Weight clipping is a well-known strategy to keep the Lipschitz constant of the critic under control, in Wasserstein GAN training. After each training iteration, all parameters of the critic are clipped to a given box, impacting the progress made by the optimizer. In this work, we propose a new strategy for weight clipping in Wasserstein GANs. Instead of directly clipping the parameters, we first obtain an equivalent model that is closer to the clipping box, and only then clip the parameters. Our motivation is to decrease the impact of the clipping strategy on the objective, at each iteration. This equivalent model is obtained by following invariant curves in the critic loss landscape, whose existence is a consequence of the positive homogeneity of common activations: rescaling the input and output signals to each activation by inverse factors preserves the loss. We provide preliminary experiments showing that the proposed strategy speeds up training on Wasserstein GANs with simple feed-forward architectures.