One-click install
npx skills add https://github.com/NVIDIA-Omniverse/content-agents --skill image-gen-nvidia-omniverse
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: image-gen
Source: https://github.com/NVIDIA-Omniverse/content-agents/tree/main/.agents/skills/image-gen
Command: npx skills add https://github.com/NVIDIA-Omniverse/content-agents --skill image-gen-nvidia-omniverse

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Generate high-quality images from natural language prompts using the wu CLI, enabling rapid visual concept generation and reducing manual artwork iteration.

Core Features & Use Cases

  • Text-to-image generation: Create images from descriptive prompts using the wu image-gen command.
  • Conditioning images: Attach one or more reference images with --image to guide style, lighting, or composition.
  • Backend flexibility: Target Gemini, OpenAI, or Nim backends to suit latency, style, or policy requirements.
  • Output control: Specify an output path with --output and integrate generated visuals into design pipelines, material references, or asset previews.
  • Workflow integration: Use generated visuals to seed texture references, concept art, or validation visuals in content pipelines.

Quick Start

Execute wu image-gen <prompt> to generate an image, optionally including conditioning images with --image arguments, and specify --output for the resulting file.

Frequently Asked Questions about image-gen

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate concept art from a text prompt using a command line interface?

Generate concept art from a text prompt by executing the wu image-gen command with your descriptive text. This produces a single image file path, accelerating visual reference creation and reducing manual artwork iteration.

Can I use reference images to guide the style and composition of generated visuals?

Yes, you can guide style, lighting, or composition by attaching one or more conditioning images using the --image argument. This directs the text-to-image generation to match your visual references.

What backends are supported for text-to-image generation with the wu CLI?

The wu CLI supports Gemini, OpenAI, and Nim backends for text-to-image generation. This backend flexibility lets you target specific latency, style, or policy requirements for your generated visuals.

Do I need to install any dependencies before generating images from prompts?

Yes, you need the wu CLI installed and available on your system PATH to generate images from prompts. Once configured, you can execute commands and specify the output path for the resulting image file.

How do I integrate generated images into a downstream design pipeline?

Integrate generated images into design pipelines by specifying an output path with the --output argument. This yields a single image file per invocation, suitable for seeding texture references, material references, or asset previews.

What are the limitations when using conditioning images for concept visual generation?

A core limitation is that each invocation outputs only a single image path, and conditioning images are optional inputs. You must manage batching and iteration externally if you need multiple variations for concept visuals.