148 lines
6.3 KiB
Python
148 lines
6.3 KiB
Python
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
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from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
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from comfy_api.latest import io
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import torch
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from .Stack import MrmthStack
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from .ParseTree import MrmthParseTree
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import copy
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class MaskMathNode(io.ComfyNode):
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"""
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Enables math expressions on Masks using Autogrow inputs.
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Inputs:
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V: Autogrow mask inputs (V0, V1, ...)
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F: Autogrow float inputs (F0, F1, ...)
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Mask: Expression to apply on input masks
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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_ag_MaskMathNode",
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category="More math",
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display_name="Mask math",
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inputs=[
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io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Mask.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.MultiType.Input(
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io.String.Input("Expression", default="V0", multiline=False),
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types=[io.String,MrmthParseTree],
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tooltip="Expression to apply on input masks",
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),
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io.Combo.Input(
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id="length_mismatch",
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options=["do nothing","error","tile", "pad"],
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display_name="on size mismatch",
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default="error",
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tooltip="How to handle mismatched mask 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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io.Int.Input(id="batching", default=0),
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io.Boolean.Input(
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id="remember_stack",
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default=False,
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display_name="Remember stack across batch",
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tooltip=(
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"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."
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),
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),
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MrmthStack.Input(id="stack", tooltip="Access stack between nodes",optional=True)
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],
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outputs=[
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io.Mask.Output(is_output_list=True),
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MrmthStack.Output(),
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],
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)
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@classmethod
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def check_lazy_status(cls, Expression, V, F, length_mismatch="tile",batching=0,remember_stack=False,stack={}):
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return checkLazyNew(Expression,V,F)
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@classmethod
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def execute(cls, V, F, Expression, length_mismatch="tile",batching=0,remember_stack=False,stack={}):
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# Identify all present tensors and their keys
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tensor_keys = [k for k, v in V.items() if v is not None]
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if not tensor_keys:
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raise ValueError("At least one input is required.")
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tensors = [V[k] for k in tensor_keys]
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stack = stack if remember_stack else (copy.deepcopy(stack) if stack is not None else {})
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# Normalize all tensors together
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normalized_tensors = normalize_to_common_shape(*tensors, mode=length_mismatch)
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V_norm = dict(zip(tensor_keys, normalized_tensors))
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# Establish reference shape
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ref_tensor = normalized_tensors[0]
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common_shape = ref_tensor.shape
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if(length_mismatch == "error"):
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for name, tensor in V.items():
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if tensor is not None and tensor.shape[0] != common_shape[0]:
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raise ValueError(f"Input '{name}' has shape {tensor.shape[0]}, expected {common_shape[0]} to match largest input.")
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# Setup legacy variables a, b, c, d
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ae = V_norm.get("V0", make_zero_like(ref_tensor))
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be = V_norm.get("V1", make_zero_like(ae))
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ce = V_norm.get("V2", make_zero_like(ae))
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de = V_norm.get("V3", make_zero_like(ae))
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# Ensure legacy are normalized
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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": 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, 2),
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"Y": getIndexTensorAlongDim(ae, 1),
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"B": getIndexTensorAlongDim(ae, 0),
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"batch": getIndexTensorAlongDim(ae, 0),
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"W": float(ae.shape[2]),
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"width": float(ae.shape[2]),
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"H": float(ae.shape[1]),
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"height": float(ae.shape[1]),
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"T": float(ae.shape[0]),
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"batch_count": float(ae.shape[0]),
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} | generate_dim_variables(ae)
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v_stacked, v_cnt = get_v_variable(V_norm, length_mismatch=length_mismatch)
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if v_stacked is not None:
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variables["V"] = v_stacked
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variables["Vcnt"] = float(v_cnt)
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variables["V_count"] = float(v_cnt)
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f_stacked, f_cnt = get_f_variable(F)
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if f_stacked is not None:
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variables["F"] = f_stacked
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variables["Fcnt"] = float(f_cnt)
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variables["F_count"] = float(f_cnt)
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# Add all dynamic inputs
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variables.update(V_norm)
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for k, val in F.items():
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variables[k] = val if val is not None else 0.0
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tree = None
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if isinstance(Expression,str):
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tree = parse_expr(Expression)
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else:
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tree = Expression
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visitor = UnifiedMathVisitor(variables, ae.shape,ae.device,state_storage=stack)
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result = visitor.visit(tree)
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result = as_tensor(result, ae.shape)
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if batching and batching > 0:
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res = torch.split(result, batching, dim=0)
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res_list = []
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for result_chunk in res:
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res_list.append(result_chunk)
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stack = stack if remember_stack else copy.deepcopy(stack)
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return (res_list, stack)
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else:
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stack = stack if remember_stack else copy.deepcopy(stack)
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return ([result], stack)
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