7.8 KiB
7.8 KiB
Documentation
- Class name: SplineEditor
- Category: KJNodes/experimental
- Output node: False
SplineEditor节点是一个专为创建和操作样条曲线设计的图形编辑器，用于生成各种类型的输出数据。它通过交互式编辑功能，允许对样条曲线的形状和特征进行精细控制。这个节点特别适用于需要自定义调度、批量掩码或坐标转换的应用场景。
Input types
Required
- points_store
- 存储控制点数据，用于生成和操作样条曲线。
- Comfy dtype: STRING
- Python dtype: str
- coordinates
- 控制点坐标的字符串表示，用于定义样条曲线的形状和轨迹。
- Comfy dtype: STRING
- Python dtype: str
- mask_width
- 指定要生成的掩码宽度，影响输出掩码的空间分辨率。
- Comfy dtype: INT
- Python dtype: int
- mask_height
- 定义掩码高度，影响输出掩码的垂直分辨率。
- Comfy dtype: INT
- Python dtype: int
- points_to_sample
- 设置从样条曲线生成的采样点数量，独立于控制点数量。
- Comfy dtype: INT
- Python dtype: int
- sampling_method
- 选择采样方法，可以是沿时间轴（用于调度）或沿路径（用于坐标）。
- Comfy dtype: COMBO[STRING]
- Python dtype: str
- interpolation
- 指定控制点之间的插值方法，影响样条曲线的平滑度和形状。
- Comfy dtype: COMBO[STRING]
- Python dtype: str
- tension
- 调整样条曲线的张力，影响其曲率以及与控制点的贴合程度。
- Comfy dtype: FLOAT
- Python dtype: float
- repeat_output
- 确定输出重复的次数，用于生成多个输出数据实例。
- Comfy dtype: INT
- Python dtype: int
- float_output_type
- 决定浮点输出的格式，可以选择列表、pandas系列或张量格式。
- Comfy dtype: COMBO[STRING]
- Python dtype: str
Optional
- min_value
- 指定输出的最小值，为生成的数据设置下限。
- Comfy dtype: FLOAT
- Python dtype: float
- max_value
- 定义输出的最大值，为生成的数据设置上限。
- Comfy dtype: FLOAT
- Python dtype: float
Output types
- mask
- Comfy dtype: MASK
- 基于定义的样条曲线生成掩码批次，适用于需要掩码输入的应用。
- Python dtype: str
- coord_str
- Comfy dtype: STRING
- 提供从样条曲线派生的坐标字符串表示，用于路径或形状的文本表示。
- Python dtype: str
- float
- Comfy dtype: FLOAT
- 输出浮点数列表、pandas系列或张量（取决于所选输出类型），表示从样条曲线采样的值。
- Python dtype: float
- count
- Comfy dtype: INT
- 返回整数计数，可能表示输出数据中的元素数量。
- Python dtype: int
Usage tips
- Infra type:
CPU - Common nodes: unknown
Source code
class SplineEditor:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"points_store": ("STRING", {"multiline": False}),
"coordinates": ("STRING", {"multiline": False}),
"mask_width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
"mask_height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
"points_to_sample": ("INT", {"default": 16, "min": 2, "max": 1000, "step": 1}),
"sampling_method": (
[
'path',
'time',
],
{
"default": 'time'
}),
"interpolation": (
[
'cardinal',
'monotone',
'basis',
'linear',
'step-before',
'step-after',
'polar',
'polar-reverse',
],
{
"default": 'cardinal'
}),
"tension": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
"repeat_output": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}),
"float_output_type": (
[
'list',
'pandas series',
'tensor',
],
{
"default": 'list'
}),
},
"optional": {
"min_value": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
"max_value": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
}
}
RETURN_TYPES = ("MASK", "STRING", "FLOAT", "INT")
RETURN_NAMES = ("mask", "coord_str", "float", "count")
FUNCTION = "splinedata"
CATEGORY = "KJNodes/experimental"
DESCRIPTION = """
# WORK IN PROGRESS
Do not count on this as part of your workflow yet,
probably contains lots of bugs and stability is not
guaranteed!!
## Graphical editor to create values for various
## schedules and/or mask batches.
**Shift + click** to add control point at end.
**Ctrl + click** to add control point (subdivide) between two points.
**Right click on a point** to delete it.
Note that you can't delete from start/end.
Right click on canvas for context menu:
These are purely visual options, doesn't affect the output:
- Toggle handles visibility
- Display sample points: display the points to be returned.
**points_to_sample** value sets the number of samples
returned from the **drawn spline itself**, this is independent from the
actual control points, so the interpolation type matters.
sampling_method:
- time: samples along the time axis, used for schedules
- path: samples along the path itself, useful for coordinates
output types:
- mask batch
example compatible nodes: anything that takes masks
- list of floats
example compatible nodes: IPAdapter weights
- pandas series
example compatible nodes: anything that takes Fizz'
nodes Batch Value Schedule
- torch tensor
example compatible nodes: unknown
"""
def splinedata(self, mask_width, mask_height, coordinates, float_output_type, interpolation,
points_to_sample, sampling_method, points_store, tension, repeat_output, min_value=0.0, max_value=1.0):
coordinates = json.loads(coordinates)
for coord in coordinates:
coord['x'] = int(round(coord['x']))
coord['y'] = int(round(coord['y']))
normalized_y_values = [
(1.0 - (point['y'] / 512) - 0.0) * (max_value - min_value) + min_value
for point in coordinates
]
if float_output_type == 'list':
out_floats = normalized_y_values * repeat_output
elif float_output_type == 'pandas series':
try:
import pandas as pd
except:
raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type")
out_floats = pd.Series(normalized_y_values * repeat_output),
elif float_output_type == 'tensor':
out_floats = torch.tensor(normalized_y_values * repeat_output, dtype=torch.float32)
# Create a color map for grayscale intensities
color_map = lambda y: torch.full((mask_height, mask_width, 3), y, dtype=torch.float32)
# Create image tensors for each normalized y value
mask_tensors = [color_map(y) for y in normalized_y_values]
masks_out = torch.stack(mask_tensors)
masks_out = masks_out.repeat(repeat_output, 1, 1, 1)
masks_out = masks_out.mean(dim=-1)
return (masks_out, str(coordinates), out_floats, len(out_floats))