly categorized into three groups. The first group consists of powerful T21 models combined with super-resolution technique, BSRGAN (59] The second group includes training-free approaches, where we evaluate FLUXX() using correspond- ing training-fit free generation methods, I-Max (60) and HiFlow [3]. Lastly, we compare with leading
Baselines.

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BESRGAN
HiFlow (FLUX)
Phvart-dgma
SANA
Diffusion-44 4K
Figure 7: Qualitative comparisons with SOTA methods our UltraHR-eval4K (4096x 4096). Compared with previous works, our method can generate realistic textures and fine-gr -gaiinr details.
Ours
Ours
Ours
Ours
Diffusion-4K
SANA
Diffusion-4K
SANA
Diffusion-4K
SANA
Diffusion-4K
SANA
Figure 8: More visual comparisons demonstrate that method consistently produces results. Additional and more diverse comparisons can be found in the supplementary material.
training-basse based UHR generation models, including Pixart-a 17
SANA [15] and Diffusion4K [16].
All baselines are evaluated under their official settings ensure a fair and consistent comparison.
Evaluation. We employ several metrics assess the quality of the generated images, with particular focus on our evaluation sets, HirraHR-eva44: To evaluate image-text consistency, we calculate the long CLIP score [6] and Fine-Grained (FG) CLIP score 622 Additionally, the Fréchet Inception Distance (FID) [33] and Inception Score (IS) 64 are computed to evaluate the overall
image quality of the generated images. Following previous works [33 I2], we compute the FID-patch and IS-patch to evaluate the local quality and details of the images, which are based on local image patches. These metrics provide a comprehensive evaluation of the overall quality, detail retention the generated images.

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Table 3: Quantitative comparison with other baselines on our UltraHR-eval4K (4096 x 4096) benchmark. The best result is highlighted in bold.
Method
FID +
patch +
ISpatch t
CLIP t
FG-CLIP t
FLUX BSRGAN SD3.5 6
37.651
43.143
11.773
5.389
31.45
28.02
BSRGAN 59
31.870
25.598
12.780
5.456
31.75
28.66
(FLUX)
37.667
37.835
11.991
4.391
31.49
27.78
(FLUX) 13
35.892
38.327
11.767
4.620
31.52
27.75
Pixart-a 117
33.171
32.198
12.212
5,390
31.78
28.65
SANA
15
37.070
38.795
11.778
5.649
31.70
28.60
4 [6
39.857
38.515
10.832
3.235
31.41
26.48
Ours(UHraaH-1000K
33.995
20.932
12.502
5.020
31.85
28.65
OuusUUtrraHR-1100-FFPPT) 31.748
15.795
12.995
5.104
31.82
28.68
Table 4: Left: User study results conducted on our UltraHR-evalde Right: Quantitative comparison on Aesthetic-Evee Eval @ 4096. The results demonstrate the superior performance of our method.
Text-Image Aligument
Preference
Method
FID
FID
CLIP 1 FG-CLIP 1
Quality
Quality
Pixart-a
44.702
SANA [5
Diffasion1IK
Pixart-7 77
14%
4%
10%
8%
16%
8%
18%
6%
SANA [3]
Diffusion4K
16
146.027
152.790
39.729
37.031
34,62
33.99
28.61
26.06
Ours
70%
78%
72%
70%
Ours
142.965
24.008
35.08
28.64
5.2 Comparison to State-of-the-Art Methods
Quantitative Comparison. Table summarizes the quantitative performance on our UltraHR- eval4K benchmark (4096 x 4096). Our method consistently achieves superior scores on key perceptual metrics such FID, FID-patch and IS, indicating its strong capability in generating high-quality images with fine- mee-graieed textures. Moreover, our method achieves competitive CLIP scores, reflecting ability to maintain semantic alignment with the input prompt. Notably, our method yields substantial improvement in FID parch, highlighting its effectiveness in synthesizing fine-grained details. This result demonstrates that our proposed approach significantly enhances the detail generation capability of pre-trained T21 models in UHR scenarios.
Qualitative Comparison. Figure presents qualitative comparisons on UlraHR-eval4K (4096 4096), focusing on the overall semantic richness and spatial layout of the generated images. While existing SOTA methods struggle to produce coherent and content-rich scenes at such ultra-high resolution, our method demonstrates strong capability in generating visually complex images with diverse and semantically meaningful elements. This highlights our model's superior capacity for global spatial reasoning and semantic planning in large-scale synthesis. In Figure 7 we further compare fine-grained textures and local details. Our method produces sharper structures realistic textures, faithfully preserving high-frequency information that other methods tend to miss or oversmooth. These results collectively demonstrate the effectiveness of our proposes dataset and method in enhancing both the global semantics and local fidelity for ultra-high-resolution text-to-image generation. Figure presents more visual comparisons.
more
User Study. As shown in Table we conducted user study with 5 volunteers evaluating 50 randomly selected cases. Images were rated on overall quality, detail quality, text-image alignment and preference. The results demonstrate the superiority of our method across all aspects.
Comparisons on Public Benchmark. We conduct a quantitative comparison on the publicly available Aesthetic-4K benchmark, specifically the Aesthetic-Evalee 44096 subset, as reported in Table
This evaluation set contains 195 image-text pairs, where all images have short side greater than 4096 pixels. Due to the limited number of samples, the reported FID scores are relatively high. Nonetheless, the results clearly demonstrate the superior performance of our method, supporting its robustness and generalizability beyond our proposed benchmark.

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Table 5: Ablation study of our key components and data scale. Model A baseline using full fine-tuning on our dataset. The comparison between C (trained on a partial dataset) and D (full dataset) validates the effectiveness of large-scale data.
Model
DOTS
SWER
Dataset
FID +
patch
CLIP
LoRA
Full
35.07
35.02
31.80)
x
Full
33.99
20.93
31,85
x
Full
32.57
19.95
31.79
C
V
v
Part
32.75
18.42
31.81
v
Full
31.74
15.79
31.82
5.3 Ablation Study
We conduct comprehensive ablation study to validate the effectiveness of our proposed training strategy and the importance of large-scale data. As shown in Table Model A serves as the baseline without our proposed DOTS and SWFR. Model B introduces DOTS, resulting in consistent improve- ments in both FII and patch-level FID, demonstrating its effectiveness in guiding the sampling process. Further incorporating SWFR (Model D) yields substantial improvements, particularly in patch-level FID, confirming that our proposed regularization enhances the detail synthesis capability of T2I models. To evaluate the impact of training data scale, we compare Model C and Model D. Model C is trained with a randomly sampled 15K subset of our UltraHR-100 100K using the same training strategy. The performance drop compared Model D clearly highlights the importance of large-scale UHR data in achieving high-fidelity and semantically aligned image generation.
Analysis for DOTS. The DOTS module em- ploys a Beta(o, 3) distribution to guide timestep sampling, where and B control the bias along the denoising trajectory. When < 8, sampling favors later steps (near i 0) that refine high- frequency details; when a > 3, it leans toward early steps (near structure. In our experiments, we set = biasing sampling toward later steps to better capture fine details crucial for ultra-high- resolution generation. An ablation study (Table () varying and B confirms this choice: larger a weakens detail learning, smaller harms semantic consistency, and overly concentrated or flattened distributions reduce diversity. These results validate (a = 2, 3 4) as balanced and effective configuration.
Table 6: Analysis for DOTS.
Method
FID
FID _iD_patch
CLIP
(a = 3, 8 = 4)
33.196
22.143
31.83
(a 1, 4)
33.727
25.095
31.79
1) emphasizing global
(a = 2,
33,874
23,850
31.82
2,
(a 2,
33.638
24.638
31.84
(a 2,8 4)
31.748
15.795
31.82
6 Conclusion
In this paper, we present UltraHR- 100K, curated dataset of 100K UHR images with rich textual annotations. Each image is carefully selected to ensure high levels of detail, visual complexity, and aesthetic appeal. Moreover, we introduce a frequency-awarr post-training method, which includes: (i) Detail-Oriented Timestep Sampling (DOTS), and (ii) Soft- Soft-Weighting Frequency Regularization (SWFR). Experiments on our proposed UltraHR-eval44 benchmark confirm that our approach significantly boosts both the visual fidelity and fine-detail accuracy of UHR image synthesis.
