614 lines
22 KiB
Python
614 lines
22 KiB
Python
import torchaudio
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import tempfile
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from typing import Optional, List
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import torch
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import os
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import ast
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import sys
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import librosa
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from loguru import logger
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from transformers import UMT5EncoderModel, AutoTokenizer
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current_dir = os.path.dirname(os.path.abspath(__file__))
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if current_dir not in sys.path:
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sys.path.append(current_dir)
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from ace_step.pipeline_ace_step import ACEStepPipeline as AP
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from ace_step.music_dcae.music_dcae_pipeline import MusicDCAE
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from ace_step.ace_models.ace_step_transformer import ACEStepTransformer2DModel
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import folder_paths
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cache_dir = folder_paths.get_temp_directory()
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models_dir = folder_paths.models_dir
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model_path = os.path.join(models_dir, "TTS", "ACE-Step-v1-3.5B")
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torch.backends.cudnn.benchmark = False
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torch.set_float32_matmul_precision('high')
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torch.backends.cudnn.deterministic = True
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torch.backends.cuda.matmul.allow_tf32 = True
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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class AudioCacher:
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def __init__(self, cache_dir: Optional[str] = None, default_format: str = "wav"):
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if cache_dir is None:
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self.cache_dir = tempfile.gettempdir()
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else:
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self.cache_dir = cache_dir
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if not os.path.exists(self.cache_dir):
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try:
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os.makedirs(self.cache_dir, exist_ok=True)
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except OSError as e:
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raise # 重新抛出异常，因为这是一个关键的初始化步骤
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self.default_format = default_format.lstrip('.') # 确保没有前导点
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self._files_to_cleanup_in_context: List[str] = [] # 用于上下文管理器
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def cache_audio_tensor(
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self,
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audio_tensor: torch.Tensor,
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sample_rate: int,
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filename_prefix: str = "cached_audio_",
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audio_format: Optional[str] = None
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) -> str:
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current_format = (audio_format or self.default_format).lstrip('.')
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try:
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with tempfile.NamedTemporaryFile(
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prefix=filename_prefix,
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suffix=f".{current_format}",
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dir=self.cache_dir,
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delete=False
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) as tmp_file:
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temp_filepath = tmp_file.name
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torchaudio.save(temp_filepath, audio_tensor, sample_rate)
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self._files_to_cleanup_in_context.append(temp_filepath)
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return temp_filepath
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except Exception as e:
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if 'temp_filepath' in locals() and os.path.exists(temp_filepath):
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try:
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os.remove(temp_filepath)
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except OSError as e_clean:
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logger.warning(f"Error cleaning temporary file {temp_filepath}: {e_clean}")
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raise RuntimeError(f"Failed to save audio: {e}") from e
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def cleanup_file(self, filepath: str) -> bool:
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"""
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清理指定的缓存文件。
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Args:
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filepath (str): 要删除的文件的路径。
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Returns:
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bool: 如果文件成功删除或文件不存在，则返回 True；如果删除失败，则返回 False。
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"""
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if not filepath:
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return True
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if os.path.exists(filepath):
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try:
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os.remove(filepath)
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if filepath in self._files_to_cleanup_in_context:
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self._files_to_cleanup_in_context.remove(filepath)
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return True
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except OSError as e:
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return False
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else:
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if filepath in self._files_to_cleanup_in_context:
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self._files_to_cleanup_in_context.remove(filepath)
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return True
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def cleanup_all_tracked_files(self) -> None:
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"""
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清理所有由当前上下文管理器实例跟踪的缓存文件。
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"""
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# 迭代列表的副本，因为 cleanup_file 可能会修改列表
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for f_path in list(self._files_to_cleanup_in_context):
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self.cleanup_file(f_path)
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self._files_to_cleanup_in_context.clear()
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def __enter__(self):
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"""进入上下文管理器时调用。"""
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# 重置跟踪文件列表，以防同一个实例被多次用于 'with' 语句
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self._files_to_cleanup_in_context = []
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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"""退出上下文管理器时调用，负责清理。"""
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self.cleanup_all_tracked_files()
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# 返回 False 以便在发生异常时重新抛出异常
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return False
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from ace_step.data_sampler import DataSampler
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def sample_data(json_data):
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return (
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json_data["audio_duration"],
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json_data["prompt"],
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json_data["lyrics"],
