fixed node issue

Author: Ubuntu

invokeai/app/invocations/bria_controlnet.py

@@ -1,6 +1,6 @@
 from invokeai.backend.bria.controlnet_bria import BRIA_CONTROL_MODES
 from pydantic import BaseModel, Field
-from invokeai.invocation_api import ImageOutput
+from invokeai.invocation_api import ImageOutput, Classification
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -40,6 +40,7 @@ class BriaControlNetOutput(BaseInvocationOutput):
     tags=["controlnet", "bria"],
     category="controlnet",
     version="1.0.0",
+    classification=Classification.Prototype,
 )
 class BriaControlNetInvocation(BaseInvocation):
     """Collect Bria ControlNet info to pass to denoiser node."""

invokeai/app/invocations/bria_denoiser.py

@@ -3,7 +3,7 @@
 from invokeai.backend.bria.controlnet_bria import BriaControlModes, BriaMultiControlNetModel
 from invokeai.backend.bria.controlnet_utils import prepare_control_images
 from invokeai.backend.bria.pipeline_bria_controlnet import BriaControlNetPipeline
-from invokeai.nodes.bria_nodes.bria_controlnet import BriaControlNetField
+from invokeai.app.invocations.bria_controlnet import BriaControlNetField
 
 import torch
 from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler

invokeai/backend/bria/pipeline_bria_controlnet.py

@@ -612,14 +612,14 @@ def encode_prompt(
 
 
 def prepare_latents(
-    batch_size,
-    num_channels_latents,
-    height,
-    width,
-    dtype,
-    device,
-    generator,
-    latents=None,
+    batch_size: int,
+    num_channels_latents: int,
+    height: int,
+    width: int,
+    dtype: torch.dtype,
+    device: torch.device,
+    generator: torch.Generator,
+    latents: Optional[torch.FloatTensor] = None,
 ):
     # VAE applies 8x compression on images but we must also account for packing which requires
     # latent height and width to be divisible by 2.