A(I)BC: understanding the flows for generative AI content
Prompt: Can you provide an overview of the various AI flows for generating content? What is currently possible in terms of text-to-image, text-to-video, etc.?
Text-to-Image
This involves converting text into images or illustrations. This can range from simple graphic representations of text to detailed images based on detailed descriptions.
Examples: Dall-E, Midjourney, Stable Diffusion, Google Imagen
Text-to-Video
Text-to-Video algorithms convert written text into video material. This can range from slideshows with images and text to fully animated videos based on text descriptions.
Examples: Runway Gen-2, Stable Video Diffusion, Pika, Deep Dream Generator
Text-to-Speech
This technology converts written text into spoken words. It is often used to create voice assistants and make text content more accessible to people with visual impairments.
Examples: Google Cloud Text-to-Speech, Amazon Polly
Speech-to-Text
Instead of generating text, Speech-to-Text converts spoken words into written text. This is often used for speech recognition applications, such as transcribing voice recordings.
Examples: Google Cloud Speech-to-Text, IBM Watson Speech to Text
Text-to-Code
Text-to-Code algorithms attempt to generate code based on natural language descriptions. This can be useful for automating programming tasks.
Examples: OpenAI’s Codex, GitHub Copilot
Image-to-Text
This flow does the opposite of Text-to-Image. It analyzes images and generates textual descriptions or labels based on what can be seen in the image. This is often used for automatic image tagging and accessibility.
Examples: Google Cloud Vision, Microsoft Azure Computer Vision
Image-to-Image
This type of AI flow converts one type of image into another. For example, converting black-and-white images into color, painting photos in the style of famous artists, and so on.
Examples: DeepArt.io, CycleGAN, Stable Diffusion
Video-to-Text
Videos can be analyzed to generate textual captions or transcripts. This is useful for video descriptions and search engine optimization.
Examples: YouTube Automatic Captions, OpenAI’s Whisper
Video-to-Video
This flow refers to editing or manipulating video content based on textual instructions, such as adjusting backgrounds, adding special effects, or improving image quality.
Examples: Runway ML, DeepAI Video Enhance, Deforum Stable Diffusion
Text-to-Music
Text-to-Music algorithms generate music based on written text or notes. This can range from simple tunes to composing complex pieces of music.
Examples: OpenAI’s MuseNet, Amper Music
Text-to-Chatbot
This technology is used to create automated chatbots that can conduct “human-like” conversations based on textual input.
Examples: Dialogflow, IBM Watson Assistant
Image-to-3D
This formula converts 2D images into 3D models, which can be useful in 3D modeling and game development.
Examples: Nerfstudio, Meshroom, RealityCapture, Luma AI
AI flows and tools are currently growing at a rapid pace. We keep our finger on the pulse for you and compile our tests and findings as clearly as possible. Check out the tools.