v1

API Reference

AI Imager exposes a simple REST API for generating and manipulating images. All endpoints accept multipart/form-data POST requests and return JSON. No authentication required.

Base URL

http://ai-image-gen.xcasper.space

Response Format

All API endpoints return a JSON object with the following shape:

JSON

{
  "url": ["https://...", "https://..."],  // array of image URLs, or null on error
  "error": null                                   // error message string, or null on success
}

Field	Type	Description
`url`	array<string> \| null	List of generated image URLs. `null` when an error occurred.
`error`	string \| null	Human-readable error message. `null` on success.

200 Success — images generated

400 Bad request — missing required fields

501 Generation failed — upstream error

POST /v1/image/prompt/generate

Text to Image

Generate one or more images from a text description using the DeepAI engine.

Request Body multipart/form-data

Field	Type	Required	Description
`prompt`	string	Required	Text description of the image to generate.
`total_images`	integer	Required	Number of images to generate (1–4 recommended).
`image_size`	string	Required	Desired resolution. One of `256x256`, `512x512`, `1024x1024`. (Passed through; actual size depends on provider.)

cURL

curl -X POST http://ai-image-gen.xcasper.space/v1/image/prompt/generate \
  -F "prompt=A futuristic city skyline at night with neon reflections" \
  -F "total_images=2" \
  -F "image_size=512x512"

Python

import requests

resp = requests.post(
    "http://ai-image-gen.xcasper.space/v1/image/prompt/generate",
    data={
        "prompt": "A futuristic city skyline at night with neon reflections",
        "total_images": 2,
        "image_size": "512x512",
    }
)
data = resp.json()
print(data["url"])  # ['https://...', 'https://...']

JavaScript

const form = new FormData();
form.append("prompt", "A futuristic city skyline at night with neon reflections");
form.append("total_images", "2");
form.append("image_size", "512x512");

const res = await fetch("/v1/image/prompt/generate", { method: "POST", body: form });
const { url, error } = await res.json();
if (error) console.error(error);
else url.forEach(src => console.log(src));

Example Response

JSON · 200 OK

{
  "url": [
    "https://api.deepai.org/job-view-file/abc123/outputs/output.jpg",
    "https://api.deepai.org/job-view-file/def456/outputs/output.jpg"
  ],
  "error": null
}

POST /v1/image/bing/generate

Magic Studio

Generate images using the Magic Studio engine (Casper Tech APIs). Falls back to DeepAI if Magic Studio is unavailable.

Request Body multipart/form-data

Field	Type	Required	Description
`prompt`	string	Required	Text description of the image to generate.
`total_images`	integer	Required	Number of images to generate.

cURL

curl -X POST http://ai-image-gen.xcasper.space/v1/image/bing/generate \
  -F "prompt=A magical forest at golden hour with fairy lights" \
  -F "total_images=2"

Python

import requests

resp = requests.post(
    "http://ai-image-gen.xcasper.space/v1/image/bing/generate",
    data={
        "prompt": "A magical forest at golden hour with fairy lights",
        "total_images": 2,
    }
)
print(resp.json()["url"])

JavaScript

const form = new FormData();
form.append("prompt", "A magical forest at golden hour with fairy lights");
form.append("total_images", "2");

const res = await fetch("/v1/image/bing/generate", { method: "POST", body: form });
const { url, error } = await res.json();

Example Response

JSON · 200 OK

{
  "url": [
    "https://cdn.magicstudio.com/output/image-abc.jpg"
  ],
  "error": null
}

POST /v1/image/mask/generate

Image + Mask

Upload an original image and a mask image, then describe the desired edit. The AI generates new images matching the prompt.

Request Body multipart/form-data

Field	Type	Required	Description
`original_image_path`	file	Required	The base image to edit (PNG, JPG, WEBP).
`masked_image_path`	file	Required	Mask image defining the edit region.
`prompt`	string	Required	Description of the desired edit.
`total_images`	integer	Required	Number of images to generate.
`image_size`	string	Required	`256x256`, `512x512`, or `1024x1024`.

cURL

curl -X POST http://ai-image-gen.xcasper.space/v1/image/mask/generate \
  -F "original_image_path=@/path/to/original.png" \
  -F "masked_image_path=@/path/to/mask.png" \
  -F "prompt=A sunlit indoor lounge with a flamingo pool" \
  -F "total_images=1" \
  -F "image_size=512x512"

