Image Batch Manager (SuperBeasts.AI)¶
Documentation¶
- Class name:
Image Batch Manager (SuperBeasts.AI) - Category:
SuperBeastsAI/Image - Output node:
False
The Image Batch Manager node is designed to reorder and process a batch of images based on specified dimensions and an optional new order. It enables the dynamic resizing and cropping of images to fit desired dimensions, and optionally reorders them according to a custom sequence, facilitating versatile image batch manipulation for various applications.
Input types¶
Required¶
width- Specifies the desired width for the output images, affecting the resizing and cropping operation to ensure images meet this width requirement.
- Comfy dtype:
INT - Python dtype:
int
height- Specifies the desired height for the output images, affecting the resizing and cropping operation to ensure images meet this height requirement.
- Comfy dtype:
INT - Python dtype:
int
ordering_enabled- Determines whether the reordering functionality is enabled, allowing images to be rearranged according to the 'new_order' parameter if specified. This parameter enables or disables the ability to customize the sequence of the image batch.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
Optional¶
new_order- Defines a custom sequence for reordering the images when 'ordering_enabled' is set to 'enabled', influencing the final arrangement of the image batch. This parameter should be a comma-separated list of indices representing the new order of images.
- Comfy dtype:
STRING - Python dtype:
str
image1- Represents one of up to twelve possible images to be included in the batch processing, contributing to the dynamic resizing, cropping, and optional reordering.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
image2- Represents one of up to twelve possible images to be included in the batch processing, contributing to the dynamic resizing, cropping, and optional reordering.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
image3- Represents one of up to twelve possible images to be included in the batch processing, contributing to the dynamic resizing, cropping, and optional reordering.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
image4- Represents one of up to twelve possible images to be included in the batch processing, contributing to the dynamic resizing, cropping, and optional reordering.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
image5- Represents one of up to twelve possible images to be included in the batch processing, contributing to the dynamic resizing, cropping, and optional reordering.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
image6- Represents one of up to twelve possible images to be included in the batch processing, contributing to the dynamic resizing, cropping, and optional reordering.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
image7- Represents one of up to twelve possible images to be included in the batch processing, contributing to the dynamic resizing, cropping, and optional reordering.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
image8- Represents one of up to twelve possible images to be included in the batch processing, contributing to the dynamic resizing, cropping, and optional reordering.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
image9- Represents one of up to twelve possible images to be included in the batch processing, contributing to the dynamic resizing, cropping, and optional reordering.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
image10- Represents one of up to twelve possible images to be included in the batch processing, contributing to the dynamic resizing, cropping, and optional reordering.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
image11- Represents one of up to twelve possible images to be included in the batch processing, contributing to the dynamic resizing, cropping, and optional reordering.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
image12- Represents one of up to twelve possible images to be included in the batch processing, contributing to the dynamic resizing, cropping, and optional reordering.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
Output types¶
image- Comfy dtype:
IMAGE - The processed batch of images, resized, cropped, and optionally reordered according to the specified parameters.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes: unknown
Source code¶
class ImageBatchManagement:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"width": ("INT", {"default": 512}),
"height": ("INT", {"default": 768}),
"ordering_enabled": (["disabled", "enabled"], {"default": "disabled"})
},
"optional": {
"new_order": ("STRING", {"default": ""}),
**{f"image{i}": ("IMAGE",) for i in range(1, 13)}
},
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "reorder"
CATEGORY = "SuperBeastsAI/Image"
def reorder(self, width, height, ordering_enabled, new_order, **kwargs):
image_keys = [f'image{i}' for i in range(1, 13)]
images = [kwargs.get(key) for key in image_keys if kwargs.get(key) is not None]
if ordering_enabled == "enabled" and new_order:
order_indices = [int(idx) - 1 for idx in new_order.split(',') if idx.strip()]
images = [images[idx] for idx in order_indices if idx < len(images)]
processed_images = []
for img in images:
pil_img = tensor2pil(img)
resized_cropped_img = resize_and_crop(pil_img, width, height)
img_tensor = pil2tensor(resized_cropped_img)
processed_images.append(img_tensor)
result = torch.cat(processed_images, dim=0) if processed_images else torch.empty(0, 3, height, width)
return (result,)