57 lines
2.4 KiB
Python
57 lines
2.4 KiB
Python
import folder_paths
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import cv2
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import os,shutil
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import torch
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import numpy as np
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from .src.gradio_demo import SadTalker
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class Video2TalkingFace:
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def __init__(self):
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self.output_dir = os.path.join(folder_paths.get_output_directory(), 'video')
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self.checkpoint_path=os.path.join(folder_paths.models_dir, 'SadTalker')
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if not os.path.exists(self.output_dir):
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os .makedirs(self.output_dir)
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if not os.path.exists(self.checkpoint_path):
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os .makedirs(self.checkpoint_path)
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{
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"videoPath": ("STRING", {"forceInput": True}),
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"audioPath": ("STRING", {"forceInput": True}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 10}),
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"enhancer": ([ 'none','lip','face' ], ),
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"filename_prefix": ("STRING", {"default": "comfyUI"}),
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},
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}
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CATEGORY = "lam"
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("视频文件名",)
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FUNCTION = "sad_talker"
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OUTPUT_NODE = True
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def sad_talker(self, videoPath,audioPath,batch_size,enhancer,filename_prefix):
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if os.path.exists(videoPath) == False:
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raise Exception('视频文件不存在')
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if os.path.exists(audioPath) == False:
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raise Exception('音频文件不存在')
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sad_talker = SadTalker(self.checkpoint_path, "custom_nodes/ComfyUI_Lam/py/src/config", lazy_load=True)
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autio_path=sad_talker.run_video_2_video(videoPath,audioPath,enhancer,batch_size,result_dir=self.output_dir)
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
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file = f"{filename}_{counter:05}_.mp4"
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shutil.move(autio_path, os.path.join(full_output_folder, file))
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autio_path=os.path.join(full_output_folder, file)
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return {"ui": {"text": "视频合成成功，保存路径："+autio_path,
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"filename_prefix": ("STRING", {"default": "comfyUI"}),}, "result": (autio_path,)}
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NODE_CLASS_MAPPINGS = {
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"Video2TalkingFace": Video2TalkingFace
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}
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# A dictionary that contains the friendly/humanly readable titles for the nodes
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NODE_DISPLAY_NAME_MAPPINGS = {
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"Video2TalkingFace": "视频转说话的人脸动画"
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}
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