• C# danbooru Stable Diffusion 提示词反推 Onnx Demo

    目录

    说明

    效果

    模型信息

    项目

    代码

    下载 

    C# danbooru Stable Diffusion 提示词反推 Onnx Demo

    说明

    模型下载地址：https://huggingface.co/deepghs/ml-danbooru-onnx

    效果

    模型信息

    Model Properties
    -------------------------
    ---------------------------------------------------------------

    Inputs
    -------------------------
    name：input
    tensor：Float[-1, 3, -1, -1]
    ---------------------------------------------------------------

    Outputs
    -------------------------
    name：output
    tensor：Float[-1, 12547]
    --------------------------------------------------------------- 

    项目

    代码

    using Microsoft.ML.OnnxRuntime;
    using Microsoft.ML.OnnxRuntime.Tensors;
    using OpenCvSharp;
    using System;
    using System.Collections.Generic;
    using System.Drawing;
    using System.IO;
    using System.Linq;
    using System.Text;
    using System.Windows.Forms;

    namespace Onnx_Demo
    {
        public partial class Form1 : Form
        {
            public Form1()
            {
                InitializeComponent();
            }

            string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
            string image_path = "";
            DateTime dt1 = DateTime.Now;
            DateTime dt2 = DateTime.Now;
            string model_path;
            Mat image;

            SessionOptions options;
            InferenceSession onnx_session;
            Tensor input_tensor;
            List input_container;
            IDisposableReadOnlyCollection result_infer;
            DisposableNamedOnnxValue[] results_onnxvalue;

            Tensor result_tensors;

            StringBuilder sb = new StringBuilder();

            public string[] class_names;

            private void button1_Click(object sender, EventArgs e)
            {
                OpenFileDialog ofd = new OpenFileDialog();
                ofd.Filter = fileFilter;
                if (ofd.ShowDialog() != DialogResult.OK) return;
                pictureBox1.Image = null;
                image_path = ofd.FileName;
                pictureBox1.Image = new Bitmap(image_path);
                textBox1.Text = "";
                image = new Mat(image_path);
            }

            private void button2_Click(object sender, EventArgs e)
            {
                if (image_path == "")
                {
                    return;
                }

                button2.Enabled = false;
                textBox1.Text = "";
                sb.Clear();
                Application.DoEvents();

                image = new Mat(image_path);

                // 将图片转为RGB通道
                Cv2.CvtColor(image, image, ColorConversionCodes.BGR2RGB);

                // 输入Tensor
                input_tensor = new DenseTensor(new[] { 1, 3, image.Height, image.Width });

                // 输入Tensor
                for (int y = 0; y < image.Height; y++)
                {
                    for (int x = 0; x < image.Width; x++)
                    {
                        input_tensor[0, 0, y, x] = image.At(y, x)[0] / 255f;
                        input_tensor[0, 1, y, x] = image.At(y, x)[1] / 255f;
                        input_tensor[0, 2, y, x] = image.At(y, x)[2] / 255f;
                    }
                }

                //将 input_tensor 放入一个输入参数的容器，并指定名称
                input_container.Add(NamedOnnxValue.CreateFromTensor("input", input_tensor));

                dt1 = DateTime.Now;
                //运行 Inference 并获取结果
                result_infer = onnx_session.Run(input_container);
                dt2 = DateTime.Now;

                // 将输出结果转为DisposableNamedOnnxValue数组
                results_onnxvalue = result_infer.ToArray();

                // 读取第一个节点输出并转为Tensor数据
                result_tensors = results_onnxvalue[0].AsTensor();

                var result_array = result_tensors.ToArray();

                double[] scores = new double[result_array.Length];
                for (int i = 0; i < result_array.Length; i++)
                {
                    double score = 1 / (1 + Math.Exp(result_array[i] * -1));
                    scores[i] = score;
                }

                List ltResult = new List();
                ScoreIndex temp;
                for (int i = 0; i < scores.Length; i++)
                {
                    temp = new ScoreIndex(i, scores[i]);
                    ltResult.Add(temp);
                }

