A innovative technique swaps learned lookup tables for binned Gaussian splats in vision transformers, enabling the generation of 8x8 image patches that render at arbitrary resolutions without fixed constraints. By leveraging differentiable splatting and custom kernels, this approach reduces blur and boundary seams in AI-generated imagery, as demonstrated through cat synthesis experiments. The method's flexibility could transform high-res workflows in generative AI, balancing visual fidelity with computational challenges.