Trending December 2023 # Novel Ai Image Generation Guide # Suggested January 2024 # Top 13 Popular

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See More : How To Use NovelAI Image Generation Free

In today’s digital age, visual content plays a crucial role in capturing attention and conveying messages effectively. Whether you are an artist, designer, marketer, or content creator, having the ability to generate compelling images is a valuable skill. This is where NovelAI steps in, offering a user-friendly platform that harnesses the power of artificial intelligence to generate stunning visuals that match your creative vision.

To embark on your image generation journey with NovelAI, the first step is to subscribe to their platform. By becoming a NovelAI member, you gain access to their extensive suite of tools and features, including the highly acclaimed image generation feature. Subscribe today and unlock the full potential of your creative prowess.

Once you have subscribed to NovelAI, it’s time to dive into the world of image generation. Start by figuring out your prompt—a concise description of the image you envision. Whether it’s a majestic landscape, a mythical creature, or a futuristic cityscape, clearly define your prompt to guide the AI in generating the desired visuals.

NovelAI offers the flexibility to pick your favorite prompts and experiment with various editing options. Tweak, refine, and enhance your prompts to achieve the perfect balance between imagination and reality. Let your creativity run wild as you explore the possibilities that NovelAI has to offer.

To fine-tune your image generation process, NovelAI provides you with the option to adjust your text prompt and generation settings. The Edit Image canvas allows you to make precise modifications, ensuring that the generated images align with your creative vision.

Experiment with different parameters such as strength, noise, and resolution aspects to refine your results further. Refocus your text prompt to emphasize specific visual characteristics or let the AI interpret your words and create stunning compositions that surpass your expectations.

In the realm of image generation, NovelAI empowers users to define the visual characteristics of their creations in two distinct ways. You can either use tags to specify the desired attributes of your character or composition, or you can let the AI interpret your words and generate visuals based on its understanding.

By using tags such as {detailed}, {ornate}, {realistic}, or {photorealistic}, you can add intricate details to objects or clothing and steer the image away from a flat 2D anime style. These tags provide you with granular control over the final output, allowing you to bring your creative vision to life.

NovelAI’s cutting-edge technology is driven by their custom NovelAI Diffusion Models, built on the foundation of Stable Diffusion. This unique approach ensures the generation of high-quality images with remarkable realism and artistic appeal.

Midjourney Free Alternatives: Exploring Image Generation Options

Are you a creative individual looking for AI image generation tools? If you’ve been using Midjourney, you may have noticed that they no longer offer free trials. But don’t worry! There are plenty of other alternatives available that offer similar features and capabilities. In this article, we will explore some of the best Midjourney free alternatives that you can consider for your creative endeavors.

See More : How to Use Midjourney to Generate AI Art?

Stable Diffusion, developed by Stability AI, is an excellent alternative to Midjourney. This AI image generator has been trained on billions of images, allowing it to produce results comparable to what you could create with Midjourney. The best part? It is completely free and open source, with no credit-based system. You have the freedom to generate as many images as you like without any limitations.

If you’re looking for a website that offers a variety of image generation and editing tools, DALL-E 2 is worth considering. While it operates on a credit-based system, meaning you have a limited number of images you can generate or edit, the platform provides a range of powerful features. Although you’ll need to pay real money to obtain more credits, the tool’s versatility makes it a viable alternative to Midjourney.

Bing Image Creator is a fantastic web-based tool that allows you to generate AI images using prompts. Available on web, Android, and iOS platforms, this free alternative offers a user-friendly interface and the ability to create unique images with ease. Simply input your prompts, and the AI will generate images based on your inputs. It’s a convenient and accessible option for those seeking an alternative to Midjourney.

Starry AI is another notable alternative that utilizes the Stable Diffusion models to generate images. When you sign up, you receive 10 credits to use immediately, and you can even earn an additional 5 free credits by sharing the tool on social media. This platform offers a seamless and enjoyable user experience, making it an attractive choice for those who want to create captivating AI-generated images.

