import os import torch import folder_paths import numpy as np from PIL import Image, ImageOps from .control import load_controlnet, is_advanced_controlnet from .nodes_main import AdvancedControlNetApply from .utils import BIGMAX, ControlWeights, TimestepKeyframeGroup, TimestepKeyframe, get_properly_arranged_t2i_weights from .logger import logger class LoadImagesFromDirectory: @classmethod def INPUT_TYPES(s): return { "required": { "directory": ("STRING", {"default": ""}), }, "optional": { "image_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}), "start_index": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}), } } RETURN_TYPES = ("IMAGE", "MASK", "INT") FUNCTION = "load_images" CATEGORY = "" def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0): if not os.path.isdir(directory): raise FileNotFoundError(f"Directory '{directory} cannot be found.'") dir_files = os.listdir(directory) if len(dir_files) == 0: raise FileNotFoundError(f"No files in directory '{directory}'.") dir_files = sorted(dir_files) dir_files = [os.path.join(directory, x) for x in dir_files] # start at start_index dir_files = dir_files[start_index:] images = [] masks = [] limit_images = False if image_load_cap > 0: limit_images = True image_count = 0 for image_path in dir_files: if os.path.isdir(image_path): continue if limit_images and image_count >= image_load_cap: break i = Image.open(image_path) i = ImageOps.exif_transpose(i) image = i.convert("RGB") image = np.array(image).astype(np.float32) / 255.0 image = torch.from_numpy(image)[None,] if 'A' in i.getbands(): mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 mask = 1. - torch.from_numpy(mask) else: mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") images.append(image) masks.append(mask) image_count += 1 if len(images) == 0: raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.") return (torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count) class ScaledSoftUniversalWeightsDeprecated: @classmethod def INPUT_TYPES(s): return { "required": { "base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "step": 0.001}, ), "flip_weights": ("BOOLEAN", {"default": False}), }, "optional": { "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ), "cn_extras": ("CN_WEIGHTS_EXTRAS",), }, "hidden": { "autosize": ("ACNAUTOSIZE", {"padding": 0}), } } RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT") FUNCTION = "load_weights" CATEGORY = "" def load_weights(self, base_multiplier, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}): weights = ControlWeights.universal(base_multiplier=base_multiplier, uncond_multiplier=uncond_multiplier, extras=cn_extras) return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) class SoftControlNetWeightsDeprecated: @classmethod def INPUT_TYPES(s): return { "required": { "weight_00": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_01": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_02": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_03": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_04": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_05": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_06": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_07": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_08": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_09": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "flip_weights": ("BOOLEAN", {"default": False}), }, "optional": { "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ), "cn_extras": ("CN_WEIGHTS_EXTRAS",), }, "hidden": { "autosize": ("ACNAUTOSIZE", {"padding": 0}), } } DEPRECATED = True RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT") FUNCTION = "load_weights" CATEGORY = "" def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}): weights_output = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, weight_07, weight_08, weight_09, weight_10, weight_11] weights_middle = [weight_12] weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras) return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) class CustomControlNetWeightsDeprecated: @classmethod def INPUT_TYPES(s): return { "required": { "weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_04": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_05": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_06": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_07": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_08": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_09": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "flip_weights": ("BOOLEAN", {"default": False}), }, "optional": { "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ), "cn_extras": ("CN_WEIGHTS_EXTRAS",), }, "hidden": { "autosize": ("ACNAUTOSIZE", {"padding": 0}), } } DEPRECATED = True RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT") FUNCTION = "load_weights" CATEGORY = "" def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}): weights_output = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, weight_07, weight_08, weight_09, weight_10, weight_11] weights_middle = [weight_12] weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras) return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) class SoftT2IAdapterWeightsDeprecated: @classmethod def INPUT_TYPES(s): return { "required": { "weight_00": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_01": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_02": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "flip_weights": ("BOOLEAN", {"default": False}), }, "optional": { "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ), "cn_extras": ("CN_WEIGHTS_EXTRAS",), }, "hidden": { "autosize": ("ACNAUTOSIZE", {"padding": 