roman220220/ACE-Step1.5-sft-MLX-8bit

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ACE-Step 1.5 sft for MLX: 8-bit

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ACE-Step 1.5 sft (ACE-Step team, MIT, trained on licensed and royalty-free data) is converted for Apple Silicon here: songs with sung, intelligible lyrics from a style description, 48 kHz stereo.

DiT and condition encoders at 8-bit (round-to-nearest, group 64): practically indistinguishable from bf16.

sft vs turbo

ACE-Step 1.5 ships two DiTs. We compared them on 3 songs × 4 seeds: pop, rock and a ballad, 30 s each. The score is Whisper large-v3-turbo's word error rate against the lyrics that were asked for (lower = the lyrics come through more clearly).

By ear: turbo's mix sounds fuller and more like a finished song, while sft puts the voice upfront and gets the words across. On electronic music sft sounded best.

Quantization

Teacher-forced error against bf16: every decoder call of the bf16 run, fed to each variant.

Lyrics intelligibility, the same 3 songs × 4 seeds (Whisper WER, lower is better; 12 tracks, so differences of a few points are noise):

Pick bf16 or 8-bit if you have the memory, GPTQ 4-bit on a 16 GB Mac.

⚠️ How to run it: the stock mlx-audio settings sing gibberish

Pulled through mlx-audio's defaults (branch `pc/add-ace`, commit 1e8264a), the sft DiT produces cacophony. Two causes, both found and measured (findings):

1. 1.The 5 Hz LM hints. mlx-audio feeds its planner's audio codes into the DiT, and that breaks the sft model. sft doesn't need a planner: run it with use_lm=False. It is also ~15 s faster.
2. 2.The unconditional CFG branch. mlx-audio encodes all-zero text there. The official pipeline uses the trained null_condition_emb. The patch below uses it.

import mlx.core as mx, mlx.nn as nn
import mlx_audio.utils as u
from mlx_audio.tts import load

# a local folder doesn't tell mlx-audio which model class it is
pick = u.get_model_class
u.get_model_class = lambda model_type, model_name, category, model_remapping: pick("ace_step", None, category, model_remapping)
model = load("roman220220/ACE-Step1.5-sft-MLX-8bit")

class NullAwareEncoder(nn.Module):          # CFG's unconditional branch, as the official pipeline does it
    def __init__(self, inner, null):
        super().__init__(); self.inner, self._null = inner, null
    def __call__(self, text_hidden_states=None, lyric_hidden_states=None, **kw):
        out, mask = self.inner(text_hidden_states=text_hidden_states, lyric_hidden_states=lyric_hidden_states, **kw)
        if not mx.any(text_hidden_states).item() and not mx.any(lyric_hidden_states).item():
            out = mx.broadcast_to(self._null.astype(out.dtype), out.shape)
        return out, mask
model.encoder = NullAwareEncoder(model.encoder, model.null_condition_emb)

result = list(model.generate(
    text="upbeat pop song with female vocals, bright synths, driving beat",
    lyrics="[Verse]\nCity lights are calling out my name\n...",
    duration=30.0, seed=1, vocal_language="en",
    use_lm=False, num_steps=50, guidance_scale=7.0, shift=1.0, guidance_interval=1.0, cfg_type="apg",
))[-1]   # result.audio: [2, samples] at 48 kHz

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LLMTray uses this model for in-chat music generation.

License

Licensed under the MIT License, the same license as the ACE-Step 1.5 model. See `LICENSE`.

This is a modified version of ACE-Step/acestep-v15-sft, together with the ACE-Step 1.5 VAE and its Qwen3-Embedding-0.6B text encoder:

• —converted to mlx-audio's MLX layout: the decoder's Conv1d weights transposed and the rotary caches dropped;
• —the DiT and condition encoders quantized to 8-bit.