93 lines
3.4 KiB
Markdown
93 lines
3.4 KiB
Markdown
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# Documentation
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- Class name: SaltScheduledImageDisplacement
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- Category: SALT/Scheduling/Image
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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SaltScheduledImageDisplacement节点用于根据提供的振幅和角度调度表，对一批图像应用预定的位移效果。该节点利用优化的位移层进行图像变换，允许在序列中动态操作图像，根据预定义的调度表调整位移效果。这种方法实现了对图像的精细控制，能够创造出随时间或序列变化的动态视觉效果。
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# Input types
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## Required
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- images
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- 需要进行位移处理的图像批次。这是定义将要经历位移变换的基础内容的关键输入。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- displacement_images
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- 用于确定批次中每张图像的位移方向和幅度的图像。这些图像在指导位移过程中起着关键作用。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Optional
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- amplitude_schedule
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- 指定批次中每张图像位移幅度的调度表。它控制位移效果的强度。
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- Comfy dtype: LIST
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- Python dtype: Optional[List[float]]
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- angle_schedule
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- 详细说明每张图像位移角度的调度表。这个参数决定了位移效果的方向。
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- Comfy dtype: LIST
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- Python dtype: Optional[List[float]]
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# Output types
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- images
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- 应用预定位移后的图像批次。这个输出展示了经过位移效果处理后的转换图像。
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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 SaltScheduledImageDisplacement:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"displacement_images": ("IMAGE",),
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},
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"optional": {
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"amplitude_schedule": ("LIST",),
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"angle_schedule": ("LIST",),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "apply_displacement"
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CATEGORY = "SALT/Scheduling/Image"
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def apply_displacement(self, images, displacement_images, amplitude_schedule=None, angle_schedule=None):
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batch_size, height, width, _ = images.shape
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device = images.device
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# Initialize the optimized displacement layer
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displacement_layer = DisplacementLayer(device)
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displaced_images = []
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for i in range(batch_size):
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# Default amplitude and angle to 0 if their schedules are not provided or are too short
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amplitude_value = amplitude_schedule[i] if amplitude_schedule and i < len(amplitude_schedule) else 0
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angle_value = angle_schedule[i] if angle_schedule and i < len(angle_schedule) else 0
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amplitude = torch.tensor([amplitude_value], dtype=torch.float, device=device)
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angle = torch.tensor([angle_value], dtype=torch.float, device=device)
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image = images[i:i+1].to(device)
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displacement_image = displacement_images[i:i+1].to(device)
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# Use the displacement layer
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displaced_image = displacement_layer(image, displacement_image, amplitude, angle)
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displaced_images.append(displaced_image)
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# Combine the batch of displaced images
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displaced_images = torch.cat(displaced_images, dim=0)
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return (displaced_images,)
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```
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