Generating modified digital images utilizing a global and spatial autoencoder

A system comprising:

• one or more memory devices comprising a digital image and a global and spatial autoencoder comprising an encoder neural network and a generator neural network;
• and
• one or more computing devices that are configured to cause the system to:
• extract from the digital image, utilizing the encoder neural network, a spatial code comprising features representing a geometric layout of the digital image and a global code representing overall style properties of the digital image;
• determine an image attribute represented by one or more of the spatial code or the global code of the digital image;
• detect a modification to the image attribute represented by one or more of the spatial code or the global code;
• and
• generate a modified digital image to reflect the modification to the image attribute by modifying one or more of the spatial code or the global code utilizing the generator neural network.

A computer-implemented method for deep image manipulation utilizing global and spatial autoencoders, the computer-implemented method comprising:

• extracting from a first digital image, utilizing an encoder neural network comprising a first set of blocks for encoding global features and a second set of blocks for encoding spatial features, a spatial code comprising features representing a geometric layout of the first digital image by extracting intermediate features using the second set of blocks to upsample features extracted from the first set of blocks;
• extracting from a second digital image, utilizing the encoder neural network, a global code comprising features representing overall style properties of the second digital image;
• and
• generating a modified digital image by combining the spatial code with the global code utilizing a generator neural network.