Trending February 2024 # Microsoft Bing Is Getting An Ai Image Generator # Suggested March 2024 # Top 8 Popular

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Microsoft Bing is getting an AI image generator in the coming weeks, which allows users to turn text into digital art.

Let’s say a picture of a Shiba Inu as an astronaut would go perfectly with a blog post you’re writing.

You turn to the search engines for a free-to-use image, but you can’t find one that matches your criteria.

With the new Image Creator tool coming to Microsoft Bing, you can generate the exact image you need by inputting descriptive text.

See an example of Image Creator in action below:

Image Creator is powered by DALL-E 2 image generator technology developed by OpenAI.

In a blog post, Microsoft says Image Creator can assist searchers with creating images that don’t exist yet.

All you have to do is type in an image description, and Image Creator will generate it for you.

Availability

Microsoft is taking a “measured” approach with the rollout of Image Creator, starting with a limited preview in select locations.

The gradual launch is due to how new the DALL-E 2 technology is.

Microsoft is exercising caution out of a commitment to responsible AI, the company says:

“It’s important, with early technologies like DALL∙E 2, to acknowledge that this is new and we expect it to continue to evolve and improve. We take our commitment to responsible AI seriously. To help prevent DALL∙E 2 from delivering inappropriate results across the Designer app and Image Creator, we are working together with our partner OpenAI, who developed DALL∙E 2, to take the necessary steps and will continue to evolve our approach.”

Image Creator will employ techniques to prevent misuse, including query blocking on sensitive topics and filters to limit the generation of images that violate Bing’s policies.

Microsoft will take feedback from the limited preview to improve Image Creator before rolling it out to everyone.

Source:  Microsoft

Featured Image: Zhuravlev Andrey/Shutterstock

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Windows 11 22H2 Startup Sound Quality Is Getting Lowered By Microsoft

Windows 11 22H2 startup sound quality is getting lowered by Microsoft

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Windows 11 users spotted a decrease in the quality of the operating system.

The startup sound for Windows 11 went from

2,304 Kbps to only 1,536 Kbps.

Nobody knows what this 33% quality reduction means for the time being.

Microsoft Executive Vice President and Chief Product Officer, Panos Panay, has been on an unrelenting campaign to educate users about just how quality-focused Windows 11 really is.

The Redmond-based tech company’s main concern is to deliver a superior product, unparalleled in quality and client friendliness.

However, it seems that Microsoft decided to reduce the quality of certain aspects of the new operating system, an action that sort of contradicts all previous statements.

We’re referring to the user-friendly and warm startup sound for Windows 11, which apparently took a quality hit when least expected.

Microsoft lowers Windows 11 startup sound quality

It seems that Microsoft has further plans for its latest OS that contradict statements made not that long ago, about everything that has to do with Windows 11 being superior.

We said that because, allegedly, the tech giant has lowered the bit rate of the Windows 11 Startup sound in the upcoming version 22H2, Sun Valley 2.

The audio file for the Windows 11 startup sound has a reduced bit rate of 1,536 Kbps, compared to the 2,304 Kbps it had in last year’s build 22463.

This change was noticed in Beta Channel build 22621, which has been confirmed to be the RTM release candidate for Windows 11 22H2.

During Nickel’s development, the Windows 11 startup sound in chúng tôi had its bitrate lowered from 2304 to 1536. chúng tôi Xeno (@XenoPanther) July 21, 2023

We’re actually looking at a 33% reduction in the bit rate, which let’s be honest, will not ultimately lead to a perceptible difference in the fidelity of the Windows 11 startup sound.

Some netizens say that Microsoft has also made changes to the music itself, mentioning that the silence at the very start of the sound has been slightly prolonged in Build 22621.

We’re not saying that this might be the end of Windows 11 as we know it. However, some users, using high-fidelity sound equipment, may be ticked off by this reduction.

As for the rest of us, happy with a regular pair of headphones or a plain pair of PC speakers won’t really realize that something happened in the first place.

