Summary of Vq4dit: Efficient Post-training Vector Quantization For Diffusion Transformers, by Juncan Deng et al.
VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion Transformers
by Juncan Deng, Shuaiting Li, Zeyu Wang, Hong Gu, Kedong Xu, Kejie Huang
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Diffusion Transformers Models (DiTs) have revolutionized image generation capabilities by transitioning from traditional UNets to transformers. However, their large parameter size hinders inference on edge devices. Vector quantization (VQ) has the potential to decompose model weight into a codebook and assignments, allowing extreme weight quantization and significantly reducing memory usage. The proposed VQ4DiT method efficiently quantizes DiTs by calibrating both codebooks and assignments, achieving optimal results. With VQ4DiT, DiT XL/2 models can be quantized to 2-bit precision on NVIDIA A100 GPUs within reasonable timeframes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make artificial intelligence (AI) models smaller without losing their ability to create high-quality images. The current models are too big for some devices, so scientists found a way to shrink them while keeping the good parts. This makes it possible to use these models on more devices. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Inference » Precision » Quantization
