Fine Tuning Text-to-Image Diffusion Models for Correcting Anomalous Images

• URL: http://arxiv.org/abs/2409.16174v1
• Date: Mon, 23 Sep 2024 00:51:47 GMT
• Title: Fine Tuning Text-to-Image Diffusion Models for Correcting Anomalous Images
• Authors: Hyunwoo Yoo,
• Abstract summary: 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)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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