(AI) implement the rest
This commit is contained in:
mcDandy
@@ -29,7 +29,7 @@ class AudioMathNode(io.ComfyNode):
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io.String.Input(id="Expression", default="I0*(1-F0)+I1*F0", tooltip="Expression to apply on input audio"),
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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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options=["error", "error", "pad"],
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default="error",
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tooltip="How to handle mismatched image batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
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)
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+60
-22
@@ -1,34 +1,31 @@
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from comfy_api.latest import io
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from .modelLikeCommon import calculate_patches
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from inspect import cleandoc
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from .helper_functions import commonLazy
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from antlr4 import InputStream, CommonTokenStream
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from .Parser.MathExprLexer import MathExprLexer
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from .Parser.MathExprParser import MathExprParser
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import re
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class CLIPMathNode(io.ComfyNode):
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"""
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This node enables the use of math expressions on CLIP weights.
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This node enables the use of math expressions on CLIP weights using Autogrow inputs.
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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_CLIPMathNode",
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node_id="mrmth_ag_CLIPMathNode",
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display_name="CLIP Math",
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category="More math",
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inputs=[
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io.Clip.Input(id="a", tooltip="Main CLIP (base)"),
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io.Clip.Input(id="b", optional=True, lazy=True, tooltip="Optional 2nd CLIP"),
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io.Clip.Input(id="c", optional=True, lazy=True, tooltip="Optional 3rd CLIP"),
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io.Clip.Input(id="d", optional=True, lazy=True, tooltip="Optional 4th CLIP"),
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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="Model", default="a*(1-w)+b*w", tooltip="Expression to apply on weights"),
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io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Clip.Input("values"), 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.String.Input(id="Expression", default="I0*(1-F0)+I1*F0", tooltip="Expression to apply on weights"),
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io.Combo.Input(
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id="length_mismatch",
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options=["broadcast", "passthrough", "pad"],
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default="broadcast",
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options=["error", "passthrough", "pad"],
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default="error",
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tooltip="How to handle mismatched layer counts. For models, this usually defaults to broadcast (zero for missing layers)."
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)
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],
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@@ -40,17 +37,58 @@ class CLIPMathNode(io.ComfyNode):
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tooltip = cleandoc(__doc__)
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@classmethod
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def check_lazy_status(cls, Model, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="broadcast"):
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return commonLazy(Model, a, b, c, d, w, x, y, z)
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def check_lazy_status(cls, Expression, V, F, length_mismatch="tile"):
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input_stream = InputStream(Expression)
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lexer = MathExprLexer(input_stream)
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stream = CommonTokenStream(lexer)
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stream.fill()
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# Support aliases
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aliases_img = {"a": "V0", "b": "V1", "c": "V2", "d": "V3"}
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aliases_flt = {"w": "F0", "x": "F1", "y": "F2", "z": "F3"}
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needed = []
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needed1 = []
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for token in filter(lambda t: t.type == MathExprParser.VARIABLE, stream.tokens):
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var_name = token.text
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if re.match(r"[VF][0-9]+", var_name):
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needed.append(var_name)
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elif var_name in aliases_img:
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needed.append(aliases_img[var_name])
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elif var_name in aliases_flt:
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needed.append(aliases_flt[var_name])
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for v in needed:
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if v.startswith("V"):
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if v not in V or V[v] is None:
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needed1.append(v)
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elif v.startswith("F"):
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if v not in F or F[v] is None:
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needed1.append(v)
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return needed1
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@classmethod
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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:
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patcher_a = a.patcher
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patcher_b = b.patcher if b else None
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patcher_c = c.patcher if c else None
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patcher_d = d.patcher if d else None
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def execute(cls, V, F, Expression, length_mismatch="tile") -> io.NodeOutput:
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# Determine reference CLIP
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a = V.get("V0")
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if a is None:
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for m in V.values():
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if m is not None:
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a = m
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break
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if a is None:
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raise ValueError("At least one input CLIP is required.")
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patches = calculate_patches(Model, patcher_a, patcher_b, patcher_c, patcher_d, w, x, y, z)
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patcher_a = a.patcher
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# Prepare CLIP patchers
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patchers_V = {}
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for k, v in V.items():
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if v is not None:
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patchers_V[k] = v.patcher
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patches = calculate_patches(Expression, patcher_a, None, None, None, 0,0,0,0, V=patchers_V, F=F)
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out_clip = a.clone()
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if patches:
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@@ -30,7 +30,7 @@ class ConditioningMathNode(io.ComfyNode):
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io.String.Input(id="Expression_pi", default="I0*(1-F0)+I1*F0", tooltip="Expression to apply on pooled_input part of conditioning"),
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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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options=["error", "error", "pad"],
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default="error",
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tooltip="How to handle mismatched image batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
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)
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+59
-29
@@ -1,10 +1,14 @@
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from inspect import cleandoc
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import torch
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from .LatentMathNode import parse_expr
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from .helper_functions import parse_expr, as_tensor
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from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
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from .helper_functions import commonLazy
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from comfy_api.latest import io
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from antlr4 import InputStream, CommonTokenStream
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from .Parser.MathExprLexer import MathExprLexer
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from .Parser.MathExprParser import MathExprParser
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import re
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class FloatMathNode(io.ComfyNode):
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@@ -12,15 +16,8 @@ class FloatMathNode(io.ComfyNode):
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This node enables the use of math expressions on Floats.
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Inputs:
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a, b, c, d: Floats, bound to variables with the same name.
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w, x, y, z: Floats, bound to variables of the expression.
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V: Autogrow float inputs (V0, V1, ...)
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FloatFunc: String, describing math expression.
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Valid functions: sin, cos, tan, abs, sqrt, min, max, norm, etc.
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Operators: +, -, *, /, ^, %.
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Constants: e, pi.
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Outputs:
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FLOAT: The result of evaluating the math expression.
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"""
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@classmethod
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@@ -30,14 +27,7 @@ class FloatMathNode(io.ComfyNode):
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category="More math",
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display_name="Float math",
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inputs=[
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io.Float.Input(id="a", force_input=True),
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io.Float.Input(id="b", default=0.0, optional=True, lazy=True, force_input=True),
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io.Float.Input(id="c", default=0.0, optional=True, lazy=True, force_input=True),
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io.Float.Input(id="d", default=0.0, optional=True, lazy=True, force_input=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.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Float.Input("values"), prefix="V", min=1, max=50)),
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io.String.Input(id="FloatFunc", default="a*(1-w)+b*w", tooltip="Expression to use on inputs"),
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],
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outputs=[
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@@ -48,20 +38,60 @@ class FloatMathNode(io.ComfyNode):
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tooltip = cleandoc(__doc__)
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@classmethod
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def check_lazy_status(cls, FloatFunc, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0,):
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return commonLazy(FloatFunc, a, b, c, d, w, x, y, z)
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def check_lazy_status(cls, FloatFunc, V):
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input_stream = InputStream(FloatFunc)
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lexer = MathExprLexer(input_stream)
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stream = CommonTokenStream(lexer)
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stream.fill()
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# Support aliases
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# Legacy FloatMathNode mapped a,b,c,d,w,x,y,z to V0-V7 roughly?
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# Actually Step 37 showed explicit mapping:
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# a->V0, b->V1, c->V2, d->V3, w->V4, x->V5, y->V6, z->V7
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aliases = {
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"a": "V0", "b": "V1", "c": "V2", "d": "V3",
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"w": "V4", "x": "V5", "y": "V6", "z": "V7"
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}
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needed = []
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needed1 = []
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for token in filter(lambda t: t.type == MathExprParser.VARIABLE, stream.tokens):
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var_name = token.text
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if re.match(r"V[0-9]+", var_name):
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needed.append(var_name)
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elif var_name in aliases:
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needed.append(aliases[var_name])
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for v in needed:
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if v not in V or V[v] is None:
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needed1.append(v)
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return needed1
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@classmethod
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def execute(cls, FloatFunc, a, b=0.0, c=0.0, d=0.0, w=0.0, x=0.0, y=0.0, z=0.0):
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def execute(cls, FloatFunc, V):
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variables = {}
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# Populate aliases
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variables["a"] = V.get("V0", 0.0)
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variables["b"] = V.get("V1", 0.0)
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variables["c"] = V.get("V2", 0.0)
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variables["d"] = V.get("V3", 0.0)
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variables["w"] = V.get("V4", 0.0)
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variables["x"] = V.get("V5", 0.0)
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variables["y"] = V.get("V6", 0.0)
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variables["z"] = V.get("V7", 0.0)
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# Populate all V inputs
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for k, val in V.items():
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variables[k] = val if val is not None else 0.0
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variables = {
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"a": a,
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"b": b,
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"c": c,
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"d": d,
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"w": w, "x": x, "y": y, "z": z
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}
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tree = parse_expr(FloatFunc);
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# scalar execution
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# UnifiedMathVisitor expects variables and a shape. Shape [1] for scalar?
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visitor = UnifiedMathVisitor(variables, [1])
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result = visitor.visit(tree)
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return (result,)
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# Result might be float or tensor(scalar)
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if torch.is_tensor(result):
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result = result[0].item()
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return (float(result),)
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+92
-29
@@ -1,40 +1,38 @@
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from .helper_functions import generate_dim_variables, parse_expr, getIndexTensorAlongDim, as_tensor, prepare_inputs, commonLazy, normalize_to_common_shape
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from .helper_functions import generate_dim_variables, parse_expr, getIndexTensorAlongDim, as_tensor, prepare_inputs, normalize_to_common_shape, make_zero_like
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from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
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from comfy_api.latest import io
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from antlr4 import InputStream, CommonTokenStream
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from .Parser.MathExprLexer import MathExprLexer
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from .Parser.MathExprParser import MathExprParser
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import re
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class ImageMathNode(io.ComfyNode):
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"""
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Enables math expressions on Images.
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Enables math expressions on Images using Autogrow inputs.
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Inputs:
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a, b, c, d: Image 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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V: Autogrow image inputs (V0, V1, ...)
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F: Autogrow float inputs (F0, F1, ...)
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Image: Expression to apply on input images
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Outputs:
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IMAGE: Result of applying expression to input images
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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_ImageMathNode",
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node_id="mrmth_ag_ImageMathNode", # New ID to avoid collision if necessary, or keep standard and user migrates? User asked to "switch", likely implies replacing functionality but maybe keeping ID? Usually replacing ID breaks workflows.
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# Strategy: Use a NEW ID for the autogrow version if we want to allow side-by-side, but typically "Autogrow switch" implies replacing the main node.
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# However, standard ComfyUI practice for breaking changes is often a new node or careful migration.
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# Looking at AudioMathNode in step 6, it used "mrmth_ag_AudioMathNode".
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# I will follow that pattern: mrmth_ag_ImageMathNode.
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category="More math",
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display_name="Image math",
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inputs=[
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io.Image.Input(id="a"),
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io.Image.Input(id="b", optional=True, lazy=True),
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io.Image.Input(id="c", optional=True, lazy=True),
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io.Image.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="Image", default="a*(1-w)+b*w", tooltip="Expression to apply on input images"),
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io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Image.Input("values"), 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.String.Input(id="Expression", default="I0*(1-F0)+I1*F0", tooltip="Expression to apply on input images"), # Changed ID to Expression to match AudioMathNode pattern, or keep Image? AudioMathNode used "Expression".
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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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options=["error", "error", "pad"],
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default="error",
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tooltip="How to handle mismatched image batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
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)
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@@ -45,24 +43,76 @@ class ImageMathNode(io.ComfyNode):
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)
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@classmethod
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def check_lazy_status(cls, Image, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
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return commonLazy(Image, a, b, c, d, w, x, y, z)
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def check_lazy_status(cls, Expression, V, F, length_mismatch="tile"):
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input_stream = InputStream(Expression)
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lexer = MathExprLexer(input_stream)
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stream = CommonTokenStream(lexer)
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stream.fill()
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# Support aliases
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aliases_img = {"a": "V0", "b": "V1", "c": "V2", "d": "V3"}
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aliases_flt = {"w": "F0", "x": "F1", "y": "F2", "z": "F3"}
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needed = []
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needed1 = []
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for token in filter(lambda t: t.type == MathExprParser.VARIABLE, stream.tokens):
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var_name = token.text
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if re.match(r"[VF][0-9]+", var_name):
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needed.append(var_name)
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elif var_name in aliases_img:
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needed.append(aliases_img[var_name])
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elif var_name in aliases_flt:
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needed.append(aliases_flt[var_name])
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for v in needed:
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if v.startswith("V"):
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if v not in V or V[v] is None:
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needed1.append(v)
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elif v.startswith("F"):
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if v not in F or F[v] is None:
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needed1.append(v)
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return needed1
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@classmethod
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def execute(cls, Image, 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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def execute(cls, V, F, Expression, length_mismatch="tile"):
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# I and F are Autogrow.Type which is dict[str, Any]
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# Determine reference image for zero-initialization (fallback for a,b,c,d)
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ref_image = None
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for img in V.values():
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if img is not None:
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ref_image = img
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break
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if ref_image is None:
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raise ValueError("At least one input is required.")
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a = V.get("V0")
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b = V.get("V1")
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c = V.get("V2")
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d = V.get("V3")
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# Fallback for a if missing (unlikely if V0 is default but possible)
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if a is None:
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a = make_zero_like(ref_image)
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ae, be, ce, de = prepare_inputs(a, b, c, d)
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print(f"DEBUG: shapes {ae.shape[0]}, {be.shape[0]}, {ce.shape[0]}, {de.shape[0]}")
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ae, be, ce, de = normalize_to_common_shape(ae, be, ce, de, mode=length_mismatch)
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if(length_mismatch == "error"):
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max_length = max(ae.shape[0], be.shape[0], ce.shape[0], de.shape[0])
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for tensor, name in zip([ae, be, ce, de], ["a", "b", "c", "d"]):
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if tensor.shape[0] != max_length:
|
||||
max_length = ae.shape[0]
|
||||
for name, tensor in V.items():
|
||||
if tensor is not None and tensor.shape[0] != max_length:
|
||||
raise ValueError(f"Input '{name}' has shape {tensor.shape[0]}, expected {max_length} to match largest input.")
|
||||
ae, be, ce, de = normalize_to_common_shape(ae, be, ce, de, mode=length_mismatch)
|
||||
|
||||
variables = {
|
||||
"a": ae, "b": be, "c": ce, "d": de,
|
||||
"w": w, "x": x, "y": y, "z": z,
|
||||
"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,
|
||||
"X": getIndexTensorAlongDim(ae, 3),
|
||||
"Y": getIndexTensorAlongDim(ae, 2),
|
||||
"B": getIndexTensorAlongDim(ae, 0),
|
||||
@@ -78,7 +128,20 @@ class ImageMathNode(io.ComfyNode):
|
||||
"N": ae.shape[3],
|
||||
"channel_count": ae.shape[3],
|
||||
} | generate_dim_variables(ae)
|
||||
tree = parse_expr(Image);
|
||||
|
||||
# Add all dynamic inputs
|
||||
for k, v in V.items():
|
||||
if v is not None:
|
||||
# Normalize all images in V to match ae.shape
|
||||
# Note: normalize_to_common_shape args are *tensors.
|
||||
# We normalize individual V item against 'ae' (the reference shape)
|
||||
norm_v = normalize_to_common_shape(ae, v, mode=length_mismatch)[1]
|
||||
variables[k] = norm_v
|
||||
|
||||
for k, v in F.items():
|
||||
variables[k] = v if v is not None else 0.0
|
||||
|
||||
tree = parse_expr(Expression);
|
||||
visitor = UnifiedMathVisitor(variables, ae.shape)
|
||||
result = visitor.visit(tree)
|
||||
result = as_tensor(result, ae.shape)
|
||||
|
||||
+100
-53
@@ -6,27 +6,20 @@ from .helper_functions import (
|
||||
parse_expr,
|
||||
as_tensor,
|
||||
normalize_to_common_shape,
|
||||
prepare_inputs
|
||||
prepare_inputs,
|
||||
make_zero_like
|
||||
)
|
||||
from .helper_functions import commonLazy
|
||||
from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
|
||||
import torch
|
||||
from antlr4 import InputStream, CommonTokenStream
|
||||
from .Parser.MathExprLexer import MathExprLexer
|
||||
from .Parser.MathExprParser import MathExprParser
|
||||
import re
|
||||
|
||||
|
||||
class LatentMathNode(io.ComfyNode):
|
||||
"""
|
||||
This node enables the use of math expressions on Latents.
|
||||
inputs:
|
||||
a, b, c, d:
|
||||
Latent, bound to variables with the same name. Defaults to zero latent if not provided.
|
||||
w, x, y, z:
|
||||
Floats, bound to variables of the expression. Defaults to 0.0 if not provided.
|
||||
Latent expression:
|
||||
String, describing expression to aply to latents.
|
||||
|
||||
outputs:
|
||||
LATENT:
|
||||
Returns a LATENT object that contains the result of the math expression applied to the input conditionings.
|
||||
This node enables the use of math expressions on Latents using Autogrow inputs.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
@@ -36,22 +29,16 @@ class LatentMathNode(io.ComfyNode):
|
||||
def define_schema(cls) -> io.Schema:
|
||||
""" """
|
||||
return io.Schema(
|
||||
node_id="mrmth_LatentMathNode",
|
||||
node_id="mrmth_ag_LatentMathNode",
|
||||
display_name="Latent math",
|
||||
category="More math",
|
||||
inputs=[
|
||||
io.Latent.Input(id="a"),
|
||||
io.Latent.Input(id="b", optional=True, lazy=True),
|
||||
io.Latent.Input(id="c", optional=True, lazy=True),
|
||||
io.Latent.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="Latent", default="a*(1-w)+b*w", tooltip="Expression to apply on input latents"),
|
||||
io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Latent.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 input latents"),
|
||||
io.Combo.Input(
|
||||
id="length_mismatch",
|
||||
options=["tile", "error", "pad"],
|
||||
options=["error", "error", "pad"],
|
||||
default="error",
|
||||
tooltip="How to handle mismatched latent batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
|
||||
)
|
||||
@@ -64,52 +51,95 @@ class LatentMathNode(io.ComfyNode):
|
||||
tooltip = cleandoc(__doc__)
|
||||
|
||||
@classmethod
|
||||
def check_lazy_status(cls, Latent, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
|
||||
return commonLazy(Latent, 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, Latent, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0, length_mismatch="tile") -> io.NodeOutput:
|
||||
def execute(cls, V, F, Expression, length_mismatch="tile") -> io.NodeOutput:
|
||||
# Determine reference latent
|
||||
ref_latent = None
|
||||
for lat in V.values():
|
||||
if lat is not None:
|
||||
ref_latent = lat
|
||||
break
|
||||
|
||||
if ref_latent is None:
|
||||
raise ValueError("At least one input is required.")
|
||||
|
||||
# Identify if any input is a NestedTensor and track original sizes for restoration
|
||||
stacked = False
|
||||
orig_split_sizes = None
|
||||
|
||||
for item in [a, b, c, d]:
|
||||
# Check all present inputs for nested tensors
|
||||
for item in V.values():
|
||||
if item is not None:
|
||||
samples = item.get("samples")
|
||||
if getattr(samples, "is_nested", False):
|
||||
stacked = True
|
||||
# Store original split sizes (batch dimension)
|
||||
# Store original split sizes (batch dimension) - assume all nested inputs share structure if mixed?
|
||||
# Or just take from the first one found.
|
||||
orig_split_sizes = [t.shape[0] for t in samples.tensors]
|
||||
break
|
||||
|
||||
# Flatten nested tensors in V
|
||||
if stacked:
|
||||
if a is not None and getattr(a.get("samples"), "is_nested", False):
|
||||
a = a.copy()
|
||||
a["samples"] = torch.cat(a["samples"].tensors, dim=0)
|
||||
if b is not None and getattr(b.get("samples"), "is_nested", False):
|
||||
b = b.copy()
|
||||
b["samples"] = torch.cat(b["samples"].tensors, dim=0)
|
||||
if c is not None and getattr(c.get("samples"), "is_nested", False):
|
||||
c = c.copy()
|
||||
c["samples"] = torch.cat(c["samples"].tensors, dim=0)
|
||||
if d is not None and getattr(d.get("samples"), "is_nested", False):
|
||||
d = d.copy()
|
||||
d["samples"] = torch.cat(d["samples"].tensors, dim=0)
|
||||
for k, val in V.items():
|
||||
if val is not None and getattr(val.get("samples"), "is_nested", False):
|
||||
new_val = val.copy()
|
||||
new_val["samples"] = torch.cat(new_val["samples"].tensors, dim=0)
|
||||
V[k] = new_val
|
||||
|
||||
a = V.get("V0")
|
||||
b = V.get("V1")
|
||||
c = V.get("V2")
|
||||
d = V.get("V3")
|
||||
|
||||
if a is None:
|
||||
a = make_zero_like(ref_latent)
|
||||
|
||||
a_c, b_c, c_c, d_c = prepare_inputs(a, b, c, d)
|
||||
at,bt,ct,dt = a_c["samples"],b_c["samples"],c_c["samples"],d_c["samples"]
|
||||
|
||||
if(length_mismatch == "error"):
|
||||
# Check only available tensors
|
||||
tensors_to_check = [t for t in [at, bt, ct, dt] if t is not None]
|
||||
max_length = max(t.shape[0] for t in tensors_to_check)
|
||||
for tensor, name in zip([at, bt, ct, dt], ["a", "b", "c", "d"]):
|
||||
if tensor is not None:
|
||||
max_length = at.shape[0]
|
||||
for name, val in V.items():
|
||||
if val is not None:
|
||||
tensor = val["samples"]
|
||||
if tensor.shape[0] != max_length:
|
||||
raise ValueError(f"Input '{name}' has shape {tensor.shape[0]}, expected {max_length} to match largest input.")
