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POST
/
api
/
v2
/
images
/
generations
/
price
cURL
curl --request POST \
  --url https://api.deapi.ai/api/v2/images/generations/price \
  --header 'Accept: <accept>' \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "prompt": "A beautiful sunset over mountains",
  "model": "Flux1schnell",
  "width": 512,
  "height": 512,
  "guidance": 7.5,
  "steps": 20,
  "seed": 42,
  "negative_prompt": "blur, darkness, noise",
  "loras": [
    {
      "name": "style_lora",
      "weight": 0.75
    }
  ]
}
'
{
  "data": {
    "price": 0.25
  }
}

Documentation Index

Fetch the complete documentation index at: https://docs.deapi.ai/llms.txt

Use this file to discover all available pages before exploring further.

Estimate the cost of an image generation job before submitting it. Request body mirrors the fields used by /api/v2/images/generations.

Authorizations

​
Authorization
string
header
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Headers

​
Accept
enum<string>
default:application/json
required
Available options:
application/json

Body

application/json

Image generation parameters

​
prompt
string
required

The main prompt for image generation

Example:

"A beautiful sunset over mountains"

​
model
string
required

The model to use for image generation. Available models can be retrieved via the GET /api/v1/client/models endpoint.

Example:

"Flux1schnell"

​
width
integer
required

Width of the generated image in pixels

Example:

512

​
height
integer
required

Height of the generated image in pixels

Example:

512

​
guidance
number
required

Guidance scale for the generation

Example:

7.5

​
steps
integer
required

Number of inference steps

Example:

20

​
seed
integer
required

Random seed for generation

Example:

42

​
negative_prompt
string | null

Elements to avoid in the generated image

Example:

"blur, darkness, noise"

​
loras
object[]

Array of LoRA models to apply

Response

Calculated price for text2img inference.

​
data
object