72 lines
2.2 KiB
Markdown
72 lines
2.2 KiB
Markdown
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# Documentation
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- Class name: AdainImage
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- Category: image/filters
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- Output node: False
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AdainImage节点应用自适应实例归一化（Adaptive Instance Normalization，AdaIN）技术对一批图像进行处理，利用参考图像来调整输入图像的风格。这个过程涉及基于参考图像的统计特性（均值和标准差）对输入图像进行归一化，从而在给定强度级别上实现风格迁移。
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# Input types
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## Required
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- images
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- 需要进行风格化的输入图像。这些是风格迁移过程的主要对象。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- reference
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- 用于提取风格特征（均值和标准差）的参考图像，这些特征将用于风格迁移。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- factor
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- 控制风格迁移强度的因子，允许在原始图像和风格化图像之间进行混合。
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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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- 经过自适应实例归一化技术处理后的风格化图像。
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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 AdainImage:
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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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"reference": ("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, reference, 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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for i in range(t.size(1)):
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r_sd, r_mean = torch.std_mean(reference[i, :, :, c], dim=None) # index by original dim order
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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) * r_sd + r_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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