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using System;
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using System.Collections.Generic;
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using System.IO;
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using System.Linq;
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using TorchSharp;
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torchvision.io.DefaultImager = new torchvision.io.SkiaImager();
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var device = TorchSharp.torch.device("cuda:0");
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var ddpm_v_sampler = TorchSharp.torch.jit.load("ddim_v_sampler.ckpt");
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ddpm_v_sampler.to(device);
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ddpm_v_sampler.eval();
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var start_token = 49406;
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var end_token = 49407;
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var dictionary = new Dictionary<string, long>(){
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{"cat", 2368},
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{"a", 320},
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{"cute", 2242},
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{"blue", 1746},
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{"wild", 3220},
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{"green", 1901},
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};
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var batch = 1;
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var prompt = "a wild cute green cat";
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var tokens = prompt.Split(' ').Select(x => dictionary[x]).ToList();
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tokens = tokens.Prepend(start_token).ToList();
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tokens = tokens.Append(end_token).ToList();
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tokens = tokens.Concat(Enumerable.Repeat<long>(0, 77 - tokens.Count)).ToList();
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var uncontional_tokens = new[]{start_token, end_token}.Concat(Enumerable.Repeat(0, 75)).ToList();
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var tokenTensor = torch.tensor(tokens.ToArray(), dtype: torch.ScalarType.Int64, device: device);
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tokenTensor = tokenTensor.reshape((long)batch, -1);
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var unconditional_tokenTensor = torch.tensor(uncontional_tokens.ToArray(), dtype: torch.ScalarType.Int64, device: device);
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unconditional_tokenTensor = unconditional_tokenTensor.reshape((long)batch, -1);
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var img = torch.randn(batch, 4, 96, 96, dtype: torch.ScalarType.Float32, device: device);
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var t = torch.ones(batch, dtype: torch.ScalarType.Int32, device: device);
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var condition = ddpm_v_sampler.invoke("clip_encoder", tokenTensor);
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var unconditional_condition = ddpm_v_sampler.invoke("clip_encoder", unconditional_tokenTensor);
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Console.WriteLine(condition);
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var timesteps = 1000;
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var ddim_steps = 50;
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int gap = timesteps / ddim_steps;
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using(var context = torch.enable_grad(false))
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{
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for(var i = timesteps-1; i >=0; i -= gap)
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{
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var t_cur = torch.full(batch, i, dtype: torch.ScalarType.Int64, device: device);
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var t_prev = torch.full(batch, i - gap >= 0? i - gap: 0, dtype: torch.ScalarType.Int64, device: device);
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img = (torch.Tensor)ddpm_v_sampler.invoke("ddim_sampler", img, condition, unconditional_condition, t_cur, t_prev);
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Console.WriteLine($"step {i}");
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}
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var decoded_images = (torch.Tensor)ddpm_v_sampler.invoke("decode_image", img);
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decoded_images = torch.clamp((decoded_images + 1.0) / 2.0, 0.0, 1.0);
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for(int i = 0; i!= batch; ++i)
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{
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var image = decoded_images[i];
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image = (image * 255.0).to(torch.ScalarType.Byte).cpu();
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torchvision.io.write_image(image, $"{i}.png", torchvision.ImageFormat.Png);
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}
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} |