Unverified60% confidenceFactExact time
Even 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.
2
Sources
60%
Confidence
Long-term
Relevance
9/1/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedSeed controls randomness at the noise level, and switching the seed with an unchanged prompt only produces minor differences in composition and detail texture, whereas Wildcards introduce an independent random dimension at the semantic level.75% similarUnverifiedAmong generation parameters, the sampling steps determine the number of denoising iterations—too few steps make the image rough, while too many yield diminishing returns; CFG Scale controls how closely the model follows the prompt—too high causes oversaturated colors and rigid composition; the random seed determines the initial noise, and a fixed seed reproduces results under the same prompt.74% similarUnverifiedEven with identical inputs, different random seeds or sampling parameters can lead to entirely different outputs from large models.73% similarUnverified即便固定所有随机种子，在不同硬件或并行度下，训练结果仍可能出现被大模型放大为显著能力差异的细微偏差68% similarUnverifiedThe seed in many inference backends is only best effort; floating-point operation order, batch scheduling, and hardware differences can all introduce non-determinism.66% similar
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