|
||||
raise ValueError(f"Input '{name}' has shape {tensor.shape[0]}, expected {max_length} to match largest input.")
|
||||
|
||||
ae, be, ce, de = normalize_to_common_shape(at, bt, ct, dt, mode=length_mismatch)
|
||||
|
||||
# parse expression once
|
||||
tree = parse_expr(Latent)
|
||||
tree = parse_expr(Expression)
|
||||
|
||||
ndim = ae.ndim
|
||||
batch_dim = 0
|
||||
@@ -124,7 +154,10 @@ class LatentMathNode(io.ComfyNode):
|
||||
|
||||
variables = {
|
||||
"a": ae, "b": be, "c": ce, "d": de,
|
||||
"w": w, "x": x, "y": y, "z": z,
|
||||
"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,
|
||||
"X": getIndexTensorAlongDim(ae, width_dim),
|
||||
"Y": getIndexTensorAlongDim(ae, height_dim),
|
||||
"B": getIndexTensorAlongDim(ae, batch_dim),
|
||||
@@ -142,8 +175,18 @@ class LatentMathNode(io.ComfyNode):
|
||||
} | generate_dim_variables(ae)
|
||||
|
||||
if time_dim is not None:
|
||||
F = getIndexTensorAlongDim(ae, time_dim)
|
||||
variables.update({"frame_idx": F, "frame": F, "frame_count": frame_count})
|
||||
F_idx = getIndexTensorAlongDim(ae, time_dim)
|
||||
variables.update({"frame_idx": F_idx, "frame": F_idx, "frame_count": frame_count})
|
||||
|
||||
# Add all dynamic inputs
|
||||
for k, v in V.items():
|
||||
if v is not None:
|
||||
v_tensor = v["samples"]
|
||||
norm_v = normalize_to_common_shape(ae, v_tensor, mode=length_mismatch)[1]
|
||||
variables[k] = norm_v
|
||||
|
||||
for k, v in F.items():
|
||||
variables[k] = v if v is not None else 0.0
|
||||
|
||||
visitor = UnifiedMathVisitor(variables, ae.shape)
|
||||
result_t = as_tensor(visitor.visit(tree), ae.shape)
|
||||
@@ -152,7 +195,11 @@ class LatentMathNode(io.ComfyNode):
|
||||
if stacked and orig_split_sizes is not None:
|
||||
from comfy.nested_tensor import NestedTensor
|
||||
# Restore original split sizes
|
||||
result_latent["samples"] = NestedTensor(torch.split(result_t, orig_split_sizes, dim=0))
|
||||
try:
|
||||
result_latent["samples"] = NestedTensor(torch.split(result_t, orig_split_sizes, dim=0))
|
||||
except Exception:
|
||||
# Fallback if split fails (e.g. result shape changed)
|
||||
result_latent["samples"] = result_t
|
||||
else:
|
||||
result_latent["samples"] = result_t
|
||||
|
||||
|
||||
+84
-31
@@ -1,43 +1,36 @@
|
||||
from .helper_functions import generate_dim_variables,parse_expr, getIndexTensorAlongDim, as_tensor, commonLazy, normalize_to_common_shape,prepare_inputs
|
||||
from .helper_functions import generate_dim_variables,parse_expr, getIndexTensorAlongDim, as_tensor, commonLazy, normalize_to_common_shape,prepare_inputs, make_zero_like
|
||||
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 MaskMathNode(io.ComfyNode):
|
||||
"""
|
||||
Enables math expressions on Masks.
|
||||
Enables math expressions on Masks using Autogrow inputs.
|
||||
|
||||
Inputs:
|
||||
a, b, c, d: Mask inputs (b, c, d default to zero if not provided)
|
||||
w, x, y, z: Float variables for expressions
|
||||
V: Autogrow mask inputs (V0, V1, ...)
|
||||
F: Autogrow float inputs (F0, F1, ...)
|
||||
Mask: Expression to apply on input masks
|
||||
|
||||
Outputs:
|
||||
MASK: Result of applying expression to input masks
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="mrmth_MaskMathNode",
|
||||
node_id="mrmth_ag_MaskMathNode",
|
||||
category="More math",
|
||||
display_name="Mask math",
|
||||
inputs=[
|
||||
io.Mask.Input(id="a"),
|
||||
io.Mask.Input(id="b", optional=True, lazy=True),
|
||||
io.Mask.Input(id="c", optional=True, lazy=True),
|
||||
io.Mask.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="Mask", default="a*(1-w)+b*w", tooltip="Expression to apply on input masks"),
|
||||
io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Mask.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 input masks"),
|
||||
io.Combo.Input(
|
||||
id="length_mismatch",
|
||||
options=["broadcast", "error", "pad"],
|
||||
default="broadcast",
|
||||
options=["error", "error", "pad"],
|
||||
default="error",
|
||||
tooltip="How to handle mismatched mask batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
|
||||
)
|
||||
],
|
||||
@@ -47,23 +40,73 @@ class MaskMathNode(io.ComfyNode):
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def check_lazy_status(cls, Mask, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="broadcast"):
|
||||
return commonLazy(Mask, 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, Mask, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0, length_mismatch="broadcast"):
|
||||
a, b, c, d = prepare_inputs(a, b, c, d)
|
||||
def execute(cls, V, F, Expression, length_mismatch="tile"):
|
||||
# Determine reference mask
|
||||
ref_mask = None
|
||||
for mask in V.values():
|
||||
if mask is not None:
|
||||
ref_mask = mask
|
||||
break
|
||||
|
||||
if ref_mask is None:
|
||||
raise ValueError("At least one input is required.")
|
||||
|
||||
a = V.get("V0")
|
||||
b = V.get("V1")
|
||||
c = V.get("V2")
|
||||
d = V.get("V3")
|
||||
|
||||
if a is None:
|
||||
a = make_zero_like(ref_mask)
|
||||
|
||||
ae, be, ce, de = prepare_inputs(a, b, c, d)
|
||||
|
||||
if(length_mismatch == "error"):
|
||||
max_length = max(a.shape[0], b.shape[0], c.shape[0], d.shape[0])
|
||||
for tensor, name in zip([a, b, c, d], ["a", "b", "c", "d"]):
|
||||
if tensor.shape[0] != max_length:
|
||||
max_length = ae.shape[0]
|
||||
for name, tensor in V.items():
|
||||
if tensor is not None and tensor.shape[0] != max_length:
|
||||
raise ValueError(f"Input '{name}' has shape {tensor.shape[0]}, expected {max_length} to match largest input.")
|
||||
ae, be, ce, de = normalize_to_common_shape(a, b, c, d, mode=length_mismatch)
|
||||
|
||||
ae, be, ce, de = normalize_to_common_shape(ae, be, ce, de, mode=length_mismatch)
|
||||
|
||||
variables = {
|
||||
"a": ae, "b": be, "c": ce, "d": de,
|
||||
"w": w, "x": x, "y": y, "z": z,
|
||||
"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,
|
||||
"X": getIndexTensorAlongDim(ae, 2),
|
||||
"Y": getIndexTensorAlongDim(ae, 1),
|
||||
"B": getIndexTensorAlongDim(ae, 0),
|
||||
@@ -75,7 +118,17 @@ class MaskMathNode(io.ComfyNode):
|
||||
"T": ae.shape[0],
|
||||
"batch_count": ae.shape[0],
|
||||
} | generate_dim_variables(ae)
|
||||
tree = parse_expr(Mask);
|
||||
|
||||
# Add all dynamic inputs
|
||||
for k, v in V.items():
|
||||
if v is not None:
|
||||
norm_v = normalize_to_common_shape(ae, v, mode=length_mismatch)[1]
|
||||
variables[k] = norm_v
|
||||
|
||||
for k, v in F.items():
|
||||
variables[k] = v if v is not None else 0.0
|
||||
|
||||
tree = parse_expr(Expression);
|
||||
visitor = UnifiedMathVisitor(variables, ae.shape)
|
||||
result = visitor.visit(tree)
|
||||
result = as_tensor(result, ae.shape)
|
||||
|
||||
+80
-17
@@ -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)
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,183 @@
|
||||
SIN=1
|
||||
COS=2
|
||||
TAN=3
|
||||
ASIN=4
|
||||
ACOS=5
|
||||
ATAN=6
|
||||
ATAN2=7
|
||||
SINH=8
|
||||
COSH=9
|
||||
TANH=10
|
||||
ASINH=11
|
||||
ACOSH=12
|
||||
ATANH=13
|
||||
ABS=14
|
||||
SQRT=15
|
||||
LN=16
|
||||
LOG=17
|
||||
EXP=18
|
||||
SMIN=19
|
||||
SMAX=20
|
||||
TMIN=21
|
||||
TMAX=22
|
||||
TNORM=23
|
||||
SNORM=24
|
||||
FLOOR=25
|
||||
CEIL=26
|
||||
ROUND=27
|
||||
GAMMA=28
|
||||
POWE=29
|
||||
SIGM=30
|
||||
CLAMP=31
|
||||
SFFT=32
|
||||
SIFFT=33
|
||||
ANGL=34
|
||||
PRNT=35
|
||||
PRINT_SHAPE=36
|
||||
NVL=37
|
||||
LERP=38
|
||||
STEP=39
|
||||
SMOOTHSTEP=40
|
||||
FRACT=41
|
||||
RELU=42
|
||||
SOFTPLUS=43
|
||||
GELU=44
|
||||
SIGN=45
|
||||
MAP=46
|
||||
EZCONV=47
|
||||
CONV=48
|
||||
SWAP=49
|
||||
PERM=50
|
||||
RESHAPE=51
|
||||
RANGE=52
|
||||
TOPK=53
|
||||
BOTK=54
|
||||
PINV=55
|
||||
SUM=56
|
||||
MEAN=57
|
||||
STD=58
|
||||
VAR=59
|
||||
QUARTILE=60
|
||||
PERCENTILE=61
|
||||
QUANTILE=62
|
||||
DOT=63
|
||||
MOMENT=64
|
||||
NOISE=65
|
||||
RAND=66
|
||||
CAUCHY=67
|
||||
EXPONENTIAL=68
|
||||
LOGNORMAL=69
|
||||
BERNOULLI=70
|
||||
POISSON=71
|
||||
COSSIM=72
|
||||
FLIP=73
|
||||
COV=74
|
||||
SORT=75
|
||||
APPEND=76
|
||||
PLUS=77
|
||||
MINUS=78
|
||||
MULT=79
|
||||
DIV=80
|
||||
MOD=81
|
||||
POW=82
|
||||
GE=83
|
||||
GT=84
|
||||
LE=85
|
||||
LT=86
|
||||
EQ=87
|
||||
EQUEALS=88
|
||||
NE=89
|
||||
PIPE=90
|
||||
LPAREN=91
|
||||
RPAREN=92
|
||||
COMMA=93
|
||||
SEMICOLON=94
|
||||
ARROW=95
|
||||
LBRACKET=96
|
||||
RBRACKET=97
|
||||
CONSTANT=98
|
||||
NUMBER=99
|
||||
VARIABLE=100
|
||||
WS=101
|
||||
'sin'=1
|
||||
'cos'=2
|
||||
'tan'=3
|
||||
'asin'=4
|
||||
'acos'=5
|
||||
'atan'=6
|
||||
'atan2'=7
|
||||
'sinh'=8
|
||||
'cosh'=9
|
||||
'tanh'=10
|
||||
'asinh'=11
|
||||
'acosh'=12
|
||||
'atanh'=13
|
||||
'abs'=14
|
||||
'sqrt'=15
|
||||
'ln'=16
|
||||
'log'=17
|
||||
'exp'=18
|
||||
'smin'=19
|
||||
'smax'=20
|
||||
'tmin'=21
|
||||
'tmax'=22
|
||||
'tnorm'=23
|
||||
'snorm'=24
|
||||
'floor'=25
|
||||
'ceil'=26
|
||||
'round'=27
|
||||
'gamma'=28
|
||||
'pow'=29
|
||||
'sigm'=30
|
||||
'clamp'=31
|
||||
'fft'=32
|
||||
'ifft'=33
|
||||
'angle'=34
|
||||
'print'=35
|
||||
'lerp'=38
|
||||
'step'=39
|
||||
'smoothstep'=40
|
||||
'fract'=41
|
||||
'relu'=42
|
||||
'softplus'=43
|
||||
'gelu'=44
|
||||
'sign'=45
|
||||
'map'=46
|
||||
'swap'=49
|
||||
'range'=52
|
||||
'topk'=53
|
||||
'botk'=54
|
||||
'pinv'=55
|
||||
'sum'=56
|
||||
'mean'=57
|
||||
'std'=58
|
||||
'var'=59
|
||||
'quantile'=62
|
||||
'dot'=63
|
||||
'moment'=64
|
||||
'cossim'=72
|
||||
'flip'=73
|
||||
'cov'=74
|
||||
'sort'=75
|
||||
'append'=76
|
||||
'+'=77
|
||||
'-'=78
|
||||
'*'=79
|
||||
'/'=80
|
||||
'%'=81
|
||||
'^'=82
|
||||
'>='=83
|
||||
'>'=84
|
||||
'<='=85
|
||||
'<'=86
|
||||
'=='=87
|
||||
'='=88
|
||||
'!='=89
|
||||
'|'=90
|
||||
'('=91
|
||||
')'=92
|
||||
','=93
|
||||
';'=94
|
||||
'->'=95
|
||||
'['=96
|
||||
']'=97
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
@@ -0,0 +1,456 @@
|
||||
// Generated from d:\stability\Data\Packages\ComfyUI\custom_nodes\more_math\more_math\Parser\MathExpr.g4 by ANTLR 4.9.2
|
||||
import org.antlr.v4.runtime.Lexer;
|
||||
import org.antlr.v4.runtime.CharStream;
|
||||
import org.antlr.v4.runtime.Token;
|
||||
import org.antlr.v4.runtime.TokenStream;
|
||||
import org.antlr.v4.runtime.*;
|
||||
import org.antlr.v4.runtime.atn.*;
|
||||
import org.antlr.v4.runtime.dfa.DFA;
|
||||
import org.antlr.v4.runtime.misc.*;
|
||||
|
||||
@SuppressWarnings({"all", "warnings", "unchecked", "unused", "cast"})
|
||||
public class MathExprLexer extends Lexer {
|
||||
static { RuntimeMetaData.checkVersion("4.9.2", RuntimeMetaData.VERSION); }
|
||||
|
||||
protected static final DFA[] _decisionToDFA;
|
||||
protected static final PredictionContextCache _sharedContextCache =
|
||||
new PredictionContextCache();
|
||||
public static final int
|
||||
SIN=1, COS=2, TAN=3, ASIN=4, ACOS=5, ATAN=6, ATAN2=7, SINH=8, COSH=9,
|
||||
TANH=10, ASINH=11, ACOSH=12, ATANH=13, ABS=14, SQRT=15, LN=16, LOG=17,
|
||||
EXP=18, SMIN=19, SMAX=20, TMIN=21, TMAX=22, TNORM=23, SNORM=24, FLOOR=25,
|
||||
CEIL=26, ROUND=27, GAMMA=28, POWE=29, SIGM=30, CLAMP=31, SFFT=32, SIFFT=33,
|
||||
ANGL=34, PRNT=35, PRINT_SHAPE=36, NVL=37, LERP=38, STEP=39, SMOOTHSTEP=40,
|
||||
FRACT=41, RELU=42, SOFTPLUS=43, GELU=44, SIGN=45, MAP=46, EZCONV=47, CONV=48,
|
||||
SWAP=49, PERM=50, RESHAPE=51, RANGE=52, TOPK=53, BOTK=54, PINV=55, SUM=56,
|
||||
MEAN=57, STD=58, VAR=59, QUARTILE=60, PERCENTILE=61, QUANTILE=62, DOT=63,
|
||||
MOMENT=64, NOISE=65, RAND=66, CAUCHY=67, EXPONENTIAL=68, LOGNORMAL=69,
|
||||
BERNOULLI=70, POISSON=71, COSSIM=72, FLIP=73, COV=74, SORT=75, APPEND=76,
|
||||
PLUS=77, MINUS=78, MULT=79, DIV=80, MOD=81, POW=82, GE=83, GT=84, LE=85,
|
||||
LT=86, EQ=87, EQUEALS=88, NE=89, PIPE=90, LPAREN=91, RPAREN=92, COMMA=93,
|
||||
SEMICOLON=94, ARROW=95, LBRACKET=96, RBRACKET=97, CONSTANT=98, NUMBER=99,
|
||||
VARIABLE=100, WS=101;
|
||||
public static String[] channelNames = {
|
||||
"DEFAULT_TOKEN_CHANNEL", "HIDDEN"
|
||||
};
|
||||
|
||||
public static String[] modeNames = {
|
||||
"DEFAULT_MODE"
|
||||
};
|
||||
|
||||
private static String[] makeRuleNames() {
|
||||
return new String[] {
|
||||
"SIN", "COS", "TAN", "ASIN", "ACOS", "ATAN", "ATAN2", "SINH", "COSH",
|
||||
"TANH", "ASINH", "ACOSH", "ATANH", "ABS", "SQRT", "LN", "LOG", "EXP",
|
||||
"SMIN", "SMAX", "TMIN", "TMAX", "TNORM", "SNORM", "FLOOR", "CEIL", "ROUND",
|
||||
"GAMMA", "POWE", "SIGM", "CLAMP", "SFFT", "SIFFT", "ANGL", "PRNT", "PRINT_SHAPE",
|
||||
"NVL", "LERP", "STEP", "SMOOTHSTEP", "FRACT", "RELU", "SOFTPLUS", "GELU",
|
||||
"SIGN", "MAP", "EZCONV", "CONV", "SWAP", "PERM", "RESHAPE", "RANGE",
|
||||
"TOPK", "BOTK", "PINV", "SUM", "MEAN", "STD", "VAR", "QUARTILE", "PERCENTILE",
|
||||
"QUANTILE", "DOT", "MOMENT", "NOISE", "RAND", "CAUCHY", "EXPONENTIAL",
|
||||
"LOGNORMAL", "BERNOULLI", "POISSON", "COSSIM", "FLIP", "COV", "SORT",
|
||||
"APPEND", "PLUS", "MINUS", "MULT", "DIV", "MOD", "POW", "GE", "GT", "LE",
|
||||
"LT", "EQ", "EQUEALS", "NE", "PIPE", "LPAREN", "RPAREN", "COMMA", "SEMICOLON",
|
||||
"ARROW", "LBRACKET", "RBRACKET", "CONSTANT", "NUMBER", "VARIABLE", "WS"
|
||||
};
|
||||
}
|
||||
public static final String[] ruleNames = makeRuleNames();
|
||||
|
||||
private static String[] makeLiteralNames() {
|
||||
return new String[] {
|
||||
null, "'sin'", "'cos'", "'tan'", "'asin'", "'acos'", "'atan'", "'atan2'",
|
||||
"'sinh'", "'cosh'", "'tanh'", "'asinh'", "'acosh'", "'atanh'", "'abs'",
|
||||
"'sqrt'", "'ln'", "'log'", "'exp'", "'smin'", "'smax'", "'tmin'", "'tmax'",
|
||||
"'tnorm'", "'snorm'", "'floor'", "'ceil'", "'round'", "'gamma'", "'pow'",
|
||||
"'sigm'", "'clamp'", "'fft'", "'ifft'", "'angle'", "'print'", null, null,
|
||||
"'lerp'", "'step'", "'smoothstep'", "'fract'", "'relu'", "'softplus'",
|
||||
"'gelu'", "'sign'", "'map'", null, null, "'swap'", null, null, "'range'",
|
||||
"'topk'", "'botk'", "'pinv'", "'sum'", "'mean'", "'std'", "'var'", null,
|
||||
null, "'quantile'", "'dot'", "'moment'", null, null, null, null, null,
|
||||
null, null, "'cossim'", "'flip'", "'cov'", "'sort'", "'append'", "'+'",
|
||||
"'-'", "'*'", "'/'", "'%'", "'^'", "'>='", "'>'", "'<='", "'<'", "'=='",
|
||||
"'='", "'!='", "'|'", "'('", "')'", "','", "';'", "'->'", "'['", "']'"
|
||||
};
|
||||
}
|
||||
private static final String[] _LITERAL_NAMES = makeLiteralNames();
|
||||
private static String[] makeSymbolicNames() {
|
||||
return new String[] {
|
||||
null, "SIN", "COS", "TAN", "ASIN", "ACOS", "ATAN", "ATAN2", "SINH", "COSH",
|
||||
"TANH", "ASINH", "ACOSH", "ATANH", "ABS", "SQRT", "LN", "LOG", "EXP",
|
||||
"SMIN", "SMAX", "TMIN", "TMAX", "TNORM", "SNORM", "FLOOR", "CEIL", "ROUND",
|
||||
"GAMMA", "POWE", "SIGM", "CLAMP", "SFFT", "SIFFT", "ANGL", "PRNT", "PRINT_SHAPE",
|
||||
"NVL", "LERP", "STEP", "SMOOTHSTEP", "FRACT", "RELU", "SOFTPLUS", "GELU",
|
||||
"SIGN", "MAP", "EZCONV", "CONV", "SWAP", "PERM", "RESHAPE", "RANGE",
|
||||
"TOPK", "BOTK", "PINV", "SUM", "MEAN", "STD", "VAR", "QUARTILE", "PERCENTILE",
|
||||
"QUANTILE", "DOT", "MOMENT", "NOISE", "RAND", "CAUCHY", "EXPONENTIAL",
|
||||
"LOGNORMAL", "BERNOULLI", "POISSON", "COSSIM", "FLIP", "COV", "SORT",
|
||||
"APPEND", "PLUS", "MINUS", "MULT", "DIV", "MOD", "POW", "GE", "GT", "LE",
|
||||
"LT", "EQ", "EQUEALS", "NE", "PIPE", "LPAREN", "RPAREN", "COMMA", "SEMICOLON",
|
||||
"ARROW", "LBRACKET", "RBRACKET", "CONSTANT", "NUMBER", "VARIABLE", "WS"
|
||||
};
|
||||
}
|
||||
private static final String[] _SYMBOLIC_NAMES = makeSymbolicNames();
|
||||
public static final Vocabulary VOCABULARY = new VocabularyImpl(_LITERAL_NAMES, _SYMBOLIC_NAMES);
|
||||
|
||||
/**
|
||||
* @deprecated Use {@link #VOCABULARY} instead.
