VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion Transformers Article Swipe
Juncan Deng
,
Shuaiting Li
,
Zeyu Wang
,
Hong Gu
,
Kedong Xu
,
Kejie Huang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.17131
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.17131
The Diffusion Transformers Models (DiTs) have transitioned the network architecture from traditional UNets to transformers, demonstrating exceptional capabilities in image generation. Although DiTs have been widely applied to high-definition video generation tasks, their large parameter size hinders inference on edge devices. Vector quantization (VQ) can decompose model weight into a codebook and assignments, allowing extreme weight quantization and significantly reducing memory usage. In this paper, we propose VQ4DiT, a fast post-training vector quantization method for DiTs. We found that traditional VQ methods calibrate only the codebook without calibrating the assignments. This leads to weight sub-vectors being incorrectly assigned to the same assignment, providing inconsistent gradients to the codebook and resulting in a suboptimal result. To address this challenge, VQ4DiT calculates the candidate assignment set for each weight sub-vector based on Euclidean distance and reconstructs the sub-vector based on the weighted average. Then, using the zero-data and block-wise calibration method, the optimal assignment from the set is efficiently selected while calibrating the codebook. VQ4DiT quantizes a DiT XL/2 model on a single NVIDIA A100 GPU within 20 minutes to 5 hours depending on the different quantization settings. Experiments show that VQ4DiT establishes a new state-of-the-art in model size and performance trade-offs, quantizing weights to 2-bit precision while retaining acceptable image generation quality.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2408.17131
- https://arxiv.org/pdf/2408.17131
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402951436
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- OpenAlex ID
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https://openalex.org/W4402951436Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2408.17131Digital Object Identifier
- Title
-
VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion TransformersWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-30Full publication date if available
- Authors
-
Juncan Deng, Shuaiting Li, Zeyu Wang, Hong Gu, Kedong Xu, Kejie HuangList of authors in order
- Landing page
-
https://arxiv.org/abs/2408.17131Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2408.17131Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2408.17131Direct OA link when available
- Concepts
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Transformer, Vector quantization, Learning vector quantization, Computer science, Training (meteorology), Artificial intelligence, Electrical engineering, Engineering, Physics, Meteorology, VoltageTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.post-training | 70 |
| abstract_inverted_index.significantly | 58 |
| abstract_inverted_index.transformers, | 14 |
| abstract_inverted_index.high-definition | 28 |
| abstract_inverted_index.state-of-the-art | 193 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |