Highly Compressed Tokenizer Can Generate Without Training

• URL: http://arxiv.org/abs/2506.08257v1
• Date: Mon, 09 Jun 2025 21:45:03 GMT
• Title: Highly Compressed Tokenizer Can Generate Without Training
• Authors: L. Lao Beyer, T. Li, X. Chen, S. Karaman, K. He,
• Abstract summary: 1D image tokenizers represent images as highly compressed one-dimensional sequences of as few as 32 discrete tokens.<n>We find that the high degree of compression achieved by a 1D tokenizer with vector quantization enables image editing and generative capabilities.<n>Our approach is demonstrated for inpainting and text-guided image editing use cases, and can generate diverse and realistic samples without requiring training of any generative model.
• Score: 0.5033155053523042
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Commonly used image tokenizers produce a 2D grid of spatially arranged tokens. In contrast, so-called 1D image tokenizers represent images as highly compressed one-dimensional sequences of as few as 32 discrete tokens. We find that the high degree of compression achieved by a 1D tokenizer with vector quantization enables image editing and generative capabilities through heuristic manipulation of tokens, demonstrating that even very crude manipulations -- such as copying and replacing tokens between latent representations of images -- enable fine-grained image editing by transferring appearance and semantic attributes. Motivated by the expressivity of the 1D tokenizer's latent space, we construct an image generation pipeline leveraging gradient-based test-time optimization of tokens with plug-and-play loss functions such as reconstruction or CLIP similarity. Our approach is demonstrated for inpainting and text-guided image editing use cases, and can generate diverse and realistic samples without requiring training of any generative model.

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