73 lines
2.3 KiB
Markdown
73 lines
2.3 KiB
Markdown
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# Documentation
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- Class name: `ControlNetHadamard`
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- Category: `Bmad/conditioning`
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- Output node: `False`
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ControlNetHadamard节点应用控制网络到一组图像和条件上，根据控制网络的输出和指定的强度调整条件。它旨在实现动态图像调节，允许根据控制网络的逻辑修改图像属性或风格。
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# Input types
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## Required
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- conds
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- 要应用于每个图像的条件，这些条件会被控制网络修改以达到预期效果。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[Tuple[torch.Tensor, Dict[str, Any]]]
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- control_net
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- 用于基于图像和指定强度修改条件的控制网络。
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- Comfy dtype: CONTROL_NET
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- Python dtype: torch.nn.Module
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- image
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- 应用条件的图像，作为控制网络修改的基础。
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- Comfy dtype: IMAGE
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- Python dtype: List[torch.Tensor]
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- strength
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- 决定控制网络对条件影响的强度，允许进行精细调整。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- conditioning
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- 经过控制网络处理后的修改条件，反映了应用的变化。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[Tuple[torch.Tensor, Dict[str, Any]]]
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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 ControlNetHadamard(nodes.ControlNetApply):
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"conds": ("CONDITIONING",),
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"control_net": ("CONTROL_NET",),
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"image": ("IMAGE",),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "apply"
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CATEGORY = "Bmad/conditioning"
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INPUT_IS_LIST = True
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OUTPUT_IS_LIST = (True,)
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def apply(self, conds, control_net, images, strength):
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control_net = control_net[0]
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strength = strength[0]
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if len(images) != len(conds):
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raise "lists sizes do not match" # maybe relax check and allow for fewer conds than images?
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print(len(images))
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print(len(images[0]))
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print(len(conds))
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new_conds = []
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for i in range(len(images)):
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new_conds.append(super().apply_controlnet(conds[i], control_net, images[i], strength)[0])
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return (new_conds,)
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```
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