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By applying specific modern state-of-the-art techniques, stable diffusion models make it possible to generate images and audio. Stable Diffusion works by modifying input data with the guide of text input and generating new creative output data. In this article, we will see how to generate new images from a given input image by employing depth-to-depth model diffusers on the PyTorch backend with a Hugging Face pipeline. We are using Hugging Face since they have made an easy-to-use diffusion pipeline available.

Learn More: Hugging Face Transformers Pipeline Functions

Learning Objectives

Understand the concept of Stable Diffusion and its application in generating images and audio using modern state-of-the-art techniques.

Gain knowledge of the key components and techniques involved in Stable Diffusion, such as latent diffusion models, denoising autoencoders, variational autoencoders, U-Net blocks, and text encoders.

Explore common applications of diffusion models, including text-to-image, text-to-videos, and text-to-3D conversions.

Learn how to set up the environment for Stable Diffusion, including utilizing GPU and installing necessary libraries and dependencies.

Develop practical skills in applying Stable Diffusion by loading and diffusing images, creating text prompts to guide the output, adjusting diffusion levels, and understanding the limitations and challenges associated with diffusion models.

This article was published as a part of the Data Science Blogathon.

What is a Stable Diffusion?

Stable Diffusion models function as latent diffusion models. It learns the latent structure of input by modeling how the data attributes diffuse through the latent space. They belong to the deep generative neural network. It is considered stable because we guide the results using original images, text, etc. On the other hand, an unstable diffusion will be unpredictable.

The Concepts of Stable Diffusion

Stable Diffusion uses the Diffusion or latent diffusion model (LDM), a probabilistic model. These models are trained like other deep learning models. Still, the objective here is removing the need for continuous applications of signal processing denoting a kind of noise in the signals in which the probability density function equals the normal distribution. We refer to this as the Gaussian noise applied to the training images. We achieve this through a sequence of denoising autoencoders (DAE). DAEs contribute by changing the reconstruction criterion. This is what alters the continuous application of signal processing. It is initialized to add a noise process to the standard autoencoder.

In a more detailed explanation, Stable Diffusion consists of 3 essential parts: First is the variational autoencoder (VAE) which, in simple terms, is an artificial neural network that performs as probabilistic graphical models. Next is the U-Net block. This convolutional neural network (CNN) was developed for image segmentation. Lastly is the text encoder part. A trained CLIP ViT-L/14 text encoder deals with this. It handles the transformations of the text prompts into an embedding space.

The VAE encoder compresses the image pixel space values into a smaller dimensional latent space to carry out image diffusion. This helps the image not to lose details. It is represented again in pixeled pictures.

Common Applications of Diffusion

Let us quickly look at three common areas where diffusion models can be applied:

Text-to-Image: This approach does not use images but a piece of text “prompt” to generate related photos.

Text-to-Videos: Diffusion models are used for generating videos out of text prompts. Current research uses this in media to do interesting feats like creating online ad videos, explaining concepts, and creating short animation videos, song videos, etc.

Text-to-3D: This stable diffusion approach converts input text to 3D images.

Applying diffusers can help generate free images that are plagiarism free. This provides content for your projects, materials, and even marketing brands. Instead of hiring a painter or photographer, you can generate your images. Instead of a voice-over artist, you can create your unique audio. Now let’s look at Image-to-image Generation.

Also Read: Bring Doodles to Life: Meta Open-Sources AI Model

Setting Up Environment

This task requires GPU and a good development environment like processing images and graphics. You are expected to ensure you have GPU available if you want to follow along with this project. We can use Google Colab since it provides a suitable environment and GPU, and you can search for it online. Follow the steps below to engage the available GPU:

Go to the Runtime tab towards the top right.

Then select GPU as a hardware accelerator from the drop-down option.

You can find all the code on GitHub.

Importing Dependencies

There are several dependencies in using the pipeline from Huggingface. We will first start by importing them into our project environment.

Installing Libraries

Some libraries are not preinstalled in Colab. We need to start by installing them before importing from them.

# Installing required libraries %pip install --quiet --upgrade diffusers transformers scipy ftfy # Installing required libraries %pip install --quiet --upgrade accelerate

Let us explain the installations we have done above. Firstly are the diffusers, transformers, scipy, and ftfy. SciPy and ftfy are standard Python libraries we employ for everyday Python tasks. We will explain the new major libraries below.