And, since we’re on the topic of Windows 11, remember that Microsoft released Build 25163 to the Dev Channel, with some pretty important changes coming to the Store.

Two days ago it also released builds 22621.436 and 22622.436 (KB5015888) to the Beta Channel, along with a new tutorial video series for Windows 11 beginners.

KB5015882 also brought some important fixes, in case you didn’t catch up yet. Also, if you are a Windows OS enthusiast, keep in mind that Microsoft will release new operating systems once every 3 years.

With that in mind, don’t forget to check all the updates surrounding Windows 12 and a potential 2023-2024 release.

Considering what you just read, what is your take on Microsoft lowering the Windows 11 startup sound quality?

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Ai Image Editor: Guide & Tools

We’re reader-supported. When you buy through links on our site, we may earn an affiliate commission.

If you’re one of the millions of new creators empowered by generative AI, you might be searching for an AI image editor to modify and enhance your projects.

From inpainting and outpainting to background removal and upscaling, these tools cater to artists and designers of all levels.

Whether you’re a seasoned graphic editor or just starting, these AI tools will help you spend less time editing and more time creating.

In this post, I will share the best AI image editor for different types of users.

I will also walk through how I use new AI tools and traditional photo editors to elevate my content.

Source: Adobe Firefly

Adobe Firefly offers a wide range of AI image editor features, such as text-to-image and generative fill.

The generative fill feature is one of the easiest to use and most impressive AI image editing features available today.

Also: Adobe Firefly Alternatives

For the full experience, you can start a Photoshop free trial or log in to your existing account.

Source: chúng tôi (Stable Diffusion)

Getimg.ai provides a powerful interface that uses generative AI to add, remove, and expand your images.

The platform is powered by Stable Diffusion, an open-source latent diffusion model capable of rendering images using text-based prompts.

Here’s a short step-by-step process for how I added a motorcycle to an image that I previously generated on Midjourney.

Step 1: Upload original imageStep 2: Erase portion of image you want to changeStep 3: Enter prompt for what you want to add (i.e., motorcycle)

Adding or removing aspects of an image using an AI model is called inpainting and outpainting.

Inpainting involves erasing portions of an image. Then, you enter a text prompt about what you want to change about the erased part of the image.

Outpainting uses AI to expand an existing image. The Stable Diffusion will apply the same style or scene and build around the edges of your original photo. You can also use a text prompt to add additional elements.

Using these Stable Diffusion features generally requires access to a powerful graphics card.

As a result, you pay a fraction of the cost to generate or edit thousands of AI images.

Canva is a popular and affordable tool that makes creating and editing content easy across several platforms like Instagram, Youtube, Blogs, and more.

I use Canva to edit the images on this site. The subscription is affordable (~$12.99 per month) and provides access to a massive library of graphics and fonts.

Canva has added several AI features perfect for hassle-free AI image editing.

One of Canva’s new AI features is similar to inpainting.

It allows you to highlight a portion of an image and use a text prompt to make visual changes.

Source: Canva

Canva also has a remove background feature that I use all the time for things like logos, product images, and quick photo editing.

Source: Canva background remover

If you don’t have Canva, I will share some free background removal options later.

Source: Stable Diffusion AUTOMATIC1111

If you are willing to go through some setup, several Stable Diffusion packages on GitHub allow you to edit images with AI locally on your desktop.

This option is great if you want to avoid paying to use a 3rd party service and have a good GPU.

If this sounds good, check out the Stable Diffusion Inpaiting Setup guide.

If you are using Midjourney, the best way to achieve maximum resolution is to use the internal upscale command.

After generating a Midjourney image, there are eight buttons below the image.

Four of them start with “U.” These buttons let you increase the resolution size of your favorite image.

Midjourney upscale command

Midjourney Upscale Command Key:

CommandLocationU1Top left imageU2Top right imageU3Bottom left imageU4Bottom right imageTable: Midjourney upscale commands

If you aren’t using Midjourney or want to increase your AI image resolution even further, you can use a dedicated AI upscaler.

