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mcDandy
2026-01-22 17:47:11 +01:00
parent d3921204ae
commit cc10c1eb8e
2 changed files with 226 additions and 72 deletions
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more_math/ConditioningMathNode.py
+119 -72
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@@ -1,47 +1,38 @@
from .helper_functions import commonLazy,parse_expr, generate_dim_variables, as_tensor, prepare_inputs, getIndexTensorAlongDim, normalize_to_common_shape
from comfy_api.latest import io
import torch
from .helper_functions import generate_dim_variables, parse_expr, getIndexTensorAlongDim, as_tensor, prepare_inputs, make_zero_like, normalize_to_common_shape
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 re
class ConditioningMathNode(io.ComfyNode):
"""
Enables math operations on conditionings.
Enables math expressions on Audio.
Inputs:
a, b, c, d: Conditioning inputs (b, c, d default to zero if not provided)
w, x, y, z: Float variables for expressions
Tensor: Expression for the tensor part (describes image composition)
pooled_output: Expression for the pooled output (condensed representation)
Outputs:
CONDITIONING: Result of applying expressions to input conditionings
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_ConditioningMathNode",
display_name="Conditioning math",
node_id="mrmth_ag_ConditioningMathNode",
category="More math",
display_name="Conditioning math",
inputs=[
io.Conditioning.Input(id="a"),
io.Conditioning.Input(id="b", optional=True, lazy=True),
io.Conditioning.Input(id="c", optional=True, lazy=True),
io.Conditioning.Input(id="d", optional=True, lazy=True),
io.Float.Input(id="w", default=0.0, optional=True, lazy=True, force_input=True),
io.Float.Input(id="x", default=0.0, optional=True, lazy=True, force_input=True),
io.Float.Input(id="y", default=0.0, optional=True, lazy=True, force_input=True),
io.Float.Input(id="z", default=0.0, optional=True, lazy=True, force_input=True),
io.String.Input(id="Tensor", default="a*(1-w)+b*w", tooltip="Expression for tensor part (image composition)"),
io.String.Input(
id="pooled_output", default="a*(1-w)+b*w", tooltip="Expression for pooled output (condensed representation)"
),
io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Conditioning.Input("values"), 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.String.Input(id="Expression", default="I0*(1-F0)+I1*F0", tooltip="Expression to apply on tensor part of conditioning"),
io.String.Input(id="Expression_pi", default="I0*(1-F0)+I1*F0", tooltip="Expression to apply on pooled_input part of conditioning"),
io.Combo.Input(
id="length_mismatch",
options=["tile", "error", "pad"],
default="error",
tooltip="How to handle mismatched conditioning segment counts. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing as zero."
tooltip="How to handle mismatched image batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
)
],
outputs=[
@@ -50,58 +41,114 @@ class ConditioningMathNode(io.ComfyNode):
)
@classmethod
def check_lazy_status(cls, Tensor, pooled_output, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
tensor_needs = set(commonLazy(Tensor, a, b, c, d, w, x, y, z))
pooled_needs = set(commonLazy(pooled_output, a, b, c, d, w, x, y, z))
return list(tensor_needs.union(pooled_needs))
def check_lazy_status(cls, Expression,Expression_pi, V, F, length_mismatch="tile"):
input_stream = InputStream(Expression)
lexer = MathExprLexer(input_stream)
stream = CommonTokenStream(lexer)
stream.fill()
input_stream = InputStream(Expression)
lexer = MathExprLexer(input_stream)
stream1 = CommonTokenStream(lexer)
stream1.fill()
# Support aliases
aliases_img = {"a": "V0", "b": "V1", "c": "V2", "d": "V3"}
aliases_flt = {"w": "F0", "x": "F1", "y": "F2", "z": "F3"}
needed = set()
needed1 = set()
for token in filter(lambda t: t.type == MathExprParser.VARIABLE, stream.tokens + stream1.tokens):
var_name = token.text
if re.match(r"[VF][0-9]+", var_name):
needed.add(var_name)
elif var_name in aliases_img:
needed.add(aliases_img[var_name])
elif var_name in aliases_flt:
needed.add(aliases_flt[var_name])
for v in needed:
if v.startswith("V"):
if v not in V or V[v] is None:
needed1.add(v)
elif v.startswith("F"):
if v not in F or F[v] is None:
needed1.add(v)
return needed1
@classmethod
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"):
# Default missing conditionings to zero
a_c, b_c, c_c, d_c = prepare_inputs(a, b, c, d)
def execute(cls, V, F, Expression, Expression_pi, length_mismatch="tile"):
dir(V["V0"])
print(V["V0"])
if(length_mismatch == "error"):
max_lengths = V.get("V0")[0][0].shape
for name, tensor in V.items():
if tensor[0][0] is not None and max_lengths!=tensor[0][0].shape:
raise ValueError(f"Input '{name}' has shape {tensor[0][0].shape}, expected {max_lengths} to match input.")