Diffusers: Diffusers is a library made available by Hugging Face for getting well-trained diffusion models for generating images. We are going to use it for accessing our pipeline and other packages.

Transformers: Transformers contain tools and APIs that help us cut training costs from scratch.

# Backend import torch # Internet access import requests # Regular Python library for Image processing from PIL import Image # Hugging face pipeline from diffusers import StableDiffusionDepth2ImgPipeline

StableDiffusionDepth2ImgPipeline is the library that reduces our code. All we need to do is pass an image and a prompt describing our expectations.

Instantiating the Pre-trained Diffusers

Next, we just make an instance of the pre-trained diffuser we imported above and assign it to our GPU. Here this is Cuda.

# Creating a variable instance of the pipeline pipe = StableDiffusionDepth2ImgPipeline.from_pretrained( "stabilityai/stable-diffusion-2-depth", torch_dtype=torch.float16, ) # Assigning to GPU"cuda") Preparing Image Data

Let’s define a function to help us check images from URLs. You can skip this step to try an image you have locally. Mount the drive in Colab.

# Accesssing images from the web import urllib.parse as parse import os import requests # Verify URL def check_url(string): try: result = parse.urlparse(string) return all([result.scheme, result.netloc, result.path]) except: return False

We can define another function to use the check_url function for loading an image.

# Load an image def load_image(image_path): if check_url(image_path): return, stream=True).raw) elif os.path.exists(image_path): return Loading Image

Now, we need an image to diffuse into another image. You can use your photo. In this example, we are using an online image for convenience. Feel free to use your URL or images.

# Loading an image URL # Displaying the Image img Creating Text Prompts

Now we have a usable image. Let’s now show some diffusion feats on it. To achieve this, we wrap prompts to the pictures. These are sets of texts with keywords describing our expectations from the Diffusion. Instead of generating a random new image, we can use prompts to guide the model’s output.

Note that we set the strength to 0.7. This is an average. Also, note the negative_prompt is set to None. We will look at this more later.

# Setting Image prompt prompt = "Some sliced tomatoes mixed" # Assigning to pipeline pipe(prompt=prompt, image=img, negative_prompt=None, strength=0.7).images[0]

Now we can continue with this step on new images. The method remains;

Loading the image to be diffused, and

Creating a text description of the target image.

You can create some examples on your own.

Creating Negative Prompts

Another approach is to create a negative prompt to counter the intended output. This makes the pipeline more flexible. We can do this by assigning a negative prompt to the negative_prompt variable.

# Loading an image URL # Displaying the Image img # Setting Image prompt prompt = "" n_prompt = "rot, bad, decayed, wrinkled" # Assigning to pipeline pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0] Adjusting Diffusion Level

You may ask about altering how much the new image changes from the first. We can achieve this by changing the strength level. We will observe the effect of different strength levels on the previous image.

At strength = 0.1

# Setting Image prompt prompt = "" n_prompt = "rot, bad, decayed, wrinkled" # Assigning to pipeline pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.1).images[0]

On strength = 0.4

# Setting Image prompt prompt = "" n_prompt = "rot, bad, decayed, wrinkled" # Assigning to pipeline pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.4).images[0]

At strength = 1.0

# Setting Image prompt prompt = "" n_prompt = "rot, bad,decayed, wrinkled" # Assigning to pipeline pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=1.0).images[0]

The strength variable makes it possible to work on the effect of Diffusion on the new image generated. This makes it more flexible and adjustable.

Limitations of Diffusion Models

Before we call it a wrap on Stable Diffusion, one must understand that one can face some limitations and challenges with these pipelines. Every new technology always has some issues at first.

We trained the stable diffusion model on images with 512×512 resolution. The implication is that when we generate new photos and desire dimensions higher than 512×512, the image quality tends to degrade. Although, there is an attempt to solve this problem by updating higher versions of the Stable Diffusion model where we can natively generate images but at 768×768 resolution. Although people attempt to improve things, as long as there is a maximum resolution, the use case will primarily limit printing large banners and flyers.

Training the dataset on the LAION database. It is a non-profit organization that provides datasets, tools, and models for research purposes. This has shown that the model could not identify human limbs and faces richly.

