用强化学习自动调参，让图像编辑更快更省资源。
AutoEdit: Automatic Hyperparameter Tuning for Image Editing
- 将调参看作扩散过程中的动态决策，用强化学习自动调整参数。
- 相比暴力搜索，计算量降低显著，且保持编辑效果最优。
- 适合需要高效图像编辑的开发者和研究者使用。
扩散模型的进展推动了文本引导图像编辑的发展，但现有方法在超参数选择上面临严峻挑战。为获得合理编辑效果，用户常需通过暴力搜索调整多个相互依赖的超参数（如反演步数、注意力修改等），导致计算成本高昂。本文将超参数搜索视为扩散去噪过程中的序列决策任务，提出一种基于强化学习的框架，构建马尔可夫决策过程，使超参数在去噪步骤中动态调整，并将编辑目标融入奖励函数。该方法通过近端策略优化实现高效求解，同时保持最优超参数配置。实验表明，与现有暴力搜索方法相比，搜索时间与计算开销显著降低，提升了基于扩散模型图像编辑框架的实际部署能力。代码已开源：https://github.com/chaupham1709/AutoEdit.git。
原文摘要 · Abstract (English)
Recent advances in diffusion models have revolutionized text-guided image editing, yet existing editing methods face critical challenges in hyperparameter identification. To get the reasonable editing performance, these methods often require the user to brute-force tune multiple interdependent hyperparameters, such as inversion timesteps and attention modification. This process incurs high computational costs due to the huge hyperparameter search space. We consider searching optimal editing's hyperparameters as a sequential decision-making task within the diffusion denoising process. Specifically, we propose a reinforcement learning framework, which establishes a Markov Decision Process that dynamically adjusts hyperparameters across denoising steps, integrating editing objectives into a reward function. The method achieves time efficiency through proximal policy optimization while maintaining optimal hyperparameter configurations. Experiments demonstrate significant reduction in search time and computational overhead compared to existing brute-force approaches, advancing the practical deployment of a diffusion-based image editing framework in the real world. Codes can be found at https://github.com/chaupham1709/AutoEdit.git.
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