From ebcb022cb4136932c9b1fa8bece68fbd2cb03824 Mon Sep 17 00:00:00 2001 From: mcDandy Date: Thu, 11 Dec 2025 22:59:05 +0100 Subject: [PATCH] Fixed conditioning and noise math node --- more_math/ConditioningMathNode.py | 10 +++++----- more_math/NoiseMathNode.py | 8 ++++---- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/more_math/ConditioningMathNode.py b/more_math/ConditioningMathNode.py index 2d6f67e..d93b8b6 100644 --- a/more_math/ConditioningMathNode.py +++ b/more_math/ConditioningMathNode.py @@ -59,11 +59,11 @@ 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"]) if a[0][1]["pooled_output"] else None}]] + b = [[torch.zeros_like(a[0][0]), {"pooled_output": torch.zeros_like(a[0][1]["pooled_output"]) if a[0][1]["pooled_output"] is not None else None}]] if c is None: - c = [[torch.zeros_like(a[0][0]), {"pooled_output": torch.zeros_like(a[0][1]["pooled_output"]) if a[0][1]["pooled_output"] else None}]] + c = [[torch.zeros_like(a[0][0]), {"pooled_output": torch.zeros_like(a[0][1]["pooled_output"]) if a[0][1]["pooled_output"] is not None else None}]] if d is None: - d = [[torch.zeros_like(a[0][0]), {"pooled_output": torch.zeros_like(a[0][1]["pooled_output"]) if a[0][1]["pooled_output"] else None}]] + d = [[torch.zeros_like(a[0][0]), {"pooled_output": torch.zeros_like(a[0][1]["pooled_output"]) if a[0][1]["pooled_output"] is not None else None}]] ta = a[0][0].clone() @@ -80,9 +80,9 @@ class ConditioningMathNode(io.ComfyNode): 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) lexer = MathExprLexer(input_stream) stream = CommonTokenStream(lexer) @@ -91,7 +91,7 @@ class ConditioningMathNode(io.ComfyNode): visitor = TensorEvalVisitor(variables,ta.shape) result1 = visitor.visit(tree) - if a[0][1]["pooled_output"]: + if a[0][1]["pooled_output"] is not None: 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) diff --git a/more_math/NoiseMathNode.py b/more_math/NoiseMathNode.py index d772dfa..833c0ae 100644 --- a/more_math/NoiseMathNode.py +++ b/more_math/NoiseMathNode.py @@ -105,10 +105,10 @@ class NoiseExecutor(): samples = input_latent["samples"] # evaluate generators / default zeros - a_val = self.a.generate_noise(input_latent) if self.a is not None else None - b_val = self.b.generate_noise(input_latent) if self.b is not None else None - c_val = self.c.generate_noise(input_latent) if self.c is not None else None - d_val = self.d.generate_noise(input_latent) if self.d is not None else None + a_val = self.a.generate_noise(input_latent) if self.a is not None else torch.zeros_like(samples) + b_val = self.b.generate_noise(input_latent) if self.b is not None else torch.zeros_like(samples) + c_val = self.c.generate_noise(input_latent) if self.c is not None else torch.zeros_like(samples) + d_val = self.d.generate_noise(input_latent) if self.d is not None else torch.zeros_like(samples) # helper to convert a returned value into a list matching ref_list def to_list(val, ref_list):