223 lines
9.0 KiB
Python
223 lines
9.0 KiB
Python
from inspect import cleandoc
|
|
from comfy_api.latest import io
|
|
from .helper_functions import (
|
|
generate_dim_variables,
|
|
getIndexTensorAlongDim,
|
|
parse_expr,
|
|
as_tensor,
|
|
normalize_to_common_shape,
|
|
make_zero_like,
|
|
get_v_variable,
|
|
get_f_variable,
|
|
checkLazyNew
|
|
)
|
|
from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
|
|
import torch
|
|
from comfy.nested_tensor import NestedTensor
|
|
from .Stack import MrmthStack
|
|
from .ParseTree import MrmthParseTree
|
|
import copy
|
|
|
|
class LatentMathNode(io.ComfyNode):
|
|
"""
|
|
This node enables the use of math expressions on Latents using Autogrow inputs.
|
|
"""
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def define_schema(cls) -> io.Schema:
|
|
""" """
|
|
return io.Schema(
|
|
node_id="mrmth_ag_LatentMathNode",
|
|
display_name="Latent math",
|
|
category="More math",
|
|
inputs=[
|
|
io.Autogrow.Input(id="V",template=io.Autogrow.TemplatePrefix(io.Latent.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 latents",
|
|
),
|
|
io.Combo.Input(
|
|
id="length_mismatch",
|
|
options=["do nothing","error","tile", "pad"],
|
|
display_name="on size mismatch",
|
|
default="error",
|
|
tooltip="How to handle mismatched latent batch sizes. tile: repeat shorter inputs; error: raise error on mismatch; pad: treat missing frames as zero."
|
|
),
|
|
io.Int.Input(id="batching"),
|
|
io.Boolean.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.Latent.Output(is_output_list=True),
|
|
MrmthStack.Output(),
|
|
|
|
],
|
|
)
|
|
|
|
tooltip = cleandoc(__doc__)
|
|
|
|
@classmethod
|
|
def check_lazy_status(cls, Expression, V, F,batching, length_mismatch="tile",remember_stack=False,stack={}):
|
|
return checkLazyNew(Expression,V,F)
|
|
|
|
@classmethod
|
|
def execute(cls, V, F, Expression,batching, length_mismatch="tile",remember_stack=False,stack={}) -> io.NodeOutput:
|
|
# Determine reference latent
|
|
ref_latent = None
|
|
for lat in V.values():
|
|
if lat is not None:
|
|
ref_latent = lat
|
|
break
|
|
|
|
if ref_latent is None:
|
|
raise ValueError("At least one input is required.")
|
|
stack = stack if remember_stack else (copy.deepcopy(stack) if stack is not None else {})
|
|
|
|
# Identify if any input is a NestedTensor and track original sizes for restoration
|
|
stacked = False
|
|
orig_split_sizes = None
|
|
|
|
# Check all present inputs for nested tensors
|
|
for item in V.values():
|
|
if item is not None:
|
|
samples = item.get("samples")
|
|
if getattr(samples, "is_nested", False):
|
|
stacked = True
|
|
# Store original split sizes (batch dimension) - assume all nested inputs share structure if mixed?
|
|
# Or just take from the first one found.
|
|
orig_split_sizes = [t.shape[0] for t in samples.tensors]
|
|
break
|
|
|
|
# Flatten nested tensors in V
|
|
if stacked:
|
|
for k, val in V.items():
|
|
if val is not None and getattr(val.get("samples"), "is_nested", False):
|
|
new_val = val.copy()
|
|
new_val["samples"] = torch.cat(new_val["samples"].tensors, dim=0)
|
|
V[k] = new_val
|
|
|
|
# Identify all present tensors and their keys
|
|
tensor_keys = [k for k, v in V.items() if v is not None]
|
|
at_list = [V[k]["samples"] for k in tensor_keys]
|
|
|
|
# Normalize all together
|
|
normalized_samples = normalize_to_common_shape(*at_list, mode=length_mismatch)
|
|
V_norm_samples = dict(zip(tensor_keys, normalized_samples))
|
|
|
|
ae = V_norm_samples.get("V0", make_zero_like(normalized_samples[0]))
|
|
be = V_norm_samples.get("V1", make_zero_like(ae))
|
|
ce = V_norm_samples.get("V2", make_zero_like(ae))
|
|
de = V_norm_samples.get("V3", make_zero_like(ae))
|
|
|
|
# Ensure legacy are normalized
|
|
ae, be, ce, de = normalize_to_common_shape(ae, be, ce, de, mode=length_mismatch)
|
|
|
|
if(length_mismatch == "error"):
|
|
for name in tensor_keys:
|
|
if V[name]["samples"].shape[0] != ae.shape[0]:
|
|
raise ValueError(f"Input '{name}' has shape {V[name]['samples'].shape[0]}, expected {ae.shape[0]} to match input.")
