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This paper explanation covers VQ-GAN, a model that combines a modified VQ-VAE with a GPT-2 transformer to synthesize high-resolution images. It discusses perceptual and patch-based adversarial losses, transformer conditioning and training, sampling strategies, and comparisons with related models.