|
||||
*/
|
||||
@Deprecated
|
||||
public static final String[] tokenNames;
|
||||
static {
|
||||
tokenNames = new String[_SYMBOLIC_NAMES.length];
|
||||
for (int i = 0; i < tokenNames.length; i++) {
|
||||
tokenNames[i] = VOCABULARY.getLiteralName(i);
|
||||
if (tokenNames[i] == null) {
|
||||
tokenNames[i] = VOCABULARY.getSymbolicName(i);
|
||||
}
|
||||
|
||||
if (tokenNames[i] == null) {
|
||||
tokenNames[i] = "<INVALID>";
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
@Deprecated
|
||||
public String[] getTokenNames() {
|
||||
return tokenNames;
|
||||
}
|
||||
|
||||
@Override
|
||||
|
||||
public Vocabulary getVocabulary() {
|
||||
return VOCABULARY;
|
||||
}
|
||||
|
||||
|
||||
public MathExprLexer(CharStream input) {
|
||||
super(input);
|
||||
_interp = new LexerATNSimulator(this,_ATN,_decisionToDFA,_sharedContextCache);
|
||||
}
|
||||
|
||||
@Override
|
||||
public String getGrammarFileName() { return "MathExpr.g4"; }
|
||||
|
||||
@Override
|
||||
public String[] getRuleNames() { return ruleNames; }
|
||||
|
||||
@Override
|
||||
public String getSerializedATN() { return _serializedATN; }
|
||||
|
||||
@Override
|
||||
public String[] getChannelNames() { return channelNames; }
|
||||
|
||||
@Override
|
||||
public String[] getModeNames() { return modeNames; }
|
||||
|
||||
@Override
|
||||
public ATN getATN() { return _ATN; }
|
||||
|
||||
public static final String _serializedATN =
|
||||
"\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2g\u0387\b\1\4\2\t"+
|
||||
"\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4\7\t\7\4\b\t\b\4\t\t\t\4\n\t\n\4\13"+
|
||||
"\t\13\4\f\t\f\4\r\t\r\4\16\t\16\4\17\t\17\4\20\t\20\4\21\t\21\4\22\t\22"+
|
||||
"\4\23\t\23\4\24\t\24\4\25\t\25\4\26\t\26\4\27\t\27\4\30\t\30\4\31\t\31"+
|
||||
"\4\32\t\32\4\33\t\33\4\34\t\34\4\35\t\35\4\36\t\36\4\37\t\37\4 \t \4!"+
|
||||
"\t!\4\"\t\"\4#\t#\4$\t$\4%\t%\4&\t&\4\'\t\'\4(\t(\4)\t)\4*\t*\4+\t+\4"+
|
||||
",\t,\4-\t-\4.\t.\4/\t/\4\60\t\60\4\61\t\61\4\62\t\62\4\63\t\63\4\64\t"+
|
||||
"\64\4\65\t\65\4\66\t\66\4\67\t\67\48\t8\49\t9\4:\t:\4;\t;\4<\t<\4=\t="+
|
||||
"\4>\t>\4?\t?\4@\t@\4A\tA\4B\tB\4C\tC\4D\tD\4E\tE\4F\tF\4G\tG\4H\tH\4I"+
|
||||
"\tI\4J\tJ\4K\tK\4L\tL\4M\tM\4N\tN\4O\tO\4P\tP\4Q\tQ\4R\tR\4S\tS\4T\tT"+
|
||||
"\4U\tU\4V\tV\4W\tW\4X\tX\4Y\tY\4Z\tZ\4[\t[\4\\\t\\\4]\t]\4^\t^\4_\t_\4"+
|
||||
"`\t`\4a\ta\4b\tb\4c\tc\4d\td\4e\te\4f\tf\3\2\3\2\3\2\3\2\3\3\3\3\3\3\3"+
|
||||
"\3\3\4\3\4\3\4\3\4\3\5\3\5\3\5\3\5\3\5\3\6\3\6\3\6\3\6\3\6\3\7\3\7\3\7"+
|
||||
"\3\7\3\7\3\b\3\b\3\b\3\b\3\b\3\b\3\t\3\t\3\t\3\t\3\t\3\n\3\n\3\n\3\n\3"+
|
||||
"\n\3\13\3\13\3\13\3\13\3\13\3\f\3\f\3\f\3\f\3\f\3\f\3\r\3\r\3\r\3\r\3"+
|
||||
"\r\3\r\3\16\3\16\3\16\3\16\3\16\3\16\3\17\3\17\3\17\3\17\3\20\3\20\3\20"+
|
||||
"\3\20\3\20\3\21\3\21\3\21\3\22\3\22\3\22\3\22\3\23\3\23\3\23\3\23\3\24"+
|
||||
"\3\24\3\24\3\24\3\24\3\25\3\25\3\25\3\25\3\25\3\26\3\26\3\26\3\26\3\26"+
|
||||
"\3\27\3\27\3\27\3\27\3\27\3\30\3\30\3\30\3\30\3\30\3\30\3\31\3\31\3\31"+
|
||||
"\3\31\3\31\3\31\3\32\3\32\3\32\3\32\3\32\3\32\3\33\3\33\3\33\3\33\3\33"+
|
||||
"\3\34\3\34\3\34\3\34\3\34\3\34\3\35\3\35\3\35\3\35\3\35\3\35\3\36\3\36"+
|
||||
"\3\36\3\36\3\37\3\37\3\37\3\37\3\37\3 \3 \3 \3 \3 \3 \3!\3!\3!\3!\3\""+
|
||||
"\3\"\3\"\3\"\3\"\3#\3#\3#\3#\3#\3#\3$\3$\3$\3$\3$\3$\3%\3%\3%\3%\3%\3"+
|
||||
"%\3%\3%\3%\3%\3%\3%\3%\3%\3%\5%\u018e\n%\3&\3&\3&\3&\3&\3&\3&\3&\3&\3"+
|
||||
"&\3&\3&\3&\5&\u019d\n&\3\'\3\'\3\'\3\'\3\'\3(\3(\3(\3(\3(\3)\3)\3)\3)"+
|
||||
"\3)\3)\3)\3)\3)\3)\3)\3*\3*\3*\3*\3*\3*\3+\3+\3+\3+\3+\3,\3,\3,\3,\3,"+
|
||||
"\3,\3,\3,\3,\3-\3-\3-\3-\3-\3.\3.\3.\3.\3.\3/\3/\3/\3/\3\60\3\60\3\60"+
|
||||
"\3\60\3\60\3\60\3\60\3\60\3\60\3\60\3\60\3\60\3\60\3\60\3\60\3\60\3\60"+
|
||||
"\3\60\3\60\5\60\u01e9\n\60\3\61\3\61\3\61\3\61\3\61\3\61\3\61\3\61\3\61"+
|
||||
"\3\61\3\61\3\61\3\61\3\61\3\61\5\61\u01fa\n\61\3\62\3\62\3\62\3\62\3\62"+
|
||||
"\3\63\3\63\3\63\3\63\3\63\3\63\3\63\3\63\3\63\3\63\3\63\5\63\u020c\n\63"+
|
||||
"\3\64\3\64\3\64\3\64\3\64\3\64\3\64\3\64\3\64\3\64\3\64\5\64\u0219\n\64"+
|
||||
"\3\65\3\65\3\65\3\65\3\65\3\65\3\66\3\66\3\66\3\66\3\66\3\67\3\67\3\67"+
|
||||
"\3\67\3\67\38\38\38\38\38\39\39\39\39\3:\3:\3:\3:\3:\3;\3;\3;\3;\3<\3"+
|
||||
"<\3<\3<\3=\3=\3=\3=\3=\3=\3=\3=\3=\3=\3=\3=\3=\3=\3=\5=\u0250\n=\3>\3"+
|
||||
">\3>\3>\3>\3>\3>\3>\3>\3>\3>\3>\3>\3>\3>\5>\u0261\n>\3?\3?\3?\3?\3?\3"+
|
||||
"?\3?\3?\3?\3@\3@\3@\3@\3A\3A\3A\3A\3A\3A\3A\3B\3B\3B\3B\3B\3B\3B\3B\3"+
|
||||
"B\3B\3B\3B\3B\3B\3B\3B\3B\3B\3B\3B\3B\3B\3B\5B\u028e\nB\3C\3C\3C\3C\3"+
|
||||
"C\3C\3C\3C\3C\3C\3C\3C\3C\3C\3C\3C\3C\3C\3C\3C\3C\3C\3C\5C\u02a7\nC\3"+
|
||||
"D\3D\3D\3D\3D\3D\3D\3D\3D\3D\3D\3D\3D\3D\3D\3D\3D\3D\5D\u02bb\nD\3E\3"+
|
||||
"E\3E\3E\3E\3E\3E\3E\3E\3E\3E\3E\3E\3E\3E\3E\3E\3E\3E\3E\3E\3E\3E\5E\u02d4"+
|
||||
"\nE\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F\3F"+
|
||||
"\3F\5F\u02ed\nF\3G\3G\3G\3G\3G\3G\3G\3G\3G\3G\3G\3G\3G\3G\3G\3G\3G\3G"+
|
||||
"\3G\3G\3G\5G\u0304\nG\3H\3H\3H\3H\3H\3H\3H\3H\3H\3H\3H\3H\3H\3H\3H\3H"+
|
||||
"\3H\3H\3H\5H\u0319\nH\3I\3I\3I\3I\3I\3I\3I\3J\3J\3J\3J\3J\3K\3K\3K\3K"+
|
||||
"\3L\3L\3L\3L\3L\3M\3M\3M\3M\3M\3M\3M\3N\3N\3O\3O\3P\3P\3Q\3Q\3R\3R\3S"+
|
||||
"\3S\3T\3T\3T\3U\3U\3V\3V\3V\3W\3W\3X\3X\3X\3Y\3Y\3Z\3Z\3Z\3[\3[\3\\\3"+
|
||||
"\\\3]\3]\3^\3^\3_\3_\3`\3`\3`\3a\3a\3b\3b\3c\3c\3c\3c\3c\5c\u036b\nc\3"+
|
||||
"d\6d\u036e\nd\rd\16d\u036f\3d\3d\6d\u0374\nd\rd\16d\u0375\5d\u0378\nd"+
|
||||
"\3e\3e\7e\u037c\ne\fe\16e\u037f\13e\3f\6f\u0382\nf\rf\16f\u0383\3f\3f"+
|
||||
"\2\2g\3\3\5\4\7\5\t\6\13\7\r\b\17\t\21\n\23\13\25\f\27\r\31\16\33\17\35"+
|
||||
"\20\37\21!\22#\23%\24\'\25)\26+\27-\30/\31\61\32\63\33\65\34\67\359\36"+
|
||||
";\37= ?!A\"C#E$G%I&K\'M(O)Q*S+U,W-Y.[/]\60_\61a\62c\63e\64g\65i\66k\67"+
|
||||
"m8o9q:s;u<w=y>{?}@\177A\u0081B\u0083C\u0085D\u0087E\u0089F\u008bG\u008d"+
|
||||
"H\u008fI\u0091J\u0093K\u0095L\u0097M\u0099N\u009bO\u009dP\u009fQ\u00a1"+
|
||||
"R\u00a3S\u00a5T\u00a7U\u00a9V\u00abW\u00adX\u00afY\u00b1Z\u00b3[\u00b5"+
|
||||
"\\\u00b7]\u00b9^\u00bb_\u00bd`\u00bfa\u00c1b\u00c3c\u00c5d\u00c7e\u00c9"+
|
||||
"f\u00cbg\3\2\7\4\2GGgg\3\2\62;\5\2C\\aac|\6\2\62;C\\aac|\5\2\13\f\17\17"+
|
||||
"\"\"\2\u039e\2\3\3\2\2\2\2\5\3\2\2\2\2\7\3\2\2\2\2\t\3\2\2\2\2\13\3\2"+
|
||||
"\2\2\2\r\3\2\2\2\2\17\3\2\2\2\2\21\3\2\2\2\2\23\3\2\2\2\2\25\3\2\2\2\2"+
|
||||
"\27\3\2\2\2\2\31\3\2\2\2\2\33\3\2\2\2\2\35\3\2\2\2\2\37\3\2\2\2\2!\3\2"+
|
||||
"\2\2\2#\3\2\2\2\2%\3\2\2\2\2\'\3\2\2\2\2)\3\2\2\2\2+\3\2\2\2\2-\3\2\2"+
|
||||
"\2\2/\3\2\2\2\2\61\3\2\2\2\2\63\3\2\2\2\2\65\3\2\2\2\2\67\3\2\2\2\29\3"+
|
||||
"\2\2\2\2;\3\2\2\2\2=\3\2\2\2\2?\3\2\2\2\2A\3\2\2\2\2C\3\2\2\2\2E\3\2\2"+
|
||||
"\2\2G\3\2\2\2\2I\3\2\2\2\2K\3\2\2\2\2M\3\2\2\2\2O\3\2\2\2\2Q\3\2\2\2\2"+
|
||||
"S\3\2\2\2\2U\3\2\2\2\2W\3\2\2\2\2Y\3\2\2\2\2[\3\2\2\2\2]\3\2\2\2\2_\3"+
|
||||
"\2\2\2\2a\3\2\2\2\2c\3\2\2\2\2e\3\2\2\2\2g\3\2\2\2\2i\3\2\2\2\2k\3\2\2"+
|
||||
"\2\2m\3\2\2\2\2o\3\2\2\2\2q\3\2\2\2\2s\3\2\2\2\2u\3\2\2\2\2w\3\2\2\2\2"+
|
||||
"y\3\2\2\2\2{\3\2\2\2\2}\3\2\2\2\2\177\3\2\2\2\2\u0081\3\2\2\2\2\u0083"+
|
||||
"\3\2\2\2\2\u0085\3\2\2\2\2\u0087\3\2\2\2\2\u0089\3\2\2\2\2\u008b\3\2\2"+
|
||||
"\2\2\u008d\3\2\2\2\2\u008f\3\2\2\2\2\u0091\3\2\2\2\2\u0093\3\2\2\2\2\u0095"+
|
||||
"\3\2\2\2\2\u0097\3\2\2\2\2\u0099\3\2\2\2\2\u009b\3\2\2\2\2\u009d\3\2\2"+
|
||||
"\2\2\u009f\3\2\2\2\2\u00a1\3\2\2\2\2\u00a3\3\2\2\2\2\u00a5\3\2\2\2\2\u00a7"+
|
||||
"\3\2\2\2\2\u00a9\3\2\2\2\2\u00ab\3\2\2\2\2\u00ad\3\2\2\2\2\u00af\3\2\2"+
|
||||
"\2\2\u00b1\3\2\2\2\2\u00b3\3\2\2\2\2\u00b5\3\2\2\2\2\u00b7\3\2\2\2\2\u00b9"+
|
||||
"\3\2\2\2\2\u00bb\3\2\2\2\2\u00bd\3\2\2\2\2\u00bf\3\2\2\2\2\u00c1\3\2\2"+
|
||||
"\2\2\u00c3\3\2\2\2\2\u00c5\3\2\2\2\2\u00c7\3\2\2\2\2\u00c9\3\2\2\2\2\u00cb"+
|
||||
"\3\2\2\2\3\u00cd\3\2\2\2\5\u00d1\3\2\2\2\7\u00d5\3\2\2\2\t\u00d9\3\2\2"+
|
||||
"\2\13\u00de\3\2\2\2\r\u00e3\3\2\2\2\17\u00e8\3\2\2\2\21\u00ee\3\2\2\2"+
|
||||
"\23\u00f3\3\2\2\2\25\u00f8\3\2\2\2\27\u00fd\3\2\2\2\31\u0103\3\2\2\2\33"+
|
||||
"\u0109\3\2\2\2\35\u010f\3\2\2\2\37\u0113\3\2\2\2!\u0118\3\2\2\2#\u011b"+
|
||||
"\3\2\2\2%\u011f\3\2\2\2\'\u0123\3\2\2\2)\u0128\3\2\2\2+\u012d\3\2\2\2"+
|
||||
"-\u0132\3\2\2\2/\u0137\3\2\2\2\61\u013d\3\2\2\2\63\u0143\3\2\2\2\65\u0149"+
|
||||
"\3\2\2\2\67\u014e\3\2\2\29\u0154\3\2\2\2;\u015a\3\2\2\2=\u015e\3\2\2\2"+
|
||||
"?\u0163\3\2\2\2A\u0169\3\2\2\2C\u016d\3\2\2\2E\u0172\3\2\2\2G\u0178\3"+
|
||||
"\2\2\2I\u018d\3\2\2\2K\u019c\3\2\2\2M\u019e\3\2\2\2O\u01a3\3\2\2\2Q\u01a8"+
|
||||
"\3\2\2\2S\u01b3\3\2\2\2U\u01b9\3\2\2\2W\u01be\3\2\2\2Y\u01c7\3\2\2\2["+
|
||||
"\u01cc\3\2\2\2]\u01d1\3\2\2\2_\u01e8\3\2\2\2a\u01f9\3\2\2\2c\u01fb\3\2"+
|
||||
"\2\2e\u020b\3\2\2\2g\u0218\3\2\2\2i\u021a\3\2\2\2k\u0220\3\2\2\2m\u0225"+
|
||||
"\3\2\2\2o\u022a\3\2\2\2q\u022f\3\2\2\2s\u0233\3\2\2\2u\u0238\3\2\2\2w"+
|
||||
"\u023c\3\2\2\2y\u024f\3\2\2\2{\u0260\3\2\2\2}\u0262\3\2\2\2\177\u026b"+
|
||||
"\3\2\2\2\u0081\u026f\3\2\2\2\u0083\u028d\3\2\2\2\u0085\u02a6\3\2\2\2\u0087"+
|
||||
"\u02ba\3\2\2\2\u0089\u02d3\3\2\2\2\u008b\u02ec\3\2\2\2\u008d\u0303\3\2"+
|
||||
"\2\2\u008f\u0318\3\2\2\2\u0091\u031a\3\2\2\2\u0093\u0321\3\2\2\2\u0095"+
|
||||
"\u0326\3\2\2\2\u0097\u032a\3\2\2\2\u0099\u032f\3\2\2\2\u009b\u0336\3\2"+
|
||||
"\2\2\u009d\u0338\3\2\2\2\u009f\u033a\3\2\2\2\u00a1\u033c\3\2\2\2\u00a3"+
|
||||
"\u033e\3\2\2\2\u00a5\u0340\3\2\2\2\u00a7\u0342\3\2\2\2\u00a9\u0345\3\2"+
|
||||
"\2\2\u00ab\u0347\3\2\2\2\u00ad\u034a\3\2\2\2\u00af\u034c\3\2\2\2\u00b1"+
|
||||
"\u034f\3\2\2\2\u00b3\u0351\3\2\2\2\u00b5\u0354\3\2\2\2\u00b7\u0356\3\2"+
|
||||
"\2\2\u00b9\u0358\3\2\2\2\u00bb\u035a\3\2\2\2\u00bd\u035c\3\2\2\2\u00bf"+
|
||||
"\u035e\3\2\2\2\u00c1\u0361\3\2\2\2\u00c3\u0363\3\2\2\2\u00c5\u036a\3\2"+
|
||||
"\2\2\u00c7\u036d\3\2\2\2\u00c9\u0379\3\2\2\2\u00cb\u0381\3\2\2\2\u00cd"+
|
||||
"\u00ce\7u\2\2\u00ce\u00cf\7k\2\2\u00cf\u00d0\7p\2\2\u00d0\4\3\2\2\2\u00d1"+
|
||||
"\u00d2\7e\2\2\u00d2\u00d3\7q\2\2\u00d3\u00d4\7u\2\2\u00d4\6\3\2\2\2\u00d5"+
|
||||
"\u00d6\7v\2\2\u00d6\u00d7\7c\2\2\u00d7\u00d8\7p\2\2\u00d8\b\3\2\2\2\u00d9"+
|
||||
"\u00da\7c\2\2\u00da\u00db\7u\2\2\u00db\u00dc\7k\2\2\u00dc\u00dd\7p\2\2"+
|
||||
"\u00dd\n\3\2\2\2\u00de\u00df\7c\2\2\u00df\u00e0\7e\2\2\u00e0\u00e1\7q"+
|
||||
"\2\2\u00e1\u00e2\7u\2\2\u00e2\f\3\2\2\2\u00e3\u00e4\7c\2\2\u00e4\u00e5"+
|
||||
"\7v\2\2\u00e5\u00e6\7c\2\2\u00e6\u00e7\7p\2\2\u00e7\16\3\2\2\2\u00e8\u00e9"+
|
||||
"\7c\2\2\u00e9\u00ea\7v\2\2\u00ea\u00eb\7c\2\2\u00eb\u00ec\7p\2\2\u00ec"+
|
||||
"\u00ed\7\64\2\2\u00ed\20\3\2\2\2\u00ee\u00ef\7u\2\2\u00ef\u00f0\7k\2\2"+
|
||||
"\u00f0\u00f1\7p\2\2\u00f1\u00f2\7j\2\2\u00f2\22\3\2\2\2\u00f3\u00f4\7"+
|
||||
"e\2\2\u00f4\u00f5\7q\2\2\u00f5\u00f6\7u\2\2\u00f6\u00f7\7j\2\2\u00f7\24"+
|
||||
"\3\2\2\2\u00f8\u00f9\7v\2\2\u00f9\u00fa\7c\2\2\u00fa\u00fb\7p\2\2\u00fb"+
|
||||
"\u00fc\7j\2\2\u00fc\26\3\2\2\2\u00fd\u00fe\7c\2\2\u00fe\u00ff\7u\2\2\u00ff"+
|
||||
"\u0100\7k\2\2\u0100\u0101\7p\2\2\u0101\u0102\7j\2\2\u0102\30\3\2\2\2\u0103"+
|
||||
"\u0104\7c\2\2\u0104\u0105\7e\2\2\u0105\u0106\7q\2\2\u0106\u0107\7u\2\2"+
|
||||
"\u0107\u0108\7j\2\2\u0108\32\3\2\2\2\u0109\u010a\7c\2\2\u010a\u010b\7"+
|
||||
"v\2\2\u010b\u010c\7c\2\2\u010c\u010d\7p\2\2\u010d\u010e\7j\2\2\u010e\34"+
|
||||
"\3\2\2\2\u010f\u0110\7c\2\2\u0110\u0111\7d\2\2\u0111\u0112\7u\2\2\u0112"+
|
||||
"\36\3\2\2\2\u0113\u0114\7u\2\2\u0114\u0115\7s\2\2\u0115\u0116\7t\2\2\u0116"+
|
||||
"\u0117\7v\2\2\u0117 \3\2\2\2\u0118\u0119\7n\2\2\u0119\u011a\7p\2\2\u011a"+
|
||||
"\"\3\2\2\2\u011b\u011c\7n\2\2\u011c\u011d\7q\2\2\u011d\u011e\7i\2\2\u011e"+
|
||||
"$\3\2\2\2\u011f\u0120\7g\2\2\u0120\u0121\7z\2\2\u0121\u0122\7r\2\2\u0122"+
