70 lines
2.1 KiB
Markdown
70 lines
2.1 KiB
Markdown
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# Documentation
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- Class name: BatchNormalizeImage
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- Category: image/filters
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- Output node: False
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BatchNormalizeImage节点用于根据给定的因子对一批图像进行归一化处理。它调整每张图像的像素值，使其标准差和均值更接近整个批次的总体特征。这一过程增强了图像数据的一致性，使其更适合进一步的处理或分析。
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# Input types
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## Required
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- images
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- images参数代表需要进行归一化处理的图像批次。它对归一化过程至关重要，因为它直接影响用于归一化的均值和标准差的计算。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- factor
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- factor参数控制原始图像与其归一化版本的混合程度。它在决定归一化图像的最终外观方面起着重要作用。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- image
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- 输出是一批根据指定因子进行归一化处理的图像，这可能会增强它们在后续图像处理任务中的适用性。
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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 BatchNormalizeImage:
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def __init__(self):
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pass
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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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"factor": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "round": 0.01}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "batch_normalize"
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CATEGORY = "image/filters"
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def batch_normalize(self, images, factor):
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t = copy.deepcopy(images) # [B x H x W x C]
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t = t.movedim(-1,0) # [C x B x H x W]
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for c in range(t.size(0)):
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c_sd, c_mean = torch.std_mean(t[c], dim=None)
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for i in range(t.size(1)):
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i_sd, i_mean = torch.std_mean(t[c, i], dim=None)
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t[c, i] = (t[c, i] - i_mean) / i_sd
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t[c] = t[c] * c_sd + c_mean
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t = torch.lerp(images, t.movedim(0,-1), factor) # [B x H x W x C]
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return (t,)
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```
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