September 5, 2026
Making the same AI image again: why it is impossible and what to do instead
Why the same prompt never gives back the same image, what a seed is really worth, what the canvas keeps of every generation, and the editing method that lets you change one detail without losing the rest.
Frank HoubreFounder of Imaginode
The request everybody phrases wrong
Rerunning the same prompt never gives back the same image: to change one detail while keeping the rest, you have to edit the existing image rather than regenerate it.
The scene repeats itself in every training session I run. Somebody gets a perfect image. Only one thing is missing: the jacket should be blue. The person reruns the same prompt with blue instead of red, and gets back an entirely different image. Different face, different set, different light.
What follows is half an hour of reruns, with the growing conviction that the tool is broken. It is not. It is the request that is badly phrased, and rephrasing it correctly changes everything.
We are going to see precisely why two identical generations do not exist, what the famous seed does and does not settle, what the canvas really keeps of each of your generations, and above all the right method: do not regenerate, edit.
Why two generations are never identical
Each generation starts from a different random noise that the model transforms step by step: two different draws give two different images, even with a strictly identical prompt.
An image model does not draw, it denoises. It starts from a mush of pixels drawn at random and turns it step by step into a coherent image, letting your text guide it. The starting point changes with every click.
Two different starting points lead to two different arrivals, even with exactly the same instructions. That is not a flaw, it is the mechanism itself. A model that always returned the same image for a given prompt would be useless: nobody could explore variants.
Add that it has no memory. Generation number twelve knows nothing about the eleven before it. It does not know you had found a good one. Every click on Generate starts from scratch, with no shared history with what came before.
The seed: what it really is
The seed is the number that determines the starting noise: fixing it lets you in theory land on the same image, provided nothing else changes and the provider honours it.
The starting noise is not truly random, it is computed from a number. That number is the seed. Same seed, same noise, and therefore in theory the same image.
In theory only, and that is where people get ideas. The seed only freezes the starting point. Change a single word of the prompt, change the format, change the resolution, and the path travelled from that same noise takes you somewhere else. The seed does not lock the content of the image, it locks the draw.
What settles the question in your case for good: an identical seed with a modified prompt will not give you your image with just a blue jacket. It will give you another image, perhaps a bit closer, never yours. The seed is for exploring methodically, not for retouching.
What the canvas really keeps of every generation
Each node keeps its history: engine, exact prompt, settings, camera, connected inputs, cost and date, with a button to copy the prompt back.
The seed is not adjustable on the canvas, and I would rather say so plainly than keep the doubt alive. Everything else, on the other hand, is kept, and that is what counts day to day.
Every Image node and every Video node keeps its generations as thumbnails. One click takes you back to one of them. And behind each thumbnail, the parameters screen gives you back the engine used, the prompt word for word, the settings, the camera settings, the inputs that were connected, the cost in credits and the date.
In practice, that solves half of real situations. You almost never want to remake an image pixel for pixel. You want to find the recipe again: which model, which prompt, which framing. That you have, and the Copy prompt button spares you retyping it.
The right question: reproduce or modify?
Reproducing an image identically has almost no real use, whereas modifying an existing one is a constant need: the two requests do not call for the same tools.
Take thirty seconds to phrase what you actually want. In almost every case, it is not the same image. It is the same image with one difference.
The jacket in blue. The background outdoors. Without the car on the right. In vertical format. Those requests have one thing in common: they start from an image that already exists and suits you. They are not asking for a generation, they are asking for a modification.
And there, the answer exists and works very well. It is called image editing, it takes up a good part of the catalogue, and it is the subject of the remaining half of this article.
Editing: starting from your image, not from noise
An editing model takes your image as input and only modifies what you describe, preserving all the rest of the composition.
An editing model does not start from random noise, it starts from your file. It analyses it, understands the scene, applies your instruction and returns the same image with the requested change. The face stays the face, the set stays the set.
Flux Kontext at 6 credits is the specialist of the genre: it requires an input image and transforms it on instruction, and it is precisely that constraint that makes it strong. It preserves what pure generation models would recompose at random.
On the canvas, you wire it up: the existing Image node plugs into a new node whose model is an editing model. You write only what has to change. The result arrives with its own thumbnail, and your two versions live side by side in the project.
