Scenario

Generates 2D game sprites and character assets using diffusion models conditioned on user-provided style references and game art direction. The system accepts reference images, text prompts, and style parameters, then outputs sprite sheets or individual frames optimized for direct import into game engines (Unity, Unreal, Godot) with metadata for animation frame boundaries and collision detection hints. Architecture uses LoRA fine-tuning on gaming art datasets to maintain visual consistency across generated asset batches.

Generates physically-based rendering (PBR) textures including albedo, normal maps, roughness, and metallic channels from text descriptions or reference images. The system uses conditional diffusion to produce texture maps that tile seamlessly and comply with PBR standards, then exports them in formats compatible with game engines (OpenGL, DirectX normal map conventions). Architecture applies post-processing to ensure proper normal map orientation and seamless tiling across UV boundaries.

Analyzes user prompts and suggests improvements to increase generation quality and consistency, using language models trained on successful Scenario generation prompts. The system accepts a user prompt, identifies ambiguities or missing details, and suggests more specific phrasings that historically produce higher-quality results. Architecture uses prompt embeddings and quality metrics from historical generations to rank suggestions.

Generates concept art and environment layouts from text descriptions, mood boards, or reference images using large-scale diffusion models fine-tuned on game concept art datasets. The system accepts multiple reference images and text prompts, then synthesizes cohesive environment concepts that maintain visual consistency across multiple variations. Architecture uses attention-based style blending to balance multiple reference influences and iterative refinement to ensure architectural coherence and spatial plausibility.

Processes multiple asset generation requests in batch mode while maintaining visual consistency across the entire asset set using a shared style embedding and iterative refinement loop. The system accepts a batch manifest specifying asset types, quantities, and style parameters, then generates assets with enforced consistency checks comparing embeddings against a reference style vector. Architecture uses a style anchor mechanism to ensure all generated assets remain visually cohesive even across different asset categories (characters, props, environments).

Allows users to fine-tune proprietary diffusion models on custom game art datasets using LoRA (Low-Rank Adaptation) to create specialized generators that understand a game's unique visual language. The system accepts uploaded image datasets, trains lightweight LoRA adapters on Scenario's infrastructure, and deploys trained models as private endpoints. Architecture uses parameter-efficient fine-tuning to reduce training time and storage overhead while maintaining generation quality.

Provides an interactive refinement workflow where users can provide feedback on generated assets (e.g., 'more detailed', 'darker colors', 'different pose') and the system regenerates variations incorporating that feedback. The system uses CLIP embeddings to encode user feedback and adjust generation parameters, then produces refined variations while maintaining consistency with previous iterations. Architecture maintains a refinement history and allows branching to explore multiple refinement directions.

Exposes RESTful API endpoints for programmatic asset generation with asynchronous processing and webhook callbacks for completion notifications. The system accepts generation requests with parameters, queues them for processing, and notifies external systems via webhooks when assets are ready. Architecture uses job queuing and status tracking to handle concurrent requests and provide polling endpoints for status checks.

Generates sequences of animation frames from a starting pose and motion description, using diffusion models conditioned on temporal consistency constraints. The system accepts a keyframe image and text description of motion (e.g., 'walk cycle', 'jump attack'), then generates intermediate frames that smoothly interpolate between keyframes while maintaining character consistency. Architecture uses optical flow estimation and temporal attention to enforce frame-to-frame coherence.

Generates variations of characters with controlled attribute changes (e.g., 'same character, different armor', 'same pose, different skin tone', 'same character, older age') using conditional diffusion with attribute-specific guidance. The system accepts a base character image and attribute modification requests, then generates variations that preserve character identity while changing specified attributes. Architecture uses CLIP-based attribute embeddings to guide generation and identity preservation losses to maintain character consistency.

Provides plugins for Unity and Unreal Engine that enable real-time asset generation preview within the editor, with direct import of generated assets into the scene. The system integrates with the game engine's asset pipeline, allowing designers to generate and preview assets without leaving the editor. Architecture uses the Scenario API to queue generation requests and streams results back to the editor plugin for immediate preview and import.