import torch from .helper_functions import generate_dim_variables, parse_expr, getIndexTensorAlongDim, as_tensor, prepare_inputs, make_zero_like, normalize_to_common_shape from .Parser.UnifiedMathVisitor import UnifiedMathVisitor from comfy_api.latest import io from antlr4 import InputStream, CommonTokenStream from .Parser.MathExprLexer import MathExprLexer from .Parser.MathExprParser import MathExprParser import re class ConditioningMathNode(io.ComfyNode): """ Enables math expressions on Audio. Inputs: I: Autogrow image inputs (I0, I1, ...) F: Autogrow float inputs (F0, F1, ...) Image: Expression """ @classmethod def define_schema(cls) -> io.Schema: return io.Schema( node_id="mrmth_ag_ConditioningMathNode", category="More math", display_name="Conditioning math", inputs=[ io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Conditioning.Input("values"), prefix="V", min=1, max=50)), 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)), io.String.Input(id="Expression", default="I0*(1-F0)+I1*F0", tooltip="Expression to apply on tensor part of conditioning"), io.String.Input(id="Expression_pi", default="I0*(1-F0)+I1*F0", tooltip="Expression to apply on pooled_input part of conditioning"), io.Combo.Input( id="length_mismatch", options=["tile", "error", "pad"], default="error", tooltip="How to handle mismatched image batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero." ) ], outputs=[ io.Conditioning.Output(), ], ) @classmethod def check_lazy_status(cls, Expression,Expression_pi, V, F, length_mismatch="tile"): input_stream = InputStream(Expression) lexer = MathExprLexer(input_stream) stream = CommonTokenStream(lexer) stream.fill() input_stream = InputStream(Expression) lexer = MathExprLexer(input_stream) stream1 = CommonTokenStream(lexer) stream1.fill() # Support aliases aliases_img = {"a": "V0", "b": "V1", "c": "V2", "d": "V3"} aliases_flt = {"w": "F0", "x": "F1", "y": "F2", "z": "F3"} needed = set() needed1 = set() for token in filter(lambda t: t.type == MathExprParser.VARIABLE, stream.tokens + stream1.tokens): var_name = token.text if re.match(r"[VF][0-9]+", var_name): needed.add(var_name) elif var_name in aliases_img: needed.add(aliases_img[var_name]) elif var_name in aliases_flt: needed.add(aliases_flt[var_name]) for v in needed: if v.startswith("V"): if v not in V or V[v] is None: needed1.add(v) elif v.startswith("F"): if v not in F or F[v] is None: needed1.add(v) return needed1 @classmethod def execute(cls, V, F, Expression, Expression_pi, length_mismatch="tile"): dir(V["V0"]) print(V["V0"]) if(length_mismatch == "error"): max_lengths = V.get("V0")[0][0].shape for name, tensor in V.items(): if tensor[0][0] is not None and max_lengths!=tensor[0][0].shape: raise ValueError(f"Input '{name}' has shape {tensor[0][0].shape}, expected {max_lengths} to match input.") max_lengths = V.get("V0")[0][1]["pooled_output"].shape for name, tensor in V.items(): if tensor[0][1]["pooled_output"] is not None and max_lengths!=tensor[0][1]["pooled_output"].shape: raise ValueError(f"Input '{name}' has shape {tensor[0][1]["pooled_output"].shape}, expected {max_lengths} to match input.") tensor={} pooled_output={} for key, conditioning in V.items(): if conditioning is not None and isinstance(conditioning, dict) and "waveform" in conditioning: tensor[key] = conditioning[0][0] pooled_output[key] = conditioning[0][1]["pooled_output"] else: tensor[key] = torch.zeros_like(V.get("V0")[0][0]) pooled_output[key] = torch.zeros_like(V.get("V0")[0][1]["pooled_output"]) new_values = normalize_to_common_shape(*tensor.values(), mode=length_mismatch) tensor.update(zip(tensor.keys(), new_values)) new_values = normalize_to_common_shape(*pooled_output.values(), mode=length_mismatch) tensor.update(zip(pooled_output.keys(), new_values)) ac,bc,cc,dc = prepare_inputs(V.get("V0"),V.get("V1"),V.get("V2"),V.get("V3")) a = ac[0][0] b = bc[0][0] c = cc[0][0] d = dc[0][0] variables = { "a": a, "b": b, "c": c, "d": d, "w": F.get("F0", 0.0) if F.get("F0") is not None else 0.0, "x": F.get("F1", 0.0) if F.get("F1") is not None else 0.0, "y": F.get("F2", 0.0) if F.get("F2") is not None else 0.0, "z": F.get("F3", 0.0) if F.get("F3") is not None else 0.0, "B": getIndexTensorAlongDim(a, 0), "batch": getIndexTensorAlongDim(a, 0), "T": a.shape[0], "batch_count": a.shape[0], } | generate_dim_variables(a) | tensor tree = parse_expr(Expression); visitor = UnifiedMathVisitor(variables, a.shape) rtensor = visitor.visit(tree) rtensor = as_tensor(rtensor, a.shape) a = ac[0][1]["pooled_output"] b = bc[0][1]["pooled_output"] c = cc[0][1]["pooled_output"] d = dc[0][1]["pooled_output"] variables = { "a": a, "b": b, "c": c, "d": d, "w": F.get("F0", 0.0) if F.get("F0") is not None else 0.0, "x": F.get("F1", 0.0) if F.get("F1") is not None else 0.0, "y": F.get("F2", 0.0) if F.get("F2") is not None else 0.0, "z": F.get("F3", 0.0) if F.get("F3") is not None else 0.0, "B": getIndexTensorAlongDim(a, 0), "batch": getIndexTensorAlongDim(a, 0), "T": a.shape[0], "batch_count": a.shape[0], } | generate_dim_variables(a) | pooled_output tree = parse_expr(Expression); visitor = UnifiedMathVisitor(variables, a.shape) rpooled = visitor.visit(tree) rpooled = as_tensor(rpooled, a.shape) vl = V["V0"] vl[0][0] = rtensor vl[0][1]["pooled_output"] = rpooled return (vl,)