Summary of Rdpm: Solve Diffusion Probabilistic Models Via Recurrent Token Prediction, by Xiaoping Wu and Jie Hu and Xiaoming Wei
RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction
by Xiaoping Wu, Jie Hu, Xiaoming Wei
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 introduces Recurrent Diffusion Probabilistic Model (RDPM), a novel generative framework that enhances diffusion processes for high-fidelity image synthesis. RDPM iteratively predicts token codes for subsequent timesteps, transforming Gaussian noise into the source data distribution, aligning with GPT-style models. The model demonstrates superior performance while requiring fewer inference steps than traditional methods. This work aims to contribute to the development of a unified model for multimodal generation, integrating continuous signal domains like images and text. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make pictures look more realistic using a special kind of computer program called Recurrent Diffusion Probabilistic Model (RDPM). RDPM takes some random noise and turns it into the actual picture you want. It’s really good at doing this and only needs to do a few steps to get it right. This technology could help make pictures, videos, and even audio look more like what we see in our everyday lives. |
Keywords
* Artificial intelligence * Diffusion * Gpt * Image synthesis * Inference * Probabilistic model * Token
