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from torch import Tensor
import math
import folder_paths
from .control_plusplus import load_controlnetplusplus, PlusPlusType, PlusPlusInput, PlusPlusInputGroup, PlusPlusImageWrapper
from .utils import BIGMAX
class PlusPlusLoaderAdvanced:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"plus_input": ("PLUS_INPUT", ),
"name": (folder_paths.get_filename_list("controlnet"), ),
}
}
RETURN_TYPES = ("CONTROL_NET", "IMAGE",)
FUNCTION = "load_controlnet_plusplus"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/ControlNet++"
def load_controlnet_plusplus(self, plus_input: PlusPlusInputGroup, name: str):
controlnet_path = folder_paths.get_full_path("controlnet", name)
controlnet = load_controlnetplusplus(controlnet_path)
controlnet.verify_control_type(name, plus_input)
return (controlnet, PlusPlusImageWrapper(plus_input),)
class PlusPlusLoaderSingle:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"name": (folder_paths.get_filename_list("controlnet"), ),
"control_type": (PlusPlusType._LIST_WITH_NONE, {"default": PlusPlusType.NONE}, ),
}
}
RETURN_TYPES = ("CONTROL_NET",)
FUNCTION = "load_controlnet_plusplus"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/ControlNet++"
def load_controlnet_plusplus(self, name: str, control_type: str):
controlnet_path = folder_paths.get_full_path("controlnet", name)
controlnet = load_controlnetplusplus(controlnet_path)
controlnet.single_control_type = control_type
controlnet.verify_control_type(name)
return (controlnet,)
class PlusPlusInputNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"control_type": (PlusPlusType._LIST,),
},
"optional": {
"prev_plus_input": ("PLUS_INPUT",),
"autosize": ("ACNAUTOSIZE", {"padding": 0}),
#"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": BIGMAX, "step": 0.01}),
}
}
RETURN_TYPES = ("PLUS_INPUT", )
FUNCTION = "wrap_images"
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/ControlNet++"
def wrap_images(self, image: Tensor, control_type: str, strength=1.0, prev_plus_input: PlusPlusInputGroup=None):
if prev_plus_input is None:
prev_plus_input = PlusPlusInputGroup()
prev_plus_input = prev_plus_input.clone()
if math.isclose(strength, 0.0):
strength = 0.0000001
pp_input = PlusPlusInput(image, control_type, strength)
prev_plus_input.add(pp_input)
return (prev_plus_input,)
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