Experience an art exhibition at the prestigious Gagosian Gallery featuring surreal & lifelike paintings created by the AI image generator DALL-E. The exhibition, curated by film director Bennett Miller, challenges the boundaries between human art & AI-generated art. Miller gained early access to DALL-E through his documentary work on AI, which raises questions about creativity & authenticity. This example highlights the growing role of AI in image creation, prompting a need to understand how to approach AI image generation. This article explores the mechanics, applications, & ethical considerations of AI image generation.
What does AI image generation involve?
AI image generators use trained artificial neural networks to create realistic images from textual input. They can fuse styles, concepts, and attributes to produce original & contextually relevant imagery.These devices derive their power from Generative AI, a specialized branch of artificial intelligence dedicated to the creation of contentThey are trained on large datasets of images, allowing them to learn different aspects & characteristics of the images & generate new ones with similar style & content. Various types of AI image generators exist, including neural style transfer, Generative Adversarial Networks (GANs), & diffusion models.
Mechanics of AI image generators – An overview
In this segment, we will explore the complex operations of the impressive AI image generators discussed earlier, with a focus on the training methods used to produce images.
Text understanding using NLP
AI image generators use NLP models like CLIP to convert text prompts into numerical representations, capturing semantic meaning & context. These high-dimensional vectors act as a guide for the AI to create images that accurately represent the input text. For example, the prompt “a red apple on a tree” would be transformed into a numerical format that guides the generator to create an image of a red apple positioned on a tree. This process enables AI image generators to interpret & visually represent text prompts.
Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs) are a type of machine learning algorithms consisting of two competing neural networks – the generator & the discriminator. GANs were introduced in 2014 by Ian Goodfellow and his colleagues, & have since become popular in the field of generative AI models. The generator creates fake samples, while the discriminator determines whether a sample is real or fake. GANs operate based on an adversarial game, where the generator aims to produce convincing fake samples, & the discriminator aims to accurately identify them. The ongoing contest between the two networks ensures continuous learning & improvement. The process involves labeled data to help the discriminator distinguish between real & fake images, & the feedback loop enables both networks to improve.
Diffusion Models
Diffusion models are generative models in machine learning that create new data by imitating the data they have been trained on. They add noise to the data & learn how to reverse it to create new, similar data. This process is similar to a master chef learning to make dishes that taste just like the ones they’ve tried before. The model starts with an original piece of data & gradually adds random noise through a series of steps. Then, it learns how the noise alters the data & aims to effectively navigate it backward. Once trained, the model reverses the process by removing the noise to get back to the original data. Finally, it uses what it learned to create new data by transforming random noise into an image based on a text prompt. This method makes diffusion models capable of generating realistic images, sounds, & other types of data.
Neural Style Transfer (NST)
Neural Style Transfer (NST) constitutes a deep learning method that blends the content from one image with the stylistic elements of another, resulting in the generation of a novel artistic composition. It uses a pretrained network to analyze visuals & applies measures to transfer the style from one image to another, resulting in a new image with desired features. The procedure encompasses three fundamental images: the content image, the style image, and the generated image. The neural networks in NST have layers that detect basic features & combine them to recognize more complex features. Content loss measures how much the content of the generated image differs from the original, while style loss measures differences in textures & patterns. Total loss combines content & style loss into a single measure, allowing for a balance between content & style. NST uses an optimization algorithm to minimize this total loss & blend content & style from different images. Other AI image-generation technologies, such as GANs, NST, & diffusion models, are also gaining attention in this rapidly evolving field.
Discovering widely used AI image creation tools
In this segment, we will provide an overview of the prominent text-to-image AI entities capable of producing remarkable visuals in response to given text prompts.
DALL-E 2
DALL-E stands as an AI image generation technology created by OpenAI, symbolizing the amalgamation of Dali and WALL-E to represent the intersection of art and artificial intelligence.DALL-E 2, released in April 2022, is built on an advanced architecture that integrates data from CLIP & utilizes the GPT-3 large language model. It comprises two primary components: the Prior & the Decoder, & is capable of generating images with four times the resolution compared to the original DALL-E. The cost of DALL-E operates on a credit-based system, with users being able to purchase credits for image generation, edit requests, or variation requests.
Midjourney
Midjourney is a text-to-picture service propelled by AI, crafted by a research lab headquartered in San Francisco, known as Midjourney, Inc.
Users can turn textual descriptions into images through a Discord bot on their official channel. The AI favors creating visually appealing, painterly images with complementary colors, balanced light & shadow, sharp details, & pleasing symmetry or perspective. The current resolution of the images is relatively low, but an upcoming release, Midjourney 6, is expected to feature higher-resolution images suitable for printing. Midjourney offers four subscription plans with access to the member gallery, Discord server, & terms for commercial usage.
Stable Diffusion
Stable Diffusion is a text-to-image AI model launched in 2022 by Stability AI, EleutherAI, & LAION. It uses the Latent Diffusion Model to generate images from text, with features like inpainting, outpainting, & image-to-image transformations. The model gradually refines images from random noise to match provided textual descriptions. It initially used a frozen CLIP ViT-L/14 text encoder but now incorporates OpenClip for more detailed images. Stable Diffusion is open-source, easy to use, & can operate on consumer-grade graphics cards, making it accessible to a wide audience. It is priced at $0.0023 per image with a free trial available, but may experience server issues due to high user demand.
Common applications & examples of AI image generators
AI image generation technology possesses numerous applications. It can inspire creativity among artists, serve as a valuable tool for educators, and expedite the product design process by swiftly visualizing new designs.
Entertainment
AI image generators create realistic environments & characters for video games & movies, saving time & resources. An exceptional example is The Frost, a 12-minute movie in which AI generates every shot. The creation process involved the Waymark AI platform, utilizing a script authored by Josh Rubin, who holds the role of executive producer at the company.The script was fed to OpenAI’s image-making model DALL-E 2, which generated every single shot in the film. Waymark further employed D-ID, an AI tool proficient in imbuing movement into static images, to animate the captured shots.
Marketing & advertising
AI-generated images are rapidly being used in marketing & advertising to create campaign visuals without the need for traditional photo shoots. The image was generated using DALL-E 2, an AI-powered generator developed by OpenAI.
The cover showcased a detailed illustration of a female astronaut on Mars, signifying the inaugural use of an AI-generated image as the cover of a prominent magazine. This event underscores the potential of AI in the creative industry.
Medical imaging
AI image generators are crucial in improving diagnostic images in the medical field by creating clearer & more detailed images of tissues and organs. For example, DALL-E 2 was found to be proficient in generating & manipulating radiological images such as X-rays, CT scans, MRIs, & ultrasounds. It can create realistic X-ray images from text prompts & reconstruct missing elements in radiological images. However, it struggles with generating images with pathological abnormalities & specific CT, MRI, or ultrasound images. The synthetic data produced by DALL-E 2 holds the potential to expedite the advancement of new deep-learning tools in radiology and address privacy concerns associated with data sharing among medical institutions.AI image generation technology is expected to unlock more possibilities across diverse sectors as it continues to evolve.
Conclusion
AI image generation technology is advancing, but it is unlikely to replace professional artists. AI lacks the nuanced creativity & emotion that human artists bring to their work. Additionally, AI image generators are limited by their reliance on text prompts, which can be stifling for artistic expression. It is more likely that AI will serve as a tool to assist & empower artists in their creative endeavors, offering new avenues for exploration & facilitating the production of high-quality art.
