Suggested changes

Co-Authored-By: Ryan Dick <[email protected]>

Author: StAlKeR7779

invokeai/app/invocations/denoise_latents.py

@@ -495,7 +495,6 @@ def parse_controlnet_field(
                     resize_mode=control_info.resize_mode,
                 )
             )
-            # MultiControlNetModel has been refactored out, just need list[ControlNetData]
 
     def prep_ip_adapter_image_prompts(
         self,

invokeai/backend/stable_diffusion/extensions/controlnet.py

@@ -8,7 +8,7 @@
 from PIL.Image import Image
 
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
-from invokeai.app.util.controlnet_utils import prepare_control_image
+from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, prepare_control_image
 from invokeai.backend.stable_diffusion.denoise_context import UNetKwargs
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningMode
 from invokeai.backend.stable_diffusion.extension_callback_type import ExtensionCallbackType
@@ -27,8 +27,8 @@ def __init__(
         weight: Union[float, List[float]],
         begin_step_percent: float,
         end_step_percent: float,
-        control_mode: str,
-        resize_mode: str,
+        control_mode: CONTROLNET_MODE_VALUES,
+        resize_mode: CONTROLNET_RESIZE_VALUES,
     ):
         super().__init__()
         self._model = model
@@ -43,8 +43,8 @@ def __init__(
 
     @contextmanager
     def patch_extension(self, ctx: DenoiseContext):
+        original_processors = self._model.attn_processors
         try:
-            original_processors = self._model.attn_processors
             self._model.set_attn_processor(ctx.inputs.attention_processor_cls())
 
             yield None
@@ -62,8 +62,6 @@ def resize_image(self, ctx: DenoiseContext):
             do_classifier_free_guidance=False,
             width=image_width,
             height=image_height,
-            # batch_size=batch_size * num_images_per_prompt,
-            # num_images_per_prompt=num_images_per_prompt,
             device=ctx.latents.device,
             dtype=ctx.latents.dtype,
             control_mode=self._control_mode,
@@ -125,7 +123,7 @@ def _run(self, ctx: DenoiseContext, soft_injection: bool, conditioning_mode: Con
         cn_unet_kwargs = UNetKwargs(
             sample=model_input,
             timestep=ctx.timestep,
-            encoder_hidden_states=None,  # set later by conditoning
+            encoder_hidden_states=None,  # set later by conditioning
             cross_attention_kwargs=dict(  # noqa: C408
                 percent_through=ctx.step_index / total_steps,
             ),
@@ -139,9 +137,14 @@ def _run(self, ctx: DenoiseContext, soft_injection: bool, conditioning_mode: Con
             weight = weight[ctx.step_index]
 
         tmp_kwargs = vars(cn_unet_kwargs)
-        tmp_kwargs.pop("down_block_additional_residuals", None)
-        tmp_kwargs.pop("mid_block_additional_residual", None)
-        tmp_kwargs.pop("down_intrablock_additional_residuals", None)
+
+        # Remove kwargs not related to ControlNet unet
+        # ControlNet guidance fields
+        del tmp_kwargs["down_block_additional_residuals"]
+        del tmp_kwargs["mid_block_additional_residual"]
+
+        # T2i Adapter guidance fields
+        del tmp_kwargs["down_intrablock_additional_residuals"]
 
         # controlnet(s) inference
         down_samples, mid_sample = self._model(