Modern text-to-image (T2I) systems use internet-scale language pretraining for text-conditioned image generation. This work investigates whether language pretraining is necessary for strong T2I generation. Halcyon, a family of early-fusion multimodal autoregressive models pretrained entirely from scratch on GPIC, a dataset of 100M image-text pairs, is introduced. Halcyon-0.52B outperforms LlamaGen-XL/8 (48.0 vs. 63.3 gFD) with 18.2 times less pretraining compute. Further experiments show that text-only pretraining does not improve T2I generation.
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Models trained without language pretraining can achieve high image generation quality, reducing reliance on large-scale data and computational resources.
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