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One approach to achieving this goal is through the use of latent diffusion models, which are a type of machine learning model that is capable of generating detailed images from text descriptions. These models work by learning to map the latent space of an image generator network to the space of text descriptions, allowing them to generate images that are highly detailed and realistic.

In this article, we will explore the concept of latent diffusion models in more detail and discuss how they can be leveraged for creative image generation. We will also discuss some of the challenges and limitations of this approach and consider the potential applications and impact of this technology.

Source: Keras

Unveiling the Mysteries of Latent Diffusion

Latent diffusion models are machine learning models designed to learn the underlying structure of a dataset by mapping it to a lower-dimensional latent space. This latent space represents the data in which the relationships between different data points are more easily understood and analyzed.

In the context of image generation, latent diffusion models are used to map the latent space of an image generator network to the space of text descriptions. This allows the model to generate images from text descriptions by sampling from the latent space and then using the image generator network to transform the samples into images.

Na the Challenges of Latent Diffusion

Despite the promise o usion models for creative image generation, there are a number of challenges and limitations to this approach.

The need for large amounts of high-quality training data: The model needs to learn the mapping between the latent space of the image generator network and the space of text descriptions, which requires a lot of data to do accurately.

Difficulty in generating highly detailed and realistic images: Latent diffusion models may still have some limitations in terms of the level of realism they are able to achieve because the image generator network may not be able to fully capture all of the subtle variations and nuances in the data, leading to some loss of realism in the generated images.

Difficulty in controlling the diversity of generated images: Latent diffusion models use a random process to sample the points in the latent space, which may lead to generating similar images or not being able to generate certain types of images.

Difficulty in controlling specific attributes of generated images: It is challenging to control the specific attributes of the generated images, such as the pose, lighting, and background of an object.

Limited ability to handle multi-modal data: Current models are not able to handle multi-modal data well, meaning it is difficult for the model to generate images that are a combination of different attributes or concepts.

Latent Diffusion in Action

There are a number of existing models that use latent diffusion for image generation.

Stable Diffusion Generative Adversarial Network (SD-GAN):

Developed by researchers at Stanford University

Uses stable diffusion to generate highly detailed and realistic images from text descriptions

Produces impressive results in a number of experimental studies

Latent Space Models (LSM) approach:

Developed by researchers at MIT

Works by mapping the latent space of an image generator network to the space of text descriptions

Allows it to generate highly detailed and realistic images from text descriptions

Has been used to generate a wide range of images, including faces, animals, and objects

Produces impressive results in a number of experimental studies

Other models that use latent diffusion for image generation:

Latent Adversarial Diffusion Network (LADN)

Latent Attribute Model (LAM)

These models have been used to generate a wide range of images and have demonstrated promising results in a number of experimental studies

The Future is Here: How Latent Diffusion is Transforming Industries?

Despite these challenges and limitations, latent diffusion models have the potential to revolutionize the way we create and share visual content. These models could significantly accelerate and enhance the creative process by enabling us to generate detailed and realistic images simply by describing them in words.

Latent diffusion models have a lot of potential applications beyond the image generation examples mentioned above. Some other potential applications that may be better than the existing applications include:

Video Generation: Latent diffusion models could be used to generate videos from text descriptions, allowing for the creation of realistic and highly detailed videos.

3D Model Generation: Latent diffusion models could be used to generate 3D models from text descriptions, allowing for the creation of highly detailed and realistic 3D models for use in video games, animation, and other applications.

Speech Generation: Latent diffusion models could be used to generate speech from text descriptions, creating realistic and natural-sounding speech.

Music Generation: Latent diffusion models could be used to generate music from text descriptions, allowing for the creation of highly detailed and realistic music.

Text-to-image Translation: Latent diffusion Models could be used to generate images from text descriptions with more control of the attributes of the image, resulting in more realistic and diverse outputs.

Multi-modal Generation: Latent diffusion models could be used to generate multi-modal outputs such as text-to-image-to-video, allowing for more diverse and realistic outputs.

Overall, these potential applications of latent diffusion models may be better than existing applications because they allow for more control and diversity in the generated outputs and may also be more useful in practical applications.

Wrapping Up

Overall, the use of latent diffusion models for creative image generation has the potential to greatly enhance and accelerate the creative process and is an exciting area of research and development in the field of artificial intelligence.

