Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens

• URL: http://arxiv.org/abs/2501.07730v2
• Date: Sat, 02 Aug 2025 07:11:16 GMT
• Title: Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens
• Authors: Dongwon Kim, Ju He, Qihang Yu, Chenglin Yang, Xiaohui Shen, Suha Kwak, Liang-Chieh Chen,
• Abstract summary: We introduce Text-Aware Transformer-based 1-Dimensional Tokenizer (TA-TiTok)<n>TA-TiTok is an efficient and powerful image tokenizer that can utilize either discrete or continuous 1-dimensional tokens.<n>We also introduce a family of text-to-image Masked Generative Models (MaskGen) trained exclusively on open data.
• Score: 46.361925096761915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image tokenizers form the foundation of modern text-to-image generative models but are notoriously difficult to train. Furthermore, most existing text-to-image models rely on large-scale, high-quality private datasets, making them challenging to replicate. In this work, we introduce Text-Aware Transformer-based 1-Dimensional Tokenizer (TA-TiTok), an efficient and powerful image tokenizer that can utilize either discrete or continuous 1-dimensional tokens. TA-TiTok uniquely integrates textual information during the tokenizer decoding stage (i.e., de-tokenization), accelerating convergence and enhancing performance. TA-TiTok also benefits from a simplified, yet effective, one-stage training process, eliminating the need for the complex two-stage distillation used in previous 1-dimensional tokenizers. This design allows for seamless scalability to large datasets. Building on this, we introduce a family of text-to-image Masked Generative Models (MaskGen), trained exclusively on open data while achieving comparable performance to models trained on private data. We aim to release both the efficient, strong TA-TiTok tokenizers and the open-data, open-weight MaskGen models to promote broader access and democratize the field of text-to-image masked generative models.

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