# !/usr/bin/env python # -*- coding: UTF-8 -*- import os import torch import numpy as np import io import torchaudio from .node_utils import gc_clear from .generate import auto_prompt_type,infer_stage2,inference_lowram_final,build_model,Separator,song_infer_lowram import time import folder_paths MAX_SEED = np.iinfo(np.int32).max current_node_path = os.path.dirname(os.path.abspath(__file__)) from .SongGeneration.codeclm.models import builders device = torch.device( "cuda:0") if torch.cuda.is_available() else torch.device( "mps") if torch.backends.mps.is_available() else torch.device( "cpu") # add checkpoints dir SongGeneration_Weigths_Path = os.path.join(folder_paths.models_dir, "SongGeneration") if not os.path.exists(SongGeneration_Weigths_Path): os.makedirs(SongGeneration_Weigths_Path) folder_paths.add_model_folder_path("SongGeneration", SongGeneration_Weigths_Path) class SongGeneration_Loader: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "infer_model": (["none"] +[i for i in folder_paths.get_filename_list("SongGeneration") if i.endswith(".pt") ],), }, } RETURN_TYPES = ("SongGeneration_Audiolm","SongGeneration_Cfg") RETURN_NAMES = ("model","cfg") FUNCTION = "main" CATEGORY = "SongGeneration" def main(self, infer_model,): infer_model_path=folder_paths.get_full_path("SongGeneration", infer_model) if infer_model != "none" else None assert infer_model_path is not None ,"模型不能为空.need infer model" model,cfg=build_model(os.path.join(SongGeneration_Weigths_Path, "ckpt"),infer_model_path) return (model,cfg) class SongGeneration_Stage1: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "vae": (folder_paths.get_filename_list("vae"),), "seperate_model": (["none"] + [i for i in folder_paths.get_filename_list("SongGeneration") if i.endswith(".safetensors") and not "fix" in i.lower()],), "prompt_pt": (["none"] + [i for i in folder_paths.get_filename_list("SongGeneration") if "prompt" in i.lower()],), "auto_prompt_audio_type": (auto_prompt_type,), "model_1rvq": (["none"] + [i for i in folder_paths.get_filename_list("SongGeneration") if i.endswith(".safetensors")],), "demucs_pt": (["none"] + [i for i in folder_paths.get_filename_list("SongGeneration") if i.endswith(".pth")],), }, "optional": { "audio": ("AUDIO",), } } RETURN_TYPES = ("SongGeneration_Cond",) RETURN_NAMES = ("cond",) FUNCTION = "main" CATEGORY = "SongGeneration" def main(self,vae,seperate_model, auto_prompt_audio_type,prompt_pt,model_1rvq,demucs_pt,**kwargs): audio=kwargs.get("audio", None) model_sep_path=folder_paths.get_full_path("SongGeneration", seperate_model) if seperate_model != "none" else None vae_model=folder_paths.get_full_path("vae", vae) prompt_pt_path=folder_paths.get_full_path("SongGeneration", prompt_pt) if prompt_pt != "none" else None if audio is not None: prompt_audio_path = os.path.join(folder_paths.get_input_directory(), f"audio_{time.strftime('%m%d%H%S')}_temp.wav") waveform=audio["waveform"].squeeze(0) buff = io.BytesIO() torchaudio.save(buff, waveform, audio["sample_rate"], format="FLAC") with open(prompt_audio_path, 'wb') as f: f.write(buff.getbuffer()) use_descriptions=False #不建议同时提供参考音频和描述文本 dm_model_path=folder_paths.get_full_path("SongGeneration", demucs_pt) if demucs_pt != "none" else None assert dm_model_path is not None ,"使用参考音频时，需要选择htdemucs模型， if use audio need htdemucs model" separator = Separator(dm_model_path, os.path.join(current_node_path, "SongGeneration/third_party/demucs/ckpt/htdemucs.yaml")) model_1rvq_path=folder_paths.get_full_path("SongGeneration", model_1rvq) if model_1rvq != "none" else None assert model_1rvq_path is not None ,"使用参考音频时，需要选择model_模型， if use audio need model_odel" audio_tokenizer = builders.get_audio_tokenizer_model(f"Flow1dVAE1rvq_{model_1rvq_path}",os.path.join(current_node_path, f'SongGeneration/conf/stable_audio_1920_vae.json'),vae_model,'inference') seperate_tokenizer = builders.get_audio_tokenizer_model(f"Flow1dVAESeparate_{model_sep_path}",os.path.join(current_node_path, f'SongGeneration/conf/stable_audio_1920_vae.json'),vae_model,'inference') else: prompt_audio_path,use_descriptions,audio_tokenizer,separator,seperate_tokenizer=None,True,None,None,None original_item=song_infer_lowram(seperate_tokenizer,separator,audio_tokenizer,prompt_pt_path, folder_paths.get_output_directory(),prompt_audio_path,auto_prompt_audio_type,) gc_clear() print("Stage1 is done.") return ({"item": original_item, "use_descriptions": use_descriptions,"model_sep_path":model_sep_path,"vae_model":vae_model},) class SongGeneration_Stage2: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "model": ("SongGeneration_Audiolm",), "cfg": ("SongGeneration_Cfg",), "cond": ("SongGeneration_Cond",), "lyric": ("STRING", {"multiline": True, "default": "[intro-short] ;\n [verse]\n 雪花舞动在无尽的天际.情缘如同雪花般轻轻逝去.希望与真挚.永不磨灭.你的忧虑.随风而逝 ;\n [chorus]\n 我怀抱着守护这片梦境.在这世界中寻找爱与虚幻.苦辣酸甜.我们一起品尝.在雪的光芒中.紧紧相拥 ;\n [inst-short] ;\n [verse]\n雪花再次在风中飘扬.情愿如同雪花般消失无踪.希望与真挚.永不消失.在痛苦与喧嚣中.你找到解脱 ;\n [chorus]\n 我环绕着守护这片梦境.在这世界中感受爱与虚假.苦辣酸甜.我们一起分享.在白银的光芒中.我们同在 ;\n [outro-short]"}), "description": ("STRING", {"multiline": False, "default": "female, dark, pop, sad, piano and drums, the bpm is 125"}), #OPTIONAL "cfg_coef": ("FLOAT", {"default": 1.5, "min": 0.1, "max": 3.0, "step": 0.1}), "temp": ("FLOAT", {"default": 0.9, "min": 0.1, "max": 2.0, "step": 0.1}), "top_k": ("INT", {"default": 50, "min": 1, "max": 100, "step": 1}), "top_p": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), "record_tokens": ("BOOLEAN", {"default": True}), "record_window": ("INT", {"default": 50, "min": 1, "max": 1000, "step": 1}), }, } RETURN_TYPES = ("SongGeneration_Cond",) RETURN_NAMES = ("cond",) FUNCTION = "main" CATEGORY = "SongGeneration" def main(self, model,cfg,cond,lyric,description,cfg_coef,temp,top_k,top_p,record_tokens,record_window): descriptions=description if cond.get("use_descriptions",False) else None items=infer_stage2(cond.get("item"),model,cfg.max_dur,lyric,descriptions,cfg_coef, temp,top_k,top_p,record_tokens ,record_window ) gc_clear() return ({"items":items,"cfg":cfg,"model_sep_path":cond["model_sep_path"],"vae_model":cond["vae_model"]},) class SongGeneration_Sampler: def __init__(self): pass @classmethod def INPUT_TYPES(s): return { "required": { "cond": ("SongGeneration_Cond",), "gen_type": (["mixed","bgm","vocal",],), "save_separate": ("BOOLEAN", {"default": False}), } } RETURN_TYPES = ("AUDIO",) RETURN_NAMES = ("audio", ) FUNCTION = "sampler_main" CATEGORY = "SongGeneration" def sampler_main(self,cond,gen_type,save_separate): cfg=cond.get("cfg") cfg.gen_type=gen_type model_sep_path=cond["model_sep_path"] vae_model=cond["vae_model"] print("start inference final,loading model") seperate_tokenizer = builders.get_audio_tokenizer_model(f"Flow1dVAESeparate_{model_sep_path}",os.path.join(current_node_path, f'SongGeneration/conf/stable_audio_1920_vae.json'),vae_model,'inference') seperate_tokenizer = seperate_tokenizer.eval().cuda() audio=inference_lowram_final(cfg,seperate_tokenizer,cfg.max_dur,cond.get("items"),folder_paths.get_output_directory(),save_separate) del seperate_tokenizer gc_clear() return (audio,) NODE_CLASS_MAPPINGS = { "SongGeneration_Loader":SongGeneration_Loader, "SongGeneration_Stage1": SongGeneration_Stage1, "SongGeneration_Stage2": SongGeneration_Stage2, "SongGeneration_Sampler": SongGeneration_Sampler, } NODE_DISPLAY_NAME_MAPPINGS = { "SongGeneration_Loader": "SongGeneration_Loader", "SongGeneration_Stage1": "SongGeneration_Stage1", "SongGeneration_Stage2": "SongGeneration_Stage2", "SongGeneration_Sampler": "SongGeneration_Sampler", }