If you’re willing to invest in a paid alternative, chúng tôi is a powerful AI image generator that offers a wide range of features. With the ability to generate images from text, edit existing images, and even create animations, chúng tôi provides a comprehensive creative toolkit. While it may require a financial commitment, the extensive capabilities make it a valuable alternative to Midjourney for professional artists and designers.

Also Read : Midjourney Multiple Negative Prompts: Enhancing Image Generation

Blue Willow is a free alternative that has gained significant popularity with over 300 million users on its Discord server. Operating on user donations, this platform offers an array of features, including the ability to generate AI images from prompts. It’s a vibrant community-driven alternative where you can connect with fellow creatives and explore the possibilities of AI-generated images.

Q: What are the alternatives to Midjourney for AI image generation?

A: There are several alternatives available for AI image generation. Some notable options include Stable Diffusion, DALL-E 2, Bing Image Creator, Starry AI, chúng tôi Adobe Firefly, and Blue Willow.

Q: Is Stable Diffusion a free alternative to Midjourney?

A: Yes, Stable Diffusion is an open-source alternative to Midjourney that offers image generation capabilities. It is completely free and doesn’t have a credit-based system.

Q: Does DALL-E 2 offer image editing tools in addition to image generation?

A: Yes, DALL-E 2 is a versatile platform that provides both image generation and editing tools. While it operates on a credit-based system, you can pay for more credits to continue using the platform.

Q: How does Bing Image Creator work for AI image generation?

A: Bing Image Creator is a web-based tool available on web, Android, and iOS platforms. It allows you to generate AI images by providing prompts. The AI then generates images based on your inputs. Bing Image Creator is a free alternative to Midjourney.

Q: What is Starry AI and how does it compare to Midjourney?

A: Starry AI is an alternative that utilizes Stable Diffusion models to generate images. When you sign up, you receive 10 free credits, and you can earn an additional 5 credits by sharing the tool on social media. Starry AI offers a seamless user experience and is a notable alternative to Midjourney.

Q: Is chúng tôi a paid alternative to Midjourney?

A: Yes, chúng tôi is a paid alternative that offers a wide range of features for AI image generation. It allows you to generate images from text, edit existing images, and even create animations. chúng tôi is suitable for professional artists and designers who are willing to invest financially in an image generation tool.

Q: What is Adobe Firefly and how does it compare to Midjourney?

A: Adobe Firefly is a beta tool developed by Adobe for creating AI-generated images and animations. While it is still in development, it shows promise as a free tool. Adobe Firefly is an exciting alternative to Midjourney, especially considering Adobe’s reputation in the creative industry.

Q: What is Blue Willow and how is it different from Midjourney?

A: Blue Willow is a free alternative with a vibrant community. It operates on user donations and offers features like generating AI images from prompts. With over 300 million users on its Discord server, Blue Willow provides a community-driven space for creatives to explore AI-generated images.

While Midjourney may no longer offer free trials, there is no shortage of alternatives available. Whether you prefer open source solutions like Stable Diffusion, versatile platforms like DALL-E 2 and Bing Image Creator, credit-based platforms like Starry AI, or comprehensive tools like chúng tôi and Adobe Firefly, you have several options to explore. Additionally, if you’re looking for a community-driven alternative, Blue Willow offers a vibrant space for creative individuals.

Remember to explore each alternative to determine which one aligns with your specific needs and preferences. Whether you prioritize open-source solutions, user-friendly interfaces, versatile features, or community engagement, there’s an option out there for you. Don’t let the absence of Midjourney’s free trial hinder your creative journey. Embrace these alternatives and continue to explore the exciting possibilities of AI-generated images.

Beginner’s Guide To Image Gradient

So, the gradient helps us measure how the image changes and based on sharp changes in the intensity levels; it detects the presence of an edge. We will dive deep into it by manually computing the gradient in a moment.

Why do we need an image gradient?

Image gradient is used to extract information from an image. It is one of the fundamental building blocks in image processing and edge detection. The main application of image gradient is in edge detection. Many algorithms, such as Canny Edge Detection, use image gradients for detecting edges.

