|
import folder_paths |
|
import comfy.utils |
|
import comfy.model_detection |
|
import comfy.model_management |
|
import comfy.lora |
|
from comfy.model_patcher import ModelPatcher |
|
|
|
from .utils import TimestepKeyframeGroup |
|
from .control import ControlNetAdvanced, load_controlnet |
|
|
|
|
|
|
|
|
|
def convert_cn_lora_from_diffusers(cn_model: ModelPatcher, lora_path: str): |
|
lora_data = comfy.utils.load_torch_file(lora_path, safe_load=True) |
|
unet_dtype = comfy.model_management.unet_dtype() |
|
for key, value in lora_data.items(): |
|
lora_data[key] = value.to(unet_dtype) |
|
diffusers_keys = comfy.utils.unet_to_diffusers(cn_model.model.state_dict()) |
|
|
|
|
|
|
|
|
|
|
|
|
|
lora_data = comfy.lora.load_lora(lora_data, to_load=diffusers_keys) |
|
|
|
|
|
|
|
|
|
|
|
return lora_data |
|
|
|
|
|
class ControlNetLoaderWithLoraAdvanced: |
|
@classmethod |
|
def INPUT_TYPES(s): |
|
return { |
|
"required": { |
|
"control_net_name": (folder_paths.get_filename_list("controlnet"), ), |
|
"cn_lora_name": (folder_paths.get_filename_list("controlnet"), ), |
|
"cn_lora_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), |
|
}, |
|
"optional": { |
|
"timestep_keyframe": ("TIMESTEP_KEYFRAME", ), |
|
} |
|
} |
|
|
|
RETURN_TYPES = ("CONTROL_NET", ) |
|
FUNCTION = "load_controlnet" |
|
|
|
CATEGORY = "Adv-ControlNet ππ
π
π
/LOOSEControl" |
|
|
|
def load_controlnet(self, control_net_name, cn_lora_name, cn_lora_strength: float, |
|
timestep_keyframe: TimestepKeyframeGroup=None |
|
): |
|
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) |
|
controlnet: ControlNetAdvanced = load_controlnet(controlnet_path, timestep_keyframe) |
|
if not isinstance(controlnet, ControlNetAdvanced): |
|
raise ValueError("Type {} is not compatible with CN LoRA features at this time.") |
|
|
|
lora_path = folder_paths.get_full_path("controlnet", cn_lora_name) |
|
lora_data = convert_cn_lora_from_diffusers(cn_model=controlnet.control_model_wrapped, lora_path=lora_path) |
|
|
|
controlnet.control_model_wrapped.add_patches(lora_data, strength_patch=cn_lora_strength) |
|
|
|
return (controlnet,) |
|
|