86 lines
3.8 KiB
Python
86 lines
3.8 KiB
Python
from .helper_functions import generate_dim_variables, parse_expr, getIndexTensorAlongDim, as_tensor, prepare_inputs, commonLazy, normalize_to_common_shape
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from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
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from comfy_api.latest import io
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class ImageMathNode(io.ComfyNode):
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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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io.Combo.Input(
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id="length_mismatch",
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options=["tile", "error", "pad"],
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default="tile",
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tooltip="How to handle mismatched image batch sizes. broadcast: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
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)
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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 check_lazy_status(cls, Image, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
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return commonLazy(Image, a, b, c, d, w, x, y, z)
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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, length_mismatch="tile"):
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ae, be, ce, de = prepare_inputs(a, b, c, d)
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print(f"DEBUG: shapes {ae.shape[0]}, {be.shape[0]}, {ce.shape[0]}, {de.shape[0]}")
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if(length_mismatch == "error"):
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max_length = max(ae.shape[0], be.shape[0], ce.shape[0], de.shape[0])
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for tensor, name in zip([ae, be, ce, de], ["a", "b", "c", "d"]):
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if tensor.shape[0] != max_length:
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raise ValueError(f"Input '{name}' has shape {tensor.shape[0]}, expected {max_length} to match largest input.")
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ae, be, ce, de = normalize_to_common_shape(ae, be, ce, de, mode=length_mismatch)
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variables = {
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"a": ae, "b": be, "c": ce, "d": de,
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"w": w, "x": x, "y": y, "z": z,
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"X": getIndexTensorAlongDim(ae, 3),
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"Y": getIndexTensorAlongDim(ae, 2),
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"B": getIndexTensorAlongDim(ae, 0),
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"batch": getIndexTensorAlongDim(ae, 0),
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"C": getIndexTensorAlongDim(ae, 1),
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"channel": getIndexTensorAlongDim(ae, 1),
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"W": ae.shape[2],
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"width": ae.shape[2],
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"H": ae.shape[1],
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"height": ae.shape[1],
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"T": ae.shape[0],
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"batch_count": ae.shape[0],
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"N": ae.shape[3],
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"channel_count": ae.shape[3],
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} | generate_dim_variables(ae)
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tree = parse_expr(Image);
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visitor = UnifiedMathVisitor(variables, ae.shape)
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result = visitor.visit(tree)
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result = as_tensor(result, ae.shape)
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return (result,)
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