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import os
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import torch
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import numpy as np
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from PIL import Image, ImageOps
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from .utils import BIGMAX, ControlWeights, TimestepKeyframeGroup, TimestepKeyframe, get_properly_arranged_t2i_weights
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from .logger import logger
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class LoadImagesFromDirectory:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"directory": ("STRING", {"default": ""}),
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},
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"optional": {
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"image_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
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"start_index": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK", "INT")
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FUNCTION = "load_images"
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CATEGORY = ""
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def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0):
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if not os.path.isdir(directory):
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raise FileNotFoundError(f"Directory '{directory} cannot be found.'")
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dir_files = os.listdir(directory)
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if len(dir_files) == 0:
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raise FileNotFoundError(f"No files in directory '{directory}'.")
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dir_files = sorted(dir_files)
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dir_files = [os.path.join(directory, x) for x in dir_files]
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dir_files = dir_files[start_index:]
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images = []
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masks = []
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limit_images = False
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if image_load_cap > 0:
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limit_images = True
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image_count = 0
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for image_path in dir_files:
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if os.path.isdir(image_path):
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continue
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if limit_images and image_count >= image_load_cap:
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break
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i = Image.open(image_path)
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i = ImageOps.exif_transpose(i)
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image = i.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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if 'A' in i.getbands():
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mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
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mask = 1. - torch.from_numpy(mask)
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else:
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mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
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images.append(image)
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masks.append(mask)
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image_count += 1
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if len(images) == 0:
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raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.")
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return (torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count)
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class ScaledSoftUniversalWeightsDeprecated:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "step": 0.001}, ),
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"flip_weights": ("BOOLEAN", {"default": False}),
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},
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"optional": {
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"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
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"cn_extras": ("CN_WEIGHTS_EXTRAS",),
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"autosize": ("ACNAUTOSIZE", {"padding": 0}),
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}
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}
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
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FUNCTION = "load_weights"
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CATEGORY = ""
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def load_weights(self, base_multiplier, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
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weights = ControlWeights.universal(base_multiplier=base_multiplier, uncond_multiplier=uncond_multiplier, extras=cn_extras)
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class SoftControlNetWeightsDeprecated:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"weight_00": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_01": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_02": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_03": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_04": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_05": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_06": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_07": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_08": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_09": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"flip_weights": ("BOOLEAN", {"default": False}),
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},
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"optional": {
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"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
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"cn_extras": ("CN_WEIGHTS_EXTRAS",),
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"autosize": ("ACNAUTOSIZE", {"padding": 0}),
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}
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}
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DEPRECATED = True
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
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FUNCTION = "load_weights"
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CATEGORY = ""
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def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
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weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights,
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uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
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weights_output = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
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weight_07, weight_08, weight_09, weight_10, weight_11]
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weights_middle = [weight_12]
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weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras)
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class CustomControlNetWeightsDeprecated:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_04": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_05": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_06": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_07": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_08": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_09": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"flip_weights": ("BOOLEAN", {"default": False}),
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},
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"optional": {
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"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
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"cn_extras": ("CN_WEIGHTS_EXTRAS",),
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"autosize": ("ACNAUTOSIZE", {"padding": 0}),
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}
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}
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DEPRECATED = True
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
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FUNCTION = "load_weights"
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CATEGORY = ""
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def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
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weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights,
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uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
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weights_output = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
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weight_07, weight_08, weight_09, weight_10, weight_11]
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weights_middle = [weight_12]
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weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras)
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class SoftT2IAdapterWeightsDeprecated:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"weight_00": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_01": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_02": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"flip_weights": ("BOOLEAN", {"default": False}),
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},
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"optional": {
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"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
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"cn_extras": ("CN_WEIGHTS_EXTRAS",),
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"autosize": ("ACNAUTOSIZE", {"padding": 0}),
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}
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}
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DEPRECATED = True
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
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FUNCTION = "load_weights"
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CATEGORY = ""
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def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights,
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uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
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weights = [weight_00, weight_01, weight_02, weight_03]
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weights = get_properly_arranged_t2i_weights(weights)
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weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class CustomT2IAdapterWeightsDeprecated:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
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"flip_weights": ("BOOLEAN", {"default": False}),
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},
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"optional": {
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"uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
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"cn_extras": ("CN_WEIGHTS_EXTRAS",),
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"autosize": ("ACNAUTOSIZE", {"padding": 0}),
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}
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}
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DEPRECATED = True
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RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
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RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
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FUNCTION = "load_weights"
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CATEGORY = ""
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def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights,
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uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
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weights = [weight_00, weight_01, weight_02, weight_03]
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weights = get_properly_arranged_t2i_weights(weights)
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weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
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return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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