Pixel-wise RL on Diffusion Models: Reinforcement Learning from Rich
  Feedback
Mo Kordzanganeh Danial Keshvary Nariman Arian
Latent diffusion models are the state-of-the-art for synthetic image
generation. To align these models with human preferences, training the models
using reinforcement learning on human feedback is crucial. Black et. al 2024
introduced denoising diffusion policy optimisation (DDPO), which accounts for
the iterative denoising nature of the generation by modelling it as a Markov
chain with a final reward. As the reward is a single value that determines the
model's performance on the entire image, the model has to navigate a very
sparse reward landscape and so requires a large sample count. In this work, we
extend the DDPO by presenting the Pixel-wise Policy Optimisation (PXPO)
algorithm, which can take feedback for each pixel, providing a more nuanced
reward to the model.