awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
Fanghua-Yu avatar

Fanghua-Yu/SUPIR

0
View on GitHub↗
5,587 stars·470 forks·Python·21 viewssupir.xpixel.group↗

SUPIR

SUPIR is an AI image upscaler and restoration system designed to remove artifacts and restore quality to real-world photographs. It functions as a diffusion-based image enhancer and restoration tool that uses large-scale model scaling to produce high-resolution results with photorealistic details.

The system balances visual aesthetics with input fidelity, allowing for a trade-off between strict adherence to the original image and the overall visual appeal of the output. It leverages large-scale model inference to improve image clarity and maintain realistic details during the upscaling process.

Features

  • Image-Conditioned Generation - Uses the original low-resolution image as a structural reference to guide the generation of high-resolution output.
  • Image Restoration Models - Employs large-scale diffusion models for pixel prediction and artifact removal during restoration.
  • Model Parameter Scaling - Leverages an oversized neural network architecture to capture complex visual priors for photorealistic results.
  • Diffusion-Based Upsamplers - Implements a diffusion-based pipeline to upscale images and refine details while maintaining fidelity to the source.
  • AI Upscaling - Increases the resolution and clarity of real-world photographs using AI-driven upscaling.
  • Image Restoration - Removes artifacts and restores image quality to produce photorealistic high-resolution results.
  • High-Fidelity Synthesis - Produces ultra-high-resolution images that maintain structural integrity and visual realism.
  • Gradual Latent Upscaling - Performs image enlargement and restoration within the latent space of a diffusion process before decoding.
  • Inference Scaling - Uses large-scale model inference to improve image clarity and reconstruct real-world photographs.
  • Visual Fidelity Controllers - Allows users to adjust fidelity controllers to balance image accuracy with aesthetic quality.
  • Generative Image Enhancements - Uses generative diffusion models to enhance image quality and visual aesthetics.
  • Hyperparameter Tuning - Provides controls to trade off strict adherence to the original image against overall visual appeal.
  • Image Restoration - Scales model capacity for photorealistic image restoration.

Star history

Star history chart for fanghua-yu/supirStar history chart for fanghua-yu/supir

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI

Projects sharing features with SUPIR

These projects share indexed features with SUPIR. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • spipm/depixelization_pocspipm avatar

    spipm/Depixelization_poc

    4,535View on GitHub↗

    This project is an AI upscaling framework and deep learning image restorer designed to estimate original source pixels from low-resolution inputs. It functions as a super-resolution reconstruction system that transforms pixelated images into high-resolution versions by restoring high-frequency details and sharpening edges. The system utilizes a convolutional neural network pipeline to analyze pixel data and perform digital image restoration. It employs pixel-shuffle upsampling to rearrange channel dimensions into spatial dimensions, which increases resolution while reducing checkerboard artif

    Python
    View on GitHub↗4,535
  • sczhou/codeformersczhou avatar

    sczhou/CodeFormer

    18,002View on GitHub↗

    CodeFormer is a deep learning framework designed for the restoration and enhancement of facial images and video sequences. It functions as a comprehensive processing engine capable of reconstructing high-quality facial features from degraded, blurry, or damaged inputs, while also providing tools for image upscaling and generative inpainting to fill missing or corrupted regions. The system distinguishes itself by utilizing a codebook-based quantization approach that maps input patches to high-quality facial representations, supported by transformer-based global modeling to ensure structural co

    Pythoncodebookcodeformerface-enhancement
    View on GitHub↗18,002
  • vladmandic/sdnextvladmandic avatar

    vladmandic/sdnext

    7,139View on GitHub↗

    SD.Next is an all-in-one web interface and multi-backend inference engine for generating, editing, and processing images and videos using diffusion models. It functions as a comprehensive tool for diffusion model management and an automated image processing pipeline for bulk operations. The project is distinguished by its hardware-backend abstraction layer, which provides automatic detection and acceleration for NVIDIA CUDA, AMD ROCm, Intel OpenVINO, and DirectML. It features a headless generative API and a programmatic command interface, allowing users to trigger tasks via REST API or CLI wi

    Pythonai-artcaptiondiffusers
    View on GitHub↗7,139
  • astriaai/headshots-starterastriaai avatar

    astriaai/headshots-starter

    4,461View on GitHub↗

    This project is an AI headshot generator and SaaS boilerplate designed to train custom models on uploaded photos to produce professional, high-resolution portraits. It functions as an image generation pipeline and model training orchestrator that manages the end-to-end workflow of processing user images to create stylized avatars. The system includes a credit-based monetization framework that handles payments via automated webhooks. It provides a complete infrastructure for AI-driven services, incorporating user account management and automated email notifications to alert users when their ge

    TypeScript
    View on GitHub↗4,461
Compare all 30 related projects→

Frequently asked questions

What does fanghua-yu/supir do?

SUPIR is an AI image upscaler and restoration system designed to remove artifacts and restore quality to real-world photographs. It functions as a diffusion-based image enhancer and restoration tool that uses large-scale model scaling to produce high-resolution results with photorealistic details.

What are the main features of fanghua-yu/supir?

The main features of fanghua-yu/supir are: Image-Conditioned Generation, Image Restoration Models, Model Parameter Scaling, Diffusion-Based Upsamplers, AI Upscaling, Image Restoration, High-Fidelity Synthesis, Gradual Latent Upscaling.

Which projects share features with fanghua-yu/supir?

Projects with overlapping indexed features include: spipm/depixelization_poc — This project is an AI upscaling framework and deep learning image restorer designed to estimate original source pixels… sczhou/codeformer — CodeFormer is a deep learning framework designed for the restoration and enhancement of facial images and video… vladmandic/sdnext — SD.Next is an all-in-one web interface and multi-backend inference engine for generating, editing, and processing… astriaai/headshots-starter — This project is an AI headshot generator and SaaS boilerplate designed to train custom models on uploaded photos to… xpixelgroup/diffbir — DiffBIR is a diffusion-based image restoration framework designed for blind image reconstruction. It utilizes… tntwise/real-video-enhancer — Real-Video-Enhancer is a cross-platform desktop application that utilizes neural networks to upscale resolution,…