Title: Native-Resolution Image Synthesis
Venue: NeurIPS 2025
Accepted: yes
Keywords: Native-resolution

Image Synthesis

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


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Native-resolution image synthesis and proposes the Native-resolution diffusion Transformer (NiT), a generative model that can synthesize images across a wide range of resolutions and aspect ratios without being constrained to a fixed format. Inspired by the variable-length generalization capabilities of LLMs, the model reformulates image generation by directly modeling visual data at its native resolution using techniques like packed variable-length token processing, axial 2D Rotary Positional Embeddings, and FlashAttention. NiT achieves state-of-the-art results on both 256×256 and 512×512 ImageNet benchmarks with a single model and demonstrates strong zero-shot generalization to unseen resolutions and aspect ratios.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n+ Impressive generalization on high-resolution compared to previous work, as shown in Tab. 2 and Tab. 3.\n+ Theory-wise, this is not new as FiT already explore similar concept of using 2D Rotary Position Embedding (RoPE). **BUT** implementation-wise, this paper propose an very interesting and efficient trick (Packed Full-Attention and Packed Adaptive Layer Normalization) to train diffusion transformer on high-resolution.\n\n**Weaknesses**\n+ The different of this work against FiT is not clear, they seem similar.\n+ The comparison with other work seem unfair as previous work utilize different VAE than the one used in this work (DC-AE).\n\nQUESTIONS:\n+ Please explain the different between FiT and this work.\n+ I think the comparison with other method is not fair. Author should retrain NiT with same VAE as previous method or pick the best previous method (i.e FiT) and train it with DC-AE for a fair comparison.\n+ There was a long line of research in native-resolution generation with GAN like ScaleSpace-GAN [1], CREPS [2], AnyRes-GAN [3], INR-GAN [4],.. Author should include them in related work for completeness for future readers/researchers.\n\nReference:\n\n[1] Wolski et. al. Learning Images Across Scales Using Adversarial Training. SIGGRAPH 2024\n\n[2] Nguyen et. al. Efficient Scale-Invariant Generator with Column-Row Entangled Pixel Synthesis. CVPR 2023\n\n[3] Chai et. al. Any-resolution Training for High-resolution Image Synthesis. ECCV 2022\n\n[4] Skorokhodov et. al. Adversarial Generation of Continuous Images. CVPR 2021\n\nLIMITATIONS:\nNo limitations besides then weaknesses I mentioned", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a Native-resolution diffusion Transformer (NiT) model for image synthesis at arbitrary resolutions and aspect ratios. The key contributions are:\n1. Introducing the concept of native-resolution image synthesis, which overcomes the limitations of fixed-resolution, square-image methods.\n2. Developing the NiT architecture that inherently integrates dynamic tokenization, Variable-Length Sequence Processing and 2D Structural Prior Injection into a single framework.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nQuality‌: The proposed NiT model shows impressive performance on both standard and zero-shot generalization tasks, outperforming existing methods. The ablation studies provide insights into the model's ability to generalize.\nClarity‌: The paper is well-written and easy to follow. The motivation, methodology, and experimental setup are clearly explained.\nSignificance‌: The native-resolution image synthesis paradigm has the potential to bridge the gap between visual generative modeling and large language models, offering a more flexible and scalable approach.\nOriginality‌: The idea of native-resolution modeling and the NiT architecture are novel.\nWeaknesses:\nQuality‌: The performance on extremely high resolutions and aspect ratios is still not satisfactory, indicating room for improvement.\nClarity‌: Some details of the implementation, such as hyperparameters and exact training procedures, could be more thoroughly described to ensure reproducibility.\nOriginality‌: While the main idea is novel, some components of the NiT architecture build upon existing techniques. More arguments are needed.\n\nQUESTIONS:\n1. Please describe the hyperparameters, data preprocessing steps, and exact training procedures in more detail. This will help ensure the reproducibility of your results.\n2. Please provide a more in-depth discussion of the limitations of the NiT model, especially regarding its performance on extremely high resolutions and aspect ratios.\n3. Please discuss both potential positive and negative societal impacts of your work. A comprehensive discussion of limitations and potential avenues for future improvement.\n4. Please include comparisons with more state-of-the-art baseline methods, especially those that are recent and relevant to your work.