Summary of Ld-pruner: Efficient Pruning Of Latent Diffusion Models Using Task-agnostic Insights, by Thibault Castells et al.
LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insights
by Thibault Castells, Hyoung-Kyu Song, Bo-Kyeong Kim, Shinkook Choi
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 Latent Diffusion Models (LDMs) have gained popularity due to their impressive results under limited computational resources. However, deploying LDMs on resource-constrained devices poses challenges regarding memory consumption and inference speed. To address this issue, researchers introduce LD-Pruner, a novel structured pruning method that compresses LDMs while preserving performance. This approach leverages the latent space during pruning to quantify the impact of pruning on model performance independently of the task at hand. By targeting components with minimal impact on output, the model converges faster during training, reducing computational cost. The compressed model achieves improved inference speed and reduced parameter count with minimal performance degradation. Experimental results demonstrate LD-Pruner’s effectiveness on three tasks: text-to-image (T2I) generation, unconditional image generation (UIG), and unconditional audio generation (UAG). Specifically, LD-Pruner reduces the inference time of Stable Diffusion (SD) by 34.9% while improving its FID by 5.2% on the MS-COCO T2I benchmark. This work paves the way for more efficient pruning methods for LDMs, enhancing their applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a powerful tool that can create amazing images and music, but it’s too big to fit on your phone or computer. To fix this problem, scientists created a new way to shrink these tools without losing their power. They call it LD-Pruner. This method helps the tool learn how to forget some of its old information, so it doesn’t need as much space and can work faster. The team tested LD-Pruner on three different tasks: creating images from text, making random music, and generating random sounds. It worked really well! For example, they took a big model that could create great images but was slow to use. By applying LD-Pruner, they made it smaller and faster without losing its ability to make good images. This is an important step towards making these powerful tools more useful for everyday people. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Inference » Latent space » Pruning
