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C# danbooru Stable Diffusion 提示词反推 Onnx Demo

news 2025/9/15 2:31:02

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
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name：input
tensor：Float[-1, 3, -1, -1]
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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<float> input_tensor;List<NamedOnnxValue> input_container;IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;DisposableNamedOnnxValue[] results_onnxvalue;Tensor<float> 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Tensorinput_tensor = new DenseTensor<float>(new[] { 1, 3, image.Height, image.Width });// 输入Tensorfor (int y = 0; y < image.Height; y++){for (int x = 0; x < image.Width; x++){input_tensor[0, 0, y, x] = image.At<Vec3b>(y, x)[0] / 255f;input_tensor[0, 1, y, x] = image.At<Vec3b>(y, x)[1] / 255f;input_tensor[0, 2, y, x] = image.At<Vec3b>(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<float>();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<ScoreIndex> ltResult = new List<ScoreIndex>();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<NamedOnnxValue>();image_path = "test_img/2.jpg";pictureBox1.Image = new Bitmap(image_path);image = new Mat(image_path);List<string> str = new List<string>();StreamReader sr = new StreamReader("model/lable.txt");string line;while ((line = sr.ReadLine()) != null){str.Add(line);}class_names = str.ToArray();}}
}