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Fix SolidMask and MaskComposite device mismatch with --gpu-only

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SolidMask had a hardcoded device="cpu" while other nodes (e.g.
EmptyImage) follow intermediate_device(). This causes a RuntimeError
when MaskComposite combines masks from different device sources
under --gpu-only.

- SolidMask: use intermediate_device() instead of hardcoded "cpu"
- MaskComposite: align source device to destination before operating
This commit is contained in:
gschreiber 2026-04-05 12:46:56 +00:00
parent 8cbbea8f6a
commit 2729bd0dc6
1 changed files with 3 additions and 1 deletions
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4
comfy_extras/nodes_mask.py
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@ -2,6 +2,7 @@ import numpy as np
import scipy.ndimage import scipy.ndimage
import torch import torch
import comfy.utils import comfy.utils
import comfy.model_management
import node_helpers import node_helpers
from typing_extensions import override from typing_extensions import override
from comfy_api.latest import ComfyExtension, IO, UI from comfy_api.latest import ComfyExtension, IO, UI
@ -188,7 +189,7 @@ class SolidMask(IO.ComfyNode):
@classmethod @classmethod
def execute(cls, value, width, height) -> IO.NodeOutput: def execute(cls, value, width, height) -> IO.NodeOutput:
out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu") out = torch.full((1, height, width), value, dtype=torch.float32, device=comfy.model_management.intermediate_device())
return IO.NodeOutput(out) return IO.NodeOutput(out)
solid = execute # TODO: remove solid = execute # TODO: remove
@ -262,6 +263,7 @@ class MaskComposite(IO.ComfyNode):
def execute(cls, destination, source, x, y, operation) -> IO.NodeOutput: def execute(cls, destination, source, x, y, operation) -> IO.NodeOutput:
output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone() output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
source = source.reshape((-1, source.shape[-2], source.shape[-1])) source = source.reshape((-1, source.shape[-2], source.shape[-1]))
source = source.to(output.device)
left, top = (x, y,) left, top = (x, y,)
right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2])) right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))