Limitations and future works. Our main limitations lie two aspects. First, while the proposed frequency- aware post-training strategy effectively enhances fine-detail synthesis, it introduces a slight degradation in text-image alignment, as shown Table Second, our dataset currently contains a relatively limited amount of portrait data, which constrains the improvement in high-resolution (UHR) portrait generation, as illustrated in Figure S In future work, we plan to develop more balanced training strategies to alleviate the alignment issue and expand our dataset with additional high-quality UHR portrait images to further improve performance portrait synthesis.
This work was supported by Natural Science Foundation of China: No. 62406135, Natural Science Foundation of Jiangsu Province: BK20241 198, and Gusu Innovation and Entrepreneur Leading Talents: No. ZXL2024362.
Acknowledgments
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References
[1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Bureu Karagol Ayan, Tim Salimans, al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing systems, 35:36479-36494 2022.
[2] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXit:2307.019522 2023.
[3] Junsong Chen, YU Jincheng, GE Chongjian, Lewei Yao, Enze Xie, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart-a: Fast training of diffusion transformer for photorealistic rext-to-image synthesis. The Twelfth International Conference on Learning Representations.
[4] Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, et al. Cogview: Mastering text-to-image generation via transformers. Advances in neural information processing systems, 34:19822-19835 19835, 2021.
[5] William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEF/CVF international conference on computer vision, pages 4195-4205, 2023.
[6] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow trans- formers for high-resolution image synthesis. In Forty-first international conference machine learning, 2024.
[7] Black-Forest Labs. Flux. httge://hhgiiagaaacc.oo//ooooooa 2024.
1-dev
[8] Bingchen Liu, Ehsan Akhgari, Alexander Visheratin, Aleks Kamko, Linmiao Xu, Shivam Shrirao, Chase Lambert, Joao Souza, Suhail Doshi, and Daiqing Li. Playground v3: Im- proving text-to-image alignment with deep-fusion large language models. arXiv preprint arXiv:2409. 10695, 2024.
[9] Leyang Li, Shilin Lu, Yan Ren, and Adams Wai-Kin Kong. Set you straight: Auto-steering denoising trajectories to sidestep unwanted concepts. arXiv preprint arXiv:2504. 12002222..77 2025.
[10] Daiheng Gao, Shilin Lu, Wenbo Zhou, Jiaming Chu, Jie Zhang, Mengxi Jia, Bang Zhang, Zhaoxin Fan, and Weiming Zhang. Eraseanything: Enabling concept erasure in rectified flow transformers. In Forty-second International Conference on Machine Learning, 2025.
[11] Xiantao Hu, Ying Tai, Xu Zhao, Chen Zhao, Zhenyu Zhang, Jun Li, Bineng Zhong, and Jian Yang. Exploiting multimodal spatial-temporal patterns for video object tracking. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 3581-3589, 2025.
[12] Jingjing Ren, Wenbo Li, Haoyu Chen, Renjing Pei, Bin Shao, Yong Guo, Long Peng, Fenglong Song, and Lei Zhu. Ultrapixel: Advancing ultra high-resolution image synthesis to new peaks. In The Thirty-eighth Annual Conference on Neural Information Processing Systems.
[13] Jiazi Bu, Pengyang Ling, Yujie Zhou, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Dahua Lin, and Jiaqi Wang. Hiflow: Training free nigh-resolution image generation with flow-aligned guidance. arXiv preprint 25Kt:::2004062..22 2025.
[14] Ruoyi Du, Dongliang Chang, Timothy Hospedales, Yi-Zhe Song, and Zhanyu Ma. Demofusion: Democratising high-resolution image generation with no $$S. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pages 6168, 2024.
[15] Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, et al. Sana: Efficient high-resolution image synthesis with linear diffusion transformers. arXiv preprint arXiv:2410.1066 2024.

---

[16] Jinjin Zhang, Qiuyu Huang, Junjie Liu, Xiefan Guo, and Di Huang. Diffusion-4k: Ultra-high- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025.
[17] Junsong Chen, Chongjian Ge, Enze Xie, Yue Wu, Lewei Yao, Xiaozhe Ren, Zhongdao Wang. Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart-o: Weak-to-strong training of diffusion transformer for 4k text-to-image generation. In European Conference on Computer Vision, pages 74-91. Springer, 2024.
[18] Chen Zhao, Zhizhou Chen, Yunzhe Xu, Enxuan Gu, Jian Li, Zili Yi, Qian Wang, Jian Yang, and Ying Tai. From zero to detail: Deconstructing -ii--iihhhdiiiiiinn image restoration from progressive spectral perspective. In Proceedings of the Computer Vision and Puttern Recognition Conference, pages 17935-17946, 2025.
[19] Chen Zhao, Weiling Cai, Chengwei Hu, and Zheng Yuan. Cycle contrastive adversarial learning with structural consistency for unsupervised high-quality image deraining transformer. Neural Networks, 178: 106428004882 2024.
[20] Yingqing He, Shaoshu Yang, Haoxin Chen, Xiaodong Cun, Menghan Xia, Yong Zhang, Xintao Wang, Ran He, Qifeng Chen, and Ying Shan. Scalecrafter: Tuning-free higher-resolution visual generation with diffusion models. In The Twelfth International Conference on Learning Representations, 2023.
[21] Linjiang Huang, Rongyao Fang, Aiping Zhang, Guanglu Song, Si Liu, Yu Liu, and Hongsheng Li. Fouriscale: A frequency perspective training-free high-resolution image synthesis. In European Conference on Computer Vision, pages 196-212. Springer, 2024.
[22] Zhiyu Jin, Xuli Shen, Bin Li, and Xiangyang Xue. Training-free diffusion model adaptation for variable-sized text-to-image synthesis. Advances in Neural Information Processing Systems, 36:70847-70860, 2023.
[23] Younghyun Kim, Geunmin Hwang, Junyu Zhang, and Eunbyung Park. Diffusehigh: Training- free progressive high-resolution image synthesis through structure guidance. In Proceedings of the AAAI conference on artificial intelligence, volume 39, pages 4338-4346, 2025.
[24] Zhengqiang Zhang, Ruihuang Li, and Lei Zhang. Frecas: Efficient higher-resolution image generation via frequency-aware cascaded sampling. The Thirteenth International Conference on Learning Representations, 2025.
[25] Zihan Zhou, Shilin Lu, Shuli Leng, Shaocong Zhang, Zhuming Lian, Xinlei Yu, and Adams Wai-Kin Kong. Dragflow: Unleashing dit priors with region based supervision for drag editing. arXiv preprint arXiv:2510.02253, 2025.
[26] Zhennan Chen, Yajie Li, Haofan Wang. Zhibo Chen, Zhengkai Jiang, Jun Li. Qian Wang, Jian Yang, and Ying Tai. Region- text-to-image generation via hard binding and soft refinement. arXiv preprint / 1.06558, 2024.
[27] Nikai Du, Zhennan Chen, Shan Gao, Zhizhou Chen, Xi Chen, Zhengkai Jiang, Jian Yang, and Ying Tai. Textcrafter: Accurately rendering multiple texts in complex visual scenes. arXiv preprint arXiv:2503.23441, 2025.
[28] Google. Gemini. gtepsn:/gemmni.oo
com/ 2025.
[29] Dewei Zhou, You Li, Fan Ma, Xiaoting Zhang, and Yi Yang. Mige: Multi-instance generation controller for text-to-image synthesis. In Proceedings of the IEFE/CVF Conference on Computer Vision and Pattern Recognition, pages 6818 1188882,, 2024.
[30] Li Zhang, Yan Zhong, Jianan Wang, Zhe Min, Liu Liu, et al. Rethinking 3d convolution in normorm space. In The Tiirth- Annual Conference on Neural Information Processing Systems, 2024.
[31] Dewei Zhou, You Li, Fan Ma, Zongxin Yang, and Yi Yang. Mige++: Advanced multi-instance generation controller for image synthesis. IEFE Transactions on Puttern Analysis and Machine Intelligence,200
12

---

[32] Li Zhang, Zean Han, Yan Zhong, Qiaojun Yu, Xingyu Wu, et al. Vocapter: Voting-based pose tracking for category-level articulated object via inter-rrame priors. In ACM Multimedia 2024, 2024.
[33] Shilin Lu, Yanzhu Liu, and Adams Wai-Kin Kong. Tf-icon: Diffusion-based training-free cross-domain image composition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2305, 2023.
[34] Ji Du, Jiesheng Wu, Desheng Kong, Weiyun Liang, Fangwei Hao, Jing Xu, Bin Wang, Guiling Wang, and Ping Li. Upgen: Unleashing potential of foundation models for training-free camouflage detection via generative models. IEEE Transactions on Image Processing, 2025.
[35] Kepan Nan, Rui Xie, Penghao Zhou, Tiehan Fan, Zhenheng Yang. Zhijie Chen, Xiang Li, Jian Yang, and Ying Tai. Openvid- mm: A large-scale high-quality dataset for text-to-video generation. arXiv preprint arXiv:2407.02371, 2024.
[36] Chenyang Si, Ziqi Huang. Yuming Jiang, and Ziwei Liu. Freeu: Free lunch in diffusion u-net. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, 2024.
pages
[37] Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. Playground v2. 5: Three insights towards enhancing aesthetic quality in text-to-image generation. arXiv preprint vrXiv:2402.17244 2024.
[38] Shilin Lu, Zilan Wang, Leyang Li, Yanzhu I Liu, and Adams Wai-Kin Kong. Mace: Mass concept erasure in diffusion models. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pages 6430-6440, 2024.
[39] Dewei Zhou, Ji Xie, Zongxin Yang, and Yi Yang. 3dis: Depth-driven decoupled instance synthesis for text-to-image generation. arXiv preprint arXiv:2410. 122669 2024.
[40] Chen Zhao, Weiling Cai, Chenyu Dong, and Chengwei Hu. Wavelet-based fourier information interaction with frequency diffusion adjustment for underwater image restoration. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8281-8291, 2024.
[41] Chen Zhao, Chenyu Dong, and Weiling Cai. Learning physical-aware diffusion model based on transformer for underwater image enhancement. arXiv preprint arXiv:2403.01497 2024.
[42] Chen Zhao, Weiling Cai, Chenyu Dong, and Ziqi Zeng. Toward sufficient spetial-frequency interaction for gradient-aware underwater image enhancement. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3220- 3224. IEEF, 2024.
[43] Zhennan Chen, Rongrong Gao, Tian-Zhu Xiang, and Fan Lin. Diffusion model for camouflaged object detection. arXiv preprint htth:2338.000003 2023.
[44] Rui Xie, Yinhong Liu, Penghao Zhou, Chen Zhao, Jun Zhou, Kai Zhang, Zhenyu Zhang, Jian Yang, Zhenheng Yang. and Ying Tai. Star: Spatial-temporal augmentation with text-to-video models for real-world video super-resolution. arXiv preprint arXiv:2501.0200 2025.
[45] Ji Du, Fangwei Hao, Mingyang Yu, Desheng Kong, Jiesheng Wu, Bin Wang. Jing Xu, and Ping Li. Shift the lens: Environment-awann unsupervised camouflaged object detection. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19271-19282, 2025.
[46] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:684006851, 2020.
[47] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502. 2020.
[48] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEF/CVF conference computer vision and pattern recognition, pages 10684-10695, 2022.
13

---

[49] Le Zhuo, Ruoyi Du, Han Xiao, Yangguang Li, Dongyang Liu, Rongje Huang, Wenze Liu, Lirui Zhao, Fu- Yun Wang, Zhanyu Ma, et al. Lumina-next. Making lumina-12x stronger and faster with next-dit. arXiv preprint arXiv:2406. 18583, 2024.
[50] Dewei Zhou, Zongxin Yang, and Yi Yang. Pyramid diffusion models for low-light image enhancement. arXiv preprint arXiv:2305. 10. 10028, 2023.
[51] Lucy Chai, Michael Gharbi, Eli Shechtman, Phillip Isola, and Richard Zhang. Any-resolution training for high-resolution image synthesis. In European conference on computer vision, pages 170-188. Springer, 2022.
[52] Li Zhang, Weiqing Meng, Yan Zhong, Bin Kong, Mingliang Xu, Jianming Du, Xue Wang, Rujing Wang, and Liu Liu. U-cope: Taking a further step to universal 9d category-level object pose estimation. In European Conference on Computer Vision, pages 254-270, Springer, 2025.
[53] Dewei Zhou, Mingwei Li, Zongxin Yang, and Yi Yang. Dreamrenderer: Taming multi-instance attribute control in large-scale text-to-image models. arXiv preprint arXiv:2503. /2885, 2025.
[54] Shilin Lu, Zihan Zhou, Jiayou Lu, Yuanzhi Zhu, and Adams Wii-Kin Kong. Robust water- marking using generative priors against image editing: From benchmarking to advances. arXiv preprint arXiv:2410.18775, 2024.
[55] Li Zhang, Mingliang Xu, Dong Li, Jianming Du, and Rujing Wang. Catmullrom splines-based regression for image forgery localization. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 7196-7204, 2024.
[56] Shilin Lu, Zhuming Lian, Zihan Zhou, Shaocong Zhang, Chen Zhao, and Adams Wai-Kin Kong. Does flux already know how to perform physically plausible image composition? arXiv preprint arXiv:2509.2 1278, 2025.
[57] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion- 5b: An open large-scall dataset for training next generation image-text models. Advances in neural information processing systems, 35:25278 255244, 2022.
[58] Mingyang Yi, Aoxue Li, Yi Xin, and Zhenguo Li. Towards understanding the working mech- anism of text-to-image diffusion model. In The Thirty-eighth Annual Conference on Neural Information P Processing Systems.
[59] Kai Zhang, Jingyun Liang, Luc Van Gool, and Radu Timofte. Designing practical degradation model for deep blind image super-resolution. In Proceedings of the IEEE/CVF international conference on computer vision, pages 4791-4800, 2021.
[60] Ruoyi Du, Dongyang Liu, Le Zhuo, Qin Qi, Hongsheng Li, Zhanyu Ma, and Peng Gao. I-max: Maximize the resolution potential of pre-trained rectified flow transformers with projected flow. arXiv preprint arXiv:2410.078: 2024.
[61] Beichen Zhang, Pan Zhang, Xiaoyi Dong, Yuhang Zang, and Jiaqi Wang. Long-clip: Unlocking the long-text capability of clip. In European Conference on Computer Vision, pages 310-325. Springer, 2024.
[62] Chunyu Xie, Bin Wang, Fanjing Kong, Jincheng Li, Dawei Liang, Gengshen Zhang, Dawei Leng, and Yuhui Yin. Fg-clip: Fine-grainee visual and textual alignment. arXiv preprint arXiv:2505.05071, 2025.
[63] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.
[64] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in neural information processing systems, 29, 2016.
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Reviews:
[{"review_id": "OudY9JlvTL", "reviewer": "Reviewer_4Jyg", "summary": "The paper tackles the problem of high-quality, detail-rich image generation, by proposing a new dataset UHR100K for training. UHR100K is collected and filtered with well-designed strategies. \nIn addition to that, a frequency-aware post-training method is proposed for detail synthesis. \nWith the new UHR100K dataset and the new training strategy, the authors improve the synthesis capability of pre-trained T2I models.", "questions": "- Why the eval set \"UltraHR-eval4K\" is named after \"4K\" while it actually only consists of 2K images (Table 2)?\n- What is $\\lambda_{freq}$ used in your experiments?", "limitations": "No limitations are discussed. It is highly recommended for the authors to discuss.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The paper shows a straight-forward post-training pipeline for enhancing the detail synthesis for T2I models. The simple idea shows promising results. However, there are still issues with the detail richness and color saturation in the results. Also, the balance between \"complicate textures\" and \"simplicity\" needs further discussions. \n\nMy current rating for the paper is Borderline Accept. \n\n## Strengths\n### Idea/Motivation\n- The motivation of proposing UHR100K is straight-forward and intuitive. Instead of using complex training-free methods, which still struggle generating detail-rich images, the authors curate more and better data as a simple solution. a\n- Helping models to capture the details in generation also makes sense. A new post-training strategy is proposed to compile the new dataset. \n\n### Method\n- The authors filter data based on content complexity, detail richness and aesthetic quality. \n- Based on the observation that fine details are usually generated in the later denoising process, the authors propose to sample from a Beta distribution, whose emphasis is inclined to later timesteps. \n- SWFR puts more focus on the high-frequency synthesis.\n\n### Results/Experiments\n- Table 3 shows the proposed dataset and SWFR brings a boost for multiple metrics.\n- Ablation study in Table 4 shows the improvement for each component.\n\n### Writing/Delivery\nThe paper is overall well-written with clear guidances for readers. \n\n## Weaknesses\n### Idea/Motivation\n- While the capability to generate rich details is generally desirable, it sometimes may not align with all aesthetic preferences. \nFor example, Minimalism asks for simplicity and discourages the complexity in pictures. Will the proposed dataset and method introduce a bias towards rich visual contents? The paper might be more comprehensive if authors could discuss and study this bias, especially the differences before and after the post-training.\n\n### Method\n- The captions are generated from an LLM (Gemini 2.0). Are those captions aligned with human perceptions? Are longer captions mean better semantics? \n- Following the last comment, other than Gemini, are any other LLMs suitable for captioning?\n\n### Results/Experiments\n- Unfortunately, although with more details, some results still show unrealistic styles, such as over-smooth or weird color tones. For example,  in the middle row of Fig. 4 (Supp.), there are no details on the skin and it is over-smoothed. Also on the page 8 in the supp., the color of \"ours\" looks unrealistic with a strong emphasis on the brown and yellow hues. Also on the page 11 in the supp., in my opinion the result from Diffusion-4K looks more natural. \n- Although achieving the best performance on quantitative metrics in Table 3, it is more convincing if a user study is included, as most of the existing metrics still struggle aligning with human perceptions. \n- There is no qualitative comparison between each component in the ablation study. \n- It might be more convincing if the training strategy and the new dataset can be applied to more base models other than SANA.\n- Given that this is a paper enhancing visual quality, it is highly recommended authors could include native images/outputs in the supplementary material as it is still far from the file limit (21.5MB v.s. 100MB). Some images shown in the supplementary material come with obvious compression loss (e.g., Fig. 11 in the supp.).\n\n### Writing/Delivery\n- L35-L36, \"a open-source\" -> \"an open-source\"\n- In Fig. 5, the colors of legend is hard to distinguish. Some colors are similar (e.g., \"$\\alpha=0.5, \\beta=0.5$\" and \"$\\alpha=2, \\beta=4$\").", "was_revised": true, "final_justification": "The papers comes with a new large-scale dataset. The authors have addressed all of my concerns in the rebuttal. I think the paper is ready for acceptance.", "rebuttal": "We appreciate this valuable suggestion. However, due to this year’s NeurIPS rebuttal guidelines, we are unable to include visual results in the response. We will include the requested visualizations in the revised version of the paper, including qualitative examples for minimalism prompts and ablation study results.\n\n---\n## For the Idea / Motivation\nWe thank the reviewer for raising the concern about potential bias toward rich visual content, which may not align with aesthetics such as minimalism. To investigate this, we conducted preliminary qualitative experiments using minimalist prompts before and after post-training. The results suggest that our method, while enhancing detail, does not significantly degrade the model’s ability to generate stylistically minimalist images. This robustness may stem from the strong generalization ability of the pretrained model.\nOur primary motivation is to improve model performance in Ultra-High-Resolution (UHR) scenarios, especially under complex content and fine-grained detail—areas where existing models often struggle. For simple or minimalist generation tasks, pretrained models such as FLUX or SD3 are often already sufficient without further UHR fine-tuning. We plan to include further discussion and illustrative minimalist examples in the revised version.\n\n---\n\n## For the Method\n\nTo assess whether the long captions generated by Gemini 2.0 align with human perception, we conducted a user study, as shown in **Table 1**. Ten volunteers each evaluated 50 caption pairs (our original long captions vs. Gemini-generated short captions) based on semantic richness (the extent to which the caption captures semantic content of the image) and semantic accuracy (how accurately the caption reflects the image content), using a 1–5 scale (1 = very poor, 2 = poor, 3 = fair, 4 = good, 5 = excellent). We report the average scores across all participants. Additionally, we collected overall preference judgments between the two captions. Results show that the long captions are consistently rated higher in both semantic richness and accuracy, and are more frequently preferred, demonstrating their stronger semantic expressiveness. Additionally, in **Table 2**, we evaluated models using only the first sentence (short version) of the long captions. Results confirm that longer, richer captions lead to higher-quality image generation. While other LLMs like GPT-4o can also generate captions, we focused on Gemini due to current resource constraints. We plan to explore more LLMs in future work.\n\n---\n\n## Experiments\n\n### 1. Unrealistic Styles\n\nWe acknowledge that in some cases, our method produces over-smoothed textures or unnatural color tones. For skin smoothness, We believe this may be due to the scarcity of portrait data in our dataset, resulting in suboptimal portrait generation performance. This is a current limitation we plan to address by collecting more high-quality UHR portrait data. Unnatural color tones may also stem from base model limitations (SANA) and the difficulty of balancing detail enhancement with natural appearance. We will refine our post-training strategy to improve visual realism.\n\n### 2. User Study\n\nAs shown in **Table 3**, we conducted a user study with 5 volunteers evaluating 50 randomly selected cases. Images were rated on overall quality, detail quality, text-image alignment and preference. The results demonstrate the superiority of our method across all aspects.\n\n### 3. Ablation Study\n\nWe will include qualitative comparisons of ablation variants in the revised version.\n\n### 4. Generalization to Other Models\n\nDue to limited computational resources, we currently focus on SANA. We plan to extend our training strategy and dataset to other models such as FLUX and SD3 in future work.\n\n### 5. Image Compression\n\nAll images were compressed using the same method due to Overleaf compilation limits. We apologize for not including original-resolution images and will release all data and outputs publicly after the paper accepted.\n\n---\n\n## Clarifications & Minor Issues\n\n- We apologize for the inconvenience caused by the grammatical and formatting issues. We will correct the grammatical error (“a open-source” → “an open-source”) and improve the color distinguishability in Figure 5.\n- The term **\"4K\"** in *UltraHR-eval4K* refers to **image resolution**, not the number of samples.\n- The frequency loss weight **$\\lambda_{\\text{freq}}$** is set to **1** in our experiments.\n\n---\n\n## Limitations & Future Work\n\nAs mentioned, the lack of high-quality portrait data limits performance in portrait generation scenarios. Additionally, while our frequency-aware post-training strategy enhances detail synthesis, it slightly compromises text-image alignment, as evidenced by the CLIP score: Model A outperforms Model D in Table 4 of the paper. In future work, we will:\n\n- Collect more high-quality UHR portrait data.\n- Explore more effective training strategies to address this trade-off and improve robustness.\n\n---\n\nTable 1. Caption User Study. Long_cap refers to our original long captions containing both global and fine-grained descriptions, while Short_cap refers to newly generated short captions that include only global descriptions.\n| Caption Type | Semantic Richness | Semantic Accuracy | Preference |\n|--------------|-------------------|-------------------|------------|\n| Short_cap    | 2.65              | 3.86              | 7%       |\n| Long_cap     | **4.87**              | **4.26**              | **93%**       |\n\n---\n\nTable 2. Quantitative Comparison on UltraHR-eval4K Using Short Captions.  SANA-s and Ours-s denote results evaluated on the UltraHR-eval4K benchmark using short captions (the first sentence of the original long captions), while SANA and Ours indicate evaluation using the full original long captions.\n| Method   | FID     | FID_patch | IS     | IS_patch |\n|----------|---------|-----------|--------|----------|\n| SANA-s   |  41.234       |    39.657       |   11.216     |    5.394      | \n| SANA     | **37.070**  | **38.795**    | **11.778** | **5.649**    | \n||\n| Ours-s   | 34.653  | 16.701    | 12.519 |  4.563   |  \n| Ours     | **31.748**  | **15.795**    | **12.995** | **5.104**    |\n\n---\n\nTable 3.  User study results conducted on our UltraHR-eval4K benchmark, evaluating overall quality, detail fidelity, text-image alignment and user preference across different methods.\n| Method   | Overall Quality | Detail Quality | Text-Image Alignment | Preference |\n|----------|-----------------|----------------|------------|-----------|\n| Pixart-σ   | 14% | 10% | 16% | 18% |\n| SANA     |   4%| 8% | 8% | 6% |\n| Diffusion4K   |   12% |4%\t|6%|\t6%|\n| Ours     |    **70%**|\t**78%**| **72%** | **70%** |"}, {"review_id": "UMVRBpbQio", "reviewer": "Reviewer_WG7N", "summary": "The paper tackles key limitations in ultra-high-resolution (UHR) text-to-image generation by introducing both a novel dataset and a post-training method for diffusion models. First, the authors release a large-scale dataset of UHR images paired with rich, detailed captions, designed to better support training and evaluation at high resolutions. They then propose a post-training technique to enhance diffusion model performance specifically in the UHR regime. Finally, the authors introduce a new benchmark built on their dataset and use it to assess the effectiveness of their method. The paper includes both quantitative and qualitative comparisons with state-of-the-art methods as well as an ablation study to evaluate the contributions of different components of their approach.", "questions": "1. Could you please specify the license under which the images in your dataset are released? It is essential to confirm that all images are sourced under permissive licenses that allow redistribution and use for the purposes outlined in this paper. If you are unable to provide clear licensing information, it may not be appropriate to consider the dataset and benchmark as formal contributions of this work.\n2. You employ the LAION aesthetic quality predictor to filter out images below a certain threshold. Given the subjective nature of aesthetic quality, could you discuss potential biases this introduces into the dataset? How might these biases propagate into downstream models trained using your dataset or benchmark? Additionally, what are the potential societal impacts, particularly concerning aesthetic preferences or representational diversity?\n3. The current discussion of limitations is restricted to the post-training method. Could you expand this to also address the limitations and downstream impacts of the dataset and benchmark, especially in terms of potential accessibility limitations, representational imbalances, or ethical concerns? This section would benefit from a deeper reflection on social implications and possible future extensions.\n4. Could you explicitly state the architecture and configuration for Model A used in the ablation study (Table 4) and include it in Table 3 for a complete comparative evaluation? This will support replicability and help contextualize performance improvements. Additionally, please discuss the negative results more thoroughly. For example, in Section 5.3, it is clear that some methods underperform on CLIP scores. Understanding these failure modes would strengthen the paper’s empirical analysis and provide valuable insights for future work..\n\nTo improve the quality score: Include Model A in Table 3 and provide a clear analysis of how it compares to other models. Expand your discussion to include both positive and negative results, providing explanations for observed limitations or failure modes.\n\nTo improve the clarity score: Clearly state the model architecture and configuration for Model A to ensure replicability. Also clarify how evaluation dataset splits are selected.\n\nTo improve the significance score: Provide explicit licensing information for your dataset and discuss the biases and limitations of your methods in more depth, particularly those associated with data filtering and curation practices.\n\nTo improve the originality score: Provide explicit licensing information for your dataset and contextualize your post-training method by comparing it to other post-training or fine-tuning techniques, including from adjacent domains. This would help clarify what is fundamentally new about your approach.\n\nFinal Note: This is a promising and well-executed paper with strong potential contributions. However, confirmation of dataset licensing is essential. If the licensing is permissive and properly addressed, I would be happy to revisit the overall evaluation positively, as the work is otherwise of high technical quality and broad interest to the NeurIPS community.", "limitations": "No. As mentioned above:\n- Given the increasing scrutiny and societal concerns around synthetic media, the authors should minimally acknowledge ethical and societal risks associated with enabling more photorealistic high-resolution image generation. A more complete analysis would enhance the credibility and responsibility of the work.\n- Overall, the discussion of limitations is notably insufficient. While the paper introduces three major contributions (dataset, benchmark, and post-training method), only the training method’s limitations are addressed. The dataset and benchmark also warrant discussion—particularly around dataset curation, representation, biases, and potential misuse, which are essential considerations for any work involving generative AI.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Quality\n\nStrengths\n- The methodology for constructing the dataset is carefully considered and well-documented. The filtering and captioning strategies demonstrate a rigorous approach to dataset curation.\n- The proposed post-training method is well-motivated and technically sound, with strong rationale provided for the introduced components.\n- The experimental design is robust and comprehensive. It includes a solid set of baselines, a well-structured ablation study, and both quantitative and qualitative evaluations which contribute to a well-rounded empirical assessment of the proposed approach.\n\nWeaknesses\n- In Table 1 and Lines 144 to 148, the paper notes the use of the LAION Aesthetics predictor to filter out images that do not meet an aesthetic threshold. However, the implications of this filtering are not discussed. Given concerns around aesthetic filtering (e.g., reinforcing biases or discarding content types disproportionately), the authors should reflect on whether this introduces systematic limitations or skews in the dataset and what the implications are if any. The shortcomings of LAION might be worth discussing here as well.\n- Lines 153 and 154 mention that 2000 images were selected for the evaluation set, but no explanation is given for the selection strategy. It is important to clarify whether this set was sampled randomly or curated for diversity, and whether any balancing criteria were used. Additionally, breaking down the distributions in Figure 3 by train/evaluation split could strengthen the claim of unbiasedness and representativeness in the evaluation set.\n- In the quantitative evaluation section from Lines 252 to 259, the paper highlights strong FID patch results for the proposed method but neglects to comment on its comparatively weak IS patch performance. The authors should discuss negative results as well for a more balanced analysis, especially since the IS patch to FID patch performance discrepancy is stark and merits analysis. Acknowledging and exploring possible reasons behind this could help surface limitations of the method and improve the robustness of the evaluation.\n- In Section 5.3, negative or inconclusive findings from the ablation study (specifically, the performance drop in CLIP score when DOTS and SWFR are used, as seen in Model D vs. Model A) are not discussed. These results suggest that the proposed training strategy does not universally improve all evaluation metrics, and discussing them would lead to a more balanced and complete picture of the method’s strengths and trade-offs.\n- For better alignment between the ablation study and the main comparison in Table 3, Model A of the ablation study should be included in Table 3. Without it, it is difficult to isolate how much of the observed performance improvement comes from the proposed training method versus the base model choice. This is especially relevant given that Model A already outperforms all models in Table 3 on CLIP and is the second-best performer FID patch.\n\nClarity\n\nStrengths\n- The introduction is well-written, with a well-defined problem statement and insightful contextualization within existing work. It effectively sets the stage for the paper’s contributions.\n- The paper's structure is logical and easy to follow. Technical concepts are explained with a high degree of precision and care. The motivations behind each design choice are clearly conveyed, contributing to strong overall readability.\n- The visual presentation of the dataset through examples and image illustrations is helpful in understanding the scale and diversity of the data, though some could be more space-efficient if space is a constraint. The dataset comparison table in Table 2 helps contextualize the proposed dataset relative to existing datasets.\n\nWeaknesses\n- The pipeline presented in Table 1 is functionally informative, but tables are not an effective way to convey procedural flows. Consider replacing this table with a pipeline diagram, which would more intuitively communicate the processing steps and their relationships.\n- In Lines 81 to 85, it would be clearer to explicitly list the three main contributions: (1) a UHR image-caption dataset; (2) a post-training strategy for diffusion models; and (3) a benchmark for UHR text-to-image generation. This would clarify the paper’s scope and focus early on.