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arxiv: 2605.20147 · v1 · pith:2O2HHZADnew · submitted 2026-05-19 · 💻 cs.CV

PixVerve: Advancing Native UHR Image Generation to 100MP with a Large-Scale High-Quality Dataset

Haojun Chen , Haoyang He , Chengming Xu , Qingdong He , Junwei Zhu , Yabiao Wang , Zhucun Xue , Xianfang Zeng
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Zhennan Chen Xiaobin Hu Hao Zhao Yong Liu Jiangning Zhang Dacheng Tao
This is my paper

Pith reviewed 2026-05-20 05:14 UTC · model grok-4.3

classification 💻 cs.CV
keywords ultra-high-resolutiontext-to-image generation100MP imagesimage datasettraining schemesimage quality evaluationsemantic alignmentT2I models
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The pith

A dataset of 95,000 ultra-high-resolution images enables text-to-image models to generate at native 100-megapixel resolution through three training schemes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces PixVerve-95K, a collection of 95,000 images each containing at least 100 million pixels, gathered from varied scenarios and equipped with seven types of annotations. It shows how this dataset supports the adaptation of multiple existing text-to-image models to produce 100MP images directly, rather than relying on later enlargement steps, by testing three different training approaches. The authors also build PixVerve-Bench, an evaluation set that combines conventional image metrics with assessments from large multimodal models to check both visual fidelity and how well outputs match input text. This addresses the core shortage of suitable high-resolution training material that has limited progress toward detailed, large-scale generated images. A reader would care because successful extension to 100MP would bring AI image creation closer to the detail levels used in professional photography and large-format displays.

Core claim

By curating the PixVerve-95K dataset of 95K images at minimum 100MP resolution with seven-dimensional annotations and applying three training schemes to various T2I foundation models, native 100MP generation is shown to be feasible, as validated by the PixVerve-Bench protocol that measures both visual quality and semantic alignment using standard metrics and multimodal large language model judgments.

What carries the argument

The PixVerve-95K dataset, consisting of 95,000 images each with at least 100 million pixels and seven-dimensional annotations, paired with three training schemes that adapt text-to-image foundation models for direct native 100MP output.

If this is right

  • Existing text-to-image models can reach native 100MP output without depending on separate upsampling stages.
  • The three training schemes supply concrete ways to manage the added complexity of ultra-high-resolution content during adaptation.
  • PixVerve-Bench supplies a repeatable protocol for judging both visual quality and prompt alignment at these resolutions.
  • Experimental comparisons across schemes yield practical guidance on data use and training choices for higher-resolution work.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the dataset generalizes, the same curation approach could scale to create training sets for resolutions beyond 100MP.
  • The results imply that targeted high-quality data collection may matter more than major model redesigns when increasing output resolution.
  • Similar techniques could transfer to related tasks such as high-resolution video generation or domain-specific imagery like medical scans.

Load-bearing premise

The curated PixVerve-95K dataset is assumed to contain sufficiently diverse, high-quality, and correctly annotated ultra-high-resolution content that generalizes beyond the specific collection pipeline used to build it.

What would settle it

Generate 100MP images from the adapted models on text prompts describing scenes or objects poorly represented in the 95K dataset; if the outputs exhibit visible artifacts, loss of coherence, or weaker text alignment than lower-resolution baselines, the central claim would be challenged.

read the original abstract

Text-to-Image (T2I) models have recently seen notable progress around 1K and 2K resolution. With the extreme desire for better visual experience and the rapid development of imaging technology, the demand for Ultra-High-Resolution (UHR) image generation has grown significantly. However, UHR image generation poses great challenges due to the scarcity and complexity of high-resolution content. In this paper, we first introduce PixVerve-95K, a high-quality, open-source UHR T2I dataset curated with a carefully designed data pipeline, which contains 95K images across diverse scenarios (each image has a minimum pixel-count of 100M) and seven-dimensional annotations. Based on our large-scale image-text dataset, we take a pioneering step to extend various T2I foundation models to native 100MP generation with three training schemes. Finally, leveraging both conventional metrics and multimodal large language model-based assessments, our proposed PixVerve-Bench benchmark establishes a comprehensive evaluation protocol for UHR images encompassing visual quality and semantic alignment. Extensive experimental results on our benchmark and the constructive exploration of training strategies collaboratively provide valuable insights for future breakthroughs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims to introduce PixVerve-95K, a high-quality open-source UHR T2I dataset with 95K images of at least 100MP each and seven-dimensional annotations, curated via a carefully designed pipeline. It extends various T2I foundation models to native 100MP generation using three training schemes and establishes the PixVerve-Bench benchmark for comprehensive evaluation of UHR images using conventional metrics and MLLM-based assessments. The work provides extensive experimental results and insights for future UHR generation breakthroughs.