                //根据分数倒序排序，取前10个
                var SortedByScore = ltResult.OrderByDescending(p => p.Score).ToList().Take(10);

                foreach (var item in SortedByScore)
                {
                    sb.Append(class_names[item.Index] + ",");
                }
                sb.Length--; // 将长度减1来移除最后一个字符

                sb.AppendLine("");
                sb.AppendLine("------------------");

                // 只取分数最高的
                // float max = result_array.Max();
                // int maxIndex = Array.IndexOf(result_array, max);
                // sb.AppendLine(class_names[maxIndex]+" "+ max.ToString("P2"));

                sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
                textBox1.Text = sb.ToString();
                button2.Enabled = true;
            }

            private void Form1_Load(object sender, EventArgs e)
            {
                model_path = "model/ml_danbooru.onnx";

                // 创建输出会话，用于输出模型读取信息
                options = new SessionOptions();
                options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
                options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行

                // 创建推理模型类，读取本地模型文件
                onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径

                // 创建输入容器
                input_container = new List();

                image_path = "test_img/2.jpg";
                pictureBox1.Image = new Bitmap(image_path);
                image = new Mat(image_path);

                List str = new List();
                StreamReader sr = new StreamReader("model/lable.txt");
                string line;
                while ((line = sr.ReadLine()) != null)
                {
                    str.Add(line);
                }
                class_names = str.ToArray();
            }

