233 lines
7.8 KiB
Markdown
233 lines
7.8 KiB
Markdown
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# Documentation
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- Class name: ImageEffectsLensChromaticAberration
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- Category: image/effects/lens
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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此节点应用色差效果到图像上，模拟光线通过镜头时的色散现象，创造出具有视觉特色的色彩边缘效果。它通过添加艺术性或真实的镜头瑕疵来增强视觉内容，常用于赋予图像更动态或复古的外观。
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# Input types
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## Required
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- images
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- 将应用色差效果的输入图像。这个参数对于定义将要进行变换的基础内容至关重要。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- shift
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- 确定模拟色差效果的像素偏移量，影响颜色通道的位移程度。
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- Comfy dtype: INT
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- Python dtype: int
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- method
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- 指定在应用色差效果时处理边缘像素的方法，确保效果在整个图像上一致应用。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- shift_type
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- 定义应用于色差效果的偏移类型，影响颜色通道位移的方向和性质。
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- Comfy dtype: INT
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- Python dtype: int
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- mixing_type
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- 控制原始和偏移颜色通道的混合方式，影响色差效果的整体强度和外观。
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- Comfy dtype: INT
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- Python dtype: int
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- transpose
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- 决定在应用色差效果之前是否以及如何转置图像，影响效果的方向。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- colors
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- 选择要偏移的颜色通道，直接影响色差效果的颜色组成。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- lens_curvy
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- 调整镜头效果的曲率，修改色差在图像上的显著程度。
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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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- 应用了色差效果的转换后图像，展示了对原始内容添加的视觉增强或艺术效果。
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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 ImageEffectsLensChromaticAberration:
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def __init__(self):
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pass
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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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"shift": ("INT", {
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"default": 10,
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"step": 1,
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}),
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"method": (["reflect", "edge", "constant"],),
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"shift_type": ("INT", {
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"default": 1,
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"min": 1,
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"max": 4,
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"step": 1,
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}),
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"mixing_type": ("INT", {
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"default": 1,
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"min": 1,
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"max": 4,
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"step": 1,
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}),
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"transpose": (["none", "rotate", "reflect"],),
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"colors": (["rb", "rg", "gb"],),
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"lens_curvy": ("FLOAT", {
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"default": 1.0,
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"max": 15.0,
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"step": 0.1,
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "node"
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CATEGORY = "image/effects/lens"
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def node(self, images, shift, method, shift_type, mixing_type, transpose, colors, lens_curvy):
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# noinspection PyUnboundLocalVariable
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def apply(image):
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img = image.clone().detach()
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if transpose == "rotate":
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img = img.permute(1, 0, 2)
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elif transpose == "none" or transpose == "reflect":
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pass
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else:
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raise ValueError("Not existing reverse.")
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r, g, b = img[:, :, 0:3].split(1, 2)
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height, width = img[:, :, 0].shape
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def get_space(size, min_val, max_val):
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size += shift * 2
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if min_val == max_val:
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return torch.linspace(min_val, max_val, size)
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else:
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return radialspace_1D(size, lens_curvy, 1.0, min_val, max_val)
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if shift_type == 1:
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f_shifts = get_space(height, -shift, shift)
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if transpose == "reflect":
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t_shifts = get_space(width, -shift, shift)
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elif shift_type == 2:
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f_shifts = get_space(height, 0, shift)
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f_shifts = torch.flip(f_shifts, dims=(0,))
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if transpose == "reflect":
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t_shifts = get_space(width, 0, shift)
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t_shifts = torch.flip(t_shifts, dims=(0,))
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elif shift_type == 3:
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f_shifts = get_space(height, 0, shift)
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if transpose == "reflect":
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t_shifts = get_space(width, 0, shift)
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elif shift_type == 4:
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f_shifts = get_space(height, shift, shift)
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if transpose == "reflect":
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t_shifts = get_space(width, shift, shift)
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else:
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raise ValueError("Not existing shift_type.")
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if mixing_type == 1:
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f_shifts = f_shifts
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s_shifts = -f_shifts
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if transpose == "reflect":
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t_shifts = t_shifts
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c_shifts = -t_shifts
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elif mixing_type == 2:
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f_shifts = -f_shifts
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s_shifts = f_shifts
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if transpose == "reflect":
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t_shifts = -t_shifts
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c_shifts = t_shifts
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elif mixing_type == 3:
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f_shifts = f_shifts
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s_shifts = f_shifts
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if transpose == "reflect":
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t_shifts = t_shifts
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c_shifts = t_shifts
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elif mixing_type == 4:
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f_shifts = -f_shifts
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s_shifts = -f_shifts
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if transpose == "reflect":
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t_shifts = -t_shifts
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c_shifts = -t_shifts
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else:
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raise ValueError("Not existing mixing_type.")
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if colors == "rb":
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def cat(f_value, s_value):
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return torch.cat([f_value, g, s_value], 2)
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f = r
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s = b
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elif colors == "rg":
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def cat(f_value, s_value):
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return torch.cat([f_value, s_value, b], 2)
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f = r
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s = g
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elif colors == "gb":
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def cat(f_value, s_value):
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return torch.cat([r, f_value, s_value], 2)
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f = g
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s = b
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else:
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raise ValueError("Not existing colors.")
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f_pad = F.pad(f.squeeze(), [shift, shift], padding_mode=method).unsqueeze(2)
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s_pad = F.pad(s.squeeze(), [shift, shift], padding_mode=method).unsqueeze(2)
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f_shifted = torch.zeros_like(f_pad)
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s_shifted = torch.zeros_like(s_pad)
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for i in range(height + (shift * 2)):
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f_shifted[i] = torch.roll(f_pad[i], shifts=int(f_shifts[i]), dims=0)
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s_shifted[i] = torch.roll(s_pad[i], shifts=int(s_shifts[i]), dims=0)
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if transpose == "reflect":
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for i in range(width + (shift * 2)):
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f_shifted[:, i] = torch.roll(f_shifted[:, i], shifts=int(t_shifts[i]), dims=0)
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s_shifted[:, i] = torch.roll(s_shifted[:, i], shifts=int(c_shifts[i]), dims=0)
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f_result = f_shifted[shift:-shift, shift:-shift, :]
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s_result = s_shifted[shift:-shift, shift:-shift, :]
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img[:, :, 0:3] = cat(f_result, s_result)
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if transpose == "rotate":
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img = img.permute(1, 0, 2)
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elif transpose == "none" or transpose == "reflect":
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pass
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else:
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raise ValueError("Not existing reverse.")
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return img
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return (torch.stack([
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apply(images[i]) for i in range(len(images))
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]),)
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```
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