How to write an editing instruction
An editing instruction has two halves: what changes, described precisely, then the list of what must not move.
This is the most useful rule in the whole article, and it holds for every editing model. First half: what you want to change, described with precision. Replace the denim jacket with a navy suit jacket, straight cut, in wool.
Second half, the one everybody forgets: the list of what does not move. Keep exactly the same face, the same hair, the same pose, the same framing, the same background, the same light. Without that list, the model feels entitled to retouch everything it comes across, and you get back a different person, nicely dressed.
Adapt the list to your image: if there are glasses, a dog, a car, name them. A model does not guess what you are attached to. This double structure is the same one described in writing a prompt that works, applied to editing.
Which editing model for which budget
GPT Image Mini edits at 2 credits, Seedream 5 Lite at 5, Flux Kontext at 6, Nano Banana 2 at 10 and Nano Banana Pro at 19 for the most compound requests.
The editing catalogue runs from 2 to 19 credits, and the choice is made on the complexity of the request more than on raw quality. GPT Image Mini at 2 credits is enough for a simple, well bounded retouch.
Seedream 5 Lite at 5 credits accepts editing and swallows enormous prompts, which is perfect when your list of what does not move is long. Flux Kontext at 6 credits stays the most consistent for changing a background or restyling a scene while keeping its structure.
Above that, Nano Banana 2 at 10 credits follows compound instructions of the kind change the light, keep the character, move the object, without recomposing everything. And Nano Banana Pro at 19 credits for nested instructions where the scene has to be understood before it is modified. Always start with the cheapest, it does the job more often than people think.
When what you want to reproduce is a character
A Reference node with three photos and an @mention makes a character recognisable from one generation to the next, which no prompt does.
There is one case where editing is not enough: when you want the same person in a completely different scene. There it is no longer about modifying an image, but about finding an element again in a brand new one.
The answer is the Reference node. You give it a name, a description and a few photos, then you type @ in any prompt to call it. The model receives the images on top of the text and copies real proportions instead of inventing them.
The subject deserves better than a paragraph, and I covered it in full in consistent character in AI. Just remember that three good photos beat four hundred words of description, and that not every model accepts references: the port does not appear on the node when the chosen model cannot read them.
When what you want to reproduce is a style
A Style node plugged into several nodes imposes the same art direction on every generation without your having to retype it.
Third scenario: what you want to reproduce is neither the image nor the character, it is the look. The same palette, the same material, the same treatment of light across ten different visuals.
Copying three lines of art direction at the top of each prompt works more or less, until the day you change one of them and forget to carry the change over to the other nine. The Style node exists for that: you describe it once, you add inspiration images to it, and you plug it into all your nodes.
Change the description in the Style node, and the next ten generations inherit it. That is exactly the mechanism described in creating an AI moodboard, and it is what holds a series of brand visuals together.
The production method: lock in stages
Explore freely at the floor rate, lock the chosen image, then only move forward through successive edits on that image.
Here is how I work, and it avoids ninety percent of the situations described above. Phase one, exploration: Flux Schnell at 1 credit, fifteen or twenty tries, no restraint. You are looking for the composition, nothing else.
Phase two, locking: a single chosen image, regenerated cleanly on a good model. From then on that image is the reference and you no longer rerun the original prompt. It lives in its node, with its history.
Phase three, corrections: only by editing, one modification at a time, each in its own node. You get a readable chain where every step is visible and reversible. And if the third correction goes badly, you go back to the second having lost nothing.
What we still cannot do
Recovering an image generated elsewhere, without its prompt or its file, remains impossible: only the image itself lets you start again from there.
Two honest limits to finish. If you have lost the image and the prompt, nobody will find them again. A generated image is not stored inside the model, it exists nowhere but in your file. That is the best reason to work in a project that keeps everything.
And if you have the image but it comes from elsewhere, from another tool or a screenshot, all is not lost: import it into a Media node and edit it. You will not find the recipe again, but you will be able to modify it, which was the real request all along.
For what comes next, two directions depending on your need: keeping the same character if a face is what is giving you trouble, or beginner mistakes if you feel you are burning credits without moving forward.