Stable Diffusion on a CPU can run in a feasible time ranging from a few seconds to a few minutes. This removes the need for a high computing environment. It can only be a bit complex when the pipeline is customized. This can demand high RAM and processor, but the available channel takes less complexity.

Lastly is the issue of Legal rights. The practice can easily suffer legal matters as the models require vast images and datasets to learn and perform well. An instance is the January 2023 lawsuits from three artists for copyright infringement against Stability AI, Midjourney, and DeviantArt. Therefore, there can be limitations in freely building these images.


In conclusion, while the concept of diffusers is cutting-edge, the Hugging Face pipeline makes it easy to integrate into our projects with an easy and very direct code underside. Using prompts on the images makes it possible to set and bring an imaginary picture to the Diffusion. Additionally, the strength variable is another critical parameter. It helps us with the level of Diffusion. We have seen how to generate new images from images.

Key Takeaways

By applying state-of-the-art techniques, stable diffusion models generate images and audio.

Typical applications of Diffusion include Text-to-image, Text-to-Videos, and Text-to-3D.

StableDiffusion Depth2ImgPipeline is the library that reduces our code, so we only need to pass an image to describe our expectations.

Reference Links

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Frequently Asked Questions

Q1. What is the Stable Diffusion method?

A. The Stable Diffusion method is a technique used in machine learning for generating realistic and high-quality synthetic images. It leverages diffusion processes to progressively refine noisy images into coherent and visually appealing samples.

Q2. Where can I use Stable Diffusion for free?

A. Stable Diffusion methods, such as Diffusion Models, are available as open-source implementations. They can be accessed and used for free on various platforms, including GitHub and other machine learning libraries.

Q3. What is an example of a Stable Diffusion?

A. An example of a Stable Diffusion technique is the Diffusion Models with denoising priors. This approach involves iteratively updating an initial noisy image by applying a series of transformations, resulting in a smoother and clearer output.

Q4. What is the best Stable Diffusion model?

A. The best Stable Diffusion model choice depends on the specific task and dataset. Different models, such as Deep Diffusion Models or variants like DALL-E, offer different capabilities and performance levels.


You're reading How To Generate Images Using Stable Diffusion?

How To Generate Beautiful Ai Images Free

AI image generation offers several benefits that make it an attractive option for individuals and businesses alike. Firstly, it eliminates the need for extensive graphic design skills or experience. With AI, anyone can create visually appealing images, regardless of their artistic background.

See More: How To Create Your Own Animated AI Avatar: 3 Easy Steps

Additionally, AI image generation is incredibly time-efficient. Instead of spending hours or even days manually designing an image, AI tools can generate them within minutes. This is particularly beneficial for projects with tight deadlines or when you need multiple images quickly.

Adobe Firefly is a remarkable AI image generation tool that enables you to create beautiful images at no cost. Compared to other tools like Bing Image Creator and Midjourney, which require a subscription or a monthly fee, Adobe Firefly offers a free alternative with excellent results.

To access Adobe Firefly, follow these simple steps:

Visit the Adobe Firefly website.

Choose to either create an account or log in with your Google account.

Once you are logged in, you can start generating AI images effortlessly.

By offering the option to log in with your Google account, Adobe Firefly ensures a hassle-free experience for users.

Now that you have access to Adobe Firefly, let’s explore how to generate your image. On the bottom bar of the tool’s interface, you will find a text box. Simply describe what you want your image to depict. It could be a description of an object, a scene, or an abstract concept.

Also Check: How to Use DALL·E 2 to Create AI Images

You can now save the image to your preferred location on your computer or device. It’s that simple!

In conclusion, Adobe Firefly provides a fantastic opportunity for individuals and businesses to create beautiful AI images without any cost. The benefits of AI image generation, such as its simplicity, cost-effectiveness, and time-efficiency, make it an ideal choice for anyone in need of stunning visuals.

We encourage you to try Adobe Firefly and explore the limitless possibilities it offers. Start creating captivating images that perfectly align with your vision and requirements.

Yes, you can use the AI-generated images created with Adobe Firefly for both personal and commercial purposes. However, it’s always a good practice to review the licensing terms and conditions provided by Adobe to ensure compliance.

Absolutely! Adobe Firefly is designed to be user-friendly and accessible to individuals with no prior design experience. Its intuitive interface and straightforward process make it a great tool for beginners.

