Pinterest researchers have introduced a reinforcement learning framework to fine-tune diffusion models, addressing issues like bias and fairness. The method outperforms existing models, demonstrating generality, robustness, and the ability to generate diverse images. It achieved better results across various tasks and encourages further research to enhance diffusion models. [50 words]
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Pinterest Researchers Present an Effective Scalable Algorithm to Improve Diffusion Models Using Reinforcement Learning (RL)
Overview
Diffusion models are a type of generative models that have shown significant advancements in Machine Learning (ML) by achieving state-of-the-art image quality. However, they face challenges such as biases and stereotypes due to the distribution of training data. To address these issues, researchers from Pinterest have introduced a reinforcement learning (RL) framework for fine-tuning diffusion models to align with human preferences.
Proposed Framework
The framework enables training over diverse tasks and uses a distribution-based reward function for reinforcement learning fine-tuning. Additionally, multi-task joint training is performed to equip the model to handle a variety of objectives simultaneously.
Evaluation and Results
The authors considered three separate reward functions for evaluation and found that their method outperformed existing models in terms of human preference. It also demonstrated a more balanced distribution in terms of skin tone bias and better performance in generating diverse image compositions. The model also excelled in multi-reward joint optimization.
Conclusion
The proposed scalable RL training framework significantly improves diffusion models, demonstrating better generality, robustness, and the ability to generate diverse images. The authors aim to inspire future research to enhance diffusion models’ capabilities and address issues like bias and fairness.
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