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magenta-torch/magenta-realtime-2.
They let the model run faster-than-real-time without calling torch.compile at
runtime (e.g. on ZeroGPU, where torch.compile is unavailable).sm_120), the ZeroGPU architecture. They will not
load on other GPUs (A100, H100, L4, T4, consumer cards, …).1model = AutoModel.from_pretrained("magenta-torch/magenta-realtime-2",
2 trust_remote_code=True, dtype=torch.bfloat16).to("cuda")
3model.compile_steps() # portable torch.compile — works on any CUDA GPUMagentaRT2ForConditionalGeneration.depthformer.decoder) — see the converter/compile
utilities in the dev repo
(fork).model.load_compiled("magenta-torch/magenta-rt-aoti-base") # binds the weight-less graphs to the model's weightstemporal.pt2 and depth.pt2 (the two hot step graphs) + metadata.