Latent diffusion models offer a promising approach to generating detailed and realistic images from text descriptions.

These models work by learning to map the latent space of an image generator network to the space of text descriptions, allowing them to generate images that are highly representative of the data.

However, this approach has challenges and limitations, including the need for large amounts of high-quality training data and the difficulty of generating fully realistic images.

I hope, you find this short article useful. Thank you for reading!

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Reitio: Enabling Frictionless Creation Of User

The Metaverse is not and will not ever refer to one company or one virtual world – it will be like describing the World Wide Web as a single webpage or labeling social media as only Facebook.

Make no mistake, virtual worlds are not new by any means. Gaming virtual worlds like Grand Theft Auto and World of Warcraft have had their fair share of success, the same goes for social virtual worlds like Roblox and Minecraft.

With that said, what’s truly revolutionary is a state where these virtual worlds could interoperate with each other: a state where virtual items in a particular virtual world could be seamlessly ported to another virtual world (and back); a condition where virtual assets are freely composable with one another; and a state where conducting virtual asset transactions, no matter its underlying virtual world, does not involve a centralized gatekeeping intermediary.

A single webpage can only fit so much information, but with a network of interconnected webpages, users can create the World Wide Web. Similarly, a single virtual world can only host so much experience, but with a network of interoperable virtual worlds, users can create the Metaverse.

Towards Metaverse Adoption

User-generated content (UGC) is the lifeblood of all networked services. Without imbuing users with free rein to create their own web pages, the World Wide Web would not grow to the degree that it is right now. Virtual worlds are not an exception: just like how people will not use Facebook or Twitter if their friends or favorite content creators are not there, people will also not visit a virtual world if there isn’t sufficient UGC piquing their interest.

However, existing 3D editors (e.g.: Blender, VoxEdit, etc.) require specialized expertise, such that it locks out ordinary people from the creative process. As a result, a large skill gap remains to create one simple 3D asset for use in a single virtual world, let alone across the Metaverse.

A thriving UGC base is of paramount importance for the Metaverse to become widely adopted. The more people participating in UGC creation, the more UGC will be generated, which attracts more users to virtual worlds (and with it, the Metaverse).

The Metaversal 3D Toolkit for the Masses

The root cause of Reitio’s existence is simple enough: creating even a half-decent 3D asset carries a steep learning curve, which locks ordinary users from participating in UGC creation, hindering virtual world adoption.

With this in mind, Reitio is built from the ground up to eliminate the steep learning curve associated with creating a 3D asset, empowering users to create 3D renditions of their imaginations and bring them to life in their virtual world of choice, instead of getting intimidated before they even started.

Being a fully web-based solution, Reitio’s mix-and-match, templates-oriented approach to 3D design ensures that the toolkit is intuitive enough to be picked up by anyone within a few minutes – the Canva for 3D assets.

Modular 3D Legos at your Fingertips

A Reitio-generated 3D asset is fundamentally made up of templates stacked on top of each other – like Lego blocks. Templates come in different flavors: free default templates are provided in-house by our internal designers, while premium templates are listed by community contributors or our virtual world partners, whereby each instance of usage will subject users to a pay-per-use royalty payment to the template creator.

The whole design experience is comparable to creating Lego structures: users start by choosing a ‘base’ template, then gradually work their way up – the extent of what defines a “finished” 3D design will ultimately depend on the user itself, just like the extent of what constitutes a “completed” Lego structure will be in the hands of that structure’s builder.

This design philosophy is what allows Reitio to be extremely user-friendly without sacrificing much of the variability and extensibility offered by more complex fully-fledged 3D editors.

A Novel Stack Primed to Serve the Next Digital Frontier

The current landscape imposes a steep learning curve on ordinary Metaverse participants, such that it locks them out from participating in generating UGCs for the Metaverse.

Reitio is developed from square one to fill this gap: a codeless, no-download, on-the-web 3D design tool so intuitive that it will only take users no longer than a few minutes to get a hang of it and generate their first Metaverse 3D asset.

“At the current pace of UGC generation, the Metaverse will never reach the critical mass of users needed for mass adoption,” said Emerson Li, co-founder, and CEO of Reitio. “User-generated content is the beating heart of the Metaverse – Reitio will allow anyone, regardless of background or experience, to create their own fully customizable 3D assets and bring them to life on their virtual world of choice across the Metaverse.”

