208 lines
8.0 KiB
Python
208 lines
8.0 KiB
Python
from inspect import cleandoc
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from comfy_api.latest import io
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import comfy.utils
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from antlr4 import CommonTokenStream, InputStream
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import torch
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from .helper_functions import ThrowingErrorListener
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from .Parser.MathExprParser import MathExprParser
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from .Parser.MathExprLexer import MathExprLexer
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from .Parser.TensorEvalVisitor import TensorEvalVisitor
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import copy
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def calculate_patches(Model, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0):
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# Parse expression
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input_stream = InputStream(Model)
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lexer = MathExprLexer(input_stream)
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stream = CommonTokenStream(lexer)
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parser = MathExprParser(stream)
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parser.addErrorListener(ThrowingErrorListener())
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tree = parser.expr()
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sd_a = a.model.state_dict()
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sd_b = b.model.state_dict() if b is not None else {}
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sd_c = c.model.state_dict() if c is not None else {}
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sd_d = d.model.state_dict() if d is not None else {}
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patches = {}
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layer_count = len(sd_a)
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from .helper_functions import getIndexTensorAlongDim
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pbar = comfy.utils.ProgressBar(layer_count)
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# Iterate over all keys in the main model 'a'
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for i, (key, tens_a) in enumerate(sd_a.items()):
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# Get corresponding tensors from other models, defaulting to zeros if missing or models not provided
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tens_b = sd_b.get(key, None)
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if tens_b is None: tens_b = torch.zeros_like(tens_a,device=tens_a.device)
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else: tens_b = tens_b.to(tens_a.device)
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tens_c = sd_c.get(key, None)
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if tens_c is None: tens_c = torch.zeros_like(tens_a,device=tens_a.device)
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else: tens_c = tens_c.to(tens_a.device)
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tens_d = sd_d.get(key, None)
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if tens_d is None: tens_d = torch.zeros_like(tens_a,device=tens_a.device)
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else: tens_d = tens_d.to(tens_a.device)
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# Variables for the visitor
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variables = {
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'a': tens_a,
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'b': tens_b,
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'c': tens_c,
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'd': tens_d,
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'w': w, 'x': x, 'y': y, 'z': z,
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'L': i, 'layer': i,
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'LC': layer_count, 'layer_count': layer_count
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}
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for dim_idx in range(tens_a.ndim):
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idx_tensor = getIndexTensorAlongDim(tens_a, dim_idx)
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idx_tensor = idx_tensor.to(tens_a.device)
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variables[f'D{dim_idx}'] = idx_tensor
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variables[f'dim_{dim_idx}'] = idx_tensor
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visitor = TensorEvalVisitor(variables, tens_a.shape)
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result_tensor = visitor.visit(tree)
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# Calculate difference for patching
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# The patch should be: result - original
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# Because ComfyUI applies: original + patch
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diff = result_tensor - tens_a
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# Allow skipping zero patches to save memory
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if torch.all(diff == 0):
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continue
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# Store patch. ComfyUI expects { key: (tensor,) } usually
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patches[key] = (diff,)
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pbar.update(1)
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return patches
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class ModelMathNode(io.ComfyNode):
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"""
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This node enables the use of math expressions on Model weights (state_dict).
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Functionally acts as a custom model merge.
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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_ModelMathNode",
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display_name="Model Math",
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category="More math",
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inputs=[
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io.Model.Input(id="a", tooltip="Main model (base)"),
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io.Model.Input(id="b", optional=True, tooltip="Optional 2nd model"),
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io.Model.Input(id="c", optional=True, tooltip="Optional 3rd model"),
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io.Model.Input(id="d", optional=True, tooltip="Optional 4th model"),
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io.Float.Input(id="w", default=0.0, optional=True, force_input=True),
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io.Float.Input(id="x", default=0.0, optional=True, force_input=True),
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io.Float.Input(id="y", default=0.0, optional=True, force_input=True),
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io.Float.Input(id="z", default=0.0, optional=True, force_input=True),
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io.String.Input(id="Model", default="a*(1-w)+b*w", tooltip="Expression to apply on weights"),
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],
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outputs=[
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io.Model.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 execute(cls, Model, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0) -> io.NodeOutput:
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patches = calculate_patches(Model, a, b, c, d, w, x, y, z)
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out_model = a.clone()
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if patches:
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out_model.add_patches(patches, 1.0, 1.0)
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return (out_model,)
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class CLIPMathNode(io.ComfyNode):
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"""
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This node enables the use of math expressions on CLIP weights.
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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_CLIPMathNode",
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display_name="CLIP Math",
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category="More math",
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inputs=[
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io.Clip.Input(id="a", tooltip="Main CLIP (base)"),
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io.Clip.Input(id="b", optional=True, tooltip="Optional 2nd CLIP"),
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io.Clip.Input(id="c", optional=True, tooltip="Optional 3rd CLIP"),
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io.Clip.Input(id="d", optional=True, tooltip="Optional 4th CLIP"),
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io.Float.Input(id="w", default=0.0, optional=True, force_input=True),
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io.Float.Input(id="x", default=0.0, optional=True, force_input=True),
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io.Float.Input(id="y", default=0.0, optional=True, force_input=True),
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io.Float.Input(id="z", default=0.0, optional=True, force_input=True),
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io.String.Input(id="Model", default="a*(1-w)+b*w", tooltip="Expression to apply on weights"),
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],
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outputs=[
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io.Clip.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 execute(cls, Model, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0) -> io.NodeOutput:
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patcher_a = a.patcher
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patcher_b = b.patcher if b else None
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patcher_c = c.patcher if c else None
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patcher_d = d.patcher if d else None
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patches = calculate_patches(Model, patcher_a, patcher_b, patcher_c, patcher_d, w, x, y, z)
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out_clip = a.clone()
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if patches:
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out_clip.add_patches(patches, 1.0, 1.0)
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return (out_clip,)
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class VAEMathNode(io.ComfyNode):
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"""
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This node enables the use of math expressions on VAE weights.
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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_VAEMathNode",
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display_name="VAE Math",
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category="More math",
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inputs=[
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io.Vae.Input(id="a", tooltip="Main VAE (base)"),
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io.Vae.Input(id="b", optional=True, tooltip="Optional 2nd VAE"),
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io.Vae.Input(id="c", optional=True, tooltip="Optional 3rd VAE"),
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io.Vae.Input(id="d", optional=True, tooltip="Optional 4th VAE"),
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io.Float.Input(id="w", default=0.0, optional=True, force_input=True),
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io.Float.Input(id="x", default=0.0, optional=True, force_input=True),
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io.Float.Input(id="y", default=0.0, optional=True, force_input=True),
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io.Float.Input(id="z", default=0.0, optional=True, force_input=True),
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io.String.Input(id="Model", default="a*(1-w)+b*w", tooltip="Expression to apply on weights"),
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],
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outputs=[
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io.Vae.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 execute(cls, Model, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0) -> io.NodeOutput:
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patcher_a = a.patcher
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patcher_b = b.patcher if b else None
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patcher_c = c.patcher if c else None
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patcher_d = d.patcher if d else None
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patches = calculate_patches(Model, patcher_a, patcher_b, patcher_c, patcher_d, w, x, y, z)
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# VAE does not have a clone method, so we shallow copy and clone the patcher
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out_vae = copy.copy(a)
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out_vae.patcher = a.patcher.clone()
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if patches:
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out_vae.patcher.add_patches(patches, 1.0, 1.0)
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return (out_vae,)
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