mcDandy
f6413f4202
Improved error messages in AudioMathNode and refactored ConditioningMathNode to simplify input handling and output structure. Updated README to clarify blur and edge function behaviors. Removed redundant 'category' field from deprecated node schemas to hide them.
83 lines
3.5 KiB
Python
83 lines
3.5 KiB
Python
from ..helper_functions import generate_dim_variables,parse_expr, getIndexTensorAlongDim, as_tensor, commonLazy, normalize_to_common_shape,prepare_inputs
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from ..Parser.UnifiedMathVisitor import UnifiedMathVisitor
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from comfy_api.latest import io
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class MaskMathNodeOLD(io.ComfyNode):
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"""
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Enables math expressions on Masks.
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Inputs:
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a, b, c, d: Mask 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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Mask: Expression to apply on input masks
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Outputs:
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MASK: Result of applying expression to input masks
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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_MaskMathNode",
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display_name="Mask math",
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is_deprecated=True,
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inputs=[
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io.Mask.Input(id="a"),
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io.Mask.Input(id="b", optional=True, lazy=True),
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io.Mask.Input(id="c", optional=True, lazy=True),
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io.Mask.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="Mask", default="a*(1-w)+b*w", tooltip="Expression to apply on input masks"),
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io.Combo.Input(
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id="length_mismatch",
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options=["error", "error", "pad"],
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default="error",
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tooltip="How to handle mismatched mask batch sizes. tile: 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.Mask.Output(),
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],
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)
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@classmethod
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def check_lazy_status(cls, Mask, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
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return commonLazy(Mask, a, b, c, d, w, x, y, z)
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@classmethod
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def execute(cls, Mask, 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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a, b, c, d = prepare_inputs(a, b, c, d)
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if(length_mismatch == "error"):
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max_length = max(a.shape[0], b.shape[0], c.shape[0], d.shape[0])
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for tensor, name in zip([a, b, c, d], ["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(a, b, c, d, 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, 2),
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"Y": getIndexTensorAlongDim(ae, 1),
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"B": getIndexTensorAlongDim(ae, 0),
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"batch": getIndexTensorAlongDim(ae, 0),
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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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} | generate_dim_variables(ae)
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tree = parse_expr(Mask);
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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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