Unverified50% confidenceCautionExact time
The seed cannot lock down all variables other than lighting—model version updates, aspect ratio changes, and even subtle server-side differences can all cause the result to drift.
1
Sources
50%
Confidence
Long-term
Relevance
10/17/2026
First Seen
Sources
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Related Claims
UnverifiedEven with the same prompt, different seeds produce completely different images; once the seed is fixed, even small changes to the number of sampling steps or the CFG guidance strength can cause differences in the output.65% similarUnverifiedVersion changes in closed-source models are opaque to the public, making it difficult for users to determine whether performance degradation stems from quantization, policy adjustments, or server-side load issues.64% similarUnverifiedEven with identical inputs, different random seeds or sampling parameters can lead to entirely different outputs from large models.62% similarUnverified即使固定PyTorch或TensorFlow的随机种子，若不设置torch.backends.cudnn.deterministic = True，计算结果仍可能因硬件调度差异无法完全复现62% similarUnverifiedThe weighting mechanisms that major models apply to information sources are not transparent and are constantly changing, meaning optimization tactics that work today may become ineffective tomorrow due to model updates.62% similar
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