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Add naive ControlNet support to TiledStableDiffusionRefineInvocation

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This commit is contained in:
Ryan Dick
2024-06-10 10:52:14 -04:00
committed by Kent Keirsey
parent d08e405017
commit 5301770525
2 changed files with 106 additions and 18 deletions
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4
invokeai/app/util/controlnet_utils.py
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@ -289,7 +289,7 @@ def prepare_control_image(
width: int,
height: int,
num_channels: int = 3,
device: str = "cuda",
device: str | torch.device = "cuda",
dtype: torch.dtype = torch.float16,
control_mode: CONTROLNET_MODE_VALUES = "balanced",
resize_mode: CONTROLNET_RESIZE_VALUES = "just_resize_simple",
@ -304,7 +304,7 @@ def prepare_control_image(
num_channels (int, optional): The target number of image channels. This is achieved by converting the input
image to RGB, then naively taking the first `num_channels` channels. The primary use case is converting a
RGB image to a single-channel grayscale image. Raises if `num_channels` cannot be achieved. Defaults to 3.
device (str, optional): The target device for the output image. Defaults to "cuda".
device (str | torch.Device, optional): The target device for the output image. Defaults to "cuda".
dtype (_type_, optional): The dtype for the output image. Defaults to torch.float16.
do_classifier_free_guidance (bool, optional): If True, repeat the output image along the batch dimension.
Defaults to True.