fix runtime errors + stack passing for noise

more_math/AudioMathNode.py

@@ -16,6 +16,7 @@ from .Parser.MathExprParser import MathExprParser
 import re
 import torch
 from .Stack import MrmthStack
+import copy
 
 class AudioMathNode(io.ComfyNode):
     """
@@ -91,7 +92,7 @@ class AudioMathNode(io.ComfyNode):
         tensor_keys = [k for k, v in V.items() if v is not None and isinstance(v, dict) and "waveform" in v]
         if not tensor_keys:
              raise ValueError("At least one audio input is required.")
-        stack = stack.deepcopy() if stack is not None else {}
+        stack = copy.deepcopy(stack) if stack is not None else {}
         waveforms = {k: V[k]["waveform"] for k in tensor_keys}
         sample_rates = {k + "sr": V[k].get("sample_rate", 44100) for k in tensor_keys}
 
@@ -154,7 +155,7 @@ class AudioMathNode(io.ComfyNode):
         visitor = UnifiedMathVisitor(variables, a_w.shape,a_w.device,state_storage=stack)
         result = visitor.visit(tree)
         result = as_tensor(result, a_w.shape)
-        
+
         if batching and batching > 0:
             res = torch.split(result, batching, dim=0)
             res_list = []

more_math/ClipMathNode.py

@@ -5,6 +5,8 @@ from .Parser.MathExprLexer import MathExprLexer
 from .Parser.MathExprParser import MathExprParser
 import re
 from .Stack import MrmthStack
+import copy
+
 
 
 class CLIPMathNode(io.ComfyNode):
@@ -75,7 +77,7 @@ class CLIPMathNode(io.ComfyNode):
     def execute(cls, V, F, Expression, length_mismatch="tile",stack={}) -> io.NodeOutput:
         # Determine reference CLIP
         a = V.get("V0")
-        stack = stack.deepcopy() if stack is not None else {}
+        stack = copy.deepcopy(stack) if stack is not None else {}
         if a is None:
              for m in V.values():
                  if m is not None:

more_math/ConditioningMathNode.py

@@ -90,7 +90,7 @@ class ConditioningMathNode(io.ComfyNode):
         tensor_keys = [k for k, v in V.items() if v is not None and isinstance(v, list) and len(v) > 0]
         if not tensor_keys:
              raise ValueError("At least one input is required.")
-        stack = stack.deepcopy() if stack is not None else {}
+        stack = copy.deepcopy(stack) if stack is not None else {}
 
         # Extract tensors and pooled outputs
         tensors = {}

more_math/FloatMathNode.py

@@ -10,6 +10,8 @@ from .Parser.MathExprLexer import MathExprLexer
 from .Parser.MathExprParser import MathExprParser
 import re
 from .Stack import MrmthStack
+import copy
+
 
 
 class FloatMathNode(io.ComfyNode):
@@ -84,7 +86,7 @@ class FloatMathNode(io.ComfyNode):
         variables["x"] = V.get("V5", 0.0)
         variables["y"] = V.get("V6", 0.0)
         variables["z"] = V.get("V7", 0.0)
-        stack = stack.deepcopy() if stack is not None else {}
+        stack = copy.deepcopy(stack) if stack is not None else {}
 
         # Populate all V inputs
         for k, val in V.items():
@@ -98,7 +100,6 @@ class FloatMathNode(io.ComfyNode):
 
         tree = parse_expr(FloatFunc);
         # scalar execution
-        # UnifiedMathVisitor expects variables and a shape. Shape [1] for scalar?
         visitor = UnifiedMathVisitor(variables, [1],state_storage=stack)
         result = visitor.visit(tree)
         # Result might be float or tensor(scalar)

more_math/GuiderMathNode.py

@@ -20,6 +20,7 @@ import comfy.utils
 import comfy.hooks
 import comfy.samplers
 from .Stack import MrmthStack
+import copy
 
 
 class GuiderMathNode(io.ComfyNode):
@@ -83,7 +84,7 @@ class GuiderMathNode(io.ComfyNode):
 
     @classmethod
     def execute(cls, V, F, Expression,Expression1,stack={}):
-        stack = stack.deepcopy() if stack is not None else {}
+        stack = copy.deepcopy(stack) if stack is not None else {}
         return (MathGuider(V, F, Expression,Expression1),stack)
 
 

more_math/ImageMathNode.py

@@ -7,6 +7,8 @@ from .Parser.MathExprParser import MathExprParser
 import re
 import torch
 from .Stack import MrmthStack
+import copy
+
 
 class ImageMathNode(io.ComfyNode):
     """
@@ -86,7 +88,7 @@ class ImageMathNode(io.ComfyNode):
              raise ValueError("At least one input is required.")
 
         tensors = [V[k] for k in tensor_keys]
-        stack = stack.deepcopy() if stack is not None else {}
+        stack = copy.deepcopy(stack) if stack is not None else {}
 
