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Defer @pollockjj's tiled-VAE and UPSCALE_MODEL MultiGPU lanes (#14066)

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* Revert "Add tiled VAE lane to MultiGPU Work Units"

This reverts commit 4d3d68e473.

The tiled VAE lane will land as part of a follow-up PR alongside the
UPSCALE_MODEL lane, separated from the threaded-loader fix PR (#14052)
to keep the upstream merge focused.

* Revert "Add UPSCALE_MODEL lane to MultiGPU CFG Split"

This reverts commit 74b0a826ea.

The UPSCALE_MODEL lane will land as part of a follow-up PR alongside the
tiled VAE lane, separated from the threaded-loader fix PR (#14052) to
keep the upstream merge focused.

---------

Co-authored-by: John Pollock <pollockjj@gmail.com>
This commit is contained in:
Jedrzej Kosinski
2026-05-22 16:44:29 -07:00
committed by GitHub
parent cb83c41db7
commit 5dc4e38b89
7 changed files with 12 additions and 564 deletions
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27
comfy_extras/nodes_multigpu.py
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@@ -13,42 +13,33 @@ import comfy.multigpu
class MultiGPUCFGSplitNode(io.ComfyNode):
"""
Attaches per-device deepclones to any connected MODEL, UPSCALE_MODEL, and/or VAE so
downstream nodes that recognize the attached state dispatch their work across multiple GPUs.
Prepares model to have sampling accelerated via splitting work units.
Place after nodes that modify the model object itself (compile, attention-switch, etc.).
Otherwise position is not order-sensitive.
Should be placed after nodes that modify the model object itself, such as compile or attention-switch nodes.
Other than those exceptions, this node can be placed in any order.
"""
@classmethod
def define_schema(cls):
return io.Schema(
node_id="MultiGPU_WorkUnits",
display_name="MultiGPU Work Units",
display_name="MultiGPU CFG Split",
category="advanced/multigpu",
description=cleandoc(cls.__doc__),
inputs=[
io.Model.Input("model", optional=True),
io.UpscaleModel.Input("upscale_model", optional=True),
io.Vae.Input("vae", optional=True),
io.Model.Input("model"),
io.Int.Input("max_gpus", default=2, min=1, step=1),
],
outputs=[
io.Model.Output(),
io.UpscaleModel.Output(),
io.Vae.Output(),
],
)
@classmethod
def execute(cls, max_gpus: int, model: ModelPatcher = None, upscale_model=None, vae=None) -> io.NodeOutput:
if model is not None:
model = comfy.multigpu.create_multigpu_deepclones(model, max_gpus, reuse_loaded=True)
if upscale_model is not None:
upscale_model = comfy.multigpu.create_upscale_model_multigpu_deepclones(upscale_model, max_gpus)
if vae is not None:
vae = comfy.multigpu.create_vae_multigpu_deepclones(vae, max_gpus)
return io.NodeOutput(model, upscale_model, vae)
def execute(cls, model: ModelPatcher, max_gpus: int) -> io.NodeOutput:
model = comfy.multigpu.create_multigpu_deepclones(model, max_gpus, reuse_loaded=True)
return io.NodeOutput(model)
class MultiGPUOptionsNode(io.ComfyNode):