Python

import requests

with open("original.png", "rb") as orig, open("mask.png", "rb") as mask:
    resp = requests.post(
        "http://ai-image-gen.xcasper.space/v1/image/mask/generate",
        files={
            "original_image_path": orig,
            "masked_image_path": mask,
        },
        data={
            "prompt": "A sunlit indoor lounge with a flamingo pool",
            "total_images": 1,
            "image_size": "512x512",
        }
    )
print(resp.json()["url"])

JavaScript

const form = new FormData();
form.append("original_image_path", originalFile);  // File object
form.append("masked_image_path", maskFile);         // File object
form.append("prompt", "A sunlit indoor lounge with a flamingo pool");
form.append("total_images", "1");
form.append("image_size", "512x512");

const res = await fetch("/v1/image/mask/generate", { method: "POST", body: form });
const { url, error } = await res.json();

Example Response

JSON · 200 OK

{
  "url": [
    "https://api.deepai.org/job-view-file/xyz789/outputs/output.jpg"
  ],
  "error": null
}

POST /v1/image/variation/generate

Image Variation

Upload a reference image and receive AI-generated variations inspired by its composition and style.

Request Body multipart/form-data

Field	Type	Required	Description
`path_to_image`	file	Required	Reference image to generate variations from (PNG, JPG, WEBP).
`total_images`	integer	Required	Number of variations to generate.
`image_size`	string	Required	`256x256`, `512x512`, or `1024x1024`.

cURL

curl -X POST http://ai-image-gen.xcasper.space/v1/image/variation/generate \
  -F "path_to_image=@/path/to/reference.png" \
  -F "total_images=2" \
  -F "image_size=512x512"

Python

import requests

with open("reference.png", "rb") as img:
    resp = requests.post(
        "http://ai-image-gen.xcasper.space/v1/image/variation/generate",
        files={"path_to_image": img},
        data={"total_images": 2, "image_size": "512x512"}
    )
print(resp.json()["url"])

JavaScript

const form = new FormData();
form.append("path_to_image", referenceFile);  // File object
form.append("total_images", "2");
form.append("image_size", "512x512");

const res = await fetch("/v1/image/variation/generate", { method: "POST", body: form });
const { url, error } = await res.json();

Example Response

JSON · 200 OK

{
  "url": [
    "https://api.deepai.org/job-view-file/aaa111/outputs/output.jpg",
    "https://api.deepai.org/job-view-file/bbb222/outputs/output.jpg"
  ],
  "error": null
}

GET /v1/history

Image History

Returns the generation history for the current session (identified by a session cookie set automatically on first visit).

Query Parameters

Parameter	Type	Required	Description
`limit`	integer	Optional	Maximum number of records to return. Default: 1000.
`target`	string	Optional	Specific field to extract: `category`, `prompt`, `time`, or `urls`.
`category`	string	Optional	Filter by category: `chatgpt`, `bing`, `masking`, or `variation`.

cURL

# Get all history
curl "http://ai-image-gen.xcasper.space/v1/history"

# Get last 5 bing prompts
curl "http://ai-image-gen.xcasper.space/v1/history?category=bing&limit=5"

# Get only image URLs
curl "http://ai-image-gen.xcasper.space/v1/history?target=urls"

Python

import requests

# Full history
resp = requests.get("http://ai-image-gen.xcasper.space/v1/history")
print(resp.json())

# Filter by category and limit
resp = requests.get(
    "http://ai-image-gen.xcasper.space/v1/history",
    params={"category": "bing", "limit": 5}
)
print(resp.json())

JavaScript

// Full history
const res = await fetch("/v1/history");
const data = await res.json();
console.log(data.data);

// Filter
const filtered = await fetch("/v1/history?category=chatgpt&limit=10");
console.log(await filtered.json());

Example Response

JSON · 200 OK

{
  "data": [
    {
      "category": "chatgpt",
      "prompt": "A futuristic city at night",
      "time": "2026-04-05T21:57:11",
      "urls": [
        "https://api.deepai.org/job-view-file/abc/outputs/output.jpg"
      ]
    }
  ]
}

Error Response

When a request fails, url is null and error contains the reason:

JSON · 400 / 501

{
  "url": null,
  "error": "Kindly fill all the fields"
}