Summary of Q-dit: Accurate Post-training Quantization For Diffusion Transformers, by Lei Chen et al.
Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
by Lei Chen, Yuan Meng, Chen Tang, Xinzhu Ma, Jingyan Jiang, Xin Wang, Zhi Wang, Wenwu Zhu
First submitted to arxiv on: 25 Jun 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 A recent advancement in diffusion models, specifically the transition from UNet-based models to Diffusion Transformers (DiTs), has significantly improved image and video generation quality and scalability. However, the substantial computational costs of these large-scale models pose a significant challenge for real-world deployment. Post-Training Quantization (PTQ) emerges as a promising solution, enabling model compression and accelerated inference for pre-trained models without retraining. The paper focuses on DiT quantization, proposing Q-DiT, a novel approach that integrates automatic quantization granularity allocation to handle variance in weights and activations across input channels and sample-wise dynamic activation quantization to adaptively capture activation changes. Experimental results on ImageNet and VBench demonstrate the effectiveness of Q-DiT, achieving significant reductions in FID and maintaining high fidelity in image and video generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DiT models can generate great images and videos, but they are very computer-intensive. To make them more practical for real-life use, researchers have been working on a technique called Post-Training Quantization (PTQ). This helps compress the model and speed up its calculations without retraining it from scratch. However, PTQ doesn’t work well with DiT models yet. The problem is that DiTs have very different patterns of data and calculations compared to traditional models, which makes it hard for PTQ to correctly compress them. In this paper, scientists propose a new approach called Q-DiT that can better handle the complexities of DiT models. They test their method on some big datasets and show that it can significantly reduce the amount of computer power needed while still producing high-quality results. |
Keywords
» Artificial intelligence » Diffusion » Inference » Model compression » Quantization » Unet
