62 lines
1.7 KiB
Markdown
62 lines
1.7 KiB
Markdown
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# Documentation
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- Class name: `BatchAverageUnJittered`
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- Category: `image/filters/jitter`
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- Output node: `False`
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BatchAverageUnJittered节点旨在通过应用平均或中值操作来处理一批图像，以减少抖动效应。它对图像的子批次进行操作，应用指定的操作来创建更平滑、更稳定的输出图像。
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# Input types
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## Required
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- images
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- 需要处理的图像批次。这个输入对于确定将应用平均或中值操作的图像集至关重要。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- operation
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- 指定要应用于图像的操作（'mean'或'median'）。这个选择影响抖动减少的方法和输出图像的最终外观。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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# Output types
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- image
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- 经过指定的平均或中值操作处理以减少抖动效应后的图像批次。
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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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class BatchAverageUnJittered:
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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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"images": ("IMAGE",),
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"operation": (["mean", "median"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply"
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CATEGORY = "image/filters/jitter"
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def apply(self, images, operation):
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t = images.detach().clone()
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batch = []
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for i in range(t.shape[0] // 9):
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if operation == "mean":
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batch.append(torch.mean(t[i*9:i*9+9], dim=0, keepdim=True))
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elif operation == "median":
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batch.append(torch.median(t[i*9:i*9+9], dim=0, keepdim=True)[0])
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return (torch.cat(batch, dim=0),)
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```
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