Save Image With Alpha¶
Documentation¶
- Class name:
SaveImageWithAlpha
- Category:
KJNodes/image
- Output node:
True
This node is designed for saving images with an alpha channel, allowing for transparency effects. It processes a batch of images and their corresponding masks, applies the masks as alpha channels to the images, optionally adds metadata (such as prompts or additional PNG information), and saves the resulting images with transparency to a specified location.
Input types¶
Required¶
images
- A batch of images to be processed and saved with alpha transparency. This input is crucial for defining the visual content of the output files.
- Comfy dtype:
IMAGE
- Python dtype:
torch.Tensor
mask
- A batch of masks corresponding to the images, used to create the alpha channel for transparency effects. This input is essential for determining which parts of each image should be transparent.
- Comfy dtype:
MASK
- Python dtype:
torch.Tensor
filename_prefix
- A prefix for the filenames of the saved images, allowing for organized storage and easy identification.
- Comfy dtype:
STRING
- Python dtype:
str
Output types¶
ui
- The output includes a UI component that displays the saved images, providing a visual confirmation of the operation's success.
Usage tips¶
- Infra type:
GPU
- Common nodes: unknown
Source code¶
class SaveImageWithAlpha:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
self.type = "output"
self.prefix_append = ""
@classmethod
def INPUT_TYPES(s):
return {"required":
{"images": ("IMAGE", ),
"mask": ("MASK", ),
"filename_prefix": ("STRING", {"default": "ComfyUI"})},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ()
FUNCTION = "save_images_alpha"
OUTPUT_NODE = True
CATEGORY = "KJNodes/image"
DESCRIPTION = """
Saves an image and mask as .PNG with the mask as the alpha channel.
"""
def save_images_alpha(self, images, mask, filename_prefix="ComfyUI_image_with_alpha", prompt=None, extra_pnginfo=None):
from PIL.PngImagePlugin import PngInfo
filename_prefix += self.prefix_append
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
results = list()
if mask.dtype == torch.float16:
mask = mask.to(torch.float32)
def file_counter():
max_counter = 0
# Loop through the existing files
for existing_file in os.listdir(full_output_folder):
# Check if the file matches the expected format
match = re.fullmatch(fr"{filename}_(\d+)_?\.[a-zA-Z0-9]+", existing_file)
if match:
# Extract the numeric portion of the filename
file_counter = int(match.group(1))
# Update the maximum counter value if necessary
if file_counter > max_counter:
max_counter = file_counter
return max_counter
for image, alpha in zip(images, mask):
i = 255. * image.cpu().numpy()
a = 255. * alpha.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
# Resize the mask to match the image size
a_resized = Image.fromarray(a).resize(img.size, Image.LANCZOS)
a_resized = np.clip(a_resized, 0, 255).astype(np.uint8)
img.putalpha(Image.fromarray(a_resized, mode='L'))
metadata = None
if not args.disable_metadata:
metadata = PngInfo()
if prompt is not None:
metadata.add_text("prompt", json.dumps(prompt))
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
# Increment the counter by 1 to get the next available value
counter = file_counter() + 1
file = f"{filename}_{counter:05}.png"
img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
results.append({
"filename": file,
"subfolder": subfolder,
"type": self.type
})
return { "ui": { "images": results } }