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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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25
comfy_extras/nodes_upscale_model.py
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@@ -81,33 +81,13 @@ class ImageUpscaleWithModel(io.ComfyNode):
output_device = comfy.model_management.intermediate_device()
multigpu_clones = getattr(upscale_model, 'multigpu_clones', None)
if multigpu_clones:
for dev, desc in multigpu_clones.items():
model_management.free_memory(memory_required, dev)
desc.to(dev)
oom = True
try:
while oom:
try:
steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
pbar = comfy.utils.ProgressBar(steps)
if multigpu_clones:
functions = {device: lambda a: upscale_model(a.float())}
for dev, desc in multigpu_clones.items():
functions[dev] = lambda a, d=desc: d(a.float())
s = comfy.utils.tiled_scale_multidim_multigpu(
in_img,
functions,
tile=(tile, tile),
overlap=overlap,
upscale_amount=upscale_model.scale,
pbar=pbar,
output_device=output_device,
)
else:
s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a.float()), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar, output_device=output_device)
s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a.float()), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar, output_device=output_device)
oom = False
except Exception as e:
model_management.raise_non_oom(e)
@@ -116,9 +96,6 @@ class ImageUpscaleWithModel(io.ComfyNode):
raise e
finally:
upscale_model.to("cpu")
if multigpu_clones:
for desc in multigpu_clones.values():
desc.to("cpu")
s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0).to(comfy.model_management.intermediate_dtype())
return io.NodeOutput(s)