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import random
import numpy as np
import torch
from PIL import Image, ImageOps, ImageSequence
import hashlib
import re
import glob
import node_helpers
from server import PromptServer
class ImageBatchLoader:
RETURN_TYPES = ("IMAGE", "STRING", "STRING", "IMAGE")
RETURN_NAMES = ("image", "filename", "image_count", "image_list")
OUTPUT_IS_LIST = (False, False, False, True)
FUNCTION = "load_batch_images"
CATEGORY = "Batch Process"
SUPPORTED_EXTENSIONS = {".png", ".jpg", ".jpeg", ".bmp", ".gif", ".webp"}
def __init__(self):
self.image_states = {}
self.current_directory = ""
self.images = []
self.search_states = {}
self._last_scan_key = None
self._all_image_paths = []
self._last_reset_on_queue = {}
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"directory": ("STRING",),
"search_title": ("STRING", {"default": ""}),
"delimiter": ("STRING", {"default": ""}),
"mode": (
["single_image", "incremental_image", "random"],
{"default": "incremental_image"},
),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
"filename_option": (
[
"filename",
"prefix",
"suffix",
"prefix & suffix",
"prefix nor suffix",
],
),
"image_list": (
"BOOLEAN",
{
"label_on": "yes",
"label_off": "no",
"default": False,
"defaultInput": False,
},
),
"subfolder": (
"BOOLEAN",
{
"label_on": "yes",
"label_off": "no",
"default": False,
"defaultInput": False,
},
),
"start_index": (
"INT",
{
"default": 0,
"min": 0,
"max": 999999,
"step": 1,
"tooltip": "Start index (1-based). Use 0 to start from beginning, or set 1 for first image, 2 for second image, etc.",
},
),
"end_index": (
"INT",
{
"default": 0,
"min": 0,
"max": 999999,
"step": 1,
"tooltip": "End index (inclusive, 1-based). Use 0 to include all remaining images, or set >0 to limit range (1=first, 2=second, etc.).",
},
),
},
"optional": {
"reset_on_queue": (
"INT",
{
"default": 1,
"min": 0,
"max": 1,
"step": 1,
"tooltip": "Any change to this value (0↔1) resets the read index to start from the first image again. Keep unchanged during a queue run to advance normally.",
},
),
},
"hidden": {"node_id": "UNIQUE_ID"},
}
def set_directory(
self,
directory,
filename_option="filename",
search_title="",
delimiter="",
subfolder=False,
):
scan_key = (directory, filename_option, search_title, delimiter, subfolder)
if scan_key != self._last_scan_key:
if not os.path.isdir(directory):
raise ValueError(
f"The provided path '{directory}' is not a valid directory."
)
# Fast list of all images (no PIL verification)
all_image_paths = self.list_images(directory, subfolder)
self._all_image_paths = all_image_paths
# Extract just the filenames for filtering
all_images = [os.path.basename(path) for path in all_image_paths]
filtered_images = self.filter_images(
directory, all_images, filename_option, search_title, delimiter
)
# Filter full paths in O(n) by basename membership
allowed_names = set(filtered_images)
self.images = [
p for p in all_image_paths if os.path.basename(p) in allowed_names
]
self.images = sorted(
self.images,
key=lambda p: [
int(part) if part.isdigit() else part.lower()
for part in re.split(r"(\d+)", os.path.basename(p))
],
)
self.current_directory = directory
self._last_scan_key = scan_key
if scan_key not in self.search_states:
self.search_states[scan_key] = 0
if not self.images:
print("No matching image files found in the provided directory.")
