Topaz Gigapixel AI

C

CodeFormer

S-Lab, Nanyang Technological University

Evaluate CodeFormer for a permitted portrait restoration: compare fidelity settings, inspect face changes and recomposition, and check the non-commercial S-Lab license.

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FidelityFx Super Resolution

AMD|N/A

FidelityFX Super Resolution (FSR) is AMD's open-source spatial upscaling technology designed to boost performance in real-time rendering applications, particularly video games. Unlike NVIDIA's DLSS which requires dedicated Tensor Cores, FSR is hardware-agnostic and runs on AMD, NVIDIA, and Intel GPUs including integrated graphics. The technology has evolved through multiple generations: FSR 1.0 used Lanczos-based spatial upscaling on single frames, FSR 2.0 introduced temporal upscaling leveraging motion vectors and previous frame data for near-native quality, and FSR 3.0 added optical flow-based frame generation to dramatically increase perceived frame rates. Quality modes range from Ultra Quality to Ultra Performance, letting users balance visual fidelity against performance gains of up to 2x or more. FSR supports DirectX 11, DirectX 12, and Vulkan APIs and is deployed across PC, Xbox, PlayStation, and portable devices like Steam Deck where it enables playable frame rates within limited GPU power budgets. Hundreds of major titles including Cyberpunk 2077, Starfield, and Hogwarts Legacy feature FSR integration, with engine-level support in Unreal Engine and Unity simplifying adoption. Released under the MIT license through AMD's GPUOpen platform, FSR encourages transparent collaboration and modification by developers and researchers. Its platform independence and open-source nature have made it one of the most widely adopted upscaling solutions in the gaming industry, shaping the future of real-time image quality enhancement.

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Real-ESRGAN

Tencent ARC|N/A

Real-ESRGAN is an open-source image upscaling and restoration model developed by Xintao Wang and collaborators at Tencent ARC Lab that enhances low-resolution, degraded, or compressed images to high-resolution outputs with remarkable detail recovery. Released in 2021 under the BSD license, Real-ESRGAN builds on the original ESRGAN architecture by introducing a high-order degradation modeling approach that simulates the complex, unpredictable quality loss found in real-world images, including compression artifacts, noise, blur, and downsampling. The model uses a U-Net architecture with Residual-in-Residual Dense Blocks as its generator network, trained with a combination of perceptual loss, GAN loss, and pixel loss to produce sharp, natural-looking upscaled results. Real-ESRGAN supports upscaling factors of 2x, 4x, and higher, and includes specialized model variants for anime and illustration content alongside the general-purpose photographic model. The model handles real-world degradations far better than its predecessor ESRGAN, which was trained only on synthetic degradation patterns. Real-ESRGAN has become one of the most widely deployed AI upscaling solutions, integrated into numerous applications including desktop tools, web services, mobile apps, and professional image editing workflows. The model runs efficiently on both CPU and GPU, with the lighter RealESRGAN-x4plus-anime variant optimized for consumer hardware. As a fully open-source project available on GitHub with pre-trained weights, it serves as the backbone for popular tools like Upscayl and various ComfyUI nodes. Real-ESRGAN is essential for photographers, content creators, game developers, and anyone who needs to enhance image resolution while preserving natural appearance and adding realistic detail.

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StableSR

Jianyi Wang|N/A

StableSR is an innovative super-resolution model developed by Jianyi Wang and collaborators that leverages the generative prior of a pre-trained Stable Diffusion model for high-quality image upscaling with realistic detail synthesis. Released in 2023 under the Apache 2.0 license, StableSR represents one of the first successful applications of diffusion-based generative models to the image super-resolution task. The model introduces a time-aware encoder that injects information from the low-resolution input image into the Stable Diffusion denoising process at each timestep, along with a controllable feature wrapping module that balances between fidelity to the original image and the richness of generated details. This architecture enables StableSR to produce upscaled images with remarkably realistic textures and fine details that go beyond what traditional regression-based super-resolution methods can achieve. The controllable feature wrapping allows users to adjust the strength of generative enhancement, providing a spectrum from conservative restoration that closely follows the input to aggressive enhancement that adds more synthesized detail. StableSR handles diverse image types including photographs, artwork, screenshots, and text-containing images, with particular strength in restoring natural textures like skin, hair, fabric, and foliage. The model is fully open source with code and pre-trained weights available on GitHub and is compatible with existing Stable Diffusion infrastructure. StableSR is valuable for photographers restoring low-resolution images, digital artists upscaling reference material, and content creators who need high-resolution outputs from limited source imagery. Its diffusion-based approach has influenced subsequent research in generative super-resolution methods.