Tiled Diffusion
This addresses the need for scalable and flexible tiling in applications like texture creation, video game assets, and digital art, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackles the problem of automating the creation of seamless image tiles, which is traditionally manual and limited in scalability, by introducing Tiled Diffusion, a method that extends diffusion models to generate cohesive tiling patterns across various domains, enabling seamless integration of multiple images without manual intervention.
Image tiling -- the seamless connection of disparate images to create a coherent visual field -- is crucial for applications such as texture creation, video game asset development, and digital art. Traditionally, tiles have been constructed manually, a method that poses significant limitations in scalability and flexibility. Recent research has attempted to automate this process using generative models. However, current approaches primarily focus on tiling textures and manipulating models for single-image generation, without inherently supporting the creation of multiple interconnected tiles across diverse domains. This paper presents Tiled Diffusion, a novel approach that extends the capabilities of diffusion models to accommodate the generation of cohesive tiling patterns across various domains of image synthesis that require tiling. Our method supports a wide range of tiling scenarios, from self-tiling to complex many-to-many connections, enabling seamless integration of multiple images. Tiled Diffusion automates the tiling process, eliminating the need for manual intervention and enhancing creative possibilities in various applications, such as seamlessly tiling of existing images, tiled texture creation, and 360$^\circ$ synthesis.