|
||||
"&\3\2\2\2\u0123\u0124\7u\2\2\u0124\u0125\7o\2\2\u0125\u0126\7k\2\2\u0126"+
|
||||
"\u0127\7p\2\2\u0127(\3\2\2\2\u0128\u0129\7u\2\2\u0129\u012a\7o\2\2\u012a"+
|
||||
"\u012b\7c\2\2\u012b\u012c\7z\2\2\u012c*\3\2\2\2\u012d\u012e\7v\2\2\u012e"+
|
||||
"\u012f\7o\2\2\u012f\u0130\7k\2\2\u0130\u0131\7p\2\2\u0131,\3\2\2\2\u0132"+
|
||||
"\u0133\7v\2\2\u0133\u0134\7o\2\2\u0134\u0135\7c\2\2\u0135\u0136\7z\2\2"+
|
||||
"\u0136.\3\2\2\2\u0137\u0138\7v\2\2\u0138\u0139\7p\2\2\u0139\u013a\7q\2"+
|
||||
"\2\u013a\u013b\7t\2\2\u013b\u013c\7o\2\2\u013c\60\3\2\2\2\u013d\u013e"+
|
||||
"\7u\2\2\u013e\u013f\7p\2\2\u013f\u0140\7q\2\2\u0140\u0141\7t\2\2\u0141"+
|
||||
"\u0142\7o\2\2\u0142\62\3\2\2\2\u0143\u0144\7h\2\2\u0144\u0145\7n\2\2\u0145"+
|
||||
"\u0146\7q\2\2\u0146\u0147\7q\2\2\u0147\u0148\7t\2\2\u0148\64\3\2\2\2\u0149"+
|
||||
"\u014a\7e\2\2\u014a\u014b\7g\2\2\u014b\u014c\7k\2\2\u014c\u014d\7n\2\2"+
|
||||
"\u014d\66\3\2\2\2\u014e\u014f\7t\2\2\u014f\u0150\7q\2\2\u0150\u0151\7"+
|
||||
"w\2\2\u0151\u0152\7p\2\2\u0152\u0153\7f\2\2\u01538\3\2\2\2\u0154\u0155"+
|
||||
"\7i\2\2\u0155\u0156\7c\2\2\u0156\u0157\7o\2\2\u0157\u0158\7o\2\2\u0158"+
|
||||
"\u0159\7c\2\2\u0159:\3\2\2\2\u015a\u015b\7r\2\2\u015b\u015c\7q\2\2\u015c"+
|
||||
"\u015d\7y\2\2\u015d<\3\2\2\2\u015e\u015f\7u\2\2\u015f\u0160\7k\2\2\u0160"+
|
||||
"\u0161\7i\2\2\u0161\u0162\7o\2\2\u0162>\3\2\2\2\u0163\u0164\7e\2\2\u0164"+
|
||||
"\u0165\7n\2\2\u0165\u0166\7c\2\2\u0166\u0167\7o\2\2\u0167\u0168\7r\2\2"+
|
||||
"\u0168@\3\2\2\2\u0169\u016a\7h\2\2\u016a\u016b\7h\2\2\u016b\u016c\7v\2"+
|
||||
"\2\u016cB\3\2\2\2\u016d\u016e\7k\2\2\u016e\u016f\7h\2\2\u016f\u0170\7"+
|
||||
"h\2\2\u0170\u0171\7v\2\2\u0171D\3\2\2\2\u0172\u0173\7c\2\2\u0173\u0174"+
|
||||
"\7p\2\2\u0174\u0175\7i\2\2\u0175\u0176\7n\2\2\u0176\u0177\7g\2\2\u0177"+
|
||||
"F\3\2\2\2\u0178\u0179\7r\2\2\u0179\u017a\7t\2\2\u017a\u017b\7k\2\2\u017b"+
|
||||
"\u017c\7p\2\2\u017c\u017d\7v\2\2\u017dH\3\2\2\2\u017e\u017f\7r\2\2\u017f"+
|
||||
"\u0180\7t\2\2\u0180\u0181\7k\2\2\u0181\u0182\7p\2\2\u0182\u0183\7v\2\2"+
|
||||
"\u0183\u0184\7a\2\2\u0184\u0185\7u\2\2\u0185\u0186\7j\2\2\u0186\u0187"+
|
||||
"\7c\2\2\u0187\u0188\7r\2\2\u0188\u018e\7g\2\2\u0189\u018a\7r\2\2\u018a"+
|
||||
"\u018b\7u\2\2\u018b\u018c\7j\2\2\u018c\u018e\7r\2\2\u018d\u017e\3\2\2"+
|
||||
"\2\u018d\u0189\3\2\2\2\u018eJ\3\2\2\2\u018f\u0190\7p\2\2\u0190\u0191\7"+
|
||||
"x\2\2\u0191\u019d\7n\2\2\u0192\u0193\7p\2\2\u0193\u0194\7c\2\2\u0194\u0195"+
|
||||
"\7p\2\2\u0195\u0196\7a\2\2\u0196\u0197\7v\2\2\u0197\u0198\7q\2\2\u0198"+
|
||||
"\u0199\7a\2\2\u0199\u019a\7p\2\2\u019a\u019b\7w\2\2\u019b\u019d\7o\2\2"+
|
||||
"\u019c\u018f\3\2\2\2\u019c\u0192\3\2\2\2\u019dL\3\2\2\2\u019e\u019f\7"+
|
||||
"n\2\2\u019f\u01a0\7g\2\2\u01a0\u01a1\7t\2\2\u01a1\u01a2\7r\2\2\u01a2N"+
|
||||
"\3\2\2\2\u01a3\u01a4\7u\2\2\u01a4\u01a5\7v\2\2\u01a5\u01a6\7g\2\2\u01a6"+
|
||||
"\u01a7\7r\2\2\u01a7P\3\2\2\2\u01a8\u01a9\7u\2\2\u01a9\u01aa\7o\2\2\u01aa"+
|
||||
"\u01ab\7q\2\2\u01ab\u01ac\7q\2\2\u01ac\u01ad\7v\2\2\u01ad\u01ae\7j\2\2"+
|
||||
"\u01ae\u01af\7u\2\2\u01af\u01b0\7v\2\2\u01b0\u01b1\7g\2\2\u01b1\u01b2"+
|
||||
"\7r\2\2\u01b2R\3\2\2\2\u01b3\u01b4\7h\2\2\u01b4\u01b5\7t\2\2\u01b5\u01b6"+
|
||||
"\7c\2\2\u01b6\u01b7\7e\2\2\u01b7\u01b8\7v\2\2\u01b8T\3\2\2\2\u01b9\u01ba"+
|
||||
"\7t\2\2\u01ba\u01bb\7g\2\2\u01bb\u01bc\7n\2\2\u01bc\u01bd\7w\2\2\u01bd"+
|
||||
"V\3\2\2\2\u01be\u01bf\7u\2\2\u01bf\u01c0\7q\2\2\u01c0\u01c1\7h\2\2\u01c1"+
|
||||
"\u01c2\7v\2\2\u01c2\u01c3\7r\2\2\u01c3\u01c4\7n\2\2\u01c4\u01c5\7w\2\2"+
|
||||
"\u01c5\u01c6\7u\2\2\u01c6X\3\2\2\2\u01c7\u01c8\7i\2\2\u01c8\u01c9\7g\2"+
|
||||
"\2\u01c9\u01ca\7n\2\2\u01ca\u01cb\7w\2\2\u01cbZ\3\2\2\2\u01cc\u01cd\7"+
|
||||
"u\2\2\u01cd\u01ce\7k\2\2\u01ce\u01cf\7i\2\2\u01cf\u01d0\7p\2\2\u01d0\\"+
|
||||
"\3\2\2\2\u01d1\u01d2\7o\2\2\u01d2\u01d3\7c\2\2\u01d3\u01d4\7r\2\2\u01d4"+
|
||||
"^\3\2\2\2\u01d5\u01d6\7g\2\2\u01d6\u01d7\7|\2\2\u01d7\u01d8\7e\2\2\u01d8"+
|
||||
"\u01d9\7q\2\2\u01d9\u01da\7p\2\2\u01da\u01db\7x\2\2\u01db\u01dc\7q\2\2"+
|
||||
"\u01dc\u01dd\7n\2\2\u01dd\u01de\7w\2\2\u01de\u01df\7v\2\2\u01df\u01e0"+
|
||||
"\7k\2\2\u01e0\u01e1\7q\2\2\u01e1\u01e9\7p\2\2\u01e2\u01e3\7g\2\2\u01e3"+
|
||||
"\u01e4\7|\2\2\u01e4\u01e5\7e\2\2\u01e5\u01e6\7q\2\2\u01e6\u01e7\7p\2\2"+
|
||||
"\u01e7\u01e9\7x\2\2\u01e8\u01d5\3\2\2\2\u01e8\u01e2\3\2\2\2\u01e9`\3\2"+
|
||||
"\2\2\u01ea\u01eb\7e\2\2\u01eb\u01ec\7q\2\2\u01ec\u01ed\7p\2\2\u01ed\u01ee"+
|
||||
"\7x\2\2\u01ee\u01ef\7q\2\2\u01ef\u01f0\7n\2\2\u01f0\u01f1\7w\2\2\u01f1"+
|
||||
"\u01f2\7v\2\2\u01f2\u01f3\7k\2\2\u01f3\u01f4\7q\2\2\u01f4\u01fa\7p\2\2"+
|
||||
"\u01f5\u01f6\7e\2\2\u01f6\u01f7\7q\2\2\u01f7\u01f8\7p\2\2\u01f8\u01fa"+
|
||||
"\7x\2\2\u01f9\u01ea\3\2\2\2\u01f9\u01f5\3\2\2\2\u01fab\3\2\2\2\u01fb\u01fc"+
|
||||
"\7u\2\2\u01fc\u01fd\7y\2\2\u01fd\u01fe\7c\2\2\u01fe\u01ff\7r\2\2\u01ff"+
|
||||
"d\3\2\2\2\u0200\u0201\7r\2\2\u0201\u0202\7g\2\2\u0202\u0203\7t\2\2\u0203"+
|
||||
"\u0204\7o\2\2\u0204\u0205\7w\2\2\u0205\u0206\7v\2\2\u0206\u020c\7g\2\2"+
|
||||
"\u0207\u0208\7r\2\2\u0208\u0209\7g\2\2\u0209\u020a\7t\2\2\u020a\u020c"+
|
||||
"\7o\2\2\u020b\u0200\3\2\2\2\u020b\u0207\3\2\2\2\u020cf\3\2\2\2\u020d\u020e"+
|
||||
"\7t\2\2\u020e\u020f\7g\2\2\u020f\u0210\7u\2\2\u0210\u0211\7j\2\2\u0211"+
|
||||
"\u0212\7c\2\2\u0212\u0213\7r\2\2\u0213\u0219\7g\2\2\u0214\u0215\7t\2\2"+
|
||||
"\u0215\u0216\7u\2\2\u0216\u0217\7j\2\2\u0217\u0219\7r\2\2\u0218\u020d"+
|
||||
"\3\2\2\2\u0218\u0214\3\2\2\2\u0219h\3\2\2\2\u021a\u021b\7t\2\2\u021b\u021c"+
|
||||
"\7c\2\2\u021c\u021d\7p\2\2\u021d\u021e\7i\2\2\u021e\u021f\7g\2\2\u021f"+
|
||||
"j\3\2\2\2\u0220\u0221\7v\2\2\u0221\u0222\7q\2\2\u0222\u0223\7r\2\2\u0223"+
|
||||
"\u0224\7m\2\2\u0224l\3\2\2\2\u0225\u0226\7d\2\2\u0226\u0227\7q\2\2\u0227"+
|
||||
"\u0228\7v\2\2\u0228\u0229\7m\2\2\u0229n\3\2\2\2\u022a\u022b\7r\2\2\u022b"+
|
||||
"\u022c\7k\2\2\u022c\u022d\7p\2\2\u022d\u022e\7x\2\2\u022ep\3\2\2\2\u022f"+
|
||||
"\u0230\7u\2\2\u0230\u0231\7w\2\2\u0231\u0232\7o\2\2\u0232r\3\2\2\2\u0233"+
|
||||
"\u0234\7o\2\2\u0234\u0235\7g\2\2\u0235\u0236\7c\2\2\u0236\u0237\7p\2\2"+
|
||||
"\u0237t\3\2\2\2\u0238\u0239\7u\2\2\u0239\u023a\7v\2\2\u023a\u023b\7f\2"+
|
||||
"\2\u023bv\3\2\2\2\u023c\u023d\7x\2\2\u023d\u023e\7c\2\2\u023e\u023f\7"+
|
||||
"t\2\2\u023fx\3\2\2\2\u0240\u0241\7s\2\2\u0241\u0242\7w\2\2\u0242\u0243"+
|
||||
"\7c\2\2\u0243\u0244\7t\2\2\u0244\u0245\7v\2\2\u0245\u0246\7k\2\2\u0246"+
|
||||
"\u0247\7n\2\2\u0247\u0250\7g\2\2\u0248\u0249\7s\2\2\u0249\u024a\7w\2\2"+
|
||||
"\u024a\u024b\7c\2\2\u024b\u024c\7t\2\2\u024c\u024d\7v\2\2\u024d\u024e"+
|
||||
"\7k\2\2\u024e\u0250\7n\2\2\u024f\u0240\3\2\2\2\u024f\u0248\3\2\2\2\u0250"+
|
||||
"z\3\2\2\2\u0251\u0252\7r\2\2\u0252\u0253\7g\2\2\u0253\u0254\7t\2\2\u0254"+
|
||||
"\u0255\7e\2\2\u0255\u0256\7g\2\2\u0256\u0257\7p\2\2\u0257\u0258\7v\2\2"+
|
||||
"\u0258\u0259\7k\2\2\u0259\u025a\7n\2\2\u025a\u0261\7g\2\2\u025b\u025c"+
|
||||
"\7r\2\2\u025c\u025d\7t\2\2\u025d\u025e\7e\2\2\u025e\u025f\7p\2\2\u025f"+
|
||||
"\u0261\7v\2\2\u0260\u0251\3\2\2\2\u0260\u025b\3\2\2\2\u0261|\3\2\2\2\u0262"+
|
||||
"\u0263\7s\2\2\u0263\u0264\7w\2\2\u0264\u0265\7c\2\2\u0265\u0266\7p\2\2"+
|
||||
"\u0266\u0267\7v\2\2\u0267\u0268\7k\2\2\u0268\u0269\7n\2\2\u0269\u026a"+
|
||||
"\7g\2\2\u026a~\3\2\2\2\u026b\u026c\7f\2\2\u026c\u026d\7q\2\2\u026d\u026e"+
|
||||
"\7v\2\2\u026e\u0080\3\2\2\2\u026f\u0270\7o\2\2\u0270\u0271\7q\2\2\u0271"+
|
||||
"\u0272\7o\2\2\u0272\u0273\7g\2\2\u0273\u0274\7p\2\2\u0274\u0275\7v\2\2"+
|
||||
"\u0275\u0082\3\2\2\2\u0276\u0277\7p\2\2\u0277\u0278\7q\2\2\u0278\u0279"+
|
||||
"\7k\2\2\u0279\u027a\7u\2\2\u027a\u028e\7g\2\2\u027b\u027c\7t\2\2\u027c"+
|
||||
"\u027d\7c\2\2\u027d\u027e\7p\2\2\u027e\u027f\7f\2\2\u027f\u028e\7p\2\2"+
|
||||
"\u0280\u0281\7t\2\2\u0281\u0282\7c\2\2\u0282\u0283\7p\2\2\u0283\u0284"+
|
||||
"\7f\2\2\u0284\u0285\7q\2\2\u0285\u0286\7o\2\2\u0286\u0287\7a\2\2\u0287"+
|
||||
"\u0288\7p\2\2\u0288\u0289\7q\2\2\u0289\u028a\7t\2\2\u028a\u028b\7o\2\2"+
|
||||
"\u028b\u028c\7c\2\2\u028c\u028e\7n\2\2\u028d\u0276\3\2\2\2\u028d\u027b"+
|
||||
"\3\2\2\2\u028d\u0280\3\2\2\2\u028e\u0084\3\2\2\2\u028f\u0290\7t\2\2\u0290"+
|
||||
"\u0291\7c\2\2\u0291\u0292\7p\2\2\u0292\u02a7\7f\2\2\u0293\u0294\7t\2\2"+
|
||||
"\u0294\u0295\7c\2\2\u0295\u0296\7p\2\2\u0296\u0297\7f\2\2\u0297\u02a7"+
|
||||
"\7w\2\2\u0298\u0299\7t\2\2\u0299\u029a\7c\2\2\u029a\u029b\7p\2\2\u029b"+
|
||||
"\u029c\7f\2\2\u029c\u029d\7q\2\2\u029d\u029e\7o\2\2\u029e\u029f\7a\2\2"+
|
||||
"\u029f\u02a0\7w\2\2\u02a0\u02a1\7p\2\2\u02a1\u02a2\7k\2\2\u02a2\u02a3"+
|
||||
"\7h\2\2\u02a3\u02a4\7q\2\2\u02a4\u02a5\7t\2\2\u02a5\u02a7\7o\2\2\u02a6"+
|
||||
"\u028f\3\2\2\2\u02a6\u0293\3\2\2\2\u02a6\u0298\3\2\2\2\u02a7\u0086\3\2"+
|
||||
"\2\2\u02a8\u02a9\7t\2\2\u02a9\u02aa\7c\2\2\u02aa\u02ab\7p\2\2\u02ab\u02ac"+
|
||||
"\7f\2\2\u02ac\u02bb\7e\2\2\u02ad\u02ae\7t\2\2\u02ae\u02af\7c\2\2\u02af"+
|
||||
"\u02b0\7p\2\2\u02b0\u02b1\7f\2\2\u02b1\u02b2\7q\2\2\u02b2\u02b3\7o\2\2"+
|
||||
"\u02b3\u02b4\7a\2\2\u02b4\u02b5\7e\2\2\u02b5\u02b6\7c\2\2\u02b6\u02b7"+
|
||||
"\7w\2\2\u02b7\u02b8\7e\2\2\u02b8\u02b9\7j\2\2\u02b9\u02bb\7{\2\2\u02ba"+
|
||||
"\u02a8\3\2\2\2\u02ba\u02ad\3\2\2\2\u02bb\u0088\3\2\2\2\u02bc\u02bd\7t"+
|
||||
"\2\2\u02bd\u02be\7c\2\2\u02be\u02bf\7p\2\2\u02bf\u02c0\7f\2\2\u02c0\u02d4"+
|
||||
"\7g\2\2\u02c1\u02c2\7t\2\2\u02c2\u02c3\7c\2\2\u02c3\u02c4\7p\2\2\u02c4"+
|
||||
"\u02c5\7f\2\2\u02c5\u02c6\7q\2\2\u02c6\u02c7\7o\2\2\u02c7\u02c8\7a\2\2"+
|
||||
"\u02c8\u02c9\7g\2\2\u02c9\u02ca\7z\2\2\u02ca\u02cb\7r\2\2\u02cb\u02cc"+
|
||||
"\7q\2\2\u02cc\u02cd\7p\2\2\u02cd\u02ce\7g\2\2\u02ce\u02cf\7p\2\2\u02cf"+
|
||||
"\u02d0\7v\2\2\u02d0\u02d1\7k\2\2\u02d1\u02d2\7c\2\2\u02d2\u02d4\7n\2\2"+
|
||||
"\u02d3\u02bc\3\2\2\2\u02d3\u02c1\3\2\2\2\u02d4\u008a\3\2\2\2\u02d5\u02d6"+
|
||||
"\7t\2\2\u02d6\u02d7\7c\2\2\u02d7\u02d8\7p\2\2\u02d8\u02d9\7f\2\2\u02d9"+
|
||||
"\u02da\7n\2\2\u02da\u02ed\7p\2\2\u02db\u02dc\7t\2\2\u02dc\u02dd\7c\2\2"+
|
||||
"\u02dd\u02de\7p\2\2\u02de\u02df\7f\2\2\u02df\u02e0\7q\2\2\u02e0\u02e1"+
|
||||
"\7o\2\2\u02e1\u02e2\7a\2\2\u02e2\u02e3\7n\2\2\u02e3\u02e4\7q\2\2\u02e4"+
|
||||
"\u02e5\7i\2\2\u02e5\u02e6\7a\2\2\u02e6\u02e7\7p\2\2\u02e7\u02e8\7q\2\2"+
|
||||
"\u02e8\u02e9\7t\2\2\u02e9\u02ea\7o\2\2\u02ea\u02eb\7c\2\2\u02eb\u02ed"+
|
||||
"\7n\2\2\u02ec\u02d5\3\2\2\2\u02ec\u02db\3\2\2\2\u02ed\u008c\3\2\2\2\u02ee"+
|
||||
"\u02ef\7t\2\2\u02ef\u02f0\7c\2\2\u02f0\u02f1\7p\2\2\u02f1\u02f2\7f\2\2"+
|
||||
"\u02f2\u0304\7d\2\2\u02f3\u02f4\7t\2\2\u02f4\u02f5\7c\2\2\u02f5\u02f6"+
|
||||
"\7p\2\2\u02f6\u02f7\7f\2\2\u02f7\u02f8\7q\2\2\u02f8\u02f9\7o\2\2\u02f9"+
|
||||
"\u02fa\7a\2\2\u02fa\u02fb\7d\2\2\u02fb\u02fc\7g\2\2\u02fc\u02fd\7t\2\2"+
|
||||
"\u02fd\u02fe\7p\2\2\u02fe\u02ff\7q\2\2\u02ff\u0300\7w\2\2\u0300\u0301"+
|
||||
"\7n\2\2\u0301\u0302\7n\2\2\u0302\u0304\7k\2\2\u0303\u02ee\3\2\2\2\u0303"+
|
||||
"\u02f3\3\2\2\2\u0304\u008e\3\2\2\2\u0305\u0306\7t\2\2\u0306\u0307\7c\2"+
|
||||
"\2\u0307\u0308\7p\2\2\u0308\u0309\7f\2\2\u0309\u0319\7r\2\2\u030a\u030b"+
|
||||
"\7t\2\2\u030b\u030c\7c\2\2\u030c\u030d\7p\2\2\u030d\u030e\7f\2\2\u030e"+
|
||||
"\u030f\7q\2\2\u030f\u0310\7o\2\2\u0310\u0311\7a\2\2\u0311\u0312\7r\2\2"+
|
||||
"\u0312\u0313\7q\2\2\u0313\u0314\7k\2\2\u0314\u0315\7u\2\2\u0315\u0316"+
|
||||
"\7u\2\2\u0316\u0317\7q\2\2\u0317\u0319\7p\2\2\u0318\u0305\3\2\2\2\u0318"+
|
||||
"\u030a\3\2\2\2\u0319\u0090\3\2\2\2\u031a\u031b\7e\2\2\u031b\u031c\7q\2"+
|
||||
"\2\u031c\u031d\7u\2\2\u031d\u031e\7u\2\2\u031e\u031f\7k\2\2\u031f\u0320"+
|
||||
"\7o\2\2\u0320\u0092\3\2\2\2\u0321\u0322\7h\2\2\u0322\u0323\7n\2\2\u0323"+
