comfyui image resize simple custom node code

 refer to code for your scratch 

:

# from .imagetransfer import ImageTransfer

# __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

from PIL import Image

import torch

from torchvision.transforms.functional import to_pil_image, to_tensor

class ImageTransfer:

@classmethod

def INPUT_TYPES(cls):

return {

"required": {

"image": ("IMAGE",),

},

}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "transfer"

CATEGORY = "Utility"

def transfer(self, image):

# Ensure we're not tracking gradients for this operation

print(f"Type of the image tensor: {image.dtype}")

print(f"Dimensions of the image tensor: {image.shape}")

with torch.no_grad():

# Check if image tensor is in the expected format [1, H, W, C]

if image.dim() == 4 and image.shape[-1] in [1, 3]:

# Permute the tensor to match [C, H, W] format expected by to_pil_image

image_permuted = image.squeeze(0).permute(2, 0, 1)

# Convert the PyTorch tensor to a PIL Image

image_pil = to_pil_image(image_permuted)

# Resize the PIL Image

resized_image_pil = image_pil.resize((256, 256), Image.LANCZOS)

# Convert the PIL Image back to a PyTorch tensor

resized_image_tensor = to_tensor(resized_image_pil)

# Permute dimensions back to [1, H, W, C] and add the batch dimension

resized_image_tensor = resized_image_tensor.permute(1, 2, 0).unsqueeze(0)

print(f"Type of the resized image tensor: {resized_image_tensor.dtype}")

print(f"Dimensions of the resized image tensor: {resized_image_tensor.shape}")

return (resized_image_tensor,)

else:

raise ValueError("Image tensor format not recognized. Expected format: [1, H, W, C].")

NODE_CLASS_MAPPINGS = {

"ImageTransfer": ImageTransfer

}

NODE_DISPLAY_NAME_MAPPINGS = {

"ImageTransfer": "Image Transfer"

}