My Trained Models

View, manage, rename, and delete your trained AI models. See training status, usage statistics, and generate new photos from any of your completed models.

How It Works

View Models

See all your trained models with training status and completion date.

Select Model

Choose a model to generate images or manage it.

Generate

Click generate to create new photos using the selected model.

See it in action

a portfolio grid of consistent AI portrait photos of the same person, studio light

a set of matching character portraits arranged neatly, cohesive style

A creator in a tidy home office thoughtfully flipping through a physical portfolio binder of glossy portrait prints, morning light across he

Close-up of hands organizing labeled photo sleeves in a filing box, each sleeve holding a single character portrait, warm shelf lighting beh

A man standing before a gallery wall of framed portraits of different stylized characters, hands in pockets, studying them in a bright airy

Overhead shot of neat stacks of portrait prints grouped into separate piles on a clean desk, a pair of reading glasses resting beside them

How does managing trained models in Imagera compare to the alternatives?

Dimension	Imagera My Trained Models	Re-training every time	Prompt-only image tools
Reusing an identity	Trained models are saved as named assets; select and generate again anytime	You rebuild the model from scratch for each new batch	No persistent identity — you re-describe the look in every prompt
Time to a new photo	A few clicks: view, select, generate from a finished model	Waiting on a fresh training run before you can generate	Fast per image, but likeness drifts between generations
Identity consistency	Same trained model yields a repeatable, consistent likeness	New training can shift the look run to run	Hard to hold one face steady across many images
Managing multiple identities	Each model is separate with its own name, status, and usage stats	Multiple identities mean repeated setup and easy mix-ups	No structured way to keep personas apart
Housekeeping	Rename, review usage, and delete models from one dashboard	Little organisation beyond a job history	Not applicable — nothing to store or curate
Cost pattern	Training is the main spend; generating after costs credits per image, credits never expire	You risk paying to train the same identity more than once	Credits per image with no reusable asset to show for it

A Naming Convention That Scales Past a Handful of Models

Once you accept that models are permanent assets, the single most useful habit is a naming scheme you can read six months from now without opening anything. Instead of naming a model after the person alone, encode the three things you will actually search on later: who it is, what it is tuned for, and which attempt it is. A pattern like subject, then intended use, then a version marker — a founder headshot set, a lifestyle character, a second pass after you adjusted the source photos — turns a flat list into something you can scan and trust. The dashboard sorts and displays what you type, so the discipline lives entirely in how you label, not in any hidden folder structure.

Version markers matter more than people expect. Because you can keep an earlier model alongside a refined one, a name that says nothing about which iteration it is forces you to open and generate a test image just to remember. Adding a simple v1 or v2, or a short date, removes that guesswork and pairs naturally with the completion date the dashboard already shows. When you retrain the same subject with better inputs, the old and new sit side by side with names that make the difference obvious, so you retire the weaker one on purpose rather than by accident.

If you run identities for more than yourself — a small roster of personas, or models built for different clients or campaigns — prefix the name with the owner or project so everything for one line of work clusters together alphabetically. You are not building a database, just borrowing one convention from filing systems: put the most important sorting key first. That one decision keeps a library of ten or twenty models navigable with the same effort a beginner spends managing two.

Where the Library Fits in a Repeatable Production Loop

A trained model is most valuable when it becomes the fixed point in a routine rather than a one-off. In practice, the loop looks like this: open My Trained Models, confirm the identity you want shows a completed status, select it, and send it into generation for the batch you need — a set of posts, a run of thumbnails, a few profile variants. Because the model is already trained, that entire cycle is measured in clicks rather than a fresh setup, which is what lets a content schedule survive contact with a busy week. The library is the thing you return to, not the thing you rebuild.

The identity you manage here is the same one the rest of the Personal Influencer suite draws on, so the loop naturally extends past plain generation. A finished model can carry into pose control when you need a specific angle, style transfer when you want a different treatment on the same face, or refinement when a favorite frame is nearly right. Managing the model well upstream — a clear name, a confirmed completed status, an eye on which version is current — is what keeps every one of those downstream steps consistent, because they all reference the identity you curated rather than a fresh, slightly different training run.

