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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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