def load_images(self, directory):
if not os.path.isdir(directory):
raise ValueError(f"Invalid directory: {directory}")
all_images = [
f
for f in os.listdir(directory)
if any(f.endswith(ext) for ext in self.SUPPORTED_EXTENSIONS)
]
paths = [os.path.join(directory, f) for f in all_images]
return sorted(
paths,
key=lambda p: [
int(part) if part.isdigit() else part.lower()
for part in re.split(r"(\d+)", os.path.basename(p))
],
)
def filter_images(self, directory, files, filename_option, search_title, delimiter):
def get_prefix(filename):
if delimiter:
return filename.split(delimiter)[0]
else:
return re.split(r"[^a-zA-Z0-9]", filename)[0]
def get_suffix(filename):
name_without_ext = os.path.splitext(filename)[0]
if delimiter:
return name_without_ext.split(delimiter)[-1]
else:
return re.split(r"[^a-zA-Z0-9]", name_without_ext)[-1]
filtered_files = files
if search_title:
if filename_option == "filename":
filtered_files = [f for f in filtered_files if search_title in f]
elif filename_option == "prefix":
search_prefix = get_prefix(search_title)
filtered_files = [
f for f in filtered_files if get_prefix(f) == search_prefix
]
elif filename_option == "suffix":
search_suffix = get_suffix(search_title)
filtered_files = [
f for f in filtered_files if get_suffix(f) == search_suffix
]
elif filename_option == "prefix & suffix":
search_prefix = get_prefix(search_title)
search_suffix = get_suffix(search_title)
filtered_files = [
f
for f in filtered_files
if get_prefix(f) == search_prefix or get_suffix(f) == search_suffix
]
elif filename_option == "prefix nor suffix":
search_prefix = get_prefix(search_title)
search_suffix = get_suffix(search_title)
filtered_files = [
f
for f in filtered_files
if get_prefix(f) != search_prefix and get_suffix(f) != search_suffix
]
return filtered_files
@classmethod
def list_images(cls, path: str, subfolder: bool = False, verify: bool = False):
images = []
if os.path.isfile(path):
files = [path]
else:
if subfolder:
files = []
for root, _, filenames in os.walk(path):
for name in filenames:
files.append(os.path.join(root, name))
else:
try:
files = [
entry.path for entry in os.scandir(path) if entry.is_file()
]
except FileNotFoundError:
files = []
valid_extensions = {".jpg", ".jpeg", ".png", ".webp", ".bmp", ".gif"}
candidate_files = [
f for f in files if os.path.splitext(f)[1].lower() in valid_extensions
]
if not verify:
return sorted(
candidate_files,
key=lambda p: [
int(part) if part.isdigit() else part.lower()
for part in re.split(r"(\d+)", os.path.basename(p))
],
)
for filename in candidate_files:
try:
with Image.open(filename) as img:
img.verify()
images.append(filename)
except Exception:
continue
return sorted(
images,
key=lambda p: [
int(part) if part.isdigit() else part.lower()
for part in re.split(r"(\d+)", os.path.basename(p))
],
)
def load_batch_images(
self,
directory,
search_title="",
delimiter="",
mode="incremental_image",
seed=0,
filename_option="filename",
image_list=False,
subfolder=False,
start_index=0,
end_index=-1,
reset_on_queue=1,
node_id=None,
):
# Ensure directory is scanned once and cached
self.set_directory(
directory, filename_option, search_title, delimiter, subfolder
)
# Use cached listing for count and names
all_images_in_dir = self._all_image_paths
image_count = str(len(all_images_in_dir))
# Determine which image list to use for index filtering
images_to_filter = self.images if self.images else all_images_in_dir
# Apply index filtering
filtered_images = images_to_filter
if filtered_images:
