Enhancing AI Face Realism: Cost-Efficient Quality Improvement in Distilled Diffusion Models with a Fully Synthetic Dataset

• URL: http://arxiv.org/abs/2505.02255v2
• Date: Thu, 08 May 2025 19:05:22 GMT
• Title: Enhancing AI Face Realism: Cost-Efficient Quality Improvement in Distilled Diffusion Models with a Fully Synthetic Dataset
• Authors: Jakub Wasala, Bartlomiej Wrzalski, Kornelia Noculak, Yuliia Tarasenko, Oliwer Krupa, Jan Kocon, Grzegorz Chodak,
• Abstract summary: This study presents a novel approach to enhance the cost-to-quality ratio of image generation with diffusion models.<n>We generate a synthetic paired dataset and train a fast image-to-image translation head.<n>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.
• Score: 2.6177855932435623
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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