Mathematical Calculation of Image gradients

Enough talking about gradients, Let’s now look at how we compute gradients manually. Let’s take a 3*3 image and try to find an edge using an image gradient. We will start by taking a center pixel around which we want to detect the edge. We have 4 main neighbors of the center pixel, which are:

(iv) P(x,y+1) bottom pixel

We will subtract the pixels opposite to each other i.e. Pbottom – Ptop and Pright – Pleft , which will give us the change in intensity or the contrast in the level of intensity of the opposite the pixel.

Change of intensity in the X direction is given by:

Gradient in Y direction = PR - PL

Change of intensity in the Y direction is given by:

Gradient in Y direction = PB - PT

Gradient for the image function is given by:

𝛥I = [𝛿I/𝛿x, 𝛿I/𝛿y]

Let us find out the gradient for the given image function:

We can see from the image above that there is a change in intensity levels only in the horizontal direction and no change in the y direction. Let’s try to replicate the above image in a 3*3 image, creating it manually-

Let us now find out the change in intensity level for the image above

GX = PR - PL Gy = PB - PT GX = 0-255 = -255 Gy = 255 - 255 = 0

𝛥I = [ -255, 0]

Let us take another image to understand the process clearly.

Let us now try to replicate this image using a grid system and create a similar 3 * 3 image.

Now we can see that there is no change in the horizontal direction of the image

GX = PR - PL , Gy = PB - PT GX = 255 - 255 = 0 Gy = 0 - 255 = -255

𝛥I = [0, -255]

But what if there is a change in the intensity level in both the direction of the image. Let us take an example in which the image intensity is changing in both the direction

Let us now try replicating this image using a grid system and create a similar 3 * 3 image.

GX = PR - PL , Gy = PB - PT GX = 0 - 255 = -255 Gy = 0 - 255 = -255

𝛥I = [ -255, -255]

Now that we have found the gradient values, let me introduce you to two new terms:

Gradient magnitude

Gradient orientation

Gradient magnitude represents the strength of the change in the intensity level of the image. It is calculated by the given formula:

Gradient Magnitude: √((change in x)² +(change in Y)²)

The higher the Gradient magnitude, the stronger the change in the image intensity

Gradient Orientation represents the direction of the change of intensity levels in the image. We can find out gradient orientation by the formula given below:

Gradient Orientation: tan-¹( (𝛿I/𝛿y) / (𝛿I/𝛿x)) * (180/𝝅) Overview of Filters

We have learned to calculate gradients manually, but we can’t do that manually each time, especially with large images. We can find out the gradient of any image by convoluting a filter over the image. To find the orientation of the edge, we have to find the gradient in both X and Y directions and then find the resultant of both to get the very edge.

Different filters or kernels can be used for finding gradients, i.e., detecting edges.

3 filters that we will be working on in this article are

Roberts filter

Prewitt filter

Sobel filter

All the filters can be used for different purposes. All these filters are similar to each other but different in some properties. All these filters have horizontal and vertical edge detecting filters.

These filters differ in terms of the values orientation and size

Roberts Filter

Suppose we have this 4*4 image

Let us look at the computation

The gradient in x-direction =

Gx = 100 *1 + 200*0 + 150*0 - 35*1 Gx = 65

The gradient in y direction =

Gy = 100 *0 + 200*1 - 150*1 + 35*0 Gy = 50

Now that we have found out both these values, let us calculate gradient strength and gradient orientation.

Gradient magnitude = √(Gx)² + (Gy)² = √(65)² + (50)² = √6725 ≅ 82

We can use the arctan2 function of NumPy to find the tan-1 in order to find the gradient orientation

Gradient Orientation = np.arctan2( Gy / Gx) * (180/ 𝝅) = 37.5685 Prewitt Filter

Prewitt filter is a 3 * 3 filter and it is more sensitive to vertical and horizontal edges as compared to the Sobel filter. It detects two types of edges – vertical and horizontal. Edges are calculated by using the difference between corresponding pixel intensities of an image.

