Summary of Pixel-space Post-training Of Latent Diffusion Models, by Christina Zhang et al.
Pixel-Space Post-Training of Latent Diffusion Models
by Christina Zhang, Simran Motwani, Matthew Yu, Ji Hou, Felix Juefei-Xu, Sam Tsai, Peter Vajda, Zijian He, Jialiang Wang
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 Latent diffusion models (LDMs) have revolutionized image generation, leveraging their ability to operate in a compressed latent space for efficient training and deployment. However, LDMs still struggle with generating high-frequency details and complex compositions accurately. We propose addressing this issue by incorporating pixel-space supervision in the post-training process to preserve high-frequency details. Our experiments demonstrate that adding a pixel-space objective significantly improves both supervised quality fine-tuning and preference-based post-training for state-of-the-art DiT transformer and U-Net diffusion models, resulting in enhanced visual quality and reduced visual flaws while maintaining text alignment quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to generate realistic images using just some basic information. This is what latent diffusion models (LDMs) do! They’re super good at creating pictures, but sometimes they get stuck on small details or making the image look weird. To fix this, we tried adding a new step in the process that focuses on the tiny details. Guess what? It worked amazingly well! Our test results showed that using this new approach improved the quality of the images and reduced the number of mistakes. This is super important for things like AI-generated art or fake news detection. |
Keywords
» Artificial intelligence » Alignment » Diffusion » Fine tuning » Image generation » Latent space » Supervised » Transformer
