Q: Key Concepts

<h3 id="keyconcepts">Key Concepts</h3> Models Model is a type of machine learning model that is trained to generate new data, such as text, images, audio, or video, based on the patterns and relationships it has learned from a training dataset. To see the list of all public models on Segmind, go to <a href="https://segmind.com/models">segmind.com/models</a> and filter by models. PixelFlow GUI Pixelflow is a node based tool that gives developer and creators the ability to access a myriad of open-sourced models and seamlessly string them together to create highly tailored AI workflows. PixelFlow APIs Workflows created on PixelFlow GUI can be converted into APIs for simpler calling from your application. You can mark the inputs and outputs to a workflows to convert them into PixelFlow APIs. Eg. workflow URL: <a href="https://api.segmind.com/workflows/67cbde50f6cf2bb5a9b16d54-v1">https://api.segmind.com/workflows/workflowID-v1</a> Playground Playgrounds allow you to use a model or a PixelFlow via a simple user interface. This is a simple form interface to give in the inputs and view the outputs without using an API key. Fine-tuning Segmind support fine tuning SDXL model. Fine-tuning refers to the process of taking a pre-trained Stable Diffusion model, which is a powerful text-to-image generative AI model, and further training it on a specific dataset to specialize it for a particular domain or task. Prompts Prompts are textual inputs or instructions provided to generative AI models like

Question 1

Rate Limits

Accepted Answer

Rate limiting serves multiple purposes:

Security: It mitigates the risk of abuse or misuse by preventing malicious users from flooding the API with excessive requests, which could disrupt services.
Fair Access: Rate limiting ensures equitable access to the API by restricting any single user or application from monopolizing resources, enabling all users to leverage the API effectively.
Performance Optimization: By managing the overall load on our infrastructure, rate limiting prevents performance degradation due to sudden spikes in API requests, maintaining a smooth and consistent user experience.

At Segmind, we prioritize the security, fairness, and reliability of our API services. Rate limiting is a crucial measure to uphold these core principles and deliver an optimal experience to all our users.

Here are the rate limits depending on your account plan.

Free

Pro

Enterprise

5 RPM

50 RPM

Custom

Question 2

Miscellaneous

Accepted Answer

Notes in Controls

To improve the user experience and provide more space for working with nodes, the "Notes" feature has been moved to the right-hand side of the screen, within the controls section. This allows you to easily add and edit notes for your workflow without cluttering the main canvas.

Creating Endpoints for Faster Inferences

For supported nodes, you can now create an endpoint directly from the advanced options within the node itself. This allows for faster inferences by dedicating resources to the model.

To create an endpoint:

Open the advanced options for a supported node.
Click the "Create Endpoint" button.
This will open a new page where you can configure and deploy your endpoint.

Canceling a Pixelflow Run

You can cancel a Pixelflow run at any time by clicking the "Cancel" button. However, it's important to note that even if you cancel the run, any nodes that have already started processing will continue to run on the backend until they are complete.

Segbot Model Recommendations

Segbot is a helpful assistant that can recommend models based on your use case. You can describe your requirements to Segbot, and it will suggest a list of suitable models.

However, it's important to remember that Segbot can only recommend models. You will still need to manually add the recommended models to your workflow from the left sidebar.

Question 3

Developer Quickstart

Accepted Answer

Step 1: Create a Free Account

Head over to the Segmind.com website and sign up for a free account. You can simply connect with your Google or Discord account.

Step 2: Use the playground to try different models

Navigate to any model from the model page to run a model. You'll see an example that you can try or feel free to tweak the prompts and other settings to get the best output for you.

Step 3: Create an API key

The Segmind Serverless API uses API keys for authentication. To create one, login to your account and head to the API Keys page on the dashboard.

Step 4: Use the API

You can authenticate by including your Segmind API key in the x-api-key section of the request.

Example cURL request:

curl --location 'https://api.segmind.com/v1/instantid' \
--header 'x-api-key: YOUR-API-KEY' \
--header 'Content-Type: application/json' \
--data '{
    }'

Example python request

import requests
api_key = "YOUR-API-KEY"
url = "https://api.segmind.com/v1/face-to-sticker"
# Request payload
data = {
}
response = requests.post(url, json=data, headers={'x-api-key': api_key})
print(response)

Question 4

Key Concepts

Accepted Answer

Key Concepts

Models

Model is a type of machine learning model that is trained to generate new data, such as text, images, audio, or video, based on the patterns and relationships it has learned from a training dataset. To see the list of all public models on Segmind, go to segmind.com/models and filter by models.

PixelFlow GUI

Pixelflow is a node based tool that gives developer and creators the ability to access a myriad of open-sourced models and seamlessly string them together to create highly tailored AI workflows.

PixelFlow APIs

Workflows created on PixelFlow GUI can be converted into APIs for simpler calling from your application. You can mark the inputs and outputs to a workflows to convert them into PixelFlow APIs. Eg. workflow URL: https://api.segmind.com/workflows/workflowID-v1

Playground

Playgrounds allow you to use a model or a PixelFlow via a simple user interface. This is a simple form interface to give in the inputs and view the outputs without using an API key.

Fine-tuning

Segmind support fine tuning SDXL model. Fine-tuning refers to the process of taking a pre-trained Stable Diffusion model, which is a powerful text-to-image generative AI model, and further training it on a specific dataset to specialize it for a particular domain or task.

Prompts

Prompts are textual inputs or instructions provided to generative AI models like

Question 5

Fine Tuning

Accepted Answer

Welcome to Segmind's Fine-Tuning Service, designed to enhance your AI models with tailored training on the Flux.1 base model. Our solution is streamlined for efficiency and flexibility, allowing you to create models that fit your specific needs in minutes.

Key Features

One-Click Presets: Simplify the fine-tuning process with our best parameter presets for various categories like Man, Woman, Styles, Objects, and Characters.
LoRA Training Support: Leverage LoRA (Low-Rank Adaptation) to enhance your Flux.1 models.
Advanced Control: For experienced users, we provide extensive options to modify training parameters for more precise model customization.
Fast Training: Achieve the best-trained model in just 15-20 minutes with our user-friendly training console.

Getting Started

Quick Start with Presets

Choose a Preset: Select from our predefined best parameter settings for your target category.
Upload your data: Upload the image data in the zip format.
Launch Training: Start the training process with a single click.
Review Output: After 15-20 minutes, review your fine-tuned model's outputs.

Advanced Customization

For developers seeking more control, you can customize the training with the advance parameters. Please refer to this article for more information: Fine-tune Your Own Flux.1 LoRA Models

How to Use

Log in to your Segmind account.
Navigate

Question 6

Model Status

Accepted Answer

Model status can be cold and warm refer to how long it takes to launch a machine learning model to accept requests.

Cold Boot: When a model hasn't been used in a while, it gets turned off to conserve resources. This is similar to completely turning off your computer. When you make a request to use the model again, it needs to be fully loaded and started up, which can take several minutes for large models. This is a cold boot.
Warm Boot: If a model has been used recently, it stays loaded and ready to accept requests. This is similar to putting your computer in sleep mode. When you use a warm model, the response is much faster because the model is already up and running.

Here's why this happens:

Segmind has a large library of models, and keeping them all running all the time would use a lot of resources.
They only run the models that are actually being used.
Cold boots happen more often for less frequently used models.

Segmind only charges you for the time the model is actually processing your request, not the boot time. So, cold boots don't affect your costs.

Frequently Read Articles

Browse All Categories