Good Seed Makes a Good Crop: Discovering Secret Seeds in Text-to-Image Diffusion Models

Katherine Xu, Lingzhi Zhang, Jianbo Shi; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3024-3034

Recent advances in text-to-image (T2I) diffusion models have facilitated creative and photorealistic image synthesis. By varying the random seeds we can generate many images for a fixed text prompt. Technically the seed controls the initial noise and in multi-step diffusion inference the noise used for reparameterization at intermediate timesteps in the reverse diffusion process. However the specific impact of the random seed on the generated images remains relatively unexplored. In this work we conduct a large-scale scientific study into the impact of random seeds during diffusion inference. Remarkably we reveal that the best 'golden' seed achieved an impressive FID of 21.60 compared to the worst 'inferior' seed's FID of 31.97. Additionally a classifier can predict the seed number used to generate an image with over 99.9% accuracy in just a few epochs establishing that seeds are highly distinguishable based on generated images. Encouraged by these findings we examined the influence of seeds on interpretable visual dimensions. We find that certain seeds consistently produce grayscale images prominent sky regions or image borders. Seeds also affect image composition including object location size and depth. Moreover by leveraging these 'golden' seeds we demonstrate improved image generation such as high-fidelity inference and diversified sampling. Our investigation extends to inpainting tasks where we uncover some seeds that tend to insert unwanted text artifacts. Overall our extensive analyses highlight the importance of selecting good seeds and offer practical utility for image generation.