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using StableDiffusionV2;
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using System;
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using System.IO;
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using TorchSharp;
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var batch = 1;
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var device = torch.device("cuda:0");
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torchvision.io.DefaultImager = new torchvision.io.SkiaImager();
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var prompt = "a wild cute green cat";
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var outputFolder = "Output";
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if(!Directory.Exists(outputFolder))
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{
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Directory.CreateDirectory(outputFolder);
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}
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var clipTokenizer = new ClipTokenizer("vocab.json", "merges.txt");
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var tokens = clipTokenizer.Tokenize(prompt);
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var uncontional_tokens = clipTokenizer.Tokenize("");
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var tokenTensor = torch.tensor(tokens, dtype: torch.ScalarType.Int64, device: device);
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var unconditional_tokenTensor = torch.tensor(uncontional_tokens, dtype: torch.ScalarType.Int64, device: device);
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tokenTensor = tokenTensor.repeat(batch, 1);
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unconditional_tokenTensor = unconditional_tokenTensor.repeat(batch, 1);
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var clipEncoder = new ClipEncoder("clip_encoder.ckpt", device);
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var img = torch.randn(batch, 4, 64, 64, dtype: torch.ScalarType.Float32, device: device);
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var condition = clipEncoder.Forward(tokenTensor);
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var unconditional_condition = clipEncoder.Forward(unconditional_tokenTensor);
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clipEncoder.Dispose();
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var ddpm = new DDPM("ddim_v_sampler.ckpt", device);
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var ddimSampler = new DDIMSampler(ddpm);
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var ddim_steps = 50;
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img = ddimSampler.Sample(img, condition, unconditional_condition, ddim_steps);
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ddpm.Dispose();
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var vae = new AutoencoderKL("autoencoder_kl.ckpt", device);
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var decoded_images = vae.Forward(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 savedPath = Path.Join(outputFolder, $"{i}.png");
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var image = decoded_images[i];
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image = (image * 255.0).to(torch.ScalarType.Byte).cpu();
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torchvision.io.write_image(image, savedPath, torchvision.ImageFormat.Png);
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Console.WriteLine($"save image to {savedPath}, enjoy");
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} |