mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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174 lines
8.4 KiB
Python
174 lines
8.4 KiB
Python
import torch, os, argparse, accelerate, warnings, torchaudio
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import math
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from diffsynth.core import UnifiedDataset
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from diffsynth.core.data.operators import ToAbsolutePath, RouteByType, DataProcessingOperator, LoadPureAudioWithTorchaudio
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from diffsynth.pipelines.ace_step import AceStepPipeline, ModelConfig
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from diffsynth.diffusion import *
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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class LoadAceStepAudio(DataProcessingOperator):
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"""Load audio file and return waveform tensor [2, T] at 48kHz."""
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def __init__(self, target_sr=48000):
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self.target_sr = target_sr
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def __call__(self, data: str):
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try:
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waveform, sample_rate = torchaudio.load(data)
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if sample_rate != self.target_sr:
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resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=self.target_sr)
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waveform = resampler(waveform)
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if waveform.shape[0] == 1:
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waveform = waveform.repeat(2, 1)
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return waveform
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except Exception as e:
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warnings.warn(f"Cannot load audio from {data}: {e}")
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return None
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class AceStepTrainingModule(DiffusionTrainingModule):
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def __init__(
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self,
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model_paths=None, model_id_with_origin_paths=None,
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tokenizer_path=None, silence_latent_path=None,
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trainable_models=None,
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lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None,
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preset_lora_path=None, preset_lora_model=None,
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use_gradient_checkpointing=True,
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use_gradient_checkpointing_offload=False,
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extra_inputs=None,
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fp8_models=None,
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offload_models=None,
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device="cpu",
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task="sft",
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):
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super().__init__()
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# ===== 解析模型配置（固定写法） =====
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model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device)
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# ===== Tokenizer 配置 =====
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text_tokenizer_config = self.parse_path_or_model_id(tokenizer_path, default_value=ModelConfig(model_id="ACE-Step/Ace-Step1.5", origin_file_pattern="Qwen3-Embedding-0.6B/"))
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silence_latent_config = self.parse_path_or_model_id(silence_latent_path, default_value=ModelConfig(model_id="ACE-Step/Ace-Step1.5", origin_file_pattern="acestep-v15-turbo/silence_latent.pt"))
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# ===== 构建 Pipeline =====
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self.pipe = AceStepPipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, text_tokenizer_config=text_tokenizer_config, silence_latent_config=silence_latent_config)
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# ===== 拆分 Pipeline Units（固定写法） =====
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self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
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# ===== 切换到训练模式（固定写法） =====
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self.switch_pipe_to_training_mode(
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self.pipe, trainable_models,
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lora_base_model, lora_target_modules, lora_rank, lora_checkpoint,
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preset_lora_path, preset_lora_model,
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task=task,
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)
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# ===== 其他配置（固定写法） =====
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self.use_gradient_checkpointing = use_gradient_checkpointing
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self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
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self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
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self.fp8_models = fp8_models
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self.task = task
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# ===== 任务模式路由（固定写法） =====
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self.task_to_loss = {
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"sft:data_process": lambda pipe, *args: args,
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"sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi),
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"sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi),
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}
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def get_pipeline_inputs(self, data):
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inputs_posi = {"prompt": data["prompt"], "positive": True}
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inputs_nega = {"positive": False}
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duration = math.floor(data['audio'][0].shape[1] / data['audio'][1]) if data.get("audio") is not None else data.get("duration", 60)
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# ===== 共享参数 =====
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inputs_shared = {
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# ===== 核心字段映射 =====
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"input_audio": data["audio"],
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# ===== 音频生成任务所需元数据 =====
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"lyrics": data["lyrics"],
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"task_type": "text2music",
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"duration": duration,
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"bpm": data.get("bpm", 100),
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"keyscale": data.get("keyscale", "C major"),
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"timesignature": data.get("timesignature", "4"),
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"vocal_language": data.get("vocal_language", "unknown"),
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# ===== 框架控制参数（固定写法） =====
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"cfg_scale": 1,
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"rand_device": self.pipe.device,
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"use_gradient_checkpointing": self.use_gradient_checkpointing,
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"use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
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}
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# ===== 额外字段注入：通过 --extra_inputs 配置的数据集列名（固定写法） =====
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inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared)
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return inputs_shared, inputs_posi, inputs_nega
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def forward(self, data, inputs=None):
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# ===== 标准实现，不要修改（固定写法） =====
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if inputs is None: inputs = self.get_pipeline_inputs(data)
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inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype)
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for unit in self.pipe.units:
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inputs = self.pipe.unit_runner(unit, self.pipe, *inputs)
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loss = self.task_to_loss[self.task](self.pipe, *inputs)
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return loss
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def ace_step_parser():
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parser = argparse.ArgumentParser(description="ACE-Step training.")
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parser = add_general_config(parser)
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parser.add_argument("--tokenizer_path", type=str, default=None, help="Tokenizer path in format model_id:origin_pattern.")
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parser.add_argument("--silence_latent_path", type=str, default=None, help="Silence latent path in format model_id:origin_pattern.")
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parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.")
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return parser
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if __name__ == "__main__":
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parser = ace_step_parser()
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args = parser.parse_args()
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# ===== Accelerator 配置（固定写法） =====
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accelerator = accelerate.Accelerator(
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gradient_accumulation_steps=args.gradient_accumulation_steps,
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kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)],
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)
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# ===== 数据集定义 =====
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dataset = UnifiedDataset(
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base_path=args.dataset_base_path,
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metadata_path=args.dataset_metadata_path,
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repeat=args.dataset_repeat,
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data_file_keys=args.data_file_keys.split(","),
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main_data_operator=None,
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special_operator_map={
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"audio": ToAbsolutePath(args.dataset_base_path) >> LoadPureAudioWithTorchaudio(target_sample_rate=48000),
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},
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)
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# ===== TrainingModule =====
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model = AceStepTrainingModule(
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model_paths=args.model_paths,
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model_id_with_origin_paths=args.model_id_with_origin_paths,
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tokenizer_path=args.tokenizer_path,
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silence_latent_path=args.silence_latent_path,
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trainable_models=args.trainable_models,
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lora_base_model=args.lora_base_model,
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lora_target_modules=args.lora_target_modules,
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lora_rank=args.lora_rank,
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lora_checkpoint=args.lora_checkpoint,
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preset_lora_path=args.preset_lora_path,
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preset_lora_model=args.preset_lora_model,
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use_gradient_checkpointing=args.use_gradient_checkpointing,
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use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
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extra_inputs=args.extra_inputs,
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fp8_models=args.fp8_models,
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offload_models=args.offload_models,
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task=args.task,
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device="cpu" if args.initialize_model_on_cpu else accelerator.device,
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)
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# ===== ModelLogger（固定写法） =====
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model_logger = ModelLogger(
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args.output_path,
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remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
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)
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# ===== 任务路由（固定写法） =====
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launcher_map = {
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"sft:data_process": launch_data_process_task,
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"sft": launch_training_task,
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"sft:train": launch_training_task,
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}
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launcher_map[args.task](accelerator, dataset, model, model_logger, args=args)
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