image-generation

Generating images, and not overpaying for drafts

Multiple leading image models share one credit balance, and the cheapest costs a single credit while the dearest costs roughly ten times that. The catalogue reports the live count. Since credits are flat per generation, the difference between a careless workflow and a careful one is about an order of magnitude on the same output.

Read the catalogue first

curl -s "https://aicontentdrop.com/v1/models?type=image"

{
  "type": "image",
  "count": 4,
  "models": [
    { "id": "z_image",       "name": "Z Image",       "credits": 1 },
    { "id": "flux_2",        "name": "Flux 2",        "credits": 3 },
    { "id": "seedream_4_5",  "name": "Seedream 4.5",  "credits": 4 },
    { "id": "nano_banana_2", "name": "Nano Banana 2", "credits": 6 }
  ]
}

The sample above is trimmed to four entries. Ids, prices and the count change as models are added, so read them rather than hard-coding the list. To see only what fits a budget:

curl -s "https://aicontentdrop.com/v1/models?type=image&max_credits=4"

The workflow that saves the credits

Iterate cheap, finish expensive. Composition, framing and subject are decided at 1–3 credits a shot; you only need the expensive model for the one image that ships.

1. Draft the idea on the cheapest model that can express it. Change one thing per attempt so you learn what actually moved the result.
2. When the composition is right, re-run that prompt on a higher-quality model for the final.
3. Quote the final before running it if the user is near their balance.

An agent that starts on the best model and iterates there burns a user's month on drafts nobody keeps.

Generating

curl -sX POST https://aicontentdrop.com/v1/generate/image \
  -H "Authorization: Bearer $ACD_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: hero-still-01" \
  -d '{"prompt":"a matte-black espresso machine on a concrete counter, morning window light, shallow depth of field","model":"seedream_4_5"}'