🔧 Image Resize

Documentation

• Class name: ImageResize+
• Category: essentials
• Output node: False

The ImageResize+ node is designed to adjust the size of an image to specified dimensions, offering a fundamental operation in image processing that can be crucial for both preprocessing before further analysis or modification, and for adapting images to fit specific display or storage requirements.

Input types

Required

• image
  • The 'image' parameter represents the image data to be resized. It is central to the node's operation, determining the source image that will undergo resizing.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• width
  • The 'width' parameter specifies the target width for the resizing operation. It directly influences the horizontal dimension of the output image.
  • Comfy dtype: INT
  • Python dtype: int
• height
  • The 'height' parameter specifies the target height for the resizing operation. It directly influences the vertical dimension of the output image.
  • Comfy dtype: INT
  • Python dtype: int
• interpolation
  • The 'interpolation' parameter defines the method used for resizing, affecting the quality and characteristics of the output image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• keep_proportion
  • The 'keep_proportion' parameter determines whether the original aspect ratio of the image should be preserved during resizing.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool
• condition
  • The 'condition' parameter allows for conditional execution of the resizing based on specific criteria.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• multiple_of
  • The 'multiple_of' parameter ensures the dimensions of the resized image are multiples of a specified value, useful for certain processing or model input requirements.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• IMAGE
  • Comfy dtype: IMAGE
  • unknown
  • Python dtype: unknown
• width
  • Comfy dtype: INT
  • The output 'width' represents the actual width of the resized image.
  • Python dtype: int
• height
  • Comfy dtype: INT
  • The output 'height' represents the actual height of the resized image.
  • Python dtype: int

Usage tips

• Infra type: GPU
• Common nodes:
  • SetNode
  • SVD_img2vid_Conditioning
  • PrepImageForClipVision

Source code

class ImageResize:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
                "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }),
                "interpolation": (["nearest", "bilinear", "bicubic", "area", "nearest-exact", "lanczos"],),
                "keep_proportion": ("BOOLEAN", { "default": False }),
                "condition": (["always", "downscale if bigger", "upscale if smaller"],),
                "multiple_of": ("INT", { "default": 0, "min": 0, "max": 512, "step": 1, }),
            }
        }

    RETURN_TYPES = ("IMAGE", "INT", "INT",)
    RETURN_NAMES = ("IMAGE", "width", "height",)
    FUNCTION = "execute"
    CATEGORY = "essentials"

    def execute(self, image, width, height, keep_proportion, interpolation="nearest", condition="always", multiple_of=0):
        _, oh, ow, _ = image.shape

        if keep_proportion is True:
            if width == 0 and oh < height:
                width = MAX_RESOLUTION
            elif width == 0 and oh >= height:
                width = ow

            if height == 0 and ow < width:
                height = MAX_RESOLUTION
            elif height == 0 and ow >= width:
                height = ow

            #width = ow if width == 0 else width
            #height = oh if height == 0 else height
            ratio = min(width / ow, height / oh)
            width = round(ow*ratio)
            height = round(oh*ratio)
        else:
            if width == 0:
                width = ow
            if height == 0:
                height = oh

        if multiple_of > 1:
            width = width - (width % multiple_of)
            height = height - (height % multiple_of)

        outputs = p(image)

        if "always" in condition or ("bigger" in condition and (oh > height or ow > width)) or ("smaller" in condition and (oh < height or ow < width)):
            if interpolation == "lanczos":
                outputs = comfy.utils.lanczos(outputs, width, height)
            else:
                outputs = F.interpolate(outputs, size=(height, width), mode=interpolation)

        outputs = pb(outputs)

        return(outputs, outputs.shape[2], outputs.shape[1],)