0}), } } DEPRECATED = True RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT") FUNCTION = "load_weights" CATEGORY = "" def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}): weights = [weight_00, weight_01, weight_02, weight_03] weights = get_properly_arranged_t2i_weights(weights) weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras) return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) class CustomT2IAdapterWeightsDeprecated: @classmethod def INPUT_TYPES(s): return { "required": { "weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), "flip_weights": ("BOOLEAN", {"default": False}), }, "optional": { "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ), "cn_extras": ("CN_WEIGHTS_EXTRAS",), }, "hidden": { "autosize": ("ACNAUTOSIZE", {"padding": 0}), } } DEPRECATED = True RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT") FUNCTION = "load_weights" CATEGORY = "" def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}): weights = [weight_00, weight_01, weight_02, weight_03] weights = get_properly_arranged_t2i_weights(weights) weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras) return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) class AdvancedControlNetApplyDEPR: @classmethod def INPUT_TYPES(s): return { "required": { "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), "control_net": ("CONTROL_NET", ), "image": ("IMAGE", ), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) }, "optional": { "mask_optional": ("MASK", ), "timestep_kf": ("TIMESTEP_KEYFRAME", ), "latent_kf_override": ("LATENT_KEYFRAME", ), "weights_override": ("CONTROL_NET_WEIGHTS", ), "model_optional": ("MODEL",), "vae_optional": ("VAE",), }, "hidden": { "autosize": ("ACNAUTOSIZE", {"padding": 0}), } } DEPRECATED = True RETURN_TYPES = ("CONDITIONING","CONDITIONING","MODEL",) RETURN_NAMES = ("positive", "negative", "model_opt") FUNCTION = "apply_controlnet" CATEGORY = "" def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, mask_optional=None, model_optional=None, vae_optional=None, timestep_kf: TimestepKeyframeGroup=None, latent_kf_override=None, weights_override: ControlWeights=None, control_apply_to_uncond=False): new_positive, new_negative = AdvancedControlNetApply.apply_controlnet(self, positive=positive, negative=negative, control_net=control_net, image=image, strength=strength, start_percent=start_percent, end_percent=end_percent, mask_optional=mask_optional, vae_optional=vae_optional, timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,) return (new_positive, new_negative, model_optional) class AdvancedControlNetApplySingleDEPR: @classmethod def INPUT_TYPES(s): return { "required": { "conditioning": ("CONDITIONING", ), "control_net": ("CONTROL_NET", ), "image": ("IMAGE", ), "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) }, "optional": { "mask_optional": ("MASK", ), "timestep_kf": ("TIMESTEP_KEYFRAME", ), "latent_kf_override": ("LATENT_KEYFRAME", ), "weights_override": ("CONTROL_NET_WEIGHTS", ), "model_optional": ("MODEL",), "vae_optional": ("VAE",), }, "hidden": { "autosize": ("ACNAUTOSIZE", {"padding": 0}), } } DEPRECATED = True RETURN_TYPES = ("CONDITIONING","MODEL",) RETURN_NAMES = ("CONDITIONING", "model_opt") FUNCTION = "apply_controlnet" CATEGORY = "" def apply_controlnet(self, conditioning, control_net, image, strength, start_percent, end_percent, mask_optional=None, model_optional=None, vae_optional=None, timestep_kf: TimestepKeyframeGroup=None, latent_kf_override=None, weights_override: ControlWeights=None): values = AdvancedControlNetApply.apply_controlnet(self, positive=conditioning, negative=None, control_net=control_net, image=image, strength=strength, start_percent=start_percent, end_percent=end_percent, mask_optional=mask_optional, vae_optional=vae_optional, timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override, control_apply_to_uncond=True) return (values[0], model_optional) class ControlNetLoaderAdvancedDEPR: @classmethod def INPUT_TYPES(s): return { "required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), ), }, "optional": { "tk_optional": ("TIMESTEP_KEYFRAME", ), } } DEPRECATED = True RETURN_TYPES = ("CONTROL_NET", ) FUNCTION = "load_controlnet" CATEGORY = "" def load_controlnet(self, control_net_name, tk_optional: TimestepKeyframeGroup=None, timestep_keyframe: TimestepKeyframeGroup=None, ): if timestep_keyframe is not None: # backwards compatibility tk_optional = timestep_keyframe controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) controlnet = load_controlnet(controlnet_path, tk_optional) return (controlnet,) class DiffControlNetLoaderAdvancedDEPR: @classmethod def INPUT_TYPES(s): return { "required": { "model": ("MODEL",), "control_net_name": (folder_paths.get_filename_list("controlnet"), ) }, "optional": { "tk_optional": ("TIMESTEP_KEYFRAME", ), }, "hidden": { "autosize": ("ACNAUTOSIZE", {"padding": 0}), } } DEPRECATED = True RETURN_TYPES = ("CONTROL_NET", ) FUNCTION = "load_controlnet" CATEGORY = "" def load_controlnet(self, control_net_name, model, tk_optional: TimestepKeyframeGroup=None, timestep_keyframe: TimestepKeyframeGroup=None ): if timestep_keyframe is not None: # backwards compatibility tk_optional = timestep_keyframe controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) controlnet = load_controlnet(controlnet_path, tk_optional, model) if is_advanced_controlnet(controlnet): controlnet.verify_all_weights() return (controlnet,)