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json_data["infer_step"],
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json_data["guidance_scale"],
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json_data["scheduler_type"],
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json_data["cfg_type"],
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json_data["omega_scale"],
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json_data["actual_seeds"][0],
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json_data["guidance_interval"],
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json_data["guidance_interval_decay"],
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json_data["min_guidance_scale"],
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json_data["use_erg_tag"],
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json_data["use_erg_lyric"],
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json_data["use_erg_diffusion"],
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", ".join(map(str, json_data["oss_steps"])),
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json_data["guidance_scale_text"] if "guidance_scale_text" in json_data else 0.0,
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json_data["guidance_scale_lyric"] if "guidance_scale_lyric" in json_data else 0.0,
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)
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data_sampler = DataSampler()
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json_data = data_sampler.sample()
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json_data = sample_data(json_data)
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audio_duration,\
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prompt, \
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lyrics,\
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infer_step, \
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guidance_scale,\
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scheduler_type, \
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cfg_type, \
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omega_scale, \
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manual_seeds, \
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guidance_interval, \
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guidance_interval_decay, \
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min_guidance_scale, \
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use_erg_tag, \
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use_erg_lyric, \
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use_erg_diffusion, \
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oss_steps, \
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guidance_scale_text, \
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guidance_scale_lyric = json_data
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device = torch.device("cpu")
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dtype = torch.float32
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if torch.cuda.is_available():
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device = torch.device("cuda")
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dtype = torch.bfloat16
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elif torch.backends.mps.is_available():
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device = torch.device("mps")
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dtype = torch.float16
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class GenerationParameters:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{ "audio_duration": ("FLOAT", {"default": audio_duration, "min": 0.0, "max": 240.0, "step": 1.0, "tooltip": "0 is a random length"}),
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"infer_step": ("INT", {"default": infer_step, "min": 1, "max": 60, "step": 1}),
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"guidance_scale": ("FLOAT", {"default": guidance_scale, "min": 0.0, "max": 200.0, "step": 0.1, "tooltip": "When guidance_scale_lyric > 1 and guidance_scale_text > 1, the guidance scale will not be applied."}),
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"scheduler_type": (["euler", "heun"], {"default": scheduler_type, "tooltip": "euler is recommended. heun will take more time."}),
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"cfg_type": (["cfg", "apg", "cfg_star"], {"default": cfg_type, "tooltip": "apg is recommended. cfg and cfg_star are almost the same."}),
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"omega_scale": ("FLOAT", {"default": omega_scale, "min": -100.0, "max": 100.0, "step": 0.1, "tooltip": "Higher values can reduce artifacts"}),
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"seed": ("INT", {"default":manual_seeds, "min": 0, "max": 4294967295, "step": 1}),
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"guidance_interval": ("FLOAT", {"default": guidance_interval, "min": 0, "max": 1, "step": 0.01, "tooltip": "0.5 means only apply guidance in the middle steps"}),
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"guidance_interval_decay": ("FLOAT", {"default": guidance_interval_decay, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Guidance scale will decay from guidance_scale to min_guidance_scale in the interval. 0.0 means no decay."}),
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"min_guidance_scale": ("INT", {"default": min_guidance_scale, "min": 0, "max": 200, "step": 1}),
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"use_erg_tag": ("BOOLEAN", {"default": use_erg_tag}),
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"use_erg_lyric": ("BOOLEAN", {"default": use_erg_lyric}),
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"use_erg_diffusion": ("BOOLEAN", {"default": use_erg_diffusion}),
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"oss_steps": ("STRING", {"default": oss_steps}),
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"guidance_scale_text": ("FLOAT", {"default": guidance_scale_text, "min": 0.0, "max": 10.0, "step": 0.1}),
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"guidance_scale_lyric": ("FLOAT", {"default": guidance_scale_lyric, "min": 0.0, "max": 10.0, "step": 0.1}),
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},
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("parameters",)
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FUNCTION = "generate"
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CATEGORY = "🎤MW/MW-ACE-Step"
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def generate(self, **kwargs):
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kwargs["manual_seeds"] = kwargs.pop("seed")
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return (str(kwargs),)
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class MultiLinePromptACES:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"multi_line_prompt": ("STRING", {
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"multiline": True,
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"default": prompt}),
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},
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}
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CATEGORY = "🎤MW/MW-ACE-Step"
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("prompt",)
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FUNCTION = "promptgen"
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def promptgen(self, multi_line_prompt: str):
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return (multi_line_prompt.strip(),)
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class MultiLineLyrics:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"multi_line_prompt": ("STRING", {
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"multiline": True,
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"default": lyrics}),
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},
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}
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CATEGORY = "🎤MW/MW-ACE-Step"
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("lyrics",)
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FUNCTION = "lyricsgen"
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def lyricsgen(self, multi_line_prompt: str):
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return (multi_line_prompt.strip(),)
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class ACEModelLoader:
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@classmethod
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def INPUT_TYPES(cls):
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models = [name for name in os.listdir(model_path) if os.path.isdir(os.path.join(model_path, name)) and not name.startswith(".")]
