Load Images from ZIP

Documentation

Class name: SaltLoadImageZip
Category: SALT/Image/Loaders
Output node: False

The SaltLoadImageZip node is designed to load and optionally resize images from a ZIP file. It supports a variety of image formats and can concatenate the loaded images into a single tensor, facilitating their use in image processing workflows.

Input types

Required

path
- Specifies the file path to the ZIP archive containing images. It is crucial for locating and accessing the desired images for processing.
- Comfy dtype: STRING
- Python dtype: str
resize_images_to_first
- Determines whether all loaded images should be resized to match the dimensions of the first image found. This ensures uniformity in image sizes, which is often necessary for batch processing or when images are used in neural networks.
- Comfy dtype: BOOLEAN
- Python dtype: bool

Output types

images
- Comfy dtype: IMAGE
- The output is a tensor containing the loaded (and possibly resized) images, ready for further processing or analysis.
- Python dtype: torch.Tensor

Usage tips

Infra type: GPU
Common nodes: unknown

Source code

class SaltLoadImageZip:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "path": ("STRING", {}),
                "resize_images_to_first": ("BOOLEAN", {"default": True})
            }
        }
    
    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("images",)

    CATEGORY = "SALT/Image/Loaders"
    FUNCTION = "load_images"

    def load_images(self, path: str, resize_images_to_first: bool = True):
        supported_formats = ('.png', '.jpg', '.jpeg', '.gif', '.tga', '.tiff', '.webp')
        images = []
        first_image_size = None
        
        with zipfile.ZipFile(path, 'r') as z:
            for file_name in z.namelist():
                if file_name.lower().endswith(supported_formats):
                    with z.open(file_name) as file:
                        image = Image.open(BytesIO(file.read()))
                        if first_image_size is None:
                            first_image_size = image.size
                        if image.size == first_image_size or resize_images_to_first:
                            images.append(image if image.size == first_image_size else self.resize_right(image, first_image_size))

        if not images:
            raise ValueError(f"The input zip `{path}` does not contain any valid images!")

        images = [pil2tensor(img) for img in images]
        images = torch.cat(images, dim=0)

        return (images, )

    def resize_right(self, image, target_size):
        img_ratio = image.width / image.height
        target_ratio = target_size[0] / target_size[1]
        resize_width, resize_height = (
            (target_size[0], round(target_size[0] / img_ratio)) if target_ratio > img_ratio else
            (round(target_size[1] * img_ratio), target_size[1])
        )
        image = image.resize((resize_width, resize_height), Image.Resampling.LANCZOS)
        x_crop, y_crop = (resize_width - target_size[0]) // 2, (resize_height - target_size[1]) // 2
        return image.crop((x_crop, y_crop, x_crop + target_size[0], y_crop + target_size[1]))

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