A working example of Prewitt filter

Suppose we have the same 4*4 image as earlier

Let us look at the computation

The gradient in x direction =

Gx = 100 *(-1) + 200*0 + 100*1 + 150*(-1) + 35*0 + 100*1 + 50*(-1) + 100*0 + 200*1 Gx = 100

The gradient in y direction =

Gy = 100 *1 + 200*1 + 200*1 + 150*0 + 35*0 +100*0 + 50*(-1) + 100*(-1) + 200*(-1) Gy = 150

Now that we have found both these values let us calculate gradient strength and gradient orientation.

Gradient magnitude = √(Gx)² + (Gy)² = √(100)² + (150)² = √32500 ≅ 180

We will use the arctan2 function of NumPy to find the gradient orientation

Gradient Orientation = np.arctan2( Gy / Gx) * (180/ 𝝅) = 56.3099 Sobel Filter

Sobel filter is the same as the Prewitt filter, and just the center 2 values are changed from 1 to 2 and -1 to -2 in both the filters used for horizontal and vertical edge detection.

A working example of Sobel filter

Suppose we have the same 4*4 image as earlier

Let us look at the computation

The gradient in x direction =

Gx = 100 *(-1) + 200*0 + 100*1 + 150*(-2) + 35*0 + 100*2 + 50*(-1) + 100*0 + 200*1 Gx = 50

The gradient in y direction =

Gy = 100 *1 + 200*2 + 100*1 + 150*0 + 35*0 +100*0 + 50*(-1) + 100*(-2) + 200*(-1) Gy = 150

Now that we have found out both these values, let us calculate gradient strength and gradient orientation.

Gradient magnitude = √(Gx)² + (Gy)² = √(50)² + (150)² = √ ≅ 58

Using the arctan2 function of NumPy to find the gradient orientation

Gradient Orientation = np.arctan2( Gy / Gx) * (180/ 𝝅) = 71.5650 Implementation using OpenCV

We will perform the program on a very famous image known as Lenna.

Let us start by installing the OpenCV package

#installing opencv !pip install cv2

After we have installed the package, let us import the package and other libraries

Using Roberts filter

Python Code:

Using Prewitt Filter #Converting image to grayscale gray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #Creating Prewitt filter kernelx = np.array([[1,1,1],[0,0,0],[-1,-1,-1]]) kernely = np.array([[-1,0,1],[-1,0,1],[-1,0,1]]) #Applying filter to the image in both x and y direction img_prewittx = cv2.filter2D(img, -1, kernelx) img_prewitty = cv2.filter2D(img, -1, kernely) # Taking root of squared sum(np.hypot) from both the direction and displaying the result prewitt = np.hypot(img_prewitty,img_prewittx) prewitt = prewitt[:,:,0] prewitt = prewitt.astype('int') plt.imshow(prewitt,cmap='gray')

OUTPUT:

Using Sobel filter gray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) kernelx = np.array([[-1,0,1],[-2,0,2],[-1,0,1]]) kernely = np.array([[1, 2, 1],[0, 0, 0],[-1,-2,-1]]) img_x = cv2.filter2D(gray_img, -1, kernelx) img_y = cv2.filter2D(gray_img, -1, kernely) #taking root of squared sum and displaying result new=np.hypot(img_x,img_y) plt.imshow(new.astype('int'),cmap='gray')

OUTPUT:

Conclusion

This article taught us the basics of Image gradient and its application in edge detection. Image gradient is one of the fundamental building blocks of image processing. It is the directional change in the intensity of the image. The main application of image gradient is in edge detection. Finding the change in intensity can conclude that it can be a boundary of an object. We can compute the gradient, its magnitude, and the orientation of the gradient manually. We usually use filters, which are of many kinds and for different results and purposes. The filters discussed in this article are the Roberts filter, Prewitt filter, and Sobel filter. We implemented the code in OpenCV, using all these 3 filters for computing gradient and eventually finding the edges.

Some key points to be noted:

Image gradient is the building block of any edge detection algorithm.

We can manually find out the image gradient and the strength and orientation of the gradient.

