Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation
Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, LiMin Wang, Ying Shan
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/tencentarc/seed-vokenOfficialIn paperpytorch★ 999
- github.com/tencentarc/open-magvit2pytorch★ 999
Abstract
We present Open-MAGVIT2, a family of auto-regressive image generation models ranging from 300M to 1.5B. 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 (1.17 rFID) on ImageNet 256 256. Furthermore, we explore its application in plain auto-regressive models and validate scalability properties. 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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| ImageNet 256x256 | Open-MAGVIT2-XL | FID | 2.33 | — | Unverified |