77 lines
2.7 KiB
Markdown
77 lines
2.7 KiB
Markdown
|
|
# Documentation
|
|
- Class name: `ImageConstantHSV`
|
|
- Category: `image/filters`
|
|
- Output node: `False`
|
|
|
|
ImageConstantHSV节点用于生成基于指定HSV（色相、饱和度、明度）颜色值的纯色图像。它允许创建具有可调节颜色属性的均匀色彩图像，从而能够生成具有精确色调的图像。
|
|
|
|
# Input types
|
|
## Required
|
|
- **`width`**
|
|
- 指定生成图像的宽度。它决定了输出图像的水平尺寸。
|
|
- Comfy dtype: `INT`
|
|
- Python dtype: `int`
|
|
- **`height`**
|
|
- 指定生成图像的高度。它决定了输出图像的垂直尺寸。
|
|
- Comfy dtype: `INT`
|
|
- Python dtype: `int`
|
|
- **`batch_size`**
|
|
- 确定在单个批次中生成的图像数量。这允许同时生成多个具有相同颜色属性的图像。
|
|
- Comfy dtype: `INT`
|
|
- Python dtype: `int`
|
|
- **`hue`**
|
|
- 设置HSV颜色模型中的色相分量，定义了颜色的基本色调。
|
|
- Comfy dtype: `FLOAT`
|
|
- Python dtype: `float`
|
|
- **`saturation`**
|
|
- 设置HSV颜色模型中的饱和度分量，控制颜色的强度。
|
|
- Comfy dtype: `FLOAT`
|
|
- Python dtype: `float`
|
|
- **`value`**
|
|
- 设置HSV颜色模型中的明度分量，决定颜色的亮度。
|
|
- Comfy dtype: `FLOAT`
|
|
- Python dtype: `float`
|
|
|
|
# Output types
|
|
- **`image`**
|
|
- 基于指定HSV值生成的纯色图像。
|
|
- Comfy dtype: `IMAGE`
|
|
- Python dtype: `torch.Tensor`
|
|
|
|
|
|
## Usage tips
|
|
- Infra type: `CPU`
|
|
- Common nodes: unknown
|
|
|
|
|
|
## Source code
|
|
```python
|
|
class ImageConstantHSV:
|
|
def __init__(self, device="cpu"):
|
|
self.device = device
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
|
|
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
|
|
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
|
|
"hue": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
|
|
"saturation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
|
|
"value": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
|
|
}}
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "generate"
|
|
|
|
CATEGORY = "image/filters"
|
|
|
|
def generate(self, width, height, batch_size, hue, saturation, value):
|
|
red, green, blue = hsv_to_rgb(hue, saturation, value)
|
|
|
|
r = torch.full([batch_size, height, width, 1], red)
|
|
g = torch.full([batch_size, height, width, 1], green)
|
|
b = torch.full([batch_size, height, width, 1], blue)
|
|
return (torch.cat((r, g, b), dim=-1), )
|
|
|
|
```
|