|
|
import os |
|
|
from PIL import Image |
|
|
from PIL import ImageOps |
|
|
import numpy as np |
|
|
import torch |
|
|
import comfy |
|
|
import folder_paths |
|
|
import base64 |
|
|
from io import BytesIO |
|
|
|
|
|
class LoadImagesFromDirBatch: |
|
|
@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 = "image" |
|
|
|
|
|
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}'.") |
|
|
|
|
|
|
|
|
valid_extensions = ['.jpg', '.jpeg', '.png', '.webp'] |
|
|
dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)] |
|
|
|
|
|
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) and os.path.ex: |
|
|
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) == 1: |
|
|
return (images[0], 1) |
|
|
elif len(images) > 1: |
|
|
image1 = images[0] |
|
|
for image2 in images[1:]: |
|
|
if image1.shape[1:] != image2.shape[1:]: |
|
|
image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1) |
|
|
image1 = torch.cat((image1, image2), dim=0) |
|
|
return (image1, len(images)) |
|
|
|
|
|
|
|
|
class LoadImagesFromDirList: |
|
|
@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") |
|
|
OUTPUT_IS_LIST = (True, True) |
|
|
|
|
|
FUNCTION = "load_images" |
|
|
|
|
|
CATEGORY = "image" |
|
|
|
|
|
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}'.") |
|
|
|
|
|
|
|
|
valid_extensions = ['.jpg', '.jpeg', '.png', '.webp'] |
|
|
dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)] |
|
|
|
|
|
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) and os.path.ex: |
|
|
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 |
|
|
|
|
|
return images, masks |
|
|
|
|
|
|
|
|
class LoadImageInspire: |
|
|
@classmethod |
|
|
def INPUT_TYPES(s): |
|
|
input_dir = folder_paths.get_input_directory() |
|
|
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] |
|
|
return {"required": { |
|
|
"image": (sorted(files) + ["#DATA"], {"image_upload": True}), |
|
|
"image_data": ("STRING", {"multiline": False}), |
|
|
} |
|
|
} |
|
|
|
|
|
CATEGORY = "InspirePack/image" |
|
|
|
|
|
RETURN_TYPES = ("IMAGE", "MASK") |
|
|
FUNCTION = "load_image" |
|
|
|
|
|
def load_image(self, image, image_data): |
|
|
image_data = base64.b64decode(image_data.split(",")[1]) |
|
|
i = Image.open(BytesIO(image_data)) |
|
|
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") |
|
|
return (image, mask.unsqueeze(0)) |
|
|
|
|
|
|
|
|
class ChangeImageBatchSize: |
|
|
@classmethod |
|
|
def INPUT_TYPES(s): |
|
|
input_dir = folder_paths.get_input_directory() |
|
|
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] |
|
|
return {"required": { |
|
|
"image": ("IMAGE",), |
|
|
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}), |
|
|
"mode": (["simple"],) |
|
|
} |
|
|
} |
|
|
|
|
|
CATEGORY = "InspirePack/image" |
|
|
|
|
|
RETURN_TYPES = ("IMAGE", ) |
|
|
FUNCTION = "load_image" |
|
|
|
|
|
def load_image(self, image, batch_size, mode): |
|
|
if mode == "simple": |
|
|
if len(image) < batch_size: |
|
|
last_frame = image[-1].unsqueeze(0).expand(batch_size - len(image), -1, -1, -1) |
|
|
image = torch.concat((image, last_frame), dim=0) |
|
|
else: |
|
|
image = image[:batch_size, :, :, :] |
|
|
return (image,) |
|
|
else: |
|
|
print(f"[WARN] ChangeImageBatchSize: Unknown mode `{mode}` - ignored") |
|
|
return (image, ) |
|
|
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = { |
|
|
"LoadImagesFromDir //Inspire": LoadImagesFromDirBatch, |
|
|
"LoadImageListFromDir //Inspire": LoadImagesFromDirList, |
|
|
"LoadImage //Inspire": LoadImageInspire, |
|
|
"ChangeImageBatchSize //Inspire": ChangeImageBatchSize, |
|
|
} |
|
|
NODE_DISPLAY_NAME_MAPPINGS = { |
|
|
"LoadImagesFromDir //Inspire": "Load Image Batch From Dir (Inspire)", |
|
|
"LoadImageListFromDir //Inspire": "Load Image List From Dir (Inspire)", |
|
|
"ChangeImageBatchSize //Inspire": "Change Image Batch Size (Inspire)" |
|
|
} |
|
|
|
