Image generation and edits

Use the Image API to generate new images from text prompts or edit one or more reference images. The image endpoints use the same base URL and Bearer token authentication as the rest of the LLM7 API.

https://api.llm7.io/v1

Authorization: Bearer <LLM7_API_TOKEN>

The Image API is intentionally strict. Unsupported fields are rejected on input, and successful responses are normalized so provider-only fields are not exposed.

Generate a new image

Send a JSON request to POST /images/generations.

curl https://api.llm7.io/v1/images/generations \
  -H "Authorization: Bearer $LLM7_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-2",
    "prompt": "A clean product photo of a matte black desk lamp on a white desk",
    "size": "1024x1024",
    "n": 1
  }'

Request fields

Field	Required	Type	Notes
`model`	yes	string	Image model ID, for example `gpt-image-2`
`prompt`	yes	string	Text instruction for the image
`size`	no	string	For example `1024x1024`; support depends on the selected model
`n`	no	integer	Only `1` is supported

Unsupported fields include response_format, quality, style, background, output_format, mask, and any other extra keys.

Python

import base64
import os

from openai import OpenAI

client = OpenAI(
    api_key=os.environ["LLM7_API_TOKEN"],
    base_url="https://api.llm7.io/v1",
)

result = client.images.generate(
    model="gpt-image-2",
    prompt="A clean product photo of a matte black desk lamp on a white desk",
    size="1024x1024",
    n=1,
)

image_bytes = base64.b64decode(result.data[0].b64_json)
with open("generated.png", "wb") as file:
    file.write(image_bytes)

Edit an image

Send multipart form data to POST /images/edits. This endpoint supports instruction-based editing and inpainting from one or more reference images. The gateway contract does not support an explicit mask field. Send the source image as image and describe the desired edit in prompt.

curl https://api.llm7.io/v1/images/edits \
  -H "Authorization: Bearer $LLM7_API_TOKEN" \
  -F "model=gpt-image-2" \
  -F "prompt=Replace the empty wall area with a framed abstract painting" \
  -F "size=1024x1024" \
  -F "image=@room.png"

Request fields

Field	Required	Type	Notes
`model`	yes	text	Image model ID, for example `gpt-image-2`
`prompt`	yes	text	Edit or inpaint instruction
`size`	no	text	For example `1024x1024`; support depends on the selected model
`image`	yes	file	Repeatable field for reference images

Current model metadata controls reference-image limits. For gpt-image-2, the gateway supports up to 3 reference images, with each image up to 8 MB. Unsupported form fields include mask, response_format, quality, style, background, output_format, and any other extra fields. Unsupported file field names are rejected; files must be sent as image.

Multiple reference images

curl https://api.llm7.io/v1/images/edits \
  -H "Authorization: Bearer $LLM7_API_TOKEN" \
  -F "model=gpt-image-2" \
  -F "prompt=Apply the color palette from the second image to the chair in the first image" \
  -F "image=@chair.png" \
  -F "image=@palette.png"

Python

import base64
import os

from openai import OpenAI

client = OpenAI(
    api_key=os.environ["LLM7_API_TOKEN"],
    base_url="https://api.llm7.io/v1",
)

with open("room.png", "rb") as image:
    result = client.images.edit(
        model="gpt-image-2",
        image=image,
        prompt="Replace the empty wall area with a framed abstract painting",
        size="1024x1024",
    )

image_bytes = base64.b64decode(result.data[0].b64_json)
with open("edited.png", "wb") as file:
    file.write(image_bytes)

Success response

Generation and edit responses use the same normalized JSON shape:

{
  "data": [
    {
      "b64_json": "<base64-encoded image bytes>"
    }
  ],
  "usage": {
    "cost_usd": "0.01200000"
  }
}

Response contract:

data is always a list with exactly one item.
data[0].b64_json is the generated or edited image encoded as base64.
usage.cost_usd is a decimal USD string for the billed request when cost is available.
URL image output is not supported.
Extra upstream fields such as url, created, revised_prompt, or provider metadata are not returned.

Decode the image:

import base64

image_bytes = base64.b64decode(response.data[0].b64_json)

Error responses

Gateway errors follow an OpenAI-style error object:

{
  "error": {
    "message": "Upstream provider returned an invalid response.",
    "type": "upstream_invalid_response",
    "param": null,
    "code": "upstream_invalid_response"
  }
}

Common cases:

HTTP status	Code	Meaning
`400`	`invalid_request` or `upstream_bad_request`	Invalid gateway request or upstream rejected the request
`401`	`missing_api_key` or `invalid_api_key`	Missing or invalid LLM7 token
`402`	`insufficient_balance`	No available balance or allowance
`413`	`invalid_request` or `reference image too large`	Request body or reference image is too large
`429`	`rate_limit_exceeded` or `upstream_rate_limited`	Gateway or upstream rate limit
`502`	`upstream_invalid_response`	Upstream success response did not match the image contract
`503`	`upstream_unavailable`	Upstream provider is temporarily unavailable

The gateway does not expose raw upstream error bodies to clients.

Getting started

Text generation

Images

AI tools

Generate a new image

Request fields

Python

Edit an image

Request fields

Multiple reference images

Python

Success response

Error responses

​Generate a new image

​Request fields

​Python

​Edit an image

​Request fields

​Multiple reference images

​Python

​Success response

​Error responses

Generate a new image

Request fields

Python

Edit an image

Request fields

Multiple reference images

Python

Success response

Error responses