# AI Character Generator

> Kenerate's AI character generator designs characters from a description in any art style — anime, painting, comic, 3D — with 80+ models. Keep a character consistent with a seed, a reference image, or a trained LoRA (500–10,000 steps). Neta Lumina and Pony XL suit anime; Seedream 5.0 Pro gives the most polish. A free account gets 25 credits.

URL: https://kenerateai.com/ai-character-generator
Publisher: Kenerate AI (https://kenerateai.com)
Last updated: 27 September 2026

## Key facts
- Styles: Any you can name
- Consistency: Seed · reference · LoRA
- LoRA training: 500–10,000 steps
- Free start: 25 credits

## Steps
1. Describe your character — Who they are, one or two defining features, outfit, and the art style — “anime”, “oil painting”, “3D render”.
2. Pick a model for the style — Seedream 5.0 Pro for polished art, Neta Lumina or Pony XL for anime, Chroma to switch styles, Flux 2 Dev on the free plan.
3. Keep the look — Reuse the seed, or upload your best image as a reference for the next scene.
4. Train a LoRA for full consistency — Upload a set of images of your character, pick a trigger word, and generate with it.

## In depth

### Designing a character that reads
Strong character prompts give the model a silhouette and a story:
- A role or archetype — sorceress, mercenary, mecha pilot.
- One or two signature features — curled horns, a chrome arm, silver braids.
- An outfit with materials and colours.
- A setting that says who they are — a ruined temple, a neon market.
- The art style, first in the prompt.

### Three levels of consistency
A seed repeats the same starting noise, so small prompt changes keep the look — good for variations. A reference image carries the face and outfit into a new scene on models that take references. A LoRA goes furthest: it is a small add-on trained on your images, a technique first described for language models and now standard for image models [1]. After training, the trigger word brings your character into any prompt.

### Choosing a style
Decide the style before the details, because it changes everything else — proportions, line, colour. The style matrix on this page is one character on Chroma with only the first line changed; use it to pick a look, then refine on the model that suits that style.

References:
[1] Hu et al., “LoRA: Low-Rank Adaptation of Large Language Models” (ICLR 2022): https://arxiv.org/abs/2106.09685

## FAQ
Q: What is an AI character generator?
A: A tool that designs a character from a description — look, outfit, personality and art style — for games, stories, D&D, VTubers or concept art.

Q: Is the AI character generator free?
A: You can start free: a new account gets 25 credits, which work on free-plan models such as Flux 2 Dev. Bigger jobs use one-time credit packs that never expire.

Q: How do I keep the same character across images?
A: Three ways, from quick to strongest: keep the seed and change one detail; upload a previous image as a reference; or train a LoRA on a set of images so the character appears whenever you use its trigger word.

Q: How does LoRA training work?
A: Upload a zip of images of your character and pick a trigger word. You can choose 500 to 10,000 training steps (1,000 is the default), then generate with the trained LoRA whenever you use its trigger word.

Q: Which art styles can it make?
A: Any you can name — anime, oil painting, comic, watercolor, art nouveau, 3D render, pin-up, pixel art. The style matrix on this page shows one character in eight.

Q: Can I turn a 3D model or sketch into a character?
A: Yes. Upload it in image to image and describe the finished character — the pose and proportions come from your image.

Q: Does it add a watermark?
A: No. Images download without a watermark.

## Related pages
- Text to image AI: https://kenerateai.com/text-to-image-ai
- Image to image AI: https://kenerateai.com/image-to-image-ai
- 3D character generator: https://kenerateai.com/3d-character-generator-ai

## Sources
- Hu et al., “LoRA: Low-Rank Adaptation of Large Language Models” (ICLR 2022): https://arxiv.org/abs/2106.09685