Add support for LoRA models in TiledStableDiffusionRefineInvocation.

invokeai/app/invocations/tiled_stable_diffusion_refine.py

@@ -1,4 +1,5 @@
 from contextlib import ExitStack
+from typing import Iterator, Tuple
 
 import numpy as np
 import numpy.typing as npt
@@ -25,6 +26,8 @@ from invokeai.app.invocations.noise import get_noise
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, prepare_control_image
+from invokeai.backend.lora import LoRAModelRaw
+from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, image_resized_to_grid_as_tensor
 from invokeai.backend.tiles.tiles import calc_tiles_min_overlap, merge_tiles_with_linear_blending
 from invokeai.backend.tiles.utils import Tile
@@ -236,11 +239,19 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
         # Crop the global noise into tiles.
         noise_tiles = [self.crop_latents_to_tile(latents=global_noise, image_tile=t) for t in tiles]
 
+        # Prepare an iterator that yields the UNet's LoRA models and their weights.
+        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
+            for lora in self.unet.loras:
+                lora_info = context.models.load(lora.lora)
+                assert isinstance(lora_info.model, LoRAModelRaw)
+                yield (lora_info.model, lora.weight)
+                del lora_info
+
         # Load the UNet model.
         unet_info = context.models.load(self.unet.unet)
 
         refined_latent_tiles: list[torch.Tensor] = []
-        with ExitStack() as exit_stack, unet_info as unet:
+        with ExitStack() as exit_stack, unet_info as unet, ModelPatcher.apply_lora_unet(unet, _lora_loader()):
             assert isinstance(unet, UNet2DConditionModel)
             scheduler = get_scheduler(
                 context=context,