GENOME vs Neural Amp Modeler

GENOME is an amp modeler, effects suite, and virtual cabinet engine for guitar and bass. Its CODEX engine loads Neural Amp Modeler, AIDA-X, and Proteus captures in one workflow, and the product page says it includes more than 40 premium AI amp captures. Signal chains support series or parallel routing, up to 12 components per lane, and dual-lane expansion. GENOME runs standalone or as a VST3, AU, or AAX plugin; listed tested DAWs include Pro Tools, Logic Pro X, Ableton Live, Cubase, Studio One, and Reaper. Parameter automation, MIDI mapping, and MIDI Learn support external hardware control. The free GENOME Intro includes four TSM-Ai amplifiers, 14 pedals, five DynIR cabinets, and four Studio FX. The GENOME license costs $89.99 as a one-time purchase and includes 24 amps, 49 pedals, 16 DynIR cabinets, and 16 Studio FX, excluding FairTone and Artist Series components. A 14-day free trial is available, and a Two notes account is required. A desktop license does not include GENOME iOS; iOS use requires an Access Pass.

Neural Amp Modeler (NAM) uses deep learning to create models of guitar amplifiers and pedals. The project is free and open source, with three code repositories under permissive licenses that are open to contributions. Users can train models online in Google Colab or install the Python-based trainer locally from PyPI. The Gateway plugin loads and plays snapshot models of gear, while ParametricOD demonstrates modeling across an overdrive's knobs and switches. The plugin repository produces VST3 and AU plugins as well as a standalone desktop application. Models can be shared through TONE3000, a community-organized online library. NAM runs on Windows, macOS, Linux, Raspberry Pi, embedded systems, and websites. Existing A1 models remain supported in NeuralAmpModelerCore and NeuralAmpModelerPlugin. Builders can incorporate NAM's open-source modeling technology into their own products. The training code depends on PyTorch Lightning; version 0.12.3 excludes compromised Lightning versions 2.6.2 and 2.6.3.