NVidia AI Denoiser

Over the holidays to keep me busy I have implemented a simple command line implementation of NVidias new AI denoiser. Here are some examples of what it can do,

Original image:


Denoised image:


The code can be found here:


I have created a windows distribution as well for those who wish to try it out here:

Denoiser windows v2.4

Denoiser windows v2.3Β Edit 23/04/2019: Updated download to correct link (oops!)

Denoiser windows v2.2

Denoiser windows v2.1

Denoiser windows v2.0

Denoiser windows v1.1

Denoiser windows v1.0



84 thoughts on “NVidia AI Denoiser

  1. Sharing some experience. Had done some testing with v1.0 & it works pretty nice on unidirectional PT renders (Cycles, LuxCoreRender). But does nothing to images generated with BiDir engines (Maxwell, Indigo).

    BTW can it be made to run through a sequence? Whole set of images in specific folder? Wanna test to see, if and how much flickering occurs.

    Liked by 1 person

  2. Thanks for taking the time to test it. The results on Bidirectional are very interesting indeed. The training data was done only on unidirectional so perhaps that is the reason for this.

    I’ll look into sequences, however EXR support is definitely at the top of my list. In the mean time I’ve created some videos previous for work that demonstrates how well it works with animation if you’re interested? Of course I’ll have to see if I can get permission to share these before.


    1. Hi ! I tried out the denoiser with an exr image with multiple planes stored like diffuse, reflect, etc.
      Worked fine the command, however when I try to open in it in After effects is just black. the size of the file reduced. that should happen no?


  3. was no problem
    i like experimenting & experiencing novelties in visual tech
    so thank you for sharing & getting back

    on BiDir
    had also assumed so because of iRay – although it’s well capable of BiDir rendering, i guess it isn’t economically feasible for company to do it just yet

    on to sequences
    was a wishful idea – as you have priorities all set, with EXR being an integral part
    it’s totally understandable and i respect that

    but… if you might know, how the batch script should look like or have a link to a site, it would mean a lot to me…

    am no coder but, still… intrigue & passion for animation burns inside πŸ™‚
    so either way i need to grind on, dig further…

    Thanks again for your work & keep it up!


      so i’ve done some more tests (now with 1.1) on interior scene renders and it seems that this denoiser works efficiently only in well lit areas, similar to your cornell box example
      and after further observation it also leads me to the question, why is there more noise in the greens?

      i wish i would know more about specifications and statistics, what kind of data was this denoiser fed with…

      oh, BTW
      Does this AI learns while working? Is such an option even possible – switching to learn mode?


      1. No problem, if people are enjoying and making use of it then it makes it worth while πŸ™‚

        > but… if you might know, how the batch script should look like or have a link to a site, it would mean a lot to me…

        It would probably be pretty easy to setup with a batch script. I think I could whip one up pretty quickly as a short term work around. It would be useful to know how the numbers in your images are formatted i.e. do they have padding like image.0001.jpg, image.0002.jpg etc…

        >why is there more noise in the greens?

        Are you supplying normal and albedo inputs with the “-n” and “-a” flags. This should improve preserving the colour a lot better than just giving it the beauty alone. If so then perhaps its just a limitation.

        >i wish i would know more about specifications and statistics, what kind of data was this denoiser fed with…

        The training data this uses is the shipped training data from Nvidia which was trained with Iray. As to the exact training set they used I do not have any information on. OptiX actually ships with tools to create your own training data which is really interesting and could improve results if it were trained with images from the same renderer you’re denoising with. So if you happen to have a couple of thousand image pairs lying around to train it with I would be interested in the results πŸ˜‰

        >Does this AI learns while working? Is such an option even possible – switching to learn mode?

        That would be sweet but sadly no its pre-trained. I’m not sure how that would work anyway as you essentially train AI by giving it a big data set of before and after results. When you give it a new before result it can use its training data to try to match a before and produce the expected after result on its own. Is this making sense? However to learn on its own how would it know if what its producing is the correct result?


