# Image generation for character products — prompts, consistency, cost

> Prompting a diffusion engine well, keeping a character recognizable across generations, and budgeting image features at 10 credits a render.

Published 2026-07-28 · eroq.ai — canonical: https://eroq.ai/blog/image-generation-for-characters


Chat retains; images convert. A character the user can *see* — in new scenes, new outfits, new moods — is the single strongest premium mechanic we know in this category. Here is how to build the feature well against `eroq-image-one`.

## Speaking diffusion

Image One is a diffusion engine, and diffusion has a dialect. It reads both prose and comma-separated tags; what it rewards is **specificity in the right order**:

```
subject and identity, action or pose, outfit, setting, lighting, camera, style
```

```json
{
  "model": "eroq-image-one",
  "prompt": "young woman with silver bob and amber eyes, leaning on a workbench, grease-stained overalls, spaceship hangar, warm practical lights, 35mm, shallow depth of field",
  "negative_prompt": "blurry, extra fingers, watermark, text"
}
```

Three rules that do most of the work:

- **Front-load identity.** Tokens earlier in the prompt weigh more. The character's defining traits — hair, eyes, build — come first, every time.
- **Always send a negative prompt.** `blurry, extra fingers, watermark, text` is the floor; add whatever your style must exclude. It is a free quality lever.
- **One scene per prompt.** Diffusion averages competing instructions into mush. "In the hangar AND at the beach" produces neither.

`cfg_scale` (prompt adherence) defaults sensibly; raise it toward 9–12 when the engine takes too much creative liberty, drop toward 4–6 when results look overcooked.

## The consistency problem

The hard problem in character imagery is that a diffusion model invents a new face every call. You cannot fully prompt your way out — but you can get surprisingly far:

1. **Fix a canonical description.** Write one 15–25 token identity block per character and prepend it *verbatim* to every image prompt. Word-for-word stability matters; synonyms drift the face.
2. **Fix the style block too.** A consistent rendering style ("35mm, soft grain") makes faces read as the same person even when features wobble.
3. **Seed galleries, don't stream them.** For a character's public gallery, generate in batches, curate the on-model results, discard the rest. At [10 credits a render](/pricing), a curated 12-image gallery costs about $1.50 including rejects — price the feature, not the attempt.

Refused or empty generations refund automatically, and a prompt the engine declines returns an explicit `content_blocked` code rather than a silent failure — so your retry logic can tell "rephrase" from "try again".

## Where images fit the product

The pattern that converts, in order of effort:

- **The reveal** — a one-time "see them" moment early in a relationship. One image, massive activation effect.
- **Scene stills** — user-triggered "show me this moment" during chat. Charge your users per render; your cost is a known 10 credits.
- **The gallery** — curated, drip-released, subscription-gated. Batch-generated off-peak.

Each maps to one `POST /v1/images/generations` call — the [reference](/docs/images) covers the parameters, and outputs return inline (base64) for you to store; nothing is retained on our side.

## Budget table

| Feature | Renders/user/mo | Credits | ~Cost |
| --- | --- | --- | --- |
| Reveal (once) | 1 | 10 | $0.10 |
| Scene stills | 12 | 120 | $1.20 |
| Gallery drops (curated 3:1) | 16 | 160 | $1.60 |

Against a $10–15/month subscription, imagery lands comfortably inside margin while being the most visible thing the subscription buys. That combination — cheap to run, premium to perceive — is why it is the first feature we tell builders to add after chat.