Adobe Firefly does not impose any limitations on the number of images you can generate. Feel free to create as many images as you need to bring your ideas to life.

Yes, once you have downloaded the AI-generated image, you can further customize it using image editing software of your choice. This allows you to add personal touches or make specific adjustments according to your requirements.

Yes, Adobe Firefly is an online tool, and therefore, it requires an internet connection for access and functionality. Make sure you have a stable internet connection while using the tool.

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How To Generate Youtube Video Titles Using Ai.

There are three main things you need to be aware of when creating content for YouTube. Content, Thumbnails and Titles! All three play an important role and should work in unison to get people watching your content so follow along as we introduce you to a brand new Ai tool to help create titles for YouTube videos that will get you way more views than you are currently getting.

Related: How to get Dislikes back on YouTube. Show Dislike count on YouTube again.

With such a massive volume of content available, grabbing viewers’ attention has become increasingly challenging. Without good thumbnails and good titles, even the best content will go unwatched. So we’re going to take a lot at some reasons why titles are so important and a little further on, how you can use Ai to create really good titles for YouTube videos.

Why YouTube Titles Are Super Important.

What Makes for a Good YouTube Video Title?

Keyword Optimization: Including relevant keywords in your title can improve your video’s discoverability. Conducting keyword research and incorporating popular search terms can help your video rank higher in search results and attract organic traffic.

Length and Formatting: YouTube truncates long titles, so it’s crucial to keep them concise. Aim for titles between 50-60 characters to ensure they are fully displayed in search results. Using capital letters, punctuation, and engaging formatting can make your title visually appealing and stand out.

Don’t copy the big channels too much: You may think that copying huge YouTube channels is the best course of action but that will usually negatively affect your channel. Why? Well, big YouTube channels rely less on the algorithms and more on their subscriber base and their reputation, this means they can usually write whatever they want for a title and just rely on their thumbnail (usually with their face) to do all the heavy lifting.

The Power of Thumbnail-Title Combination

Visual Representation: Thumbnails provide a visual representation of your video’s content. Choose an image that accurately represents the essence of your video and captures attention. Incorporate bold colours, clear visuals, and compelling imagery that aligns with your title and content. It’s also a good idea to put your face in the thumbnail if that is relevant to your channel.

Consistency and Branding: Consistent branding across your thumbnails helps establish recognition and familiarity with your channel. Use consistent colours, fonts, and visual elements that align with your brand identity, making it easier for viewers to identify your videos in search results. Again this is where your face comes in handy! Use it if you are confident!

Contrast and Text Overlay: Adding text overlay to your thumbnails can reinforce the message conveyed in your title. Ensure the text is clear, legible, and large enough to be easily read in small thumbnail sizes. Just don’t overdo the amount of text on your thumbnail. Keep it simple.

The best Ai to Generate YouTube Video Titles?

How To Generate Ssl Certificates On Linux Using Openssl

The process of generating SSL/TLS certificates is a common task for many Linux system administrators. Luckily, even if you are not an administrator, it is easy to do so using OpenSSL, an open-source tool that is installed by default on many Linux distributions. Here we explain what OpenSSL is, how to install it, and most importantly, how to use it to generate SSL and TLS certificates on your system.

What Is OpenSSL?

OpenSSL is a library developed by the OpenSSL Project to provide open-source SSL and TLS implementations for the encryption of network traffic. It is readily available for a variety of Unix-based distributions and can be used to generate certificates, RSA private keys, and perform general cryptography-related tasks.

Limitation of Self-Signed SSL Certificate

When you use OpenSSL to generate a SSL certificate, it is considered “self-signed.” It means that the SSL certificate is signed with its own private key and not from a Certificate Authority (CA).

As such, the SSL certificate cannot be “trusted” and should not be used for any public facing site. If used, the users will likely see warnings from their browsers about the certificate.

A self-signed certificate is useful for local development or any apps running in the background that don’t face the Internet.

Alternatively, you can use LetsEncrypt or obtain a certificate verified by a trusted authority, such as Comodo CA.


Most Linux distributions already have a version of OpenSSL built in by default. If not, you can easily install it.

You can install it on Ubuntu and Debian by using the apt command:





On CentOS (or its alternative), you can install it by using the yum command:


yum install


You can also easily download it from its website as a “.tar.gz” file.