\n\nLIMITATIONS:\nProviding a more comprehensive discussion of the model's limitations, especially in extreme conditions. On the other hand, while the experiments look very impressive, I need more arguments on the idea of the proposed method, since it is built on some existing techniques.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces \"native-resolution image synthesis,\" a new paradigm for generative modeling that handles images of arbitrary resolutions and aspect ratios. The authors propose the Native-resolution diffusion Transformer (NiT), which is inspired by Large Language Models and processes variable-length visual tokens without resizing or cropping inputs. The proposed model is shown to achieve state-of-the-art results on standard benchmarks and demonstrates impressive zero-shot generalization to unseen resolutions and aspect ratios.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper addresses a significant and practical limitation of current generative models, which are typically constrained to fixed, square resolutions. The proposed native-resolution approach is a novel and impactful contribution to the field.\n2. The technical design of the NiT architecture is sound and well-motivated, cleverly adapting concepts like variable-length sequence processing and rotary positional embeddings (2D RoPE) from LLMs to the vision domain.\n\nWeaknesses:\n1. The evaluation relies heavily on quantitative metrics. The paper would benefit from a more extensive qualitative analysis in the main text, particularly showing failure cases to better illustrate where the model struggles with object coherence or detail at unseen scales.\n2. While the generalization is strong, the model's performance degrades noticeably at extremely high resolutions like 2048x2048 and on extreme aspect ratios. A deeper analysis of these failure points would provide a more complete understanding of the method's limitations.\n3. The ablation study confirms the importance of native-resolution training data but could further explore how the specific distribution of resolutions and aspect ratios within the training set impacts generalization performance.\n\nQUESTIONS:\nI like this paper which would benefit to the community in my opinion.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Native-resolution Image Synthesis, a new approach to image generation. It introduces the Native-resolution diffusion Transformer (NiT), which handles images of any resolution or aspect ratio. NiT uses variable-length visual tokens to overcome the limitations of fixed-size models. Trained only on ImageNet, it achieves state-of-the-art results on both 256×256 and 512×512 benchmarks. It also generalizes well to unseen sizes like 1024×1024 and unusual aspect ratios.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. This paper effectively addresses the challenge of using images with diverse resolutions during training. Unlike previous methods that rely on fixed-size inputs, the proposed NiT model learns directly from native-resolution images. As a result, it achieves strong and stable performance not only on standard benchmarks like 256×256 and 512×512, but also on unseen higher resolutions and aspect ratios.\n\n2. The methodology is clearly presented, with well-structured explanations of each component. \n\n\n**Weakness**\n1.The paper briefly mentions multi-resolution training in related work, which has been proven effective and practical in large-scale settings. Its approach is especially similar to the frame packing strategy used in CogVideoX. However, whether the proposed method retains its efficiency and effectiveness at large scale remains an open question, as the paper does not provide empirical validation under such conditions.\n\n2. The paper mentions training with images of diverse resolutions, but lacks concrete details on the experimental setup. For example, it does not specify how image resolutions are mixed within a batch or across GPUs during training. Compared to bucket sampling, it's unclear whether the proposed method can truly support more diverse and continuous resolution ranges in practice. Additionally, since the model relies on a high compression ratio DC-AE with a downsampling factor of 32, all image resolutions must be multiples of 32. This constraint may limit the flexibility and granularity of supported resolutions in real-world applications.\n\n3. The paper emphasizes its ability to resolution extrapolate. However, combining multi-resolution training with RoPE-style positional encoding already enables some level of resolution extrapolation. While the paper presents strong results in this area, it would be helpful to clarify how much of the performance gain comes from the proposed architectural design versus the known benefits of RoPE and resolution diversity in training.\n\nQUESTIONS:\nSee weakness\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}