Fine Tuning Text-to-Image Diffusion Models for Correcting Anomalous Images
<|reference_start|>Fine Tuning Text-to-Image Diffusion Models for Correcting Anomalous Images: Since the advent of GANs and VAEs, image generation models have continuously evolved, opening up various real-world applications with the introduction of Stable Diffusion and DALL-E models. These text-to-image models can generate high-quality images for fields such as art, design, and advertising. However, they often produce aberrant images for certain prompts. This study proposes a method to mitigate such issues by fine-tuning the Stable Diffusion 3 model using the DreamBooth technique. Experimental results targeting the prompt "lying on the grass/street" demonstrate that the fine-tuned model shows improved performance in visual evaluation and metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Frechet Inception Distance (FID). User surveys also indicated a higher preference for the fine-tuned model. This research is expected to make contributions to enhancing the practicality and reliability of text-to-image models.<|reference_end|>
@article{yoo2024fine,
    title={Fine Tuning Text-to-Image Diffusion Models for Correcting Anomalous
  Images},
    author={Hyunwoo Yoo},
    journal={arXiv preprint arXiv:2409.16174},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16174},
    primaryClass={cs.CV}
}