Summary of Memory-efficient Fine-tuning For Quantized Diffusion Model, by Hyogon Ryu et al.
Memory-Efficient Fine-Tuning for Quantized Diffusion Model
by Hyogon Ryu, Seohyun Lim, Hyunjung Shim
First submitted to arxiv on: 9 Jan 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 emergence of billion-parameter diffusion models has revolutionized generative AI, but their large-scale architecture poses challenges in fine-tuning and deployment due to resource demands and slow inference speed. This paper explores fine-tuning quantized diffusion models, revealing that current methods neglect distinct patterns in model weights and different roles throughout time steps. To address these limitations, the authors introduce TuneQDM, a novel memory-efficient fine-tuning method specifically designed for quantized diffusion models. By introducing separable functions to consider inter-channel weight patterns and optimizing scales in a timestep-specific manner, TuneQDM achieves performance on par with its full-precision counterpart while offering significant memory efficiency. Experimental results demonstrate that TuneQDM consistently outperforms the baseline in single- and multi-subject generations, exhibiting high subject fidelity and prompt fidelity comparable to the full precision model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fine-tuning big AI models is like trying to get a really smart robot to do new tricks. These robots (called diffusion models) are super powerful but use lots of energy and take a long time to work. Scientists have been working on ways to make these robots smaller and faster, so they can be used in more places. This paper talks about a new way to fine-tune these robots called TuneQDM. It’s like a special tool that helps the robot learn new tricks without using too much energy or taking too long. The scientists tested this tool on some big AI models and found that it worked really well, even better than other tools they tried. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Inference » Precision » Prompt