I recommend checking out this free tool called Upscale Media.

Source: Upscale Media

This app lets you increase the resolution by 2-4x, which is usually more than enough.

Sometimes you just want to remove the background from an image quickly.

If you have an iPhone, Apple added a feature that lets you select particular objects in a photo by simply holding down your finger.

Source: Apple

If you’re on a desktop, I recommend using RemoveBG.

You simply upload your photo, and the site’s AI will identify the subject and remove the unwanted background.

This process is perfect if you need a transparent PNG image.

Combining an AI image generator like Midjourney with RemoveBG lets you mix and match the subject and background of your favorite AI images.

I’ll show you what I mean.

Remove background from one Midjourney image and layer on top of another

With AI image generators like Midjourney and Stable Diffusion, anyone who can describe something with words now has the power to create visual art.

Photoshop and other image editing tools are commonplace to graphic designers, but most people interested in AI art have probably never used these tools.

So you generated a beautiful image in Midjourney, but you want to change the color of the background, or you want to remove a particular object.

We actually don’t need AI for this step.

Many free image editing tools online, like PhotoPea, have features that enable you to edit AI images quickly.

For example, the spot healing brush in PhotoPea lets you easily remove unwanted text or items in a Midjourney image.

PhotoPea spot healing brush

Even Canva or PowerPoint allows you to stack and crop images into layers, which for most use cases, is good enough.

Eventually, a powerful and intuitive AI editing tool will exist.

But at the moment, AI image generation plus simple photo editors can get us most of the way there.

In the meantime, I’m on the lookout for new AI tools like AI image editors that can make our lives easier.

Be sure to subscribe to our short and sweet newsletter if you don’t feel like endlessly sifting through Twitter and TikTok to find AI nuggets.

Midjourney’s Enthralling Ai Art Generator Goes Live For Everyone

This is the second time that the platform has opened to all as a beta. On July 18, the platform opened up for 24 hours. In an email sent out to Midjourney beta testers on Tuesday, however, founder David Holz wrote that the “Midjourney beta is now open to everyone.”

Midjourney is one of the more interesting entrants in the small but growing field of AI art, which takes user-generated queries, runs them through an AI algorithm, and lets the algorithm pull from its source images and apply various artistic techniques to the resulting image. Midjourney’s images look somewhat similar in quality to those outputted by Latitude’s Voyage, though Latitude charges $14.99 for 20 image credits as part of its paid plan.

For now, beta users will receive about 25 free images as part of the Midjourney beta. After that you’ll have to pay either $10/mo for 200 images per month, or a standard membership of $30 per month for unlimited use. Midjourney will allow corporate use of the generated images for a special enterprise membership. Otherwise, the images belong to you.

On Sept. 29, the “godfather” of AI art, DALL-E, also went “free” on a similar, credit-based plan. DALL-E, however, seems far more literal than Midjourney. If you’re hoping for a more photo-realistic prompt, stick with DALL-E; however, if you prefer more artistic creations, Midjourney seems like the better bet. Meta also showed off Make-A-Video, which takes AI art and interpolates them to create GIFs and small video clips.

There’s only one catch with Midjourney: prompts and the resulting images are generated via Discord, the social networking/chat app. To be part of Midjourney, you’ll need a free Discord membership. (PCWorld’s beginner’s guide to Discord can help you get settled if you’re new to the service.) The following link will sign you up for the Midjourney service: discord.gg/midjourney.

While Midjourney does provide a user manual, using it boils down to this. Within Discord, you’ll need to join one of the quickly growing number of “#newbies” channels — it doesn’t matter which one. Once inside the channel, simply type “/imagine” and then the text prompt. Remember, even though there’s AI generating the output, guiding the style is up to you. It’s worth scrolling up and down the various “newbies” channels to see what prompts generate which responses, and which styles you can apply.

Each Midjourney prompt will generate a new matrix of four images. Here, I used “walking in a literal sea of stars” as a prompt.

Mark Hachman / IDG

Some results will amaze you; others will disappoint. There’s a bit of luck in it all. I wasn’t nearly as impressed with my “a kraken appears from beneath San Francisco Bay,” but the resulting image from “a castle on an asteroid floating through space” at the top of this story is pretty awesome.

Each time you enter a request, you’ll receive an almost immediate result, in a matrix of four images that are all variations of the theme. Below each image you’ll also see several buttons. Each button has a meaning: the “U” buttons upscale a particular image, for 1 to 4; the “V” buttons ask the AI to provide a variation the theme on the particular image. The circular arrow is a “do over” for the entire array. Be careful! Simply tapping each button generates the command, and charges you an image credit in return.

How to get more Midjourney image credits for free

By default, all of the prompts that you generate are “fast,” or immediate. You can get more image credits, for “free,” by switching to the “/relax” command. This puts your request in a queue, rather than processing it immediately. It’s unclear how long this additional queue time actually is, but Midjourney won’t charge you a credit for using it.

Unfortunately, this loophole only works as a subscriber. On the other hand, if you do end up signing up for the $10/mo plan and get hooked, but can’t justify spending $30 per month on cool images, you might try this option!

This story was updated in Sept. 29 to add details about DALL-E and Make-A-Video.

An Approach Towards Neural Network Based Image Clustering

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

Introduction:

Now you might say Don’t Be Lazy and mine the data you need, to which my reply would be Nah!! So, how do we tackle this problem? That is exactly what this article is all about, to apply Deep Learning directly on testing data (here images) without the hassles of creating a training data set and training a Neural Network on that data set.

A Convolution Neural Network as a Feature Extractor

Before I go any further, we first need to discuss why do we need a feature extractor? And how a Convolution Neural Network (C.N.N) can be made to act as one. 

Need of a Feature Extractor for Image Data and how a CNN acts like one:

Let’s say an algorithm needs two eyes, one nose, and a mouth, like features, to classify an image as a face, but in different images, these features are present at different pixel locations and hence simply flattening the image and giving it to an algorithm will not work. Here is where Convolution Layers of a C.N.N comes to play.  These layers act as a feature extractor for us and break down the image into finer and finer details. Consider the example given below:

This is an image of a cat and here is how the first Convolution layer of Vgg16 sees it

Notice the different images, these are the feature maps learned by our CNN, some feature maps focus on the outline, some on textures, while some on finer details like ears and mouth, convolution layers at the next stage breaks down these features even to finer details. Check what is learned by our C.N.N at the 9th layer. It seems here our CNN learned about textures.

Now that we have seen a Convolution layer can learn specific features of an image, the next part of this article will walk you through its coding.

Seeing what different Convolution layers of C.N.N see (Code):

The following code displays how you can achieve the above results using Vgg16 a pre-trained CNN:

## lets Define a Function that can show Features learned by CNN's nth convolusion layer ### preprocessing for img for vgg16 ## Now lets define a model which will help us ## see what vgg16 sees ## let make predictions to see what the Cnn sees ## Plotting Features

If you have reached till here, in this article now you know what a CNN sees and how to visualize it. Back to the problem at hand, the next part of this article focuses on how you can make an efficient clustering algorithm using the above knowledge.

Designing an Image Clustering Algorithm

For this section I will be working with the keep-babies- safe data set on Kaggle. This was the challenge hosted by Hacker Earth where we were supposed to create an image clustering model to classify the given images into two categories, namely toys or consumer products, and read the text written on the consumer products.  The following are a few images from this data set.

