Search Results for "amd"

...Instead of relying on the upscaling method originally integrated by a game developer, the software intercepts the game’s rendering pipeline and redirects it to alternative technologies chosen by the user. This makes it possible to swap technologies such as NVIDIA DLSS, AMD FSR, or Intel XeSS even if the game only supports one of them by default. The tool effectively acts as a compatibility layer between the game engine and multiple upscaling frameworks, enabling cross-GPU access to features that might otherwise be restricted to specific hardware ecosystems. In addition to replacing upscalers, OptiScaler can enable frame generation features in titles that do not officially support them, improving frame rates and perceived smoothness during gameplay.

...It features blazing-fast TensorRT inference by NVIDIA, which can speed up AI processes significantly. Pre-packaged, without the need to install Docker or WSL (Windows Subsystem for Linux) - and NCNN inference by Tencent which is lightweight and runs on NVIDIA, AMD and even Apple Silicon - in contrast to the mammoth of an inference PyTorch is, which only runs on NVIDIA GPUs.

Replacing DLSS 2.x with FSR 2.1.2 for various games such as Cyberpunk 2077, Red Dead Redemption 2, etc. It includes a DLL wrapper/injector (winmm.dll) to disable Nvidia GeForce RTX GPU checking, so AMD Radeon / Nvidia GTX users can enjoy the mod. Download the latest relase from Release Page. Extract the contents of the archive next to the game EXE file in your games folder. Make sure the game is running in DX12 mode. Download the latest relase from Release Page. Extract the contents of the archive next to the game EXE file in your games folder.

Super-resolution using a CNN, based on the work of the DGtal team. First of all, an Nvidia graphics card (neither AMD nor Intel integrated) is highly recommended to parallelize the CNN. You will then need to install CUDA. No CUDA = dozens of times slower. This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test batch and lrto the learning rate. ...