Added minimal viable node support for ControlNet in TextToImageInvocation.

invokeai/app/invocations/generate.py

@@ -4,7 +4,9 @@ from functools import partial
 from typing import Literal, Optional, Union
 
 import numpy as np
+from diffusers import ControlNetModel
 from torch import Tensor
+import torch
 
 from pydantic import BaseModel, Field
 
@@ -53,6 +55,9 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
     seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
     model:       str = Field(default="", description="The model to use (currently ignored)")
     progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
+    control_model: Optional[str] = Field(default=None, description="The control model to use")
+    control_image: Optional[ImageField] = Field(default=None, description="The processed control image")
+    # control_strength: Optional[float] = Field(default=1.0, ge=0, le=1, description="The strength of the controlnet")
     # fmt: on
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
@@ -70,20 +75,36 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
         )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Handle invalid model parameter
         model = choose_model(context.services.model_manager, self.model)
 
+        # loading controlnet image (currently requires pre-processed image)
+        control_image = (
+            None if self.control_image is None
+            else context.services.images.get(
+                self.control_image.image_type, self.control_image.image_name
+            )
+        )
+        # loading controlnet model
+        if (self.control_model is None or self.control_model==''):
+            control_model = None
+        else:
+            # FIXME: change this to dropdown menu?
+            control_model = ControlNetModel.from_pretrained(self.control_model,
+                                                            torch_dtype=torch.float16).to("cuda")
+
         # Get the source node id (we are invoking the prepared node)
         graph_execution_state = context.services.graph_execution_manager.get(
             context.graph_execution_state_id
         )
         source_node_id = graph_execution_state.prepared_source_mapping[self.id]
 
-        outputs = Txt2Img(model).generate(
+        txt2img = Txt2Img(model, control_model=control_model)
+        outputs = txt2img.generate(
             prompt=self.prompt,
             step_callback=partial(self.dispatch_progress, context, source_node_id),
+            control_image=control_image,
             **self.dict(
-                exclude={"prompt"}
+                exclude={"prompt", "control_image" }
             ),  # Shorthand for passing all of the parameters above manually
         )
         # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object