model-code

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

@@ -0,0 +1,52 @@
+"""
+Ace-Step 1.5 Base (non-turbo, 24 layers) — Text-to-Music inference example.
+
+Uses cfg_scale=7.0 (standard CFG guidance) and more steps for higher quality.
+"""
+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-base/model.safetensors"
+        ),
+        ModelConfig(
+            model_id="ACE-Step/Ace-Step1.5",
+            origin_file_pattern="acestep-v15-base/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 cinematic orchestral piece with soaring strings and heroic brass"
+lyrics = "[Intro - Orchestra]\n\n[Verse 1]\nAcross the mountains, through the valley\nA journey of a thousand miles\n\n[Chorus]\nRise above the stormy skies\nLet the music carry you"
+
+audio = pipe(
+    prompt=prompt,
+    lyrics=lyrics,
+    duration=30.0,
+    seed=42,
+    num_inference_steps=20,
+    cfg_scale=7.0,  # Base model uses CFG
+    shift=3.0,
+)
+
+sf.write("acestep-v15-base.wav", audio.cpu().numpy(), pipe.sample_rate)
+print(f"Saved, shape: {audio.shape}")