Shuttle Jaguar
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Model Type | Text-to-Image Diffusion |
| Supported Formats | bfloat16, fp8, GGUF |
| Author | ShuttleAI |
What is shuttle-jaguar?
Shuttle Jaguar is an advanced text-to-image AI model designed to generate high-quality, cinematic, and realistic images from textual descriptions. What sets it apart is its ability to produce results in just four inference steps, making it highly efficient while maintaining image quality. The model is optimized for various hardware configurations through multiple precision formats.
Implementation Details
The model can be implemented using the Diffusers library or through ShuttleAI's API. It supports various optimization techniques including CPU offloading and torch.compile for enhanced performance. The implementation allows for customizable parameters such as image dimensions, guidance scale, and inference steps.
- Supports multiple precision formats (bfloat16, fp8, GGUF)
- Compatible with 🧨 Diffusers library and ComfyUI
- Optimized for CUDA acceleration
- Configurable sequence length up to 256 tokens
Core Capabilities
- Fast image generation in just 4 inference steps
- High-quality aesthetic and cinematic image output
- Flexible resolution support up to 1024x1024
- Customizable guidance scale for controlled generation
- Memory-efficient operation with CPU offloading options
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to generate high-quality images in just four inference steps, combined with its support for multiple precision formats and optimization options, makes it stand out in terms of efficiency and versatility.
Q: What are the recommended use cases?
Shuttle Jaguar is ideal for applications requiring quick, high-quality image generation from text descriptions, particularly when aesthetic and cinematic quality is important. It's suitable for both production environments and research purposes, with its Apache 2.0 license allowing for commercial use.
