(AI) implement the rest
This commit is contained in:
mcDandy
+100
-53
@@ -6,27 +6,20 @@ from .helper_functions import (
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parse_expr,
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as_tensor,
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normalize_to_common_shape,
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prepare_inputs
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prepare_inputs,
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make_zero_like
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)
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from .helper_functions import commonLazy
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from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
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import torch
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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 LatentMathNode(io.ComfyNode):
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"""
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This node enables the use of math expressions on Latents.
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inputs:
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a, b, c, d:
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Latent, bound to variables with the same name. Defaults to zero latent if not provided.
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w, x, y, z:
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Floats, bound to variables of the expression. Defaults to 0.0 if not provided.
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Latent expression:
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String, describing expression to aply to latents.
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outputs:
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LATENT:
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Returns a LATENT object that contains the result of the math expression applied to the input conditionings.
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This node enables the use of math expressions on Latents using Autogrow inputs.
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"""
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def __init__(self):
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@@ -36,22 +29,16 @@ class LatentMathNode(io.ComfyNode):
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def define_schema(cls) -> io.Schema:
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""" """
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return io.Schema(
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node_id="mrmth_LatentMathNode",
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node_id="mrmth_ag_LatentMathNode",
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display_name="Latent math",
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category="More math",
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inputs=[
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io.Latent.Input(id="a"),
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io.Latent.Input(id="b", optional=True, lazy=True),
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io.Latent.Input(id="c", optional=True, lazy=True),
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io.Latent.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="Latent", default="a*(1-w)+b*w", tooltip="Expression to apply on input latents"),
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io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Latent.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 latents"),
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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 latent 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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@@ -64,52 +51,95 @@ class LatentMathNode(io.ComfyNode):
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tooltip = cleandoc(__doc__)
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@classmethod
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def check_lazy_status(cls, Latent, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="tile"):
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return commonLazy(Latent, 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, 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:
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def execute(cls, V, F, Expression, length_mismatch="tile") -> io.NodeOutput:
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# Determine reference latent
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ref_latent = None
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for lat in V.values():
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if lat is not None:
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ref_latent = lat
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break
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if ref_latent is None:
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raise ValueError("At least one input is required.")
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# Identify if any input is a NestedTensor and track original sizes for restoration
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stacked = False
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orig_split_sizes = None
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for item in [a, b, c, d]:
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# Check all present inputs for nested tensors
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for item in V.values():
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if item is not None:
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samples = item.get("samples")
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if getattr(samples, "is_nested", False):
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stacked = True
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# Store original split sizes (batch dimension)
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# Store original split sizes (batch dimension) - assume all nested inputs share structure if mixed?
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# Or just take from the first one found.
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orig_split_sizes = [t.shape[0] for t in samples.tensors]
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break
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# Flatten nested tensors in V
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if stacked:
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if a is not None and getattr(a.get("samples"), "is_nested", False):
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a = a.copy()
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a["samples"] = torch.cat(a["samples"].tensors, dim=0)
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if b is not None and getattr(b.get("samples"), "is_nested", False):
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b = b.copy()
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b["samples"] = torch.cat(b["samples"].tensors, dim=0)
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if c is not None and getattr(c.get("samples"), "is_nested", False):
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c = c.copy()
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c["samples"] = torch.cat(c["samples"].tensors, dim=0)
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if d is not None and getattr(d.get("samples"), "is_nested", False):
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d = d.copy()
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d["samples"] = torch.cat(d["samples"].tensors, dim=0)
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for k, val in V.items():
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if val is not None and getattr(val.get("samples"), "is_nested", False):
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new_val = val.copy()
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new_val["samples"] = torch.cat(new_val["samples"].tensors, dim=0)
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V[k] = new_val
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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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if a is None:
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a = make_zero_like(ref_latent)
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a_c, b_c, c_c, d_c = prepare_inputs(a, b, c, d)
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at,bt,ct,dt = a_c["samples"],b_c["samples"],c_c["samples"],d_c["samples"]
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if(length_mismatch == "error"):
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# Check only available tensors
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tensors_to_check = [t for t in [at, bt, ct, dt] if t is not None]
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max_length = max(t.shape[0] for t in tensors_to_check)
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for tensor, name in zip([at, bt, ct, dt], ["a", "b", "c", "d"]):
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if tensor is not None:
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max_length = at.shape[0]
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for name, val in V.items():
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if val is not None:
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tensor = val["samples"]
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if 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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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(at, bt, ct, dt, mode=length_mismatch)
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# parse expression once
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tree = parse_expr(Latent)
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tree = parse_expr(Expression)
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ndim = ae.ndim
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batch_dim = 0
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@@ -124,7 +154,10 @@ class LatentMathNode(io.ComfyNode):
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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, width_dim),
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"Y": getIndexTensorAlongDim(ae, height_dim),
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"B": getIndexTensorAlongDim(ae, batch_dim),
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@@ -142,8 +175,18 @@ class LatentMathNode(io.ComfyNode):
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} | generate_dim_variables(ae)
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if time_dim is not None:
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F = getIndexTensorAlongDim(ae, time_dim)
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variables.update({"frame_idx": F, "frame": F, "frame_count": frame_count})
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F_idx = getIndexTensorAlongDim(ae, time_dim)
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variables.update({"frame_idx": F_idx, "frame": F_idx, "frame_count": frame_count})
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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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v_tensor = v["samples"]
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norm_v = normalize_to_common_shape(ae, v_tensor, 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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visitor = UnifiedMathVisitor(variables, ae.shape)
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result_t = as_tensor(visitor.visit(tree), ae.shape)
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@@ -152,7 +195,11 @@ class LatentMathNode(io.ComfyNode):
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if stacked and orig_split_sizes is not None:
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from comfy.nested_tensor import NestedTensor
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# Restore original split sizes
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result_latent["samples"] = NestedTensor(torch.split(result_t, orig_split_sizes, dim=0))
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try:
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result_latent["samples"] = NestedTensor(torch.split(result_t, orig_split_sizes, dim=0))
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except Exception:
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# Fallback if split fails (e.g. result shape changed)
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result_latent["samples"] = result_t
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else:
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result_latent["samples"] = result_t
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