AuraSR-v2
| Property | Value |
|---|---|
| Parameter Count | 618M |
| Model Type | GAN-based Super-Resolution |
| License | Apache 2.0 |
| Framework | PyTorch |
What is AuraSR-v2?
AuraSR-v2 is an advanced GAN-based super-resolution model designed specifically for upscaling generated images. Based on the GigaGAN architecture, it offers sophisticated image-conditioned upscaling capabilities with a focus on maintaining artistic quality and detail enhancement.
Implementation Details
Built using PyTorch, AuraSR-v2 implements a variation of the GigaGAN architecture, utilizing 618M parameters for optimal performance. The model supports 4x upscaling through an overlapped processing method, ensuring seamless and high-quality results.
- Implements GigaGAN architecture with custom modifications
- Uses F32 tensor type for precise computations
- Supports direct integration through Python API
- Includes overlapped processing for enhanced quality
Core Capabilities
- 4x image upscaling with maintained quality
- Specialized processing for AI-generated images
- Efficient processing through PyTorch optimization
- Simple integration through pip installation
Frequently Asked Questions
Q: What makes this model unique?
AuraSR-v2 stands out for its specialized focus on upscaling AI-generated images, implementing the GigaGAN architecture with custom optimizations for enhanced quality.
Q: What are the recommended use cases?
The model is ideal for upscaling AI-generated artwork, enhancing low-resolution images, and improving image quality in digital art workflows where 4x upscaling is needed.