Treating the library as the entry point also changes how you plan. Rather than deciding shot by shot whether to train, you decide once per identity and then think in batches: this week from the founder model, next week a stylized set from a character model, an occasional refresh when you retrain with newer photos. The credits you spend land on generations from an asset you already own, so the meaningful investment stays where it belongs — at training time — and the recurring work becomes light, predictable, and easy to hand to a routine.

Deciding When to Reuse, Retrain, or Run a Parallel Model

The everyday default is reuse. If a model already produces a likeness you are happy with, there is rarely a reason to touch its training — you simply select it and generate again, which is exactly the behavior the library rewards. Reuse costs only the credits for the images you make, and because credits do not expire, a model trained months ago is ready the day you come back. Most weeks, the right answer is to open the dashboard, pick a finished model, and create, without questioning whether a new training run is needed at all.

Retraining is the right call when the underlying inputs have genuinely improved or the look has drifted from what you now want — you have better source photos, a cleaner set, or a different styling target in mind. When you retrain, keep the earlier model until the new one has proven itself across a few real generations, then use the completion dates and your version markers to confirm which is current and delete the one you no longer need. The usage statistics help here too: a model you have barely generated from is easy to replace, while one you rely on constantly deserves a careful side-by-side comparison before you retire it.

Running a parallel model is the answer when the goal is genuinely different rather than better. A separate stylized character, a distinct persona, or a configuration tuned for a specific look should live as its own asset instead of overwriting something that already works, because each model is independent and none interfere with each other. The simple test: if you might ever want both outputs, keep both models; if the new version is meant to supersede the old, retrain and eventually delete. Holding those three moves — reuse, retrain, parallel — clearly apart is what keeps a growing library intentional instead of cluttered.

What My Trained Models Is — Your AI Model Library

My Trained Models is the control room for every AI model you have created inside Imagera's Personal Influencer suite. Once you finish training a model on a person or a specific configuration, it does not disappear into a list of anonymous jobs — it lands here as a named, reusable asset you can open, inspect, and put back to work whenever you need it. Think of it less like a training screen and more like a library shelf: each spine is a distinct identity you can pull down on demand.

The page is built for people who create more than once. If you have trained a single model, this is where you confirm it finished cleanly and start generating. If you have trained several — a founder for a personal brand, a couple of stylised character variants, a separate configuration you tuned for a particular look — this is where they live side by side so you never lose track of which model produced which images. You can view training status, check completion dates, read usage statistics, rename models to something you will recognise later, delete the ones you no longer need, and generate fresh photos from any model that has finished training.

It is designed for creators, marketers, and small teams who treat AI identities as long-lived assets rather than throwaway experiments. The value shows up over weeks and months: instead of re-uploading photos and re-training every time you want a new shot, you return to a model you already trust and generate again in a few clicks.

How It Works: View, Select, and Generate in Three Steps

The workflow is deliberately short because the heavy lifting — the training — already happened. The first step is simply to view your models. The dashboard lays out everything you have trained with its current training status and completion date, so you can tell at a glance which models are ready to use and which are still processing. That status view matters: a model that is mid-training is not yet a finished asset, and the page makes that distinction obvious rather than letting you generate against something incomplete.

The second step is to select a model. Choosing one opens it for action — this is the point where you decide whether you want to manage the model (rename it, review its usage statistics, or remove it) or move straight to creating with it. Selecting a completed model tells Imagera which trained identity to draw on for the images you are about to make, so the likeness stays consistent with everything else that model has produced.

The third step is to generate. With a completed model selected, one click sends it into generation to create new photos using that identity. Because the model is already trained, you are not waiting on a fresh training run each time — you are reusing an existing asset, which is the entire point of keeping a model library. Repeat the loop as often as you like: view, select, generate, and come back tomorrow to the same model without rebuilding anything.

