What methods can be used for image generation (GAN, VAE, diffusion, autoregressive)? What are the pros and cons of each.

Main methods of image generation:

1. GAN (Generative Adversarial Networks)

   • Pros: generate very realistic images, suitable for high-resolution tasks.
   • Cons: complex to train, unstable, may suffer from vanishing gradient problem and mode collapse.

2. VAE (Variational Autoencoders)

   • Pros: stable training, good for exploring latent space, allows generating diverse images.
   • Cons: often generate less sharp and blurry images compared to GAN.

3. Diffusion models

   • Pros: capable of generating high-quality and diverse images, more stable training, work well with noise.
   • Cons: generation can be slow due to a large number of diffusion steps.

4. Autoregressive models

   • Pros: generate images step-by-step, allowing control over the process, model complex dependencies well.
   • Cons: generation can be slow as each pixel depends on previous ones.

The choice of method depends on the task: if high realism is needed — GAN or diffusion models; if stability and exploration of latent space are important — VAE; for control and sequential generation — autoregressive models.