  This paper addresses the challenge of example-based non-stationary texture
synthesis. We introduce a novel twostep approach wherein users first modify a
reference texture using standard image editing tools, yielding an initial rough
target for the synthesis. Subsequently, our proposed method, termed
"self-rectification", automatically refines this target into a coherent,
seamless texture, while faithfully preserving the distinct visual
characteristics of the reference exemplar. Our method leverages a pre-trained
diffusion network, and uses self-attention mechanisms, to gradually align the
synthesized texture with the reference, ensuring the retention of the
structures in the provided target. Through experimental validation, our
approach exhibits exceptional proficiency in handling non-stationary textures,
demonstrating significant advancements in texture synthesis when compared to
existing state-of-the-art techniques. Code is available at
https://github.com/xiaorongjun000/Self-Rectification
