Summary of Not All Prompts Are Made Equal: Prompt-based Pruning Of Text-to-image Diffusion Models, by Alireza Ganjdanesh et al.
Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion Models
by Alireza Ganjdanesh, Reza Shirkavand, Shangqian Gao, Heng Huang
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper presents a novel approach to pruning text-to-image (T2I) diffusion models, enabling resource-constrained organizations to deploy these models after fine-tuning on their internal target data. The proposed Adaptive Prompt-Tailored Pruning (APTP) method addresses the limitations of static and dynamic pruning methods by learning to determine the required capacity for an input text prompt and routing it to a specialized model. APTP outperforms single-model pruning baselines in terms of FID, CLIP, and CMMD scores when applied to Stable Diffusion (SD) V2.1 using CC3M and COCO datasets. The paper’s analysis reveals that the clusters learned by APTP are semantically meaningful, and it demonstrates APTP’s ability to automatically discover challenging prompts for SD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about making a special kind of computer program called a “text-to-image model” work more efficiently on devices with limited power. These models can create images based on text descriptions, but they use too much energy right now. The scientists developed a new way to trim down these models so they use less energy while still being good at creating images. They tested this new method on two big datasets and found that it worked better than the old ways of doing things. This is important because it could help organizations with limited resources use these powerful image-creating tools. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Prompt » Pruning
