The inverse folding of proteins has tremendous applications in protein design and protein engineering. While machine learning approaches for inverse folding have made significant advancements in recent years, efficient generation of diverse and high-quality sequences remains a significant challenge, limiting their practical utility in protein design and engineering. We propose to do probabilistic flow framework that introduces three key designs for designing an amino acid sequence with target fold.At the input level,compare to existing inverse folding methods, rather than sampling sequences from the backbone scaffold, we demonstrate that analyzing a protein structure via the local chemical environment (micro-environment) at each residue can come to comparable performance.At the method level, rather than optimizing the recovery ratio, we generate diverse suggestions. At the data level, during training, we propose to do data augmentation with sequence with high sequence similarity, and train a probability flow model to capture the diverse sequence information. We demonstrate that we achieve comparable recovery ratio as the SOTA inverse folding models while only using micro-environment as inputs, and further show that we outperforms existing inverse folding methods in several zero-shot thermal stability change prediction tasks.