Basic Usage

Now that you have OpenSSL installed, we can have a look at some of the basic functions the program provides.

You can start by viewing the version and other relevant information about your OpenSSL installation:

openssl version


You can check out the manual provided:



Generating a Certificate Using a Configuration File

Generating a certificate using OpenSSL is possible in many ways. One of them is by using a configuration file which will specify details about the organization.

To start, you can create a configuration file called “config.conf” and edit it using Nano:




Here is an example of the content of the configuration file:

[req] default_bits = 2048 prompt = no default_md = sha256 req_extensions = req_ext x509_extensions= v3_ca distinguished_name = dn [dn] C = US ST = California L = Los Angeles O = Org OU = Sales [ v3_ca ] subjectKeyIdentifier=hash authorityKeyIdentifier=keyid:always,issuer:always basicConstraints = CA:true [req_ext] subjectAltName = @alt_names [alt_names] DNS.1 = chúng tôi can just copy and paste this into the file and make the necessary changes to reflect your organization’s information.

Next, you have to generate an RSA private key, which will then be used to generate a root certificate -:

openssl genrsa


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The -out flag is used in this case to specify the name of the key that will be generated. A key size of 2048 bits is also specified, which is the default for RSA keys.

You will also have to generate a Certificate Signing Request (CSR):

openssl req



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In this case, the -key flag is used to specify the RSA key, the -out flag specifies the name of the CSR file and the -config flag is used to specify the name of the config file.

After this, you can generate a root certificate, which is used to generate our final certificate:

openssl req






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In the process of generating this root certificate, the -sha256 flag is used to specify SHA256 as the message digest.

Now, as for the final step, we can finally type the following to generate our certificate:

openssl x509







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chúng tôi -CA flag specifies the root certificate, the -CAkey flag specifies the private key and -extfile specifies the name of the configuration file. The “final.crt” file will be the SSL certificate you want.

Generating a Certificate without a Configuration File

Alternatively, you can also generate a certificate using OpenSSL without a configuration file.

You can start by generating an RSA private key:

openssl genrsa


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Next, you will have to generate a CSR:

openssl req



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chúng tôi generating a CSR, you will be prompted to answer questions about your organization.

Finally, we can generate the certificate itself:

openssl x509





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chúng tôi of Keys and Certificates

Keys and certificates are easily checked and verified using OpenSSL, with the -check flag:

openssl rsa



chúng tôi can check certificate signing requests:

openssl req




chúng tôi certificates as well:

openssl x509




chúng tôi Asked Questions 1. Do I still have to worry about Heartbleed?

Heartbleed (CVE-2014-0160) is an old vulnerability found in OpenSSL in 2014. TLS-servers and clients running OpenSSL both were affected. A patch was quickly released a few days after its discovery, and this vulnerability isn’t something to worry about in 2023 as long as you are running a modern and up-to-date version of OpenSSL.

If you are using OpenSSL on Debian and Ubuntu-based systems, you can always update it by running the following commands:

sudo apt update && sudo apt upgrade openssl 2. How long do SSL certificates last before they expire?

This depends on the value you choose when generating the certificate. This can be specified by using the -days flag when generating a certificate.

Image credit: Sls written on wooden cube block by 123RF

Severi Turusenaho

Technical Writer - Linux & Cybersecurity.

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How To Pick Memorable Images For Your Content

It’s pretty clear that images are a big factor for improving content marketing pieces.

While “the more the better” isn’t factually accurate as adding 100 images for the sake of images isn’t smart, more than zero is a good start.

Images can help readers break down sections of expansive content and pull the key information from it.

How do you know what images are memorable and which to include in your content to do that?

I’ll show you.

How Many Images Should Content Have?

Before we jump into picking memorable images for your content, it’s important to know how many images your content pieces need.

There is no set, magical number that will launch your conversions through the roof.

It’s not like adding 15 images instead of 11 is going to make a world of difference in your content.

In fact, adding too many images for the sole purpose of imagery can backfire on you.

Image totals depend completely on the:

Type of content you are writing.



Are you writing content for a beginner audience? You probably need more images to explain steps and show how specific tools work.

If you are writing to CEOs, showcasing a tool walkthrough doesn’t really make sense. Why? CEOs are rarely in the day to day grunt-work using marketing tools. They are delegating that task to someone else.

Using images in content is more complex than meets the eye.