  4. … πŸ™‚ was imagining a concept of a master artist teaching AI to help on projects later. Something along the lines of personal “AIssistant” – long term study/project. Basically, for starters alike macro maker – recognition of repeating patterns, actions to automate after. Similarly applied in case of rendering, since artists usually develop own styles with years. Just wishfully brainstorming πŸ˜‰

    Seen you’ve updated. Will try some testing over weekend.

    TYVM for explanations & stay well


  5. I want to test your app but when I click on the application file nothing happens, a window opens for half a second and closes immediately after. Same on both computers (windows 10). Could you help me with that? Thank you


    1. Hey Alexis,
      So its a command line application, this means that you need to use it in cmd. If you’re not familiar with using cmd here is a crash course to running the denoiser.
      1. Launch cmd
      2. Enter this into the command line
      C:/Location of denoiser/Denoiser.exe -i C:/Location of image to be denoised/noiseyImage.jpg -o C:/Location to save image/denoisedImage.jpg
      3. Hit enter
      4. You should see something similar to the following written by cmd

      Launching Nvidia AI Denoiser command line app v2.0
      Created by Declan Russell (25/12/2017 ~ Merry Christmas!)
      Input image: C:/Location of image to be denoised/noiseyImage.jpg
      Loaded successfully
      Output image: C:/Location to save image/denoisedImage.jpg
      Denoising complete
      Saving to: C:/Location to save image/denoisedImage.jpg

      5. Hopefully everything has worked out and you have some nice noise free images

      Hope this helps, let me know if you run into any problems πŸ™‚


      1. Ok thanks, very clear mini-tut!

        Now it works and does the steps as expected but it doesn’t change anything to my image: the denoisedImage.jpg is exactly the same as the non-denoised image. Even if I add the albedo and normal maps…

        I tested on both computers and I have 2 different problems :

        1. on my desktop it does the steps but saves an image that is the same as the input, no change whatsoever between input and output

        2. on my laptop it says [optix]: Unknown exception. (for the laptop I think it’s a driver problem, you say we need 390.xx or higher, but for my gtx980m there is no 390.xx or higher.)



  6. Hi Declan
    I have used your Denoiser.exe for some Images and it is producing very good output, but I have found one issue with that after denoising the image I am facing antialising problem. the edges are becoming so much dirty. do you have any thoughts on this?


    1. Hi Vijay,
      I haven’t experienced any aliasing issues myself with the denoiser. Would you be able to send me an example image of the results you are getting so I can get a better idea of what to look for?


  7. I’ve tried using the denoiser not with renders, but with photos, so this may be the problem but the thing doesnt work like… At all. It somewhat manipulates images, changing them a bit, but the noise stays the same. Tried several ISO values, .png and .jpg files, updated the GPU driver, though it was 391 already – nothing changed.
    The test image was denoised normally though, meaning it certainly works, so i dont know what the problem is even.


    1. Hey Alexandr,
      So I too have tried this on photographs and found similar results. Its all down to the training data that the denoiser uses. In my experiments it seems that it doesn’t perform very well with the colour noise that you get in photographs. However it does seem to work quite well with monochrome photographs as it has similar monochrome noise that most renders like the example image would have.


    1. Awesome! So the denoiser ships with two sets of training data. One that was trained on HDR images and one on LDR images. You will likely get better results leaving the HDR training data enabled. I’ve just left it as an option just in case there are cases it doesn’t work well. I may remove it in the future.


  8. I’ve tested it on two machines and one of them’s giving me trouble. The denoiser.exe process won’t terminate itself after having output the result. Have to kill the process manually for batch script to continue. OS: Windows 7SP1, latest drivers, gtx1060. Other than that, promising results!