\n- Typo in Line 144: the term should be “Aesthetic” (not “Aesthetics”).\n- Line 141: Add a citation or footnote for Shannon entropy, as it’s a referenced concept and not common knowledge to all readers.\n- Figure 3 appears too early in the paper; it should be moved to Section 3, where the dataset is formally introduced, for better alignment between figures and content.\n- Lines 165 to 170 repeat points made earlier in the paper. Consider either integrating this material into the earlier sections (e.g., Introduction or Dataset description) or trimming it for conciseness.\n- In Section 5.3, the underlying model architecture and configuration used for Model A should be clearly specified. Since its scores in Table 4 do not align with any models in Table 3, it is unclear what base model was used. Providing this information is necessary to enable replication of the results and to properly contextualize the improvements shown in the ablation study.\n\nSignificance\n\nStrengths\n- The proposed UHR dataset, if confirmed to be publicly usable (pending clarification on image licensing), has the potential to serve as a valuable resource for the community. High-quality, caption-rich ultra-high-resolution image datasets remain scarce, and this contribution could help advance research in text-to-image generation at scale.\n- The training strategy introduced in the paper demonstrates promise, with empirical results suggesting improved performance across several metrics. If validated more broadly, this could become a useful tool for practitioners working with diffusion models in the UHR regime.\n- The UltraHR-eval4K benchmark, derived from the proposed dataset, adds further utility and could help standardize evaluation practices in UHR image synthesis tasks.\n\nWeaknesses\n- The significance of the proposed training method is primarily evaluated on the authors’ own benchmark. A more convincing demonstration of significance would involve testing the generalizability of the proposed method—i.e., evaluating models trained with the new strategy on existing, established benchmarks for text-to-image generation.\n- Given the increasing scrutiny and societal concerns around synthetic media, the authors should minimally acknowledge ethical and societal risks associated with enabling more photorealistic high-resolution image generation. A more complete analysis would enhance the credibility and responsibility of the work.\n- Overall, the discussion of limitations is notably insufficient. While the paper introduces three major contributions (dataset, benchmark, and post-training method), only the training method’s limitations are addressed. The dataset and benchmark also warrant discussion—particularly around dataset curation, representation, biases, and potential misuse, which are essential considerations for any work involving generative AI.\n\nOriginality\n\nStrengths \n- The proposed dataset is novel in scale, resolution, and quality, and stands out for its rigorous collection methodology as presented in Table 2 and detailed in Section 3. Pending clarification on image licensing, the dataset can contribute meaningfully to the field.\n- The post-training method introduced offers a relatively new approach to improving text-to-image generation in the ultra-high-resolution regime. \n- The related work discussion in Section 2 is comprehensive and does a good job of situating the paper within the landscape of image generation and large-scale diffusion training. The comparative framing helps clarify how the proposed contributions differ from or extend prior work, underscoring their originality.\n\nWeaknesses\n- The paper would benefit from a more explicit comparison to existing post-training or fine-tuning strategies for diffusion models (even from adjacent domains which apply a similar approach) to better contextualize the novelty of the proposed method. Identifying and comparing the proposed approach to similar strategies would help underscore how the proposed approach builds upon or diverges from established ideas.", "was_revised": true, "final_justification": "The authors provided important clarifications that improved the technical quality and clarity of the paper. \n\nMost significantly, they addressed the licensing concerns regarding their dataset and acknowledged that not specifying the license is unacceptable. My initial recommended score was based on the assumption that their failure to specify the license of the source material for the dataset would constitute an ethics violation and prevent their dataset from being considered within their contributions. However, their clarification remedied that, and I'm willing to adjust the recommended score upwards to factor in the dataset which improves the overall significance and originality of the paper.\n\nHowever, I'd like to caveat that this is contingent on their response to the licensing concerns being valid; I'm not an expert in this area, so I'm only improving my recommended score based on my reading of their response. The ethics reviewer should hold the final say on whether or not their response is adequate and thus decide if the paper is free of ethics violations.", "rebuttal": "## Quality\n1) We appreciate the reviewer’s valuable comments on aesthetic filtering. 1) Existing datasets, including LAION, OpenVid-1M, OpenHumanVid, and OmniStyle-1M, commonly employ aesthetic filtering to enhance visual quality by filtering out low-quality samples. Our filtering strategy aligns with these practices, aiming to ensure a cleaner training corpus. Notably, to prevent overly aggressive filtering, we removed only the bottom 50% of images based on aesthetic scores. This approach serves as a lightweight filter, primarily targeting severely degraded or visually corrupted content. 2) We recognize that aesthetic filtering may introduce biases by excluding images with monochrome or low contrast, text-heavy or document-like images, and non-photographic compositions. Although these may seem less “aesthetic,” they often contain important semantic information for text-to-image tasks, so filtering them reduces content diversity. 3) The LAION Aesthetics Predictor is trained on subjective aesthetic scores and may inherently reflect cultural or stylistic biases, favoring certain photographic conventions. It tends to exhibit reduced tolerance for minimalistic, abstract, or utilitarian designs (e.g., charts, educational diagrams, UI mockups). Such biases can inadvertently lead to the exclusion of valuable non-photographic content. \n\n2) The UltraHR-eval4K evaluation set was randomly sampled from the entire dataset under the constraint that both height and width exceed 4096 pixels. This ensures that the evaluation set remains unbiased and reflects the natural data distribution within the full dataset. As shown in **Table 1**, we provide statistical insights for our UltraHR-eval4K benchmark. We will include these statistics in the revised version of the paper\n\n3) FID measures the distance between two data distributions. Our model achieves strong performance on FID_patch, indicating that the generated image patches exhibit high structural and textural similarity to real high-quality images, reflecting the effectiveness of our approach in preserving fine-grained local details. However, the IS_patch score is relatively low, which warrants further analysis. We attribute this disparity primarily to the characteristics and inherent limitations of the IS. IS evaluates the quality and diversity of generated images via a pre-trained classifier. In the context of UHR image generation, evaluating local patches using IS presents two key challenges: (1) UHR image patches often lack clear semantic category attributes, which reduces the classifier's confidence and thus lowers IS scores; and (2) the classifier used in IS is typically trained on low-resolution data, which introduces a significant distribution gap when applied to high-resolution content—further undermining its reliability. Moreover, since IS is based on classification confidence, it is not well-suited to assess the local detail quality of UHR images. As further evidence, as shown in **Table 2**, Model A—trained solely on our UHR dataset—already yields a low IS_patch score. This strongly suggests that the discrepancy stems more from the characteristics of the data than from the design of our method.\n\n4) We sincerely apologize for not providing a more detailed discussion of this issue in the paper. In the ablation study, the observed drop in CLIP score when comparing Model D to Model A reflects a limitation of our frequency-aware post-training strategy, which incorporates both DOTS and SWFR. This strategy primarily focuses on enhancing detail learning by allocating more training capacity to the denoising stages in the later sampling steps. However, this reallocation inevitably reduces the emphasis on the earlier denoising steps, which are more critical for modeling global semantics. As a result, the model’s ability to generate semantically rich and globally coherent content may be compromised, thereby affecting the CLIP score.\n\n5) As shown in **Table 2**, we have already included Model A in the main comparison. Model A refers to the variant trained solely on our proposed dataset using full fine-tuning. The experimental results demonstrate that our curated dataset alone can substantially enhance the generative capability of the baseline model. To ensure a fair comparison, the training setup, hardware environment, and number of training iterations are kept consistent with those of Model D.\n\n---\n\n## Clarity\n1) We will replace the current table with a pipeline figure to present the data processing steps in a clearer and more objective manner.\n\n2) In the revised paper, we will explicitly state the three key contributions to clarify the scope and focus of the paper.\n\n3) We will correct “Aesthetics” to “Aesthetic” in Line 144 in the revised version.\n\n4) We will add an appropriate citation or footnote for Shannon entropy **[1]** in Line 141 to ensure clarity and proper attribution for readers unfamiliar with the concept.\n\n5) We agree that Figure 3 would be better placed in Section 3. We will revise the paper accordingly to improve the alignment between figures and corresponding content.