Kandinsky 2.1

Kandinsky inherits best practices from Dall-E 2 and Latent diffusion, while introducing some new ideas.

~0.00s

~$5

~0.00s

~$5

 1import requests
 2import json
 3
 4url = "https://api.segmind.com/v1/kandinsky2.1-txt2im"
 5headers = {
 6    "x-api-key": "YOUR_API_KEY",
 7    "Content-Type": "application/json"
 8}
 9
10data = {
11    "prompt": "tiny isometric city on a tiny floating island, highly detailed, 3d render",
12    "negative_prompt": "NONE",
13    "scheduler": "DDIM",
14    "samples": 1,
15    "num_inference_steps": 25,
16    "guidance_scale": 7.5,
17    "seed": 1024,
18    "img_width": 512,
19    "img_height": 512,
20    "base64": false
21}
22
23response = requests.post(url, headers=headers, json=data)
24
25if response.status_code == 200:
26    result = response.json()
27    print(json.dumps(result, indent=2))
28else:
29    print(f"Error: {response.status_code}")
30    print(response.text)

 1import requests
 2import json
 3
 4url = "https://api.segmind.com/v1/kandinsky2.1-txt2im"
 5headers = {
 6    "x-api-key": "YOUR_API_KEY",
 7    "Content-Type": "application/json"
 8}
 9
10data = {
11    "prompt": "tiny isometric city on a tiny floating island, highly detailed, 3d render",
12    "negative_prompt": "NONE",
13    "scheduler": "DDIM",
14    "samples": 1,
15    "num_inference_steps": 25,
16    "guidance_scale": 7.5,
17    "seed": 1024,
18    "img_width": 512,
19    "img_height": 512,
20    "base64": false
21}
22
23response = requests.post(url, headers=headers, json=data)
24
25if response.status_code == 200:
26    result = response.json()
27    print(json.dumps(result, indent=2))
28else:
29    print(f"Error: {response.status_code}")
30    print(response.text)

API Endpoint

POSThttps://api.segmind.com/v1/kandinsky2.1-txt2im

Parameters

base64optional

boolean

Base64 encoding of the output image.

Default: false

guidance_scaleoptional

number

Scale for classifier-free guidance

img_heightoptional

integer

Image resolution.

Allowed values :

5121024

img_widthoptional

integer

Image resolution.

Allowed values :

5121024

negative_promptoptional

string

Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'

num_inference_stepsoptional

integer

Number of denoising steps.

Range: 10 - 40

promptoptional

string

Prompt to render

samplesoptional

integer

Number of samples to generate.

Default: 1Range: 1 - 8

scheduleroptional

string

Type of scheduler.

Allowed values :

"DDIM""P-Sampler"

seedoptional

integer

Seed for image generation.

Response Type

Returns: Text/JSON

Common Error Codes

The API returns standard HTTP status codes. Detailed error messages are provided in the response body.

400

Bad Request

Invalid parameters or request format

401

Unauthorized

Missing or invalid API key

403

Forbidden

Insufficient permissions

404

Not Found

Model or endpoint not found

406

Insufficient Credits

Not enough credits to process request

429

Rate Limited

Too many requests

500

Server Error

Internal server error

502

Bad Gateway

Service temporarily unavailable

504

Timeout

Request timed out