zhaoyl18/SEIKO
SEIKO is a novel reinforcement learning method to efficiently fine-tune diffusion models in an online setting. Our methods outperform all baselines (PPO, classifier-based guidance, direct reward backpropagation) for fine-tuning Stable Diffusion.
This project helps researchers and developers fine-tune diffusion models to generate data (like images, biological sequences, or molecules) that meet specific, high-value criteria. You provide a pre-trained diffusion model and a way to evaluate the 'goodness' of generated samples, and it outputs a refined model capable of consistently producing high-reward results. This is for machine learning practitioners and scientists working on generative AI for specialized applications.
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Use this if you need to efficiently train a diffusion model to generate items with a specific, measurable property, such as aesthetically pleasing images or molecules with high bioactivity, especially when evaluating these properties can be costly or time-consuming.
Not ideal if your goal is general-purpose data generation without a specific, optimizable 'reward' metric, or if you do not have the infrastructure (like GPUs) to train large generative models.
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Language
Python
License
MIT
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Last pushed
Jul 18, 2024
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