NVIDIA Launches Blueprint for 3D-Guided Generative AI Image Creation

NVIDIA unveils a new workflow blueprint empowering users to control image composition in Artificial Intelligence–generated visuals using 3D scene guidance.

NVIDIA has introduced the AI Blueprint for 3D-guided generative AI for RTX PCs, providing creators and developers with powerful tools to control the composition of Artificial Intelligence–generated images. Traditional text-based prompts have simplified scene creation, but they struggle with nuanced aspects like camera angles and object placement, leaving users wanting greater creative oversight. NVIDIA´s blueprint addresses these limitations by integrating a 3D scene draft as a depth map, crafted in Blender, which guides the image generator—FLUX.1-dev from Black Forest Labs—in conjunction with user prompts to deliver customized results.

This depth map-driven technique offers advantages over pure text input, enabling users to intuitively manipulate every aspect of a scene, from object location to camera viewpoint, without requiring intricate 3D models or detailed textures. The system´s foundation leverages ComfyUI for chaining generative AI models and includes a Blender plug-in for seamless integration. The workflow also incorporates NVIDIA´s NIM microservice to optimize deployment and speed when running FLUX.1-dev on GeForce RTX GPUs, utilizing TensorRT and quantized formats such as FP4 and FP8 for substantial performance gains and reduced memory requirements. A GeForce RTX 4080 GPU or higher is recommended for optimal use.

NVIDIA´s blueprint is designed to lower barriers for both AI artists and developers. It comes as a prebuilt package with Blender, ComfyUI, essential plug-ins, deployment instructions, and all necessary nodes and microservices, ensuring both an easy start for newcomers and a flexible platform for experienced developers to extend. The solution benefits from the high-speed inference made possible by NVIDIA´s latest RTX and Blackwell architectures, halving model size requirements compared to previous standards. As part of a broader suite of over ten available NIM microservices targeting diverse AI tasks, this blueprint marks a significant advance in generative visual workflows and is available for immediate download, supporting real-time experimentation and customization on RTX-enabled PCs and workstations.

75

Impact Score

HMS researchers design Artificial Intelligence tool to quicken drug discovery

Harvard Medical School researchers unveiled PDGrapher, an Artificial Intelligence tool that identifies gene target combinations to reverse disease states up to 25 times faster than current methods. The Nature-published study outlines a shift from single-target screening to multi-gene intervention design.

Contact Us

Got questions? Use the form to contact us.

Contact Form

Clicking next sends a verification code to your email. After verifying, you can enter your message.