Abstract: Systems and techniques are described for processing image data using a residual model that can be configured with an adjustable number of sampling steps. For example, a process can include obtaining a latent representation of an image and processing, using a decoder of a machine learning model, the latent representation of the image to generate an initial reconstructed image. The process can further include processing, using the residual model, the initial reconstructed image and noise data to predict a plurality of predictions of a residual over a number of sampling steps. The residual represents a difference between the image and the initial reconstructed image. The process can include obtaining, from the plurality of predictions of the residual, a final residual representing the difference between the image and the initial reconstructed image. The process can further include combining the initial reconstructed image and the residual to generate a final reconstructed image.