We learned how to find the gradient and detect edges using different filters coded using OpenCV.

Chatgpt + Enterprise Data: The Next Generation Of Ai

ChatGPT is an OpenAI large language model that may be used for generating text, translate languages, compose various types of creative material, and provide helpful answers to your questions. ChatGPT may be used to build a range of AI-powered customer service apps when combined with enterprise data.

ChatGPT + Enterprise data with Azure OpenAI and Cognitive Search

This sample shows how to use the Retrieval Augmented Generation pattern to create ChatGPT-like experiences over your own data. It makes use of Azure OpenAI Service to connect to the ChatGPT model (gpt-35-turbo) and Azure Cognitive Search to index and retrieve data.

The repo includes example data, so you may try it from beginning to end. We utilize a fictitious company named Contoso Electronics in our sample application, and the experience allows its workers to ask questions regarding benefits, internal policies, and job descriptions and positions.

Features

Interactions for chat and Q&A

Investigates several possibilities for assisting users in determining the credibility of replies through citations, source material monitoring, and so on.

Shows methodologies for data preparation, quick creation, and orchestration of model (ChatGPT) and retriever (Cognitive Search) interaction.

To adjust the functionality and experiment with options, use the settings directly in the UX.

Azure Resource Costs and Configuration Prerequisites for Local Deployment

To run the project locally, you will need the following prerequisites:

Azure Developer CLI

Python 3+

Important: Python and the pip package manager must be in the PATH environment variable in Windows for the setup scripts to work.

Important: Ensure you can run python --version from the console. On Ubuntu, you might need to run sudo apt install python-is-python3 to link python to python3.

Node.js

Git

PowerShell 7+ (pwsh) – For Windows users only.

Important: Ensure you can run pwsh.exe from a PowerShell command. If this fails, you likely need to upgrade PowerShell.

Note: Your Azure account must have Microsoft.Authorization/roleAssignments/write permissions, such as User Access Administrator or Owner.

Installation and Project Initialization

Create a new folder and switch to it in the terminal.

Run the command azd auth login to authenticate with your Azure account.

Run the command azd init -t azure-search-openai-demo to initialize the project using the provided template.

For the target location, the regions currently supporting the models used in this sample are East US or South-Central US. You can check for an up-to-date list of regions and models here.

Starting from scratch:

If you don’t have any pre-existing Azure services and want to start from a fresh deployment, execute the following command:

Run azd up – This will provision Azure resources and deploy the sample application to those resources, including building the search index based on the files found in the ./data folder.

Note: It may take a minute for the application to be fully deployed. If you see a “Python Developer” welcome screen, wait a minute and refresh the page.

Using Existing Resources:

If you want to use existing resources instead of creating new ones, follow these steps:

To change the name of an existing OpenAI service, use the command azd env set AZURE_OPENAI_SERVICE {Name of existing OpenAI service}.

To set the name of the existing resource group where the OpenAI service is provisioned, use the command azd env set AZURE_OPENAI_RESOURCE_GROUP {Name of existing resource group}

If your ChatGPT deployment is not the default ‘chat,’ use the command azd env set AZURE_OPENAI_CHATGPT_DEPLOYMENT {Name of existing ChatGPT deployment}.

If your GPT deployment is not the default ‘davinci,’ use the command azd env set AZURE_OPENAI_GPT_DEPLOYMENT {Name of existing GPT deployment}

Run the command azd up to deploy or re-deploy the repository’s local clone utilizing the existing resources.

Note: If you want to use existing Search and Storage Accounts, refer to the ./infra/main.parameters.json file for a list of environment variables to pass to azd env set in order to configure those existing resources.

Running Locally:

To run the project locally, follow these steps:

Run the command azd login to authenticate with your Azure account.

Change the directory to the app folder.

Run ./start.ps1 or ./start.sh or use the “VS Code Task: Start App” command to start the project locally.

Sharing Environments:

If you want to share a completely deployed and existing environment with someone else, follow these steps:

They must install the Azure CLI on their computer.

