Cosmos-UpsamplePrompt1-12B-Transfer
| Property | Value |
|---|---|
| Model Type | Multimodal Transformer |
| Architecture | Pixtral 12B |
| License | NVIDIA Open Model License & Apache 2.0 |
| Input Types | Text + Video |
| Release Date | March 18, 2025 |
What is Cosmos-UpsamplePrompt1-12B-Transfer?
Cosmos-UpsamplePrompt1-12B-Transfer is NVIDIA's advanced multimodal AI model designed to enhance and enrich text prompts based on video context. The model specializes in transforming simple input descriptions into detailed, structured narratives that capture the nuances present in control videos, making it particularly valuable for conditional world generation tasks.
Implementation Details
Built on the Pixtral 12B architecture, this model processes both text strings and MP4 video inputs to generate enriched text outputs. It's optimized for NVIDIA Ampere and Hopper architectures, running on Linux systems through the Cosmos-Transfer1 runtime engine.
- Supports commercial applications under NVIDIA's Open Model License
- Processes 3D video inputs alongside 1D text inputs
- Generates structured, detailed text descriptions while maintaining contextual accuracy
- Compatible with enterprise-grade deployment scenarios
Core Capabilities
- Detailed scene description generation from video context
- Maintenance of consistent description structure
- Enhanced prompt quality for world generation models
- Commercial-ready deployment capabilities
- Global deployment support
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to transform simple prompts into rich, detailed descriptions while maintaining consistency with video content sets it apart. It's specifically designed to enhance the quality of inputs for world generation models, making it a valuable tool in the AI content generation pipeline.
Q: What are the recommended use cases?
The model is ideal for research and development purposes, particularly in scenarios requiring detailed scene descriptions from video inputs. It's well-suited for applications in content generation, video understanding, and automated description systems that require high-quality, detailed text outputs.
