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Store backend in AI Implementation struct
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@@ -1,16 +1,23 @@
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using Flowframes.IO;
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using System;
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using System.Collections.Generic;
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using System.Collections.Generic;
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namespace Flowframes.Data
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{
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class Implementations
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{
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public static AI rifeCuda = new AI("RIFE_CUDA", "RIFE", "CUDA/Pytorch Implementation of RIFE (Nvidia Only!)", "rife-cuda", new int[] { 2, 4, 8, 16 });
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public static AI rifeNcnn = new AI("RIFE_NCNN", "RIFE (NCNN)", "Vulkan/NCNN Implementation of RIFE", "rife-ncnn", new int[] { 2, 4, 8 }, true);
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public static AI flavrCuda = new AI("FLAVR_CUDA", "FLAVR", "Experimental Pytorch Implementation of FLAVR (Nvidia Only!)", "flavr-cuda", new int[] { 2, 4, 8 });
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public static AI dainNcnn = new AI("DAIN_NCNN", "DAIN (NCNN)", "Vulkan/NCNN Implementation of DAIN", "dain-ncnn", new int[] { 2, 3, 4, 5, 6, 7, 8 });
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public static AI xvfiCuda = new AI("XVFI_CUDA", "XVFI", "CUDA/Pytorch Implementation of XVFI (Nvidia Only!)", "xvfi-cuda", new int[] { 2, 3, 4, 5, 6, 7, 8, 9, 10 });
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public static AI rifeCuda = new AI(AI.Backend.Pytorch, "RIFE_CUDA", "RIFE",
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"CUDA/Pytorch Implementation of RIFE (Nvidia Only!)", "rife-cuda", new int[] { 2, 4, 8, 16 });
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public static AI rifeNcnn = new AI(AI.Backend.Ncnn, "RIFE_NCNN", "RIFE (NCNN)",
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"Vulkan/NCNN Implementation of RIFE", "rife-ncnn", new int[] { 2, 4, 8 }, true);
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public static AI flavrCuda = new AI(AI.Backend.Pytorch, "FLAVR_CUDA", "FLAVR",
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"Experimental Pytorch Implementation of FLAVR (Nvidia Only!)", "flavr-cuda", new int[] { 2, 4, 8 });
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public static AI dainNcnn = new AI(AI.Backend.Ncnn, "DAIN_NCNN", "DAIN (NCNN)",
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"Vulkan/NCNN Implementation of DAIN", "dain-ncnn", new int[] { 2, 3, 4, 5, 6, 7, 8 });
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public static AI xvfiCuda = new AI(AI.Backend.Pytorch, "XVFI_CUDA", "XVFI",
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"CUDA/Pytorch Implementation of XVFI (Nvidia Only!)", "xvfi-cuda", new int[] { 2, 3, 4, 5, 6, 7, 8, 9, 10 });
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public static List<AI> networks = new List<AI> { rifeCuda, rifeNcnn, flavrCuda, dainNcnn, xvfiCuda };
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