## Introduction
Discussions about artificial intelligence (AI) in media and entertainment often differ due to various viewpoints on its role in content creation and consumption. People’s experiences and philosophies can influence their understanding of AI’s part in content creation processes.
At the core of these discussions are generative AI models that create specific content like images, videos, music, voice, and 3D assets using large datasets. These models are advancing rapidly, with frequent updates making it challenging to stay updated with the latest advancements.
However, no single model is perfectly suited for all content pipeline tasks. Applications, hardware, interfaces, and workflows can affect inference, which is the operation of a trained model with inputs like prompts or images to generate outputs. This variability means that the most suitable model and platform can differ based on individual needs.
While there are many ways to discuss generative AI, this article does not attempt to define the “best” model or tool for content creation, as this is subjective. Instead, it aims to provide insights into different types of generative AI models and where they can be accessed.
## Generative AI Explained
AI is a broad concept encompassing different technologies. In content creation, it involves AI models, software, tools, and features, making it hard to distinguish their functions. It helps to categorize AI by its availability and use:
– **AI as Products**: Commercial AI solutions for access or deployment.
– **AI as Tools**: Products integrated into creative workflows to generate content.
– **AI as Features**: Features embedded in software enhancing the creative process.
Generative AI can be commercially distributed via web platforms, APIs, downloadable models, or hosted solutions. Organizations deploying generative AI have infrastructure considerations influencing scalability and integration with existing workflows.
Generative AI tools in the content creation pipeline allow creators to produce various content types. These tools, powered by AI models, enable experimentation and offer new workflow opportunities previously difficult, time-consuming, or costly. However, no single model is “best” for every task, and research is necessary to find models aligning with specific needs.
Some mainstream software uses ML for automated tasks in content creation, like image classification and audio enhancement.
## Different Types of Generative AI Models
The generative AI landscape evolves rapidly, with new models and updates frequently. Below are tables of popular generative AI base models for different content types:
### Image Models
Tables highlighting generative AI models for creating 2D images.
### Video Models
Tables highlighting generative AI models for video generation.
### Audio Models
Tables highlighting generative AI models for music, voice, and sound effects.
### 3D Models
Tables highlighting generative AI models for 3D content, including 2Dā3D outputs and more.
## How to Access and Use Generative AI Models
Generative AI models are accessible through locally hosted software or cloud and web-based platforms, including new tools and traditional applications integrating AI features. Not all platforms offer the same models or features. For instance, Adobe Photoshop offers limited models compared to other platforms. Download AI models from repositories like Huggingface or GitHub to run them locally using tools like Comfy UI or TouchDesigner, allowing offline work and data control. Some models may require cloud-based platforms due to size or complexity.
Cloud-based platforms like Adobe Firefly, Artlist, Freepik, Higgsfield, Krea, and OpenArt offer multiple generative AI models, processed on their servers, requiring only a web browser and internet connection. These platforms often provide exclusive tools for subscribers. Platforms like MidJourney offer models only through their website and require a subscription, while others like ComfyUI Cloud and Flora use node-based workflows for structured content generation.
## Where Can Generative AI Models Be Useful in Content Creation?
Generative AI can be used in various ways across the content creation pipeline, whether on local hardware or through online platforms. It serves as a supplementary tool, enabling artists to perform more creative tasks and avoid repetitive ones. In pre-production, AI aids in generating mood boards or refining storyboards. In production, it offers alternative methods for music, graphics, or video production. Post-production uses AI for visual effects, frame completion, audio enhancement, color adjustment, and asset preparation.
Understanding generative AI capabilities and training data helps select suitable platforms and avoid legal risks. Not all models are safe for commercial use, and reviewing licensing terms is crucial.
Whether deploying AI, hosting, or generating content, hardware remains critical for enabling effective work. Creators can explore AI opportunities with recommended systems for generative AI from Puget Systems. For larger deployments, server-class solutions are recommended.
## Additional Resources
Puget Systems offers workstations tailored for various workflows. Their solutions page provides different configurations for content creation, engineering, and scientific computing. A custom configuration page and expert consultations are available for personalized workstation needs.