Scenario

You upload your own references and Scenario trains a model on them, so the output follows your art direction instead of a generic AI look. Ten to 30 images suit a style, and 5 to 15 suit a specific character or object. A Multi-LoRA merge can combine several styles and subjects in one generation.

References and trained models hold a look from shot to shot, which matters most when you need the same character in many poses or the same environment from different angles. That consistency is the main reason teams pick Scenario over a general AI art generator.

You chain models and tools as nodes and save the chain as a workflow or a small app, then rerun it whenever the same job comes up. An agent can build the workflow from a description, or you can give your own agent Scenario skills, so the same process runs in the app, through the API or from an agent.

One workspace handles image, video, 3D and audio, and game asset generation for each type draws on the leading models as they launch. You can swap one model for another inside a workflow and keep your references and prompts, so switching models does not mean starting the asset over.

Scenario exposes an API that connects to the app's own features, so you can drive generation and model training from code. Teams plug it into Unity, Unreal or a custom pipeline, automate batch generation, and even ship custom models for player-facing user-generated content. Full docs live at docs.scenario.com.

On enterprise plans the rules run before any model does. Model allowlists, per-project spending caps, SSO, an audit log and an optional IP Detection add-on apply to every member, API key and agent. IP Detection checks prompts and input images against your own policy, and a blocked request never reaches a model.