标题： 基于稳定扩散的过程感知和高保真微观结构生成 显示英文标题

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

摘要： 基于加工参数合成现实的微观结构图像对于理解材料设计中的工艺-结构关系至关重要。 然而，由于训练用的显微图像有限以及加工变量的连续性，这项任务仍然具有挑战性。 为克服这些挑战，我们提出了一种基于Stable Diffusion 3.5 Large (SD3.5-Large) 的新颖的工艺感知生成建模方法，这是一种最先进的文本到图像扩散模型，专门用于微观结构生成。 我们的方法引入了数值感知嵌入，将连续变量（退火温度、时间及放大倍数）直接编码到模型的条件中，从而在指定工艺条件下实现可控的图像生成，并捕捉由工艺驱动的微观结构变化。 为解决数据稀缺和计算限制问题，我们通过DreamBooth和低秩适应（LoRA）仅微调模型权重的一小部分，高效地将预训练模型转移到材料领域。 我们使用基于微调的U-Net和VGG16编码器的语义分割模型，在24张标记的显微图像上验证了真实性。 它实现了97.1%的准确率和85.7%的平均IoU，优于之前的方法。 使用物理描述符和空间统计进行的定量分析显示合成与真实微观结构之间有很强的一致性。 具体而言，两点相关性和线性路径误差分别保持在2.1%和0.6%以下。 我们的方法是SD3.5-Large在工艺感知微观结构生成中的首次应用，为数据驱动的材料设计提供了一种可扩展的方法。 显示英文摘要

摘要： 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.