Summary of Rissole: Parameter-efficient Diffusion Models Via Block-wise Generation and Retrieval-guidance, by Avideep Mukherjee et al.
RISSOLE: Parameter-efficient Diffusion Models via Block-wise Generation and Retrieval-Guidance
by Avideep Mukherjee, Soumya Banerjee, Piyush Rai, Vinay P. Namboodiri
First submitted to arxiv on: 30 Aug 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 The paper proposes a retrieval-augmented generation (RAG) approach to design compact-sized deep generative models for resource-constrained devices. The RAG approach leverages corresponding blocks of retrieved images to condition the training and generation stages of block-wise denoising diffusion models, ensuring coherence across generated blocks. The method is showcased using latent diffusion models (LDMs) as the base model, but can be applied to other variants of denoising diffusion models. Experiments demonstrate the effectiveness of the approach in achieving compact model sizes while maintaining excellent generation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to create images using computers. They wanted to make this process more efficient so that it could work on devices with limited power. To do this, they divided the image into smaller blocks and generated each block separately. However, making sure these blocks fit together seamlessly was a challenge. The solution is called retrieval-augmented generation (RAG), which uses previously seen images to help generate new ones. This approach ensures that the generated blocks look natural and cohesive. The team tested their method using a specific type of image generator and found that it worked well, producing high-quality images while using fewer resources. |
Keywords
* Artificial intelligence * Rag * Retrieval augmented generation
