model-code

examples/ace_step/model_inference/acestep-v15-sft.py

@@ -0,0 +1,52 @@
+"""
+Ace-Step 1.5 SFT (supervised fine-tuned, 24 layers) — Text-to-Music inference example.
+
+SFT variant is fine-tuned for specific music styles.
+"""
+from diffsynth.pipelines.ace_step import AceStepPipeline, ModelConfig
+import torch
+import soundfile as sf
+
+
+pipe = AceStepPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(
+            model_id="ACE-Step/Ace-Step1.5",
+            origin_file_pattern="acestep-v15-sft/model.safetensors"
+        ),
+        ModelConfig(
+            model_id="ACE-Step/Ace-Step1.5",
+            origin_file_pattern="acestep-v15-sft/model.safetensors"
+        ),
+        ModelConfig(
+            model_id="ACE-Step/Ace-Step1.5",
+            origin_file_pattern="Qwen3-Embedding-0.6B/model.safetensors"
+        ),
+    ],
+    tokenizer_config=ModelConfig(
+        model_id="ACE-Step/Ace-Step1.5",
+        origin_file_pattern="Qwen3-Embedding-0.6B/"
+    ),
+    vae_config=ModelConfig(
+        model_id="ACE-Step/Ace-Step1.5",
+        origin_file_pattern="vae/"
+    ),
+)
+
+prompt = "A jazzy lo-fi beat with smooth saxophone and vinyl crackle, late night vibes"
+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"
+
+audio = pipe(
+    prompt=prompt,
+    lyrics=lyrics,
+    duration=30.0,
+    seed=42,
+    num_inference_steps=20,
+    cfg_scale=7.0,
+    shift=3.0,
+)
+
+sf.write("acestep-v15-sft.wav", audio.cpu().numpy(), pipe.sample_rate)
+print(f"Saved, shape: {audio.shape}")