max_lengths = V.get("V0")[0][1]["pooled_output"].shape
for name, tensor in V.items():
if tensor[0][1]["pooled_output"] is not None and max_lengths!=tensor[0][1]["pooled_output"].shape:
raise ValueError(f"Input '{name}' has shape {tensor[0][1]["pooled_output"].shape}, expected {max_lengths} to match input.")
# We process the first segment of each conditioning
ta_full, da = a_c[0]
tb_full, db = b_c[0]
tc_full, dc = c_c[0]
td_full, dd = d_c[0]
tensor={}
pooled_output={}
for key, conditioning in V.items():
if conditioning is not None and isinstance(conditioning, dict) and "waveform" in conditioning:
tensor[key] = conditioning[0][0]
pooled_output[key] = conditioning[0][1]["pooled_output"]
else:
tensor[key] = torch.zeros_like(V.get("V0")[0][0])
pooled_output[key] = torch.zeros_like(V.get("V0")[0][1]["pooled_output"])
new_values = normalize_to_common_shape(*tensor.values(), mode=length_mismatch)
tensor.update(zip(tensor.keys(), new_values))
new_values = normalize_to_common_shape(*pooled_output.values(), mode=length_mismatch)
tensor.update(zip(pooled_output.keys(), new_values))
ac,bc,cc,dc = prepare_inputs(V.get("V0"),V.get("V1"),V.get("V2"),V.get("V3"))
ta, tb, tc, td = normalize_to_common_shape(ta_full, tb_full, tc_full, td_full, mode=length_mismatch)
B_val = getIndexTensorAlongDim(ta, 0)
a = ac[0][0]
b = bc[0][0]
c = cc[0][0]
d = dc[0][0]
variables = {
"a": ta, "b": tb, "c": tc, "d": td, "w": w, "x": x, "y": y, "z": z,
"B": B_val, "batch": B_val,
"T": ta.shape[0], "batch_count": ta.shape[0],
"N": ta.shape[1], "channel_count": ta.shape[1],
} | generate_dim_variables(ta)
"a": a, "b": b, "c": c, "d": d,
"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, 0),
"batch": getIndexTensorAlongDim(a, 0),
"T": a.shape[0],
"batch_count": a.shape[0],
} | generate_dim_variables(a) | tensor
tree = parse_expr(Tensor)
visitor = UnifiedMathVisitor(variables, ta.shape)
result_tensor = as_tensor(visitor.visit(tree), ta.shape)
tree = parse_expr(Expression);
visitor = UnifiedMathVisitor(variables, a.shape)
rtensor = visitor.visit(tree)
rtensor = as_tensor(rtensor, a.shape)
new_dict = da.copy()
pa = da.get("pooled_output")
if pa is not None:
pb = db.get("pooled_output")
pc = dc.get("pooled_output")
pd = dd.get("pooled_output")
pb = pb if pb is not None else torch.zeros_like(pa)
pc = pc if pc is not None else torch.zeros_like(pa)
pd = pd if pd is not None else torch.zeros_like(pa)
a = ac[0][1]["pooled_output"]
b = bc[0][1]["pooled_output"]
c = cc[0][1]["pooled_output"]
d = dc[0][1]["pooled_output"]
variables = {
"a": a, "b": b, "c": c, "d": d,
"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, 0),
"batch": getIndexTensorAlongDim(a, 0),
"T": a.shape[0],
"batch_count": a.shape[0],
} | generate_dim_variables(a) | pooled_output
pa, pb, pc, pd = normalize_to_common_shape(pa, pb, pc, pd, mode=length_mismatch)
variables_pooled = {"a": pa, "b": pb, "c": pc, "d": pd, "w": w, "x": x, "y": y, "z": z} | generate_dim_variables(pa)
tree_p = parse_expr(pooled_output)
visitor_p = UnifiedMathVisitor(variables_pooled, pa.shape)
result_pooled = as_tensor(visitor_p.visit(tree_p), pa.shape)
new_dict["pooled_output"] = result_pooled
# Create new conditioning list
output_cond = [(result_tensor, new_dict)]
if len(a) > 1:
output_cond.extend(a[1:])
return (output_cond,)
tree = parse_expr(Expression);
visitor = UnifiedMathVisitor(variables, a.shape)
rpooled = visitor.visit(tree)
rpooled = as_tensor(rpooled, a.shape)
vl = V["V0"]
vl[0][0] = rtensor
vl[0][1]["pooled_output"] = rpooled
return (vl,)
more_math/deprecated/ConditioningMathNode.py
+107
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@@ -0,0 +1,107 @@
from .helper_functions import commonLazy,parse_expr, generate_dim_variables, as_tensor, prepare_inputs, getIndexTensorAlongDim, normalize_to_common_shape
from comfy_api.latest import io
import torch
from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
class ConditioningMathNode(io.ComfyNode):
"""
Enables math operations on conditionings.
Inputs:
a, b, c, d: Conditioning inputs (b, c, d default to zero if not provided)
w, x, y, z: Float variables for expressions
Tensor: Expression for the tensor part (describes image composition)
pooled_output: Expression for the pooled output (condensed representation)
Outputs:
CONDITIONING: Result of applying expressions to input conditionings
"""
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="mrmth_ConditioningMathNode",
display_name="Conditioning math",
category="More math",
inputs=[
io.Conditioning.Input(id="a"),
io.Conditioning.Input(id="b", optional=True, lazy=True),
io.Conditioning.Input(id="c", optional=True, lazy=True),
io.Conditioning.Input(id="d", optional=True, lazy=True),
io.Float.Input(id="w", default=0.0, optional=True, lazy=True, force_input=True),
io.Float.Input(id="x", default=0.0, optional=True, lazy=True, force_input=True),
io.Float.Input(id="y", default=0.0, optional=True, lazy=True, force_input=True),
io.Float.Input(id="z", default=0.0, optional=True, lazy=True, force_input=True),
io.String.Input(id="Tensor", default="a*(1-w)+b*w", tooltip="Expression for tensor part (image composition)"),
io.String.Input(
id="pooled_output", default="a*(1-w)+b*w", tooltip="Expression for pooled output (condensed representation)"
),
io.Combo.Input(
id="length_mismatch",
options=["tile", "error", "pad"],
default="error",
tooltip="How to handle mismatched conditioning segment counts. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing as zero."
)
],
outputs=[
io.Conditioning.Output(),
],
)
@classmethod
def check_lazy_status(cls, Tensor, pooled_output, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
tensor_needs = set(commonLazy(Tensor, a, b, c, d, w, x, y, z))
pooled_needs = set(commonLazy(pooled_output, a, b, c, d, w, x, y, z))
return list(tensor_needs.union(pooled_needs))
@classmethod
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"):
# Default missing conditionings to zero
a_c, b_c, c_c, d_c = prepare_inputs(a, b, c, d)
# We process the first segment of each conditioning
ta_full, da = a_c[0]
tb_full, db = b_c[0]
tc_full, dc = c_c[0]
td_full, dd = d_c[0]
ta, tb, tc, td = normalize_to_common_shape(ta_full, tb_full, tc_full, td_full, mode=length_mismatch)
B_val = getIndexTensorAlongDim(ta, 0)
variables = {
"a": ta, "b": tb, "c": tc, "d": td, "w": w, "x": x, "y": y, "z": z,
"B": B_val, "batch": B_val,
"T": ta.shape[0], "batch_count": ta.shape[0],
"N": ta.shape[1], "channel_count": ta.shape[1],
} | generate_dim_variables(ta)
tree = parse_expr(Tensor)
visitor = UnifiedMathVisitor(variables, ta.shape)
result_tensor = as_tensor(visitor.visit(tree), ta.shape)
new_dict = da.copy()
pa = da.get("pooled_output")
if pa is not None:
pb = db.get("pooled_output")
pc = dc.get("pooled_output")
pd = dd.get("pooled_output")
pb = pb if pb is not None else torch.zeros_like(pa)
pc = pc if pc is not None else torch.zeros_like(pa)
pd = pd if pd is not None else torch.zeros_like(pa)
pa, pb, pc, pd = normalize_to_common_shape(pa, pb, pc, pd, mode=length_mismatch)
variables_pooled = {"a": pa, "b": pb, "c": pc, "d": pd, "w": w, "x": x, "y": y, "z": z} | generate_dim_variables(pa)
tree_p = parse_expr(pooled_output)
visitor_p = UnifiedMathVisitor(variables_pooled, pa.shape)
result_pooled = as_tensor(visitor_p.visit(tree_p), pa.shape)
new_dict["pooled_output"] = result_pooled
# Create new conditioning list
output_cond = [(result_tensor, new_dict)]
if len(a) > 1:
output_cond.extend(a[1:])
return (output_cond,)