Summary of Rare-to-frequent: Unlocking Compositional Generation Power Of Diffusion Models on Rare Concepts with Llm Guidance, by Dongmin Park et al.
Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance
by Dongmin Park, Sebin Kim, Taehong Moon, Minkyu Kim, Kangwook Lee, Jaewoong Cho
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 A state-of-the-art text-to-image diffusion model can struggle to generate rare compositions of concepts, such as objects with unusual attributes. Our research demonstrates that the compositional generation power of these models can be significantly enhanced by Large Language Model (LLM) guidance. We show through empirical and theoretical analysis that exposing frequent concepts relevant to the target rare concept during the diffusion sampling process yields more accurate concept composition. Based on this, we propose a training-free approach called R2F that plans and executes rare-to-frequent concept guidance throughout the diffusion inference by leveraging abundant semantic knowledge in LLMs. Our framework is flexible across any pre-trained diffusion models and LLMs, and can be seamlessly integrated with region-guided diffusion approaches. Extensive experiments on three datasets, including our newly proposed benchmark RareBench, demonstrate that R2F surpasses existing models like SD3.0 and FLUX by up to 28.1% in text-to-image alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Rare text-to-image diffusion models can struggle to create rare concept compositions, such as unusual objects. We found that using Large Language Models (LLMs) helps these models generate more accurate concepts. Our approach, R2F, is a training-free way to guide the model to generate rare concepts by using LLMs to plan and execute the process. This framework can be used with any pre-trained diffusion model or LLM and integrates well with other approaches. We tested our method on three datasets and found that it works better than existing models like SD3.0 and FLUX, improving text-to-image alignment by up to 28.1%. |
Keywords
» Artificial intelligence » Alignment » Diffusion » Diffusion model » Inference » Large language model
