112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
from .helper_functions import (
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generate_dim_variables,
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parse_expr,
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getIndexTensorAlongDim,
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as_tensor,
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normalize_to_common_shape,
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make_zero_like,
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get_v_variable,
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get_f_variable,
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checkLazyNew
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)
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from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
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from comfy_api.latest import io
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import torch
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from .Stack import MrmthStack
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import copy
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from .ParseTree import MrmthParseTree
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class StringMathNode(io.ComfyNode):
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"""
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Enables math expressions on Audio.
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Inputs:
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I: Autogrow image inputs (I0, I1, ...)
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F: Autogrow float inputs (F0, F1, ...)
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Image: Expression
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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_ag_AudioMathNode",
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category="More math",
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display_name="Audio math",
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inputs=[
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io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.String.Input("values", optional=True), prefix="V", min=1, max=50)),
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io.Autogrow.Input(id="F", template=io.Autogrow.TemplatePrefix(io.Float.Input("float", default=0.0, optional=True, lazy=True, force_input=True), prefix="F", min=1, max=50)),
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io.MultiType.Input(
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io.String.Input("Expression", default="", multiline=False),
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types=[io.String,MrmthParseTree],
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tooltip="3D model file or path string",
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),
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io.Int.Input(id="batching", default=0),
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io.Bool.Input(
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id="remember_stack",
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default=False,
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display_name="Remember stack across batch",
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tooltip=(
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"If enabled, stack is copied at output leading to changes being remembered during batch operations (node runs multiple times in sucession). If disabled each batch gets it's own copy of the stack."
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),
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),
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MrmthStack.Input(id="stack", tooltip="Access stack between nodes",optional=True)
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],
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outputs=[
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io.Audio.Output(is_output_list=True),
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MrmthStack.Output(),
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],
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)
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@classmethod
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def check_lazy_status(cls, Expression, V, F,batching=0, remember_stack=False,stack={}):
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return checkLazyNew(Expression,V,F)
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@classmethod
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def execute(cls, V, F, Expression, batching=0, remember_stack=False, stack={}):
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variables = {
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"a": V.get(0,""), "b": V.get(1,""), "c": V.get(2,""), "d": V.get(3,""),
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"w": F.get("F0", 0.0) if F.get("F0") is not None else 0.0,
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"x": F.get("F1", 0.0) if F.get("F1") is not None else 0.0,
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"y": F.get("F2", 0.0) if F.get("F2") is not None else 0.0,
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"z": F.get("F3", 0.0) if F.get("F3") is not None else 0.0
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}
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v_stacked, v_cnt = get_v_variable(V, length_mismatch="do nothing")
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if v_stacked is not None:
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variables["V"] = v_stacked
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variables["Vcnt"] = float(v_cnt)
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variables["V_count"] = float(v_cnt)
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f_stacked, f_cnt = get_f_variable(F)
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if f_stacked is not None:
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variables["F"] = f_stacked
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variables["Fcnt"] = float(f_cnt)
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variables["F_count"] = float(f_cnt)
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for k, val in F.items():
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variables[k] = val if val is not None else 0.0
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tree = None
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if isinstance(Expression,str):
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tree = parse_expr(Expression)
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else:
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tree = Expression
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visitor = UnifiedMathVisitor(variables, len(V[0]),torch.device("cpu"),state_storage=stack)
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result = visitor.visit(tree)
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result = str(result)
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if batching and batching > 0:
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def chunks(s, n):
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"""Produce `n`-character chunks from `s`."""
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for start in range(0, len(s), n):
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yield s[start:start+n]
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stack = stack if remember_stack else copy.deepcopy(stack)
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return (chunks(result,batching), stack)
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else:
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stack = stack if remember_stack else copy.deepcopy(stack)
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return (result, stack)
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