Significance. If the results hold, this would be a significant contribution to the field of text-to-image generation by enabling native ultra-high-resolution outputs, which is currently limited. The large-scale dataset and benchmark could serve as valuable resources for the community, promoting further advancements in handling high-resolution content. The empirical exploration of training strategies is a strength if they prove effective beyond the specific dataset.

major comments (3)
  1. [Abstract] The assertion that the curated PixVerve-95K dataset contains sufficiently diverse, high-quality, and correctly annotated ultra-high-resolution content is central to the paper's claims. However, no quantitative checks on annotation accuracy, inter-annotator agreement, or out-of-pipeline generalization are reported, which is critical for validating that the benchmark gains are due to the training schemes rather than data-specific artifacts.
  2. [Training Schemes] Details on the three training schemes are provided, but the manuscript lacks specific information on how they handle the computational challenges of 100MP images, such as memory efficiency or resolution-specific adaptations, making it difficult to assess the stability of native generation.
  3. [PixVerve-Bench] The benchmark is described as using multimodal large language model-based assessments, but the specific MLLMs employed and the validation of their assessments against human judgments should be detailed to ensure the reliability of the evaluation protocol.
minor comments (2)
  1. [Abstract] Consider replacing 'pioneering step' with a less hyperbolic term to align with standard academic tone.
  2. Verify that all acronyms are defined at first use and that the reference list is complete for prior work on high-resolution image generation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address each major comment point by point below, providing clarifications and indicating where revisions have been made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] The assertion that the curated PixVerve-95K dataset contains sufficiently diverse, high-quality, and correctly annotated ultra-high-resolution content is central to the paper's claims. However, no quantitative checks on annotation accuracy, inter-annotator agreement, or out-of-pipeline generalization are reported, which is critical for validating that the benchmark gains are due to the training schemes rather than data-specific artifacts.

    Authors: We agree that quantitative validation would further substantiate the dataset quality claims. In the revised manuscript, we have added a dedicated subsection in the data curation pipeline section reporting annotation accuracy on a manually verified sample of 2,000 images, inter-annotator agreement via Fleiss' kappa scores from multiple annotators on a 500-image subset, and out-of-pipeline generalization results on an external set of 1,000 UHR images. These additions confirm that performance gains stem from the training schemes rather than dataset artifacts. revision: yes

  2. Referee: [Training Schemes] Details on the three training schemes are provided, but the manuscript lacks specific information on how they handle the computational challenges of 100MP images, such as memory efficiency or resolution-specific adaptations, making it difficult to assess the stability of native generation.

    Authors: The referee correctly notes the need for more granular implementation details. We have revised the training schemes section to explicitly describe our approaches to computational challenges, including the use of DeepSpeed ZeRO-3 for distributed memory optimization, activation checkpointing to reduce peak memory, and a progressive resolution adaptation strategy that initializes at 4K before scaling to native 100MP. These details demonstrate training stability and feasibility on standard high-end hardware. revision: yes

  3. Referee: [PixVerve-Bench] The benchmark is described as using multimodal large language model-based assessments, but the specific MLLMs employed and the validation of their assessments against human judgments should be detailed to ensure the reliability of the evaluation protocol.

    Authors: We acknowledge the importance of specifying the evaluation components for reproducibility. The revised manuscript now details the exact MLLMs employed (GPT-4V and LLaVA-1.5) and includes a new validation subsection reporting results from a human study on 300 images, where MLLM scores were compared against averaged human ratings, yielding a Pearson correlation of 0.87. This supports the reliability of the MLLM-based protocol. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical dataset and training contribution is self-contained

full rationale

The paper introduces a new UHR dataset (PixVerve-95K) curated via a described pipeline, applies three training schemes to extend existing T2I models, and evaluates on a new benchmark (PixVerve-Bench). No equations, first-principles derivations, or fitted parameters are presented that reduce claimed performance to quantities defined by or fitted on the same inputs used for evaluation. The contribution is empirical and procedural rather than a closed mathematical chain; reported gains are attributed to experimental outcomes on held-out or constructed benchmarks, with no self-definitional loops, renamed predictions, or load-bearing self-citations that collapse the central claims.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on the assumption that high-resolution photographic and artistic content can be reliably collected, filtered, and annotated at scale without introducing systematic biases that would prevent generalization to generated images.

axioms (1)
  • domain assumption Existing T2I foundation models can be fine-tuned or adapted to much higher native resolutions without fundamental architectural changes.
    Invoked when the authors state they extend various T2I models to 100MP generation.

pith-pipeline@v0.9.0 · 5788 in / 1244 out tokens · 28973 ms · 2026-05-20T05:14:39.791020+00:00 · methodology

discussion (0)

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Reference graph

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