Paul Shapiro 
@jroakes this has nothing to do with SEO but do you have a machine where you can run a CUDA model on it?
JR Oakes 
@fighto it should be able to run Cuda on most Nvidia GPUs. AFAIK, Cuda is just the bridging technology fot interfacing vector data to GPUs
@fighto I will normally run Colab, we spin up a GPU cluster to run training
@jroakes it’s not too big, but you can tell me no KBNet https://paperswithcode.com/task/image-denoising
Papers with Code - Image Denoising
**Image Denoising** is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image. <span style="color:grey; opacity: 0.6">( Image credit: [Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior](https://arxiv.org/pdf/1707.05414v5.pdf) )</span>
@fighto Yeah, those models look super small. Do you need a GPU to run?
@jroakes yeah, the pre-trained models were trained using CUDA, so when I tried running them I got an error
@fighto I haven’t had time to dig into it, but pytorch and tensorflow models shouldn’t mind where the processing is done. Weather on a GPU, CPU, or TPU. Most of the academic work is coded on their particular system so there is probably some code in their files that just specifies that the data should be on the GPU device. Normally like xxxx.to_device(‘gpu’) or similar. Updating that to move the data to the CPU should resolve the problem.
@fighto Looking at https://github.com/zhangyi-3/KBNet/blob/main/Denoising/test_real_denoising_sidd.py. Just remove all the .cuda() methods and it should work fine. I have never worked with the basicsr library they are using, but looks like a pytorch model. The .cuda() methods means move the weights from the CPU to GPU.
