71 lines
2.1 KiB
Markdown
71 lines
2.1 KiB
Markdown
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# Documentation
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- Class name: BatchNormalizeLatent
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- Category: latent/filters
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- Output node: False
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BatchNormalizeLatent节点对潜在表示应用批量归一化，将每个潜在样本的分布调整为标准分布，从而稳定并可能改善生成过程。
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# Input types
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## Required
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- latents
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- 需要进行归一化的潜在表示。这个输入对归一化过程至关重要，因为它直接修改了这些潜在表示的分布。
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- Comfy dtype: LATENT
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- Python dtype: Dict[str, torch.Tensor]
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- factor
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- 一个缩放因子，用于在原始潜在表示和归一化后的潜在表示之间进行插值，允许对归一化效果进行可控调整。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- latent
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- 经过归一化处理的潜在表示，根据指定的factor进行调整，以潜在地提高生成模型的性能。
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- Comfy dtype: LATENT
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- Python dtype: Dict[str, 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 BatchNormalizeLatent:
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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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"latents": ("LATENT", ),
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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 = ("LATENT",)
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FUNCTION = "batch_normalize"
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CATEGORY = "latent/filters"
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def batch_normalize(self, latents, factor):
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latents_copy = copy.deepcopy(latents)
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t = latents_copy["samples"] # [B x C x H x W]
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t = t.movedim(0,1) # [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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latents_copy["samples"] = torch.lerp(latents["samples"], t.movedim(1,0), factor) # [B x C x H x W]
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return (latents_copy,)
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```
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