Discover The Power Of Konqueror

Many Linux users have a set of applications – browser, file manager, image viewer – that they’re loyal to. In most cases, these applications correspond to the default setup of a Linux distribution. If you’re a KDE user, you’ve probably heard of Konqueror. It’s a powerful application that has been a part of KDE for years, but it’s often unfairly neglected in favor of newer apps. Did you know you can use Konqueror not only as a file manager, but also as a web browser, PDF viewer and document editor?

If this sounds interesting, you can install Konqueror from the repositories of Ubuntu, Debian, Arch and other distributions, or from the source. Note that you’ll have to install many packages as Konqueror’s dependencies if you don’t already have KDE on your system. I recommend you also install a package called “konq-plugins” which contains browser extensions.

Using Konqueror as a Web Browser

The UserAgent Changer extension can modify Konqueror’s identification – it can “pretend” to be a different browser. The “Web Browsing” section in the Configure Konqueror dialog lets you enable Do Not Track headers as a part of browser identification. The only essential thing that Konqueror lacks is private browsing, but other features make up for it.

Split View is one such feature. Accessed via the Window menu or with keyboard shortcuts (Ctrl+Shift+L to split vertically, Ctrl+Shift+T horizontally), Split View divides the active tab into as many small frames as you want. You can open new links in separate frames to preview multiple websites at the same time.

File Management and Beyond

Speaking of file opening, Konqueror can handle several filetypes – you can use it to open PDF files, edit text documents, preview and even convert between basic image formats (JPG, PNG, TIFF, GIF, BMP). It can also display Linux info and man pages in a nice, readable format; just type man:/[name] into the address bar.

Konqueror’s versatility is made possible by KParts, a KDE component framework that’s used to manage file types or embed applications into one another. Technically, any KDE application that supports KParts can be embedded into Konqueror, meaning that Konqueror can “take over” its functionality. This is how Konqueror works as a file manager – it embeds Dolphin and offers all its features. Users switching from Dolphin will surely appreciate this.

Konqueror’s power derives from the convenience and seamless integration of features that would otherwise require opening several applications. True, you need to have Okular, Dolphin and other apps installed if you want to use Konqueror as anything other than a browser, so some people might consider this embedding as “cheating” or even “useless.” On the other hand, it’s extremely practical when working with multiple files since you can view them all in one window or quickly switch between tabs. Konqueror can act as a container for other apps and eliminate clutter from your workflow, and you can always go back to using it as a lighweight web browser.

Ivana Isadora Devcic

Ivana Isadora is a freelance writer, translator and copyeditor fluent in English, Croatian and Swedish. She’s a Linux user & KDE fan interested in startups, productivity and personal branding. Find out how to connect with Ivana here.

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55 Best Stable Diffusion Pixel Art Prompts

Stable Diffusion is a deep learning, text-to-image model released in 2023, which can generate detailed images based on text descriptions.

We’ll start by generating different images using Stable Diffusion, such as buildings, characters, and background textures.

The key to getting good results with pixel art is to use the right text prompts and experiment with different combinations to guide the AI towards the desired outcome.

For buildings: Use text prompts like “pixel art of a house,” “warm and cozy,” and reference popular games like Animal Crossing, which have a pixel art style.

For characters: Avoid using the term “pixel art” in the prompt, as it may not yield good results. Instead, try terms like “pixelated full body,” “character icon concept art,” and “Pixel Perfect.”

Don’t worry, these are just simple tips in order to experiment with your pixel art using Stable Diffusion, but below are the prompts that you can use to generate pixelated ai art in Diffusion.

Pixel art is very popular category of Ai image generation as it looks awesome when made with good prompts in Stable Diffusion or even Midjourney.

But keep in mind that the prompt might not give you results that are free of mistakes, and you might need some negative prompts. However, if you try and try again, you can make the best pixel art there is.

Let’s get started.

