AI Model Training

What is AI Model Training?

AI model training is the process of teaching an AI by showing it millions of examples until it learns how to produce the right kind of output.

At a glance

Also known as
Model trainingAI trainingMachine learning trainingNeural network training
Used for
Teaching AI systems to generate images or videoBuilding custom character modelsAdapting AI to specific styles or subjects
Common tools
Gradient descent algorithmsGPU clustersLoRA fine-tuningDreamBooth
Related terms
Fine-tuningLoRADiffusion modelTraining dataModel weightsAI art

Ready to create?

Direct scenes, design characters, and ship full films

All-in-one AI creative platform with simple, transparent pricing, no speed throttles, and an infinite Canvas for max creativity.

Try Morphic
How it compares

How it compares

AI model trainingAI inference

Training is the process of teaching the model by adjusting its weights across a large dataset. Inference is the process of using a trained model to generate new outputs from a given input at production time. Training happens once and is computationally expensive; inference happens every time the model is used and is much faster. The quality of inference output is a direct product of the quality and completeness of the training that preceded it.

Think of it like…

Imagine you want to teach a friend to identify different types of dogs. You show them picture after picture of dogs, and each time you say what breed it is. At first they get most of them wrong, but slowly they start to notice that poodles have curly hair, and German Shepherds have pointy ears, and so on. After seeing enough examples, they start getting it right almost every time. That is exactly what AI model training is. You show the AI system millions of examples with the right answers, and it gradually adjusts itself until it can get the answers right most of the time without being told. How it works in simple terms: the model has millions of tiny dials that can each be turned slightly up or down. Training finds the best position for all those dials by trying slightly different positions over and over until the model's outputs match what was expected. Where you encounter this: every AI tool you use has gone through training. The style of image a generator produces, the types of motion a video model creates, and the subjects a model handles well or poorly are all direct results of the data and process used during training.

Pro tip

When working with custom model training or fine-tuning for consistent character or style generation, the quality and diversity of your training images matters more than the quantity. A smaller set of thirty to fifty carefully curated, well-lit, varied-pose reference images typically produces better fine-tune results than several hundred inconsistent or repetitive examples.

Types and variations

  • Pre-training from scratch is the process of training a foundation model on a large dataset from random initial weights, requiring enormous computational resources.
  • Fine-tuning adapts a pre-trained model to a specific task or domain using a smaller, targeted dataset and much less computation.
  • LoRA training adds a small set of additional parameters to a pre-trained model that can be trained quickly to represent a specific style, character, or subject.
  • DreamBooth is a fine-tuning technique specifically designed to teach a model the visual appearance of a particular subject from a small number of reference images.
  • Reinforcement learning from human feedback uses human ratings of model outputs to guide further training toward preferred behaviours.

Ready to make your first scene in Morphic?

Try Morphic

Common use cases

  • AI model training underpins the development of every commercial AI image and video generation tool.
  • Custom fine-tuning is used by creators who need consistent character rendering, branded visual styles, or subject-specific generation that a general foundation model does not reliably produce.
  • Studios and agencies train custom models on proprietary visual assets to produce AI-generated content that is consistent with their existing brand identity.
  • Game developers fine-tune models on their own concept art to generate new assets that fit an established visual language.
  • Independent AI filmmakers train character models to maintain consistent appearance across a series of generated scenes.

Ready to create?

Direct scenes, design characters, and ship full films

All-in-one AI creative platform with simple, transparent pricing, no speed throttles, and an infinite Canvas for max creativity.

Try Morphic

FAQs

What is AI model training?
AI model training is the process of teaching an AI system to perform a task by exposing it to large amounts of data and adjusting its internal parameters iteratively until it produces accurate or high-quality outputs. It is the foundational process behind every AI creative tool available today.
How does AI model training work?
Training works by presenting the model with many examples and a measure of how far its outputs are from the desired results. An algorithm called gradient descent then adjusts the model's parameters in small increments to reduce that error, and this cycle is repeated across the full training dataset many thousands of times.
What is the difference between training and inference?
Training is the resource-intensive process of teaching the model by adjusting its parameters across a large dataset. Inference is using the already-trained model to generate new outputs from a given input. Training happens once; inference happens every time the model is used in production.
What is fine-tuning in AI model training?
Fine-tuning is the process of adapting a pre-trained foundation model to a specific task, style, or subject by continuing training on a smaller, targeted dataset. It requires significantly less computation than training from scratch and is the primary method creators use to customise AI tools for specific characters, aesthetics, or use cases.
What is LoRA training?
LoRA is a fine-tuning technique that adds a small set of additional parameters to a pre-trained model and trains only those new parameters, leaving the foundation model weights unchanged. It is computationally efficient and widely used for training AI models to consistently represent specific characters, art styles, or subjects.
How does training data affect AI output quality?
The training dataset directly determines the range of subjects, styles, and concepts the model can handle. Models trained on high-quality, diverse datasets produce more capable and reliable outputs. Gaps or biases in training data produce corresponding gaps and biases in what the model can generate.
Can I train my own AI model for image or video generation?
Yes, custom fine-tuning and LoRA training workflows allow creators to adapt pre-trained foundation models to specific characters, styles, or visual assets using small datasets and accessible tools. Full pre-training from scratch requires substantial computational resources and is generally not practical for individual creators.
Why does AI model training matter for creative production?
Understanding model training helps creators know why AI tools behave as they do, what their limits are, and how to customise them for specific production needs. Custom training is essential for maintaining visual consistency across a series of AI-generated scenes, characters, or branded content.
Can't find what you are looking for?
Contact us and let us know.
bg