Skip to yearly menu bar Skip to main content


Free-form Flows: Make Any Architecture a Normalizing Flow

Felix Draxler · Peter Sorrenson · Lea Zimmermann · Armand Rousselot · Ullrich K├Âthe

MR1 & MR2 - Number 153
[ ]
Fri 3 May 8 a.m. PDT — 8:30 a.m. PDT


Normalizing Flows are generative models that directly maximize the likelihood. Previously, the design of normalizing flows was largely constrained by the need for analytical invertibility. We overcome this constraint by a training procedure that uses an efficient estimator for the gradient of the change of variables formula. This enables any dimension-preserving neural network to serve as a generative model through maximum likelihood training. Our approach allows placing the emphasis on tailoring inductive biases precisely to the task at hand. Specifically, we achieve excellent results in molecule generation benchmarks utilizing E(n)-equivariant networks at greatly improved sampling speed. Moreover, our method is competitive in an inverse problem benchmark, while employing off-the-shelf ResNet architectures. We publish our code at

Live content is unavailable. Log in and register to view live content