Style transfer is an application in which the style of one image is transferred onto the content of another. Here are a few ways it can be used with AI-generated images:
Direct style transfer:
Apply a pre-trained style transfer model, such as the famous neural style transfer by Gatys et al to directly apply the style of a reference image.
This may be included as a post-processing or merged in the training process of the generative model.
Style-Aware Generative Models
Training a model that can inherently take style into account. This can happen through:
Joint Learning: Training the model to simultaneously generate content and style.
Conditional Generation: Conditioning the model on a style vector or reference image.
Style-Based Generative Adversarial Networks (GANs):
Use GANs with a separate style encoder that maps a style vector to a style representation. The generator can then use this style representation to generate images with the desired style.
Style Mixing: Combine multiple styles by interpolating between their corresponding style vectors. This opens up the possibility of even more creative and diverse image generation.
Style Transfer as a Regularizer :Apply the style transfer process as an external regulizer, guiding the model towards pictures of one style, but keeping content fidelity intact.
Transfer Learning
Fine-tune a pre-trained style transfer network on your chosen dataset for additional refinement toward your specific requirements.
With these additional techniques in style transfer, the aesthetic appeal of AI-generated images can be taken to a new level for creating better-looking and more interesting material.
