Summary of Reward Guided Latent Consistency Distillation, by Jiachen Li et al.
Reward Guided Latent Consistency Distillation
by Jiachen Li, Weixi Feng, Wenhu Chen, William Yang Wang
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes Reward Guided Latent Consistency Distillation (RG-LCD), an approach to efficiently generate high-fidelity images through text-to-image synthesis. The method builds upon Latent Consistency Distillation (LCD), which distills a latent consistency model from a pre-trained teacher latent diffusion model, reducing the number of inference steps needed. However, this efficiency comes at the cost of sample quality. To address this trade-off, RG-LCD integrates feedback from a reward model during training to maximize the reward associated with single-step generation. The authors demonstrate that when trained with a good reward model, RG-LCD’s 2-step generations are favored by humans over longer-generation teacher LDM samples, achieving a 25-time inference acceleration without sacrificing quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better pictures from words. It’s like writing a story and having the computer draw what you described. The problem is that these computers need to do lots of calculations to create good pictures. This makes it slow. To solve this, researchers created a way to teach the computer to generate better pictures with fewer steps. They also made sure the pictures are as good as if the computer had taken many more steps. It’s like giving the computer a hint about what you want the picture to look like. |
Keywords
» Artificial intelligence » Diffusion model » Distillation » Image synthesis » Inference
