GRACE is a two-stage framework for efficient video generation that compresses a pretrained video autoencoder while maintaining compatibility with a pretrained DiT model. It uses a frozen base latent and learns a residual latent to preserve information, aligning the compressed latent with the DiT's feature space for optimized generation.
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GRACE enables efficient video generation by compressing autoencoders while maintaining compatibility with pretrained models, reducing computational costs without sacrificing quality.
The paper introduces SGF+, a method for autoregressive video generation that decouples the roles of denoising and context writing by assigning separate parameters to each, improving visual quality and temporal consistency without additional training data or longer training periods.
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