\n\n6) We will review Lines 165–170 and either incorporate key points into earlier sections or remove repetition to improve clarity and flow.\n\n7) Model A shares the same architecture as our full model D, both based on SANA. Specifically, Model A refers to the variant trained solely on our proposed dataset using full fine-tuning. To ensure a fair comparison, the training setup, hardware environment, and number of training iterations are kept consistent with those of Model D.\n\n[1] C. E. Shannon, \"A mathematical theory of communication,\" in The Bell System Technical Journal.\n\n---\n\n## Significance\n1) We conduct a quantitative comparison on the publicly available Aesthetic-Eval@4096 benchmark, as reported in **Table 3**. This evaluation set contains 195 image-text pairs, where all images have a short side greater than 4096 pixels. The results clearly demonstrate the superior performance of our method, supporting its robustness and generalizability beyond our proposed benchmark.\n\n2) We acknowledge that advances in photorealistic high-resolution image generation raise important ethical and societal concerns, including risks related to misinformation, privacy, and misuse of synthetic media. We emphasize the need for increased awareness of these issues and encourage the community to collaboratively develop ethical guidelines and mitigation strategies. We will include a brief discussion of these ethical challenges in the revised manuscript.\n\n3) The main limitation of our dataset currently lies in the relatively small amount of portrait data, which results in limited improvements over prior methods in UHR portrait generation, as also illustrated in Figure 8 of the paper. In future work, we plan to collect more high-quality UHR portrait images to enhance our model’s performance specifically in UHR portrait synthesis.\n\n---\n\n## Originality\n\n1)  We have added a comparison experiment with LoRA, as shown in **Table 4**, to further highlight the effectiveness of our proposed frequency-aware post-training strategy. Compared to existing post-training approach, our method demonstrates particularly notable improvements in detail enhancement, as reflected by the FID_patch metric.\n\n---\n\n## Questions\n1) We confirm that our dataset will be released under the Creative Commons Attribution-NonCommercial (CC BY-NC) license, which permits redistribution and use for non-commercial purposes, such as academic research. This license ensures a permissive usage framework while maintaining responsible use boundaries. We are fully committed to open science—upon acceptance of the paper, we will release all data, code, and trained models to the community. The primary goal of our dataset is to support and enrich open research in the field of ultra-high-resolution text-to-image generation.\n\n2) For aesthetic, please refer to our response to Comment 1 in the Quality section.\n\n3) For limitations, please refer to our response to Comment 3 in the Significance section. Furthermore, we have discussed the broader social implications and ethical considerations in our response to Comment 2 in the Significance section.\n\n4) Please refer to Comments 3 and 4 in the Quality section for a detailed discussion and clarification regarding Model A.\n\n---\n\nTable 1. (a) Distribution statistics of image resolutions in the UltraHR-eval4K benchmark.\n| Resolution  | 0k-4k  | 4k-5k   | 5k-6k   | 6k-7k   | 7k-8k   |  8k-   |\n|---------|--------|--------|--------|--------|--------|--------|\n| Quantity|  0 |  69 |  887 | 740 |  233  |  71  |   \n\n(b) Caption length distribution in our UltraHR-eval4K benchmark.\n| Length  | 0-60  | 60-100   | 100-120   | 120-140   | 140-   |\n|---------|--------|--------|--------|--------|--------|\n| Quantity|  7 |  349  |  1073 | 521 |  50  |    \n\n---\n\nTable 2. Quantitative comparison on  our UltraHR-eval4K (4096 × 4096) benchmark.\n| Method  | FID   | FID_patch   | IS   | IS_patch   | CLIP   | FG-CLIP   |\n|---------|--------|--------|--------|--------|--------|--------|\n| SANA|   37.070|  38.795 | 11.778|  **5.649** |  31.70|  28.60| \n| Model A | 33.995 | 20.932 | 12.502 | 5.020  | **31.85** | 28.65  |\n| Ours (Model D)   | **31.748** | **15.795** | **12.995** | 5.104  | 31.82  | **28.68**  |\n\n---\n\nTable 3. Quantitative comparison on Aesthetic-Eval@4096.  \n| Method  |    FID     | FID_patch    | CLIP  | FG-CLIP |\n|-------------|-------------|-------------|---------------|-------------------|\n| Pixart-σ    | 150.593 | 44.702  | 34.88 | 28.48  |\n| SANA    | 146.027 | 37.031  | 34.62 | 28.61  |\n| DIFF4K  | 152.790 | 39.729  | 33.99 | 26.06  |\n| Ours    | **142.965** | **24.008** | **35.08** | **28.64**  |\n\n---\n\nTable 4. Comparison experiment with LoRA.\n| Method  |    FID     | FID_patch    | CLIP  | \n|-------------|-------------|-------------|---------------|\n| LoRA  |  35.07 | 35.02  | 31.80 |\n| Ours    | **31.74** | **15.79** | **31.82** |"}, {"review_id": "O54tS0qoGI", "reviewer": "Reviewer_1nz3", "summary": "This paper introduces UltraHR-100K, a large-scale dataset designed for ultra-high-resolution (UHR) text-to-image synthesis, consisting of 100K images with resolutions exceeding 3K. To address the challenge of fine-grained detail synthesis in UHR scenarios, the authors propose a frequency-aware post-training strategy that includes Detail-Oriented Timestep Sampling (DOTS) and Soft-Weighting Frequency Regularization (SWFR). Experimental results demonstrate the effectiveness of the proposed method, although some missing comparisons and qualitative studies could further strengthen the work.", "questions": "Please refer to the Weaknesses section for potential directions to enhance the paper. While these additions are not essential for acceptance, addressing them could improve the paper’s completeness and persuasiveness.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths\n1. The paper presents a rigorously curated UHR dataset (UltraHR-100K) with significantly larger scale and higher quality than previous datasets such as Aesthetic-4K. The multi-dimensional filtering process (GLCM-based texture analysis, Shannon entropy, and aesthetic score) is clearly motivated and ensures both diversity and fidelity of the data.\n2. Leveraging the proposed frequency-aware post-training strategy, the model achieves strong performance in UHR image synthesis. The qualitative results (e.g., Figure 6) are visually compelling and show clear improvements over baselines.\n\nWeaknesses\n1. The evaluation is conducted exclusively on the authors’ own UltraHR-eval4K benchmark. Including comparisons on established public datasets such as Aesthetic-4K would provide stronger and more objective evidence of the method’s generalizability and robustness, since hyperparameter tuning can significantly influence performance.\n2. The ablation studies are entirely quantitative. Incorporating qualitative ablation would offer more intuitive insights into how each component contributes to improved visual quality.", "was_revised": true, "final_justification": "I have read the rebuttal and appreciate the authors’ clarifications. They have addressed my main concerns, and I will keep my score as 5.", "rebuttal": "Firstly, it is a great honor to receive such high praise. We appreciate this helpful suggestion.\n\n## For the comparisons on established public datasets\nWe conduct a quantitative comparison on the publicly available Aesthetic-4K benchmark, specifically the Aesthetic-Eval@4096 subset, as reported in Table 1. This evaluation set contains 195 image-text pairs, where all images have a short side greater than 4096 pixels. Due to the limited number of samples, the reported FID scores are relatively high. Nonetheless, the results clearly demonstrate the superior performance of our method, supporting its robustness and generalizability beyond our proposed benchmark.\n\n---\n\n## For the qualitative ablation\nWe apologize for not including qualitative results in the ablation study. Due to NeurIPS rebuttal guidelines, we are unable to provide visualizations in the response. However, we will include intuitive qualitative comparisons of different ablation variants in the revised version of the paper to better illustrate the contribution of each component.\n\n---\n\nTable 1. Quantitative comparison on Aesthetic-Eval@4096.  \nWe report FID, FID_patch, CLIP, and FG-CLIP metrics. The results demonstrate the superior performance of our model on this public UHD benchmark.\n\n| Method  |    FID     | FID_patch    | CLIP  | FG-CLIP |\n|-------------|-------------|-------------|---------------|-------------------|\n| Pixart-σ    | 150.593 | 44.702  | 34.88 | 28.48  |\n| SANA    | 146.027 | 37.031  | 34.62 | 28.61  |\n| DIFF4K  | 152.790 | 39.729  | 33.99 | 26.06  |\n| Ours    | **142.965** | **24.008** | **35.08** | **28.64**  |"}, {"review_id": "n0tewlEauR", "reviewer": "Reviewer_UXyJ", "summary": "This paper addresses the challenges in ultra-high-resolution (UHR) text-to-image generation, namely the lack of large-scale high-quality datasets and tailored training strategies for fine-grained detail synthesis. The authors construct a comprehensive dataset, emphasizing its rigorous curation based on detail richness, content complexity, and aesthetic quality. They also propose a frequency-aware post-training method, comprising Detail-Oriented Timestep Sampling (DOTS) and Soft-Weighting Frequency Regularization (SWFR), to enhance high-frequency detail preservation in diffusion models.", "questions": "Please see the weaknesses part.", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "[Strengths]\n1. This paper constructs a large-scale text-to-image dataset with high-quality images and texts.\n2. The model trained based on the proposed dataset demonstrates favorable performance.\n\n\n[Weaknesses]\n1. The vast majority of this paper's contributions lie in the proposal of a large dataset, with limited technical innovations and more engineering-oriented content. It seems more appropriate for submission to the NeurIPS Datasets & Benchmarks Track rather than the main conference.\n2. As a dataset-focused paper, the comparison between the proposed dataset and existing ones is rather superficial. Additionally, it is recommended to add citations for the comparative methods listed in Table 2.\n3. Regarding the DOTS module proposed in Section 4, the paper lacks clarity on the specific strategies for selecting α and β, as well as the parameter sensitivity analysis.