IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative Models

Diffusion-based models have recently transformed conditional image generation, achieving unprecedented fidelity in generating photorealistic and semantically accurate images. However, consistently generating high-quality images remains challenging, partly due to the lack of mechanisms for conditioning outputs on perceptual quality. In this work, we propose methods to integrate image quality assessment (IQA) models into diffusion-based generators, enabling quality-aware image generation. First, we experiment with gradient-based guidance to optimize image quality directly and show this approach has limited generalizability. To address this, we introduce IQA-Adapter, a novel architecture that conditions generation on target quality levels by learning the relationship between images and quality scores. When conditioned on high target quality, IQA-Adapter shifts the distribution of generated images towards a higher-quality subdomain. This approach achieves up to a 10% improvement across multiple objective metrics, as confirmed by a subjective study, while preserving generative diversity and content. Additionally, IQA-Adapter can be used inversely as a degradation model, generating progressively more distorted images when conditioned on lower quality scores. Our quality-aware methods also provide insights into the adversarial robustness of IQA models, underscoring the potential of quality conditioning in generative modeling and the importance of robust IQA methods.

YaART: Yet Another ART Rendering Technology

In the rapidly progressing field of generative models, the development of efficient and high-fidelity text-to-image diffusion systems represents a significant frontier. This study introduces YaART, a novel production-grade text-to-image cascaded diffusion model aligned to human preferences using Reinforcement Learning from Human Feedback (RLHF). During the development of YaART, we especially focus on the choices of the model and training dataset sizes, the aspects that were not systematically investigated for text-to-image cascaded diffusion models before. In particular, we comprehensively analyze how these choices affect both the efficiency of the training process and the quality of the generated images, which are highly important in practice. Furthermore, we demonstrate that models trained on smaller datasets of higher-quality images can successfully compete with those trained on larger datasets, establishing a more efficient scenario of diffusion models training. From the quality perspective, YaART is consistently preferred by users over many existing state-of-the-art models.