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Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens
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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.
Forward citations
Cited by 6 Pith papers
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FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens
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1D binary image latents reduce a 1024x1024 image to 128 discrete tokens and support text-to-image generation with diffusion and autoregressive models.
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Discrete-JEPA learns discrete semantic image tokens through latent predictive coding without pixel reconstruction, and achieves stable long-horizon prediction on synthetic symbolic tasks.
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GRAT accelerates pretrained diffusion transformers by grouping tokens and restricting each group's attention to neighboring blocks or criss-cross rows and columns, achieving large speedups with near-full-attention quality.
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Text-Guided Token Communication for Wireless Image Transmission
A text-guided token transmission system using pre-trained image and text models outperforms a deep JSCC baseline on perceptual and semantic metrics, but relies on an assumption that text is available at the receiver.
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DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer
DC-AR generates 512x512 images in 12 masked autoregressive steps plus 20 diffusion refinement steps, using a 32x compressed 2D tokenizer, and reports gFID 5.49 on MJHQ-30K.
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