Stable Diffusion img2img

This model uses diffusion-denoising mechanism as first proposed by SDEdit, Stable Diffusion is used for text-guided image-to-image translation. This model uses the weights from Stable Diffusion to generate new images from an input image using StableDiffusionImg2ImgPipeline from diffusers

~7.64s

~$0.004

Playground API

Pricing

~7.64s

~$0.004

 1import requests
 2import json
 3
 4url = "https://api.segmind.com/v1/sd1.5-img2img"
 5headers = {
 6    "x-api-key": "YOUR_API_KEY",
 7    "Content-Type": "application/json"
 8}
 9
10data = {
11    "image": "/sd-img2img-input.jpeg",
12    "samples": 1,
13    "prompt": "A fantasy landscape, trending on artstation, mystical sky",
14    "negative_prompt": "nude, disfigured, blurry",
15    "scheduler": "DDIM",
16    "num_inference_steps": 25,
17    "guidance_scale": 10.5,
18    "strength": 0.75,
19    "seed": 98877465625,
20    "img_width": 512,
21    "img_height": 512,
22    "base64": false
23}
24
25response = requests.post(url, headers=headers, json=data)
26
27if response.status_code == 200:
28    result = response.json()
29    print(json.dumps(result, indent=2))
30else:
31    print(f"Error: {response.status_code}")
32    print(response.text)

 1import requests
 2import json
 3
 4url = "https://api.segmind.com/v1/sd1.5-img2img"
 5headers = {
 6    "x-api-key": "YOUR_API_KEY",
 7    "Content-Type": "application/json"
 8}
 9
10data = {
11    "image": "/sd-img2img-input.jpeg",
12    "samples": 1,
13    "prompt": "A fantasy landscape, trending on artstation, mystical sky",
14    "negative_prompt": "nude, disfigured, blurry",
15    "scheduler": "DDIM",
16    "num_inference_steps": 25,
17    "guidance_scale": 10.5,
18    "strength": 0.75,
19    "seed": 98877465625,
20    "img_width": 512,
21    "img_height": 512,
22    "base64": false
23}
24
25response = requests.post(url, headers=headers, json=data)
26
27if response.status_code == 200:
28    result = response.json()
29    print(json.dumps(result, indent=2))
30else:
31    print(f"Error: {response.status_code}")
32    print(response.text)

API Endpoint

POSThttps://api.segmind.com/v1/sd1.5-img2img

Parameters

imagerequired

string (uri)

Input Image.

promptrequired

string

Prompt to render

base64optional

boolean

Base64 encoding of the output image.

Default: false

guidance_scaleoptional

number

Scale for classifier-free guidance

Default: 7.5Range: 0.1 - 25

img_heightoptional

integer

Image resolution.

Default: 512

Allowed values :

512768

img_widthoptional

integer

Image resolution.

Default: 512

Allowed values :

512768

negative_promptoptional

string

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

num_inference_stepsoptional

integer

Number of denoising steps.

Default: 20Range: 20 - 100

samplesoptional

integer

Number of samples to generate.

Default: 1Range: 1 - 4

scheduleroptional

string

Type of scheduler.

Default: "DDIM"

Allowed values (12 total):

"DDIM""DPM Multi""DPM Single""Euler a""Euler""Heun""DPM2 a Karras""DPM2 Karras""LMS""PNDM"+2 more

seedoptional

integer

Seed for image generation.

Default: -1

strengthoptional

number

How much to transform the reference image

Default: 1Range: 0.1 - 1

Response Type

Returns: Image

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