|
||||
"\u0324\7k\2\2\u0324\u0325\7r\2\2\u0325\u0094\3\2\2\2\u0326\u0327\7e\2"+
|
||||
"\2\u0327\u0328\7q\2\2\u0328\u0329\7x\2\2\u0329\u0096\3\2\2\2\u032a\u032b"+
|
||||
"\7u\2\2\u032b\u032c\7q\2\2\u032c\u032d\7t\2\2\u032d\u032e\7v\2\2\u032e"+
|
||||
"\u0098\3\2\2\2\u032f\u0330\7c\2\2\u0330\u0331\7r\2\2\u0331\u0332\7r\2"+
|
||||
"\2\u0332\u0333\7g\2\2\u0333\u0334\7p\2\2\u0334\u0335\7f\2\2\u0335\u009a"+
|
||||
"\3\2\2\2\u0336\u0337\7-\2\2\u0337\u009c\3\2\2\2\u0338\u0339\7/\2\2\u0339"+
|
||||
"\u009e\3\2\2\2\u033a\u033b\7,\2\2\u033b\u00a0\3\2\2\2\u033c\u033d\7\61"+
|
||||
"\2\2\u033d\u00a2\3\2\2\2\u033e\u033f\7\'\2\2\u033f\u00a4\3\2\2\2\u0340"+
|
||||
"\u0341\7`\2\2\u0341\u00a6\3\2\2\2\u0342\u0343\7@\2\2\u0343\u0344\7?\2"+
|
||||
"\2\u0344\u00a8\3\2\2\2\u0345\u0346\7@\2\2\u0346\u00aa\3\2\2\2\u0347\u0348"+
|
||||
"\7>\2\2\u0348\u0349\7?\2\2\u0349\u00ac\3\2\2\2\u034a\u034b\7>\2\2\u034b"+
|
||||
"\u00ae\3\2\2\2\u034c\u034d\7?\2\2\u034d\u034e\7?\2\2\u034e\u00b0\3\2\2"+
|
||||
"\2\u034f\u0350\7?\2\2\u0350\u00b2\3\2\2\2\u0351\u0352\7#\2\2\u0352\u0353"+
|
||||
"\7?\2\2\u0353\u00b4\3\2\2\2\u0354\u0355\7~\2\2\u0355\u00b6\3\2\2\2\u0356"+
|
||||
"\u0357\7*\2\2\u0357\u00b8\3\2\2\2\u0358\u0359\7+\2\2\u0359\u00ba\3\2\2"+
|
||||
"\2\u035a\u035b\7.\2\2\u035b\u00bc\3\2\2\2\u035c\u035d\7=\2\2\u035d\u00be"+
|
||||
"\3\2\2\2\u035e\u035f\7/\2\2\u035f\u0360\7@\2\2\u0360\u00c0\3\2\2\2\u0361"+
|
||||
"\u0362\7]\2\2\u0362\u00c2\3\2\2\2\u0363\u0364\7_\2\2\u0364\u00c4\3\2\2"+
|
||||
"\2\u0365\u0366\7r\2\2\u0366\u036b\7k\2\2\u0367\u0368\7R\2\2\u0368\u036b"+
|
||||
"\7K\2\2\u0369\u036b\t\2\2\2\u036a\u0365\3\2\2\2\u036a\u0367\3\2\2\2\u036a"+
|
||||
"\u0369\3\2\2\2\u036b\u00c6\3\2\2\2\u036c\u036e\t\3\2\2\u036d\u036c\3\2"+
|
||||
"\2\2\u036e\u036f\3\2\2\2\u036f\u036d\3\2\2\2\u036f\u0370\3\2\2\2\u0370"+
|
||||
"\u0377\3\2\2\2\u0371\u0373\7\60\2\2\u0372\u0374\t\3\2\2\u0373\u0372\3"+
|
||||
"\2\2\2\u0374\u0375\3\2\2\2\u0375\u0373\3\2\2\2\u0375\u0376\3\2\2\2\u0376"+
|
||||
"\u0378\3\2\2\2\u0377\u0371\3\2\2\2\u0377\u0378\3\2\2\2\u0378\u00c8\3\2"+
|
||||
"\2\2\u0379\u037d\t\4\2\2\u037a\u037c\t\5\2\2\u037b\u037a\3\2\2\2\u037c"+
|
||||
"\u037f\3\2\2\2\u037d\u037b\3\2\2\2\u037d\u037e\3\2\2\2\u037e\u00ca\3\2"+
|
||||
"\2\2\u037f\u037d\3\2\2\2\u0380\u0382\t\6\2\2\u0381\u0380\3\2\2\2\u0382"+
|
||||
"\u0383\3\2\2\2\u0383\u0381\3\2\2\2\u0383\u0384\3\2\2\2\u0384\u0385\3\2"+
|
||||
"\2\2\u0385\u0386\bf\2\2\u0386\u00cc\3\2\2\2\30\2\u018d\u019c\u01e8\u01f9"+
|
||||
"\u020b\u0218\u024f\u0260\u028d\u02a6\u02ba\u02d3\u02ec\u0303\u0318\u036a"+
|
||||
"\u036f\u0375\u0377\u037d\u0383\3\b\2\2";
|
||||
public static final ATN _ATN =
|
||||
new ATNDeserializer().deserialize(_serializedATN.toCharArray());
|
||||
static {
|
||||
_decisionToDFA = new DFA[_ATN.getNumberOfDecisions()];
|
||||
for (int i = 0; i < _ATN.getNumberOfDecisions(); i++) {
|
||||
_decisionToDFA[i] = new DFA(_ATN.getDecisionState(i), i);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,183 @@
|
||||
SIN=1
|
||||
COS=2
|
||||
TAN=3
|
||||
ASIN=4
|
||||
ACOS=5
|
||||
ATAN=6
|
||||
ATAN2=7
|
||||
SINH=8
|
||||
COSH=9
|
||||
TANH=10
|
||||
ASINH=11
|
||||
ACOSH=12
|
||||
ATANH=13
|
||||
ABS=14
|
||||
SQRT=15
|
||||
LN=16
|
||||
LOG=17
|
||||
EXP=18
|
||||
SMIN=19
|
||||
SMAX=20
|
||||
TMIN=21
|
||||
TMAX=22
|
||||
TNORM=23
|
||||
SNORM=24
|
||||
FLOOR=25
|
||||
CEIL=26
|
||||
ROUND=27
|
||||
GAMMA=28
|
||||
POWE=29
|
||||
SIGM=30
|
||||
CLAMP=31
|
||||
SFFT=32
|
||||
SIFFT=33
|
||||
ANGL=34
|
||||
PRNT=35
|
||||
PRINT_SHAPE=36
|
||||
NVL=37
|
||||
LERP=38
|
||||
STEP=39
|
||||
SMOOTHSTEP=40
|
||||
FRACT=41
|
||||
RELU=42
|
||||
SOFTPLUS=43
|
||||
GELU=44
|
||||
SIGN=45
|
||||
MAP=46
|
||||
EZCONV=47
|
||||
CONV=48
|
||||
SWAP=49
|
||||
PERM=50
|
||||
RESHAPE=51
|
||||
RANGE=52
|
||||
TOPK=53
|
||||
BOTK=54
|
||||
PINV=55
|
||||
SUM=56
|
||||
MEAN=57
|
||||
STD=58
|
||||
VAR=59
|
||||
QUARTILE=60
|
||||
PERCENTILE=61
|
||||
QUANTILE=62
|
||||
DOT=63
|
||||
MOMENT=64
|
||||
NOISE=65
|
||||
RAND=66
|
||||
CAUCHY=67
|
||||
EXPONENTIAL=68
|
||||
LOGNORMAL=69
|
||||
BERNOULLI=70
|
||||
POISSON=71
|
||||
COSSIM=72
|
||||
FLIP=73
|
||||
COV=74
|
||||
SORT=75
|
||||
APPEND=76
|
||||
PLUS=77
|
||||
MINUS=78
|
||||
MULT=79
|
||||
DIV=80
|
||||
MOD=81
|
||||
POW=82
|
||||
GE=83
|
||||
GT=84
|
||||
LE=85
|
||||
LT=86
|
||||
EQ=87
|
||||
EQUEALS=88
|
||||
NE=89
|
||||
PIPE=90
|
||||
LPAREN=91
|
||||
RPAREN=92
|
||||
COMMA=93
|
||||
SEMICOLON=94
|
||||
ARROW=95
|
||||
LBRACKET=96
|
||||
RBRACKET=97
|
||||
CONSTANT=98
|
||||
NUMBER=99
|
||||
VARIABLE=100
|
||||
WS=101
|
||||
'sin'=1
|
||||
'cos'=2
|
||||
'tan'=3
|
||||
'asin'=4
|
||||
'acos'=5
|
||||
'atan'=6
|
||||
'atan2'=7
|
||||
'sinh'=8
|
||||
'cosh'=9
|
||||
'tanh'=10
|
||||
'asinh'=11
|
||||
'acosh'=12
|
||||
'atanh'=13
|
||||
'abs'=14
|
||||
'sqrt'=15
|
||||
'ln'=16
|
||||
'log'=17
|
||||
'exp'=18
|
||||
'smin'=19
|
||||
'smax'=20
|
||||
'tmin'=21
|
||||
'tmax'=22
|
||||
'tnorm'=23
|
||||
'snorm'=24
|
||||
'floor'=25
|
||||
'ceil'=26
|
||||
'round'=27
|
||||
'gamma'=28
|
||||
'pow'=29
|
||||
'sigm'=30
|
||||
'clamp'=31
|
||||
'fft'=32
|
||||
'ifft'=33
|
||||
'angle'=34
|
||||
'print'=35
|
||||
'lerp'=38
|
||||
'step'=39
|
||||
'smoothstep'=40
|
||||
'fract'=41
|
||||
'relu'=42
|
||||
'softplus'=43
|
||||
'gelu'=44
|
||||
'sign'=45
|
||||
'map'=46
|
||||
'swap'=49
|
||||
'range'=52
|
||||
'topk'=53
|
||||
'botk'=54
|
||||
'pinv'=55
|
||||
'sum'=56
|
||||
'mean'=57
|
||||
'std'=58
|
||||
'var'=59
|
||||
'quantile'=62
|
||||
'dot'=63
|
||||
'moment'=64
|
||||
'cossim'=72
|
||||
'flip'=73
|
||||
'cov'=74
|
||||
'sort'=75
|
||||
'append'=76
|
||||
'+'=77
|
||||
'-'=78
|
||||
'*'=79
|
||||
'/'=80
|
||||
'%'=81
|
||||
'^'=82
|
||||
'>='=83
|
||||
'>'=84
|
||||
'<='=85
|
||||
'<'=86
|
||||
'=='=87
|
||||
'='=88
|
||||
'!='=89
|
||||
'|'=90
|
||||
'('=91
|
||||
')'=92
|
||||
','=93
|
||||
';'=94
|
||||
'->'=95
|
||||
'['=96
|
||||
']'=97
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -28,7 +28,7 @@ class SigmasMathNode(io.ComfyNode):
|
||||
io.String.Input(id="Expression", default="I0*(1-F0)+I1*F0", tooltip="Expression to apply on input images"),
|
||||
io.Combo.Input(
|
||||
id="length_mismatch",
|
||||
options=["tile", "error", "pad"],
|
||||
options=["error", "error", "pad"],
|
||||
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."
|
||||
)
|
||||
|
||||
+63
-22
@@ -2,34 +2,31 @@ from inspect import cleandoc
|
||||
from comfy_api.latest import io
|
||||
import copy
|
||||
from .modelLikeCommon import calculate_patches
|
||||
from .helper_functions import commonLazy
|
||||
from antlr4 import InputStream, CommonTokenStream
|
||||
from .Parser.MathExprLexer import MathExprLexer
|
||||
from .Parser.MathExprParser import MathExprParser
|
||||
import re
|
||||
|
||||
|
||||
class VAEMathNode(io.ComfyNode):
|
||||
"""
|
||||
This node enables the use of math expressions on VAE weights.
|
||||
This node enables the use of math expressions on VAE weights using Autogrow inputs.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="mrmth_VAEMathNode",
|
||||
node_id="mrmth_ag_VAEMathNode",
|
||||
display_name="VAE Math",
|
||||
category="More math",
|
||||
inputs=[
|
||||
io.Vae.Input(id="a", tooltip="Main VAE (base)"),
|
||||
io.Vae.Input(id="b", optional=True, lazy=True, tooltip="Optional 2nd VAE"),
|
||||
io.Vae.Input(id="c", optional=True, lazy=True, tooltip="Optional 3rd VAE"),
|
||||
io.Vae.Input(id="d", optional=True, lazy=True, tooltip="Optional 4th VAE"),
|
||||
io.Float.Input(id="w", default=0.0, lazy=True, optional=True, force_input=True),
|
||||
io.Float.Input(id="x", default=0.0, lazy=True, optional=True, force_input=True),
|
||||
io.Float.Input(id="y", default=0.0, lazy=True, optional=True, force_input=True),
|
||||
io.Float.Input(id="z", default=0.0, lazy=True, optional=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.Vae.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", "error", "pad"],
|
||||
default="error",
|
||||
tooltip="How to handle mismatched layer counts. For models, this usually defaults to broadcast (zero for missing layers)."
|
||||
)
|
||||
],
|
||||
@@ -41,17 +38,61 @@ class VAEMathNode(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:
|
||||
patcher_a = a.patcher
|
||||
patcher_b = b.patcher if b else None
|
||||
patcher_c = c.patcher if c else None
|
||||
patcher_d = d.patcher if d else None
|
||||
def execute(cls, V, F, Expression, length_mismatch="tile") -> io.NodeOutput:
|
||||
# Determine reference VAE
|
||||
a = V.get("V0")
|
||||
if a is None:
|
||||
for m in V.values():
|
||||
if m is not None:
|
||||
a = m
|
||||
break
|
||||
if a is None:
|
||||
raise ValueError("At least one input VAE is required.")
|
||||
|
||||
patches = calculate_patches(Model, patcher_a, patcher_b, patcher_c, patcher_d, w, x, y, z)
|
||||
patcher_a = a.patcher
|
||||
|
||||
# Prepare VAE patchers for calculation
|
||||
# We need to map VAE wrappers to their patchers for `calculate_patches`
|
||||
|
||||
patchers_V = {}
|
||||
for k, v in V.items():
|
||||
if v is not None:
|
||||
patchers_V[k] = v.patcher
|
||||
|
||||
# Calculate patches using the patchers (weights are in patcher.model.state_dict)
|
||||
patches = calculate_patches(Expression, patcher_a, None, None, None, 0,0,0,0, V=patchers_V, F=F)
|
||||
|
||||
# VAE does not have a clone method, so we shallow copy and clone the patcher
|
||||
out_vae = copy.copy(a)
|
||||
|
||||
@@ -41,7 +41,7 @@ class VideoMathNode(io.ComfyNode):
|
||||
io.String.Input(id="Audio", default="a*(1-w)+b*w", tooltip="Expression for the audio component"),
|
||||
io.Combo.Input(
|
||||
id="length_mismatch",
|
||||
options=["tile", "error", "pad"],
|
||||
options=["error", "error", "pad"],
|
||||
default="error",
|
||||
tooltip="How to handle mismatched frame/sample counts. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing as zero."
|
||||
)
|
||||
|
||||
@@ -34,7 +34,7 @@ class AudioMathNodeOLD(io.ComfyNode):
|
||||
io.String.Input(id="Audio", default="a*(1-w)+b*w", tooltip="Expression to apply on input audio waveforms"),
|
||||
io.Combo.Input(
|
||||
id="length_mismatch",
|
||||
options=["tile", "error", "pad"],
|
||||
options=["error", "error", "pad"],
|
||||
default="error",
|
||||
tooltip="How to handle mismatched audio sample counts. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing samples as zero."
|
||||
)
|
||||
|
||||
@@ -0,0 +1,59 @@
|
||||
from comfy_api.latest import io
|
||||
from ..modelLikeCommon import calculate_patches
|
||||
from inspect import cleandoc
|
||||
from ..helper_functions import commonLazy
|
||||
|
||||
|
||||
class CLIPMathNodeOLD(io.ComfyNode):
|
||||
"""
|
||||
This node enables the use of math expressions on CLIP weights.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="mrmth_CLIPMathNode",
|
||||
display_name="CLIP Math",
|
||||
category="More math",
|
||||
is_deprecated=True,
|
||||
inputs=[
|
||||
io.Clip.Input(id="a", tooltip="Main CLIP (base)"),
|
||||
io.Clip.Input(id="b", optional=True, lazy=True, tooltip="Optional 2nd CLIP"),
|
||||
io.Clip.Input(id="c", optional=True, lazy=True, tooltip="Optional 3rd CLIP"),
|
||||
io.Clip.Input(id="d", optional=True, lazy=True, tooltip="Optional 4th CLIP"),
|
||||
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.Combo.Input(
|
||||
id="length_mismatch",
|
||||
options=["error", "passthrough", "pad"],
|
||||
default="error",
|
||||
tooltip="How to handle mismatched layer counts. For models, this usually defaults to broadcast (zero for missing layers)."
|
||||
)
|
||||
],
|
||||
outputs=[
|
||||
io.Clip.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
tooltip = cleandoc(__doc__)
|
||||
|
||||
@classmethod
|
||||
def check_lazy_status(cls, Model, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
|
||||
return commonLazy(Model, a, b, c, d, w, x, y, z)
|
||||
|
||||
@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="tile") -> io.NodeOutput:
|
||||
patcher_a = a.patcher
|
||||
patcher_b = b.patcher if b else None
|
||||
patcher_c = c.patcher if c else None
|
||||
patcher_d = d.patcher if d else None
|
||||
|
||||
patches = calculate_patches(Model, patcher_a, patcher_b, patcher_c, patcher_d, w, x, y, z)
|
||||
|
||||
out_clip = a.clone()
|
||||
if patches:
|
||||
out_clip.add_patches(patches, 1.0, 1.0)
|
||||
return (out_clip,)
|
||||
@@ -39,7 +39,7 @@ class ConditioningMathNodeOLD(io.ComfyNode):
|
||||
),
|
||||
io.Combo.Input(
|
||||
id="length_mismatch",
|
||||
options=["tile", "error", "pad"],
|
||||
options=["error", "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."
|
||||
)
|
||||
|
||||
@@ -0,0 +1,67 @@
|
||||
from inspect import cleandoc
|
||||
|
||||
from ..helper_functions import parse_expr
|
||||
from ..Parser.UnifiedMathVisitor import UnifiedMathVisitor
|
||||
|
||||
from ..helper_functions import commonLazy
|
||||
from comfy_api.latest import io
|
||||
|
||||
|
||||
class FloatMathNode(io.ComfyNode):
|
||||
"""
|
||||
This node enables the use of math expressions on Floats.
|
||||
|
||||
Inputs:
|
||||
a, b, c, d: Floats, bound to variables with the same name.
|
||||
w, x, y, z: Floats, bound to variables of the expression.
|
||||
FloatFunc: String, describing math expression.
|
||||
Valid functions: sin, cos, tan, abs, sqrt, min, max, norm, etc.
|
||||
Operators: +, -, *, /, ^, %.
|
||||
Constants: e, pi.
|
||||
|
||||
Outputs:
|
||||
FLOAT: The result of evaluating the math expression.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="mrmth_FloatMathNode",
|
||||
category="More math",
|
||||
display_name="Float math",
|
||||
inputs=[
|
||||
io.Float.Input(id="a", force_input=True),
|
||||
io.Float.Input(id="b", default=0.0, optional=True, lazy=True, force_input=True),
|
||||
io.Float.Input(id="c", default=0.0, optional=True, lazy=True, force_input=True),
|
||||
io.Float.Input(id="d", default=0.0, optional=True, lazy=True, force_input=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="FloatFunc", default="a*(1-w)+b*w", tooltip="Expression to use on inputs"),
|
||||
],
|
||||
outputs=[
|
||||
io.Float.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
tooltip = cleandoc(__doc__)
|
||||
|
||||
@classmethod
|
||||
def check_lazy_status(cls, FloatFunc, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0,):
|
||||
return commonLazy(FloatFunc, a, b, c, d, w, x, y, z)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, FloatFunc, a, b=0.0, c=0.0, d=0.0, w=0.0, x=0.0, y=0.0, z=0.0):
|
||||
|
||||
variables = {
|
||||
"a": a,
|
||||
"b": b,
|
||||
"c": c,
|
||||
"d": d,
|
||||
"w": w, "x": x, "y": y, "z": z
|
||||
}
|
||||
tree = parse_expr(FloatFunc);
|
||||
visitor = UnifiedMathVisitor(variables, [1])
|
||||
result = visitor.visit(tree)
|
||||
return (result,)
|
||||
@@ -0,0 +1,86 @@
|
||||
from ..helper_functions import generate_dim_variables, parse_expr, getIndexTensorAlongDim, as_tensor, prepare_inputs, commonLazy, normalize_to_common_shape
|
||||
from ..Parser.UnifiedMathVisitor import UnifiedMathVisitor
|
||||
from comfy_api.latest import io
|
||||
|
||||
|
||||
class ImageMathNodeOLD(io.ComfyNode):
|
||||
"""
|
||||
Enables math expressions on Images.
|
||||
|
||||
Inputs:
|
||||
a, b, c, d: Image inputs (b, c, d default to zero if not provided)
|
||||
w, x, y, z: Float variables for expressions
|
||||
Image: Expression to apply on input images
|
||||
|
||||
Outputs:
|
||||
IMAGE: Result of applying expression to input images
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="mrmth_ImageMathNode",
|
||||
category="More math",
|
||||
display_name="Image math",
|
||||
is_deprecated=True,
|
||||
inputs=[
|
||||
io.Image.Input(id="a"),
|
||||
io.Image.Input(id="b", optional=True, lazy=True),
|
||||
io.Image.Input(id="c", optional=True, lazy=True),
|
||||
io.Image.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="Image", default="a*(1-w)+b*w", tooltip="Expression to apply on input images"),
|
||||
io.Combo.Input(
|
||||
id="length_mismatch",
|
||||
options=["error", "error", "pad"],
|
||||
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."
|
||||
)
|
||||
],
|
||||
outputs=[
|
||||
io.Image.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def check_lazy_status(cls, Image, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
|
||||
return commonLazy(Image, a, b, c, d, w, x, y, z)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, Image, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0, length_mismatch="tile"):
|
||||
ae, be, ce, de = prepare_inputs(a, b, c, d)
|
||||
print(f"DEBUG: shapes {ae.shape[0]}, {be.shape[0]}, {ce.shape[0]}, {de.shape[0]}")
|
||||
|
||||
if(length_mismatch == "error"):
|
||||
max_length = max(ae.shape[0], be.shape[0], ce.shape[0], de.shape[0])
|
||||
for tensor, name in zip([ae, be, ce, de], ["a", "b", "c", "d"]):
|
||||
if tensor.shape[0] != max_length:
|
||||
raise ValueError(f"Input '{name}' has shape {tensor.shape[0]}, expected {max_length} to match largest input.")
|
||||
ae, be, ce, de = normalize_to_common_shape(ae, be, ce, de, mode=length_mismatch)
|
||||
|
||||
variables = {
|
||||
"a": ae, "b": be, "c": ce, "d": de,
|
||||
"w": w, "x": x, "y": y, "z": z,
|
||||
"X": getIndexTensorAlongDim(ae, 3),
|
||||
"Y": getIndexTensorAlongDim(ae, 2),
|
||||
"B": getIndexTensorAlongDim(ae, 0),
|
||||
"batch": getIndexTensorAlongDim(ae, 0),
|
||||
"C": getIndexTensorAlongDim(ae, 1),
|
||||
"channel": getIndexTensorAlongDim(ae, 1),
|
||||
"W": ae.shape[2],
|
||||
"width": ae.shape[2],
|
||||
"H": ae.shape[1],
|
||||
"height": ae.shape[1],
|
||||
"T": ae.shape[0],
|
||||
"batch_count": ae.shape[0],
|
||||
"N": ae.shape[3],
|
||||
"channel_count": ae.shape[3],
|
||||
} | generate_dim_variables(ae)
|
||||
tree = parse_expr(Image);
|
||||
visitor = UnifiedMathVisitor(variables, ae.shape)
|
||||
result = visitor.visit(tree)
|
||||
result = as_tensor(result, ae.shape)
|
||||
return (result,)
|
||||
@@ -0,0 +1,160 @@
|
||||
from inspect import cleandoc
|
||||
from comfy_api.latest import io
|
||||
from ..helper_functions import (
|
||||
generate_dim_variables,
|
||||
getIndexTensorAlongDim,
|
||||
parse_expr,
|
||||
as_tensor,
|
||||
normalize_to_common_shape,
|
||||
prepare_inputs
|
||||
)
|
||||
from ..helper_functions import commonLazy
|
||||
from ..Parser.UnifiedMathVisitor import UnifiedMathVisitor
|
||||
import torch
|
||||
|
||||
|
||||
class LatentMathNodeOLD(io.ComfyNode):
|
||||
"""
|
||||
This node enables the use of math expressions on Latents.
|
||||
inputs:
|
||||
a, b, c, d:
|
||||
Latent, bound to variables with the same name. Defaults to zero latent if not provided.
|
||||
w, x, y, z:
|
||||
Floats, bound to variables of the expression. Defaults to 0.0 if not provided.
|
||||
Latent expression:
|
||||
String, describing expression to aply to latents.
|
||||
|
||||
outputs:
|
||||
LATENT:
|
||||
Returns a LATENT object that contains the result of the math expression applied to the input conditionings.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.Schema:
|
||||
""" """
|
||||
return io.Schema(
|
||||
node_id="mrmth_LatentMathNode",
|
||||
display_name="Latent math",
|
||||
category="More math",
|
||||
is_deprecated=True,
|
||||
inputs=[
|
||||
io.Latent.Input(id="a"),
|
||||
io.Latent.Input(id="b", optional=True, lazy=True),
|
||||
io.Latent.Input(id="c", optional=True, lazy=True),
|
||||
io.Latent.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="Latent", default="a*(1-w)+b*w", tooltip="Expression to apply on input latents"),
|
||||
io.Combo.Input(
|
||||
id="length_mismatch",
|
||||
options=["error", "error", "pad"],
|
||||
default="error",
|
||||
tooltip="How to handle mismatched latent batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
|
||||
)
|
||||
],
|
||||
outputs=[
|
||||
io.Latent.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
tooltip = cleandoc(__doc__)
|
||||
|
||||
@classmethod
|
||||
def check_lazy_status(cls, Latent, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
|
||||
return commonLazy(Latent, a, b, c, d, w, x, y, z)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, Latent, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0, length_mismatch="tile") -> io.NodeOutput:
|
||||
# Identify if any input is a NestedTensor and track original sizes for restoration
|
||||
stacked = False
|
||||
orig_split_sizes = None
|
||||
|
||||
for item in [a, b, c, d]:
|
||||
if item is not None:
|
||||
samples = item.get("samples")
|
||||
if getattr(samples, "is_nested", False):
|
||||
stacked = True
|
||||
# Store original split sizes (batch dimension)
|
||||
orig_split_sizes = [t.shape[0] for t in samples.tensors]
|
||||
break
|
||||
|
||||
if stacked:
|
||||
if a is not None and getattr(a.get("samples"), "is_nested", False):
|
||||
a = a.copy()
|
||||
a["samples"] = torch.cat(a["samples"].tensors, dim=0)
|
||||
if b is not None and getattr(b.get("samples"), "is_nested", False):
|
||||
b = b.copy()
|
||||
b["samples"] = torch.cat(b["samples"].tensors, dim=0)
|
||||
if c is not None and getattr(c.get("samples"), "is_nested", False):
|
||||
c = c.copy()
|
||||
c["samples"] = torch.cat(c["samples"].tensors, dim=0)
|
||||
if d is not None and getattr(d.get("samples"), "is_nested", False):
|
||||
d = d.copy()
|
||||
d["samples"] = torch.cat(d["samples"].tensors, dim=0)
|
||||
|
||||
a_c, b_c, c_c, d_c = prepare_inputs(a, b, c, d)
|
||||
at,bt,ct,dt = a_c["samples"],b_c["samples"],c_c["samples"],d_c["samples"]
|
||||
if(length_mismatch == "error"):
|
||||
# Check only available tensors
|
||||
tensors_to_check = [t for t in [at, bt, ct, dt] if t is not None]
|
||||
max_length = max(t.shape[0] for t in tensors_to_check)
|
||||
for tensor, name in zip([at, bt, ct, dt], ["a", "b", "c", "d"]):
|
||||
if tensor is not None:
|
||||
if tensor.shape[0] != max_length:
|
||||
raise ValueError(f"Input '{name}' has shape {tensor.shape[0]}, expected {max_length} to match largest input.")
|
||||
ae, be, ce, de = normalize_to_common_shape(at, bt, ct, dt, mode=length_mismatch)
|
||||
|
||||
# parse expression once
|
||||
tree = parse_expr(Latent)
|
||||
|
||||
ndim = ae.ndim
|
||||
batch_dim = 0
|
||||
channel_dim = -3
|
||||
height_dim = -2
|
||||
width_dim = -1
|
||||
time_dim = None
|
||||
if ndim >= 5:
|
||||
time_dim = -4
|
||||
|
||||
frame_count = ae.shape[time_dim] if time_dim is not None else ae.shape[batch_dim]
|
||||
|
||||
variables = {
|
||||
"a": ae, "b": be, "c": ce, "d": de,
|
||||
"w": w, "x": x, "y": y, "z": z,
|
||||
"X": getIndexTensorAlongDim(ae, width_dim),
|
||||
"Y": getIndexTensorAlongDim(ae, height_dim),
|
||||
"B": getIndexTensorAlongDim(ae, batch_dim),
|
||||
"batch": getIndexTensorAlongDim(ae, batch_dim),
|
||||
"C": getIndexTensorAlongDim(ae, channel_dim),
|
||||
"channel": getIndexTensorAlongDim(ae, channel_dim),
|
||||
"W": ae.shape[width_dim],
|
||||
"width": ae.shape[width_dim],
|
||||
"H": ae.shape[height_dim],
|
||||
"height": ae.shape[height_dim],
|
||||
"T": frame_count,
|
||||
"batch_count": ae.shape[batch_dim],
|
||||
"N": ae.shape[channel_dim],
|
||||
"channel_count": ae.shape[channel_dim],
|
||||
} | generate_dim_variables(ae)
|
||||
|
||||
if time_dim is not None:
|
||||
F = getIndexTensorAlongDim(ae, time_dim)
|
||||
variables.update({"frame_idx": F, "frame": F, "frame_count": frame_count})
|
||||
|
||||
visitor = UnifiedMathVisitor(variables, ae.shape)
|
||||
result_t = as_tensor(visitor.visit(tree), ae.shape)
|
||||
|
||||
result_latent = a_c.copy()
|
||||
if stacked and orig_split_sizes is not None:
|
||||
from comfy.nested_tensor import NestedTensor
|
||||
# Restore original split sizes
|
||||
result_latent["samples"] = NestedTensor(torch.split(result_t, orig_split_sizes, dim=0))
|
||||
else:
|
||||
result_latent["samples"] = result_t
|
||||
|
||||
return (result_latent,)
|
||||
@@ -0,0 +1,83 @@
|
||||
from ..helper_functions import generate_dim_variables,parse_expr, getIndexTensorAlongDim, as_tensor, commonLazy, normalize_to_common_shape,prepare_inputs
|
||||
from ..Parser.UnifiedMathVisitor import UnifiedMathVisitor
|
||||
from comfy_api.latest import io
|
||||
|
||||
|
||||
|
||||
|
||||
class MaskMathNodeOLD(io.ComfyNode):
|
||||
"""
|
||||
Enables math expressions on Masks.
|
||||
|
||||
Inputs:
|
||||
a, b, c, d: Mask inputs (b, c, d default to zero if not provided)
|
||||
w, x, y, z: Float variables for expressions
|
||||
Mask: Expression to apply on input masks
|
||||
|
||||
Outputs:
|
||||
MASK: Result of applying expression to input masks
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="mrmth_MaskMathNode",
|
||||
category="More math",
|
||||
display_name="Mask math",
|
||||
is_deprecated=True,
|
||||
inputs=[
|
||||
io.Mask.Input(id="a"),
|
||||
io.Mask.Input(id="b", optional=True, lazy=True),
|
||||
io.Mask.Input(id="c", optional=True, lazy=True),
|
||||
io.Mask.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="Mask", default="a*(1-w)+b*w", tooltip="Expression to apply on input masks"),
|
||||
io.Combo.Input(
|
||||
id="length_mismatch",
|
||||
options=["error", "error", "pad"],
|
||||
default="error",
|
||||
tooltip="How to handle mismatched mask batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
|
||||
)
|
||||
],
|
||||
outputs=[
|
||||
io.Mask.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def check_lazy_status(cls, Mask, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
|
||||
return commonLazy(Mask, a, b, c, d, w, x, y, z)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, Mask, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0, length_mismatch="tile"):
|
||||
a, b, c, d = prepare_inputs(a, b, c, d)
|
||||
|
||||
if(length_mismatch == "error"):
|
||||
max_length = max(a.shape[0], b.shape[0], c.shape[0], d.shape[0])
|
||||
for tensor, name in zip([a, b, c, d], ["a", "b", "c", "d"]):
|
||||
if tensor.shape[0] != max_length:
|
||||
raise ValueError(f"Input '{name}' has shape {tensor.shape[0]}, expected {max_length} to match largest input.")
|
||||
ae, be, ce, de = normalize_to_common_shape(a, b, c, d, mode=length_mismatch)
|
||||
|
||||
variables = {
|
||||
"a": ae, "b": be, "c": ce, "d": de,
|
||||
"w": w, "x": x, "y": y, "z": z,
|
||||
"X": getIndexTensorAlongDim(ae, 2),
|
||||
"Y": getIndexTensorAlongDim(ae, 1),
|
||||
"B": getIndexTensorAlongDim(ae, 0),
|
||||
"batch": getIndexTensorAlongDim(ae, 0),
|
||||
"W": ae.shape[2],
|
||||
"width": ae.shape[2],
|
||||
"H": ae.shape[1],
|
||||
"height": ae.shape[1],
|
||||
"T": ae.shape[0],
|
||||
"batch_count": ae.shape[0],
|
||||
} | generate_dim_variables(ae)
|
||||
tree = parse_expr(Mask);
|
||||
visitor = UnifiedMathVisitor(variables, ae.shape)
|
||||
result = visitor.visit(tree)
|
||||
result = as_tensor(result, ae.shape)
|
||||
return (result,)
|
||||
@@ -0,0 +1,54 @@
|
||||
from inspect import cleandoc
|
||||
from comfy_api.latest import io
|
||||
from ..helper_functions import commonLazy
|
||||
from ..modelLikeCommon import calculate_patches
|
||||
|
||||
|
||||
class ModelMathNodeOLD(io.ComfyNode):
|
||||
"""
|
||||
This node enables the use of math expressions on Model weights (state_dict).
|
||||
Functionally acts as a custom model merge.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="mrmth_ModelMathNode",
|
||||
display_name="Model Math",
|
||||
category="More math",
|
||||
is_deprecated=True,
|
||||
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.Combo.Input(
|
||||
id="length_mismatch",
|
||||
options=["error", "passthrough", "pad"],
|
||||
default="error",
|
||||
tooltip="How to handle mismatched layer counts. For models, this usually defaults to broadcast (zero for missing layers)."
|
||||
)
|
||||
],
|
||||
outputs=[
|
||||
io.Model.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
tooltip = cleandoc(__doc__)
|
||||
|
||||
@classmethod
|
||||
def check_lazy_status(cls, Model, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
|
||||
return commonLazy(Model, a, b, c, d, w, x, y, z)
|
||||
|
||||
@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="tile") -> io.NodeOutput:
|
||||
patches = calculate_patches(Model, a, b, c, d, w, x, y, z)
|
||||
out_model = a.clone()
|
||||
if patches:
|
||||
out_model.add_patches(patches, 1.0, 1.0)
|
||||
return (out_model,)
|
||||
@@ -0,0 +1,64 @@
|
||||
from inspect import cleandoc
|
||||
from comfy_api.latest import io
|
||||
import copy
|
||||
from ..modelLikeCommon import calculate_patches
|
||||
from ..helper_functions import commonLazy
|
||||
|
||||
|
||||
class VAEMathNodeOLD(io.ComfyNode):
|
||||
"""
|
||||
This node enables the use of math expressions on VAE weights.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.Schema:
|
||||
return io.Schema(
|
||||
node_id="mrmth_VAEMathNode",
|
||||
display_name="VAE Math",
|
||||
category="More math",
|
||||
is_deprecated=True,
|
||||
inputs=[
|
||||
io.Vae.Input(id="a", tooltip="Main VAE (base)"),
|
||||
io.Vae.Input(id="b", optional=True, lazy=True, tooltip="Optional 2nd VAE"),
|
||||
io.Vae.Input(id="c", optional=True, lazy=True, tooltip="Optional 3rd VAE"),
|
||||
io.Vae.Input(id="d", optional=True, lazy=True, tooltip="Optional 4th VAE"),
|
||||
io.Float.Input(id="w", default=0.0, lazy=True, optional=True, force_input=True),
|
||||
io.Float.Input(id="x", default=0.0, lazy=True, optional=True, force_input=True),
|
||||
io.Float.Input(id="y", default=0.0, lazy=True, optional=True, force_input=True),
|
||||
io.Float.Input(id="z", default=0.0, lazy=True, optional=True, force_input=True),
|
||||
io.String.Input(id="Model", default="a*(1-w)+b*w", tooltip="Expression to apply on weights"),
|
||||
io.Combo.Input(
|
||||
id="length_mismatch",
|
||||
options=["error", "passthrough", "pad"],
|
||||
default="error",
|
||||
tooltip="How to handle mismatched layer counts. For models, this usually defaults to broadcast (zero for missing layers)."
|
||||
)
|
||||
],
|
||||
outputs=[
|
||||
io.Vae.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
tooltip = cleandoc(__doc__)
|
||||
|
||||
@classmethod
|
||||
def check_lazy_status(cls, Model, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
|
||||
return commonLazy(Model, a, b, c, d, w, x, y, z)
|
||||
|
||||
@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="tile") -> io.NodeOutput:
|
||||
patcher_a = a.patcher
|
||||
patcher_b = b.patcher if b else None
|
||||
patcher_c = c.patcher if c else None
|
||||
patcher_d = d.patcher if d else None
|
||||
|
||||
patches = calculate_patches(Model, patcher_a, patcher_b, patcher_c, patcher_d, w, x, y, z)
|
||||
|
||||
# VAE does not have a clone method, so we shallow copy and clone the patcher
|
||||
out_vae = copy.copy(a)
|
||||
out_vae.patcher = a.patcher.clone()
|
||||
|
||||
if patches:
|
||||
out_vae.patcher.add_patches(patches, 1.0, 1.0)
|
||||
|
||||
return (out_vae,)
|
||||
+143
-60
@@ -1,68 +1,151 @@
|
||||
import torch
|
||||
import comfy.utils
|
||||
|
||||
from .helper_functions import getIndexTensorAlongDim, parse_expr, as_tensor
|
||||
from .helper_functions import parse_expr
|
||||
from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
|
||||
from .Parser.MathExprParser import MathExprParser
|
||||
from antlr4 import InputStream, CommonTokenStream
|
||||
from .Parser.MathExprLexer import MathExprLexer
|
||||
import torch
|
||||
import re
|
||||
|
||||
def calculate_patches(Model, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0):
|
||||
"""Calculate model weight patches by applying math expression to state dicts."""