The following code will walk you through my solution for this problem:

##################### Making Essential Imports ############################ import sklearn import os import sys import matplotlib.pyplot as plt import cv2 import pytesseract import numpy as np import pandas as pd import tensorflow as tf conf = r'-- oem 2' ##################################### # Defining a skeleton for our # # DataFrame # ##################################### DataFrame = { 'photo_name' : [], 'flattenPhoto' : [], 'text' : [], } ####################################################################################### # The Approach is to apply transfer learning hence using Resnet50 as my # # pretrained model # ####################################################################################### MyModel = tf.keras.models.Sequential() MyModel.add(tf.keras.applications.ResNet50( include_top = False, weights='imagenet', pooling='avg', )) # freezing weights for 1st layer MyModel.layers[0].trainable = False ### Now defining dataloading Function def LoadDataAndDoEssentials(path, h, w): img = cv2.imread(path) DataFrame['text'].append(pytesseract.image_to_string(img, config = conf)) img = cv2.resize(img, (h, w)) ## Expanding image dims so this represents 1 sample img = img = np.expand_dims(img, 0) img = tf.keras.applications.resnet50.preprocess_input(img) extractedFeatures = MyModel.predict(img) extractedFeatures = np.array(extractedFeatures) DataFrame['flattenPhoto'].append(extractedFeatures.flatten()) ### with this all done lets write the iterrrative loop def ReadAndStoreMyImages(path): list_ = os.listdir(path) for mem in list_: DataFrame['photo_name'].append(mem) imagePath = path + '/' + mem LoadDataAndDoEssentials(imagePath, 224, 224) ### lets give the address of our Parent directory and start path = 'enter your data's path here' ReadAndStoreMyImages(path) ###################################################### # lets now do clustering # ###################################################### Training_Feature_vector = np.array(DataFrame['flattenPhoto'], dtype = 'float64') from sklearn.cluster import AgglomerativeClustering kmeans = AgglomerativeClustering(n_clusters = 2) kmeans.fit(Training_Feature_vector) A little explanation for the above code:

The above code uses Resnet50, a pre-trained  C.N.N, for feature extraction, we just remove its head or the final layer of neurons used for prediction of classes, we then feed our image to the CNN and gets a feature vector as an output, which essentially is a flattened array of all the feature maps learned by our CNN at the second last layer of Resnet50. This output vector can be given to any clustering algorithm (say kmeans(n_cluster = 2) or agglomerative clustering) which classify our images into the desired number of classes. Let me show you the clusters that were made by this approach.

The code for this visualization is as follows

## lets make this a dataFrame import seaborn as sb import matplotlib.pyplot as plt dimReducedDataFrame = pd.DataFrame(Training_Feature_vector) dimReducedDataFrame = dimReducedDataFrame.rename(columns = { 0: 'V1', 1 : 'V2'}) dimReducedDataFrame['Category'] = list (df['Class_of_image']) plt.figure(figsize = (10, 5)) sb.scatterplot(data = dimReducedDataFrame, x = 'V1', y = 'V2',hue = 'Category') plt.grid(True) plt.show() Conclusion

This article describes image clustering by explaining how you can cluster visually similar images together using deep learning and clustering. It is entirely possible to cluster similar images together without even the need to create a data set and training a CNN on it.

Also, here are a few links to my notebooks that you might find useful:

My Solution to the problem described above

Thanks for reading!!

Related

How To Unify Colors In An Image With Photoshop

How To Unify Colors In An Image With Photoshop

Written by Steve Patterson.

In this tutorial, we’ll learn how to unify colors in an image with Photoshop. As photographers, artists and designers, color is one of the most powerful tools we have for conveying the message, mood or theme of an image. But like all good things, too much of it can be bad. In photography, it’s all too easy to capture too many colors in the scene, distracting the viewer’s eye and lessening the overall impact of the image.

Of course, we can always try to control or minimize colors before we take the shot. But that’s not always possible or practical. What we need, then, is a way to unify the colors in the image afterwards. By “unify the colors”, I mean taking colors that are very different from each other and making them more similar.

