AI image generators use text prompts to quickly create stunning images. While easy, affordable, and fun – AI image generators can also be dangerous when used to produce false images that have real world implications.

AI image generators are neural networks fed billions of image-text pairs. Over time they learn what dogs look like, what Vermeers resemble and other aspects of art history.

Lensa AI

Artificial Intelligence (AI) has generated much excitement this year with regards to creating interesting art. DALL-E Mini app became widely known for its goofy prompts that generated artistic renditions of users; TikTok filters also allowed people to be transformed into anime characters. Recently, Lensa AI gained momentum for creating stylized portraits using fantasy and sci-fi elements; however its popularity has raised ethical and copyright issues among its users.

The app uses the open-source neural network model Stable Diffusion, which employs images sourced from across the web to learn techniques. While public, some sources of data may contain copyrighted material; a Twitter user pointed out that Stable Diffusion used images of other artists’ signatures that violated their rights; some artists have accused its company of taking credit for their works by including it into magic avatars on its app.

Lensa’s magical avatars are created from photos uploaded by users. When uploading, Lensa searches its database of image styles until one best matches their personality, and displays their results on-screen for users to save to their phone or use as profile pictures across social media networks.

Some have used Prisma Labs’ app Lensa AI to produce images that sexualize women and objectify minorities, and create lewd or nude portraits. Prisma Labs defended its model, saying that its purpose isn’t meant to replace artists but act as an “assistance tool.” Furthermore, Lensa AI creators have collaborated with National Endowment for the Arts to produce more realistic models of faces.


Wombo recently unveiled its PicSo text-to-art AI generator as a recent addition to their growing number of text-to-art apps. Named after Pablo Picasso to symbolize that everyone can become great artists, PicSo uses machine learning technology to search for themes based on words you enter, before skillfully blending them together into distinctive images based on styles like Van Gogh, Epic Sketching, Octane Render Render Cyberpunk or CG Render Render rendering.

PicSo is designed with user friendliness in mind, with an intuitive and precise art output. You can use PicSo for anything from creating social media profile pictures or avatars, to book covers or game characters; its unique features have made it a go-to app among users of all kinds.

Jasper, a free text-to-art AI tool, quickly and accurately generates high-quality digital artwork using prompts you provide. Utilizing Stable Diffusion denoising technique, which continuously refines an image until its details match that of text prompt, Jasper produces clear artwork free from noise that is easily downloaded for downloading.

Jasper stands out among text-to-art AIs by being highly intuitive in responding to prompts, providing a variety of choices. This AI can help those wanting to create specific images without the time or skills themselves – perfect for bloggers seeking fresh content! Furthermore, its user-friendly interface makes Jasper simple and efficient – fast and free as well.


Recently on Twitter you may have come across bizarre AI-generated images seemingly appearing out of nowhere. These were created with Dream, an app allowing users to generate “AI-powered paintings” by typing brief descriptions for an AI to paint from. Sometimes the results are so stunning as to appear to be genuine human artwork.

This tool utilizes an image-to-text algorithm that identifies various aspects of photographs and emphasizes them. This type of artificial intelligence, commonly referred to as deep learning, allows machines to learn without direct human input.

There are various AI image generators on the market, each offering unique strengths and weaknesses. One popular option is StyleGAN, which utilizes GANs to produce high-resolution images with realistic details and textures; BigGAN similarly employs GANs to generate quality images.

While this new technology is exciting, there remain concerns over prejudice in its machines. Algorithms relying on existing data can easily be fooled by biased information which leads to biased results that appear racist or sexist.

As such, it’s essential that individuals understand how these systems function and how they might be applied in the future. Furthermore, copyright laws vary across countries – rights may belong either to those who created an algorithm or trained the machine, or may belong directly to the person or user who commissioned it. Furthermore, understanding this technology’s limits as well as potential misuse is equally vital.

Stable Diffusion

Stable Diffusion, the latest AI model to make waves, can create striking visuals from text descriptions. It has quickly been adopted into apps, webs, and services transforming how we experience art. Before its release with Stable Diffusion’s release only select models like DALL*E 2 and Midjourney were capable of this type of application (Craiyon being better for memes), but now anyone with access to an adequate GPU can create their own generative art works with Stable Diffusion’s release.

Stable Diffusion stands out from other image-to-text generators by using natural language interpretation and creation to generate images. Furthermore, it offers edit capabilities of existing images by changing content, adding or removing objects or otherwise editing their appearance. Available for desktop computers and mobile devices alike – Stable Diffusion offers numerous free features like an image cropping tool and an option to save images as JPEGs files for added convenience.

The application works by analyzing text prompts and producing images based on its training data. If a text prompt is “apple,” for instance, then the computer will create an image depicting it using specific artistic styles. Furthermore, you can adjust an image using various settings and filters until you achieve your desired effect.

To make these adjustments, the user must enter their description of what they wish for in the text box. As accuracy of text determines quality of image generated, it is vitally important that users write clear and precise descriptions of their desired outcomes so as to reduce time spent creating images.


Craiyon is an artificial intelligence (AI) image generator that makes creating images simple by entering text prompts. With its intuitive user interface and diverse selection of styles, this AI image maker makes for the ideal visuals tool for graphic designers, bloggers and other users looking to add visuals to their work. However, be mindful that Craiyon may sometimes produce blurry and hard to interpret results that might make the AI unsuitable for certain uses or may contain biases that reduce output quality.

Boris Dayma created this software, then known as DALL-E mini, for use in a coding competition. Designed as a lighter version of OpenAI’s DALL-E with a smaller architecture that still manages to generate recognizable images when given text prompts, DALL-E mini is still capable of making images that can be recognized.

Whilst its results may not be as realistic as DALL-E 2, its mini version still can produce highly entertaining images – like one showing a basketball playing Demogorgon from Stranger Things! No wonder why it has become such a hit on social media!

Craiyon’s model draws upon millions of Internet images with accompanying captions, training its AI to combine concepts into new images. However, these new images may not always be accurate or recognizable and even display racial or gender stereotypes when given certain prompts – for instance when asked for “a lawyer”, it returned images depicting men wearing black judge robes – an alarming example of AI showing biases that need to be overcome responsibly by using its output regularly and checking its output for errors.

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