150 lines
6.2 KiB
Python
150 lines
6.2 KiB
Python
from tokenize import String
|
|
from .helper_functions import (
|
|
generate_dim_variables,
|
|
parse_expr,
|
|
getIndexTensorAlongDim,
|
|
as_tensor,
|
|
normalize_to_common_shape,
|
|
make_zero_like,
|
|
get_v_variable,
|
|
get_f_variable,
|
|
checkLazyNew
|
|
)
|
|
from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
|
|
from comfy_api.latest import io
|
|
from antlr4 import InputStream, CommonTokenStream
|
|
from .Parser.MathExprLexer import MathExprLexer
|
|
from .Parser.MathExprParser import MathExprParser
|
|
import torch
|
|
from .Stack import MrmthStack
|
|
import copy
|
|
from .ParseTree import MrmthParseTree
|
|
|
|
class AudioMathNode(io.ComfyNode):
|
|
"""
|
|
Enables math expressions on Audio.
|
|
|
|
Inputs:
|
|
I: Autogrow image inputs (I0, I1, ...)
|
|
F: Autogrow float inputs (F0, F1, ...)
|
|
Image: Expression
|
|
"""
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> io.Schema:
|
|
return io.Schema(
|
|
node_id="mrmth_ag_AudioMathNode",
|
|
category="More math",
|
|
display_name="Audio math",
|
|
inputs=[
|
|
io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Audio.Input("values", optional=True), prefix="V", min=1, max=50)),
|
|
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)),
|
|
io.MultiType.Input(
|
|
io.String.Input("Expression", default="", multiline=False),
|
|
types=[io.String,MrmthParseTree],
|
|
tooltip="3D model file or path string",
|
|
),
|
|
io.Combo.Input(
|
|
id="length_mismatch",
|
|
options=["do nothing","error","tile", "pad"],
|
|
display_name="on size mismatch",
|
|
default="error",
|
|
tooltip="How to handle mismatched image batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
|
|
),
|
|
io.Int.Input(id="batching", default=0),
|
|
MrmthStack.Input(id="stack", tooltip="Access stack between nodes",optional=True)
|
|
],
|
|
outputs=[
|
|
io.Audio.Output(is_output_list=True),
|
|
MrmthStack.Output(),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def check_lazy_status(cls, Expression, V, F, length_mismatch="tile",batching=0,stack={}):
|
|
return checkLazyNew(Expression,V,F)
|
|
|
|
|
|
@classmethod
|
|
def execute(cls, V, F, Expression, length_mismatch="tile",batching=0,stack={}):
|
|
# Identify all present audio inputs and their keys
|
|
tensor_keys = [k for k, v in V.items() if v is not None and isinstance(v, dict) and "waveform" in v]
|
|
if not tensor_keys:
|
|
raise ValueError("At least one audio input is required.")
|
|
stack = copy.deepcopy(stack) if stack is not None else {}
|
|
waveforms = {k: V[k]["waveform"] for k in tensor_keys}
|
|
sample_rates = {k + "sr": V[k].get("sample_rate", 44100) for k in tensor_keys}
|
|
|
|
# Normalize all waveforms together
|
|
normalized_waveforms = normalize_to_common_shape(*waveforms.values(), mode=length_mismatch)
|
|
V_norm_waveforms = dict(zip(tensor_keys, normalized_waveforms))
|
|
|
|
ref_waveform = normalized_waveforms[0]
|
|
common_shape = ref_waveform.shape
|
|
sample_rate = V[tensor_keys[0]].get("sample_rate", 44100)
|
|
|
|
if(length_mismatch == "error"):
|
|
for name in tensor_keys:
|
|
if waveforms[name].shape != common_shape:
|
|
raise ValueError(f"Input '{name}' has shape ({waveforms[name].shape[0]}, {waveforms[name].shape[2]}), expected ({common_shape[0]}, {common_shape[2]}) to match input.")
|
|
|
|
# Setup legacy variables a, b, c, d
|
|
a_w = V_norm_waveforms.get("V0", make_zero_like(ref_waveform))
|
|
b_w = V_norm_waveforms.get("V1", make_zero_like(a_w))
|
|
c_w = V_norm_waveforms.get("V2", make_zero_like(a_w))
|
|
d_w = V_norm_waveforms.get("V3", make_zero_like(a_w))
|
|
|
|
# Ensure legacy are normalized
|
|
a_w, b_w, c_w, d_w = normalize_to_common_shape(a_w, b_w, c_w, d_w, mode=length_mismatch)
|
|
|
|
variables = {
|
|
"a": a_w, "b": b_w, "c": c_w, "d": d_w,
|
|
"w": F.get("F0", 0.0) if F.get("F0") is not None else 0.0,
|
|
"x": F.get("F1", 0.0) if F.get("F1") is not None else 0.0,
|
|
"y": F.get("F2", 0.0) if F.get("F2") is not None else 0.0,
|
|
"z": F.get("F3", 0.0) if F.get("F3") is not None else 0.0,
|
|
"B": getIndexTensorAlongDim(a_w, 0),
|
|
"C": getIndexTensorAlongDim(a_w, 1),
|
|
"channel": getIndexTensorAlongDim(a_w, 1),
|
|
"S": getIndexTensorAlongDim(a_w, 2),
|
|
"sample": getIndexTensorAlongDim(a_w, 2),
|
|
"R": sample_rate,
|
|
"sample_rate": sample_rate,
|
|
"batch": getIndexTensorAlongDim(a_w, 0),
|
|
"T": float(a_w.shape[0]),
|
|
"batch_count": float(a_w.shape[0]),
|
|
} | generate_dim_variables(a_w) | V_norm_waveforms | sample_rates
|
|
|
|
v_stacked, v_cnt = get_v_variable(V_norm_waveforms, length_mismatch=length_mismatch)
|
|
if v_stacked is not None:
|
|
variables["V"] = v_stacked
|
|
variables["Vcnt"] = float(v_cnt)
|
|
variables["V_count"] = float(v_cnt)
|
|
|
|
f_stacked, f_cnt = get_f_variable(F)
|
|
if f_stacked is not None:
|
|
variables["F"] = f_stacked
|
|
variables["Fcnt"] = float(f_cnt)
|
|
variables["F_count"] = float(f_cnt)
|
|
|
|
for k, val in F.items():
|
|
variables[k] = val if val is not None else 0.0
|
|
|
|
tree = None
|
|
if isinstance(Expression,str):
|
|
tree = parse_expr(Expression)
|
|
else:
|
|
tree = Expression
|
|
visitor = UnifiedMathVisitor(variables, a_w.shape,a_w.device,state_storage=stack)
|
|
result = visitor.visit(tree)
|
|
result = as_tensor(result, a_w.shape)
|
|
|
|
if batching and batching > 0:
|
|
res = torch.split(result, batching, dim=0)
|
|
res_list = []
|
|
for result_chunk in res:
|
|
res_list.append({"waveform": result_chunk, "sample_rate": sample_rate})
|
|
return (res_list, stack)
|
|
else:
|
|
return ([{"waveform": result, "sample_rate": sample_rate}], stack)
|