| import os | |
| import torch | |
| import numpy as np | |
| from PIL import Image, ImageOps | |
| from .control import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, TimestepKeyframe | |
| from .logger import logger | |
| class LoadImagesFromDirectory: | |
| def INPUT_TYPES(s): | |
| return { | |
| "required": { | |
| "directory": ("STRING", {"default": ""}), | |
| }, | |
| "optional": { | |
| "image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}), | |
| "start_index": ("INT", {"default": 0, "min": 0, "step": 1}), | |
| } | |
| } | |
| RETURN_TYPES = ("IMAGE", "MASK", "INT") | |
| FUNCTION = "load_images" | |
| CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/deprecated" | |
| 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 TimestepKeyframeNodeDeprecated: | |
| def INPUT_TYPES(s): | |
| return { | |
| "required": { | |
| "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ), | |
| }, | |
| "optional": { | |
| "control_net_weights": ("CONTROL_NET_WEIGHTS", ), | |
| "t2i_adapter_weights": ("T2I_ADAPTER_WEIGHTS", ), | |
| "latent_keyframe": ("LATENT_KEYFRAME", ), | |
| "prev_timestep_keyframe": ("TIMESTEP_KEYFRAME", ), | |
| } | |
| } | |
| RETURN_TYPES = ("TIMESTEP_KEYFRAME", ) | |
| FUNCTION = "load_keyframe" | |
| CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes" | |
| def load_keyframe(self, | |
| start_percent: float, | |
| control_net_weights: ControlWeights=None, | |
| latent_keyframe: LatentKeyframeGroup=None, | |
| prev_timestep_keyframe: TimestepKeyframeGroup=None): | |
| if not prev_timestep_keyframe: | |
| prev_timestep_keyframe = TimestepKeyframeGroup() | |
| keyframe = TimestepKeyframe(start_percent, control_net_weights, latent_keyframe) | |
| prev_timestep_keyframe.add(keyframe) | |
| return (prev_timestep_keyframe,) | |