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Co-Authored-By: Ryan Dick <14897797+RyanJDick@users.noreply.github.com>
This commit is contained in:
Sergey Borisov 2024-07-26 20:51:12 +03:00
parent 6af659b1da
commit 5b84e117b2
1 changed files with 13 additions and 8 deletions
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21
invokeai/backend/stable_diffusion/extensions/t2i_adapter.py
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@ -62,8 +62,6 @@ class T2IAdapterExt(ExtensionBase):
image=self._image,
latents_height=latents_height,
latents_width=latents_width,
max_unet_downscale=self._max_unet_downscale,
resize_mode=self._resize_mode,
)
def _run_model(
@ -72,21 +70,28 @@ class T2IAdapterExt(ExtensionBase):
image: Image,
latents_height: int,
latents_width: int,
max_unet_downscale: int,
resize_mode: CONTROLNET_RESIZE_VALUES,
):
input_height = latents_height // max_unet_downscale * model.total_downscale_factor
input_width = latents_width // max_unet_downscale * model.total_downscale_factor
# Resize the T2I-Adapter input image.
# We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the
# result will match the latent image's dimensions after max_unet_downscale is applied.
input_height = latents_height // self._max_unet_downscale * model.total_downscale_factor
input_width = latents_width // self._max_unet_downscale * model.total_downscale_factor
# Note: We have hard-coded `do_classifier_free_guidance=False`. This is because we only want to prepare
# a single image. If CFG is enabled, we will duplicate the resultant tensor after applying the
# T2I-Adapter model.
#
# Note: We re-use the `prepare_control_image(...)` from ControlNet for T2I-Adapter, because it has many
# of the same requirements (e.g. preserving binary masks during resize).
t2i_image = prepare_control_image(
image=image,
do_classifier_free_guidance=False,
width=input_width,
height=input_height,
num_channels=model.config["in_channels"], # mypy treats this as a FrozenDict
num_channels=model.config["in_channels"],
device=model.device,
dtype=model.dtype,
resize_mode=resize_mode,
resize_mode=self._resize_mode,
)
return model(t2i_image)