How do we do that? As we’ll learn in this tutorial, it’s actually very easy, especially with Photoshop. All we need to do is choose a single color to use for the overall theme of the image, and then mix, or blend, that color in with the photo’s original colors. Let’s see how it works!

I’m using Photoshop CC but everything we’ll be learning is fully compatible with Photoshop CS6 and earlier, so everyone can follow along. You can get the latest Photoshop version here.

Let’s get started!

Why Do We Need To Unify Colors? Too Many Colors

First, let’s look at a simplified version of the problem and the solution. When we’re done, we’ll take what we’ve learned and apply it to an actual photo. Here’s a quick design I made in Photoshop using six shapes, each filled with a different color. Along the top, we have red, yellow and green, and on the bottom, we have cyan, blue and magenta:

Six shapes, each adding a different color to the image.

If I was designing something for, say, a child’s birthday party, this might work. But in most cases, I think you would agree that there are too many different colors in this image. In terms of color theory, we would say that there are too many different hues, with “hue” being what most people think of as the actual color itself (as opposed to the saturation or lightness of the color).

So if there are too many colors, what can we do about it? Well, we could always convert the image to black and white which would certainly solve the problem. Or, we could unify the colors so they look more similar to each other. How do we do that? We do it by either choosing one of the existing colors in the image, or choosing a completely different color, and then mixing that color in with the others.

Choosing A Unifying Color

If we look in my Layers panel, we see the image sitting on the Background layer (I’ve flattened the layers here just to keep things simple):

The Layers panel showing the image on the Background layer.

Then, I’ll choose Solid Color from the top of the list:

Choosing a Solid Color fill layer.

Photoshop will pop open its Color Picker where we can choose the color we want to use. The color you need may depend on the mood you’re trying to convey or on the theme of a larger, overall design. For this example, I’ll choose a shade of orange:

Choosing a color from the Color Picker.

Photoshop fills the document with the color.

The reason that the color is blocking the image is because, if we look in the Layers panel, we see that Photoshop has placed my Solid Color fill layer, named “Color Fill 1”, above the image on the Background layer. Any layer that sits above another layer in the Layers panel appears in front of that layer in the document:

The Layers panel showing the fill layer above the Background layer.

Related: Understanding Layers in Photoshop

Mixing The Colors – The “Color” Blend Mode

Changing the blend mode of the fill layer to Color.

By changing the blend mode to Color, we allow our Solid Color fill layer to affect only the colors in the image below it. It no longer has any effect on the tonal values (the brightness) of the image.

If we look at my document after changing the blend mode to Color, we see that my shapes are now once again visible. But, rather than appearing with their original colors, they now appear as different shades of the same color (the color I chose in the Color Picker):

The shapes re-appear, but now they’re all the same hue.

Mixing The Colors – Layer Opacity

We’re on the right track, but since our goal here is to make the colors more similar, not make them all the same hue, I still need a way to mix the color from the fill layer in with the original colors of the shapes. To do that, all I need to do is adjust the opacity of the fill layer. You’ll find the Opacity option in the upper right of the Layers panel, directly across from the Blend Mode option.

Opacity controls the transparency of the layer. By default, the opacity value is set to 100%, which means that the layer is 100% visible. Lowering the opacity value makes the layer more transparent, allowing the layer(s) below it to partially show through. If we lower the opacity of our Solid Color fill layer, we’ll allow the colors from the original image to show through the fill layer’s color, effectively mixing the colors from both layers together!

To show you what I mean, I’m actually going to start by lowering my opacity value all the way down to 0%:

Lowering the opacity of the fill layer to 0%.

At 0% opacity, the fill layer becomes 100% transparent, and we’re back to seeing the shapes in their original colors, completely unaffected by the fill layer:

The result with the Solid Color fill layer’s opacity set to 0%.

Watch what happens, though, as I start to increase the fill layer’s opacity. I’ll start by increasing it to 25%:

Increasing the fill layer’s opacity to 25%.