Prompt: "16-bit pixel art, cyberpunk cityscape, neon lights, and futuristic vehicles --ar 16:9 --s 2500 --upbeta --q 3"

24-bit pixel art, enchanted forest with magical creatures, style of Legend of Zelda: A Link to the Past (SNES) 1991

32-bit pixel art, steampunk airship battle, dynamic clouds, and detailed gears –ar 3:2 –s 3000 –upbeta –q 4

16-bit pixel art, underwater Atlantis city, with bioluminescent creatures and ancient architecture –ar 9:16 –s 3500 –upbeta –q 3

24-bit pixel art, ancient Egyptian tomb, filled with hieroglyphics and mystical artifacts, style of Castlevania 1986

8-bit pixel art, haunted mansion, with creepy creatures and eerie atmosphere, style of Cave Story 2004

16-bit pixel art, medieval village market, with lively characters and vibrant stalls –ar 1:1 –s 2000 –upbeta –q 3

8-bit pixel art, wild west town, with saloons, horses, and classic western characters, style of Metal Slug 1996

24-bit pixel art, futuristic dystopian city, with towering skyscrapers and flying vehicles, style of Legend of Zelda: A Link to the Past (SNES) 1991

Prompt: "24-bit pixel art, futuristic dystopian city, with towering skyscrapers and flying vehicles, style of Legend of Zelda: A Link to the Past (SNES) 1991"

32-bit pixel art, abandoned amusement park, with creepy attractions and eerie lighting –ar 2:1 –s 3200 –upbeta –q 4

16-bit pixel art, mysterious forest, with glowing mushrooms, and magical creatures –ar 1:2 –s 2200 –upbeta –q 3

24-bit pixel art, ancient Greek city, with marble temples and mythological creatures, style of Castlevania 1986

8-bit pixel art, colorful candy world, with sugary landscapes and confectionary creatures, style of Owlboy 2024

16-bit pixel art, post-apocalyptic wasteland, with dilapidated buildings and mutated wildlife –ar 8:5 –s 2400 –upbeta –q 3

Prompt: "Blasphemous Game + pixel art + pixel art style + cover art style + award winning ilustration --v 4"

32-bit pixel art, magical library filled with ancient books, floating orbs, and mystical creatures –ar 3:4 –s 2600 –upbeta –q 4

8-bit pixel art, classic dungeon crawler, with traps, monsters, and treasure chests, style of Metal Slug 1996

24-bit pixel art, gothic cathedral, with stained glass windows and intricate architecture, style of Legend of Zelda: A Link to the Past (SNES) 1991

16-bit pixel art, underwater volcanic landscape, with vibrant coral reefs and unique sea creatures –ar 5:8 –s 2300 –upbeta –q 3

24-bit pixel art, secret spy base, with high-tech gadgets, hidden rooms, and daring agents, style of Castlevania 1986

8-bit pixel art, epic space battle, with starships, lasers, and explosions, style of Owlboy 2024

16-bit pixel art, mystical fairy village, with whimsical houses, magical creatures, and enchanting scenery –ar 7:8 –s 2500 –upbeta –q 3

32-bit pixel art, colorful fiesta, with lively dancers, vibrant decorations, and festive atmosphere –ar 8:9 –s 3000 –upbeta –q 4

8-bit pixel art, medieval castle siege, with knights, catapults, and dramatic battles, style of Legend of Zelda: A Link to the Past (SNES) 1991

Prompt: "8bit, pixel art, isometric, topdown fantasy, City of Dresden, RPG, high fantasy, artstation, concept art, abandoned castle, moss, final fantasy, legend of zelda, 8bit video game art, pixel artistry"

24-bit pixel art, enchanted swamp, with magical plants, mysterious creatures, and foggy atmosphere, style of Cave Story 2004

24-bit pixel art, mountain monastery, with monks, ancient scrolls, and spiritual atmosphere, style of Owlboy 2024

Prompt: "Pixel art, House of Pixel, many details, collage 1350x350 Pixel, retro super nintendo style"

24-bit pixel art, interdimensional nexus, with portals to various fantastical worlds, style of Metal Slug 1996 –ar 13:14 –s 3600 –upbeta –q 3

16-bit pixel art, post-apocalyptic wasteland, with mutated creatures, scavengers, and dilapidated structures, style of Legend of Zelda: A Link to the Past (SNES) 1991 –ar 14:15 –s 3700 –upbeta –q 2

8-bit pixel art, magical floating islands, with ancient ruins, enchanted forests, and mythical creatures, style of Cave Story 2004 –ar 16:17 –s 3900 –upbeta –q 3

Prompt: "8-bit pixel art, haunted mansion, with ghostly apparitions, eerie sounds, and hidden secrets, style of Metal Slug 1996 --ar 20:21 --s 4300 --upbeta --q 3"

