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Add LoadImageWithFilename and LoadImageFolder nodes

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- Enhanced LoadImageWithFilename to output filename as STRING
- Added LoadImageFolder node for batch loading entire folders
- Added proper imports and error handling
- Created comprehensive README documentation
- Added Python-specific .gitignore file

Based on ComfyUI issue #8699 for filename output functionality
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thalismind
2025-07-20 21:37:16 -05:00
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.gitignore
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README.md
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# ComfyUI LoadImageWithFilename
This custom node extends ComfyUI's image loading functionality with filename output and folder loading capabilities.
## Features
### LoadImageWithFilename
- **Enhanced Load Image Node**: Based on the original ComfyUI LoadImage node
- **Filename Output**: Returns the filename of the loaded image as a STRING output
- **Compatible**: Maintains all original functionality (IMAGE and MASK outputs)
### LoadImageFolder
- **Folder Loading**: Load all images from a selected folder
- **Batch Processing**: Returns all images concatenated as a single tensor
- **Filename Tracking**: Returns a comma-separated list of all loaded filenames
- **Error Handling**: Gracefully handles corrupted or unsupported image files
## Installation
1. Place `nodes.py` in your ComfyUI `custom_nodes` directory
2. Restart ComfyUI
3. The new nodes will appear in the "image" category
## Usage
### LoadImageWithFilename
- **Input**: Select an image file from the dropdown
- **Outputs**:
- `image`: The loaded image tensor
- `mask`: The image mask (if available)
- `filename`: The filename of the loaded image
### LoadImageFolder
- **Input**: Select a folder from the dropdown
- **Outputs**:
- `image`: All images from the folder concatenated as a single tensor
- `mask`: All masks concatenated as a single tensor
- `filenames`: Comma-separated list of all loaded filenames
## Requirements
- ComfyUI
- PIL (Pillow)
- PyTorch
- NumPy
## Notes
- The LoadImageFolder node will skip any non-image files in the selected folder
- If no valid images are found in a folder, empty tensors will be returned
- All images in a folder must have the same dimensions for proper concatenation
- Error messages are printed to console for any files that fail to load
## Based On
- [ComfyUI LoadImage Node](https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py)
- [Issue #8699](https://github.com/comfyanonymous/ComfyUI/issues/8699) - Request for filename output functionality
nodes.py
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import os
import hashlib
import torch
import numpy as np
from PIL import Image, ImageOps, ImageSequence
import folder_paths
import node_helpers
class LoadImageWithFilename:
@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))]
files = folder_paths.filter_files_content_types(files, ["image"])
return {"required":
{"image": (sorted(files), {"image_upload": True})},
}
CATEGORY = "image"
RETURN_TYPES = ("IMAGE", "MASK", "STRING")
RETURN_NAMES = ("image", "mask", "filename")
FUNCTION = "load_image"
def load_image(self, image):
image_path = folder_paths.get_annotated_filepath(image)
img = node_helpers.pillow(Image.open, image_path)
output_images = []
output_masks = []
w, h = None, None
excluded_formats = ['MPO']
for i in ImageSequence.Iterator(img):
i = node_helpers.pillow(ImageOps.exif_transpose, i)
if i.mode == 'I':
i = i.point(lambda i: i * (1 / 255))
image = i.convert("RGB")
if len(output_images) == 0:
w = image.size[0]
h = image.size[1]
if image.size[0] != w or image.size[1] != h:
continue
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)
elif i.mode == 'P' and 'transparency' in i.info:
mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
output_images.append(image)
output_masks.append(mask.unsqueeze(0))
if len(output_images) > 1 and img.format not in excluded_formats:
