  Recent advances in text-to-image (T2I) diffusion models have facilitated
creative and photorealistic image synthesis. By varying the random seeds, we
can generate various 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.
