用卷积网络实现任意分辨率图像快速生成，速度更快质量更高。
FlowDCN: Exploring DCN-like Architectures for Fast Image Generation with Arbitrary Resolution
- 纯卷积设计，计算复杂度线性增长，支持任意分辨率输入。
- 在256×256 ImageNet上达到4.30 sFID，收敛速度是变压器模型的1/5。
- 参数减少8%、算力降低20%，适合高效部署和多分辨率应用。
任意分辨率图像生成在AIGC中仍具挑战性，需应对不同分辨率与长宽比的同时保持高质量。现有基于Transformer的扩散模型存在二次计算开销且分辨率外推能力有限。本文提出FlowDCN，一种纯卷积生成模型，具有线性时间与内存复杂度，可高效生成任意分辨率高质量图像。其采用新型可学习分组变形卷积块，提升对多分辨率的适应性。FlowDCN在256×256 ImageNet基准上达到4.30 sFID，优于主流变压器模型，在收敛速度（仅需1/5图像）、视觉质量、参数量（减少8%）和计算量（减少20%）方面均表现更优，展现出高扩展性与灵活性。
原文摘要 · Abstract (English)
Arbitrary-resolution image generation still remains a challenging task in AIGC, as it requires handling varying resolutions and aspect ratios while maintaining high visual quality. Existing transformer-based diffusion methods suffer from quadratic computation cost and limited resolution extrapolation capabilities, making them less effective for this task. In this paper, we propose FlowDCN, a purely convolution-based generative model with linear time and memory complexity, that can efficiently generate high-quality images at arbitrary resolutions. Equipped with a new design of learnable group-wise deformable convolution block, our FlowDCN yields higher flexibility and capability to handle different resolutions with a single model. FlowDCN achieves the state-of-the-art 4.30 sFID on $256\times256$ ImageNet Benchmark and comparable resolution extrapolation results, surpassing transformer-based counterparts in terms of convergence speed (only $\frac{1}{5}$ images), visual quality, parameters ($8\%$ reduction) and FLOPs ($20\%$ reduction). We believe FlowDCN offers a promising solution to scalable and flexible image synthesis.
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