Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets (2412.07775v6)

Abstract: While one commonly trains large diffusion models by collecting datasets on target downstream tasks, it is often desired to align and finetune pretrained diffusion models with some reward functions that are either designed by experts or learned from small-scale datasets. Existing post-training methods for reward finetuning of diffusion models typically suffer from lack of diversity in generated samples, lack of prior preservation, and/or slow convergence in finetuning. In response to this challenge, we take inspiration from recent successes in generative flow networks (GFlowNets) and propose a reinforcement learning method for diffusion model finetuning, dubbed Nabla-GFlowNet (abbreviated as $\nabla$-GFlowNet), that leverages the rich signal in reward gradients for probabilistic diffusion finetuning. We show that our proposed method achieves fast yet diversity- and prior-preserving finetuning of Stable Diffusion, a large-scale text-conditioned image diffusion model, on different realistic reward functions.

• The paper introduces Nabla-GFlowNet, leveraging reward gradients to tune diffusion models while ensuring sample diversity.
• It employs gradient-informed objectives and a residual objective to integrate rich reward signals and preserve pretrained model knowledge.
• Experimental results show that Nabla-GFlowNet outperforms baselines in balancing reward alignment, output diversity, and convergence speed.

Overview of "Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets"

The paper "Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets" introduces an innovative approach to fine-tuning diffusion models on targeted reward functions. This is particularly relevant in scenarios where pretrained models, equipped with large-scale data, need alignment towards specific objectives or reward functions derived from expert input or Reinforcement Learning from Human Feedback (RLHF).

Core Contributions

1. Introduction of Nabla-GFlowNet: The paper proposes Nabla-GFlowNet ($$), a novel method harnessing the signal from reward gradients. This method leverages <a href="https://www.emergentmind.com/topics/generative-flow-networks-gflownets" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Generative Flow Networks</a> (GFlowNets) to achieve diversity-preserving fine-tuning, optimized for scenarios where traditional methods might fail due to lack of sample diversity or slow convergence.</li> <li><strong>Gradient-Informed Objectives</strong>: Nabla-GFlowNet incorporates Gradient-Informed GFlowNet objectives, introduced as the$$ objective, which integrates the rich information available in the gradients of the reward function. This is posited as a significant improvement over previous techniques that rely solely on scalar rewards.
2. Residual Objective for Prior Preservation: In its pursuit of maintaining the diversity and fidelity of the generated samples, the paper introduces a residual objective tailored to preserve the prior knowledge inherent in pretrained diffusion models. This is achieved by aligning the model outputs with a pretrained prior, combined with the new reward function.

Methodology

• Diffusion Models and MDP Construction: The paper builds on diffusion models by representing each denoising step as a transition in a Markov Decision Process (MDP). This facilitates the integration of RL techniques in model fine-tuning processes.
• Generative Flow Networks (GFlowNets) Framework: Unlike conventional models focusing on maximization, GFlowNets explore a broad state-action space, promoting diversity by sampling according to unnormalized density functions. The paper exploits this by embedding GFlowNet paradigms into diffusion model fine-tuning, supported by the newly devised Gradient-Informed objectives.

Experimental Results

Experiments demonstrate that the proposed method achieves a superior balance between reward alignment, diversity, and convergence speed compared to baseline models like DDPO and traditional GFlowNets adjustments. Particularly in complex scenarios such as aesthetic score alignments and human preference scoring (HPSv2), the Nabla-GFlowNet method excels in maintaining a wide range of diverse outputs while adhering closely to specified reward objectives.

Implications and Future Directions

The integration of reward gradients into diffusion model fine-tuning could redefine how AI models adjust to specific tasks, potentially decreasing the time and computational resources required for model adaptation significantly. The novel approach of utilizing residual objectives for maintaining prior knowledge opens avenues for more robust fine-tuning strategies, ensuring pretrained models can be aligned to specific tasks without losing inherent learned features.

Future investigations might explore alternate gradient-based strategies and broader applications of the technique across different types of diffusion models and sampling strategies. Additionally, exploring the theoretical underpinnings of such gradient-informed optimization processes within the field of GFlowNets might prove beneficial.

In conclusion, this paper sets a precedent for leveraging gradient information within probabilistic frameworks to enhance model alignment capabilities, demonstrating its efficacy through comprehensive empirical validations. Such advances in model fine-tuning processes are likely to significantly influence future developments in AI alignment and task-specific adaptation.