Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation

• URL: http://arxiv.org/abs/2311.16201v2
• Date: Wed, 25 Sep 2024 17:58:21 GMT
• Title: Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation
• Authors: Yuhui Zhang, Brandon McKinzie, Zhe Gan, Vaishaal Shankar, Alexander Toshev,
• Abstract summary: We adapt a pre-trained language model for auto-regressive text-to-image generation. We find that pre-trained language models offer limited help.
• Score: 82.5217996570387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in image tokenizers, such as VQ-VAE, have enabled text-to-image generation using auto-regressive methods, similar to language modeling. However, these methods have yet to leverage pre-trained language models, despite their adaptability to various downstream tasks. In this work, we explore this gap by adapting a pre-trained language model for auto-regressive text-to-image generation, and find that pre-trained language models offer limited help. We provide a two-fold explanation by analyzing tokens from each modality. First, we demonstrate that image tokens possess significantly different semantics compared to text tokens, rendering pre-trained language models no more effective in modeling them than randomly initialized ones. Second, the text tokens in the image-text datasets are too simple compared to normal language model pre-training data, which causes the catastrophic degradation of language models' capability.

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