Krea AI training: how to train and test a custom style
Prepare a coherent set of examples, train a custom style in Krea and test whether it carries the visual qualities you wanted. Keep the recipe alongside the results.
Updated 5 min read
Krea AI training lets you build a custom style from example images, or clips for a video model. Open Home → assets → Train, choose Image or Video, upload a coherent set of examples and configure the training. Krea’s custom-style guide (opens in a new tab) warns that a trained style generates with the model it was trained on. Save the tested recipe in fenbs so another AI can understand your choices without assuming the style itself transfers between models.
Where do you start the training?
Start by deciding what the examples should teach: an art style, one character or one object. These are supported training subjects in Krea’s Training overview (opens in a new tab). A folder that mixes all three without a clear goal is difficult to judge, even if every individual picture looks good.
- Write one sentence naming the visual quality or subject you want to keep across outputs.
- Open Home → assets → Train. Choose Image or Video for the kind of training you intend to do.
- Upload the selected examples. Remove files that contradict the intended style or subject.
- Configure Custom Style, the trigger word and the model, along with the available steps, learning rate and batch size settings.
- Record those choices before running the training. Test the result against the goal you wrote first.
The controls above come from the style-training guide (opens in a new tab). There is no single setting value that this guide can promise will suit every dataset. Use the documented controls deliberately, and change one part of the experiment at a time so you can explain what improved.
What makes a useful training set?
Choose examples that agree about the thing you want to teach. Krea emphasizes cohesion over collecting a large, inconsistent set. For a hypothetical editorial illustration style, your selection might share a restrained palette, flat shapes and light paper texture while depicting different subjects.
For a character, the training guidance (opens in a new tab) recommends consistent attire with varied poses and angles. For an object, avoid mixing similar but different products. Otherwise your review has an avoidable ambiguity: did the output miss the intended object, or did the input set teach several objects?
- Keep examples that clearly show the quality named in your goal.
- Remove an attractive outlier if it teaches a conflicting look.
- Keep an index of the selected source files so a later revision uses the intended set.
- Separate the training files from the images you will use to judge the result.
- Record any exclusions, such as a different product version or an unwanted visual treatment.
Check current dataset allowances and access on Krea’s pricing page (opens in a new tab) rather than assuming that every account has the same capacity. The useful question is whether your selected examples fit the training you can run. More files are not a substitute for a clearer visual goal.
How should you choose settings and a trigger word?
Treat the model choice as part of the recipe. Krea’s newer style guide (opens in a new tab) makes the result model-specific, even though the older overview describes broader style-code use. Do not train with one model on the assumption that every other Krea model will accept the result.
Write down the trigger word exactly as configured. Also record the selected model, steps, learning rate and batch size. These are experimental inputs, not a certificate of success. A record that says only “trained the brand style” gives the next reviewer very little to compare.
Before adjusting a setting, state what you are trying to fix. For example: “The output matches the palette but loses the broad shapes.” This turns the next run into a specific question. Keep the explanation with the settings through fenbs’s shared work record, rather than relying on a screenshot with no reason attached.
How do you test whether the style is useful?
Use a small, repeatable set of test prompts. Include an ordinary subject, a difficult subject and a composition that differs from the training examples. These are review suggestions, not Krea presets. The aim is to see whether the result carries the desired visual qualities into a new request.
| Test | Look for | Record |
|---|---|---|
| Ordinary subject | The palette and broad shapes you wanted | Which qualities survived and which did not |
| Unfamiliar subject | A useful interpretation beyond the source examples | Any repeated unwanted details |
| Different composition | A recognizable style with a new arrangement | Whether the request remained understandable |
| Identity or product check | Important features that should remain consistent | Specific differences that need a person’s review |
Do not judge only the best output. Keep at least one failure that explains the style’s boundary. Exact text and logos can still produce artifacts, according to Krea’s training guidance (opens in a new tab). Inspect those details directly before accepting an image for use.
For wider review habits, see AI for graphic designers. Keep this experiment focused on training; the separate Krea enhancement guide addresses refining an existing image.
What should you change when the result misses the goal?
Start with the examples, not a guess that a more complicated setting will solve everything. Compare the failed output with the intended visual quality. Look for a training image that introduced the wrong treatment, a subject that appears too often or a goal that contains conflicting requirements.
Make a named revision of the experiment in your own notes. Describe what changed in the selected files or settings, then reuse the same test prompts. Keep earlier results for comparison. Krea documents that training data is saved and can be reused when refining a style or training with a different model; that does not make an existing trained style portable to that model.
If a different person will continue, give them the dataset index, exact test prompts, accepted examples and unresolved defects. The AI design handoff guide covers that next step. A useful handoff explains the evidence behind “better,” rather than leaving another reviewer to choose by taste alone.
What belongs in the lasting training recipe?
Keep the goal, source-file index, trigger word, model and settings together with a short account of the tests. Mark outputs as accepted, rejected or still under review. Add the reason: “palette approved, lettering needs replacement” is more actionable than “almost right.”
The AI at work hub has more examples of turning a useful session into a repeatable workflow. For training, the portable part is the explanation and evidence. Another AI can help review or plan the next experiment without inheriting Krea’s trained model state.
Never lose the work you did in an AI session
After a useful AI session, save it in fenbs: the key observations, findings, research and context. Then carry on from exactly where you left off, on any device and in any other AI. The next AI you connect reads it at the start of the session, so you never have to explain the same problem again.
Create a free accountFree for up to 5 people and 50 open tasks · No credit card · Works with Claude, ChatGPT, Cursor and more
How to keep your training recipe with fenbs
Save enough to carry on from exactly where you left off, on any device and in another AI. Keep the source assets in their chosen storage; the record should tell someone which set matters and what the last test showed.
- Create a free account.
- Write what you wanted the style to do, and link to the selected source files.
- Save the trigger word, model, settings and test prompts, followed by what passed and failed.
- Write the next change to try. Ask your next connected assistant to read that record before helping.
fenbs does not train a Krea style or move it between models. If you only need a single experiment inside Krea, a short local record may be enough. fenbs helps when several sessions or AI apps need to carry forward the same tested findings.