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return {
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"required": {
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"dcae_checkpoint": (models, {"default": "music_dcae_f8c8"}),
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"vocoder_checkpoint": (models, {"default": "music_vocoder"}),
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"ace_step_checkpoint": (models, {"default": "ace_step_transformer"}),
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"text_encoder_checkpoint": (models, {"default": "umt5-base"}),
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}
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}
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RETURN_TYPES = ("ACE_MODELS",)
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RETURN_NAMES = ("models",)
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FUNCTION = "load"
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CATEGORY = "🎤MW/MW-ACE-Step"
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def load(self, dcae_checkpoint, vocoder_checkpoint, ace_step_checkpoint, text_encoder_checkpoint):
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dcae_checkpoint = os.path.join(model_path, "music_dcae_f8c8")
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vocoder_checkpoint = os.path.join(model_path, "music_vocoder")
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ace_step_checkpoint = os.path.join(model_path, "ace_step_transformer")
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text_encoder_checkpoint = os.path.join(model_path, "umt5-base")
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for path in [dcae_checkpoint, vocoder_checkpoint, ace_step_checkpoint, text_encoder_checkpoint]:
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if not os.path.exists(path):
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raise FileNotFoundError(f"Checkpoint not found: {path}")
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models = (
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MusicDCAE(
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dcae_checkpoint_path=dcae_checkpoint,
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vocoder_checkpoint_path=vocoder_checkpoint
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),
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ACEStepTransformer2DModel.from_pretrained(ace_step_checkpoint, torch_dtype=dtype),
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UMT5EncoderModel.from_pretrained(text_encoder_checkpoint, torch_dtype=dtype),
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AutoTokenizer.from_pretrained(text_encoder_checkpoint),
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device,
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dtype
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)
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return (models,)
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ap = None
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class ACEStepGen:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"models": ("ACE_MODELS",),
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"prompt": ("STRING", {"forceInput": True}),
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"lyrics": ("STRING", {"forceInput": True}),
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"parameters": ("STRING", {"forceInput": True}),
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# "unload_model": ("BOOLEAN", {"default": True}),
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},
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"optional": {
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"ref_audio": ("AUDIO",),
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"ref_audio_strength": ("FLOAT", {"default": 0.5, "min": 0.01, "max": 1.0, "step": 0.01}),
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"cpu_offload": ("BOOLEAN", {"default": True}),
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},
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}
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CATEGORY = "🎤MW/MW-ACE-Step"
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RETURN_TYPES = ("AUDIO",)
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RETURN_NAMES = ("music",)
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FUNCTION = "acestepgen"
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def acestepgen(self,
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models,
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prompt: str,
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lyrics: str,
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parameters: str,
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ref_audio=None,
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ref_audio_strength=None,
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cpu_offload=False,
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# unload_model=True
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):
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parameters = ast.literal_eval(parameters)
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global ap
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if ap is None:
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ap = AP(*models, cpu_offload=cpu_offload)
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ac = AudioCacher(cache_dir=cache_dir)
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audio2audio_enable = False
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ref_audio_input = None
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if ref_audio is not None:
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ref_audio_path = ac.cache_audio_tensor(ref_audio["waveform"].squeeze(0), ref_audio["sample_rate"], filename_prefix="ref_audio_")
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audio2audio_enable = True
