# Stable Diffusion Dataset Generation for Downstream Classification Tasks

arXiv: https://arxiv.org/abs/2405.02698 · HTML: https://mlsanity.com/paper/2405.02698/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 4 May 2024.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 6 of 6)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Dataset Distillation by Matching Training Trajectories](https://mlsanity.com/paper/2203.11932/) (2022)
- [Fake it till you make it: Learning transferable representations from synthetic ImageNet clones](https://mlsanity.com/paper/2212.08420/) (2022)
- [Classification Accuracy Score for Conditional Generative Models](https://mlsanity.com/paper/1905.10887/) (2019)
- [SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning](https://mlsanity.com/paper/2109.12062/) (2021)
- [Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques](https://mlsanity.com/paper/2305.10118/) (2023)

## Built on by (most cited first, 9 of 9)

- [Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing](https://mlsanity.com/paper/2407.20830/) (2024)
- [When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators](https://mlsanity.com/paper/2602.19946/) (2026)
- [Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attacks](https://mlsanity.com/paper/2407.15526/) (2024)
- [Your Image Generator Is Your New Private Dataset](https://mlsanity.com/paper/2504.04582/) (2025)
- [Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model](https://mlsanity.com/paper/2502.19700/) (2025)
- [PolyGen: Fully Synthetic Vision-Language Training via Multi-Generator Ensembles](https://mlsanity.com/paper/2602.01370/) (2026)
- [Neuro-Symbolic Scene Graph Conditioning for Synthetic Image Dataset Generation](https://mlsanity.com/paper/2503.17224/) (2025)
- [Improving Physical Object State Representation in Text-to-Image Generative Systems](https://mlsanity.com/paper/2505.02236/) (2025)
- [On Approaches to Building Surrogate ODE Models for Diffusion Bridges](https://mlsanity.com/paper/2512.12671/) (2025)

## Abstract (arXiv)

Recent advances in generative artificial intelligence have enabled the creation of high-quality synthetic data that closely mimics real-world data. This paper explores the adaptation of the Stable Diffusion 2.0 model for generating synthetic datasets, using Transfer Learning, Fine-Tuning and generation parameter optimisation techniques to improve the utility of the dataset for downstream classification tasks. We present a class-conditional version of the model that exploits a Class-Encoder and optimisation of key generation parameters. Our methodology led to synthetic datasets that, in a third of cases, produced models that outperformed those trained on real datasets.