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(AI) implement the rest

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mcDandy
2026-01-24 23:15:03 +01:00
parent c61aec2faf
commit 0fcb7bf7bd
33 changed files with 10749 additions and 330 deletions
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more_math/ModelMathNode.py
+80 -17
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@@ -2,34 +2,32 @@ from inspect import cleandoc
from comfy_api.latest import io
from .helper_functions import commonLazy
from .modelLikeCommon import calculate_patches
from antlr4 import InputStream, CommonTokenStream
from .Parser.MathExprLexer import MathExprLexer
from .Parser.MathExprParser import MathExprParser
import re
class ModelMathNode(io.ComfyNode):
"""
This node enables the use of math expressions on Model weights (state_dict).
This node enables the use of math expressions on Model weights (state_dict) using Autogrow inputs.
Functionally acts as a custom model merge.
"""
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="mrmth_ModelMathNode",
node_id="mrmth_ag_ModelMathNode",
display_name="Model Math",
category="More math",
inputs=[
io.Model.Input(id="a", tooltip="Main model (base)"),
io.Model.Input(id="b", optional=True, lazy=True, tooltip="Optional 2nd model"),
io.Model.Input(id="c", optional=True, lazy=True, tooltip="Optional 3rd model"),
io.Model.Input(id="d", optional=True, lazy=True, tooltip="Optional 4th model"),
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="Model", default="a*(1-w)+b*w", tooltip="Expression to apply on weights"),
io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Model.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 weights"),
io.Combo.Input(
id="length_mismatch",
options=["broadcast", "passthrough", "pad"],
default="broadcast",
options=["error", "passthrough", "pad"],
default="error",
tooltip="How to handle mismatched layer counts. For models, this usually defaults to broadcast (zero for missing layers)."
)
],
@@ -41,12 +39,77 @@ class ModelMathNode(io.ComfyNode):
tooltip = cleandoc(__doc__)
@classmethod
def check_lazy_status(cls, Model, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="broadcast"):
return commonLazy(Model, a, b, c, d, w, x, y, z)
def check_lazy_status(cls, Expression, V, F, length_mismatch="tile"):
input_stream = InputStream(Expression)
lexer = MathExprLexer(input_stream)
stream = CommonTokenStream(lexer)
stream.fill()
# Support aliases
aliases_img = {"a": "V0", "b": "V1", "c": "V2", "d": "V3"}
aliases_flt = {"w": "F0", "x": "F1", "y": "F2", "z": "F3"}
needed = []
needed1 = []
for token in filter(lambda t: t.type == MathExprParser.VARIABLE, stream.tokens):
var_name = token.text
if re.match(r"[VF][0-9]+", var_name):
needed.append(var_name)
elif var_name in aliases_img:
needed.append(aliases_img[var_name])
elif var_name in aliases_flt:
needed.append(aliases_flt[var_name])
for v in needed:
if v.startswith("V"):
if v not in V or V[v] is None:
needed1.append(v)
elif v.startswith("F"):
if v not in F or F[v] is None:
needed1.append(v)
return needed1
@classmethod
def execute(cls, Model, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0, length_mismatch="broadcast") -> io.NodeOutput:
patches = calculate_patches(Model, a, b, c, d, w, x, y, z)
def execute(cls, V, F, Expression, length_mismatch="tile") -> io.NodeOutput:
# Determine reference model for cloning
a = V.get("V0")
if a is None:
# Try finding first valid model
for m in V.values():
if m is not None:
a = m
break
if a is None:
raise ValueError("At least one input model is required.")
# Prepare variables
# V0..V3 map to a..d for backward compatibility in calculate_patches
b = V.get("V1")
c = V.get("V2")
d = V.get("V3")
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
# Note: calculate_patches usually takes specific args. We might need to update it to support dynamic V/F or just pass everything.
# Looking at Step 34, calculate_patches signature: (Model, a, b, c, d, w, x, y, z)
# We need to verify if calculate_patches handles V/F. It probably doesn't.
# We should check 'modelLikeCommon.py' to see if update is needed.
# Assume for now we pass a,b,c,d,w,x,y,z as standard.
# But for full autogrow support (more than 4 inputs), calculate_patches needs update.
# The prompt didn't explicitly ask to update modelLikeCommon, but "switch to Autogrow" implies full functionality.
# I'll check modelLikeCommon.py after this block.
# For now, I will pass V and F to calculate_patches if I modify it, or I will stick to legacy args if I don't modify it.
# However, to support V4+, I MUST modify calculate_patches.
# Let's pass the V and F dicts to a modified calculate_patches, or overload it.
# I will update modelLikeCommon.py as part of this task.
patches = calculate_patches(Expression, a, b, c, d, w, x, y, z, V=V, F=F)
out_model = a.clone()
if patches:
out_model.add_patches(patches, 1.0, 1.0)