Dec 6, 2022 · We thus highlight two competing objectives of VQ tokenizers for image synthesis: semantic compression and details preservation.

We thus highlight two competing objectives of VQ tokeniz- ers for image synthesis: semantic compression and de- tails preservation. Different from previous work ...

VQ tokenizers learn to quantize images into discrete codes, and then decode the codes to recover the input images, which process is termed as reconstruction.

To demonstrate the effectiveness of our two-phase tokenizer learning on generation quality, we decode the transformer-sampled indices to image space using the ...

It is found that improving the reconstruction fidelity of VQ tokenizers does not necessarily improve the generation, instead, learning to compress semantic ...

We thus highlight two competing objectives of VQ tokeniz-ers for image synthesis: semantic compression and details preservation.

Vector-Quantized (VQ-based) generative models usually consist of two basic components, i.e., VQ tokenizers and generative transformers.

We thus highlight two competing objectives of VQ tokenizers for image synthesis: semantic compression and details preservation. Different from previous work ...

Sep 22, 2024 · All of these methods employ a visual tokenizer to convert continuous visual signals into sequences of discrete tokens, allowing them to be ...

Duration: 4:34
Posted: May 28, 2024