Files
mcDandy-more_math/more_math/VaeMathNode.py
T
mcDandy 101e783e1e (AI) add option to pad or repeat input when size not matches
Nested tensors are broken, tests do not work since my cleanup after the AI.
2026-01-14 22:56:02 +01:00

64 lines
2.6 KiB
Python

from inspect import cleandoc
from comfy_api.latest import io
import copy
from .modelLikeCommon import calculate_patches
from .helper_functions import commonLazy
class VAEMathNode(io.ComfyNode):
"""
This node enables the use of math expressions on VAE weights.
"""
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="mrmth_VAEMathNode",
display_name="VAE Math",
category="More math",
inputs=[
io.Vae.Input(id="a", tooltip="Main VAE (base)"),
io.Vae.Input(id="b", optional=True, lazy=True, tooltip="Optional 2nd VAE"),
io.Vae.Input(id="c", optional=True, lazy=True, tooltip="Optional 3rd VAE"),
io.Vae.Input(id="d", optional=True, lazy=True, tooltip="Optional 4th VAE"),
io.Float.Input(id="w", default=0.0, lazy=True, optional=True, force_input=True),
io.Float.Input(id="x", default=0.0, lazy=True, optional=True, force_input=True),
io.Float.Input(id="y", default=0.0, lazy=True, optional=True, force_input=True),
io.Float.Input(id="z", default=0.0, lazy=True, optional=True, force_input=True),
io.String.Input(id="Model", default="a*(1-w)+b*w", tooltip="Expression to apply on weights"),
io.Combo.Input(
id="length_mismatch",
options=["broadcast", "passthrough", "pad"],
default="broadcast",
tooltip="How to handle mismatched layer counts. For models, this usually defaults to broadcast (zero for missing layers)."
)
],
outputs=[
io.Vae.Output(),
],
)
tooltip = cleandoc(__doc__)
@classmethod
def check_lazy_status(cls, Model, a, b=[], c=[], d=[], w=0, x=0, y=0, z=0, length_mismatch="broadcast"):
return commonLazy(Model, a, b, c, d, w, x, y, z)
@classmethod
def execute(cls, Model, a, b=None, c=None, d=None, w=0.0, x=0.0, y=0.0, z=0.0, length_mismatch="broadcast") -> io.NodeOutput:
patcher_a = a.patcher
patcher_b = b.patcher if b else None
patcher_c = c.patcher if c else None
patcher_d = d.patcher if d else None
patches = calculate_patches(Model, patcher_a, patcher_b, patcher_c, patcher_d, w, x, y, z)
# VAE does not have a clone method, so we shallow copy and clone the patcher
out_vae = copy.copy(a)
out_vae.patcher = a.patcher.clone()
if patches:
out_vae.patcher.add_patches(patches, 1.0, 1.0)
return (out_vae,)