Image Generators

Midjourney

www.midjourney.com/website

We are a small self-funded team focused on design, human infrastructure, and AI. We have 11 full-time staff and an incredible set of advisors.

Tools for human imagination.

runwayml.com

Ideogram

ideogram.ai

Image generation for everyone

Recraft | AI for designers, creatives, sellers, and teams

www.recraft.ai

Recraft is a top-ranked text-to-image model and design platform for photorealism, vector generation, custom styles, mockups, and more

Transform Your Content

flixai.com

AI Video Generator & AI Photo Generator — Create stunning videos and photos with the power of artificial intelligence.

Krea

www.krea.ai

Defining and Describing Image Generators

• 
• Image generators are systems that learn the visual structure of images and produce new images from noise, text, or other inputs.
• Image generators are a class of generative-AI systems that synthesize visual content rather than merely classify or retrieve it. Common approaches include generative adversarial networks, autoregressive transformers, and diffusion models.[1][6][10]
• In a typical text-to-image workflow, a text encoder represents the prompt, a generative model constructs an image representation, and a decoder or upsampler produces the final image. DALL·E 2 used CLIP-related representations and a hierarchical diffusion pipeline, while Imagen used a large pretrained T5 text encoder with cascaded diffusion.[10][13]
• Diffusion-based generators begin with random noise and iteratively remove it, learning to reverse a process that gradually corrupts an image with Gaussian noise.[1][10][14]
• The concept applies to creative production, design exploration, visual prototyping, advertising, entertainment, education, and research. Its importance comes from reducing the time and cost required to create or iterate visual material, while introducing concerns about provenance, bias, copyright, and misuse.[2][9]

Uses in Context

• Creative ideation: Designers invoke image generators to explore multiple visual directions from a short description or reference image. Krea.ai, for example, is described as offering real-time image and video generation for artists and marketers.[9]
• Text-to-image production: The term commonly refers to systems that convert natural-language prompts into images, including DALL·E, Imagen, and Stable Diffusion.[2][3]
• Image variation and editing: Generators can create variations, extend compositions, or modify selected regions rather than producing an image entirely from scratch. The broader development of text-to-image systems has expanded image generation beyond unconditional synthesis.[3][13]
• Commercial content creation: Businesses use generators for marketing imagery, product concepts, campaign exploration, and other visual assets; startup and digital-native use cases include personalized model training and real-time creative tools.[9]
• Technical research: In machine learning, “image generation” describes experiments in modeling image distributions, comparing fidelity, diversity, controllability, and sampling efficiency.[1][6][14]

History of Use

Origins

• The modern technical lineage includes variational autoencoders, generative adversarial networks, and diffusion models. The GAN framework was introduced by Ian Goodfellow and collaborators in the 2014 paper Generative Adversarial Nets , which defined a generator that creates samples and a discriminator that distinguishes generated samples from real data.[6][11]
• Diffusion-based generation traces to Sohl-Dickstein and collaborators’ 2015 formulation, which framed generation as reversing a fixed Gaussian noising process.[1]
• Ho, Jain, and Abbeel formalized denoising diffusion probabilistic models in 2020 by training a network to predict noise and sampling through iterative denoising.[6]
• Text-to-image generation became a distinct mainstream research direction with OpenAI’s Zero-Shot Text-to-Image Generation , which described DALL·E as a 12-billion-parameter autoregressive transformer modeling text and discrete image tokens in a single sequence.[13]

Evolution

• 2014 — Adversarial generation: GANs introduced a generator–discriminator competition that produced sharp synthetic images, especially in relatively narrow domains such as faces.[6][13]
• 2020–2021 — Diffusion overtakes GAN-based synthesis: Diffusion models offered a comparatively stable training objective and strong image quality, although their iterative sampling initially made them slower.[4][14]
• 2021–2022 — Latent and language-conditioned systems: Latent diffusion moved denoising into a compressed representation, reducing computational requirements; DALL·E 2, Imagen, and related systems demonstrated high-quality text-conditioned generation.[3][10][13]
• 2022 onward — Accessible and open generation: Stable Diffusion helped bring high-quality generation to consumer hardware through latent diffusion, while diffusion systems such as DALL·E 2 and Imagen became prominent research and product reference points.[7][13]

