Why the Same Prompt Gives Different Results: AI Seeds

You typed the exact same prompt twice and got two different images. Did something break?

No. That's how most AI image and video models are built. The variation comes from a random starting point, and understanding it (the idea behind ai seed randomness) saves you credits and frustration. This post explains where the randomness comes from, what a seed does, and how to work with the variation instead of fighting it.

Where the randomness comes from

Most image models you'll use are diffusion models. They don't paint from a blank canvas. They start from a field of random noise and refine it, step by step, into a picture that matches your prompt.

The Hugging Face Diffusers documentation puts it plainly: diffusion is a random process that generates a different output every time. Its pipelines create the initial noisy tensors with torch.randn, which "uses a different random seed each time."

So your prompt describes the destination. The noise decides which road the model takes to get there. Different noise, different composition, different faces, different small details, even with identical words.

What a seed actually is

A seed is a number that fixes that starting noise. Runware's learning docs describe it as a "deterministic starting point for the pseudo-random processes in AI generation". Same seed plus the same inputs gives you the same starting noise, and (in the right conditions) the same result.

If you don't choose a seed, the system picks one for you. In Runware's case it draws a random one and returns the value it used, so you can record it. That's why "just run it again" feels like rolling dice: it is.

What a seed is not

• It's not a style. A seed doesn't carry a look from one prompt to another. It only fixes the noise.
• It's not portable between models. Runware's docs are blunt: two different models given the same seed will produce completely different output. Each model reads the noise through its own architecture.
• It's not a guarantee. More on that below.

Why a fixed seed still might not match

Even when you control the seed, exact repeats aren't promised. Two sources I read say so directly.

The PyTorch reproducibility notes state that "completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms", and that results may not match between CPU and GPU even with identical seeds. The Diffusers guide repeats the point: you can try to limit randomness, but it is not guaranteed even with an identical seed.

Runware adds two more limits:

• Some schedulers add fresh randomness at every denoising step, so a fixed seed can still produce slightly different results between runs.
• Support varies by model. Their docs say roughly half of their catalog exposes a seed parameter, and the rest reseed on every call.

For you as a creator, this means three practical things. A hosted model may update behind the scenes. A seed that worked last month may not behave the same today. And the safest record of a result you love is the file itself, not the number.

Why video varies even more

Video models start from noise too, but they have to settle motion, camera, lighting and timing across many frames. There are more places for two runs to diverge. A subject might walk left in one clip and right in the next, or the camera might drift differently.

I looked for Google's own documentation on seed behaviour in Veo and couldn't confirm the details from a primary source, so I won't make claims about how any specific video model handles seeds. What you can rely on is the general rule: expect more run to run variation in video than in a single still, and plan for it.

What this means in Mediafy Studio

A quick note to avoid confusion. In Mediafy Studio, Seeds are saved sets of reference images for a consistent look. That's a different thing from the random-number seed in this post. It isn't LoRA training, and the studio doesn't train custom models.

I can't verify that every model in the studio exposes a numeric seed control, so I won't promise one. The techniques below work regardless, because they reduce randomness through your inputs rather than through a number.

How to get more consistent results

Think of it as narrowing the space the model can wander in.

1. Write a tighter prompt. A vague prompt leaves the model many valid answers, and the noise picks one. Specify subject, setting, light and lens, as in AI Image Prompt Structure: Subject, Setting, Light, Lens. In Mediafy's Studio, the Enhance button can expand a short prompt into a fuller one, and Prompt Enhancer: Turn a Short Idea Into a Full Prompt covers it.
2. Compare like with like. Change one thing at a time. If you rewrite the prompt, switch models and change the aspect ratio together, you can't tell what caused the difference.
3. Generate a small batch, then pick. Treat the first run as a draft. Make a few variations, keep the best, and refine from there.
4. Start from an image, not just text. For video, an image to start from removes a big chunk of the guesswork. See Image to Video: Pick a Start Frame That Animates Well and First and Last Frame Video: Planning a Controlled Shot.
5. Use a reference for faces. If the same person has to appear again and again, a random seed won't do it. Design a character once and reuse it, as covered in Keep a Consistent AI Character Across Images and Video. In the Studio you can type @ to bring in a saved character.
6. Save what works. Keep the prompt, model name and settings next to the output. My Prompts stores prompts, and Assets holds every result.

A simple test you can run

Pick one model and run this prompt four times:

A ceramic coffee mug on a oak table by a window, morning light, 50mm lens, shallow depth of field

Notice what changes: probably the mug shape, the window position and the table grain. Now run a much more specific version:

A matte white ceramic mug with a thin blue rim, centred on a pale oak table, soft window light from the left, 50mm lens, shallow depth of field, steam rising

The second set should look more alike. That's the lesson: specificity does what people hope a seed will do. It won't make the runs identical, but it narrows the spread.

Common mistakes

• Assuming a bad result means a bad prompt. Sometimes it's just a bad roll. Try again before rewriting.
• Assuming a good result means a good prompt. A lucky roll can hide a weak prompt that fails on the next run.
• Reusing a seed across models. It won't transfer.
• Expecting a seed to fix a face or style. It fixes noise, nothing more.
• Not budgeting for retries. Generation costs money. Credits in Mediafy are pay-as-you-go, the credit cost shows on the Generate button before you run it, and failed generations are refunded automatically. The details are in How Mediafy Credits Work: Costs, Packs and Refunds. Planning for a few attempts per final image is realistic.

The takeaway

Different results from the same prompt aren't a bug. The model starts from random noise, and a seed is the number that pins that noise down. Even then, exact repeats depend on the model, the platform and the version, so don't count on them.

Control what you can: tighter prompts, one change at a time, reference images, saved characters and a habit of keeping your best outputs. If you want to try that workflow, you can sign in to Mediafy Studio with a Google account. To learn the wider craft, there's also the AI Creator Lab course.

Sources

• Hugging Face, Diffusers documentation, "Reproducibility": https://huggingface.co/docs/diffusers/en/using-diffusers/reusing_seeds
• Runware, Docs, "Seed: Controlling randomness deterministically": https://runware.ai/docs/learn/seed
• PyTorch, Documentation, "Reproducibility" (randomness notes): https://docs.pytorch.org/docs/2.15/notes/randomness.html