InstructRL4Pix: Training Diffusion for Image Editing by Reinforcement Learning
This addresses the limitation of existing models in localizing edits in complex images, offering an unsupervised approach for more precise image manipulation based on natural language commands.
The paper tackles the problem of instruction-based image editing by proposing a reinforcement learning method to train a diffusion model, using attention maps and PPO to optimize editing goals, achieving accurate editing for tasks like object insertion and removal.
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.