82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
from inspect import cleandoc
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from .helper_functions import comonLazy, eval_tensor_expr, generate_dim_variables, 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 ConditioningMathNode(MathNodeBase):
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"""
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Enables math operations on conditionings.
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Inputs:
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a, b, c, d: Conditioning 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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Tensor: Expression for the tensor part (describes image composition)
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pooled_output: Expression for the pooled output (condensed representation)
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Outputs:
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CONDITIONING: Result of applying expressions to input conditionings
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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_ConditioningMathNode",
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display_name="Conditioning math",
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category="More math",
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inputs=[
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io.Conditioning.Input(id="a"),
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io.Conditioning.Input(id="b", optional=True, lazy=True),
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io.Conditioning.Input(id="c", optional=True, lazy=True),
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io.Conditioning.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="Tensor", default="a*(1-w)+b*w", tooltip="Expression for tensor part (image composition)"),
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io.String.Input(
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id="pooled_output", default="a*(1-w)+b*w", tooltip="Expression for pooled output (condensed representation)"
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),
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],
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outputs=[
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io.Conditioning.Output(),
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],
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)
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tooltip = cleandoc(__doc__)
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@classmethod
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def check_lazy_status(cls, Tensor, pooled_output, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0):
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tensor_needs = set(comonLazy(Tensor, a, b, c, d))
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pooled_needs = set(comonLazy(pooled_output, a, b, c, d))
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return list(tensor_needs.union(pooled_needs))
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@classmethod
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def execute(cls, Tensor, pooled_output, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0):
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# Default missing conditionings to zero
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a, b, c, d = cls.prepare_inputs(a, b, c, d)
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# Extract tensors
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ta, tb, tc, td = a[0][0], b[0][0], c[0][0], d[0][0]
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# Evaluate tensor expression
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variables = {"a": ta, "b": tb, "c": tc, "d": td, "w": w, "x": x, "y": y, "z": z} | generate_dim_variables(ta)
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result_tensor = as_tensor(eval_tensor_expr(Tensor, variables, ta.shape), ta.shape)
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# Evaluate pooled_output expression if available
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pa = a[0][1].get("pooled_output")
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if pa is not None:
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pb = b[0][1].get("pooled_output")
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pc = c[0][1].get("pooled_output")
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pd = d[0][1].get("pooled_output")
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variables = {"a": pa, "b": pb, "c": pc, "d": pd, "w": w, "x": x, "y": y, "z": z} | generate_dim_variables(pa)
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result_pooled = as_tensor(eval_tensor_expr(pooled_output, variables, pa.shape), pa.shape)
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else:
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result_pooled = None
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return ([[result_tensor, {"pooled_output": result_pooled}]],)
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