output_image = torch.cat(output_images, dim=0)
output_mask = torch.cat(output_masks, dim=0)
else:
output_image = output_images[0]
output_mask = output_masks[0]
# Extract filename from the image path
filename = os.path.basename(image_path)
return (output_image, output_mask, filename)
@classmethod
def IS_CHANGED(s, image):
image_path = folder_paths.get_annotated_filepath(image)
m = hashlib.sha256()
with open(image_path, 'rb') as f:
m.update(f.read())
return m.digest().hex()
@classmethod
def VALIDATE_INPUTS(s, image):
if not folder_paths.exists_annotated_filepath(image):
return "Invalid image file: {}".format(image)
return True
class LoadImageFolder:
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
folders = [f for f in os.listdir(input_dir) if os.path.isdir(os.path.join(input_dir, f))]
return {"required":
{"folder": (sorted(folders),)},
}
CATEGORY = "image"
RETURN_TYPES = ("IMAGE", "MASK", "STRING")
RETURN_NAMES = ("image", "mask", "filenames")
FUNCTION = "load_folder"
def load_folder(self, folder):
input_dir = folder_paths.get_input_directory()
folder_path = os.path.join(input_dir, folder)
# Get all image files in the folder
image_files = []
for file in os.listdir(folder_path):
file_path = os.path.join(folder_path, file)
if os.path.isfile(file_path):
# Check if it's an image file
try:
with Image.open(file_path) as img:
image_files.append(file)
except:
continue
if not image_files:
# Return empty tensors if no images found
empty_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
empty_mask = torch.zeros((1, 64, 64), dtype=torch.float32)
return (empty_image, empty_mask, "")
# Load all images in the folder
all_images = []
all_masks = []
all_filenames = []
for filename in sorted(image_files):
file_path = os.path.join(folder_path, filename)
try:
img = node_helpers.pillow(Image.open, file_path)
output_images = []
output_masks = []
w, h = None, None
excluded_formats = ['MPO']
for i in ImageSequence.Iterator(img):
i = node_helpers.pillow(ImageOps.exif_transpose, i)
if i.mode == 'I':
i = i.point(lambda i: i * (1 / 255))
image = i.convert("RGB")
if len(output_images) == 0:
w = image.size[0]
h = image.size[1]
if image.size[0] != w or image.size[1] != h:
continue
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)
elif i.mode == 'P' and 'transparency' in i.info:
mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
output_images.append(image)
output_masks.append(mask.unsqueeze(0))
if len(output_images) > 1 and img.format not in excluded_formats:
output_image = torch.cat(output_images, dim=0)
output_mask = torch.cat(output_masks, dim=0)
else:
output_image = output_images[0]
output_mask = output_masks[0]
all_images.append(output_image)
all_masks.append(output_mask)
all_filenames.append(filename)
except Exception as e:
print(f"Error loading {filename}: {e}")
continue
if not all_images:
# Return empty tensors if no images could be loaded
empty_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
empty_mask = torch.zeros((1, 64, 64), dtype=torch.float32)
return (empty_image, empty_mask, "")
# Concatenate all images and masks
combined_images = torch.cat(all_images, dim=0)
combined_masks = torch.cat(all_masks, dim=0)
filenames_str = ",".join(all_filenames)
return (combined_images, combined_masks, filenames_str)
@classmethod
def IS_CHANGED(s, folder):
input_dir = folder_paths.get_input_directory()
folder_path = os.path.join(input_dir, folder)
if not os.path.exists(folder_path):
return "INVALID"
# Create a hash based on folder contents
m = hashlib.sha256()
for file in sorted(os.listdir(folder_path)):
file_path = os.path.join(folder_path, file)
if os.path.isfile(file_path):
try:
with open(file_path, 'rb') as f:
m.update(f.read())
except:
continue
return m.digest().hex()
@classmethod
def VALIDATE_INPUTS(s, folder):
input_dir = folder_paths.get_input_directory()
folder_path = os.path.join(input_dir, folder)
if not os.path.exists(folder_path):
return "Invalid folder: {}".format(folder)
if not os.path.isdir(folder_path):
return "Not a directory: {}".format(folder)
return True