  In this work, we propose a method to 'hack' generative models, pushing their
outputs away from the original training distribution towards a new objective.
We inject a small-scale trainable module between the intermediate layers of the
model and train it for a low number of iterations, keeping the rest of the
network frozen. The resulting output images display an uncanny quality, given
by the tension between the original and new objectives that can be exploited
for artistic purposes.