Before starting to add images to your next post, first consider your target audience in detail to understand what they expect, want, and what can be directly applied in their day-to-day as efficiently as possible.

Now, let’s go over some surefire ways to pick memorable images for your next post.

1. Avoid Stock Photos in Body Content

Stock photos can feel magical at first:

Precreated, crystal clear, high-quality images that fit almost any topic and search query.

But when it comes to aiding your body content in a post, stock photos suck big time.

Stock photos can be fantastic for featured images, but they add zero value when randomly throw into the body of your content.

Why? They are too generic to fit your subject and simple screenshots could produce more effective visual stimulus.

Stock photos don’t have the data-backing, either. Studies have repeatedly shown them to produce even negative effects.

Avoid using stock photos in the body content and try using more tutorials, walkthroughs, and screenshots.

2. Showcase More Screenshots, Tutorials & Walkthroughs

Memorable content contains tidbits that are either actionable in nature or thought-provoking.

And both of these require some form of guided walkthrough, tutorial, or screenshots to showcase examples.

Tutorials are only effective if you show, not tell.

Through-provoking posts demand images to show what you are talking about and bring it to life.

The easiest way to add images to your posts is by doing any of the above.

Showcase step-by-step how you used a specific marketing tool to get the job done. Show what settings you tweaked:

Show the exact piece of information you want readers to focus on when they look at your image:

Most images can be complex and detailed with dozens of different factors at play.

Highlighting and calling-out specific sections of the image saves the reader time and frustration by literally signaling the important aspects for them.

Using a tool like Skitch or Evernote, you can screenshot and highlight images to do just that:

If you are just using basic screenshots without these functions, readers could get frustrated and confused, leaving them with less value than you want to give and that your post surely offers.

No matter what type of post you are writing, try including examples via screenshots and walkthroughs. Provide call-outs to focus attention on the important aspects of the image and your images will provide tons more value to readers.

3. Reserve GIFs for Informal Posts or Niche Audiences

GIFs are funny, lighthearted, and can spice up otherwise boring content.

Infamous writers like Lianna Patch use them all the time in their content:

But GIFs don’t work for every writer, every audience, and every type of content published.

Lianna has a specific style that’s informative yet witty, hilarious, and informal.

It works for her audience and her ability to craft funny yet accurate content.

But not all audiences are going to be receptive to GIFs. And not all posts need them.

When it comes to using GIFs, reserve them for more informal opinion pieces or niche audiences.

4. Develop Custom Visuals to Synthesize Information

Struggling to find memorable images for a blog post that aren’t:




Cited from external sources where you might not have legal rights to use it from?

Then custom visuals are your answer.

Developing custom visuals for your content can help you add credibility, synthesize complex concepts, data, numbers, and even drive more backlinks to your site.

As an example, on my company’s site, our blog posts are always complete with custom visuals to help break down complex information, like this writing services results comparison:

This 13,000+ word case study would be nearly impossible to read, digest, or understand without these custom visuals.

Custom visuals like these also provide the benefit of getting easier backlinks to your site.

Look at almost any content marketing piece online and there will be sourced and cited images from top pages.

And almost always these images are branded, unique, custom developed images.

Even if you aren’t writing about unique data that can be developed into charts and tables, you can still produce nice custom images.

For example, I had these images developed for another piece of content on my company website:

Instead of breaking down numbers, it breaks down a concept on types of B2B companies using real-world analogies and an easy to understand visual aid.

These images are cheap and easy to develop on your own or with freelancers and can add tons of value to plain text content.

Need more memorable images for your content pieces? Develop your own custom images!

You can do this on your own if you are familiar with graphic design or use tools like Canva, etc.

Or you can hire freelancers to get the work done on sites like Design Crowd, UpWork, and more.


Content without images isn’t inherently bad. But content with images is almost always more enjoyable and even improves social sharing and recall.

So it’s essentially a no-brainer. If you can fit images in naturally, that is.

Avoid stock photos within the body of your content. They are great for featured images, but images in your text for the sake of images can hinder performance.

Do you love GIFs? So do I, but they aren’t appropriate for every audience and post. If you are writing a long-form, formal guide, they probably should be left out.

One surefire way to add memorable images is via tutorials and walkthroughs. These are easy ways to include helpful images that break up your content.

If you feel like going all out, spring for custom visuals that can help your audience synthesize complex topics or data.

