Image Upscale Models
Explore the best AI models for image upscale
Real-ESRGAN
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.
Topaz Gigapixel AI
Topaz Gigapixel AI is a commercial desktop application for AI-powered image upscaling and enhancement developed by Topaz Labs, positioned as an industry-standard tool for professional photographers, graphic designers, and image processing specialists. Available on Windows and macOS, the software uses a proprietary hybrid neural network architecture that combines multiple AI models to upscale images by up to 600 percent while preserving and even enhancing fine details, textures, and sharpness. Topaz Gigapixel AI includes specialized processing modes for different content types including faces, standard photography, computer graphics, and low-resolution sources, with each mode optimized to produce the best possible results for its target content. The software features intelligent face detection and enhancement that improves facial details during upscaling, producing natural-looking results even from very low-resolution source images. Topaz Gigapixel AI supports batch processing for handling large volumes of images and integrates with Adobe Lightroom and Photoshop as a plugin, fitting seamlessly into professional photography workflows. The application processes images locally on the user's machine using GPU acceleration, ensuring privacy and fast processing without requiring an internet connection. Output quality is widely regarded as among the best available in commercial upscaling software, with particular strength in preserving natural textures and avoiding the artificial smoothing common in many AI upscalers. As a proprietary product with a one-time purchase or subscription model, Topaz Gigapixel AI is particularly valued by professional photographers enlarging prints, real estate photographers enhancing property images, forensic analysts improving evidence imagery, and archivists restoring historical photographs to modern resolution standards.
Upscayl
Upscayl is a free and open-source desktop application for AI-powered image upscaling, built on top of Real-ESRGAN and other super-resolution models. Developed by Nayam Amarshe and TGS963, Upscayl provides a user-friendly graphical interface that makes advanced AI image upscaling accessible to non-technical users on Windows, macOS, and Linux platforms. The application wraps multiple AI upscaling models in an Electron-based desktop app, allowing users to enhance image resolution with just a few clicks without any command-line knowledge or Python environment setup. Upscayl includes several pre-installed upscaling models optimized for different content types including general photography, digital art, anime, and sharpening, with each model producing different aesthetic characteristics suited to its target content. Users can select upscaling factors of 2x, 3x, or 4x and process individual images or entire folders through batch processing. The application supports common image formats including PNG, JPG, and WebP, and provides options for output format and quality settings. Upscayl also supports custom model loading, allowing users to import additional NCNN-compatible upscaling models from the community. Released under the AGPL-3.0 license, Upscayl is fully open source with its code available on GitHub and has accumulated a large community of users and contributors. The application runs entirely locally with no internet connection required, ensuring privacy for sensitive images. Upscayl is particularly popular among photographers, graphic designers, content creators, and hobbyists who need a simple, free solution for enhancing image quality without subscriptions or cloud processing dependencies.
CodeFormer
CodeFormer is a state-of-the-art blind face restoration model developed by researchers at Nanyang Technological University in collaboration with Tencent ARC, presented at NeurIPS 2022. The model employs a unique Transformer-based architecture with a discrete codebook lookup mechanism to restore severely degraded facial images with exceptional fidelity. Its most distinguishing feature is an adjustable w parameter ranging from 0.0 to 1.0 that gives users precise control over the balance between identity preservation and restoration quality. Architecturally, CodeFormer consists of three core components: a VQGAN encoder-decoder that learns discrete visual codes from high-quality face datasets, a codebook that stores these learned representations, and a Transformer module that predicts optimal code combinations during restoration. This approach enables the model to produce plausible facial details even under extreme degradation because it draws information from learned priors rather than solely from the corrupted input. In benchmark evaluations on CelebA-HQ and WIDER-Face datasets, CodeFormer achieves superior results across FID, NIQE, and identity similarity metrics compared to previous methods. Practical applications include restoring old family photographs, enhancing faces in AI-generated images, extracting facial details from low-resolution video frames, and professional photo retouching. The model is open source, integrates with popular tools like ComfyUI, AUTOMATIC1111 WebUI, and Fooocus, and offers cloud inference through Replicate API and Hugging Face Spaces demos for accessible experimentation.
SUPIR
SUPIR is an advanced AI image restoration and upscaling model developed by Tencent ARC researchers in 2024 that harnesses the generative power of SDXL, a large-scale Stable Diffusion model, for photo-realistic image enhancement. SUPIR stands for Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration in the Wild. The model introduces a degradation-aware encoder that analyzes the specific types of quality loss present in an input image and generates intelligent text prompts to guide the restoration process, effectively telling the diffusion model what kind of content needs to be restored and how. This intelligent prompting approach enables SUPIR to produce remarkably detailed and natural-looking upscaled results that go beyond simple pixel interpolation to generate semantically meaningful detail. The model leverages the vast visual knowledge embedded in SDXL's pre-trained weights to synthesize realistic textures, facial features, text, and fine patterns during upscaling. SUPIR excels particularly at restoring severely degraded images where traditional upscaling methods fail, including old photographs, heavily compressed web images, and low-resolution captures. The model supports high upscaling factors while maintaining coherent content and natural appearance. Released under a research-only license, SUPIR is open source with code and weights available on GitHub. While computationally intensive due to its SDXL backbone, the model produces results that represent the current frontier of AI-powered image restoration quality. SUPIR is particularly valuable for professional photographers restoring archival images, forensic analysts enhancing surveillance footage, and digital artists who need maximum quality from limited source material.
SwinIR
SwinIR is a Transformer-based image restoration model developed by Jingyun Liang and the research team at ETH Zurich that achieves state-of-the-art performance across multiple restoration tasks including super-resolution, image denoising, and JPEG compression artifact removal. Released in August 2021 under the Apache 2.0 license, SwinIR adapts the Swin Transformer architecture for image processing by leveraging shifted window attention mechanisms that efficiently capture both local detail and global context in images. The model consists of three main modules: a shallow feature extraction layer, a deep feature extraction module built from Swin Transformer blocks with residual connections, and a reconstruction module that produces the restored high-quality output. With only 12 million parameters, SwinIR is remarkably lightweight compared to many competing models while delivering superior or comparable results. The model supports multiple super-resolution scales including 2x, 3x, and 4x upscaling, classical and lightweight variants for different quality-speed trade-offs, and separate configurations optimized for denoising at various noise levels and JPEG artifact removal at different quality factors. SwinIR demonstrated that Transformer architectures could outperform CNN-based approaches in low-level image processing tasks, marking an important milestone in the field. The model is fully open source with pre-trained weights available on GitHub and integrates well with standard deep learning frameworks. SwinIR is widely used in academic research as a baseline for image restoration benchmarks and in practical applications by photographers, graphic designers, and content creators who need high-quality image enhancement. Its efficient architecture makes it suitable for deployment on consumer hardware without specialized GPU requirements.
FidelityFx Super Resolution
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.
StableSR
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.