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.

我们对随机种子在扩散推断期间的影响进行了大规模的科学研究，并发现种子对生成的图像具有显著影响。我们进一步研究了种子对可解释的视觉维度的影响，并展示了通过使用这些优质的种子进行改进的图像生成，例如高保真推断和多样化采样。本研究突出了选择良好的种子的重要性，并为图像生成提供了实际效用。

优秀的种子造就丰收：在文本 - 图像扩散模型中发现秘密种子

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

Fine-tuning pretrained contextual word embedding models to supervised
downstream tasks has become commonplace in natural language processing. This
process, however, is often brittle: even with the same hyperparameter values,
distinct random seeds can lead to substantially different results. To better
understand this phenomenon, we experiment with four datasets from the GLUE
benchmark, fine-tuning BERT hundreds of times on each while varying only the
random seeds. We find substantial performance increases compared to previously
reported results, and we quantify how the performance of the best-found model
varies as a function of the number of fine-tuning trials. Further, we examine
two factors influenced by the choice of random seed: weight initialization and
training data order. We find that both contribute comparably to the variance of
out-of-sample performance, and that some weight initializations perform well
across all tasks explored. On small datasets, we observe that many fine-tuning
trials diverge part of the way through training, and we offer best practices
for practitioners to stop training less promising runs early. We publicly
release all of our experimental data, including training and validation scores
for 2,100 trials, to encourage further analysis of training dynamics during
fine-tuning.

该研究通过对 GLUE 基准测试中的四个数据集进行 BERT 的微调，发现同样参数下，不同的随机种子会导致非常不同的结果表现，并且在权重初始化和训练数据排序的两个方面探究其对结果的影响。此外，该研究对微调方法的实现提出了最佳实践，并公开其所有实验数据以供更深入的分析。

微调预训练语言模型：权重初始化，数据排序和早期停止

Fine-Tuning Pretrained Language Models: Weight Initializations, Data  Orders, and Early Stopping

Consistently checking the statistical significance of experimental results is
one of the mandatory methodological steps to address the so-called
"reproducibility crisis" in deep reinforcement learning. In this tutorial
paper, we explain how the number of random seeds relates to the probabilities
of statistical errors. For both the t-test and the bootstrap confidence
interval test, we recall theoretical guidelines to determine the number of
random seeds one should use to provide a statistically significant comparison
of the performance of two algorithms. Finally, we discuss the influence of
deviations from the assumptions usually made by statistical tests. We show that
they can lead to inaccurate evaluations of statistical errors and provide
guidelines to counter these negative effects. We make our code available to
perform the tests.

深度强化学习实验结果的统计显著性检验是应对所谓的 "再现性危机" 的方法之一。本文介绍了随机种子数量与统计误差概率的关系，并提出确定随机种子数量的理论准则，最后讨论了统计检验通常假设的偏差对评估统计误差的影响，并提供应对这些负面影响的指导方针。