         # Normalize all tensors together to find the common target shape
         normalized_tensors = normalize_to_common_shape(*tensors, mode=length_mismatch)
@@ -154,7 +156,7 @@ class ImageMathNode(io.ComfyNode):
         visitor = UnifiedMathVisitor(variables, ae.shape,ae.device,state_storage=stack)
         result = visitor.visit(tree)
         result = as_tensor(result, ae.shape)
-        
+
         if batching and batching > 0:
             res = torch.split(result, batching, dim=0)
             res_list = []

more_math/LatentMathNode.py

@@ -18,6 +18,7 @@ from .Parser.MathExprParser import MathExprParser
 import re
 from comfy.nested_tensor import NestedTensor
 from .Stack import MrmthStack
+import copy
 
 class LatentMathNode(io.ComfyNode):
     """
@@ -100,7 +101,7 @@ class LatentMathNode(io.ComfyNode):
 
         if ref_latent is None:
              raise ValueError("At least one input is required.")
-        stack = stack.deepcopy() if stack is not None else {}
+        stack = copy.deepcopy(stack) if stack is not None else {}
 
         # Identify if any input is a NestedTensor and track original sizes for restoration
         stacked = False

more_math/MaskMathNode.py

@@ -7,6 +7,7 @@ from .Parser.MathExprParser import MathExprParser
 import re
 import torch
 from .Stack import MrmthStack
+import copy
 
 
 class MaskMathNode(io.ComfyNode):
@@ -85,7 +86,7 @@ class MaskMathNode(io.ComfyNode):
              raise ValueError("At least one input is required.")
 
         tensors = [V[k] for k in tensor_keys]
-        stack = stack.deepcopy() if stack is not None else {}
+        stack = copy.deepcopy(stack) if stack is not None else {}
         # Normalize all tensors together
         normalized_tensors = normalize_to_common_shape(*tensors, mode=length_mismatch)
         V_norm = dict(zip(tensor_keys, normalized_tensors))
@@ -148,7 +149,7 @@ class MaskMathNode(io.ComfyNode):
         visitor = UnifiedMathVisitor(variables, ae.shape,ae.device,state_storage=stack)
         result = visitor.visit(tree)
         result = as_tensor(result, ae.shape)
-        
+
         if batching and batching > 0:
             res = torch.split(result, batching, dim=0)
             res_list = []

more_math/ModelMathNode.py

@@ -5,6 +5,7 @@ from .Parser.MathExprLexer import MathExprLexer
 from .Parser.MathExprParser import MathExprParser
 import re
 from .Stack import MrmthStack
+import copy
 
 class ModelMathNode(io.ComfyNode):
     """
@@ -76,7 +77,7 @@ class ModelMathNode(io.ComfyNode):
     def execute(cls, V, F, Expression, length_mismatch="tile",stack={}) -> io.NodeOutput:
         # Determine reference model for cloning
         a = V.get("V0")
-        stack = stack.deepcopy() if stack is not None else {}
+        stack = copy.deepcopy(stack) if stack is not None else {}
 
         if a is None:
             # Try finding first valid model

more_math/NoiseMathNode.py

@@ -6,6 +6,7 @@ from .Parser.MathExprLexer import MathExprLexer
 import re
 from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
 from .Stack import MrmthStack
+import copy
 
 class NoiseMathNode(io.ComfyNode):
     """
@@ -74,8 +75,8 @@ class NoiseMathNode(io.ComfyNode):
 
     @classmethod
     def execute(cls, Noise, V,F,stack={}):
-        stack = stack.deepcopy() if stack is not None else {}
-        return (NoiseExecutor(V,F, Noise,stack),)
+        stack = copy.deepcopy(stack) if stack is not None else {}
+        return (NoiseExecutor(V,F, Noise,stack),stack)
 
 
 class NoiseExecutor:

more_math/SigmasMathNode.py

@@ -6,6 +6,7 @@ from .Parser.MathExprLexer import MathExprLexer
 from .Parser.MathExprParser import MathExprParser
 import re
 from .Stack import MrmthStack
+import copy
 
 class SigmasMathNode(io.ComfyNode):
     """
@@ -87,7 +88,7 @@ class SigmasMathNode(io.ComfyNode):
 
         if ref_image is None:
              raise ValueError("At least one input is required.")
-        stack = stack.deepcopy() if stack is not None else {}
+        stack = copy.deepcopy(stack) if stack is not None else {}
         a = V.get("V0")
         b = V.get("V1")
         c = V.get("V2")

more_math/VaeMathNode.py

@@ -83,8 +83,7 @@ class VAEMathNode(io.ComfyNode):
                       break
         if a is None:
              raise ValueError("At least one input VAE is required.")
-        stack = stack.deepcopy() if stack is not None else {}
-
+        stack = copy.deepcopy(stack) if stack is not None else {}
 
         # Prepare VAE patchers for calculation
         # We need to map VAE wrappers to their patchers for `calculate_patches`
@@ -106,4 +105,4 @@ class VAEMathNode(io.ComfyNode):
         if patches:
             out_vae.patcher.add_patches(patches, 1.0, 1.0)
 
-        return (out_vae,)
+        return (out_vae,stack)

more_math/VideoMathNode.py

@@ -6,6 +6,7 @@ from .Parser.MathExprLexer import MathExprLexer
 from .Parser.MathExprParser import MathExprParser
 import re
 from .Stack import MrmthStack
+import copy
 
 class VideoMathNode(io.ComfyNode):
     """
@@ -95,7 +96,7 @@ class VideoMathNode(io.ComfyNode):
         # Use first normalized tensor to establish the reference shape
         ref_tensor = normalized_tensors[0]
         common_shape = ref_tensor.shape
-        stack = stack.deepcopy() if stack is not None else {}
+        stack = copy.deepcopy(stack) if stack is not None else {}
 
         # Setup legacy variables a, b, c, d
         ae = V_norm.get("V0", make_zero_like(ref_tensor))