Best Real-World Examples

• Generative Adversarial Networks — The foundational adversarial framework introduced by Goodfellow and collaborators in 2014.[6][11]
• Denoising Diffusion Probabilistic Models — A diffusion formulation that generates images through repeated denoising steps.[6][14]
• DALL·E — An autoregressive text-to-image model that represented text and discrete image tokens in one sequence.[13]
• DALL·E 2 — A text-to-image system using CLIP-related representations and a hierarchical diffusion pipeline.[10]
• Imagen — A text-to-image system combining a large T5 text encoder with cascaded diffusion upsampling.[10][13]
• Stable Diffusion — A latent-diffusion approach designed to make high-resolution synthesis more computationally accessible.[13]
• Krea.ai — A creative suite offering real-time image and video generation and personalized model training for artists and marketers.[9]

Case Studies

Sources

Defining and Describing Image Generators

Uses in Context

• In creative production , “text-to-image” describes systems that convert natural-language prompts into visual outputs, including DALL-E 2, Stable Diffusion, and Imagen.[3]
• In design , image generators are used for ideation, visual exploration, concept art, and rapid variations before a final human-directed design is produced.[14]
• In image editing , the same model family can support controlled modification, inpainting, outpainting, and composition changes rather than only generating an image from nothing.[1]
• In research , image generators are evaluated as generative models that learn a data distribution and sample new images from it; GANs do this through competition between a generator and discriminator.[2][12]
• In synthetic-data production , generated images can provide additional visual examples for computer-vision systems and experiments, although their usefulness depends on fidelity, diversity, and licensing.[1]
• In popular culture , “AI-generated art” commonly refers to images made with systems such as Midjourney, Stable Diffusion, and other prompt-driven tools that became broadly visible in 2022.[7]

History of Use

Origins

• The modern technical lineage includes variational autoencoders, generative adversarial networks, normalizing flows, autoregressive models, transformers, and diffusion methods.[1]
• Ian Goodfellow and collaborators introduced generative adversarial networks in the 2014 paper Generative Adversarial Nets . The approach uses a generator that creates samples and a discriminator that attempts to distinguish generated samples from real data.[3][12]
• Diffusion-based generation was introduced in foundational form by Sohl-Dickstein and collaborators in 2015, while denoising diffusion probabilistic models later provided a practical route to high-quality image synthesis.[10][14]
• Early AI visual-art work also predates contemporary neural image generators: Harold Cohen began developing the AARON system in the late 1960s.[7]

Evolution

• 2014 — Adversarial generation: GANs made it possible to generate visually convincing images by training a generator and discriminator in competition, though training could be unstable and prone to mode collapse.[3][9]
• 2020–2021 — Diffusion and language–vision alignment: DDPM-style denoising and language–image representation methods established the technical basis for later high-quality text-to-image systems.[9][14]
• 2022 — Consumer-scale text-to-image generation: DALL-E 2, Imagen, Midjourney, and Stable Diffusion brought prompt-driven image synthesis into widespread public use; Stable Diffusion’s latent-diffusion approach was released through work involving Stability AI, CompVis, and Runway.[7][14]

Best Real-World Examples

• Stable Diffusion — a source-available latent-diffusion image generator associated with Stability AI, CompVis, and Runway.[7]
• Midjourney — a 2022 text-to-image service that helped popularize prompt-driven visual creation.[7]
• DALL-E — an autoregressive and later diffusion-based family demonstrating controlled text-to-image generation.[6][14]
• Imagen — a text-to-image diffusion system reported by Google researchers in 2022.[2][3]
• VQGAN-CLIP — an open-source approach that combined a visual generator with CLIP-based text guidance and influenced early creator experimentation.[7]
• GANs — the generator–discriminator framework that shaped image synthesis research during the 2010s.[12]
• DreamFusion — a research finding showing how a pretrained text-to-image model could guide optimization of 3D representations from text.[10]

Case Studies

Sources