Tips for Getting the Most Out of Your Model Library

Name your models the moment they finish. Default or auto-generated names are fine on day one and useless by week three, when you have several models and cannot remember which configuration produced the look you liked. Renaming to something descriptive — the person, the intended use, or the styling you tuned for — turns the dashboard from a pile of jobs into a genuinely navigable library. A minute spent naming now saves a frustrating hunt later.

Use the training status and completion date as a quality checkpoint. Before you commit credits to a batch of generations, confirm the model you are about to use actually shows a completed status. Generating only from finished models is the simplest way to avoid disappointing output, and the completion date is a handy reminder of how recently you built each identity — useful when you have iterated and want to make sure you are working from your latest version rather than an earlier attempt.

Lean on usage statistics to decide what to keep. Over time, some models earn their place because you generate from them constantly, while others were experiments you never returned to. The usage data helps you see which is which, so you can keep your library tidy and delete the models you no longer need. And because each model is independent, you are free to train as many as you want — one per person or per configuration — without them interfering with each other, so there is no penalty for keeping a small stable of well-labelled, frequently used identities.

Real Scenarios Where Managing Trained Models Pays Off

The clearest case is a personal brand that ships content on a schedule. A creator trains a model of themselves once, then comes back to My Trained Models every week to generate a new batch of photos for posts, thumbnails, and profiles — same face, same believable likeness, no re-training. The library is what makes that cadence sustainable, because the identity is always one click from a fresh generation instead of an afternoon of setup.

Another common pattern is running several identities in parallel. Someone managing more than one persona — a real self plus one or two stylised characters, for example — needs each to stay distinct and consistent. Because every model is a separate asset with its own name, status, and stats, the dashboard keeps those personas cleanly apart, so a shoot for one character never accidentally borrows the look of another. Selecting the right model before generating is all it takes to switch between them.

There is also the iteration scenario. Creators often train a model, generate from it, learn what they would tweak, and train a refined version. Keeping both in the library — with descriptive names and completion dates — lets you compare outputs and retire the weaker configuration once you are confident in the newer one. When a model has served its purpose, deleting it keeps the workspace focused on the identities you actually use, rather than an ever-growing list of half-remembered experiments.

What Makes Imagera's Approach to Model Management Different

Many AI image tools treat a trained model as a fire-and-forget job: you train, you download or generate once, and the model effectively vanishes into a history log. Imagera treats it as a persistent asset. My Trained Models gives every identity a home where it can be viewed, renamed, measured by usage, and reused indefinitely — which changes the economics of creating, because the real cost of a model is amortised across every generation you run from it rather than paid again each session.

The dashboard also sits inside a connected suite rather than standing alone. A model you manage here is the same trained identity you can carry into the rest of Imagera's Personal Influencer tools — generation, pose control, style transfer, face swap, and refinement all draw on identities you own. That integration is what makes the library worth curating: the models you keep are not isolated files, they are the throughline that keeps a whole body of work looking like the same person. Managing them well upstream keeps everything downstream consistent.

Pricing follows the same asset-first logic. Training a model is where the meaningful spend happens; after that, generating from a finished model costs a modest amount of credits per image, billed as you go from a balance shared across the suite. Credits do not expire, so a model you trained months ago is still ready to generate the day you come back to it. The library, in other words, is not just organisation — it is what lets a one-time investment keep producing.

Questions People Ask Before Using My Trained Models

The most common question is whether you can keep more than one model, and the answer is yes — you can train as many as you like, with each model representing a different person or a different training configuration. There is no requirement to overwrite an old model to make a new one; they coexist in the library as independent assets, which is exactly what lets you run several identities or several iterations at the same time.

People also ask what they can actually do to a model from this page. In practice you can view its training status and completion date, rename it so it is easy to find, review its usage statistics to understand how much you rely on it, delete it when it has outlived its purpose, and — for any model that has finished training — generate new photos from it directly. It is a management surface and a launch point in one, not a separate training step.

A frequent follow-up is whether returning to an old model means starting over. It does not. Because the training is already complete and stored, coming back later is just view, select, and generate — no re-uploading, no waiting on a new training run. That reusability is the whole reason the library exists, and it is what turns a single well-trained model into a source of consistent images you can draw on again and again.