    1. I can confirm the issue mentioned by mdresner and several people reported the same issue when using D-Noise in Blender, which also finally makes use of denoiser.exe. In my case Win 7, gtx1070, latest NVidia drivers. When I make the most direct test by calling “denoiser.exe -i test.png -o denoised.png” from cmd the program reads the inputfile, then says “denoising…”, then “denoising complete”, then writes the output file (and creates it if it does not exist) containing the nicely denoised image, then says “Done!” and that’s it. The process denoiser.exe stays alive and the input prompt does not come back until the process is explicitely killed. With Blender this means that Blender hangs after each application of denoising until the process has been killed using the task manager.
      Its seems to be an issue with denoiser.exe under Win7. When I use Win 10 with the identical hardware everything runs without problems as it should.
      The denoising results I find really impressive.


      1. This is exactly my case. Every machine with windows 10 I tested ran fine, and windows 7 gave me trouble. I’ve tried running the .exe from powershell as well as cmd


  9. Thanks so much for posting your work. I’d love to give this a try but I get this error.

    [OptiX]: Unknown exception

    Here some information about the equipment I am using:

    NVIDIA System Information report created on: 02/13/2019 15:48:33
    System name: ASUS-A53S

    Operating System: Windows 10 Home, 64-bit
    DirectX version: 12.0
    GPU processor: GeForce 610M
    Driver version: 388.57
    Direct3D API version: 11.2
    Direct3D feature level: 11_0
    CUDA Cores: 48
    Core clock: 850 MHz
    Shader clock: 1700 MHz
    Memory data rate: 1600 MHz
    Memory interface: 64-bit
    Memory bandwidth: 12.80 GB/s
    Total available graphics memory: 5008 MB
    Dedicated video memory: 2048 MB DDR3
    System video memory: 0 MB
    Shared system memory: 2960 MB
    Video BIOS version: 75.19.4D.00.03
    IRQ: Not used
    Bus: PCI Express x16 Gen2
    Device Id: 10DE 1058 16521043
    Part Number: 1317 0000


    NvUpdtr.dll NVIDIA Update Components
    NvUpdt.dll NVIDIA Update Components
    NvGFTrayPluginr.dll NVIDIA GeForce Experience
    NvGFTrayPlugin.dll NVIDIA GeForce Experience
    nvui.dll NVIDIA User Experience Driver Component
    nvxdplcy.dll NVIDIA User Experience Driver Component
    nvxdbat.dll NVIDIA User Experience Driver Component
    nvxdapix.dll NVIDIA User Experience Driver Component
    NVCPL.DLL NVIDIA User Experience Driver Component
    nvCplUIR.dll 8.1.940.0 NVIDIA Control Panel
    nvCplUI.exe 8.1.940.0 NVIDIA Control Panel
    nvWSSR.dll NVIDIA Workstation Server
    nvWSS.dll NVIDIA Workstation Server
    nvViTvSR.dll NVIDIA Video Server
    nvViTvS.dll NVIDIA Video Server
    NVSTVIEW.EXE NVIDIA 3D Vision Photo Viewer
    NVSTTEST.EXE NVIDIA 3D Vision Test Application
    NVSTRES.DLL NVIDIA 3D Vision Module
    nvDispSR.dll NVIDIA Display Server
    NVMCTRAY.DLL NVIDIA Media Center Library
    nvDispS.dll NVIDIA Display Server
    PhysX 09.15.0428 NVIDIA PhysX
    NVCUDA.DLL NVIDIA CUDA 9.1.112 driver
    nvGameSR.dll NVIDIA 3D Settings Server
    nvGameS.dll NVIDIA 3D Settings Server


    1. Hi Dana,
      Which version are you using? Version 2.2 drops support for Keplar architecture (nvidia 600 series) but you should still be able to use the previous versions. Also you will need to update your Nvidia driver.

