We present NablAFx, an open-source framework developed to support research in differentiable black-box and gray-box modeling of audio effects. Built in PyTorch, NablAFx offers a versatile ecosystem to configure, train, evaluate, and compare various architectural approaches. It includes classes to manage model architectures, datasets, and training, along with features to compute and log losses, metrics and media, and plotting functions to facilitate detailed analysis. It incorporates implementations of established black-box architectures and conditioning methods as well as differentiable DSP blocks and controllers, enabling the creation of both parametric and non-parametric gray-box signal chains. Beside established conditioning methods like concatenation, featurewise linear modulation (FiLM) and temporal feature-wise linear modulation (TFiLM), we propose three further methods: time-varying concatenation (TVCond), tiny TFiLM (TTFiLM) and time-varying FiLM (TVFiLM), as efficient implementations of time-varying conditioning similar to TFiLM. We also propose the Static Rational Linearity as a flexible and efficient differentiable processor to learn nonlinear functions. The code is accessible at https://github.com/mcomunita/nablafx.
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