Catalog
concept#Artificial Intelligence#Machine Learning#Architecture#Data

Image Generation

Generation of images by algorithmic models, typically using generative AI such as diffusion models or GANs.

Image generation refers to methods that automatically produce visual content using trained models.
Emerging
High

Classification

  • High
  • Technical
  • Technical
  • Intermediate

Technical context

Cloud API providers (e.g., OpenAI, Stability APIs)Design tools (e.g., Figma, Adobe Photoshop) for post-processingData platforms for training data and labeling

Principles & goals

Clear definition of style, quality and intended use.Transparency about training data and licensing.Iterative validation of outputs with users and domain experts.
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Unintentional reproduction of copyrighted content.
  • Bias and discriminatory representations in generated images.
  • Misuse for deepfakes or misleading content.
  • Version prompts and build reproducible pipelines.
  • Combine automated quality checks with human reviews.
  • Ensure documented data provenance and license compliance.

I/O & resources

  • Text prompts, style guidelines, reference images
  • Training datasets, label information
  • Compute resources (GPU/TPU) and inference environment
  • Generated image files at specified resolution
  • Metadata about prompts, model seed and quality ratings
  • Versioned assets for production or training

Description

Image generation refers to methods that automatically produce visual content using trained models. It includes diffusion models, GANs and multimodal text-to-image pipelines. Applications range from marketing assets and product design to synthetic datasets; quality, control, ethical implications and production cost are key decision criteria.

✔

  • Rapid production of visual variants without photoshoots.
  • Cost-effective prototyping and design iterations.
  • Enables generation of rare or hard-to-capture scenes.

✖

  • Quality and consistency strongly depend on model and prompting.
  • Legal and ethical constraints on training data and outputs.
  • Limited control over fine details and brand representation.

  • Perceptual image quality (MOS)

    User or expert ratings for subjective image quality.

  • Prompt stability

    Consistency of outputs given identical prompts.

  • Inference latency

    Time between request and available image outputs.

Marketing campaign with AI images

A retailer generates product images in multiple styles for web A/B testing.

Automated UI mockups

A design team uses text-to-image models to quickly visualize layout variants.

Synthetic training data for object detection

Engineers create diverse viewpoints and conditions to make a model more robust.

1

Define goals and acceptance criteria; involve relevant stakeholders.

2

Evaluate suitable models and infrastructure (cloud vs. on-prem).

3

Run a pilot with clear test cases and evaluate outputs.

4

Introduce scaling, governance and monitoring.

⚠️ Technical debt & bottlenecks

  • No central prompt and model catalog available.
  • Ad-hoc post-processing instead of reproducible scripts.
  • Incomplete logging of inputs, outputs and metadata.
Lack of training data diversityCompute resources for inference and fine-tuningReview and approval processes
  • Generating deceptive images for targeted misinformation.
  • Using copyrighted training data without permission.
  • Automatically replacing real people in ads without consent.
  • Underestimating review effort for legal risks.
  • Approaching brand colors/designs without style governance.
  • Missing traceability of model outputs and seeds.
Prompt engineering and model understandingImage editing and post-processingPrivacy and licensing awareness
Privacy and license complianceLatency and scalability of generation APIQuality assurance and post-processing workflows
  • • Licensing and copyright rules for training data
  • • Performance limits for real-time applications
  • • Organizational policies on ethical use