This research report introduces Allegro, a novel, open-source text-to-video generation model that surpasses existing open-source and many commercial models in quality and temporal consistency. The authors detail Allegro's architecture, a multi-stage training process leveraging a custom-designed Video Variational Autoencoder (VideoVAE) and Video Diffusion Transformer (VideoDiT), and a rigorous data curation pipeline resulting in a dataset of 106 million images and 48 million videos. Extensive evaluations, including user studies, demonstrate Allegro's superior performance across various metrics, though some limitations remain, particularly regarding large-scale motion. The authors also provide insights into future improvements, including expanding model capabilities and enhancing data diversity. Finally, the complete Allegro model and code are released under the Apache 2.0 license.

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