ReStyle extends encoder-based GAN inversion by predicting residuals relative to current latent estimates in a self-correcting iterative manner. The encoder takes an input image and progressively refines its estimate of the corresponding latent code across multiple passes, rather than predicting the final latent code in a single forward pass. This approach achieves improved accuracy over state-of-the-art encoder methods with minimal inference overhead, enabling better real-image manipulation through GAN-based generative models.