How we optimized FLUX.1 Kontext [dev]

FLUX.1 Kontext [dev] has been optimized using a method called TaylorSeer, which enhances the image prediction process by utilizing cached image changes and Taylor Series approximations to reduce computational redundancy during the diffusion transformation across multiple timesteps. The core idea involves predicting image features at later timesteps by employing a truncated Taylor expansion, which captures non-linear changes more effectively than simple linear approximations. By caching derivatives and selectively computing only certain steps, the process speeds up significantly without compromising image quality, particularly at the crucial beginning and end stages of image generation. This optimization reduces model calls from about 30 to as few as 10–15, depending on the chosen speed settings, allowing for faster processing while maintaining high accuracy in the generated images. The implementation details of this approach can be explored in the denoise() and taylor_utils.py files within the FLUX.1 Kontext repository, which is now open-source for further exploration and experimentation.