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We introduce Quantized Language-Image Pretraining (QLIP), a visual tokenization method that combines state-of-the-art reconstruction quality with state-of-the-art zero-shot image understanding. QLIP trains a binary-spherical-quantization-based autoencoder with reconstruction and language-image alignment objectives. We are the first to show that the two objectives do not need to be at odds. We balance the two loss terms dynamically during training and show that a two-stage training pipeline effectively mixes the large-batch requirements of image-language pre-training with the memory bottleneck imposed by the reconstruction objective. We validate the effectiveness of QLIP for multimodal understanding and text-conditioned image generation with a single model. Specifically, QLIP serves as a drop-in replacement for the visual encoder for LLaVA and the image tokenizer for LlamaGen with comparable or even better performance. Finally, we demonstrate that QLIP enables a unified mixed-modality auto-regressive model for understanding and generation.
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Image and Video Tokenization with Binary Spherical Quantization
This work introduces a transformer-based image and video tokenizer leveraging Binary Spherical Quantization (BSQ). The method projects high-dimensional visual embeddings onto a lower-dimensional hypersphere followed by binary quantization. BSQ offers three key benefits: (1) parameter efficiency without requiring an explicit codebook, (2) scalability to arbitrary token dimensions, and (3) high compression capability—up to 100× compression of visual data with minimal distortion. The tokenizer architecture includes a transformer encoder-decoder with block-wise causal masking to handle variable-length video inputs. The resulting model, BSQ-ViT, achieves state-of-the-art visual reconstruction performance on image and video benchmarks while delivering 2.4× higher throughput compared to previous best methods. Additionally, BSQ-ViT supports video compression via autoregressive priors for adaptive arithmetic coding, achieving results comparable to leading video compression standards. Furthermore, it enables masked language models to achieve competitive image synthesis quality relative to GAN- and diffusion-based approaches.
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- Award ID(s):
- 2505865
- PAR ID:
- 10631957
- Publisher / Repository:
- https://doi.org/10.48550/arXiv.2406.07548
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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