[Paper] End-to-End Training for Unified Tokenization and Latent Denoising

Summary

UNITE introduces an autoencoder architecture that unifies tokenization and latent denoising for Latent Diffusion Models (LDMs). This approach streamlines the traditionally complex, multi-stage training process of LDMs by using a Generative Encoder that functions as both an image tokenizer and a latent generator via weight sharing.

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