Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation
This open-source project democratizes access to advanced auto-regressive visual generation technology for researchers and developers.
The Open-MAGVIT2 project replicated Google's MAGVIT-v2 tokenizer with a super-large codebook, achieving state-of-the-art reconstruction performance on ImageNet and UCF benchmarks, and produced auto-regressive image generation models from 300M to 1.5B parameters.
The Open-MAGVIT2 project produces an open-source replication of Google's MAGVIT-v2 tokenizer, a tokenizer with a super-large codebook (i.e., $2^{18}$ codes), and achieves the state-of-the-art reconstruction performance on ImageNet and UCF benchmarks. We also provide a tokenizer pre-trained on large-scale data, significantly outperforming Cosmos on zero-shot benchmarks (1.93 vs. 0.78 rFID on ImageNet original resolution). Furthermore, we explore its application in plain auto-regressive models to validate scalability properties, producing a family of auto-regressive image generation models ranging from 300M to 1.5B. To assist auto-regressive models in predicting with a super-large vocabulary, we factorize it into two sub-vocabulary of different sizes by asymmetric token factorization, and further introduce ``next sub-token prediction'' to enhance sub-token interaction for better generation quality. We release all models and codes to foster innovation and creativity in the field of auto-regressive visual generation.