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.

通过使用增强学习引导图像编辑方法（InstructRL4Pix）来生成由目标对象的注意力地图引导的图像扩散模型，该方法通过计算注意力地图之间的距离作为奖励函数来最大化奖励模型的输出，并使用邻近策略优化（PPO）对扩散模型进行微调，以实现基于自然人命令的准确图像编辑。实验证实 InstructRL4Pix 突破了传统数据集的限制，利用无监督学习来优化编辑目标，并实现了精确的图像编辑。