from .helper_functions import generate_dim_variables, parse_expr, getIndexTensorAlongDim, as_tensor, normalize_to_common_shape, make_zero_like, get_v_variable, get_f_variable, checkLazyNew from .Parser.UnifiedMathVisitor import UnifiedMathVisitor from comfy_api.latest import io import torch from .Stack import MrmthStack from .ParseTree import MrmthParseTree import copy class ImageMathNode(io.ComfyNode): """ Enables math expressions on Images using Autogrow inputs. Inputs: V: Autogrow image inputs (V0, V1, ...) F: Autogrow float inputs (F0, F1, ...) Image: Expression to apply on input images """ @classmethod def define_schema(cls) -> io.Schema: return io.Schema( node_id="mrmth_ag_ImageMathNode", category="More math", display_name="Image math", inputs=[ io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Image.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.MultiType.Input( io.String.Input("Expression", default="I0*(1-F0)+I1*F0", multiline=False), types=[io.String,MrmthParseTree], tooltip="Expression to apply on input images", ), io.Combo.Input( id="length_mismatch", options=["do nothing","error","tile", "pad"], display_name="on size mismatch", 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." ), io.Int.Input(id="batching", default=0), io.Bool.Input( id="remember_stack", default=False, display_name="Remember stack across batch", tooltip=( "If enabled, stack is copied at output leading to changes being remembered during batch operations (node runs multiple times in sucession). If disabled each batch gets it's own copy of the stack." ), ), MrmthStack.Input(id="stack", tooltip="Access stack between nodes",optional=True) ], outputs=[ io.Image.Output(is_output_list=True), MrmthStack.Output(), ], ) @classmethod def check_lazy_status(cls, Expression, V, F, length_mismatch="tile",batching=0,remember_stack=False,stack={}): return checkLazyNew(Expression,V,F) @classmethod def execute(cls, V, F, Expression, length_mismatch="error",batching=0,remember_stack=False,stack={}): # I and F are Autogrow.Type which is dict[str, Any] # Identify all present tensors and their keys tensor_keys = [k for k, v in V.items() if v is not None] if not tensor_keys: raise ValueError("At least one input is required.") tensors = [V[k] for k in tensor_keys] stack = stack if remember_stack else (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) V_norm = dict(zip(tensor_keys, normalized_tensors)) # Use first normalized tensor to establish the reference shape ref_tensor = normalized_tensors[0] common_shape = ref_tensor.shape # Setup legacy variables a, b, c, d ae = V_norm.get("V0", make_zero_like(ref_tensor)) be = V_norm.get("V1", make_zero_like(ae)) ce = V_norm.get("V2", make_zero_like(ae)) de = V_norm.get("V3", make_zero_like(ae)) # Ensure legacy variables are normalized in case they were zero-initialized ae, be, ce, de = normalize_to_common_shape(ae, be, ce, de, mode=length_mismatch) if(length_mismatch == "error"): for name, tensor in V.items(): if tensor is not None and tensor.shape[0] != common_shape[0]: raise ValueError(f"Input '{name}' has shape {tensor.shape[0]}, expected {common_shape[0]} to match largest input.") variables = { "a": ae, "b": be, "c": ce, "d": de, "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, "X": getIndexTensorAlongDim(ae, 2), "Y": getIndexTensorAlongDim(ae, 1), "B": getIndexTensorAlongDim(ae, 0), "batch": getIndexTensorAlongDim(ae, 0), "C": getIndexTensorAlongDim(ae, 3), "channel": getIndexTensorAlongDim(ae, 3), "W": float(ae.shape[2]), "width": float(ae.shape[2]), "H": float(ae.shape[1]), "height": float(ae.shape[1]), "T": float(ae.shape[0]), "batch_count": float(ae.shape[0]), "N": float(ae.shape[3]), "channel_count": float(ae.shape[3]), } | generate_dim_variables(ae) # Add all dynamic inputs variables.update(V_norm) v_stacked, v_cnt = get_v_variable(V_norm, length_mismatch=length_mismatch) if v_stacked is not None: variables["V"] = v_stacked variables["Vcnt"] = float(v_cnt) variables["V_count"] = float(v_cnt) f_stacked, f_cnt = get_f_variable(F) if f_stacked is not None: variables["F"] = f_stacked variables["Fcnt"] = float(f_cnt) variables["F_count"] = float(f_cnt) for k, val in F.items(): variables[k] = val if val is not None else 0.0 tree = None if isinstance(Expression,str): tree = parse_expr(Expression) else: tree = Expression 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 = [] for result_chunk in res: res_list.append(result_chunk) stack = stack if remember_stack else copy.deepcopy(stack) return (res_list, stack) else: stack = stack if remember_stack else copy.deepcopy(stack) return ([result], stack)