Reinforcement Learning: Enhancing Diffusion Models through Training – Discovering the Power of AI at the Berkeley Artificial Intelligence Research Blog
Introduction:
to match the training data or maximize a specific objective. In this post, we introduce a new approach to training diffusion models using reinforcement learning (RL) called denoising diffusion policy optimization (DDPO). Unlike traditional methods that rely on approximations, DDPO leverages the entire sequence of denoising steps to maximize the reward of the final sample. We demonstrate the effectiveness of DDPO by finetuning Stable Diffusion on four different tasks: compressibility, incompressibility, aesthetic quality, and prompt-image alignment. We showcase the impressive results achieved through RL finetuning, including improved image generation and surprising generalization to unseen objects and activities. However, we also highlight the challenge of overoptimization and the need for further research in this area. With DDPO, diffusion models can be further enhanced to produce even more impressive and tailored outputs.
Full Article: Reinforcement Learning: Enhancing Diffusion Models through Training – Discovering the Power of AI at the Berkeley Artificial Intelligence Research Blog
Training Diffusion Models with Reinforcement Learning: A New Approach for Better Results
Diffusion models have become increasingly popular for generating complex, high-dimensional outputs in various fields, from AI art to drug design. These models transform random noise into meaningful samples, such as images or protein structures. While most diffusion models are trained to match the training data, their true value lies in their ability to achieve downstream objectives.
In this article, we explore how reinforcement learning can be used to train diffusion models directly on these downstream objectives. By finetuning Stable Diffusion on different objectives, including image compressibility, aesthetic quality, and prompt-image alignment, we can achieve impressive results. Moreover, we demonstrate how AI models can improve each other without human intervention, using feedback from a large vision-language model.
Introducing Denoising Diffusion Policy Optimization (DDPO)
To turn diffusion into an RL problem, we assume access to a reward function that evaluates the quality of a sample. The goal is to train the diffusion model to generate samples that maximize this reward function. While diffusion models are typically trained using maximum likelihood estimation, we face challenges in the RL setting where we rely solely on samples and rewards.
To address these challenges, we propose a new algorithm called Denoising Diffusion Policy Optimization (DDPO). DDPO approaches diffusion as a multi-step Markov decision process (MDP), allowing us to leverage powerful RL algorithms designed specifically for MDPs. By considering the entire sequence of denoising steps, DDPO maximizes rewards more effectively.
Implementing DDPO for Better Results
We evaluate two variants of DDPO: DDPOSF, which employs the simple score function estimator known as REINFORCE, and DDPOIS, which utilizes a more powerful importance sampled estimator. Based on our evaluation, DDPOIS outperforms the other variant, closely resembling proximal policy optimization (PPO) in its implementation.
We apply DDPOIS to finetune Stable Diffusion v1-4 on four different tasks, each defined by a specific reward function:
1. Compressibility: Measures how easily an image can be compressed using the JPEG algorithm. The reward is the negative file size of the image when saved as a JPEG.
2. Incompressibility: Measures how difficult it is to compress an image using the JPEG algorithm. The reward is the positive file size of the image when saved as a JPEG.
3. Aesthetic Quality: Evaluates the visual appeal of an image to the human eye. The reward is determined by the LAION aesthetic predictor, a neural network trained on human preferences.
4. Prompt-Image Alignment: Assesses how well an image represents the content requested in a given prompt. We employ BERTScore to compute the similarity between the generated description and the original prompt.
During finetuning, we use specific prompts for each task to guide Stable Diffusion. We notice significant improvements in generating images that align with the prompt, especially for challenging scenarios and unusual prompts.
Surprising Generalization and Overoptimization
We observe unexpected generalization in our models, mirroring findings in RL-trained language models. For instance, our aesthetic quality model, trained on a specific set of animal prompts, generalizes well to unseen animals and everyday objects. Similarly, our prompt-image alignment model shows generalization to both unseen animals and activities, even novel combinations of the two.
However, an inevitable challenge in reward-based finetuning is overoptimization. Our models tend to sacrifice meaningful image content to maximize the reward, which can be detrimental for downstream applications. Additionally, we discover that LLaVA, the large vision-language model used for evaluation, is vulnerable to typographic attacks, where DDPO successfully fools it by making subtle modifications.
Conclusion
Reinforcement learning offers a promising approach to training diffusion models on downstream objectives. Through DDPO, we achieve better results by considering the entire denoising process as an MDP. While unexpected generalization and overoptimization pose challenges, the advancements made in this study pave the way for further improvements in training diffusion models using RL techniques.
Summary: Reinforcement Learning: Enhancing Diffusion Models through Training – Discovering the Power of AI at the Berkeley Artificial Intelligence Research Blog
Training diffusion models with reinforcement learning (RL) allows for the generation of complex and high-dimensional outputs. These models have been successful in various applications such as AI art, drug design, and continuous control. While diffusion models are typically trained to match training data, RL allows for training based on downstream objectives. This post explores how diffusion models can be trained using RL, specifically through a method called denoising diffusion policy optimization (DDPO). The performance of DDPO is evaluated on different reward functions, including image compressibility, aesthetic quality, and prompt-image alignment. The results show that DDPO outperforms other algorithms and demonstrates surprising generalization capabilities. However, overoptimization and typographic attacks are challenges that need to be addressed in future work.
