1const axios = require('axios');
4const api_key = "YOUR API-KEY";
5const url = "";
7const data = {
8  "prompt": "a toy panda standing on a pile of broccoli",
9  "negative_prompt": "(deformed iris, deformed pupils, semi-realistic, cgi, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, BadDream, UnrealisticDream",
10  "scheduler": "dpmpp_2m",
11  "num_inference_steps": 100,
12  "guidance_scale": 7.5,
13  "samples": 1,
14  "seed": 2313248373,
15  "img_width": 512,
16  "img_height": 512,
17  "base64": false
20(async function() {
21    try {
22        const response = await, data, { headers: { 'x-api-key': api_key } });
23        console.log(;
24    } catch (error) {
25        console.error('Error:',;
26    }
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


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,

min : 100

guidance_scalefloat ( default: 7.5 )

Scale for classifier-free guidance

min : 0.1,

min : 25

samplesint ( default: 1 ) Affects Pricing

Number of samples to generate.

min : 1,

min : 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.

Segmind Small-SD: The Next Generation in Efficient Text-to-Image Synthesis

Introducing Segmind Small-SD, an innovative generative AI model from Segmind, designed with the specific goal of accelerating and democratizing AI applications. This new compact and high-speed Stable Diffusion model is open-sourced and readily accessible on Huggingface. The inspiration behind Segmind Small-SD comes from the pioneering research delineated in the paper "On Architectural Compression of Text-to-Image Diffusion Models." Based on this groundwork, Segmind presents two compact versions: SD-Small and SD-Tiny, with SD-Small offering a 35% reduction in parameters compared to the base model while maintaining a similar level of image fidelity.

The technological core of Segmind Small-SD is built around Knowledge Distillation (KD), a concept that mimics a teacher-student learning process within the realm of AI. Here, a larger, pre-trained model (the teacher) assists a smaller model (the student) in training on a condensed dataset. This unique distillation method includes matching outputs at every block of the U-nets from the teacher model, ensuring the maintenance of model quality during the size reduction. The KD process includes a multi-faceted loss function that not only considers the traditional loss but also the variance between the latents generated by the teacher and the student model, and notably, the feature-level loss — the difference between the block outputs from the teacher and student.

The key advantage of the Segmind Small-SD model is its blend of speed, efficiency, and quality. Offering up to 85% faster inferences, these models significantly reduce the time required to produce results, providing a perfect balance between high performance and economic viability. Despite their reduced size, these models can generate high-quality images, making them an excellent solution for tasks that demand rapid image generation without compromising quality.

Segmind Small-SD use cases

  1. Digital Content Creation: Rapid production of superior-quality images for various digital content platforms, such as blogs, social media, and more.

  2. Game Asset Creation: For game developers, the model can efficiently generate unique game assets, enhancing creativity and speed.

  3. Customized Marketing: Quicker generation of personalized visuals for digital marketing efforts, boosting customer interaction.

  4. AI-Assisted Art and Design: Artists and designers can leverage it for fast creation of distinctive, AI-aided visual content.

  5. AI Research: For researchers in AI, faster inference means more rapid prototyping, testing, and discoveries, thereby speeding up the overall research process.

Segmind Small-SD License

Segmind Small-SD is licensed under CreativeML Open RAIL-M. This license encourages both the open and responsible use of the model. It is inspired by permissive open-source licenses in terms of granting IP rights while also adding use-based restrictions to prevent misuse of the technology, be it due to technical limitations or ethical considerations. While derivative versions of the model can be released under different licensing terms, they must always include the same use-based restrictions as the original license. Thus, the license strikes a balance between open and responsible AI development, promoting open-science in the field of AI while simultaneously addressing potential misuse.