mlsanityData to 14 Oct 2026

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

arXiv 2505.02255 ↗First posted 4 May 2025cs.CV cs.AIcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 21 of its 30 references that are in the map. First posted on arXiv 4 May 2025.

Builds on

21 of 30 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference6 Oct
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  3. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  5. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 16

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  3. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  5. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  6. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  7. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  8. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  9. 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 991
  10. 2017GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibriumcited by 213
  11. 2022Diffusion-based Image Translation using Disentangled Style and Content Representationcited by 149
  12. 2024LoRA vs Full Fine-tuning: An Illusion of Equivalencecited by 109
4 more
  1. 2023Qualitative Failures of Image Generation Models and Their Application in Detecting Deepfakescited by 31
  2. 2023DiffI2I: Efficient Diffusion Model for Image-to-Image Translationcited by 24
  3. 2022Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentationcited by 18
  4. 2022An Adaptive and Stability-Promoting Layerwise Training Approach for Sparse Deep Neural Network Architecturecited by 2

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  1. 2024Improving face generation quality and prompt following with synthetic captions
  2. 2026Identity-Conditioned Latent Consistency Distillation for Face Synthesis
  3. 2023Consistency Models
  4. 2026DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer
  5. 2023DiffPortrait3D: Controllable Diffusion for Zero-Shot Portrait View Synthesis
  6. 2022Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2
  7. 2024Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data
  8. 2024Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget