使用Stable Diffusion模型进行图片融合的代码示例

以下是使用Stable Diffusion模型进行图片融合的Python代码示例：

import torch
from torch import nn
import torchvision.transforms as transforms
from torchvision.utils import save_image
from PIL import Image

# 定义Stable Diffusion模型类
class StableDiffusion(nn.Module):
    def __init__(self, image_size=256, channels=3, num_steps=1000, noise_level=0.05):
        super().__init__()
        self.image_size = image_size
        self.channels = channels
        self.num_steps = num_steps
        self.noise_level = noise_level
        self.diffusion_steps = torch.linspace(0, 1, num_steps + 1)[1:].to(device)

        self.beta = 0.5
        self.alpha = self.beta * (1 - self.diffusion_steps) / self.noise_level ** 2

        self.conv1 = nn.Conv2d(in_channels=self.channels, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1)
        self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1)
        self.conv5 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, stride=1, padding=1)

        self.norm1 = nn.BatchNorm2d(64)
        self.norm2 = nn.BatchNorm2d(128)
        self.norm3 = nn.BatchNorm2d(256)
        self.norm4 = nn.BatchNorm2d(512)
        self.norm5 = nn.BatchNorm2d(1024)

        self.up1 = nn.Upsample(scale_factor=2, mode='nearest')
        self.up2 = nn.Upsample(scale_factor=2, mode='nearest')
        self.up3 = nn.Upsample(scale_factor=2, mode='nearest')
        self.up4 = nn.Upsample(scale_factor=2, mode='nearest')

        self.down1 = nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=3, stride=1, padding=1)
        self.down2 = nn.Conv2d(in_channels=512, out_channels=256, kernel_size=3, stride=1, padding=1)
        self.down3 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=3, stride=1, padding=1)
        self.down4 = nn.Conv2d(in_channels=128, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.down5 = nn.Conv2d(in_channels=64, out_channels=self.channels, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        # 计算噪声
        noise = torch.randn(x.shape).to(device) * self.noise_level
        # 添加噪声
        y = x + noise
        # 通过一系列卷积层和上采样进行特征提取和上采样
        y = self.up1(nn.functional.leaky_relu(self.norm1(self.conv1(y))))
        y = self.up2(nn.functional.leaky_relu(self.norm2(self.conv2(y))))
        y = self.up3(nn.functional.leaky_relu(self.norm3(self.conv3(y))))
        y = self.up4(nn.functional.leaky_relu(self.norm4(self.conv4(y))))
        y = nn.functional.leaky_relu(self.norm5(self.conv5(y)))
        # 通过一系列卷积层进行下采样和重构
        y = self.down1(y)
        y = self.down2(nn.functional.leaky_relu(y))
        y = self.down3(nn.functional.leaky_relu(y))
        y = self.down4(nn.functional.leaky_relu(y))
        y = self.down5(nn.functional.leaky_relu(y))

        # 通过扩展alpha和beta来匹配y的形状
        alpha = self.alpha.view(1, -1, 1, 1)
        beta = self.beta.view(1, -1, 1, 1)

        # 通过循环进行扩散过程
        for i in range(self.num_steps):
            y = (1 - alpha[:, i:i + 1]) * y + alpha[:, i:i + 1] * torch.randn(x.shape).to(device)
            y = y / (1 + beta[:, i:i + 1] * self.diffusion_steps[i])
        return y

# 加载图片
image1 = Image.open('image1.jpg').convert('RGB')
image2 = Image.open('image2.jpg').convert('RGB')

# 调整图像大小
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor(),
])

# 转换图像
image1 = transform(image1).unsqueeze(0).to(device)
image2 = transform(image2).unsqueeze(0).to(device)

# 创建Stable Diffusion模型
model = StableDiffusion().to(device)

# 进行图片融合
diffused_image = (model(image1) + model(image2)) / 2

# 保存融合后的图片
save_image(diffused_image.cpu(), 'diffused_image.jpg')

需要注意的是，此处的`image1`和`image2`应该是相同大小的RGB图像，否则需要进行调整大小才能进行融合。此外，`num_steps`和`noise_level`参数需要根据具体情况进行调整。