How we optimized FLUX.1 Kontext [dev]

What this post added

This post details the implementation of TaylorSeer for optimizing the FLUX.1 Kontext image generation model. It explains the mathematical basis of Taylor Series approximation for predicting intermediate image states, the caching mechanism for derivatives, and the step-by-step logic within the `denoise()` function. The post outlines two paths: full computation and approximation using Taylor Series, and describes how the latent image is updated in each case. It also discusses the `generate_compute_step_map()` function for controlling the acceleration level and provides code snippets for the caching state, derivative approximation, and latent update.