Add LatentCutToBatch node. (#11411)
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comfyanonymous 2025-12-18 19:21:40 -08:00 committed by GitHub
parent 28eaab608b
commit 894802b0f9
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@ -5,6 +5,7 @@ import nodes
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
import logging
import math
def reshape_latent_to(target_shape, latent, repeat_batch=True):
if latent.shape[1:] != target_shape[1:]:
@ -207,6 +208,47 @@ class LatentCut(io.ComfyNode):
samples_out["samples"] = torch.narrow(s1, dim, index, amount)
return io.NodeOutput(samples_out)
class LatentCutToBatch(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentCutToBatch",
category="latent/advanced",
inputs=[
io.Latent.Input("samples"),
io.Combo.Input("dim", options=["t", "x", "y"]),
io.Int.Input("slice_size", default=1, min=1, max=nodes.MAX_RESOLUTION, step=1),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, samples, dim, slice_size) -> io.NodeOutput:
samples_out = samples.copy()
s1 = samples["samples"]
if "x" in dim:
dim = s1.ndim - 1
elif "y" in dim:
dim = s1.ndim - 2
elif "t" in dim:
dim = s1.ndim - 3
if dim < 2:
return io.NodeOutput(samples)
s = s1.movedim(dim, 1)
if s.shape[1] < slice_size:
slice_size = s.shape[1]
elif s.shape[1] % slice_size != 0:
s = s[:, :math.floor(s.shape[1] / slice_size) * slice_size]
new_shape = [-1, slice_size] + list(s.shape[2:])
samples_out["samples"] = s.reshape(new_shape).movedim(1, dim)
return io.NodeOutput(samples_out)
class LatentBatch(io.ComfyNode):
@classmethod
def define_schema(cls):
@ -435,6 +477,7 @@ class LatentExtension(ComfyExtension):
LatentInterpolate,
LatentConcat,
LatentCut,
LatentCutToBatch,
LatentBatch,
LatentBatchSeedBehavior,
LatentApplyOperation,