\n4. Is Google Imagen a comparable approach to the proposed method? If so, it is not discussed in the paper.\n5. It is suggested to use vector graphics for figures, such as Figure 1.", "was_revised": true, "final_justification": "I appreciate the authors' detailed response, but I maintain my original assessment for the following reasons:\n\n1. While the authors emphasize their frequency-aware post-training strategy, the core innovation remains dataset-centric. The proposed DOTS and SWFR modules, though useful, are incremental improvements rather than paradigm-shifting contributions. As noted in my initial review, this work would be better suited for the Datasets & Benchmarks Track.  \n2. Regarding Q2, the authors do not directly address my question, and they talk about some other issues.\n\nSo I keep my initial rating.", "rebuttal": "## Q1: The vast majority of this paper's contributions lie in the proposal of a large dataset, with limited technical innovations and more engineering-oriented content. \n\nAnswer: We respectfully disagree with the reviewer’s assessment that our work is primarily a dataset paper with limited technical contributions. While the construction of the UltraHR dataset is indeed a key component of our work, our paper also proposes a **novel and effective frequency-aware post-training strategy** tailored for ultra-high-resolution (UHR) text-to-image generation. This algorithmic contribution is clearly distinct from the dataset itself and has been positively acknowledged by other reviewers.\n\nFor instance, **Reviewer 4Jyg** noted:  \n> *“Helping models to capture the details in generation also makes sense. A new post-training strategy is proposed to compile the new dataset.”*\n\nSimilarly, **Reviewer WG7N** highlighted:  \n> *“The proposed post-training method is well-motivated and technically sound, with strong rationale provided for the introduced components.”*  \n\nand further emphasized that\n\n> *“The post-training method introduced offers a relatively new approach to improving text-to-image generation in the ultra-high-resolution regime.”*\n\n**Reviewer 1nz3** also affirmed the value of our method:  \n> *“Leveraging the proposed frequency-aware post-training strategy, the model achieves strong performance in UHR image synthesis.”*\n\nAlthough our method is relatively simple, we believe that **simplicity does not undermine novelty or impact**. On the contrary, **simple yet effective approaches often demonstrate greater potential for scalability, reproducibility, and broader adoption** within the community. \n\nWe therefore believe our work presents both a **significant dataset** and a **technically novel contribution**, which together offer substantial value to the **main NeurIPS conference**.\n\n---\n\n## Q2: As a dataset-focused paper, the comparison between the proposed dataset and existing ones is rather superficial.\n\nAnswer:  We thank the reviewer for the valuable feedback and apologize for the missing citations in Table 2. We will add all relevant references in the revised version to ensure proper attribution and clarity.\n\nRegarding the comparison with existing datasets, we respectfully clarify that our UltraHR-100K dataset presents **significant improvements** over previous datasets in terms of ** resolution, scale, and diversity** . Beyond basic comparison, we also introduce a **novel, task-specific pipeline for curating high-quality ultra-high-resolution (UHR) data**, which is a key contribution of our work. This pipeline incorporates multi-dimensional filtering and tailored captioning strategies that are specifically designed to address the unique challenges of UHR image-text data curation.\n\nThe motivation and significance of our dataset have been well recognized by other reviewers. For example, **Reviewer 4Jyg** noted:  \n> *\"The motivation of proposing UHR100K is straight-forward and intuitive. Instead of using complex training-free methods, which still struggle generating detail-rich images, the authors curate more and better data as a simple solution.\"*\n\n**Reviewer WG7N** emphasized:  \n> *\"The methodology for constructing the dataset is carefully considered and well-documented. The filtering and captioning strategies demonstrate a rigorous approach to dataset curation.\"*  \n> *\"The proposed UHR dataset has the potential to serve as a valuable resource for the community. High-quality, caption-rich ultra-high-resolution image datasets remain scarce, and this contribution could help advance research in text-to-image generation at scale.\"*\n\n**Reviewer 1nz3** also acknowledged the strength of our dataset:  \n> *\"The paper presents a rigorously curated UHR dataset (UltraHR-100K) with significantly larger scale and higher quality than previous datasets such as Aesthetic-4K. The multi-dimensional filtering process is clearly motivated and ensures both diversity and fidelity of the data.\"*\n\nGiven the above, we believe that **UltraHR-100K will make a substantial contribution to the field of ultra-high-resolution image synthesis and serve as a valuable open resource for the community.**\n\n---\n\n## Q3: Regarding the DOTS module proposed in Section 4, the paper lacks clarity on the specific strategies for selecting α and β, as well as the parameter sensitivity analysis.\n\nAnswer: We thank the reviewer for pointing this out. We apologize for the lack of detailed explanation in the main paper and have included a more comprehensive description in the supplementary materials. Specifically, the DOTS module utilizes a **Beta(α, β)** distribution to guide the timestep sampling process. The parameters α and β are used to control the sampling bias toward different phases of the denoising trajectory:\n\n- When **α < β**, the distribution favors **later denoising steps** (closer to *t = 0*), which tend to focus on **high-frequency detail refinement**.\n- When **α > β**, the sampling shifts toward **early denoising steps** (closer to *t = 1*), which contributes more to **global semantic structure**.\n\nIn our main experiments, we set **α = 2** and **β = 4**, intentionally biasing the sampling toward later timesteps to better capture fine details, which are critical for ultra-high-resolution image generation.\n\nTo further investigate the sensitivity of α and β, we conducted an ablation study, as shown in **Table 1**. Below is a summary of our findings:\n\n- **(α = 3, β =4)**: Increasing α shifts the distribution rightward, leading to more early-step sampling. This **negatively impacts high-frequency learning**, as the model receives less supervision in later steps.\n- **(α =1, β =4)**: Decreasing α moves the distribution leftward, favoring later-step sampling. This can **reduce semantic fidelity**, as the model may not learn sufficient global structure.\n- **(α =2, β =5)**: Increases the distribution’s peak near the center, causing **sampling to concentrate too narrowly**, limiting diversity in supervision.\n- **(α =2, β =3)**: Lowers the distribution peak, increasing sampling across both early and late steps. While more balanced, this can **dilute the benefits** of our targeted sampling strategy.\n\nThese results support our choice of **(α = 2, β = 4)** as a **balanced and effective configuration** that encourages detail learning without sacrificing semantic structure. We will include these discussions and the corresponding table in the revised paper to improve clarity.\n\n---\n\n## Q4: Is Google Imagen a comparable approach to the proposed method? If so, it is not discussed in the paper.\n\nAnswer: We appreciate the reviewer’s suggestion regarding Google Imagen. While Google Imagen is indeed a powerful closed-source text-to-image generation model, it is not directly comparable to our proposed method for several reasons:  \n1）Imagen, as described in the original paper **[1]**, focuses on generating 256×256 or 1024×1024 images. However, it does not natively support ultra-high-resolution generation (e.g., ≥ 4K resolution), which is the primary focus of our work. \n2）Imagen is not publicly released, making it infeasible to evaluate on our UltraHR benchmarks or to integrate into our framework for a fair comparison. As a result, a quantitative or qualitative comparison is currently inaccessible to the research community. We will clarify this distinction in the updated version and will consider mentioning Imagen explicitly to avoid confusion.\n\n[1] Saharia, Chitwan, et al. \"Photorealistic text-to-image diffusion models with deep language understanding.\" Advances in neural information processing systems 35 (2022): 36479-36494.\n\n---\n\n## Q5: It is suggested to use vector graphics for figures, such as Figure 1.\n\nAnswer: We thank the reviewer for pointing this out and sincerely apologize for the current rasterized version of Figure 1. We fully agree that vector graphics would improve visual clarity and readability. We will update all relevant figures, including Figure 1, to vector format (e.g., PDF or SVG) in the revised version to ensure better scalability and presentation quality.\n\n---\n\nTable 1. Ablation study for DOTS.\n| Method  | FID   | FID_patch      | CLIP   | \n|---------|--------|--------|--------|\n|**(α = 3, β =4)** |  33.196  | 22.143  | 31.83 |  \n|**(α = 1, β =4)** | 33.727 | 25.095  | 31.79 |  \n|**(α = 2, β =5)** |   33.874 |  23.850 | 31.82 | \n|**(α = 2, β =3)** | 33.638 | 24.638 |  **31.84** | \n| Ours-**(α = 2, β =4)**   | **31.748** | **15.795** |31.82  |"}]

Author rebuttal:

Meta-review: This paper tries to enhance high-resolution text-to-image generation by proposing a new dataset and two techniques: Detail-Oriented Timestep Sampling (DOTS), to focus learning on detail-critical denoising steps, and Soft-Weighting Frequency Regularization (SWFR), to softly constrain frequency components. All reviewers are on the positive side, in particular acknowledging the importance of the dataset. The authors believe that they can resolve concerns regarding ethical reviews, and have promised to release the dataset for public research. In this sense, this dataset, with sufficient diversity, fine details, and aesthetics, together with the designs in algorithm, could be valuable contributions to the community. Based on the recommendations of the reviewers, AC believes that this paper merits acceptance.
Decision: Accept (poster)