(AI) implement the rest
This commit is contained in:
mcDandy
+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:
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max_length = ae.shape[0]
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for name, tensor in V.items():
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if tensor is not None and tensor.shape[0] != max_length:
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raise ValueError(f"Input '{name}' has shape {tensor.shape[0]}, expected {max_length} to match largest input.")
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ae, be, ce, de = normalize_to_common_shape(ae, be, ce, de, mode=length_mismatch)
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variables = {
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"a": ae, "b": be, "c": ce, "d": de,
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"w": w, "x": x, "y": y, "z": z,
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"w": F.get("F0", 0.0) if F.get("F0") is not None else 0.0,
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"x": F.get("F1", 0.0) if F.get("F1") is not None else 0.0,
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"y": F.get("F2", 0.0) if F.get("F2") is not None else 0.0,
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"z": F.get("F3", 0.0) if F.get("F3") is not None else 0.0,
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"X": getIndexTensorAlongDim(ae, 3),
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"Y": getIndexTensorAlongDim(ae, 2),
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"B": getIndexTensorAlongDim(ae, 0),
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@@ -78,7 +128,20 @@ class ImageMathNode(io.ComfyNode):
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"N": ae.shape[3],
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"channel_count": ae.shape[3],
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} | generate_dim_variables(ae)
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tree = parse_expr(Image);
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# Add all dynamic inputs
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for k, v in V.items():
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if v is not None:
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# Normalize all images in V to match ae.shape
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# Note: normalize_to_common_shape args are *tensors.
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# We normalize individual V item against 'ae' (the reference shape)
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norm_v = normalize_to_common_shape(ae, v, mode=length_mismatch)[1]
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variables[k] = norm_v
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for k, v in F.items():
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variables[k] = v if v is not None else 0.0
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tree = parse_expr(Expression);
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visitor = UnifiedMathVisitor(variables, ae.shape)
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result = visitor.visit(tree)
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result = as_tensor(result, ae.shape)
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