      Let me know if this fixes the problem πŸ™‚


      1. Hi Declan,

        I’ve tried all three versions. with the same results. I am using the latest driver from NVIDIA, 391.35, but I’ve tried an earlier version, 391.24, too



      2. Hi Dana,
        Sorry you are having problems with this. I’ve done some deeper research into this and unfortunately it turns out that the Geforce 610m is actually built on the Fermi architecture and will be unable to run the denoiser :/


  10. Hi Declan,

    No problem. Thanks for checking. I guess I need to pick up something that I KNOW will work for one of my desktops. What would you suggest on the low end?



    1. Hmm thats a tough question as it really depends on budget and use case. In my opinion the 10 series are really good value for money but they do not have all the cool new AI/Raytracing hardware that the new cards have. So who knows how they will age.

      Before you buy a new card perhaps you would be willing to try the Intel AI denoiser instead? Its essentially the same algorithm but on CPU. I’ve just published a release here https://github.com/DeclanRussell/IntelOIDenoiser.

      The training data is different however. So far in my brief testing I have found that some scenes work better with the Nvidia denoiser and others are better with the Intel.

      Hope this helps πŸ™‚


      1. Hi Declan,

        Thanks for the suggestion. I’ll check out your new code too, but it looks like I’ll need to compile it. Do you plan to do a binary release?

        Best regards,


  11. I just found the release. But just fyi this is the error I get when trying to run scons:

    scons: Reading SConscript files …
    KeyError: ‘CUDA_PATH’:
    File “D:\IntelOIDenoiser-master\SConstruct”, line 6:
    CUDA_INCLUDE_PATH = os.environ[‘CUDA_PATH’] + “/include”
    File “C:\Python27\lib\os.py”, line 425:
    return self.data[key.upper()]


  12. Hi, this looks very interesting, and I wanted to try it out with images from an old halo raytracer (HaloSim3: https://www.atoptics.co.uk/halo/halfeat.htm ), however the running the batch file just gives me this error:

    Input image: Test1.png
    Loaded successfully
    Output image: denoised_Test1.png
    [OptiX]: Unknown exception

    Any idea what I’m doing wrong?
    Tried on a GTX1080, driver version 417.35


      1. Hi Declan,

        thats like the icing on the cake. Thanks so much.
        One question, does your denoiser keep the alpha channel in the images when batch processing?
        Grant’s version for Blender drops it at the moment and I need the alpha channel to stay in the images (its a couple of hundred png files…



      2. Hi Fran,
        No problem πŸ™‚ It should keep the same format as what it has been given, so in theory it should keep the alpha channel. That being said I haven’t explicitly tested this so if it doesn’t let me know and I’ll see if I can sort out a fix.



  13. Hi! Thanks for your work. Can you help me?
    GeForce GTX 1050ti
    Driver version 416.34
    [Optix]: Failed to load Optix library


    1. Hi Tom,
      Thanks! I probably will at some point but currently AFAIK there isn’t any advantage to doing so as the denoiser will be the same between versions. Feel free to correct me on this if there is something that OptiX 7 will bring that you think will be beneficial.



    1. Hey MJ,
      Thanks, glad to see you have had some success with it. Can you give me some information about how the image was rendered, which renderer and which output filter did you use? These AI denoisers typically only work with either box filtering or FIS filtering. If neither of these options still work, feel free to try the intel version of this denoiser which has different training data and therefore may give different results (https://declanrussell.com/portfolio/intel-open-image-denoiser-2/).


  14. Hi,
    I would really like to make this work and integrate it to my photo workflow.
    I tried version 2.3 & 2.4 on DNG, JPG & TIF images.
    I am running Windows 10, my driver 461.40 for Nvidia GTX-745

    The results:
    c:\Users\Administrator\Desktop\Denoiser_v24>Denoiser -i 6400.tif -o 6400dn.jpg
    Launching Nvidia AI Denoiser command line app v2.4
    Created by Declan Russell (25/12/2017 ~ Merry Christmas!)
    Input image: 6400.tif
    Loaded successfully
    Output image: 6400dn.jpg
    Denoising complete in 5.722 seconds
    [OptiX]: Unknown error (Details: Function “bufferMap” caught exception: Encountered a CUDA error: cudaDriver().CuMemcpyDtoH( dstHost, srcDevice, byteCount ) returned (702): Launch timeout, file: , line: 0)

    Do you have a solution?