        }
    }

    1. using Microsoft.ML.OnnxRuntime;
    2. using Microsoft.ML.OnnxRuntime.Tensors;
    3. using OpenCvSharp;
    4. using System;
    5. using System.Collections.Generic;
    6. using System.Drawing;
    7. using System.IO;
    8. using System.Linq;
    9. using System.Text;
    10. using System.Windows.Forms;
    11.  
    12. namespace Onnx_Demo
    13. {
    14.     public partial class Form1 : Form
    15.     {
    16.         public Form1()
    17.         {
    18.             InitializeComponent();
    19.         }
    20.  
    21.         string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
    22.         string image_path = "";
    23.         DateTime dt1 = DateTime.Now;
    24.         DateTime dt2 = DateTime.Now;
    25.         string model_path;
    26.         Mat image;
    27.  
    28.         SessionOptions options;
    29.         InferenceSession onnx_session;
    30.         Tensor<float> input_tensor;
    31.         List<NamedOnnxValue> input_container;
    32.         IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
    33.         DisposableNamedOnnxValue[] results_onnxvalue;
    34.  
    35.         Tensor<float> result_tensors;
    36.  
    37.         StringBuilder sb = new StringBuilder();
    38.  
    39.         public string[] class_names;
    40.  
    41.         private void button1_Click(object sender, EventArgs e)
    42.         {
    43.             OpenFileDialog ofd = new OpenFileDialog();
    44.             ofd.Filter = fileFilter;
    45.             if (ofd.ShowDialog() != DialogResult.OK) return;
    46.             pictureBox1.Image = null;
    47.             image_path = ofd.FileName;
    48.             pictureBox1.Image = new Bitmap(image_path);
    49.             textBox1.Text = "";
    50.             image = new Mat(image_path);
    51.         }
    52.  
    53.         private void button2_Click(object sender, EventArgs e)
    54.         {
    55.             if (image_path == "")
    56.             {
    57.                 return;
    58.             }
    59.  
    60.             button2.Enabled = false;
    61.             textBox1.Text = "";
    62.             sb.Clear();
    63.             Application.DoEvents();
    64.  
    65.             image = new Mat(image_path);
    66.  
    67.             // 将图片转为RGB通道
    68.             Cv2.CvtColor(image, image, ColorConversionCodes.BGR2RGB);
    69.  
    70.             // 输入Tensor
    71.             input_tensor = new DenseTensor<float>(new[] { 1, 3, image.Height, image.Width });
    72.  
    73.             // 输入Tensor
    74.             for (int y = 0; y < image.Height; y++)
    75.             {
    76.                 for (int x = 0; x < image.Width; x++)
    77.                 {
    78.                     input_tensor[0, 0, y, x] = image.At<Vec3b>(y, x)[0] / 255f;
    79.                     input_tensor[0, 1, y, x] = image.At<Vec3b>(y, x)[1] / 255f;
    80.                     input_tensor[0, 2, y, x] = image.At<Vec3b>(y, x)[2] / 255f;
    81.                 }
    82.             }
    83.  
    84.             //将 input_tensor 放入一个输入参数的容器，并指定名称
    85.             input_container.Add(NamedOnnxValue.CreateFromTensor("input", input_tensor));
    86.  
    87.             dt1 = DateTime.Now;
    88.             //运行 Inference 并获取结果
    89.             result_infer = onnx_session.Run(input_container);
    90.             dt2 = DateTime.Now;
    91.  
    92.             // 将输出结果转为DisposableNamedOnnxValue数组
    93.             results_onnxvalue = result_infer.ToArray();
    94.  
    95.             // 读取第一个节点输出并转为Tensor数据
    96.             result_tensors = results_onnxvalue[0].AsTensor<float>();
    97.  
    98.             var result_array = result_tensors.ToArray();
    99.  
    100.             double[] scores = new double[result_array.Length];
    101.             for (int i = 0; i < result_array.Length; i++)
    102.             {
    103.                 double score = 1 / (1 + Math.Exp(result_array[i] * -1));
    104.                 scores[i] = score;
    105.             }
    106.  
    107.             List<ScoreIndex> ltResult = new List<ScoreIndex>();
    108.             ScoreIndex temp;
    109.             for (int i = 0; i < scores.Length; i++)
    110.             {
    111.                 temp = new ScoreIndex(i, scores[i]);
    112.                 ltResult.Add(temp);
    113.             }
    114.  
    115.             //根据分数倒序排序，取前10个
    116.             var SortedByScore = ltResult.OrderByDescending(p => p.Score).ToList().Take(10);
    117.  
    118.             foreach (var item in SortedByScore)
    119.             {
    120.                 sb.Append(class_names[item.Index] + ",");
    121.             }
    122.             sb.Length--; // 将长度减1来移除最后一个字符
    123.  
    124.             sb.AppendLine("");
    125.             sb.AppendLine("------------------");
    126.  
    127.             // 只取分数最高的
    128.             // float max = result_array.Max();
    129.             // int maxIndex = Array.IndexOf(result_array, max);
    130.             // sb.AppendLine(class_names[maxIndex]+" "+ max.ToString("P2"));
    131.  
    132.             sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
    133.             textBox1.Text = sb.ToString();
    134.             button2.Enabled = true;
    135.         }
    136.  
    137.         private void Form1_Load(object sender, EventArgs e)
    138.         {
    139.             model_path = "model/ml_danbooru.onnx";
    140.  
    141.             // 创建输出会话，用于输出模型读取信息
    142.             options = new SessionOptions();
    143.             options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
    144.             options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行
    145.  
    146.             // 创建推理模型类，读取本地模型文件
    147.             onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径
    148.  
    149.             // 创建输入容器
    150.             input_container = new List<NamedOnnxValue>();
    151.  
    152.             image_path = "test_img/2.jpg";
    153.             pictureBox1.Image = new Bitmap(image_path);
    154.             image = new Mat(image_path);
    155.  
    156.             List<string> str = new List<string>();
    157.             StreamReader sr = new StreamReader("model/lable.txt");
    158.             string line;
    159.             while ((line = sr.ReadLine()) != null)
    160.             {
    161.                 str.Add(line);
    162.             }
    163.             class_names = str.ToArray();
    164.         }
    165.  
    166.     }
    167. }

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  • 原文地址：https://blog.csdn.net/lw112190/article/details/136677149