67 lines
1.9 KiB
Markdown
67 lines
1.9 KiB
Markdown
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# Documentation
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- Class name: JWLoopImageSequence
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- Category: jamesWalker55
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- Output node: False
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JWLoopImageSequence节点旨在调整图像序列中的帧数，使其匹配指定的目标帧数。它可以截断序列至目标长度，或者重复序列（或其部分）以达到所需长度，确保输出序列恰好具有指定数量的帧。
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# Input types
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## Required
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- images
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- 表示要处理的图像序列的张量。这是用于调整序列长度的主要输入。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- target_frames
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- 一个整数，指定输出图像序列中所需的帧数。这决定了输入序列是否会被截断或延长。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- image
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- 表示经过调整的图像序列的张量，具有指定数量的帧。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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@register_node("JWLoopImageSequence", "Loop Image Sequence")
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class LoopImageSequence:
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CATEGORY = "jamesWalker55"
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INPUT_TYPES = lambda: {
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"required": {
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"images": ("IMAGE",),
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"target_frames": ("INT", {"default": 16, "step": 1}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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def execute(self, images: torch.Tensor, target_frames: int):
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if len(images) > target_frames:
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images = images[0:target_frames]
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elif len(images) < target_frames:
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to_cat = []
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for _ in range(target_frames // len(images)):
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to_cat.append(images)
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extra_frames = target_frames % len(images)
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if extra_frames > 0:
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to_cat.append(images[0:extra_frames])
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images = torch.cat(to_cat, dim=0)
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assert len(images) == target_frames
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else: # len(images) == target_frames
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pass
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return (images,)
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```
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