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fix error on flux2

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mcDandy
2025-11-27 20:46:07 +01:00
parent ec2714aa78
commit 670df99a5f
1 changed files with 22 additions and 19 deletions
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src/more_math/ConditioningMathNode.py
+22 -19
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@@ -20,7 +20,7 @@ class ConditioningMathNode(io.ComfyNode):
String, describing expression to mix tensor part of conditioning. Tensor probably describes the composition of the image as it has the say what is on the image.
Pooled output expression:
String, describing expression to mix pooled_output part of conditioning. pooled_output is Tensor compressed into 1 token.
outputs:
CONDITIONING:
Returns a CONDITIONING object that contains the result of the math expression applied to the input conditionings.
@@ -59,23 +59,24 @@ class ConditioningMathNode(io.ComfyNode):
@classmethod
def execute(cls, Tensor,pooled_output, a, b=None, c=None, d=None,w=0.0,x=0.0,y=0.0,z=0.0):
if b is None:
b = [[torch.zeros_like(a[0][0]), {"pooled_output": torch.zeros_like(a[0][1]["pooled_output"])}]]
b = [[torch.zeros_like(a[0][0]), {"pooled_output": torch.zeros_like(a[0][1]["pooled_output"])}]]
if c is None:
c = [[torch.zeros_like(a[0][0]), {"pooled_output": torch.zeros_like(a[0][1]["pooled_output"])}]]
c = [[torch.zeros_like(a[0][0]), {"pooled_output": torch.zeros_like(a[0][1]["pooled_output"])}]]
if d is None:
d = [[torch.zeros_like(a[0][0]), {"pooled_output": torch.zeros_like(a[0][1]["pooled_output"])}]]
ta = a[0][0].clone()
tb = b[0][0].clone()
pa = a[0][1]["pooled_output"].clone()
pb = b[0][1]["pooled_output"].clone()
tc = c[0][0].clone()
td = d[0][0].clone()
pc = c[0][1]["pooled_output"].clone()
pd = d[0][1]["pooled_output"].clone()
print(ta.shape,pa.shape);
if a[0][1]["pooled_output"]:
pa = a[0][1]["pooled_output"].clone()
pb = b[0][1]["pooled_output"].clone()
pc = c[0][1]["pooled_output"].clone()
pd = d[0][1]["pooled_output"].clone()
print(ta.shape,pa.shape if pa != None else 0);
variables = {'a': ta, 'b': tb, 'c': tc, 'd': td, 'w': w, 'x': x, 'y': y, 'z': z}
input_stream = InputStream(Tensor)
@@ -85,17 +86,19 @@ class ConditioningMathNode(io.ComfyNode):
tree = parser.expr()
visitor = TensorEvalVisitor(variables,ta.shape)
result1 = visitor.visit(tree)
variables = {'a': pa, 'b': pb, 'c': pc, 'd': pd, 'w': w, 'x': x, 'y': y, 'z': z}
input_stream = InputStream(Tensor)
lexer = MathExprLexer(input_stream)
stream = CommonTokenStream(lexer)
parser = MathExprParser(stream)
tree = parser.expr()
visitor = TensorEvalVisitor(variables,pa.shape)
result2 = visitor.visit(tree)
if a[0][1]["pooled_output"]:
variables = {'a': pa, 'b': pb, 'c': pc, 'd': pd, 'w': w, 'x': x, 'y': y, 'z': z}
input_stream = InputStream(Tensor)
lexer = MathExprLexer(input_stream)
stream = CommonTokenStream(lexer)
parser = MathExprParser(stream)
tree = parser.expr()
visitor = TensorEvalVisitor(variables,pa.shape)
result2 = visitor.visit(tree)
else:
result2 = None
print("No pooled_output found in input conditioning, skipping pooled_output calculation.")
result = [[result1, {"pooled_output": result2}]]
return (result,)