|
|
|
|
# parse expression once
|
|
tree = None
|
|
if isinstance(Expression,str):
|
|
tree = parse_expr(Expression)
|
|
else:
|
|
tree = Expression
|
|
|
|
ndim = ae.ndim
|
|
batch_dim = 0
|
|
channel_dim = -3
|
|
height_dim = -2
|
|
width_dim = -1
|
|
time_dim = None
|
|
if ndim >= 5:
|
|
time_dim = -4
|
|
|
|
frame_count = ae.shape[time_dim] if time_dim is not None else ae.shape[batch_dim]
|
|
|
|
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, width_dim),
|
|
"Y": getIndexTensorAlongDim(ae, height_dim),
|
|
"B": getIndexTensorAlongDim(ae, batch_dim),
|
|
"batch": getIndexTensorAlongDim(ae, batch_dim),
|
|
"C": getIndexTensorAlongDim(ae, channel_dim),
|
|
"channel": getIndexTensorAlongDim(ae, channel_dim),
|
|
"W": float(ae.shape[width_dim]),
|
|
"width": float(ae.shape[width_dim]),
|
|
"H": float(ae.shape[height_dim]),
|
|
"height": float(ae.shape[height_dim]),
|
|
"T": float(frame_count),
|
|
"batch_count": float(ae.shape[batch_dim]),
|
|
"N": float(ae.shape[channel_dim]),
|
|
"channel_count": float(ae.shape[channel_dim]),
|
|
} | generate_dim_variables(ae)
|
|
|
|
if time_dim is not None:
|
|
F_idx = getIndexTensorAlongDim(ae, time_dim)
|
|
variables.update({"frame_idx": F_idx, "frame": F_idx, "frame_count": frame_count})
|
|
|
|
# Add all dynamic inputs
|
|
variables.update(V_norm_samples)
|
|
|
|
v_stacked, v_cnt = get_v_variable(V_norm_samples, 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, v in F.items():
|
|
variables[k] = v if v is not None else 0.0
|
|
|
|
visitor = UnifiedMathVisitor(variables, ae.shape,ae.device,state_storage=stack)
|
|
result_t = as_tensor(visitor.visit(tree), ae.shape)
|
|
|
|
result_latent = ref_latent.copy()
|
|
if(batching>0):
|
|
res = torch.split(result_t,batching)
|
|
results=[]
|
|
results1=[]
|
|
for i in range(len(res)):
|
|
result_tensor = res[i] if i<len(res) else torch.zeros([1])
|
|
results.append(result_tensor)
|
|
for result_t in results:
|
|
rl = result_latent.copy()
|
|
if stacked and orig_split_sizes is not None:
|
|
# Restore original split sizes
|
|
try:
|
|
rl["samples"] = NestedTensor(torch.split(result_t, orig_split_sizes, dim=0))
|
|
except Exception:
|
|
# Fallback if split fails (e.g. result shape changed)
|
|
rl["samples"] = result_t
|
|
else:
|
|
rl["samples"] = result_t
|
|
results1.append(rl)
|
|
stack = stack if remember_stack else copy.deepcopy(stack)
|
|
return (results1,stack)
|
|
rl = result_latent.copy()
|
|
rl["samples"] = result_t
|
|
stack = stack if remember_stack else copy.deepcopy(stack)
|
|
return ([rl],stack)
|