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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}'.")
# Filter files by extension
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]
# 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) 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}'.")
# Filter files by extension
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]
# 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) 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)"
}
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