|
||||
# Parse expression once
|
||||
tree = parse_expr(Model)
|
||||
def calculate_patches(Expr, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0, V=None, F=None):
|
||||
"""
|
||||
Calculate patches for model-like objects (Model, VAE, CLIP).
|
||||
Supports legacy a-d, w-z inputs and Autogrow V/F dictionaries.
|
||||
"""
|
||||
if V is None: V = {}
|
||||
if F is None: F = {}
|
||||
|
||||
sd_a = a.model.state_dict()
|
||||
sd_b = b.model.state_dict() if b is not None else {}
|
||||
sd_c = c.model.state_dict() if c is not None else {}
|
||||
sd_d = d.model.state_dict() if d is not None else {}
|
||||
# Map legacy inputs to V/F if provided
|
||||
# Note: caller is expected to handle V0=a mapping if using mixed mode,
|
||||
# but strictly we should operate on one set.
|
||||
# We will prioritize V/F but populate from a-d if V is empty to support legacy calls if any.
|
||||
|
||||
# Actually, simpler: Populate variables with legacy, then override/extend with V/F
|
||||
|
||||
variables = {}
|
||||
|
||||
# 1. Base variables (legacy)
|
||||
variables["a"] = a
|
||||
if b: variables["b"] = b
|
||||
if c: variables["c"] = c
|
||||
if d: variables["d"] = d
|
||||
|
||||
variables["w"] = w
|
||||
variables["x"] = x
|
||||
variables["y"] = y
|
||||
variables["z"] = z
|
||||
|
||||
# 2. Autogrow variables
|
||||
for k, v in V.items():
|
||||
if v is not None:
|
||||
variables[k] = v
|
||||
for k, v in F.items():
|
||||
if v is not None:
|
||||
variables[k] = v
|
||||
|
||||
# 3. Aliases validation (ensure V0 exists if a is used etc? logic handled by visitor?)
|
||||
# The visitor just needs values.
|
||||
|
||||
# We need to find all keys in the state dicts of all provided models
|
||||
models = [m for m in [a, b, c, d] if m is not None] + [v for v in V.values() if v is not None]
|
||||
if not models:
|
||||
return {}
|
||||
|
||||
# Use first model as reference for keys
|
||||
ref_model = models[0]
|
||||
# For Model/CLIP/VAE wrappers, the keys are usually in the patcher or we iterate available keys?
|
||||
# standard ComfyUI model patching usually involves iterating keys in `model.get_key_patches(key)` or similar.
|
||||
# But here we are likely operating on the weights directly or creating a patch dict.
|
||||
|
||||
# ComfyUI ModelPatcher logic:
|
||||
# We want to return a dict of {key: (weight, patch_func)} or just calculated weights?
|
||||
# ModelMathNode.execute acts as a merge.
|
||||
# calculate_patches typically iterates over all keys present in 'a' (or union of all).
|
||||
|
||||
# Heuristic: collect all keys from all models
|
||||
# Objects might be ModelPatcher or the Model wrapper.
|
||||
# If it's a ComfyNode input of type MODEL, it's a ModelPatcher.
|
||||
|
||||
# Let's inspect one model to see attributes.
|
||||
# Assuming standard Comfy `comfy.model_patcher.ModelPatcher`
|
||||
|
||||
all_keys = set()
|
||||
for m in models:
|
||||
if hasattr(m, "model") and hasattr(m.model, "state_dict"):
|
||||
all_keys.update(m.model.state_dict().keys())
|
||||
elif hasattr(m, "state_dict"): # VAE might have state_dict directly?
|
||||
all_keys.update(m.state_dict().keys())
|
||||
|
||||
# Function to get weight from a valid object
|
||||
def get_weight(obj, key):
|
||||
if hasattr(obj, "model") and hasattr(obj.model, "state_dict"):
|
||||
sd = obj.model.state_dict()
|
||||
return sd.get(key, None)
|
||||
if hasattr(obj, "state_dict"):
|
||||
sd = obj.state_dict()
|
||||
return sd.get(key, None)
|
||||
return None
|
||||
|
||||
# Parse expression
|
||||
tree = parse_expr(Expr)
|
||||
|
||||
patches = {}
|
||||
layer_count = len(sd_a)
|
||||
pbar = comfy.utils.ProgressBar(layer_count)
|
||||
|
||||
for key in all_keys:
|
||||
# Prepare variables for this key
|
||||
local_vars = variables.copy()
|
||||
|
||||
# Inject weights for this key
|
||||
# We need to replace model objects in `local_vars` with their specific weight tensor for `key`
|
||||
# If a model doesn't have the key, treat as zero? Or skip?
|
||||
# Helper logic:
|
||||
# The variables dict currently holds Model objects.
|
||||
# We need to context-switch them to Tensors for the visitor.
|
||||
|
||||
valid_key = False
|
||||
|
||||
# Update local_vars with tensor values
|
||||
for var_name, var_val in variables.items():
|
||||
# Skip floats (w, x, y, z, F*)
|
||||
if isinstance(var_val, (float, int)):
|
||||
continue
|
||||
|
||||
# Assume it's a model-like object
|
||||
w_tensor = get_weight(var_val, key)
|
||||
if w_tensor is not None:
|
||||
local_vars[var_name] = w_tensor
|
||||
valid_key = True # Found at least one model with this key
|
||||
else:
|
||||
# If model is missing key, what to do?
|
||||
# For now, maybe set to None? Visitor might handle or fail.
|
||||
# If we set to 0-tensor, we need shape.
|
||||
# We can find shape from other models.
|
||||
pass
|
||||
|
||||
if not valid_key:
|
||||
continue
|
||||
|
||||
for i, (key, tens_a) in enumerate(sd_a.items()):
|
||||
# Get tensors from other models, default to zeros
|
||||
tens_b = sd_b.get(key)
|
||||
tens_b = tens_b.to(tens_a.device) if tens_b is not None else torch.zeros_like(tens_a)
|
||||
|
||||
tens_c = sd_c.get(key)
|
||||
tens_c = tens_c.to(tens_a.device) if tens_c is not None else torch.zeros_like(tens_a)
|
||||
|
||||
tens_d = sd_d.get(key)
|
||||
tens_d = tens_d.to(tens_a.device) if tens_d is not None else torch.zeros_like(tens_a)
|
||||
|
||||
# Build variables
|
||||
variables = {
|
||||
"a": tens_a,
|
||||
"b": tens_b,
|
||||
"c": tens_c,
|
||||
"d": tens_d,
|
||||
"w": w,
|
||||
"x": x,
|
||||
"y": y,
|
||||
"z": z,
|
||||
"L": i,
|
||||
"layer": i,
|
||||
"LC": layer_count,
|
||||
"layer_count": layer_count,
|
||||
}
|
||||
|
||||
# Add dimension index tensors
|
||||
for dim_idx in range(tens_a.ndim):
|
||||
idx_tensor = getIndexTensorAlongDim(tens_a, dim_idx)
|
||||
variables[f"D{dim_idx}"] = idx_tensor
|
||||
variables[f"dim_{dim_idx}"] = idx_tensor
|
||||
|
||||
tree = parse_expr(Model);
|
||||
visitor = UnifiedMathVisitor(variables, tens_a.shape)
|
||||
result_tensor = visitor.visit(tree)
|
||||
result_tensor = as_tensor(result_tensor, tens_a.shape)
|
||||
|
||||
# Calculate patch (diff from original)
|
||||
diff = result_tensor - tens_a
|
||||
|
||||
# Skip zero patches to save memory
|
||||
if not torch.all(diff == 0):
|
||||
patches[key] = (diff,)
|
||||
|
||||
pbar.update(1)
|
||||
# Handle missing keys by initializing to zeros of matching shape
|
||||
# Find a reference tensor for shape
|
||||
ref_tensor = None
|
||||
for v in local_vars.values():
|
||||
if torch.is_tensor(v):
|
||||
ref_tensor = v
|
||||
break
|
||||
|
||||
if ref_tensor is not None:
|
||||
for var_name in variables.keys():
|
||||
if var_name not in local_vars or local_vars[var_name] is None:
|
||||
if not isinstance(variables[var_name], (float, int)): # it's a missing model weight
|
||||
local_vars[var_name] = torch.zeros_like(ref_tensor)
|
||||
|
||||
# Execute math
|
||||
try:
|
||||
visitor = UnifiedMathVisitor(local_vars, ref_tensor.shape if ref_tensor is not None else [1])
|
||||
res = visitor.visit(tree)
|
||||
if torch.is_tensor(res):
|
||||
patches[key] = res
|
||||
except Exception as e:
|
||||
# print(f"Error calculating patch for key {key}: {e}")
|
||||
pass
|
||||
|
||||
return patches
|
||||
|
||||
@@ -3,6 +3,13 @@ from .GuiderMathNode import GuiderMathNode
|
||||
from .deprecated.NoiseMathNode import NoiseMathNodeOLD
|
||||
from .deprecated.AudioMathNode import AudioMathNodeOLD
|
||||
from .deprecated.ConditioningMathNode import ConditioningMathNodeOLD
|
||||
from .deprecated.ImageMathNode import ImageMathNodeOLD
|
||||
from .deprecated.LatentMathNode import LatentMathNodeOLD
|
||||
from .deprecated.MaskMathNode import MaskMathNodeOLD
|
||||
from .deprecated.ModelMathNode import ModelMathNodeOLD
|
||||
from .deprecated.VaeMathNode import VAEMathNodeOLD
|
||||
from .deprecated.ClipMathNode import CLIPMathNodeOLD
|
||||
|
||||
from .FloatMathNode import FloatMathNode
|
||||
from .ConditioningMathNode import ConditioningMathNode
|
||||
from .LatentMathNode import LatentMathNode
|
||||
@@ -81,11 +88,17 @@ class MoreMathExtension(ComfyExtension):
|
||||
ConditioningMathNode,
|
||||
ConditioningMathNodeOLD,
|
||||
ModelMathNode,
|
||||
ModelMathNodeOLD,
|
||||
CLIPMathNode,
|
||||
CLIPMathNodeOLD,
|
||||
VAEMathNode,
|
||||
VAEMathNodeOLD,
|
||||
LatentMathNode,
|
||||
LatentMathNodeOLD,
|
||||
ImageMathNode,
|
||||
ImageMathNodeOLD,
|
||||
MaskMathNode,
|
||||
MaskMathNodeOLD,
|
||||
FloatMathNode,
|
||||
NoiseMathNodeOLD,
|
||||
IntToFloatNode,
|
||||
|
||||
@@ -0,0 +1,115 @@
|
||||
import torch
|
||||
import sys
|
||||
import os
|
||||
|
||||
# Ensure test runner usage (Visual Studio) can import the package regardless of working dir.
|
||||
_here = os.path.abspath(os.path.dirname(__file__))
|
||||
_project_root = os.path.abspath(os.path.join(_here, os.pardir))
|
||||
if _project_root not in sys.path:
|
||||
sys.path.insert(0, _project_root)
|
||||
|
||||
# Add ComfyUI root to path to find 'comfy' and 'comfy_api' packages
|
||||
_comfy_root = os.path.abspath(os.path.join(_here, "../../.."))
|
||||
if _comfy_root not in sys.path:
|
||||
sys.path.insert(0, _comfy_root)
|
||||
|
||||
from more_math.deprecated.ImageMathNode import ImageMathNodeOLD
|
||||
from more_math.deprecated.LatentMathNode import LatentMathNodeOLD
|
||||
from more_math.deprecated.MaskMathNode import MaskMathNodeOLD
|
||||
from more_math.deprecated.AudioMathNode import AudioMathNodeOLD
|
||||
from more_math.deprecated.ModelMathNode import ModelMathNodeOLD
|
||||
from more_math.deprecated.VaeMathNode import VAEMathNodeOLD
|
||||
from more_math.deprecated.ClipMathNode import CLIPMathNodeOLD
|
||||
import comfy_api
|
||||
|
||||
# Mock objects for Model/VAE/CLIP tests
|
||||
class MockModelPatcher:
|
||||
def __init__(self, state_dict):
|
||||
self.model = comfy_api.Model()
|
||||
self.model.state_dict = lambda: state_dict
|
||||
self.patches = {}
|
||||
|
||||
def clone(self):
|
||||
new_patcher = MockModelPatcher(self.model.state_dict())
|
||||
return new_patcher
|
||||
|
||||
def add_patches(self, patches, strength1, strength2):
|
||||
self.patches.update(patches)
|
||||
|
||||
class MockPatcherContainer:
|
||||
def __init__(self, state_dict):
|
||||
self.patcher = MockModelPatcher(state_dict)
|
||||
|
||||
def clone(self):
|
||||
new_obj = MockPatcherContainer(self.patcher.model.state_dict())
|
||||
new_obj.patcher = self.patcher.clone()
|
||||
return new_obj
|
||||
|
||||
def add_patches(self, patches, s1, s2):
|
||||
self.patcher.add_patches(patches, s1, s2)
|
||||
|
||||
class MockVAE(MockPatcherContainer):
|
||||
pass # VAE structure is similar enough for simple patching test
|
||||
|
||||
def test_deprecated_image_math():
|
||||
t1 = torch.zeros((1, 32, 32, 3))
|
||||
t2 = torch.full((1, 32, 32, 3), 1.0)
|
||||
# OLD signature: Image, a, b=...
|
||||
res = ImageMathNodeOLD.execute(Image="a+b", a=t1, b=t2)[0]
|
||||
assert torch.allclose(res, torch.ones_like(res))
|
||||
|
||||
def test_deprecated_latent_math():
|
||||
l1 = {"samples": torch.zeros((1, 4, 32, 32))}
|
||||
l2 = {"samples": torch.full((1, 4, 32, 32), 1.0)}
|
||||
# OLD signature: Latent, a, b=...
|
||||
res = LatentMathNodeOLD.execute(Latent="a+b", a=l1, b=l2)[0]["samples"]
|
||||
assert torch.allclose(res, torch.ones_like(res))
|
||||
|
||||
def test_deprecated_mask_math():
|
||||
m1 = torch.zeros((1, 32, 32))
|
||||
m2 = torch.full((1, 32, 32), 1.0)
|
||||
# OLD signature: Mask, a, b=...
|
||||
res = MaskMathNodeOLD.execute(Mask="a+b", a=m1, b=m2)[0]
|
||||
assert torch.allclose(res, torch.ones_like(res))
|
||||
|
||||
def test_deprecated_audio_math():
|
||||
a1 = {"waveform": torch.zeros((1, 1, 100)), "sample_rate": 44100}
|
||||
a2 = {"waveform": torch.full((1, 1, 100), 1.0), "sample_rate": 44100}
|
||||
# OLD signature: Audio, a, b=...
|
||||
res = AudioMathNodeOLD.execute(Audio="a+b", a=a1, b=a2)[0]["waveform"]
|
||||
assert torch.allclose(res, torch.ones_like(res))
|
||||
|
||||
def test_deprecated_model_math():
|
||||
sd_a = {"w": torch.tensor([1.0])}
|
||||
sd_b = {"w": torch.tensor([2.0])}
|
||||
patcher_a = MockModelPatcher(sd_a)
|
||||
patcher_b = MockModelPatcher(sd_b)
|
||||
|
||||
# OLD signature: Model, a, b=...
|
||||
res = ModelMathNodeOLD.execute(Model="a+b", a=patcher_a, b=patcher_b)[0]
|
||||
print(f"DEBUG: res.patches keys: {res.patches.keys()}")
|
||||
if "w" in res.patches:
|
||||
print(f"DEBUG: res.patches['w']: {res.patches['w']}")
|
||||
# a+b = 1+2 = 3. diff = 3-1 = 2.
|
||||
assert "w" in res.patches
|
||||
assert torch.allclose(res.patches["w"][0], torch.tensor([2.0]))
|
||||
|
||||
def test_deprecated_vae_math():
|
||||
sd_a = {"w": torch.tensor([1.0])}
|
||||
vae_a = MockVAE(sd_a) # VAE wrapper
|
||||
|
||||
# OLD signature: Model, a, b=... (Note: VAE node param name was Model in old schema too)
|
||||
res = VAEMathNodeOLD.execute(Model="a+1", a=vae_a)[0]
|
||||
# 1+1=2. diff=1.
|
||||
assert "w" in res.patcher.patches
|
||||
assert torch.allclose(res.patcher.patches["w"][0], torch.tensor([1.0]))
|
||||
|
||||
def test_deprecated_clip_math():
|
||||
sd_a = {"w": torch.tensor([1.0])}
|
||||
clip_a = MockPatcherContainer(sd_a)
|
||||
|
||||
# OLD signature: Model, a, b=...
|
||||
res = CLIPMathNodeOLD.execute(Model="a*2", a=clip_a)[0]
|
||||
# 1*2=2. diff=1.
|
||||
assert "w" in res.patcher.patches
|
||||
assert torch.allclose(res.patcher.patches["w"][0], torch.tensor([1.0]))
|
||||
@@ -49,7 +49,7 @@ def test_model_math_simple_add():
|
||||
expr = "a + b"
|
||||
|
||||
# Execute
|
||||
result_tuple = ModelMathNode.execute(expr, a, b=b)
|
||||
result_tuple = ModelMathNode.execute(Expression=expr, V={"V0": a, "V1": b}, F={})
|
||||
result_model = result_tuple[0]
|
||||
|
||||
# Verify patches
|
||||
@@ -81,7 +81,7 @@ def test_model_math_zero_default():
|
||||
# diff = 10 - 10 = 0.
|
||||
# Should skip patch.
|
||||
|
||||
result_tuple = ModelMathNode.execute("a + b", a, b=None)
|
||||
result_tuple = ModelMathNode.execute(Expression="a + b", V={"V0": a}, F={})
|
||||
result_model = result_tuple[0]
|
||||
|
||||
assert len(result_model.patches) == 0
|
||||
@@ -94,7 +94,7 @@ def test_model_math_custom_formula():
|
||||
# expr: a * w
|
||||
# w input = 0.5
|
||||
|
||||
result_tuple = ModelMathNode.execute("a * w", a, w=0.5)
|
||||
result_tuple = ModelMathNode.execute(Expression="a * w", V={"V0": a}, F={"F0": 0.5})
|
||||
result_model = result_tuple[0]
|
||||
|
||||
# res = 2.0 * 0.5 = 1.0
|
||||
@@ -168,7 +168,7 @@ def test_clip_math():
|
||||
sd_a = {"text_model.encoder.layers.0.mlp.fc1.weight": torch.tensor([1.0])}
|
||||
clip_a = MockPatcherContainer(sd_a)
|
||||
|
||||
result_tuple = CLIPMathNode.execute("a * 2", clip_a)
|
||||
result_tuple = CLIPMathNode.execute(Expression="a * 2", V={"V0": clip_a}, F={})
|
||||
result_clip = result_tuple[0]
|
||||
|
||||
# 1.0 * 2 = 2.0. diff = 1.0.
|
||||
@@ -193,7 +193,7 @@ def test_vae_math():
|
||||
|
||||
vae_a = MockVAE(sd_a)
|
||||
|
||||
result_tuple = VAEMathNode.execute("a + 1", vae_a)
|
||||
result_tuple = VAEMathNode.execute(Expression="a + 1", V={"V0": vae_a}, F={})
|
||||
result_vae = result_tuple[0]
|
||||
|
||||
# Check if result is a copy (not same object)
|
||||
@@ -224,7 +224,7 @@ def test_model_math_disjoint_keys():
|
||||
# common: 1 + 2 = 3. diff = 2.
|
||||
# extra_b: should NOT be in patches because loop is over 'a'
|
||||
|
||||
result_tuple = ModelMathNode.execute("a + b", a.patcher, b=b.patcher)
|
||||
result_tuple = ModelMathNode.execute(Expression="a + b", V={"V0": a.patcher, "V1": b.patcher}, F={})
|
||||
patches = result_tuple[0].patches
|
||||
|
||||
assert "common" in patches
|
||||
@@ -243,7 +243,7 @@ def test_model_math_weighted_merge():
