Title: UltraHR-100K: Enhancing UHR Image Synthesis with A Large-Scale High-Quality Dataset
Venue: NeurIPS 2025
Accepted: yes
Keywords: Ultra-High-Resolution Image Synthesis
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}.


Peer reviews:
comments: 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.

STRENGTHS AND WEAKNESSES:
[Strengths]
1. This paper constructs a large-scale text-to-image dataset with high-quality images and texts.
2. The model trained based on the proposed dataset demonstrates favorable performance.


[Weaknesses]
1. 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.
2. 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.
3. 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.
4. Is Google Imagen a comparable approach to the proposed method? If so, it is not discussed in the paper.
5. It is suggested to use vector graphics for figures, such as Figure 1.

QUESTIONS:
Please see the weaknesses part.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-06-16

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nPlease 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nQuality\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nNo. 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nThe 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 \"$\\alpha=2, \\beta=4$\").\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nNo limitations are discussed. It is highly recommended for the authors to discuss.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}