To start the project on their system, use azd init -t azure-search-openai-demo

Use the command azd env refresh -e {environment name} to refresh the environment. To run this command, they will need the azd environment name, subscription Id, and location. These values are contained in the./azure/{env name}/.env file. This script will populate the.env file in their azd environment with the settings required to execute the app locally.

Execute pwsh ./scripts/roles.ps1. This will provide the user all of the required roles, allowing them to operate the app locally. You may need to execute this script for them if they do not have the appropriate rights to establish roles in the subscription. Set the AZURE_PRINCIPAL_ID environment variable in the azd.env file or in the current shell to their Azure ID, which they may acquire by running az account show.

Getting Started with the Application:

To get started with the application:

In Azure:

Navigate to the Azure WebApp that azd has deployed. You may discover the URL written out after azd finishes (as “Endpoint”) or on the Azure portal.

Running locally:

Navigate to 127.0.0.1:5000 in your web browser.

Once you’re in the web app:

Experiment with different themes in the conversation or Q&A setting. Experiment with follow-up questions, clarification requests, and requests to simplify or expound on replies in chat.

Investigate the citations and sources for the generated replies.

Also Read: How to Upload a Document to ChatGPT

A Guide To Google’s Advanced Image Search

Searching for images on Google is a simple process.

Many of us can quickly find a picture of something we are searching for by performing a basic Google image search.

This is the standard image search functionality that most of us are used to seeing when we’re looking for a photo on Google.

It is an extremely common search result format, that clearly layouts and categorizes various types of image results.

Advanced Search Filters

By navigating to chúng tôi you can start to perform your standard image search.

The basic search bar appears for you to enter your query.

You can filter image results in the following ways:

Image Size

Here you can choose from large, medium, small, or an icon.

This can help to quickly locate an image based on the specific size you are after.

Whether it be a larger “hero” image or a smaller thumbnail, this feature can make it a speedier process to specify sizes.

Image Color

You have the option of black and white, transparent, or a specific color such as blue, red, yellow, etc.

This can help to easily narrow down an image search to pick up on any certain tones or colors you’re after.

Say you are writing a blog post on beach vacations, and want some images with light blue water, you can quickly find those using this filter.

Image Usage Rights

Labeled for reuse with modification, labeled for reuse, labeled for noncommercial reuse with modification, labeled for non-commercial reuse.

This is helpful in order to easily identify what photos are up for reuse and which ones are not.

Image Type

Options include clip art, line drawing, and GIF.

This can help to easily locate images based on animation or illustration type.

Time

Options include the past 24 hours, past week, past month, past year.

This can help to pin down more recent photos that may be more relevant, dependent on the topic you are after.

Google Advanced Image Search

Now, by navigating to Google’s Advanced Image Search, you will find that this tool uses all of the filters listed above, and then some.

If you still cannot find a specific image that you are after with the basic filters, this is a great tool to try.

This Exact Word or Phrase

This option lets you find images after inputting multiple keywords, to narrow down and specify your search further.

This is very similar to using quotes when searching for something online.

Aspect Ratio

This feature allows you to search specifically for certain image aspect ratios.

So, if you wanted to see an image that should be wide, tall, panoramic, etc., you can find those images here.

Region

This feature allows you to see which photos are public in a specific part of the world.

This makes it easy to pin down photos from places you plan to visit, etc.

Site or Domain SafeSearch

Enable or disable SafeSearch to block inappropriate content.

File Type

If you are after specific file types, you can pick which image file format Google should look for (e.g., JPG, PNG, SVG).

Reverse Image Search

By going to chúng tôi and then selecting “images” in the top right corner, you are brought to Google’s reverse image search.

Now, when you select the camera icon, you can then search for other images by uploading an image.

You can either place an image URL or upload your own specific image.

This is useful for a few different reasons.

Refine & Narrow Your Search

A reverse image search can help you find images that fit a granular set of search criteria, saving you time scrolling through hundreds of images to locate what you’re after.

It helps to refine and narrow your search, creating a better overall user experience.

Pinpoint Image Sources

Say you had saved an image of something when you were searching – for instance, an in-end table that you had been interested in.

