🔧 Image Resize¶
Documentation¶
- Class name:
ImageResize+ - Category:
essentials/image manipulation - Output node:
False
This node is designed for resizing images to a specified width and height, offering a straightforward way to adjust image dimensions for various applications. It abstracts the complexities of image processing, providing an easy-to-use interface for resizing operations.
Input types¶
Required¶
image- The input image to be resized. This parameter is crucial as it determines the source image that will undergo the resizing process.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
width- Specifies the desired width of the resized image. This parameter directly influences the output image's dimensions.
- Comfy dtype:
INT - Python dtype:
int
height- Specifies the desired height of the resized image. This parameter directly influences the output image's dimensions.
- Comfy dtype:
INT - Python dtype:
int
interpolation- Defines the method used for interpolating pixels in the resizing process, affecting the quality and appearance of the resized image.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
method- Determines how the image is resized, influencing whether and how the aspect ratio is preserved or altered.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
condition- Specifies under what conditions the resizing should occur, allowing for conditional resizing based on the original and target dimensions.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
multiple_of- Ensures the dimensions of the resized image are multiples of this value, useful for certain processing or model input requirements.
- Comfy dtype:
INT - Python dtype:
int
Output types¶
IMAGE- Comfy dtype:
IMAGE - The resized image. This output reflects the changes in dimensions specified by the width and height parameters.
- Python dtype:
torch.Tensor
- Comfy dtype:
width- Comfy dtype:
INT - The actual width of the resized image, which may differ from the requested width due to constraints like maintaining aspect ratio or alignment to a multiple.
- Python dtype:
int
- Comfy dtype:
height- Comfy dtype:
INT - The actual height of the resized image, which may differ from the requested height due to constraints like maintaining aspect ratio or alignment to a multiple.
- Python dtype:
int
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes:
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"],),
"method": (["stretch", "keep proportion", "fill / crop", "pad"],),
"condition": (["always", "downscale if bigger", "upscale if smaller", "if bigger area", "if smaller area"],),
"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/image manipulation"
def execute(self, image, width, height, method="stretch", interpolation="nearest", condition="always", multiple_of=0, keep_proportion=False):
_, oh, ow, _ = image.shape
x = y = x2 = y2 = 0
pad_left = pad_right = pad_top = pad_bottom = 0
if keep_proportion:
method = "keep proportion"
if multiple_of > 1:
width = width - (width % multiple_of)
height = height - (height % multiple_of)
if method == 'keep proportion' or method == 'pad':
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
ratio = min(width / ow, height / oh)
new_width = round(ow*ratio)
new_height = round(oh*ratio)
if method == 'pad':
pad_left = (width - new_width) // 2
pad_right = width - new_width - pad_left
pad_top = (height - new_height) // 2
pad_bottom = height - new_height - pad_top
width = new_width
height = new_height
elif method.startswith('fill'):
width = width if width > 0 else ow
height = height if height > 0 else oh
ratio = max(width / ow, height / oh)
new_width = round(ow*ratio)
new_height = round(oh*ratio)
x = (new_width - width) // 2
y = (new_height - height) // 2
x2 = x + width
y2 = y + height
if x2 > new_width:
x -= (x2 - new_width)
if x < 0:
x = 0
if y2 > new_height:
y -= (y2 - new_height)
if y < 0:
y = 0
width = new_width
height = new_height
else:
width = width if width > 0 else ow
height = height if height > 0 else oh
if "always" in condition \
or ("downscale if bigger" == condition and (oh > height or ow > width)) or ("upscale if smaller" == condition and (oh < height or ow < width)) \
or ("bigger area" in condition and (oh * ow > height * width)) or ("smaller area" in condition and (oh * ow < height * width)):
outputs = image.permute(0,3,1,2)
if interpolation == "lanczos":
outputs = comfy.utils.lanczos(outputs, width, height)
else:
outputs = F.interpolate(outputs, size=(height, width), mode=interpolation)
if method == 'pad':
if pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0:
outputs = F.pad(outputs, (pad_left, pad_right, pad_top, pad_bottom), value=0)
outputs = outputs.permute(0,2,3,1)
if method.startswith('fill'):
if x > 0 or y > 0 or x2 > 0 or y2 > 0:
outputs = outputs[:, y:y2, x:x2, :]
else:
outputs = image
if multiple_of > 1 and (outputs.shape[2] % multiple_of != 0 or outputs.shape[1] % multiple_of != 0):
width = outputs.shape[2]
height = outputs.shape[1]
x = (width % multiple_of) // 2
y = (height % multiple_of) // 2
x2 = width - ((width % multiple_of) - x)
y2 = height - ((height % multiple_of) - y)
outputs = outputs[:, y:y2, x:x2, :]
outputs = torch.clamp(outputs, 0, 1)
return(outputs, outputs.shape[2], outputs.shape[1],)