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ref_audio_strength = ref_audio_strength
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ref_audio_input = ref_audio_path
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audio_output = ap(
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prompt=prompt,
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lyrics=lyrics,
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task="audio2audio",
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audio2audio_enable=audio2audio_enable,
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ref_audio_strength=ref_audio_strength,
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ref_audio_input=ref_audio_input,
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**parameters
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)
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audio, sr = audio_output[0][0].unsqueeze(0), audio_output[0][1]
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# if unload_model:
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# ap.cleanup()
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# ap = None
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return ({"waveform": audio, "sample_rate": sr},)
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class ACEStepRepainting:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"models": ("ACE_MODELS",),
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"src_audio": ("AUDIO",),
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"prompt": ("STRING", {"forceInput": True}),
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"lyrics": ("STRING", {"forceInput": True}),
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"parameters": ("STRING", {"forceInput": True}),
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"repaint_start": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
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"repaint_end": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
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"repaint_variance": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 1.0, "step": 0.01}),
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"seed": ("INT", {"default":0, "min": 0, "max": 4294967295, "step": 1}),
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# "unload_model": ("BOOLEAN", {"default": True}),
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"cpu_offload": ("BOOLEAN", {"default": True}),
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},
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}
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CATEGORY = "🎤MW/MW-ACE-Step"
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RETURN_TYPES = ("AUDIO",)
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RETURN_NAMES = ("music",)
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FUNCTION = "acesteprepainting"
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def acesteprepainting(self,
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models,
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src_audio,
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prompt: str,
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lyrics: str,
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parameters: str,
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repaint_start,
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repaint_end,
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repaint_variance,
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seed,
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# unload_model=True,
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cpu_offload=False
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):
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retake_seeds = [str(seed)]
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ac = AudioCacher(cache_dir=cache_dir)
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src_audio_path = ac.cache_audio_tensor(src_audio["waveform"].squeeze(0), src_audio["sample_rate"], filename_prefix="src_audio_")
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audio_duration = librosa.get_duration(filename=src_audio_path)
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if repaint_end > audio_duration:
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repaint_end = audio_duration
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parameters = ast.literal_eval(parameters)
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parameters["audio_duration"] = audio_duration
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global ap
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if ap is None:
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ap = AP(*models, cpu_offload=cpu_offload)
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audio_output = ap(
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prompt=prompt,
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lyrics=lyrics,
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task="repaint",
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retake_seeds=retake_seeds,
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src_audio_path=src_audio_path,
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repaint_start=repaint_start,
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repaint_end=repaint_end,
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retake_variance=repaint_variance,
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**parameters)
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audio, sr = audio_output[0][0].unsqueeze(0), audio_output[0][1]
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ac.cleanup_file(src_audio_path)
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# if unload_model:
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# ap.cleanup()