16-bit pixel art, inside an active volcano, with flowing lava, rock formations, and fire elemental creatures, style of Castlevania 1986 –ar 17:18 –s 4000 –upbeta –q 2

32-bit pixel art, colossal clockwork mechanism, with intricate gears, pulleys, and time-themed puzzles –ar 19:20 –s 4200 –upbeta –q 4

8-bit pixel art, haunted mansion, with ghostly apparitions, eerie sounds, and hidden secrets, style of Metal Slug 1996 –ar 20:21 –s 4300 –upbeta –q 3

24-bit pixel art, ancient Egyptian temple, with hieroglyphics, golden treasures, and mysterious traps, style of Cave Story 2004 –ar 22:23 –s 4500 –upbeta –q 3

32-bit pixel art, sprawling futuristic metropolis, with hovercars, towering skyscrapers, and diverse inhabitants –ar 23:24 –s 4600 –upbeta –q 4

Pixel Art Diffusion v3.0 is a custom-trained unconditional diffusion model specifically designed for pixel art creation. It has been trained on over 4,000 pixel art landscapes and portraits, providing a focused dataset for pixel art generation.

This Stable Diffusion model is trained using the dreambooth platform to create pixel art in two distinct styles. Use the trigger word “pixelsprite” for sprite art or “16bitscene” for scene art.

Using Stable Diffusion with Photoshop:

As an alternative to using specialized models, you can combine the power of Stable Diffusion with Photoshop to create pixel art.

Next, open the image in Photoshop and use its built-in pixelating tools to refine the image and achieve a pixel-perfect look.

By leveraging its text-to-image capabilities and refining your images with a solid image editing software, you can generate unique and visually appealing assets tailored to your specific needs.

Also we have some awesome prompt lists and guides reading which you can master the popular Ai tools like Stable Diffusion, ChatGPT, Midjourney and more.

Fix 0Xc1800103 – 0X90002 Media Creation Tool Error

Media Creation Tool is a Microsoft program that is used for the installation of Windows 11/10 into a DVD or USB in order to create a backup and easy access for later reinstallations. People use this Windows backup in case they have an issue with their current Windows and need to reinstall it to fix the issue or just install Windows in other computers. However, like in any program, some Windows users experience the 0xC1800103 – 0x90002 Media Creation Tool Error while using the Media Creation Tool to install Windows 11/10 on a USB or DVD.

There was a problem running this tool, Erroe Code 0xC1800103 – 0x90002

How to Fix Media Creation Tool Error 0xC1800103 – 0x90002

Whenever you get the 0xC1800103 – 0x90002 Media Creation Tool error on a Windows computer, you should first restart the computer and then restart the installation process. If the error persists, use the following solutions:

Turn off VPN on your computer

Repair System Files

Clear SoftwareDistribution folder

Set correct date, time and language settings

Delete $Windows.~BT and $Windows.~WS folders.

1] Turn off the VPN on your computer

If the error comes up again, then you should try the next solution.

2] Repair System Files

For any program, process, and even Windows OS, properly, the system files must be in good condition. This error is one of the issues you encounter in Windows PC as a result of corrupt or missing System files. To resolve the error, you must repair the system files and then start the installation again. This can be performed using the built-in Windows troubleshooting method, and we’ve covered how to do it in the linked article.

3] Clear SoftwareDistribution folder

Open the Windows Search Bar and type Command Prompt. On the resulting menu, select Run as Administrator.

Type each of these commands and press Enter to stop some Windows services temporarily. Make sure you type these commands and hit Enter one by one.

net stop wuauserv

net stop cryptSvc

net stop bits

net stop msiserver

In the run command box, type %SystemRoot%SoftwareDistributionDownload and press Enter.

Now delete all the files in the resulting folder.

After doing that, you have to restart the Windows services we stopped earlier. So, reopen the command prompt and run this command one after the other.

net start wuauserv net start cryptSvc net start bits net start msiserver

Now, you should restart your PC, and Windows will be forced to replace it and thereby fix this error

Related: Windows Media Creation Tool error – Problem running this tool or starting setup

4] Set the correct date, time, and language settings 5] Delete $Windows.~BT and $Windows.~WS folders

Another method to fix this issue is to delete $Windows.~BT and $Windows.~WS directories, as the folder may be why you are getting the 0xC1800103 – 0x90002 Media Creation Tool error.

