diff --git a/internal_controlnet/external_code.py b/internal_controlnet/external_code.py
index 8b631b069..157f08efc 100644
--- a/internal_controlnet/external_code.py
+++ b/internal_controlnet/external_code.py
@@ -115,154 +115,7 @@ def pixel_perfect_resolution(
     return int(np.round(estimation))
 
 
-<<<<<<< HEAD
 def to_base64_nparray(encoding: str) -> np.ndarray:
-=======
-InputImage = Union[np.ndarray, str]
-InputImage = Union[Dict[str, InputImage], Tuple[InputImage, InputImage], InputImage]
-
-
-@dataclass
-class ControlNetUnit:
-    """
-    Represents an entire ControlNet processing unit.
-    """
-
-    enabled: bool = True
-    module: str = "none"
-    model: str = "None"
-    weight: float = 1.0
-    image: Optional[Union[InputImage, List[InputImage]]] = None
-    resize_mode: Union[ResizeMode, int, str] = ResizeMode.INNER_FIT
-    low_vram: bool = False
-    processor_res: int = -1
-    threshold_a: float = -1
-    threshold_b: float = -1
-    guidance_start: float = 0.0
-    guidance_end: float = 1.0
-    pixel_perfect: bool = False
-    control_mode: Union[ControlMode, int, str] = ControlMode.BALANCED
-    # Whether to crop input image based on A1111 img2img mask. This flag is only used when `inpaint area`
-    # in A1111 is set to `Only masked`. In API, this correspond to `inpaint_full_res = True`.
-    inpaint_crop_input_image: bool = True
-    # If hires fix is enabled in A1111, how should this ControlNet unit be applied.
-    # The value is ignored if the generation is not using hires fix.
-    hr_option: Union[HiResFixOption, int, str] = HiResFixOption.BOTH
-
-    # Whether save the detected map of this unit. Setting this option to False prevents saving the
-    # detected map or sending detected map along with generated images via API.
-    # Currently the option is only accessible in API calls.
-    save_detected_map: bool = True
-
-    # Weight for each layer of ControlNet params.
-    # For ControlNet:
-    # - SD1.5: 13 weights (4 encoder block * 3 + 1 middle block)
-    # - SDXL: 10 weights (3 encoder block * 3 + 1 middle block)
-    # For T2IAdapter
-    # - SD1.5: 5 weights (4 encoder block + 1 middle block)
-    # - SDXL: 4 weights (3 encoder block + 1 middle block)
-    # For IPAdapter
-    # - SD15: 16 (6 input blocks + 9 output blocks + 1 middle block)
-    # - SDXL: 11 weights (4 input blocks + 6 output blocks + 1 middle block)
-    # Note1: Setting advanced weighting will disable `soft_injection`, i.e.
-    # It is recommended to set ControlMode = BALANCED when using `advanced_weighting`.
-    # Note2: The field `weight` is still used in some places, e.g. reference_only,
-    # even advanced_weighting is set.
-    advanced_weighting: Optional[List[float]] = None
-
-    # The effective region mask that unit's effect should be restricted to.
-    effective_region_mask: Optional[np.ndarray] = None
-
-    # Whether to crop image to effective mask's bounding box.
-    resize_to_effective_region: bool = False
-
-    # The tensor input for ipadapter. When this field is set in the API,
-    # the base64string will be interpret by torch.load to reconstruct ipadapter
-    # preprocessor output.
-    # Currently the option is only accessible in API calls.
-    ipadapter_input: Optional[List[Any]] = None
-
-    def __eq__(self, other):
-        if not isinstance(other, ControlNetUnit):
-            return False
-
-        return vars(self) == vars(other)
-
-    def accepts_multiple_inputs(self) -> bool:
-        """This unit can accept multiple input images."""
-        return self.module in (
-            "ip-adapter_clip_sdxl",
-            "ip-adapter_clip_sdxl_plus_vith",
-            "ip-adapter_clip_sd15",
-            "ip-adapter_face_id",
-            "ip-adapter_face_id_plus",
-            "instant_id_face_embedding",
-        )
-
-    @staticmethod
-    def infotext_excluded_fields() -> List[str]:
-        return [
-            "image",
-            "enabled",
-            # API-only fields.
-            "advanced_weighting",
-            "ipadapter_input",
-            # End of API-only fields.
-            # Note: "inpaint_crop_image" is img2img inpaint only flag, which does not
-            # provide much information when restoring the unit.
-            "inpaint_crop_input_image",
-            "effective_region_mask",
-            "resize_to_effective_region",
-        ]
-
-    @property
-    def is_animate_diff_batch(self) -> bool:
-        return getattr(self, "animatediff_batch", False)
-
-    @property
-    def uses_clip(self) -> bool:
-        """Whether this unit uses clip preprocessor."""
-        return any(
-            (
-                ("ip-adapter" in self.module and "face_id" not in self.module),
-                self.module
-                in ("clip_vision", "revision_clipvision", "revision_ignore_prompt"),
-            )
-        )
-
-    @property
-    def is_inpaint(self) -> bool:
-        return "inpaint" in self.module
-
-    def bound_check_params(self) -> None:
-        """
-        Checks and corrects negative parameters in ControlNetUnit 'unit' in place.
-        Parameters 'processor_res', 'threshold_a', 'threshold_b' are reset to
-        their default values if negative.
-        """
-        preprocessor = Preprocessor.get_preprocessor(self.module)
-        for unit_param, param in zip(
-            ("processor_res", "threshold_a", "threshold_b"),
-            ("slider_resolution", "slider_1", "slider_2"),
-        ):
-            value = getattr(self, unit_param)
-            cfg: PreprocessorParameter = getattr(preprocessor, param)
-            if value < 0:
-                setattr(self, unit_param, cfg.value)
-                logger.info(
-                    f"[{self.module}.{unit_param}] Invalid value({value}), using default value {cfg.value}."
-                )
-
-    def get_actual_preprocessor(self) -> Preprocessor:
-        if self.module == "ip-adapter-auto":
-            return Preprocessor.get_preprocessor(self.module).get_preprocessor_by_model(
-                self.model
-            )
-        return Preprocessor.get_preprocessor(self.module)
-
-
-def to_base64_nparray(encoding: str):
->>>>>>> 0365a14 (Plumbing)
     """
     Convert a base64 image into the image type the extension uses
     """