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Vinpolar/Khala-MusicGeneration-v1.0-MPS

sourceHugging Facecc-by-nc-4.0updated 4mo agoView on Hugging Face
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Model Card

Khala — Apple Silicon (MPS) pre-converted weights

Pre-converted, vanilla-PyTorch weights for running **Khala** — a high-fidelity, unified acoustic-token song generator — on Apple Silicon (MPS) or CPU, with no NVIDIA / Megatron / TransformerEngine / FlashAttention stack.

These are format conversions of the original weights, not a retrain. They are produced by gathering the upstream Megatron torch_dist checkpoint and the DAC-RVQ decoder, then renaming tensors to the de-Megatron KhalaModel layout. The numerics match the original (backbone greedy decode is bit-identical to the CUDA reference, 64/64 tokens).

Files

FileWhat it is
khala_backbone.safetensorsBackbone GPT (q0/q1 coarse acoustic tokens), KhalaModel naming
khala_superres.safetensorsSuper-resolution GPT (expands to q0…q63), non-causal
decoder_weights.ptDAC-RVQ decoder (generator-only), stereo, 64 quantizers
backbone_megatron_args.jsonBackbone config (consumed by KhalaConfig.from_megatron_args)
superres_megatron_args.jsonSuper-res config
decoder_config.yamlDecoder config

Usage

Download into the directory the vanilla runtime reads (_cuda_artifacts/, or set KHALA_VANILLA_WEIGHTS):

bash
hf download Vinpolar/Khala-MusicGeneration-v1.0-MPS --local-dir _cuda_artifacts

Then run the CLI generator or the Mac web stack from the Khala repo (Apple Silicon section of the README):

bash
# one-off track from the command line
KHALA_BACKEND=vanilla .venv-mac/bin/python -u tools/generate_vanilla.py \
    --duration 3 --tags "upbeat, pop, piano"

# or the full web UI (worker + API)
bash backend/run_backend_mac.sh        # --device cpu also supported

Attribution & license

  • —Original model, weights, and research: liujiafeng / the Khala team — liujiafeng/Khala-MusicGeneration-v1.0, paper.
  • —Released under CC BY-NC 4.0, the same license as the original weights. This repository only re-packages those weights for Apple-Silicon inference.
  • —⚠️ The upstream team noted (2026-05-07) a possible inference-quality precision issue under investigation; treat generation quality as not yet final.