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feat(nodes): restore unet check on CreateGradientMaskInvocation

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Special handling for inpainting models
This commit is contained in:
psychedelicious 2024-04-23 20:25:12 +10:00 committed by Kent Keirsey
parent a461537087
commit 1bef13db37
1 changed files with 25 additions and 12 deletions
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19
invokeai/app/invocations/latent.py
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@ -51,6 +51,7 @@ from invokeai.app.util.controlnet_utils import prepare_control_image
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
from invokeai.backend.lora import LoRAModelRaw
from invokeai.backend.model_manager import BaseModelType, LoadedModel
from invokeai.backend.model_manager.config import MainConfigBase, ModelVariantType
from invokeai.backend.model_patcher import ModelPatcher
from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
@ -204,6 +205,13 @@ class CreateGradientMaskInvocation(BaseInvocation):
title="[OPTIONAL] Image",
ui_order=6,
)
unet: Optional[UNetField] = InputField(
description="OPTIONAL: If the Unet is a specialized Inpainting model, masked_latents will be generated from the image with the VAE",
default=None,
input=Input.Connection,
title="[OPTIONAL] UNet",
ui_order=5,
)
vae: Optional[VAEField] = InputField(
default=None,
description="OPTIONAL: Only connect for specialized Inpainting models, masked_latents will be generated from the image with the VAE",
@ -253,8 +261,11 @@ class CreateGradientMaskInvocation(BaseInvocation):
expanded_image_dto = context.images.save(expanded_mask_image)
masked_latents_name = None
if self.vae is not None and self.image is not None:
# both fields must be present at the same time
if self.unet is not None and self.vae is not None and self.image is not None:
# all three fields must be present at the same time
main_model_config = context.models.get_config(self.unet.unet.key)
assert isinstance(main_model_config, MainConfigBase)
if main_model_config.variant is ModelVariantType.Inpaint:
mask = blur_tensor
vae_info: LoadedModel = context.models.load(self.vae.vae)
image = context.images.get_pil(self.image.image_name)
@ -263,7 +274,9 @@ class CreateGradientMaskInvocation(BaseInvocation):
image_tensor = image_tensor.unsqueeze(0)
img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
masked_latents = ImageToLatentsInvocation.vae_encode(
vae_info, self.fp32, self.tiled, masked_image.clone()
)
masked_latents_name = context.tensors.save(tensor=masked_latents)
return GradientMaskOutput(