You can then rerun the Media Creation Tool and see if the error has gone.

Read: How to download Windows 11/10 ISO without using Media Creation Tool

Does Windows Media Creation Tool still work?

Yes, Windows Media Creation Tools smoothly on Windows 11 as well as Windows 10 without any error. It allows users to download Windows 11/10 install files on a DVD or USB for later installation. That said, if you want to download Windows ISO or create a bootable flash drive, you can certainly make use of the Media Creation Tool.

Read: Fix Error Code 0x80042405-0xA001A on Media Creation Tool

Why am I getting the 0xC1800103 – 0x90002 Media Creation Tool Error?

Most of the time, corrupt files or other problems with your computer’s files cause the 0xC1800103 – 0x90002 Media Creation Tool Error. We’ve also learned that the problem may also be brought on by a VPN that is actively running on your computer. Therefore, you should be aware of those factors to prevent the issue and use the solutions above to fix it if you are already encountering it.

Make An Animated Gif With Stable Diffusion (Step

Stable Diffusion AI is a great way to synthesize images from text. Do you can spice up your artworks with animation?

In this step-by-step guide, we will add an animated waterfall and moving clouds to the image of a fantasy steam punk castle shown below. The prompt for generating this image can be found in this page.

Original image of a fantasy steam punk castle.

After this tutorial, you will be able to recreate this final animated GIF image:

Animated waterfall and moving clouds added.

Stable Diffusion GUI

Starting image

The prompt and parameters necessary to generate the original image can be found in this page. Alternatively, you can save the following image to your computer for this tutorial.

Starting image for this tutorial.

Step 1: Add a waterfall

We will use inpainting to add the waterfall.

In the Stable Diffusion GUI, go to img2img tab and select the inpaint tab.

Upload the starting image by dragging and dropping it to the inpaint image box.

Use the paintbrush tool to create a mask like below. The masked area is where the waterfall will be located.

Inpaint mask for the waterfall.

Put in the following settings:

PromptwaterfallSampling methodEulerSampling steps40CFG20Batch size8Parameters for inpainting the waterfall

Hit Generate. You should get 8 different images with a waterfall added.

Increase CFG value if you see an unwanted object. Decrease it if you prefer to have more variations.

Hit generate again until you see an image you like. Save that image to your computer.

I picked the image below for the castle image with waterfall added.

Waterfall added by inpainting.

This image will be our base image for the animated GIF. In the next step, we will create small variations of this image and use them as the frames for the animated GIF.

Step 2: Animate the water fall and the clouds

An animated GIF is nothing but a series of images displayed consecutively. The variations between images should be subtle but noticeable in order to create a perception of animation.

To do so, we first drag and drop the castle image with the waterfall to the inpaint box.

Create the inpaint mask like below.

Inpaint mask for animating the cloud and the waterfall.

Use the following parameters for inpainting.

Promptcloudy, steamSampling methodEulerSampling steps40CFG20Denoising strength0.2Batch size8Parameters for inpainting the waterfall

Recall we don’t want to change the image too much so using a low denoising strength like 0.2 is very important. (Recall that denoising strength of 0 changes nothing and at 1 your original image in the masked area will be completely ignored.)

Below are a few images I generated. If you pay close attention, you should see they have small differences.

You will want to cherry-pick the images you want to use for the animated GIF.

You will need 5 to 10 images. Save them to your computer.

Increase denoising strength if you want more variations. Decrease if you want less.

Step 3: Create the animated GIF

Now we will make the animated GIF.

Finally, save the animated GIF. The animated GIF should look like:

Animated waterfall and moving clouds added.

For comparison, this is the animated GIF created with denoising strength 0.1.

Animated GIF created with denoising strength 0.1. The variation is more subtle.

Tweaks for your own artwork

Note that the settings in this article are specific for the image we used. You may need to play with the following parameters when you create your own artworks.

Prompts for inpainting: Change to what you want to add in place of the mask.

CFG: Increase if you see irrelevant objects or too much variations.

Denoising strength: Increase if you want more changes in your animated GIF.

GIF delay time and crossfade parameters: Adjust to smooth out the animations.


In this tutorial, we detailed how to use inpainting to create images for making an animated GIF. I hope this serves as a good starting point for you to create your own artworks.

Don’t hesitate to drop me a line if you have any questions!

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