    1. Hi Pierre,

      Apologies for the late reply. Unfortunately your GPU is Kepler architecture, which means that its too old to run the OptiX denoiser. You will need Maxwell architecture or higher. Just FYI, if you’re looking to use this for photography, from my own experiments I would recommend using a different denoising solution. The denoiser is trained on CG images and cannot remove colour noise well (or at all). Monochrome photos can work occasionally, but given the difference in training data I wouldn’t be surprised if you ran into issues now and again.



      1. “The denoiser is trained on CG images and cannot remove colour noise well (or at all).” Hmm yes I have found the same. Can you propose viable solutions for de-noising colour noise?


      2. You would need to use another denoising solution. I’ve not had any experience with any personally, but I’ve heard good things about Neat video. Maybe that is worth a try?


    1. Hi John, Unfortunately the denoiser is a propriety library from Nvidia that is CUDA based. So there is no way for me to port it to GLSL. The flame team could of course integrate the library it flame with the use of some GL-CUDA interoperability. However you would need to talk to their dev team abut that.


  15. Hi Declan, I imagine you already know Intel published the 1.4 OID with some great new features and improvements ! So if you have insomnia it will be awesome πŸ™‚
    Thanks again ++


  16. Hey Declan, thanks for sharing your tools! Do you have any thoughts on the new temporal denoiser in Optix 7.3? Is that something you’re thinking of implementing at any point?


  17. Hello? please can you help? i’m getting [OptiX]: Unknown exception problem. I have a driver of 425 version, GeForce 920m! what should i do?


    1. Glad you enjoy the tool! Unfortunately, I don’t really know enough about fstorm to know what would be involved in integrating this into it. That may be more of a question for the developers of fstorm. You should be able to use the CMD tool here on images output by fstorm though regardless.


  18. Hi Declan. Thank you for the great job. It works – except the switches. I mean I don’t know how to use repeat and the rest commands. Could you give some help, please?
    Thank you.


    1. Hey! Thank you πŸ™‚ Which switches specifically trying to use? There are some details in the help “Denoiser.exe -h”. The -repeat option is really just for performance profiling. It just loops the denoiser N times to compute some average execution time. Probably not super useful for you unless you are doing some research.


      1. Thank you Declan. I would just like to see a completed command line, because the order and syntax is not clear for me. denoiser -i picture/pict.jpg -o picture/pict2.jpg means a normal denoising procedure. If I want to refine iterations does look like this? (For instance:)
        denoiser -i -repeat [5] picture/pict.jpg -o picture/pict2.jpg
        Thank you.


    2. Replying here to your second message because wordpress’s bad design won’t let me reply directly.

      Here is the simplest command line you can have

      C:/Location of denoiser/Denoiser.exe -i C:/Location of image to be denoised/noiseyImage.exr -o C:/Location to save image/denoisedImage.exr

      This will take and input image and give you some denoised output image. Unfortunately, there is no way to “refine” the denoised result. The denoiser is based on a machine learning, so the result you get is what you get, it either works or it doesn’t. However, you can help the denoiser by giving it additional information, such as your albedo and the normal of your image if you have them (I am assuming that you are denoising computer generated images and not photos? The denoiser doesn’t work for photos). You would do this like so,

      C:/Location of denoiser/Denoiser.exe -i C:/Location of image to be denoised/noiseyImage.exr -a C:/Location of image to be denoised/albedo.exr -n C:/Location of image to be denoised/normal.exr -o C:/Location to save image/denoisedImage.exr

      Some things to note. If you’re providing a normal feature image, you should use some file format that supports negative pixel value, such as EXR. Otherwise, you will have incorrect information fed to the denoiser.

      Hope this helps πŸ™‚


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