1const axios = require('axios');
2
3
4const api_key = "YOUR API-KEY";
5const url = "https://api.segmind.com/v1/sd1.5-realisticvision";
6
7const data = {
8 "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)",
9 "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))",
10 "scheduler": "dpmpp_2m",
11 "num_inference_steps": 25,
12 "guidance_scale": 6,
13 "samples": 1,
14 "seed": 4082622942,
15 "img_width": 512,
16 "img_height": 768,
17 "base64": false
18};
19
20(async function() {
21 try {
22 const response = await axios.post(url, data, { headers: { 'x-api-key': api_key } });
23 console.log(response.data);
24 } catch (error) {
25 console.error('Error:', error.response.data);
26 }
27})();Attributes
Prompt to render
Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'
Type of scheduler.
Allowed values:
Number of denoising steps.
min : 20,
min : 100
Scale for classifier-free guidance
min : 0.1,
min : 25
Number of samples to generate.
min : 1,
min : 4
Seed for image generation.
Width of the image.
Allowed values:
Height of the Image
Allowed values:
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.
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
- Creating realistic portraits for digital art.
- Generating diverse characters for video games or animations.
- Producing unique avatars for social media or virtual reality platforms.
- Designing fictional characters for books or graphic novels.
- 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.