# Determine actual start and end indices
# start_index is 1-indexed: 1 = first image, 2 = second image, etc.
# start_index=0 means start from beginning (index 0)
# end_index=0 means end at last image (no limit, use all remaining)
# Only non-zero values apply as limits
# Convert 1-indexed to 0-indexed: subtract 1 if > 0
actual_start = (start_index - 1) if start_index > 0 else 0
actual_end = (
(end_index - 1) if end_index > 0 else (len(filtered_images) - 1)
)
# Validate indices
if actual_start < 0:
actual_start = 0
if actual_start >= len(filtered_images):
actual_start = (
len(filtered_images) - 1 if len(filtered_images) > 0 else 0
)
if actual_end < 0:
actual_end = 0
if actual_end >= len(filtered_images):
actual_end = len(filtered_images) - 1
if actual_end < actual_start:
actual_end = actual_start
# Only apply slicing if we have valid indices and images
if len(filtered_images) > 0 and actual_start <= actual_end:
# Slice the images list (actual_end+1 because slice is exclusive on end)
filtered_images = filtered_images[actual_start : actual_end + 1]
else:
filtered_images = []
# Only load all images if image_list is True
if image_list:
# Check if we have any images to load after filtering
if not filtered_images:
return (torch.zeros(1, 64, 64, 3)), "no_images_found", image_count, []
all_loaded_images = self.load_all_images(
path=directory,
subfolder=subfolder,
node_id=node_id,
filepaths=filtered_images,
)
if all_loaded_images:
return (
all_loaded_images[0],
os.path.splitext(os.path.basename(filtered_images[0]))[0],
image_count,
all_loaded_images,
)
else:
return (torch.zeros(1, 64, 64, 3)), "no_images_found", image_count, []
else:
# For regular mode, return empty list for image_list output (fast)
empty_list = []
if not filtered_images:
return (
(torch.zeros(1, 64, 64, 3)),
"no_images_found",
image_count,
empty_list,
)
search_key = (
directory,
filename_option,
search_title,
delimiter,
subfolder,
start_index,
end_index,
)
if self._last_reset_on_queue.get(search_key) != reset_on_queue:
self.search_states[search_key] = 0
self._last_reset_on_queue[search_key] = reset_on_queue
if mode == "single_image":
image, filename = self.load_image_by_index(search_key, filtered_images)
return image, filename, image_count, empty_list
elif mode == "incremental_image":
image, filename = self.load_image_by_index(search_key, filtered_images)
return image, filename, image_count, empty_list
elif mode == "random":
random.seed(seed)
rnd_index = random.randint(0, len(filtered_images) - 1)
image, filename = self.load_image_by_path(filtered_images[rnd_index])
return image, filename, image_count, empty_list
else:
raise ValueError(f"Unknown mode: {mode}")
def load_all_images(
self,
path: str = None,
subfolder: bool = False,
node_id: str = None,
filepaths: list = None,
):
"""Load all images for the image_list output"""
images = []
if filepaths is None:
# Fallback to listing if not provided
filepaths = self.list_images(path, subfolder)
for index, image_path in enumerate(filepaths):
try:
img = node_helpers.pillow(Image.open, image_path)
img = node_helpers.pillow(ImageOps.exif_transpose, img)
if img.mode == "I":
img = img.point(lambda i: i * (1 / 255))
img = img.convert("RGB")
image_np = np.array(img).astype(np.float32) / 255.0
image_tensor = torch.from_numpy(image_np)[None, ...]
images.append(image_tensor)
if node_id:
PromptServer.instance.send_sync(
"progress",
{"node": node_id, "max": len(filepaths), "value": index},
)
except Exception as e:
print(f"Error loading image {image_path}: {str(e)}")
continue
return images
def load_image_by_index(self, search_key, filtered_images):
if not filtered_images:
print("No images loaded.")
return None, None
if search_key not in self.search_states:
self.search_states[search_key] = 0
current_index = self.search_states[search_key]
if current_index >= len(filtered_images):
current_index = 0
file_path = filtered_images[current_index]
self.search_states[search_key] = (current_index + 1) % len(filtered_images)
return self.load_image_by_path(file_path)
def load_image_by_path(self, path):
try:
image = Image.open(path)
image = ImageOps.exif_transpose(image).convert("RGB")
filename = os.path.basename(path)
# 去除文件扩展名
filename = os.path.splitext(filename)[0]
return self.pil2tensor(image), filename
except Exception as e:
print(f"Error loading image: {str(e)}")
return (torch.zeros(1, 64, 64, 3)), "error"
def pil2tensor(self, image):
image_np = np.array(image).astype(np.float32) / 255.0
if len(image_np.shape) == 2:
image_np = np.expand_dims(image_np, axis=-1)
image_np = np.expand_dims(image_np, axis=0)
return torch.from_numpy(image_np)
@classmethod
def IS_CHANGED(cls, directory, **kwargs):
if not os.path.exists(directory):
return ""
try:
loader = cls()
paths = loader.load_images(directory)
return hashlib.sha256(",".join(paths).encode()).hexdigest()
except Exception as e:
print(f"Error checking for changes: {str(e)}")
return ""
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