93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
import torch
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import numpy as np
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class ImageToVideo:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"图片": ("IMAGE",),
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"时长": ("FLOAT", {
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"default": 5.0,
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"min": 0.1,
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"max": 300.0,
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"step": 0.1
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}),
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"帧率": ("FLOAT", { # 修改为FLOAT类型
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"default": 30.0,
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"min": 1.0,
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"max": 120.0,
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"step": 0.1,
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"display": "slider"
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}),
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"批处理大小": ("INT", {
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"default": 30,
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"min": 1,
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"max": 120,
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"step": 1,
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"description": "每批处理的帧数，较小的值会降低内存使用"
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})
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}
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}
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RETURN_TYPES = ("IMAGE", "INT", "FLOAT", "FLOAT") # 最后返回FLOAT类型帧率
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RETURN_NAMES = ("视频帧", "总帧数", "实际时长", "帧率")
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FUNCTION = "create_video_frames"
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CATEGORY = "🍺DD系列节点"
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def log_progress(self, current, total):
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"""输出进度信息"""
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percentage = (current / total) * 100
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print(f"生成进度: {current}/{total} 帧 ({percentage:.1f}%)")
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def create_video_frames(self, 图片, 时长, 帧率, 批处理大小):
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try:
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# 计算需要的总帧数（四舍五入处理）
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total_frames = int(round(时长 * 帧率))
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actual_duration = total_frames / 帧率 # 实际时长（考虑帧数取整）
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# 初始化进度
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frames_processed = 0
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# 确保输入图片格式正确
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if isinstance(图片, torch.Tensor):
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if 图片.ndim == 3:
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图片 = 图片.unsqueeze(0)
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# 使用批处理方式生成帧
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batches = []
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while frames_processed < total_frames:
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# 计算当前批次应处理的帧数
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current_batch_size = min(批处理大小, total_frames - frames_processed)
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# 为当前批次生成帧
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batch_frames = 图片.repeat(current_batch_size, 1, 1, 1)
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batches.append(batch_frames)
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# 更新进度
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frames_processed += current_batch_size
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self.log_progress(frames_processed, total_frames)
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# 合并所有批次
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video_frames = torch.cat(batches, dim=0)
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print(f"\n视频帧生成完成:")
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print(f"- 总帧数: {total_frames}")
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print(f"- 实际时长: {actual_duration:.3f} 秒")
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print(f"- 帧率: {帧率:.2f} FPS")
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print(f"- 帧尺寸: {video_frames.shape[-2]}x{video_frames.shape[-1]}")
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return (video_frames, total_frames, actual_duration, 帧率)
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except Exception as e:
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print(f"错误: 生成视频帧时发生异常 - {str(e)}")
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raise e
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NODE_CLASS_MAPPINGS = {
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"DD-ImageToVideo": ImageToVideo
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"DD-ImageToVideo": "DD Image To Video"
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}
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