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# ap = None
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return ({"waveform": audio, "sample_rate": sr},)
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class ACEStepEdit:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"models": ("ACE_MODELS",),
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"src_audio": ("AUDIO",),
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"prompt": ("STRING", {"forceInput": True}),
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"lyrics": ("STRING", {"forceInput": True}),
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"parameters": ("STRING", {"forceInput": True}),
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"edit_prompt": ("STRING", {"forceInput": True}),
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"edit_lyrics": ("STRING", {"forceInput": True}),
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"edit_n_min": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 1.0, "step": 0.01}),
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"edit_n_max": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"seed": ("INT", {"default":0, "min": 0, "max": 4294967295, "step": 1}),
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# "unload_model": ("BOOLEAN", {"default": True}),
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"cpu_offload": ("BOOLEAN", {"default": True}),
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},
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}
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CATEGORY = "🎤MW/MW-ACE-Step"
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RETURN_TYPES = ("AUDIO",)
|
|
RETURN_NAMES = ("music",)
|
|
FUNCTION = "acestepedit"
|
|
|
|
def acestepedit(self,
|
|
models,
|
|
src_audio,
|
|
prompt: str,
|
|
lyrics: str,
|
|
parameters: str,
|
|
edit_prompt,
|
|
edit_lyrics,
|
|
edit_n_min,
|
|
edit_n_max,
|
|
seed,
|
|
# unload_model=True,
|
|
cpu_offload=False
|
|
):
|
|
retake_seeds = [str(seed)]
|
|
ac = AudioCacher(cache_dir=cache_dir)
|
|
src_audio_path = ac.cache_audio_tensor(src_audio["waveform"].squeeze(0), src_audio["sample_rate"], filename_prefix="src_audio_")
|
|
|
|
audio_duration = librosa.get_duration(filename=src_audio_path)
|
|
parameters = ast.literal_eval(parameters)
|
|
parameters["audio_duration"] = audio_duration
|
|
global ap
|
|
if ap is None:
|
|
ap = AP(*models, cpu_offload=cpu_offload)
|
|
|
|
audio_output = ap(
|
|
prompt=prompt,
|
|
lyrics=lyrics,
|
|
task="edit",
|
|
retake_seeds=retake_seeds,
|
|
src_audio_path=src_audio_path,
|
|
edit_target_prompt = edit_prompt,
|
|
edit_target_lyrics = edit_lyrics,
|
|
edit_n_min = edit_n_min,
|
|
edit_n_max = edit_n_max,
|
|
**parameters)
|
|
|
|
audio, sr = audio_output[0][0].unsqueeze(0), audio_output[0][1]
|
|
|
|
ac.cleanup_file(src_audio_path)
|
|
# if unload_model:
|
|
# ap.cleanup()
|
|
# ap = None
|
|
|
|
return ({"waveform": audio, "sample_rate": sr},)
|
|
|
|
|
|
class ACEStepExtend:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
|
|
return {
|
|
"required": {
|
|
"models": ("ACE_MODELS",),
|
|
"src_audio": ("AUDIO",),
|
|
"prompt": ("STRING", {"forceInput": True}),
|
|
"lyrics": ("STRING", {"forceInput": True}),
|
|
"parameters": ("STRING", {"forceInput": True}),
|
|
"left_extend_length": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
|
|
"right_extend_length": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1}),
|
|
# "repaint_variance": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 1.0, "step": 0.01}),
|
|
"seed": ("INT", {"default":0, "min": 0, "max": 4294967295, "step": 1}),
|
|
# "unload_model": ("BOOLEAN", {"default": True}),
|
|
"cpu_offload": ("BOOLEAN", {"default": True}),
|
|
},
|
|
}
|
|
|
|
CATEGORY = "🎤MW/MW-ACE-Step"
|
|
RETURN_TYPES = ("AUDIO",)
|
|
RETURN_NAMES = ("music",)
|
|
FUNCTION = "acestepextend"
|
|
|
|
def acestepextend(self,
|
|
models,
|
|
src_audio,
|
|
prompt: str,
|
|
lyrics: str,
|
|
parameters: str,
|
|
left_extend_length,
|
|
right_extend_length,
|
|
seed,
|
|
# unload_model=True,
|
|
cpu_offload=False
|
|
):
|
|
retake_seeds = [str(seed)]
|
|
ac = AudioCacher(cache_dir=cache_dir)
|
|
src_audio_path = ac.cache_audio_tensor(src_audio["waveform"].squeeze(0), src_audio["sample_rate"], filename_prefix="src_audio_")
|
|
|
|
audio_duration = librosa.get_duration(filename=src_audio_path)
|
|
repaint_start = -left_extend_length
|
|
repaint_end = audio_duration + right_extend_length
|
|
|
|
parameters = ast.literal_eval(parameters)
|
|
parameters["audio_duration"] = audio_duration
|
|
global ap
|
|
if ap is None:
|
|
ap = AP(*models, cpu_offload=cpu_offload)
|
|
|
|
audio_output = ap(
|
|
prompt=prompt,
|
|
lyrics=lyrics,
|
|
task="extend",
|
|
retake_seeds=retake_seeds,
|
|
src_audio_path=src_audio_path,
|
|
repaint_start=repaint_start,
|
|
repaint_end=repaint_end,
|
|
retake_variance=1.0,
|
|
**parameters)
|
|
|
|
audio, sr = audio_output[0][0].unsqueeze(0), audio_output[0][1]
|
|
|
|
ac.cleanup_file(src_audio_path)
|
|
# if unload_model:
|
|
# ap.cleanup()
|
|
# ap = None
|
|
|
|
return ({"waveform": audio, "sample_rate": sr},)
|
|
|
|
|
|
from .text2lyric import LyricsLangSwitch
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"ACEModelLoader": ACEModelLoader,
|
|
"LyricsLangSwitch": LyricsLangSwitch,
|
|
"ACEStepGen": ACEStepGen,
|
|
"GenerationParameters": GenerationParameters,
|
|
"MultiLinePromptACES": MultiLinePromptACES,
|
|
"MultiLineLyrics": MultiLineLyrics,
|
|
"ACEStepRepainting": ACEStepRepainting,
|
|
"ACEStepEdit": ACEStepEdit,
|
|
"ACEStepExtend": ACEStepExtend,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"ACEModelLoader": "ACE-Step Model Loader",
|
|
"LyricsLangSwitch": "ACE-Step Lyrics Language Switch",
|
|
"ACEStepGen": "ACE-Step",
|
|
"GenerationParameters": "ACE-Step Parameters",
|
|
"MultiLinePromptACES": "ACE-Step Prompt",
|
|
"MultiLineLyrics": "ACE-Step Lyrics",
|
|
"ACEStepRepainting": "ACE-Step Repainting",
|
|
"ACEStepEdit": "ACE-Step Edit",
|
|
"ACEStepExtend": "ACE-Step Extend",
|
|
} |