Allegro: Open the Black Box of Commercial-Level Video Generation Model
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Cited by 36 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Significant advancements have been made in the field of video generation, with the open-source community contributing a wealth of research papers and tools for training high-quality models. However, despite these efforts, the available information and resources remain insufficient for achieving commercial-level performance. In this report, we open the black box and introduce Allegro, an advanced video generation model that excels in both quality and temporal consistency. We also highlight the current limitations in the field and present a comprehensive methodology for training high-performance, commercial-level video generation models, addressing key aspects such as data, model architecture, training pipeline, and evaluation. Our user study shows that Allegro surpasses existing open-source models and most commercial models, ranking just behind Hailuo and Kling. Code: https://github.com/rhymes-ai/Allegro , Model: https://huggingface.co/rhymes-ai/Allegro , Gallery: https://rhymes.ai/allegrogallery .
Key facts. Cited by 70 papers in the map (data through 14 Oct 2026). Builds on 44 of its 53 references that are in the map. First posted on arXiv 20 Oct 2024.
Builds on
44 of 53 references.
References · 44
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Citing papers per year: 2024: 13; 2025: 38; 2026: 19.
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