Optimizing Multi-Round Enhanced Training in Diffusion Models for Improved Preference Understanding (2504.18204v1)

Abstract: Generative AI has significantly changed industries by enabling text-driven image generation, yet challenges remain in achieving high-resolution outputs that align with fine-grained user preferences. Consequently, multi-round interactions are necessary to ensure the generated images meet expectations. Previous methods enhanced prompts via reward feedback but did not optimize over a multi-round dialogue dataset. In this work, we present a Visual Co-Adaptation (VCA) framework incorporating human-in-the-loop feedback, leveraging a well-trained reward model aligned with human preferences. Using a diverse multi-turn dialogue dataset, our framework applies multiple reward functions, such as diversity, consistency, and preference feedback, while fine-tuning the diffusion model through LoRA, thus optimizing image generation based on user input. We also construct multi-round dialogue datasets of prompts and image pairs aligned with user intent. Experiments demonstrate that our method outperforms state-of-the-art baselines, significantly improving image consistency and alignment with user intent. Our approach consistently surpasses competing models in user satisfaction, especially in multi-turn dialogue scenarios.

Optimizing Multi-Round Enhanced Training in Diffusion Models for Improved Preference Understanding

The research paper titled "Optimizing Multi-Round Enhanced Training in Diffusion Models for Improved Preference Understanding" presents a sophisticated approach to align generative AI models with human preferences, particularly in the context of text-to-image generation using multi-round dialogue frameworks. This paper is motivated by the limitations observed in existing generative models when they attempt to produce high-resolution images that accurately reflect complex user instructions.

Core Contributions

The paper introduces the Visual Co-Adaptation (VCA) framework designed to incorporate human-in-the-loop feedback. The framework utilizes multi-round interactions to refine generative processes, thereby improving the alignment with user preferences. This is facilitated through a reward model trained to optimize image outputs according to various feedback mechanisms, including diversity, consistency, and mutual information. By leveraging a vast dialogue dataset, the framework effectively enhances the generation process, showcasing superior performance over state-of-the-art baselines in maintaining image consistency and alignment with user expectations.

Theoretical Foundations and Methodology

The authors underpin their methodology with solid theoretical analysis. They provide a convergence theorem demonstrating that the distribution of the model's latent variables converges to the target distribution in total variation norm as the number of dialogue rounds increases. This theoretical guarantee ensures the model's ability to accurately reflect user intentions.

The framework operationalizes through the integration of:

• Multi-Round Diffusion Processes: These processes iteratively refine image generation using Gaussian noise applied at distinct steps, informed by human feedback embedded within prompt refinements.
• Reward-Based Optimization: Here, a dynamic combination of diversity, consistency, and mutual information rewards guide the model's outputs. The framework adapts its attention mechanisms through parameter updates based on LoRA, efficiently balancing trade-offs among objectives to achieve global optimality.

Evaluation and Results

Experiments demonstrate the framework's effectiveness in surpassing existing models across various user-centric metrics, including consistency, aesthetic appeal, and semantic alignment. The results are shaped by rigorous comparative analyses involving human evaluations, preference scores, and CLIP scores, among others. Ablation studies further emphasize the critical contributions of each reward component, illustrating their impact on the model's output quality.

Implications and Future Directions

The findings have significant implications for enhancing generative AI accessibility, particularly for non-experts in creative fields. By effectively capturing intricate user preferences, this framework can be instrumental in applications spanning design, advertising, and entertainment.

Future research may explore:

• Expanding the framework's adaptability to different model architectures or other multi-modal domains.
• Investigating the scalability of such frameworks to larger datasets or more complex dialogue structures.
• Developing more refined human-machine interfaces to further lower the user knowledge barrier.

In summary, the paper provides a robust approach to optimizing dialogue-driven diffusion models for superior preference understanding, paving the way for more intuitive and efficacious generative AI systems.