Summary of Gradient-free Decoder Inversion in Latent Diffusion Models, by Seongmin Hong et al.
Gradient-free Decoder Inversion in Latent Diffusion Models
by Seongmin Hong, Suh Yoon Jeon, Kyeonghyun Lee, Ernest K. Ryu, Se Young Chun
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper proposes an efficient gradient-free decoder inversion method for latent diffusion models (LDMs), which can be applied to diverse latent models. The current state-of-the-art approach employs gradient descent inspired by generative adversarial networks, but this requires larger GPU memory and longer computation time for larger latent spaces. For instance, recent video LDMs can generate more than 16 frames, but GPUs with 24 GB memory can only perform gradient-based decoder inversion for 4 frames. The proposed method utilizes Adam optimizer and learning rate scheduling, significantly reducing computation time and memory usage while achieving comparable error levels to prior gradient-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to invert decoders in latent diffusion models (LDMs). This is important because LDMs are used in many applications like generating videos. The current method for inverting decoders uses something called gradient descent, which can be slow and use too much computer memory. The researchers found that this method takes too long to work with larger video sequences. To solve this problem, they developed a new method that doesn’t use gradients, making it faster and more efficient. This new method is useful for applications like hiding secret messages in videos. |
Keywords
» Artificial intelligence » Decoder » Diffusion » Gradient descent
