Why the seed matters
Diffusion starts from noise and every image is a deterministic function of that noise plus your settings. The seed is the recipe for the noise. That makes it the most useful tool for learning: fix the seed, change one thing, compare. It is exactly the method behind our artist style comparison: 1,731 artists, one seed, four fixed prompts, only the artist name changes, so the differences you see are the artist's effect and nothing else.
How to use it in practice
- Generate with seed -1 until a composition you like appears.
- Read the seed in the image info (Automatic1111 writes it in the PNG metadata and under the image) and paste it into the seed field.
- Now refine the prompt or the negative prompt word by word: the composition stays, the details move.
- When you change the sampler, the steps or the CFG, the same seed still gives a close image, which lets you compare settings fairly.
The prompt examples on this site list the seed with every image so you can start from a known result.
What breaks reproducibility
- A different model or LoRA: the noise is the same but the denoiser is not, the image changes completely.
- A different image size: the noise tensor has another shape, so the picture is unrelated even with the same seed.
- A different GPU or software version: tiny floating-point differences can shift details; the composition usually survives.
- SDXL and SD 3 add sub-seeds and noise offsets in some interfaces; leave them at their defaults for exact repeats.
Variations around a seed
Automatic1111 has a variation seed and a variation strength slider: at 0.1 to 0.3 you get siblings of the image, same pose and palette with small changes, useful to pick the best hands or face. An X/Y/Z plot with seeds on one axis and a prompt word on the other produces a comparison grid in one run.