Learning from human feedback has been shown to improve text-to-image models. These
techniques first learn a reward function that captures what humans care about in the task …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

Diffusion models are a class of flexible generative models trained with an approximation to
the log-likelihood objective. However, most use cases of diffusion models are not concerned …

Deep Generative AI has been a long-standing essential topic in the machine learning
community, which can impact a number of application areas like text generation and …

We present Direct Reward Fine-Tuning (DRaFT), a simple and effective method for fine-
tuning diffusion models to maximize differentiable reward functions, such as scores from …

Diffusion Models (DMs), also referred to as score-based diffusion models, utilize neural
networks to specify score functions. Unlike most other probabilistic models, DMs directly …

Using reinforcement learning with human feedback (RLHF) has shown significant promise in
fine-tuning diffusion models. Previous methods start by training a reward model that aligns …

Recent works have demonstrated that using reinforcement learning (RL) with multiple
quality rewards can improve the quality of generated images in text-to-image (T2I) …

We introduce beta diffusion, a novel generative modeling method that integrates demasking
and denoising to generate data within bounded ranges. Using scaled and shifted beta …

We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework
including best practices for fine-tuning diffusion-based policies (eg Diffusion Policy) in …