UltraSharp
| Property | Value |
|---|---|
| License | CC BY-NC-SA 4.0 |
| Architecture | ESRGAN |
| Scale | 4x |
| Iterations | 150,000 |
| Model URL | https://huggingface.co/Kim2091/UltraSharp |
What is UltraSharp?
UltraSharp is a sophisticated 4x upscaling model based on the ESRGAN architecture, specifically designed to excel at processing JPEG compressed images. Developed by Kim2091, this model represents a significant advancement in image upscaling technology, trained across diverse high-quality datasets including RAW images, SignatureEdits, AdobeMIT-5K, and DIV2K.
Implementation Details
The model was trained with advanced techniques including KernelGAN blur kernels, noise patches, and custom augmentation presets. It utilizes multiple loss functions: pixel, feature, cx, ssim, lpips, and fft. Training was conducted over approximately 480 epochs with a batch size of 4-8 and HR size of 128.
- Gradient Clipping for enhanced model stability
- On-the-fly training with custom augmentation presets
- Pretrained on 4x-UniScale-Balanced model
- Comprehensive dataset size between 2,000-8,000 full-size images
Core Capabilities
- Superior performance on JPEG compressed images
- Universal application across various image types
- Exceptional detail generation and texture preservation
- Effective restoration of highly compressed images
- Balanced processing maintaining image authenticity
Frequently Asked Questions
Q: What makes this model unique?
UltraSharp stands out for its specialized ability to handle JPEG compression artifacts while maintaining exceptional detail generation. Its training across diverse datasets and multiple loss functions enables it to produce high-quality upscaled images with natural textures.
Q: What are the recommended use cases?
The model is universally applicable but performs best on JPEG compressed images. It's particularly effective for: restoration of compressed images, general image upscaling, and situations requiring detailed texture preservation in the upscaled result.
