条件扩散模型-结合条件输入的图像生成技术详解

pip install torch torchvision matplotlib numpy

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from torchvision.utils import save_image
import numpy as np

class SimpleConditionalDiffusionModel(nn.Module):
    def __init__(self, input_dim, condition_dim, hidden_dim):
        super(SimpleConditionalDiffusionModel, self).__init__()
        self.fc1 = nn.Linear(input_dim + condition_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, input_dim)
    
    def forward(self, x, c):
        # Concatenate input image and condition
        x = torch.cat([x, c], dim=1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

def add_noise(image, noise_level):
    noise = torch.randn_like(image) * noise_level
    return image + noise

def denoise(model, noisy_image, condition, num_steps):
    for _ in range(num_steps):
        noisy_image = model(noisy_image, condition)
    return noisy_image

# Example usage
input_dim = 784  # 28x28 image
condition_dim = 10  # Example condition (e.g., digit class)
hidden_dim = 256
noise_level = 0.1
num_steps = 10

model = SimpleConditionalDiffusionModel(input_dim, condition_dim, hidden_dim)
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Generate dummy data
image = torch.randn(1, input_dim)  # Random image
condition = torch.randn(1, condition_dim)  # Random condition

# Add noise
noisy_image = add_noise(image, noise_level)

# Denoise
generated_image = denoise(model, noisy_image, condition, num_steps)

# Save image
save_image(generated_image.view(1, 28, 28), 'generated_image.png')

from torchvision.datasets import MNIST
from torch.utils.data import DataLoader, Dataset

class ConditionalDataset(Dataset):
    def __init__(self, mnist_data):
        self.data = mnist_data.data
        self.labels = mnist_data.targets
        self.transform = transforms.ToTensor()

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        image = self.transform(self.data[idx])
        label = torch.nn.functional.one_hot(self.labels[idx], num_classes=10).float()
        return image.view(-1), label

# Load MNIST dataset
mnist_data = MNIST(root='data', train=True, download=True)
dataset = ConditionalDataset(mnist_data)
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)

import torch.nn.functional as F

def train(model, dataloader, optimizer, epochs=5):
    for epoch in range(epochs):
        model.train()
        total_loss = 0
        for images, conditions in dataloader:
            optimizer.zero_grad()
            noisy_images = add_noise(images, noise_level)
            outputs = model(noisy_images, conditions)
            loss = F.mse_loss(outputs, images)  # Example loss function
            loss.backward()
            optimizer.step()
            total_loss += loss.item()

        print(f"Epoch [{epoch + 1}/{epochs}], Loss: {total_loss / len(dataloader)}")

# Initialize model, optimizer, and start training
model = SimpleConditionalDiffusionModel(input_dim=784, condition_dim=10, hidden_dim=256)
optimizer = optim.Adam(model.parameters(), lr=0.001)
train(model, dataloader, optimizer)

def evaluate(model, dataloader):
    model.eval()
    with torch.no_grad():
        for images, conditions in dataloader:
            noisy_images = add_noise(images, noise_level)
            generated_images = denoise(model, noisy_images, conditions, num_steps)

            # Example of saving or displaying images
            save_image(generated_images.view(-1, 28, 28), 'evaluated_image.png')
            break  # For demonstration purposes, process only one batch

evaluate(model, dataloader)