98 lines
4.8 KiB
Python
98 lines
4.8 KiB
Python
from .helper_functions import commonLazy,parse_expr, generate_dim_variables, as_tensor, prepare_inputs, getIndexTensorAlongDim, normalize_to_common_shape
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from comfy_api.latest import io
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import torch
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from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
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class ConditioningMathNode(io.ComfyNode):
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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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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 conditioning segment counts. broadcast: repeat shorter inputs; error: raise error on mismatch; pad: treat missing as zero."
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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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@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, length_mismatch="tile"):
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tensor_needs = set(commonLazy(Tensor, a, b, c, d, w, x, y, z))
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pooled_needs = set(commonLazy(pooled_output, a, b, c, d, w, x, y, z))
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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, length_mismatch="tile"):
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# Default missing conditionings to zero
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ta, tb, tc, td = prepare_inputs(a, b, c, d)
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da=db=dc=dd=None
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output_cond = a
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if len(a)>1: da,db,dc,dd = a[1]["pooled_output"], b[1]["pooled_output"] if b else None, c[1]["pooled_output"] if c else None, d[1]["pooled_output"] if d else None
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ta, tb, tc, td = normalize_to_common_shape(ta[0][0], tb[0][0], tc[0][0], td[0][0], mode=length_mismatch)
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B_val = getIndexTensorAlongDim(ta[0][0], 0)
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variables = {
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"a": ta, "b": tb, "c": tc, "d": td, "w": w, "x": x, "y": y, "z": z,
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"B": B_val, "batch": B_val,
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"T": ta.shape[0], "batch_count": ta.shape[0],
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"N": ta.shape[1], "channel_count": ta.shape[1],
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} | generate_dim_variables(ta)
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tree = parse_expr(Tensor);
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visitor = UnifiedMathVisitor(variables, ta.shape)
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result = visitor.visit(tree)
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result_tensor = as_tensor(result, ta.shape)
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if(da is not None):
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new_dict = da.copy()
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pa = da.get("pooled_output")
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if pa is not None:
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pb = db.get("pooled_output")
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pc = dc.get("pooled_output")
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pd = dd.get("pooled_output")
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pb = pb if pb is not None else torch.zeros_like(pa)
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pc = pc if pc is not None else torch.zeros_like(pa)
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pd = pd if pd is not None else torch.zeros_like(pa)
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pa, pb, pc, pd = normalize_to_common_shape(pa, pb, pc, pd, mode=length_mismatch)
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variables_pooled = {"a": pa, "b": pb, "c": pc, "d": pd, "w": w, "x": x, "y": y, "z": z} | generate_dim_variables(pa)
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tree = parse_expr(pooled_output);
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visitor = UnifiedMathVisitor(variables_pooled, pa.shape)
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result_pooled = visitor.visit(tree)
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new_dict["pooled_output"] = result_pooled
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output_cond[1]["pooled_output"] = new_dict
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output_cond[0][0] = result_tensor
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return (output_cond,)
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