  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
