How to Install and Uninstall nvidia-texture-tools.i686 Package on Fedora 34

Last updated: September 20,2024

1. Install "nvidia-texture-tools.i686" package

This guide covers the steps necessary to install nvidia-texture-tools.i686 on Fedora 34


    
        
            $
        

        
            
                sudo dnf update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf install
            
                        
                nvidia-texture-tools.i686
            
                    
    
    Copied

2. Uninstall "nvidia-texture-tools.i686" package

This tutorial shows how to uninstall nvidia-texture-tools.i686 on Fedora 34:


    
        
            $
        

        
            
                sudo dnf remove
            
                        
                nvidia-texture-tools.i686
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the nvidia-texture-tools.i686 package on Fedora 34

Last metadata expiration check: 2:21:56 ago on Tue Sep 6 02:10:55 2022.
Available Packages
Name : nvidia-texture-tools
Version : 2.1.2
Release : 1.fc34
Architecture : i686
Size : 408 k
Source : nvidia-texture-tools-2.1.2-1.fc34.src.rpm
Repository : fedora
Summary : Collection of image processing and texture manipulation tools
URL : https://github.com/castano/nvidia-texture-tools/wiki
License : MIT and ASL 2.0 and BSD
Description : The NVIDIA Texture Tools is a collection of image processing and texture
: manipulation tools, designed to be integrated in game tools and asset
: conditioning pipelines.
:
: The primary features of the library are mipmap and normal map generation,
: format conversion and DXT compression.
:
: DXT compression is based on Simon Brown's squish library. The library also
: contains an alternative GPU-accelerated compressor that uses CUDA and is
: one order of magnitude faster.