By increasing the opacity to 25%, I’m telling Photoshop to mix 25% of the fill layer’s color with 75% of the original colors, and here’s the result. Since each shape now has some of the orange from the fill layer mixed into it, the orange is unifying their colors so they no longer look quite so different. The effect is subtle at the moment, but even so, we can already see that they’re becoming more similar:

The result with the fill layer’s opacity set to 25%.

If I increase the opacity of the fill layer to 50%:

Increasing the fill layer’s opacity to 50%.

I’m now mixing 50% of the fill layer’s color in with 50% of the original colors, and now the shapes are looking even more similar:

The result with the fill layer’s opacity set to 50%.

And, if I increase the fill layer’s opacity to 75%:

Increasing the fill layer’s opacity to 75%.

Photoshop is now mixing 75% of the fill layer’s color with only 25% of the original colors, creating a very strong color theme:

The result with the fill layer’s opacity set to 75%.

Changing The Unifying Color

Photoshop re-opens the Color Picker, allowing me to choose a different color. This time, I’ll choose a pinkish purple:

Choosing a new color from the Color Picker.

The result after changing the fill color.

At the moment, I still have the opacity of my fill layer set to 75%. If the effect is too strong, all I need to do is lower the opacity. I’ll lower it down to 50%:

Lowering the fill layer’s opacity to 50%.

And now, the shapes are still being unified by the new color, but the effect is more subtle:

The result after lowering the fill layer’s opacity.

How To Unify Colors In An Image

And that’s really all there is to it! So now that we’ve looked at the basic theory behind unifying colors with Photoshop, let’s take what we’ve learned and apply it to an actual photo. You can use any photo you like. I’ll use this one since it contains lots of different colors (colorful umbrellas photo from Adobe Stock:

The original image. Photo credit: Adobe Stock.

Step 1: Add A Solid Color Fill Layer

Then, we’ll choose Solid Color from the top of the list:

Choosing a Solid Color fill layer.

Step 2: Choose Your Color

Choose your color from the Color Picker.

Step 3: Change The Fill Layer’s Blend Mode To “Color”

Next, back in the Layers panel, change the blend mode of the Solid Color fill layer from Normal to Color:

Changing the blend mode of the fill layer to Color.

Your image will re-appear, but at the moment, it’s completely colorized by the fill layer:

The image after changing the blend mode to Color.

Step 4: Lower The Fill Layer’s Opacity

To mix the fill layer’s color in with the original colors of the image, simply lower the fill layer’s opacity. The exact value you need will depend on your image, so keep an eye on it as you adjust the opacity until you’re happy with the results. For this image, I’ll lower the opacity to 25%:

Lower the opacity to blend the colors together.

This mixes 25% of the fill layer with 75% of the original image, unifying the colors nicely:

The result after lowering the fill layer’s opacity.

Before And After

To make the difference with my image easier to see, here’s a split-view comparison showing the original colors on the left and the unified colors on the right:

The original (left) and unified (right) colors.

Sampling A Unifying Color From The Image

Finally, let’s look at how to choose a unifying color directly from the image itself. So far, we’ve been choosing colors from the Color Picker. But let’s say I want to choose a color from one of the umbrellas. To do that, the first thing I’ll do is lower the opacity of my fill layer all the way down to 0%. This will make the fill layer completely transparent for a moment so I’m seeing the original colors in the image:

To choose a color from the image, first lower the fill layer’s opacity to 0%.

The sampled color appears in the Color Picker.

Increasing the fill layer’s opacity to 20%.

And here’s the result. Just as we saw earlier, I was able to instantly change the color theme of the image simply by changing the color of my fill layer, and then adjusting the opacity as needed:

The final result.

And there we have it! That’s how to easily unify colors in an image using nothing more than a Solid Color fill layer, the Color blend mode, and the layer opacity option, in Photoshop! Check out our Photo Retouching section for more image editing tutorials. And don’t forget, all of our Photoshop tutorials are now available for download as PDFs!

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