77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
import torch
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from .helper_functions import generate_dim_variables, getIndexTensorAlongDim, comonLazy, eval_tensor_expr, make_zero_like, as_tensor
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from comfy_api.latest import io
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from .MathNodeBase import MathNodeBase
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class ImageMathNode(MathNodeBase):
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"""
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Enables math expressions on Images.
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Inputs:
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a, b, c, d: Image inputs (b, c, d default to zero if not provided)
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w, x, y, z: Float variables for expressions
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Image: Expression to apply on input images
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Outputs:
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IMAGE: Result of applying expression to input images
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"""
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="mrmth_ImageMathNode",
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category="More math",
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display_name="Image math",
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inputs=[
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io.Image.Input(id="a"),
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io.Image.Input(id="b", optional=True, lazy=True),
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io.Image.Input(id="c", optional=True, lazy=True),
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io.Image.Input(id="d", optional=True, lazy=True),
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io.Float.Input(id="w", default=0.0, optional=True, lazy=True, force_input=True),
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io.Float.Input(id="x", default=0.0, optional=True, lazy=True, force_input=True),
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io.Float.Input(id="y", default=0.0, optional=True, lazy=True, force_input=True),
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io.Float.Input(id="z", default=0.0, optional=True, lazy=True, force_input=True),
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io.String.Input(id="Image", default="a*(1-w)+b*w", tooltip="Expression to apply on input images"),
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],
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outputs=[
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io.Image.Output(),
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],
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)
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@classmethod
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def execute(cls, Image, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0):
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a, b, c, d = cls.prepare_inputs(a, b, c, d)
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variables = {
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"a": a,
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"b": b,
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"c": c,
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"d": d,
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"w": w,
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"x": x,
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"y": y,
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"z": z,
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"X": getIndexTensorAlongDim(a, 3),
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"Y": getIndexTensorAlongDim(a, 2),
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"B": getIndexTensorAlongDim(a, 0),
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"batch": getIndexTensorAlongDim(a, 0),
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"C": getIndexTensorAlongDim(a, 1),
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"channel": getIndexTensorAlongDim(a, 1),
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"W": a.shape[3],
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"width": a.shape[3],
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"H": a.shape[2],
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"height": a.shape[2],
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"T": a.shape[0],
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"batch_count": a.shape[0],
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"N": a.shape[1],
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"channel_count": a.shape[1],
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} | generate_dim_variables(a)
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result = eval_tensor_expr(Image, variables, a.shape)
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return (as_tensor(result, a.shape),)
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