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53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
"""
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Ace-Step 1.5 SFT (supervised fine-tuned, 24 layers) — Text-to-Music inference example.
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SFT variant is fine-tuned for specific music styles.
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"""
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from diffsynth.pipelines.ace_step import AceStepPipeline, ModelConfig
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import torch
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import soundfile as sf
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pipe = AceStepPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(
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model_id="ACE-Step/Ace-Step1.5",
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origin_file_pattern="acestep-v15-sft/model.safetensors"
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),
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ModelConfig(
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model_id="ACE-Step/Ace-Step1.5",
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origin_file_pattern="acestep-v15-sft/model.safetensors"
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),
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ModelConfig(
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model_id="ACE-Step/Ace-Step1.5",
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origin_file_pattern="Qwen3-Embedding-0.6B/model.safetensors"
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),
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],
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tokenizer_config=ModelConfig(
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model_id="ACE-Step/Ace-Step1.5",
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origin_file_pattern="Qwen3-Embedding-0.6B/"
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),
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vae_config=ModelConfig(
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model_id="ACE-Step/Ace-Step1.5",
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origin_file_pattern="vae/"
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),
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)
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prompt = "A jazzy lo-fi beat with smooth saxophone and vinyl crackle, late night vibes"
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lyrics = "[Intro - Vinyl crackle]\n\n[Verse 1]\nMidnight city, neon glow\nSmooth jazz flowing to and fro\n\n[Chorus]\nLay back, let the music play\nJazzy nights, dreams drift away"
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audio = pipe(
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prompt=prompt,
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lyrics=lyrics,
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duration=30.0,
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seed=42,
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num_inference_steps=20,
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cfg_scale=7.0,
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shift=3.0,
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)
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sf.write("acestep-v15-sft.wav", audio.cpu().numpy(), pipe.sample_rate)
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print(f"Saved, shape: {audio.shape}")
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