Summary of Subject-driven Text-to-image Generation Via Preference-based Reinforcement Learning, by Yanting Miao et al.
Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning
by Yanting Miao, William Loh, Suraj Kothawade, Pascal Poupart, Abdullah Rashwan, Yeqing Li
First submitted to arxiv on: 16 Jul 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 Text-to-image generative models have gained popularity for synthesizing high-quality images from textual prompts. However, these models often struggle to generate specific subjects or novel renditions under varying conditions. Existing methods like DreamBooth and Subject-driven Text-to-Image (SuTI) have made progress in this area but require expensive setups, overlooking the need for efficient training and avoiding overfitting to reference images. This work presents a new reward function called -Harmonic, which provides a reliable signal for early stopping and regularization. By combining the Bradley-Terry preference model with -Harmonic, the proposed Reward Preference Optimization (RPO) algorithm offers a simpler setup requiring fewer negative samples and gradient steps. Unlike existing methods, RPO fine-tunes only the U-Net component without training a text encoder or optimizing text embeddings. The -Harmonic reward function is validated through preference labels and early stopping, achieving state-of-the-art results on DreamBench with a CLIP-I score of 0.833 and a CLIP-T score of 0.314. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create realistic images from words! This paper helps make that possible by improving a type of AI called text-to-image generative models. These models are great at creating new images, but they often struggle to focus on specific subjects or create unique versions under different conditions. The authors suggest a new way to guide these models using a special reward function that makes training faster and more efficient. This approach doesn’t require a lot of extra data or complex setup, making it more practical for real-world use. By fine-tuning the model’s “brain” (called U-Net) without needing to train a separate text encoder, this method achieves impressive results on a benchmarking task. |
Keywords
» Artificial intelligence » Early stopping » Encoder » Fine tuning » Optimization » Overfitting » Regularization
