Realistic Vision

This model corresponds to the Stable Diffusion Realistic Vision checkpoint for detailed images at the cost of a super detailed prompt

Playground

Try the model in real time below.

output image


Examples

Check out what others have created with Realistic Vision
Example preview

((selfie)) photo of an american girl and guy, smiling, (yosemite:1.3), mountains, wearing a backpack, red top, hiking jacket, rocks, river, wood, analog style (look at viewer:1.2) (skin texture), close up, cinematic light, ((night sky:1.2)), (milkiway:1.4), sidelighting, Fujiflim XT3, DSLR, 50mm, (long windblown hair)

seed: 4082622942guidance_scale: 6

API

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

POST
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 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/sd1.5-realisticvision" # Request payload data = { "prompt": "((selfie)) photo of an american girl and guy, smiling, (yosemite:1.3), mountains, wearing a backpack, red top, hiking jacket, rocks, river, wood, analog style (look at viewer:1.2) (skin texture), close up, cinematic light, ((night sky:1.2)), (milkiway:1.4), sidelighting, Fujiflim XT3, DSLR, 50mm, (long windblown hair)", "negative_prompt": "\"(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, render, 3d, 2d, sketch, painting, digital art, drawing, disfigured, ((nsfw)), ((breasts))", "scheduler": "dpmpp_2m", "num_inference_steps": 25, "guidance_scale": 6, "samples": 1, "seed": 4082622942, "img_width": 512, "img_height": 768, "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


promptstr *

Prompt to render


negative_promptstr ( default: None )

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


schedulerenum:str ( default: UniPC )

Type of scheduler.

Allowed values:


num_inference_stepsint ( default: 20 ) Affects Pricing

Number of denoising steps.

min : 20,

max : 100


guidance_scalefloat ( default: 7.5 )

Scale for classifier-free guidance

min : 0.1,

max : 25


samplesint ( default: 1 ) Affects Pricing

Number of samples to generate.

min : 1,

max : 4


seedint ( default: -1 )

Seed for image generation.


img_widthenum:int ( default: 512 ) Affects Pricing

Width of the image.

Allowed values:


img_heightenum:int ( default: 512 ) Affects Pricing

Height of the Image

Allowed values:


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.


Pricing

Serverless Pricing

Buy credits that can be used anywhere on Segmind

$ 0.0015 /per second
FEATURES

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Realistic Vision v3

The Realistic Vision model is a state-of-the-art AI model based on Stable Diffusion 1.5 that is capable of creating super realistic portraits that look like real photos. It can generate portraits in different styles, ages, and clothing, and can even create people with specific clothing. The portraits created by the model are described as absolutely amazing and mind-blowing.

The Realistic Vision model operates on a stable diffusion framework and uses SD 1.5 as it's base model. Suggested schedulers are Euler A and DPM++ SDE Karras. It works best when you combine it with an upscaler like ESRGAN.

The Realistic Vision model is capable of creating realistic and modern pictures. The model is flexible with the prompts, allowing users to use square brackets and negative prompts. Although the images created using the model look great, the clothing in the photos may appear run-down, adding a touch of authenticity to the images.

Realistic Vision v3 use cases

  1. Creating realistic portraits for digital art.

  2. Generating diverse characters for video games or animations.

  3. Producing unique avatars for social media or virtual reality platforms.

  4. Designing fictional characters for books or graphic novels.

  5. Providing a tool for fashion designers to visualize different styles and outfits on various models.

Realistic Vision v3 license

The license for the Realistic Vision model, known as the "CreativeML Open RAIL-M" license, is designed to promote both open and responsible use of the model. You may add your own copyright statement to your modifications and provide additional or different license terms for your modifications. You are accountable for the output you generate using the model, and no use of the output can contravene any provision as stated in the license.

F.A.Q.

Frequently Asked Questions

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