Program.cs

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using TorchSharp;
torchvision.io.DefaultImager = new torchvision.io.SkiaImager();
var device = TorchSharp.torch.device("cuda:0");
var ddpm_v_sampler = TorchSharp.torch.jit.load("ddim_v_sampler.ckpt");
ddpm_v_sampler.to(device);
ddpm_v_sampler.eval();

var start_token = 49406;
var end_token = 49407;
var dictionary = new Dictionary<string, long>(){
    {"cat", 2368},
    {"a", 320},
    {"cute", 2242},
    {"blue", 1746},
    {"wild", 3220},
    {"green", 1901},
};

var batch = 1;

var prompt = "a wild cute green cat";
var tokens = prompt.Split(' ').Select(x => dictionary[x]).ToList();
tokens = tokens.Prepend(start_token).ToList();
tokens = tokens.Append(end_token).ToList();
tokens = tokens.Concat(Enumerable.Repeat<long>(0, 77 - tokens.Count)).ToList();
var uncontional_tokens = new[]{start_token, end_token}.Concat(Enumerable.Repeat(0, 75)).ToList();
var tokenTensor = torch.tensor(tokens.ToArray(), dtype: torch.ScalarType.Int64, device: device);
tokenTensor = tokenTensor.reshape((long)batch, -1);
var unconditional_tokenTensor = torch.tensor(uncontional_tokens.ToArray(), dtype: torch.ScalarType.Int64, device: device);
unconditional_tokenTensor = unconditional_tokenTensor.reshape((long)batch, -1);
var img = torch.randn(batch, 4, 96, 96, dtype: torch.ScalarType.Float32, device: device);
var t = torch.ones(batch, dtype: torch.ScalarType.Int32, device: device);
var condition = ddpm_v_sampler.invoke("clip_encoder", tokenTensor);
var unconditional_condition = ddpm_v_sampler.invoke("clip_encoder", unconditional_tokenTensor);
Console.WriteLine(condition);
var timesteps = 1000;
var ddim_steps = 50;
int gap = timesteps / ddim_steps;
using(var context = torch.enable_grad(false))
{
    for(var i = timesteps-1; i >=0; i -= gap)
    {
        var t_cur = torch.full(batch, i, dtype: torch.ScalarType.Int64, device: device);
        var t_prev = torch.full(batch, i - gap >= 0? i - gap: 0, dtype: torch.ScalarType.Int64, device: device);
        img = (torch.Tensor)ddpm_v_sampler.invoke("ddim_sampler", img, condition, unconditional_condition, t_cur, t_prev);
        Console.WriteLine($"step {i}");
    }

    var decoded_images = (torch.Tensor)ddpm_v_sampler.invoke("decode_image", img);
    decoded_images = torch.clamp((decoded_images + 1.0) / 2.0, 0.0, 1.0);

    for(int i = 0; i!= batch; ++i)
    {
        // c * h * w
        var image = decoded_images[i];
        image = (image * 255.0).to(torch.ScalarType.Byte).cpu();
        torchvision.io.write_image(image, $"{i}.png", torchvision.ImageFormat.Png);
    }
}