Added support for ControlNet and MultiControlNet to legacy non-nodal Txt2Img in backend/generator. Although backend/generator will likely disappear by v3.x, right now they are very useful for testing core ControlNet and MultiControlNet functionality while node codebase is rapidly evolving.

invokeai/backend/generator/base.py

@@ -75,9 +75,11 @@ class InvokeAIGenerator(metaclass=ABCMeta):
     def __init__(self,
                  model_info: dict,
                  params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
+                 **kwargs,
                  ):
         self.model_info=model_info
         self.params=params
+        self.kwargs = kwargs
 
     def generate(self,
                  prompt: str='',
@@ -118,9 +120,12 @@ class InvokeAIGenerator(metaclass=ABCMeta):
             model=model,
             scheduler_name=generator_args.get('scheduler')
         )
-        uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt,model=model)
+
+        # get conditioning from prompt via Compel package
+        uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt, model=model)
+
         gen_class = self._generator_class()
-        generator = gen_class(model, self.params.precision)
+        generator = gen_class(model, self.params.precision, **self.kwargs)
         if self.params.variation_amount > 0:
             generator.set_variation(generator_args.get('seed'),
                                     generator_args.get('variation_amount'),
@@ -276,7 +281,7 @@ class Generator:
     precision: str
     model: DiffusionPipeline
 
-    def __init__(self, model: DiffusionPipeline, precision: str):
+    def __init__(self, model: DiffusionPipeline, precision: str, **kwargs):
         self.model = model
         self.precision = precision
         self.seed = None

invokeai/backend/generator/txt2img.py

@@ -4,6 +4,10 @@ invokeai.backend.generator.txt2img inherits from invokeai.backend.generator
 import PIL.Image
 import torch
 
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from diffusers.models.controlnet import ControlNetModel, ControlNetOutput
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
+
 from ..stable_diffusion import (
     ConditioningData,
     PostprocessingSettings,
@@ -13,8 +17,13 @@ from .base import Generator
 
 
 class Txt2Img(Generator):
-    def __init__(self, model, precision):
+    def __init__(self, model, precision,
-        super().__init__(model, precision)
+                 control_model: Optional[Union[ControlNetModel, List[ControlNetModel]]] = None,
+                 **kwargs):
+        self.control_model = control_model
+        if isinstance(self.control_model, list):
+            self.control_model = MultiControlNetModel(self.control_model)
+        super().__init__(model, precision, **kwargs)
 
     @torch.no_grad()
     def get_make_image(
@@ -42,9 +51,12 @@ class Txt2Img(Generator):
         kwargs are 'width' and 'height'
         """
         self.perlin = perlin
+        control_image = kwargs.get("control_image", None)
+        do_classifier_free_guidance = cfg_scale > 1.0
 
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
+        pipeline.control_model = self.control_model
         pipeline.scheduler = sampler
 
         uc, c, extra_conditioning_info = conditioning
@@ -61,6 +73,37 @@ class Txt2Img(Generator):
             ),
         ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
 
+        # FIXME: still need to test with different widths, heights, devices, dtypes
+        #        and add in batch_size, num_images_per_prompt?
+        if control_image is not None:
+            if isinstance(self.control_model, ControlNetModel):
+                control_image = pipeline.prepare_control_image(
+                    image=control_image,
+                    do_classifier_free_guidance=do_classifier_free_guidance,
+                    width=width,
+                    height=height,
+                    # batch_size=batch_size * num_images_per_prompt,
+                    # num_images_per_prompt=num_images_per_prompt,
+                    device=self.control_model.device,
+                    dtype=self.control_model.dtype,
+                )
+            elif isinstance(self.control_model, MultiControlNetModel):
+                images = []
+                for image_ in control_image:
+                    image_ = self.model.prepare_control_image(
+                        image=image_,
+                        do_classifier_free_guidance=do_classifier_free_guidance,
+                        width=width,
+                        height=height,
+                        # batch_size=batch_size * num_images_per_prompt,
+                        # num_images_per_prompt=num_images_per_prompt,
+                        device=self.control_model.device,
+                        dtype=self.control_model.dtype,
+                    )
+                    images.append(image_)
+                control_image = images
+            kwargs["control_image"] = control_image
+
         def make_image(x_T: torch.Tensor, _: int) -> PIL.Image.Image:
             pipeline_output = pipeline.image_from_embeddings(
                 latents=torch.zeros_like(x_T, dtype=self.torch_dtype()),
@@ -68,6 +111,7 @@ class Txt2Img(Generator):
                 num_inference_steps=steps,
                 conditioning_data=conditioning_data,
                 callback=step_callback,
+                **kwargs,
             )
 
             if (