  Instruction-based image editing has made a great process in using natural
human language to manipulate the visual content of images. However, existing
models are limited by the quality of the dataset and cannot accurately localize
editing regions in images with complex object relationships. In this paper, we
propose Reinforcement Learning Guided Image Editing Method(InstructRL4Pix) to
train a diffusion model to generate images that are guided by the attention
maps of the target object. Our method maximizes the output of the reward model
by calculating the distance between attention maps as a reward function and
fine-tuning the diffusion model using proximal policy optimization (PPO). We
evaluate our model in object insertion, removal, replacement, and
transformation. Experimental results show that InstructRL4Pix breaks through
the limitations of traditional datasets and uses unsupervised learning to
optimize editing goals and achieve accurate image editing based on natural
human commands.
