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How we optimized FLUX.1 Kontext [dev]

Try it out here: FLUX.1 Kontext [dev] In addition to making our FLUX.1 Kontext [dev] implementation open-source, we wanted to provide more guidance on how we chose to optimize it without compromising on quality. In this post, you will mainly learn about TaylorSeer optimization, a method to approximate intermediate image predictions by using cached image changes (derivatives) and formulae derived from Taylor Series approximations. Fellow optimization nerds, read on. (We pulled most of our implementation info from the following paper .) If you head to the predict function in predict.py from our FLUX.1 Kontext [dev] repo , you will find the main logic. (Highly suggest working through the repo and using this post as a guide for understanding its structure.) Let’s break it down. On TaylorSeer When generating a new image with FLUX.1 Kontext, you apply a diffusion transformation across multiple timesteps — around 30 steps in a row. At each step, a stack of transformer layers predicts an update to the image you are denoising. This process can take a while. …

Announced

15 Jul 2025 16:00 UTC