If you ever generated an AI face with (DALL-E, Midjourney, Stable Diffusion) you will often notice that the eyes in the image are not symmetrical and look weird. Now you can use the perfect tool for fixing that problem, it is called: CodeFormer. CodeFormer can help with Face Restoration, Face Color Enhancement and Restoration, and Face Inpainting.

It seems like AI-generated art has suddenly exploded everywhere in the last month. A friend showed me what he was creating with DALL·E 2, and down a rabbit hole I fell. And man, what a fun rabbit hole it is too for photography and illustration alike! AI-generated art is literally that: Artworks created by an AI generator. Fed millions upon millions of images and trained by humans and machines, these tools can produce incredibly detailed, sophisticated and realistic images based on natural language text input.

OpenAI has added a new "outpainting" function to its text-to-image AI model DALL-E that lets the system generate new visuals that expand the borders of any given picture. In the example above, you can see how DALL-E, with the help of human prompting, "imagines" what's outside the frame of Johannes Vermeer's portrait "Girl with a Pearl Earring." Note, how, even from the limited information provided by the portrait, the system is able to match Vermeer's style, mimicking the shadows and highlights of the original. In the timelapse below, you can also see how the artist responsible, August Kamp, had to expand the image in small sections at a time, often redoing DALL-E's generations in order to get the outcome she wanted. Not seen in this video but definitely worth highlighting, is the fact that the system is not generating these extensions just by itself.

A few months back I wrote a Spectator piece about a phenomenal new'neural network' – a subspecies of artificial intelligence – which promises to revolutionise art and how humans interact with art. The network is called Dall-e 2, and it remains a remarkable chunk of not-quite-sentient tech. However, such is the astonishing, accelerating speed of development in AI, Dall-e 2 has already been overtaken. Just last week a British company called Stability AI launched an artificial intelligence model which has been richly fed, like a lean greyhound given fillet steak, on several billion images, equipping it to make brand new images when prompted by a linguistic message. It is called Stable Diffusion and it is revolutionary in multiple ways, perhaps the most important being this: unlike other models, the'owners' are letting anyone use Stable Diffusion from the get-go (with intrinsic restrictions on sexual or prejudicial imagery and so on).

You'll never look at the world's most famous paintings in the same way again. An artificial intelligence company has created a new tool that allows users to imagine a world beyond the frame of their favorite artwork. Named "Outpainting," the tool was designed by employees at the San Francisco firm OpenAI as part of their innovative text-to-image system, DALL-E 2. The easy-use tool -- launched last week -- requires just two steps, with users simply selecting the artwork they want to play with and then typing in what they want to see added. The words are then transformed into images via AI technology, which are attached to the artwork, expanding it outward and changing its entire meaning. "Outpainting helps users extend their creativity by continuing an image beyond its original borders … simply by using a natural language description," OpenAI explained in a press release.

OpenAI has added a new "outpointing" function to its text-to-image AI model DALL-E that lets the system generate new visuals that expand the borders of any given picture. In the example above, you can see how DALL-E, with the help of human prompting, "imagines" what's outside the frame of Johannes Vermeer's portrait "Girl with a Pearl Earring." Note, how, even from the limited information provided by the portrait, the system is able to match Vermeer's style, mimicking the shadows and highlights of the original. In the timelapse below, you can also see how the artist responsible, August Kamp, had to expand the image in small sections at a time, often redoing DALL-E's generations in order to get the outcome she wanted. Not seen in this video but definitely worth highlighting, is the fact that the system is not generating these extensions just by itself.

Manas Bhatia has a bold vision of the future -- one where residential skyscrapers covered in trees, plants and algae act as "air purification towers." In a series of detailed images, the New Delhi-based architect and computational designer has brought the idea to life. His imagined buildings are depicted rising high above a futuristic metropolis, their curved forms inspired by shapes found in nature. But the pictures were not entirely of his own imagination. The architect's conceptual towers were created using AI imaging software.

Stable Diffusion latent diffusion image synthesis model a couple of weeks ago may be one of the most significant technological disclosures since DeCSS in 1999; it's certainly the biggest event in AI-generated imagery since the 2017 deepfakes code was copied over to GitHub and forked into what would become DeepFaceLab and FaceSwap, as well as the real-time streaming deepfake software DeepFaceLive. At a stroke, user frustration over the content restrictions in DALL-E 2's image synthesis API were swept aside, as it transpired that Stable Diffusion's NSFW filter could be disabled by changing a sole line of code. Porn-centric Stable Diffusion Reddits sprung up almost immediately, and were as quickly cut down, while the developer and user camp divided on Discord into the official and NSFW communities, and Twitter began to fill up with fantastical Stable Diffusion creations. At the moment, each day seems to bring some amazing innovation from the developers who have adopted the system, with plugins and third-party adjuncts being hastily written for Krita, Photoshop, Cinema4D, Blender, and many other application platforms. In the meantime, promptcraft – the now- professional art of'AI whispering', which may end up being the shortest career option since'Filofax binder' – is already becoming commercialized, while early monetization of Stable Diffusion is taking place at the Patreon level, with the certainty of more sophisticated offerings to come, for those unwilling to navigate Conda-based installs of the source code, or the proscriptive NSFW filters of web-based implementations.

AI image generation is here in a big way. A newly released open source image synthesis model called Stable Diffusion allows anyone with a PC and a decent GPU to conjure up almost any visual reality they can imagine. It can imitate virtually any visual style, and if you feed it a descriptive phrase, the results appear on your screen like magic. Some artists are delighted by the prospect, others aren't happy about it, and society at large still seems largely unaware of the rapidly evolving tech revolution taking place through communities on Twitter, Discord, and Github. Image synthesis arguably brings implications as big as the invention of the camera--or perhaps the creation of visual art itself. Even our sense of history might be at stake, depending on how things shake out.

Findings from a new paper indicate that state-of-the-art AI is significantly less able to recognize and interpret AI-synthesized images than people, which may be of concern in a coming climate where machine learning models are increasingly trained on synthetic data, and where it won't necessarily be known if the data is'real' or not. Here we see the resnext101_32x8d_wsl prediction model struggling in the'bagel' category. In the tests, a recognition failure was deemed to have occurred if the core target word (in this case'bagel') was not featured in the top five predicted results. The new research tested two categories of computer vision-based recognition framework: object recognition, and visual question answering (VQA). On the left, inference successes and failures from an object recognition system; on the right, VQA tasks designed to probe AI understanding of scenes and images in a more exploratory and significant way.