arXiv Open Access 2024

Allegro: Open the Black Box of Commercial-Level Video Generation Model

Yuan Zhou Qiuyue Wang Yuxuan Cai Huan Yang
Lihat Sumber

Abstrak

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 $\textbf{Allegro}$, 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/allegro_gallery .

Topik & Kata Kunci

Penulis (4)

Y

Yuan Zhou

Q

Qiuyue Wang

Y

Yuxuan Cai

H

Huan Yang

Format Sitasi

Zhou, Y., Wang, Q., Cai, Y., Yang, H. (2024). Allegro: Open the Black Box of Commercial-Level Video Generation Model. https://arxiv.org/abs/2410.15458

Akses Cepat

Lihat di Sumber
Informasi Jurnal
Tahun Terbit
2024
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