Enhancing AI Face Realism: Cost-Efficient Quality Improvement in Distilled Diffusion Models with a Fully Synthetic Dataset
Journal:
arXiv
Published Date:
May 4, 2025
Abstract
This study presents a novel approach to enhance the cost-to-quality ratio of
image generation with diffusion models. We hypothesize that differences between
distilled (e.g. FLUX.1-schnell) and baseline (e.g. FLUX.1-dev) models are
consistent and, therefore, learnable within a specialized domain, like portrait
generation. We generate a synthetic paired dataset and train a fast
image-to-image translation head. Using two sets of low- and high-quality
synthetic images, our model is trained to refine the output of a distilled
generator (e.g., FLUX.1-schnell) to a level comparable to a baseline model like
FLUX.1-dev, which is more computationally intensive. Our results show that the
pipeline, which combines a distilled version of a large generative model with
our enhancement layer, delivers similar photorealistic portraits to the
baseline version with up to an 82% decrease in computational cost compared to
FLUX.1-dev. This study demonstrates the potential for improving the efficiency
of AI solutions involving large-scale image generation.
