  We present Readout Guidance, a method for controlling text-to-image diffusion
models with learned signals. Readout Guidance uses readout heads, lightweight
networks trained to extract signals from the features of a pre-trained, frozen
diffusion model at every timestep. These readouts can encode single-image
properties, such as pose, depth, and edges; or higher-order properties that
relate multiple images, such as correspondence and appearance similarity.
Furthermore, by comparing the readout estimates to a user-defined target, and
back-propagating the gradient through the readout head, these estimates can be
used to guide the sampling process. Compared to prior methods for conditional
generation, Readout Guidance requires significantly fewer added parameters and
training samples, and offers a convenient and simple recipe for reproducing
different forms of conditional control under a single framework, with a single
architecture and sampling procedure. We showcase these benefits in the
applications of drag-based manipulation, identity-consistent generation, and
spatially aligned control. Project page: https://readout-guidance.github.io.
