APEX: Asynchronous Prefix Denoising for Extreme Compression
We introduce APEX, short for Asynchronous Prefix denoising for EXtreme compression, which pairs a hierarchical latent space with asynchronous denoising over channel groups.
ProofPaper ↗
Key points
- Fewer latent tokens promise cheaper diffusion.
- Yet aggressive latent compression creates a reconstruction-generation dilemma: adding channels improves reconstruction but makes generation harder.
- Our autoencoder orders channel prefixes from structure to detail, while one shared denoiser generates them at staggered local times without adding tokens.
- On ImageNet-512, APEX outperforms existing methods at every evaluated channel count under 64x and 128x compression.
Sources (2)
- [1]APEX: Asynchronous Prefix Denoising for Extreme CompressionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 01:06 AM
We introduce APEX, short for Asynchronous Prefix denoising for EXtreme compression, which pairs a hierarchical latent space with asynchronous denoising over channel groups.
Fewer latent tokens promise cheaper diffusion.
- [2]APEX: Asynchronous Prefix Denoising for Extreme CompressionHugging Face Daily Papers · Oct 9, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
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