Texture models based on Generative Adversarial Networks (GANs) use zero-padding to implicitly encode positional information of the image features. However, when extending the spatial input to generate images at large sizes, zero-padding can often lead to degradation of quality due to the incorrect positional information at the center of the image and limit the diversity within the generated images. In this paper, we propose a novel approach for generating stochastic texture images at large arbitrary sizes using GANs model that is based on patch-by-patch generation. Instead of zero-padding, the model uses \textit{local padding} in the generator that shares border features between the generated patches; providing positional context and ensuring consistency at the boundaries. The proposed models are trainable on a single texture image and have a constant GPU scalability with respect to the output image size, and hence can generate images of infinite sizes. We show in the experiments that our method has a significant advancement beyond existing texture models in terms of the quality and diversity of the generated textures. Furthermore, the implementation of local padding in the state-of-the-art super-resolution models effectively eliminates tiling artifacts enabling large-scale super-resolution. Our code is available at \url{https://github.com/ai4netzero/Infinite_Texture_GANs

翻译：基于生成对抗网络的纹理模型通过零填充隐式编码图像特征的位置信息。然而，当扩展空间输入以生成大尺寸图像时，零填充常因图像中心位置信息错误而导致质量下降，并限制生成图像的多样性。本文提出一种新方法，利用逐补丁生成的GAN模型生成任意大尺寸的随机纹理图像。生成器采用局部填充替代零填充，在生成的补丁之间共享边界特征，从而提供位置上下文并确保边界一致性。所提模型可在单幅纹理图像上训练，且GPU内存消耗与输出图像尺寸成恒定比例关系，因此能够生成无限尺寸图像。实验表明，我们的方法在生成纹理的质量和多样性方面显著超越现有纹理模型。此外，将局部填充应用于最先进的超分辨率模型能有效消除平铺伪影，实现大规模超分辨率重建。我们的代码已开源：\url{https://github.com/ai4netzero/Infinite_Texture_GANs}