Tame the performance of code you didn't write: A journey into stable diffusion` The author embarked on a journey to understand the Stable Diffusion codebase, which is written in Python and uses deep learning and neural networks. They set up Sentry, an error tracking tool, to instrument the code and gain insights into its performance. The author created a Sentry transaction spanning the `txt2img` function and used Profiling flame graphs to visualize the code execution. By analyzing the flame graph, they identified slow-running functions and added them to the list of functions to instrument. This process allowed them to drill down into performance bottlenecks and gain a better understanding of the code's behavior. The author concluded that by using Sentry and Profiling, developers can transform an opaque codebase into one that can be reasoned about, allowing for improved performance and debugging capabilities.