Process-aware and high-fidelity microstructure generation using stable diffusion

• URL: http://arxiv.org/abs/2507.00459v1
• Date: Tue, 01 Jul 2025 06:16:53 GMT
• Title: Process-aware and high-fidelity microstructure generation using stable diffusion
• Authors: Hoang Cuong Phan, Minh Tien Tran, Chihun Lee, Hoheok Kim, Sehyok Oh, Dong-Kyu Kim, Ho Won Lee,
• Abstract summary: We present a novel process-aware generative modeling approach based on Stable Diffusion 3.5 Large (SD3.5-Large)<n>Our method introduces numeric-aware embeddings that encode continuous variables directly into the model's conditioning.<n>We validate realism using a semantic segmentation model based on a fine-tuned U-Net with a VGG16 encoder on 24 labeled micrographs.
• Score: 0.8060624778923473
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Synthesizing realistic microstructure images conditioned on processing parameters is crucial for understanding process-structure relationships in materials design. However, this task remains challenging due to limited training micrographs and the continuous nature of processing variables. To overcome these challenges, we present a novel process-aware generative modeling approach based on Stable Diffusion 3.5 Large (SD3.5-Large), a state-of-the-art text-to-image diffusion model adapted for microstructure generation. Our method introduces numeric-aware embeddings that encode continuous variables (annealing temperature, time, and magnification) directly into the model's conditioning, enabling controlled image generation under specified process conditions and capturing process-driven microstructural variations. To address data scarcity and computational constraints, we fine-tune only a small fraction of the model's weights via DreamBooth and Low-Rank Adaptation (LoRA), efficiently transferring the pre-trained model to the materials domain. We validate realism using a semantic segmentation model based on a fine-tuned U-Net with a VGG16 encoder on 24 labeled micrographs. It achieves 97.1% accuracy and 85.7% mean IoU, outperforming previous methods. Quantitative analyses using physical descriptors and spatial statistics show strong agreement between synthetic and real microstructures. Specifically, two-point correlation and lineal-path errors remain below 2.1% and 0.6%, respectively. Our method represents the first adaptation of SD3.5-Large for process-aware microstructure generation, offering a scalable approach for data-driven materials design.

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