Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915422696112128 |
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| author | Kim, Dongwon He, Ju Yu, Qihang Yang, Chenglin Shen, Xiaohui Kwak, Suha Chen, Liang-Chieh |
| author_facet | Kim, Dongwon He, Ju Yu, Qihang Yang, Chenglin Shen, Xiaohui Kwak, Suha Chen, Liang-Chieh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07730 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens Kim, Dongwon He, Ju Yu, Qihang Yang, Chenglin Shen, Xiaohui Kwak, Suha Chen, Liang-Chieh Computer Vision and Pattern Recognition 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. |
| title | Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.07730 |