| | 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: |
| | @classmethod |
| | 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] |
| | |
| | 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: |
| | @classmethod |
| | 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,) |
| |
|