You saved the image to your computer, however, cannot remember what website you had pulled it from.

Performing a reverse image search can help you to quickly pinpoint the source.

This can save you a lot of time and hassle, for various types of search results.

Integrate Advanced Image Search

There are billions of image searches happening every day.

Yet, many don’t know the full functionality and capabilities that Google offers for performing more robust image searches.

Utilizing these capabilities can help you save a significant amount of time, especially when searching for a specific image, or certain parameters that an image needs to meet.

More Resources:

Image Credits

All screenshots taken by author, July 2023

Roblox Decals And Image Id Codes Guide

One of the best things about ROBLOX is just how customizable it is. It’s a space where you can express yourself from server to server. One of the best ways to do that is to use decals to assign custom images to objects in the world. You can change a boring wall to a wall with a picture of your favorite sci-fi spaceship or a cute and cuddly kitten.

ROBLOX decals are images that you can apply to many surfaces in ROBLOX. They help you create the world you want to be in and are used in almost every game aspect. You can search for decals in the library by typing a keyword in the “Search for decal” field. There are many available to use right away, with everything from designs to pop media to celebrities.

Each object in the world that you can edit has six parts to it. You can apply a decal on any of these six parts. You can combine different ones to create a variety of effects beyond what a single decal could achieve.

ROBLOX images ID codes also called asset IDs are the unique codes assigned to each decal available on ROBLOX. When you go into the library of assets in Roblox Studio to search for decals, you must take note of the asset ID. You will use it when you apply the decal to a surface.

For example, a Pikachu decal ID you can use is 46059313.

You can apply a ROBLOX decal to any part of an object that accepts it. You adjust which decal is on a part in Roblox Studio.

You can create your own ROBLOX decals and use them on servers that let you change the way objects look. However, you can’t just upload it and use it right away. ROBLOX is a moderated platform, and ROBLOX must first approve anything you add to the landscape. In this way, the community is kept safe and appropriate for all users.

Images shouldn’t be larger than 1024×1024, or else they will be scaled down.

Once you’ve created a decal you want to use, upload it from your profile page. It will be checked automatically and in most cases, quickly approved.

When you’ve created a decal and are ready to seek approval, upload it to get the process started.

Your decal will be available once it’s approved.

The time varies depending on what you uploaded and how it’s processed. Most players say the initial check doesn’t take more than 20 minutes most of the time and speculate that AI does it. However, it can take longer to check specific images and approve them. Some people have said their decals have taken more than a day to gain approval.

Sometimes ROBLOX will choose not to approve your decal. This is often because it violates the community standards or their terms of use.

Community Standards

Roblox values four specific community standards: safety; civility and respect; fairness and transparency; security and privacy. Keep these in mind when you create your decal. Remember that there are lots of children on ROBLOX, and everything should be family-friendly. It’s also important to remember that kindness matters a lot in the game. If you keep it clean and kind, you won’t have a problem getting approval.

If you’re concerned that your images might violate the Terms of Use, read through the agreement before submitting your decal. People upload all kinds of decals, and as long as you follow the Community Standards, it seems unlikely that it would violate the Terms of Use.

If you’re experiencing an issue with your decals, the fix is probably pretty simple.

If all the decals on a server appear blurry, it’s probably a connection issue. Try logging off, resetting your network, and then logging back in to see whether the decals look clearer. If you see blurry images throughout Roblox, consider doing some troubleshooting.

You shouldn’t upload an image that you find through a search. Instead, it needs to be something you’ve changed and made your own. If you just grabbed an image from a search engine, it might not be approved.

Other than that, it might just be a matter of time. All you can do if your decal isn’t being approved is wait to hear from the moderators. If more than a week has gone by without a message, you could try reaching out to ROBLOX support.

Explain the issue and consider linking to a copy of the decal on Imgur or a similar site. That way, it’s easy for the moderator to find the image you’re asking about.

You can use the Spray Paint device to add decals to areas that allow them easily. You have to use the asset ID to apply them. You can buy the Spray Paint device from the shop.

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