InfinityEBSD : Metrics-Guided Infinite-Size EBSD Map Generation With Diffusion Models

TL;DR

InfinityEBSD introduces a diffusion-based approach to generate large, realistic EBSD microstructure maps conditioned on physical metrics, enabling scalable, statistically representative microstructure synthesis for materials analysis.

Contribution

The paper presents a novel diffusion model conditioned on microstructural metrics to generate large-scale, realistic EBSD maps, supporting both extension of existing maps and creation of new ones from descriptors.

Findings

Generated maps match statistical descriptors of target metrics.

The method maintains morphological diversity and spatial coherence.

Generated maps are compatible with standard analysis software.

Abstract

Materials performance is deeply linked to their microstructures, which govern key properties such as strength, durability, and fatigue resistance. EBSD is a major technique for characterizing these microstructures, but acquiring large and statistically representative EBSD maps remains slow, costly, and often limited to small regions. In this work, we introduce InfinityEBSD, a diffusion-based method for generating monophase realistic EBSD maps of arbitrary size, conditioned on physically meaningful microstructural metrics. This approach supports two primary use cases: extending small experimental EBSD maps to arbitrary sizes, and generating entirely new maps directly from statistical descriptors, without any input map. Conditioning is achieved through eight microstructural descriptors, including grain size, grain perimeter, grain inertia ratio, coordination number and disorientation angle distribution, allowing the model to generate maps that are both visually realistic and physically interpretable. A patch-wise geometric extension strategy ensures spatial continuity across grains, enabling the model to produce large-scale EBSD maps while maintaining coherent grain boundaries and orientation transitions. The generated maps can also be exported as valid Channel Text Files (CTF) for immediate post-processing and analysis in software such as MTEX or simulation environments like DIGIMU. We quantitatively validate our results by comparing distributions of the guiding metrics before and after generation, showing that the model respects the statistical targets while introducing morphological diversity. InfinityEBSD demonstrates that diffusion models, guided by physical metrics, can bridge the gap between synthetic and realistic materials representation, paving the way for future developments such as 3D realistic microstructure generation from 2D data.