213 lines
8.3 KiB
Python
213 lines
8.3 KiB
Python
from inspect import cleandoc
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from comfy_api.latest import io
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from .helper_functions import (
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generate_dim_variables,
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getIndexTensorAlongDim,
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parse_expr,
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as_tensor,
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normalize_to_common_shape,
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make_zero_like,
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get_v_variable,
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get_f_variable,
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checkLazyNew
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)
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from .Parser.UnifiedMathVisitor import UnifiedMathVisitor
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import torch
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from comfy.nested_tensor import NestedTensor
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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 LatentMathNode(io.ComfyNode):
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"""
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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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pass
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@classmethod
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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_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.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.MultiType.Input(
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io.String.Input("Expression", default="I0*(1-F0)+I1*F0", multiline=False),
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types=[io.String,MrmthParseTree],
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tooltip="Expression to apply on input latents",
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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 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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io.Int.Input(id="batching"),
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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.Latent.Output(is_output_list=True),
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MrmthStack.Output(),
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],
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)
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tooltip = cleandoc(__doc__)
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@classmethod
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def check_lazy_status(cls, Expression, V, F,batching, length_mismatch="tile",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,batching, length_mismatch="tile",stack={}) -> 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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stack = copy.deepcopy(stack) if stack is not None else {}
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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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# 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) - 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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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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# 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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at_list = [V[k]["samples"] for k in tensor_keys]
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# Normalize all together
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normalized_samples = normalize_to_common_shape(*at_list, mode=length_mismatch)
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V_norm_samples = dict(zip(tensor_keys, normalized_samples))
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ae = V_norm_samples.get("V0", make_zero_like(normalized_samples[0]))
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be = V_norm_samples.get("V1", make_zero_like(ae))
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ce = V_norm_samples.get("V2", make_zero_like(ae))
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de = V_norm_samples.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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if(length_mismatch == "error"):
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for name in tensor_keys:
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if V[name]["samples"].shape[0] != ae.shape[0]:
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raise ValueError(f"Input '{name}' has shape {V[name]['samples'].shape[0]}, expected {ae.shape[0]} to match input.")
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# parse expression once
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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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ndim = ae.ndim
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batch_dim = 0
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channel_dim = -3
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height_dim = -2
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width_dim = -1
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time_dim = None
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if ndim >= 5:
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time_dim = -4
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frame_count = ae.shape[time_dim] if time_dim is not None else ae.shape[batch_dim]
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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, width_dim),
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"Y": getIndexTensorAlongDim(ae, height_dim),
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"B": getIndexTensorAlongDim(ae, batch_dim),
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"batch": getIndexTensorAlongDim(ae, batch_dim),
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"C": getIndexTensorAlongDim(ae, channel_dim),
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"channel": getIndexTensorAlongDim(ae, channel_dim),
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"W": float(ae.shape[width_dim]),
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"width": float(ae.shape[width_dim]),
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"H": float(ae.shape[height_dim]),
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"height": float(ae.shape[height_dim]),
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"T": float(frame_count),
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"batch_count": float(ae.shape[batch_dim]),
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"N": float(ae.shape[channel_dim]),
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"channel_count": float(ae.shape[channel_dim]),
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} | generate_dim_variables(ae)
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if time_dim is not None:
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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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variables.update(V_norm_samples)
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v_stacked, v_cnt = get_v_variable(V_norm_samples, 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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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,ae.device,state_storage=stack)
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result_t = as_tensor(visitor.visit(tree), ae.shape)
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result_latent = ref_latent.copy()
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if(batching>0):
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res = torch.split(result_t,batching)
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results=[]
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results1=[]
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for i in range(len(res)):
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result_tensor = res[i] if i<len(res) else torch.zeros([1])
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results.append(result_tensor)
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for result_t in results:
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rl = result_latent.copy()
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if stacked and orig_split_sizes is not None:
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# Restore original split sizes
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try:
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rl["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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rl["samples"] = result_t
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else:
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rl["samples"] = result_t
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results1.append(rl)
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return (results1,stack)
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rl = result_latent.copy()
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rl["samples"] = result_t
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return ([rl],stack)
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