ACE-Step 1.5 — Neural Engine and XL weights (v3)

ACE-Step 1.5 — Neural Engine and XL weights for WhisperKit (v3)

Format conversions of ACE-Step 1.5 (MIT): https://huggingface.co/ACE-Step/Ace-Step1.5,
https://huggingface.co/ACE-Step/acestep-v15-xl-turbo and
https://huggingface.co/ACE-Step/acestep-5Hz-lm-4B. Nothing retrained.
These add to release acestep-v2, whose text encoder, condition encoder,
hints, audio decoder, silence latent and tokenizer every version still uses.

ACE-Step 1.5 on the Neural Engine

Built by ports/acestep/convert_neural_engine.py.

File	Contents
ace_dit_ane_c{0..3}.mlmodelc__*	The 2B turbo transformer's 24 layers as four compiled Core ML programs of six, float16. Asset names stand for paths: __ is /.
ace_dit_outer_f16.safetensors	What surrounds the layers — timestep embeddings, patch projections, conditioning projection, output norm — float16, run in MLX

Core ML runs fixed shapes on the Neural Engine, so each program carries one
function per length (384 to 5,760 positions, up to 7:40 rendered) and per
conditioning size (512 or 2,368 tokens), sharing one copy of the weights.
Padding is masked out of every attention. The sliding-window layers attend
in blocks of 128 queries against 384 keys instead of the whole sequence.
Against PyTorch float32 on real weights, one six-layer program agrees to
cosine 0.99999.

ACE-Step 1.5 XL

Built by ports/acestep/convert_large.py. Int8 projections (group size 64),
float16 otherwise; parts because a release asset is capped at 2 GB.

File	Contents
ace_xl_decoder_q8.part{1..3}.safetensors	The 4B XL turbo transformer: 32 layers, width 2,560
ace_xl_cond_q8.safetensors	The condition-encoder tensors XL retrained — its two input embeddings and norms, the timbre token and the text projection. The other 192 encoder tensors are bit-identical to the 2B's and are read from ace_cond_q8
ace_lm4b_q8.part{1..3}.safetensors	The 4B planner (acestep-5Hz-lm-4B)

Licence: MIT, as upstream.