More Resources:

Image Credits

Featured Image: chúng tôi taken by author, February 2023

How To Fix Images Not Loading In Chrome

By default, Google Chrome is set to display images on the sites you visit in this browser. If you find the browser doesn’t display images for a site, that site may be experiencing problems serving pictures. If your issue persists with other sites, your browser may be the problem.

You or someone else may have disabled the image load option in Chrome, turned off JavaScript, or one of your extensions may be causing Chrome not to load your images. This guide will look at potential ways to fix your issue.

Table of Contents

Use a Different Browser to Access Your Webpage

When Chrome doesn’t display images on a site, switch to another web browser on your computer and see if you can load the pictures. You may try other browsers like Microsoft Edge, Mozilla Firefox, Vivaldi, or Opera.

If your site images load in other web browsers, the Chrome browser has issues. In this case, read on to discover more fixes.

If your other browsers also fail to load images, the site has a problem. In this case, the site admin will have to enable picture loading or fix issues that prevent images from being served.

Allow Sites to Display Images in Google Chrome

Chrome offers an option to let you disable and enable the loading of images in your web browser. If you or someone else has set this option off, you’ll have to turn the option back on to see the photos on your sites.

It’s quick and easy to toggle on this option in Chrome. Here’s how.

Open Chrome, select the three dots at the top-right corner, and select Settings.

Select Privacy and security on the left and Site Settings on the right.

Scroll down the page and choose Images.

Activate the Sites can show images option.

Relaunch Chrome and open your site. Your site images should load without issues.

Enable JavaScript in Chrome to Show Pictures

Some sites use JavaScript to serve images, and if you’ve disabled this feature in Chrome, you’ll have to turn the option on to see your images.

You can turn on JavaScript in Chrome as follows:

Launch Chrome, select the three dots at the top-right corner, and choose Settings.

Select Privacy and security on the left and Site Settings on the right.

Scroll down and choose JavaScript.

Enable the Sites can use JavaScript option.

Reopen Chrome, and your issue should be resolved.

Use Incognito Mode in Chrome

Chrome’s incognito mode isolates your current browsing session from your other browsing sessions and your past browsing data. It’s worth using this mode to see if your browser data is causing interference with your site images.

Select the three dots at the top-right corner of Chrome and choose New Incognito window.

Open your site in the new window that launches.

If your site images load in the incognito window, Chrome’s browsing history or extensions may be problematic. In this case, follow the following methods to resolve your issue.

Turn Off Chrome’s Extensions

Chrome allows you to install extensions so you can get the most out of your favorite web browser. Sometimes, one or more of these extensions become problematic, causing various issues with the browser.

It’s worth disabling your extensions to see if that fixes the image not loading issue. You can toggle off one extension at a time to find the culprit.

Launch your site and see if your photos load. If they do, enable one extension at a time to find the problematic one.

When you find the culprit extension, remove that extension by selecting Remove on Chrome’s Extensions page.

Clear Chrome Cache and Browsing Data

Chrome stores cache and other browsing files to speed up and enhance your web browsing experience. When these files go corrupt or become problematic, your browser starts to suffer.

Therefore, it’s worth clearing your browser’s cache and other data to see if that helps fix your image loading problem.

Close and reopen Chrome, and your site pictures should load.

Rename Chrome’s Data Folder

One way to fix many issues with Chrome is to rename the browser’s data folder. Chrome stores your browser’s configuration in this folder, and renaming the folder forces Chrome to recreate the configuration.

That helps resolve many issues with Chrome.

Open Chrome, and your browser will reconfigure the options.

If you use a Mac computer, your Chrome data folder is located at the following path:

On Linux, you’ll find Chrome’s data folder here:

Update Google Chrome

An obsolete version of any app can cause various issues. If you haven’t updated Chrome in a long time, Chrome’s older version is why your site images aren’t loading.

Chrome receives and installs any browser updates automatically. If that doesn’t happen for some reason, you can run a manual check to find and install the latest updates.

Close and reopen Chrome to bring your updates into effect.

Make Chrome Graphical Again

Chrome’s image loading issue isn’t too difficult to fix. The error usually occurs when you’ve misconfigured an option in the browser or your browser data has gone corrupt. Once you’ve fixed these items, your browser will start displaying your site images as usual.

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