213 lines
7.8 KiB
Markdown
213 lines
7.8 KiB
Markdown
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# Documentation
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- Class name: SplineEditor
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- Category: KJNodes/experimental
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- Output node: False
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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。
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# Input types
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## Required
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- points_store
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- 存储控制点数据，用于生成和操作样条曲线。
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- Comfy dtype: STRING
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- Python dtype: str
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- coordinates
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- 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。
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- Comfy dtype: STRING
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- Python dtype: str
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- mask_width
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- 指定要生成的掩码宽度，影响输出掩码的空间分辨率。
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- Comfy dtype: INT
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- Python dtype: int
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- mask_height
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- 定义掩码高度，影响输出掩码的垂直分辨率。
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- Comfy dtype: INT
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- Python dtype: int
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- points_to_sample
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- 设置从样条曲线生成的采样点数量，独立于控制点数量。
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- Comfy dtype: INT
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- Python dtype: int
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- sampling_method
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- 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）。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- interpolation
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- 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。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- tension
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- 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。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- repeat_output
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- 确定输出重复的次数，用于生成多个输出数据实例。
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- Comfy dtype: INT
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- Python dtype: int
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- float_output_type
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- 决定浮点输出的格式，可以选择列表、pandas系列或张量格式。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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## Optional
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- min_value
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- 指定输出的最小值，为生成的数据设置下限。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- max_value
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- 定义输出的最大值，为生成的数据设置上限。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- mask
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- Comfy dtype: MASK
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- 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。
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- Python dtype: str
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- coord_str
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- Comfy dtype: STRING
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- 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。
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- Python dtype: str
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- float
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- Comfy dtype: FLOAT
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- 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。
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- Python dtype: float
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- count
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- Comfy dtype: INT
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- 返回整数计数，可能表示输出数据中的元素数量。
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- Python dtype: int
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class SplineEditor:
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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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"points_store": ("STRING", {"multiline": False}),
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"coordinates": ("STRING", {"multiline": False}),
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"mask_width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
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"mask_height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
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"points_to_sample": ("INT", {"default": 16, "min": 2, "max": 1000, "step": 1}),
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"sampling_method": (
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[
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'path',
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'time',
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],
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{
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"default": 'time'
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}),
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"interpolation": (
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[
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'cardinal',
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'monotone',
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'basis',
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'linear',
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'step-before',
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'step-after',
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'polar',
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'polar-reverse',
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],
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{
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"default": 'cardinal'
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}),
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"tension": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"repeat_output": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}),
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"float_output_type": (
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[
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'list',
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'pandas series',
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'tensor',
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],
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{
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"default": 'list'
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}),
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},
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"optional": {
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"min_value": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
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"max_value": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("MASK", "STRING", "FLOAT", "INT")
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RETURN_NAMES = ("mask", "coord_str", "float", "count")
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FUNCTION = "splinedata"
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CATEGORY = "KJNodes/experimental"
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DESCRIPTION = """
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# WORK IN PROGRESS
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Do not count on this as part of your workflow yet,
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probably contains lots of bugs and stability is not
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guaranteed!!
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## Graphical editor to create values for various
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## schedules and/or mask batches.
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**Shift + click** to add control point at end.
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**Ctrl + click** to add control point (subdivide) between two points.
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**Right click on a point** to delete it.
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Note that you can't delete from start/end.
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Right click on canvas for context menu:
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These are purely visual options, doesn't affect the output:
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- Toggle handles visibility
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- Display sample points: display the points to be returned.
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**points_to_sample** value sets the number of samples
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returned from the **drawn spline itself**, this is independent from the
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actual control points, so the interpolation type matters.
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sampling_method:
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- time: samples along the time axis, used for schedules
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- path: samples along the path itself, useful for coordinates
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output types:
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- mask batch
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example compatible nodes: anything that takes masks
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- list of floats
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example compatible nodes: IPAdapter weights
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- pandas series
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example compatible nodes: anything that takes Fizz'
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nodes Batch Value Schedule
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- torch tensor
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example compatible nodes: unknown
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"""
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def splinedata(self, mask_width, mask_height, coordinates, float_output_type, interpolation,
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points_to_sample, sampling_method, points_store, tension, repeat_output, min_value=0.0, max_value=1.0):
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coordinates = json.loads(coordinates)
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for coord in coordinates:
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coord['x'] = int(round(coord['x']))
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coord['y'] = int(round(coord['y']))
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normalized_y_values = [
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(1.0 - (point['y'] / 512) - 0.0) * (max_value - min_value) + min_value
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for point in coordinates
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]
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if float_output_type == 'list':
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out_floats = normalized_y_values * repeat_output
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elif float_output_type == 'pandas series':
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try:
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import pandas as pd
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except:
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raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type")
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out_floats = pd.Series(normalized_y_values * repeat_output),
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elif float_output_type == 'tensor':
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out_floats = torch.tensor(normalized_y_values * repeat_output, dtype=torch.float32)
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# Create a color map for grayscale intensities
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color_map = lambda y: torch.full((mask_height, mask_width, 3), y, dtype=torch.float32)
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# Create image tensors for each normalized y value
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mask_tensors = [color_map(y) for y in normalized_y_values]
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masks_out = torch.stack(mask_tensors)
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masks_out = masks_out.repeat(repeat_output, 1, 1, 1)
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masks_out = masks_out.mean(dim=-1)
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return (masks_out, str(coordinates), out_floats, len(out_floats))
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```
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