Model Conditioning to Unlock Generative AI for Scalable and Controlled Asset Creation
Integrating generative AI into a workflow to create precise on-brand images can be problematic if there is no control over the visual input of the product. You can have specific geometry, color, logos, and brand guidelines be misinterpreted or lost without certain conditioning.
Model conditioning means providing a model with specific information or rules to help it make better predictions or decisions based on what you want it to do. To condition an LLM, you provide text-based instructions, examples, context, or previous conversation history. For image generators, you can provide text or a sample image. But this only provides so much control over the AI model. This is why 3D conditioning is required.
Setting the stage in 3D enables artists to have ultimate creative control or direction over the output of the generated visuals. Building an easy-to-use UI for end-user interaction enables non-technical teams to iterate and create content in a controlled and conditioned framework, while keeping branded assets untouched by the AI.
Building a 3D-Conditioned Workflow for Precise Visual Generative AI
Building a 3D-conditioned workflow for precise visual generative AI involves a handful of key components:
- On-brand hero asset: A finalized asset, built by an artist and typically approved by a brand manager and art director, which should be considered the hero asset.
- A simple, untextured 3D scene: Provided by a 3D artist, to use for staging the hero asset and controlling layout and composition.
- Custom application: Built with the Kit App Template based on Kit 106.2.
- Generative AI microservices and kit extensions: Add generative AI functionality to your custom application. In this case, a diffusion model takes care of inpainting.
- Solution testing: Verifies the functionality and performance of your integrated workflow.
Marketing Ecosystem Builds with NVIDIA Omniverse Blueprints
Developers at independent software vendors (ISVs) and production services agencies are building the next generation of content creation solutions, infused with controllable generative AI, built on OpenUSD. For example, Accenture Song, GRIP, Monks, WPP, and Collective World are adopting Omniverse Blueprints to accelerate development.
Developing a Scalable AI Solution for On-Brand Asset Creation
This blueprint provides you with an example architecture of how to build controllable generative AI applications. You or your client can now get the most out of your app:
- Multimodal AI-generated final-frame campaign assets
- Rapid concepting and ideation for key visuals
- Batch processing of prompt inputs, generating potentially hundreds of visual outputs from predefined text prompts fed from a database
By implementing this blueprint, you or your client get the following benefits:
- Accelerated time to market: Significantly decrease the time it takes to create high-resolution branded assets to allow for products to be taken to market faster.
- Low-effort localization: Enable the creation of localized imagery instantly to help brands meet certain cultural trends or requirements for different markets.
- Increased productivity: Easy-to-use tools that use 3D data can lower the technical skillset traditionally associated with high-fidelity asset creation.
Get Started
In this post, we introduced the NVIDIA Omniverse Blueprint for 3D conditioning for precise visual generative AI and showed you ways to benefit from building generative AI applications for brand-accurate visual asset generation and content production. For more information, see the following resources:
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FAQs
Q: What is model conditioning?
A: Model conditioning means providing a model with specific information or rules to help it make better predictions or decisions based on what you want it to do.
Q: Why is 3D conditioning required?
A: Because model conditioning only provides so much control over the AI model, and 3D conditioning enables artists to have ultimate creative control or direction over the output of the generated visuals.
Q: What are the benefits of implementing this blueprint?
A: The benefits include accelerated time to market, low-effort localization, and increased productivity.
Q: Who is adopting Omniverse Blueprints?
A: Developers at independent software vendors (ISVs) and production services agencies, such as Accenture Song, GRIP, Monks, WPP, and Collective World.