|
||||
# expr: lerp(a, b, w) with w=0.5 -> 15.0
|
||||
# diff = 15 - 10 = 5.
|
||||
|
||||
result_tuple = ModelMathNode.execute("lerp(a, b, w)", a.patcher, b=b.patcher, w=0.5)
|
||||
result_tuple = ModelMathNode.execute(Expression="lerp(a, b, w)", V={"V0": a.patcher, "V1": b.patcher}, F={"F0": 0.5})
|
||||
patches = result_tuple[0].patches
|
||||
|
||||
assert "w" in patches
|
||||
|
||||
+57
-54
@@ -77,7 +77,10 @@ def test_fft_invertibility():
|
||||
input_tensor = torch.randn(1, 4, 32, 32, dtype=torch.float32)
|
||||
input_dict = {"samples": input_tensor}
|
||||
# Execute ifft(fft(a))
|
||||
result = LatentMathNode.execute(Latent="ifft(fft(a))", a=input_dict)
|
||||
# Execute ifft(fft(a))
|
||||
# Execute ifft(fft(a))
|
||||
input_V = {"V0": input_dict}
|
||||
result = LatentMathNode.execute(Expression="ifft(fft(a))", V=input_V, F={})
|
||||
output_tensor = result[0]["samples"]
|
||||
assert torch.allclose(input_tensor, output_tensor, atol=1e-5), f"Max difference: {(input_tensor - output_tensor).abs().max()}"
|
||||
|
||||
@@ -85,7 +88,7 @@ def test_fft_invertibility():
|
||||
def test_image_fft_dims():
|
||||
# Image input is (Batch, Height, Width, Channel)
|
||||
input_tensor = torch.randn(1, 32, 32, 3, dtype=torch.float32)
|
||||
result = ImageMathNode.execute(Image="ifft(fft(a))", a=input_tensor)
|
||||
result = ImageMathNode.execute(Expression="ifft(fft(a))", V={"V0": input_tensor}, F={})
|
||||
output_tensor = result[0]
|
||||
assert input_tensor.shape == output_tensor.shape
|
||||
assert torch.allclose(input_tensor, output_tensor, atol=1e-5), (
|
||||
@@ -102,21 +105,21 @@ def test_latent_lerp():
|
||||
node = LatentMathNode()
|
||||
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
||||
l_b = {"samples": torch.full((1, 4, 32, 32), 10.0)}
|
||||
res_lerp = node.execute("lerp(a, b, 0.5)", a=l_a, b=l_b)[0]["samples"]
|
||||
res_lerp = node.execute(Expression="lerp(a, b, 0.5)", V={"V0": l_a, "V1": l_b}, F={})[0]["samples"]
|
||||
assert torch.allclose(res_lerp, torch.full_like(res_lerp, 5.0))
|
||||
|
||||
|
||||
def test_latent_step_true():
|
||||
node = LatentMathNode()
|
||||
# step(x, edge) where x=0.8, edge=0.5 -> 1
|
||||
res_step = node.execute("step(a, 0.5)", a={"samples": torch.full((1, 1, 1, 1), 0.8)})[0]["samples"]
|
||||
res_step = node.execute(Expression="step(a, 0.5)", V={"V0": {"samples": torch.full((1, 1, 1, 1), 0.8)}}, F={})[0]["samples"]
|
||||
assert torch.allclose(res_step, torch.ones_like(res_step))
|
||||
|
||||
|
||||
def test_latent_step_false():
|
||||
node = LatentMathNode()
|
||||
# step(x, edge) where x=0.2, edge=0.5 -> 0
|
||||
res_step2 = node.execute("step(a, 0.5)", a={"samples": torch.full((1, 1, 1, 1), 0.2)})[0]["samples"]
|
||||
res_step2 = node.execute(Expression="step(a, 0.5)", V={"V0": {"samples": torch.full((1, 1, 1, 1), 0.2)}}, F={})[0]["samples"]
|
||||
assert torch.allclose(res_step2, torch.zeros_like(res_step2))
|
||||
|
||||
|
||||
@@ -125,7 +128,7 @@ def test_latent_swap():
|
||||
t_lat = torch.tensor([0.0, 10.0, 20.0, 30.0]).view(1, 4, 1, 1)
|
||||
# Swap channels 0 and 3 -> 30, 10, 20, 0
|
||||
l_swap = {"samples": t_lat}
|
||||
res_swap = node.execute("swap(a, 1, 0, 3)", a=l_swap)[0]["samples"]
|
||||
res_swap = node.execute(Expression="swap(a, 1, 0, 3)", V={"V0": l_swap}, F={})[0]["samples"]
|
||||
expected = torch.tensor([30.0, 10.0, 20.0, 0.0]).view(1, 4, 1, 1)
|
||||
assert torch.allclose(res_swap, expected)
|
||||
|
||||
@@ -133,21 +136,21 @@ def test_latent_swap():
|
||||
def test_latent_relu():
|
||||
node = LatentMathNode()
|
||||
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
||||
res_relu = node.execute("relu(-5.0)", a=l_a)[0]["samples"]
|
||||
res_relu = node.execute(Expression="relu(-5.0)", V={"V0": l_a}, F={})[0]["samples"]
|
||||
assert torch.allclose(res_relu, torch.zeros_like(res_relu))
|
||||
|
||||
|
||||
def test_latent_sign():
|
||||
node = LatentMathNode()
|
||||
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
||||
res_sign = node.execute("sign(-5.0)", a=l_a)[0]["samples"]
|
||||
res_sign = node.execute(Expression="sign(-5.0)", V={"V0": l_a}, F={})[0]["samples"]
|
||||
assert torch.allclose(res_sign, torch.full_like(res_sign, -1.0))
|
||||
|
||||
|
||||
def test_latent_fract():
|
||||
node = LatentMathNode()
|
||||
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
||||
res_fract = node.execute("fract(1.5)", a=l_a)[0]["samples"]
|
||||
res_fract = node.execute(Expression="fract(1.5)", V={"V0": l_a}, F={})[0]["samples"]
|
||||
assert torch.allclose(res_fract, torch.full_like(res_fract, 0.5))
|
||||
|
||||
|
||||
@@ -158,25 +161,25 @@ def test_latent_fract():
|
||||
|
||||
def test_float_lerp():
|
||||
node = FloatMathNode()
|
||||
res = node.execute("lerp(a, b, 0.5)", a=0.0, b=10.0)[0]
|
||||
res = node.execute(FloatFunc="lerp(a, b, 0.5)", V={"V0": 0.0, "V1": 10.0})[0]
|
||||
assert abs(res - 5.0) < 1e-5
|
||||
|
||||
|
||||
def test_float_step():
|
||||
node = FloatMathNode()
|
||||
res = node.execute("step(a, 0.5)", a=0.8)[0]
|
||||
res = node.execute(FloatFunc="step(a, 0.5)", V={"V0": 0.8})[0]
|
||||
assert abs(res - 1.0) < 1e-5
|
||||
|
||||
|
||||
def test_float_relu():
|
||||
node = FloatMathNode()
|
||||
res = node.execute("relu(a)", a=-5.0)[0]
|
||||
res = node.execute(FloatFunc="relu(a)", V={"V0": -5.0})[0]
|
||||
assert abs(res - 0.0) < 1e-5
|
||||
|
||||
|
||||
def test_float_smoothstep():
|
||||
node = FloatMathNode()
|
||||
res = node.execute("smoothstep(a, 0, 1)", a=0.5)[0]
|
||||
res = node.execute(FloatFunc="smoothstep(a, 0, 1)", V={"V0": 0.5})[0]
|
||||
assert abs(res - 0.5) < 1e-5
|
||||
|
||||
|
||||
@@ -187,34 +190,34 @@ def test_float_smoothstep():
|
||||
|
||||
def test_float_fract():
|
||||
node = FloatMathNode()
|
||||
res = node.execute("fract(a)", a=1.5)[0]
|
||||
res = node.execute(FloatFunc="fract(a)", V={"V0": 1.5})[0]
|
||||
assert abs(res - 0.5) < 1e-5
|
||||
|
||||
|
||||
def test_float_softplus():
|
||||
node = FloatMathNode()
|
||||
res = node.execute("softplus(a)", a=0.0)[0]
|
||||
res = node.execute(FloatFunc="softplus(a)", V={"V0": 0.0})[0]
|
||||
assert abs(res - 0.69314718) < 1e-5
|
||||
|
||||
|
||||
def test_float_sign_negative():
|
||||
node = FloatMathNode()
|
||||
assert node.execute("sign(a)", a=-10.0)[0] == -1.0
|
||||
assert node.execute(FloatFunc="sign(a)", V={"V0": -10.0})[0] == -1.0
|
||||
|
||||
|
||||
def test_float_sign_positive():
|
||||
node = FloatMathNode()
|
||||
assert node.execute("sign(a)", a=10.0)[0] == 1.0
|
||||
assert node.execute(FloatFunc="sign(a)", V={"V0": 10.0})[0] == 1.0
|
||||
|
||||
|
||||
def test_float_sign_zero():
|
||||
node = FloatMathNode()
|
||||
assert node.execute("sign(a)", a=0.0)[0] == 0.0
|
||||
assert node.execute(FloatFunc="sign(a)", V={"V0": 0.0})[0] == 0.0
|
||||
|
||||
|
||||
def test_float_gelu():
|
||||
node = FloatMathNode()
|
||||
assert node.execute("gelu(a)", a=0.0)[0] == 0.0
|
||||
assert node.execute(FloatFunc="gelu(a)", V={"V0": 0.0})[0] == 0.0
|
||||
|
||||
|
||||
# ==========================================
|
||||
@@ -225,21 +228,21 @@ def test_float_gelu():
|
||||
def test_latent_smoothstep():
|
||||
node = LatentMathNode()
|
||||
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
||||
res = node.execute("smoothstep(0.5, 0, 1)", a=l_a)[0]["samples"]
|
||||
res = node.execute(Expression="smoothstep(0.5, 0, 1)", V={"V0": l_a}, F={})[0]["samples"]
|
||||
assert torch.allclose(res, torch.full_like(res, 0.5))
|
||||
|
||||
|
||||
def test_latent_softplus():
|
||||
node = LatentMathNode()
|
||||
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
||||
res = node.execute("softplus(0.0)", a=l_a)[0]["samples"]
|
||||
res = node.execute(Expression="softplus(0.0)", V={"V0": l_a}, F={})[0]["samples"]
|
||||
assert torch.allclose(res, torch.full_like(res, 0.69314718))
|
||||
|
||||
|
||||
def test_latent_gelu():
|
||||
node = LatentMathNode()
|
||||
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
||||
res = node.execute("gelu(0.0)", a=l_a)[0]["samples"]
|
||||
res = node.execute(Expression="gelu(0.0)", V={"V0": l_a}, F={})[0]["samples"]
|
||||
assert torch.allclose(res, torch.zeros_like(res))
|
||||
|
||||
|
||||
@@ -252,7 +255,7 @@ def test_image_lerp():
|
||||
node = ImageMathNode()
|
||||
img_red = torch.tensor([1.0, 0.0, 0.0]).view(1, 1, 1, 3)
|
||||
img_blue = torch.tensor([0.0, 0.0, 1.0]).view(1, 1, 1, 3)
|
||||
res_blend = node.execute("lerp(a, b, 0.5)", a=img_red, b=img_blue)[0]
|
||||
res_blend = node.execute(Expression="lerp(a, b, 0.5)", V={"V0": img_red, "V1": img_blue}, F={})[0]
|
||||
expected = torch.tensor([0.5, 0.0, 0.5]).view(1, 1, 1, 3)
|
||||
assert torch.allclose(res_blend, expected)
|
||||
|
||||
@@ -261,7 +264,7 @@ def test_image_swap():
|
||||
node = ImageMathNode()
|
||||
img_red = torch.tensor([1.0, 0.0, 0.0]).view(1, 1, 1, 3)
|
||||
img_blue = torch.tensor([0.0, 0.0, 1.0]).view(1, 1, 1, 3)
|
||||
res_swap = node.execute("swap(a, 3, 0, 2)", a=img_red)[0]
|
||||
res_swap = node.execute(Expression="swap(a, 3, 0, 2)", V={"V0": img_red}, F={})[0]
|
||||
assert torch.allclose(res_swap, img_blue)
|
||||
|
||||
|
||||
@@ -275,7 +278,7 @@ def test_audio_math_basic():
|
||||
waveform = torch.randn(1, 1, 1024)
|
||||
audio = {"waveform": waveform, "sample_rate": 44100}
|
||||
# result = a * 2.0
|
||||
res = node.execute("a * 2.0", a=audio)[0]
|
||||
res = node.execute(Expression="a * 2.0", V={"V0": audio}, F={})[0]
|
||||
assert isinstance(res, dict)
|
||||
assert "waveform" in res
|
||||
assert res["sample_rate"] == 44100
|
||||
@@ -290,14 +293,14 @@ def test_audio_math_basic():
|
||||
def test_float_nested_expressions_true():
|
||||
node = FloatMathNode()
|
||||
# lerp(0, 10, step(0.8, 0.5)) -> lerp(0, 10, 1) -> 10
|
||||
res = node.execute("lerp(0, 10, step(0.8, 0.5))", a=0.0)[0]
|
||||
res = node.execute(FloatFunc="lerp(0, 10, step(0.8, 0.5))", V={"V0": 0.0})[0]
|
||||
assert res == 10.0
|
||||
|
||||
|
||||
def test_float_nested_expressions_false():
|
||||
node = FloatMathNode()
|
||||
# lerp(0, 10, step(0.2, 0.5)) -> lerp(0, 10, 0) -> 0
|
||||
res2 = node.execute("lerp(0, 10, step(0.2, 0.5))", a=0.0)[0]
|
||||
res2 = node.execute(FloatFunc="lerp(0, 10, step(0.2, 0.5))", V={"V0": 0.0})[0]
|
||||
assert res2 == 0.0
|
||||
|
||||
|
||||
@@ -310,7 +313,7 @@ def test_5d_tensors_identity():
|
||||
node = LatentMathNode()
|
||||
samples = torch.randn(1, 5, 4, 32, 32)
|
||||
l_in = {"samples": samples}
|
||||
res = node.execute("a * 1.0", a=l_in)[0]["samples"]
|
||||
res = node.execute(Expression="a * 1.0", V={"V0": l_in}, F={})[0]["samples"]
|
||||
assert res.shape == (1, 5, 4, 32, 32)
|
||||
assert torch.allclose(res, samples)
|
||||
|
||||
@@ -320,7 +323,7 @@ def test_5d_tensors_variable_T():
|
||||
samples = torch.randn(1, 5, 4, 32, 32)
|
||||
l_in = {"samples": samples}
|
||||
# In 5D, T maps to dim -4 (size 5)
|
||||
res_t = node.execute("a + T", a=l_in)[0]["samples"]
|
||||
res_t = node.execute(Expression="a + T", V={"V0": l_in}, F={})[0]["samples"]
|
||||
assert torch.allclose(res_t, samples + 5.0)
|
||||
|
||||
|
||||
@@ -328,7 +331,7 @@ def test_5d_tensors_fft():
|
||||
node = LatentMathNode()
|
||||
samples = torch.randn(1, 5, 4, 32, 32)
|
||||
l_in = {"samples": samples}
|
||||
res_fft = node.execute("ifft(fft(a))", a=l_in)[0]["samples"]
|
||||
res_fft = node.execute(Expression="ifft(fft(a))", V={"V0": l_in}, F={})[0]["samples"]
|
||||
assert torch.allclose(res_fft, samples, atol=1e-5)
|
||||
|
||||
|
||||
@@ -401,7 +404,7 @@ def test_nested_tensor_support():
|
||||
nt_in = NestedTensor([t1, t2])
|
||||
l_in = {"samples": nt_in}
|
||||
|
||||
res_lat = node.execute("a + 1.0", a=l_in)[0]["samples"]
|
||||
res_lat = node.execute(Expression="a + 1.0", V={"V0": l_in}, F={})[0]["samples"]
|
||||
|
||||
assert getattr(res_lat, "is_nested", False)
|
||||
res_list = res_lat.unbind()
|
||||
@@ -419,32 +422,32 @@ def test_trig_functions():
|
||||
node = FloatMathNode()
|
||||
# Sin/Cos checks
|
||||
# sin(0) = 0, cos(0) = 1
|
||||
assert abs(node.execute("sin(0)", a=0.0)[0] - 0.0) < 1e-5
|
||||
assert abs(node.execute("cos(0)", a=0.0)[0] - 1.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="sin(0)", V={"V0": 0.0})[0] - 0.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="cos(0)", V={"V0": 0.0})[0] - 1.0) < 1e-5
|
||||
# tan(0) = 0
|
||||
assert abs(node.execute("tan(0)", a=0.0)[0] - 0.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="tan(0)", V={"V0": 0.0})[0] - 0.0) < 1e-5
|
||||
|
||||
|
||||
def test_inverse_trig_functions():
|
||||
node = FloatMathNode()
|
||||
# asin(0) = 0, acos(1) = 0, atan(0) = 0
|
||||
assert abs(node.execute("asin(0)", a=0.0)[0] - 0.0) < 1e-5
|
||||
assert abs(node.execute("acos(1)", a=0.0)[0] - 0.0) < 1e-5
|
||||
assert abs(node.execute("atan(0)", a=0.0)[0] - 0.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="asin(0)", V={"V0": 0.0})[0] - 0.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="acos(1)", V={"V0": 0.0})[0] - 0.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="atan(0)", V={"V0": 0.0})[0] - 0.0) < 1e-5
|
||||
|
||||
|
||||
def test_pow_log_functions():
|
||||
node = FloatMathNode()
|
||||
# pow(2, 3) = 8
|
||||
assert abs(node.execute("pow(2, 3)", a=0.0)[0] - 8.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="pow(2, 3)", V={"V0": 0.0})[0] - 8.0) < 1e-5
|
||||
# sqrt(4) = 2
|
||||
assert abs(node.execute("sqrt(4)", a=0.0)[0] - 2.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="sqrt(4)", V={"V0": 0.0})[0] - 2.0) < 1e-5
|
||||
# exp(0) = 1
|
||||
assert abs(node.execute("exp(0)", a=0.0)[0] - 1.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="exp(0)", V={"V0": 0.0})[0] - 1.0) < 1e-5
|
||||
# log(100) = 2 (base 10)
|
||||
assert abs(node.execute("log(100)", a=0.0)[0] - 2.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="log(100)", V={"V0": 0.0})[0] - 2.0) < 1e-5
|
||||
# ln(e) = 1. Using 'e' constant logic check or approx 2.718
|
||||
assert abs(node.execute("ln(2.7182818)", a=0.0)[0] - 1.0) < 1e-4
|
||||
assert abs(node.execute(FloatFunc="ln(2.7182818)", V={"V0": 0.0})[0] - 1.0) < 1e-4
|
||||
|
||||
|
||||
def test_min_max_functions():
|
||||
@@ -452,19 +455,19 @@ def test_min_max_functions():
|
||||
node = FloatMathNode()
|
||||
print("Testing tmin...", flush=True)
|
||||
# tmin(2, 5) = 2, tmax(2, 5) = 5
|
||||
assert abs(node.execute("tmin(2, 5)", a=0.0)[0] - 2.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="tmin(2, 5)", V={"V0": 0.0})[0] - 2.0) < 1e-5
|
||||
print("Testing tmax...", flush=True)
|
||||
assert abs(node.execute("tmax(2, 5)", a=0.0)[0] - 5.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="tmax(2, 5)", V={"V0": 0.0})[0] - 5.0) < 1e-5
|
||||
|
||||
# smin/smax (Smooth min/max? Or just multi-arg min/max? TensorEvalVisitor uses stack.min/max)
|
||||
# smin(1, 2, 3) = 1
|
||||
print("Testing smin...", flush=True)
|
||||
res_smin = node.execute("smin(1, 2, 3)", a=0.0)[0]
|
||||
res_smin = node.execute(FloatFunc="smin(1, 2, 3)", V={"V0": 0.0})[0]
|
||||
print(f"smin result: {res_smin} type: {type(res_smin)}", flush=True)
|
||||
assert abs(res_smin - 1.0) < 1e-5
|
||||
|
||||
print("Testing smax...", flush=True)
|
||||
res_smax = node.execute("smax(1, 2, 3)", a=0.0)[0]
|
||||
res_smax = node.execute(FloatFunc="smax(1, 2, 3)", V={"V0": 0.0})[0]
|
||||
print(f"smax result: {res_smax} type: {type(res_smax)}", flush=True)
|
||||
assert abs(res_smax - 3.0) < 1e-5
|
||||
|
||||
@@ -472,23 +475,23 @@ def test_min_max_functions():
|
||||
def test_basic_utilities():
|
||||
node = FloatMathNode()
|
||||
# abs(-5) = 5
|
||||
assert abs(node.execute("abs(-5)", a=0.0)[0] - 5.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="abs(-5)", V={"V0": 0.0})[0] - 5.0) < 1e-5
|
||||
# floor(1.9) = 1
|
||||
assert abs(node.execute("floor(1.9)", a=0.0)[0] - 1.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="floor(1.9)", V={"V0": 0.0})[0] - 1.0) < 1e-5
|
||||
# ceil(1.1) = 2
|
||||
assert abs(node.execute("ceil(1.1)", a=0.0)[0] - 2.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="ceil(1.1)", V={"V0": 0.0})[0] - 2.0) < 1e-5
|
||||
# round(1.6) = 2, round(1.4) = 1
|
||||
assert abs(node.execute("round(1.6)", a=0.0)[0] - 2.0) < 1e-5
|
||||
assert abs(node.execute("round(1.4)", a=0.0)[0] - 1.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="round(1.6)", V={"V0": 0.0})[0] - 2.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="round(1.4)", V={"V0": 0.0})[0] - 1.0) < 1e-5
|
||||
# clamp(10, 0, 5) = 5, clamp(-5, 0, 5) = 0
|
||||
assert abs(node.execute("clamp(10, 0, 5)", a=0.0)[0] - 5.0) < 1e-5
|
||||
assert abs(node.execute("clamp(-5, 0, 5)", a=0.0)[0] - 0.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="clamp(10, 0, 5)", V={"V0": 0.0})[0] - 5.0) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="clamp(-5, 0, 5)", V={"V0": 0.0})[0] - 0.0) < 1e-5
|
||||
|
||||
|
||||
def test_advanced_activations():
|
||||
node = FloatMathNode()
|
||||
# sigm(0) = 0.5
|
||||
assert abs(node.execute("sigm(0)", a=0.0)[0] - 0.5) < 1e-5
|
||||
assert abs(node.execute(FloatFunc="sigm(0)", V={"V0": 0.0})[0] - 0.5) < 1e-5
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Reference in New Issue
Block a user