SSD Img2Img

This model uses SSD-1B to generate images by passing a text prompt and an initial image to condition the generation

API

If you're looking for an API, you can choose from your desired programming language.

POST

import requests
import base64

# Use this function to convert an image file from the filesystem to base64
def image_file_to_base64(image_path):
    with open(image_path, 'rb') as f:
        image_data = f.read()
    return base64.b64encode(image_data).decode('utf-8')

# Use this function to fetch an image from a URL and convert it to base64
def image_url_to_base64(image_url):
    response = requests.get(image_url)
    image_data = response.content
    return base64.b64encode(image_data).decode('utf-8')

api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/ssd-img2img"

# Request payload
data = {
  "image": image_url_to_base64("https://segmind-sd-models.s3.amazonaws.com/outputs/ssd_img2img_input.jpeg"),  # Or use image_file_to_base64("IMAGE_PATH")
  "prompt": "photo of beautiful age 18 girl, pink hair, beautiful, close up, young, dslr, 8k, 4k, ultrarealistic, realistic, natural skin, textured skin",
  "negative_prompt": "painting, drawing, sketch, cartoon, anime, manga, render, CG, 3d, watermark, signature, label, long neck",
  "samples": 1,
  "scheduler": "Euler a",
  "num_inference_steps": 30,
  "guidance_scale": 7.5,
  "seed": 452361789,
  "strength": 0.9,
  "base64": False
}

headers = {'x-api-key': api_key}

response = requests.post(url, json=data, headers=headers)
print(response.content)  # The response is the generated image

RESPONSE

image/jpeg

HTTP Response Codes

200 - OKImage Generated

401 - UnauthorizedUser authentication failed

404 - Not FoundThe requested URL does not exist

405 - Method Not AllowedThe requested HTTP method is not allowed

406 - Not AcceptableNot enough credits

500 - Server ErrorServer had some issue with processing

Attributes

imageimage *

Input Image

promptstr *

Prompt to render

negative_promptstr ( default: None )

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

samplesint ( default: 1 ) Affects Pricing

Number of samples to generate.

min : 1,

max : 4

schedulerenum:str ( default: DPM2 Karras )

Type of scheduler.

Allowed values:

num_inference_stepsint ( default: 25 ) Affects Pricing

Number of denoising steps.

min : 20,

max : 100

guidance_scalefloat ( default: 7.5 )

Scale for classifier-free guidance

min : 1,

max : 25

seedint ( default: -1 )

Seed for image generation.

min : -1,

max : 999999999999999

strengthfloat ( default: 7.5 )

Scale for classifier-free guidance

min : 0,

max : 0.99

base64boolean ( default: 1 )

Base64 encoding of the output image.

To keep track of your credit usage, you can inspect the response headers of each API call. The x-remaining-credits property will indicate the number of remaining credits in your account. Ensure you monitor this value to avoid any disruptions in your API usage.

Segmind Stable Diffusion 1B (SSD-1B) Img2Img

Segmind Stable Diffusion 1B (SSD-1B) Img2Img is a cutting-edge AI model that is reshaping the landscape of image-to-image transformations. Leveraging advanced machine learning techniques, SSD-1B excels at converting conceptual prompts into vivid visuals, refining images, and facilitating seamless image translations guided by nuanced text inputs. It stands as a pivotal tool for creatives, marketers, and software developers who are looking to break new ground in the realm of visual content creation.

At the core of SSD-1B Img2Img is a sophisticated algorithm adept at intricate visual content manipulation. With the ability to take an existing image and, through text-driven prompts, transform it into a new piece that resonates with the creator's intent, the modle shines in performing style transfers, enhancing details, and altering subjects while preserving the essence of the original image.

Advantages

Text-Directed Transformation: Employs text prompts to direct the transformation process, aligning the output closely with the creator's objectives .
Fluid Style Adaptation:Capably adjusts the style from one image to another, ensuring transitions are coherent and purposeful.
Enhanced Detailing:Elevates the intricacies within images, enhancing definition and vibrancy.
Expansive Creative Range: Provides a broad spectrum of creative possibilities, ranging from nuanced tweaks to complete conceptual overhauls.

Use Cases

Creative Artwork: Enables artists to push the boundaries of their work, exploring new styles and themes effortlessly.
Marketing Material: Assists marketers in crafting images that are in sync with brand stories, maintaining uniformity across all visual communications.
Product Design: Allows designers to swiftly generate multiple product iterations, accelerating the design process.
Entertainment Media: Supports content creators in the entertainment sector to adjust and refine visual elements to match dynamic narratives.
Educational Tools: Aids educators in developing bespoke visual aids that enhance the learning experience for complex subjects.

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