stable diffusion 代码
时间: 2024-09-27 07:01:33 浏览: 17
"Stable Diffusion"通常指的是稳定的扩散过程,它是指在一个物理系统中,分子或原子由于热运动随机扩散,当扩散速度足够慢并且不会因为浓度梯度导致大规模的宏观移动时,形成的一种稳定状态。这个概念在计算机科学领域中并不常见,但它可能会出现在模拟、数据分析或者优化算法中,比如用于图像处理或生成对抗网络(GANs)中的稳定性控制。
如果是在编程中讨论与之相关的代码,这可能涉及到某种形式的数值模拟,例如在Python的开源库如FEniCS、SciPy或者TensorFlow Probability中,会用到数值解算偏微分方程(PDEs)来模拟扩散过程。这类代码通常包括定义扩散方程,设置边界条件,以及采用有限差分、有限元等方法求解。
下面是一个简化的Python示例,展示如何使用NumPy和Scipy库模拟一维稳态扩散:
```python
import numpy as np
from scipy.sparse import diags
# 定义扩散系数和网格大小
D = 0.5
L = 1.0
N = 100
dx = L / (N - 1)
# 创建二维离散矩阵
A = diags([-D * dx**2, D * dx**2], [-1, 1]) / dx**2
# 设置边界条件(假设左侧固定,右侧自由)
b = np.zeros(N)
b[0] = 1 # 左侧边界值
# 求解稳态扩散方程
steady_state = np.linalg.solve(A.toarray(), b)
```
相关问题
支持stable diffusion和支持stable diffusion web区别
根据提供的引用内容,可以得知 Stable Diffusion 是一个画像生成 AI,能够模拟和重建几乎任何可以以视觉形式想象的概念,而无需文本提示输入之外的任何指导。而 Stable Diffusion Web UI 是一个基于 Stable Diffusion 的基础应用,利用 gradio 模块搭建出交互程序,可以在低代码 GUI 中立即访问 Stable Diffusion。Stable Diffusion Web UI 提供了多种功能,如 txt2img、img2img、inpaint 等,还包含了许多模型融合改进、图片质量修复等附加升级。通过调节不同参数可以生成不同效果,用户可以根据自己的需要和喜好进行创作。因此,Stable Diffusion 是一个 AI 模型,而 Stable Diffusion Web UI 是一个基于 Stable Diffusion 的应用程序,提供了更多的功能和交互性。
Stable Diffusion图片融合代码
以下是基于PyTorch实现的Stable Diffusion图片融合代码,其中包括了模型的定义、训练和推理过程:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import ImageFolder
from tqdm import tqdm
class Unet(nn.Module):
def __init__(self):
super(Unet, self).__init__()
self.down1 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)
self.down2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)
self.down3 = nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1)
self.down4 = nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1)
self.down5 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
self.down6 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
self.down7 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
self.down8 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
self.up1 = nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1)
self.up2 = nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1)
self.up3 = nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1)
self.up4 = nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1)
self.up5 = nn.ConvTranspose2d(1024, 256, kernel_size=4, stride=2, padding=1)
self.up6 = nn.ConvTranspose2d(512, 128, kernel_size=4, stride=2, padding=1)
self.up7 = nn.ConvTranspose2d(256, 64, kernel_size=4, stride=2, padding=1)
self.up8 = nn.ConvTranspose2d(128, 3, kernel_size=4, stride=2, padding=1)
def forward(self, x):
down1 = F.leaky_relu(self.down1(x), negative_slope=0.2)
down2 = F.leaky_relu(self.down2(down1), negative_slope=0.2)
down3 = F.leaky_relu(self.down3(down2), negative_slope=0.2)
down4 = F.leaky_relu(self.down4(down3), negative_slope=0.2)
down5 = F.leaky_relu(self.down5(down4), negative_slope=0.2)
down6 = F.leaky_relu(self.down6(down5), negative_slope=0.2)
down7 = F.leaky_relu(self.down7(down6), negative_slope=0.2)
down8 = F.leaky_relu(self.down8(down7), negative_slope=0.2)
up1 = F.leaky_relu(self.up1(down8), negative_slope=0.2)
up2 = F.leaky_relu(self.up2(torch.cat([up1, down7], dim=1)), negative_slope=0.2)
up3 = F.leaky_relu(self.up3(torch.cat([up2, down6], dim=1)), negative_slope=0.2)
up4 = F.leaky_relu(self.up4(torch.cat([up3, down5], dim=1)), negative_slope=0.2)
up5 = F.leaky_relu(self.up5(torch.cat([up4, down4], dim=1)), negative_slope=0.2)
up6 = F.leaky_relu(self.up6(torch.cat([up5, down3], dim=1)), negative_slope=0.2)
up7 = F.leaky_relu(self.up7(torch.cat([up6, down2], dim=1)), negative_slope=0.2)
up8 = torch.sigmoid(self.up8(torch.cat([up7, down1], dim=1)))
return up8
class DiffusionModel(nn.Module):
def __init__(self, num_steps, betas, model):
super(DiffusionModel, self).__init__()
self.num_steps = num_steps
self.betas = betas
self.model = model
self.noise_schedule = nn.Parameter(torch.zeros(num_steps))
def forward(self, x):
z = torch.randn(x.shape).to(x.device)
x_prev = x
for i in range(self.num_steps):
t = (i + 1) / self.num_steps
noise_level = (self.noise_schedule[i] ** 0.5).view(-1, 1, 1, 1)
x_tilde = x_prev * noise_level + (1 - noise_level ** 2) ** 0.5 * z
x_prev = x_prev + self.betas[i] * (self.model(x_tilde) - x_prev)
return x_prev
def train(model, dataloader, optimizer, device):
model.train()
for x, _ in tqdm(dataloader):
x = x.to(device)
optimizer.zero_grad()
loss = ((model(x) - x) ** 2).mean()
loss.backward()
optimizer.step()
def validate(model, dataloader, device):
model.eval()
total_loss = 0
with torch.no_grad():
for x, _ in tqdm(dataloader):
x = x.to(device)
loss = ((model(x) - x) ** 2).mean()
total_loss += loss.item() * x.shape[0]
return total_loss / len(dataloader.dataset)
def main():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(256),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = ImageFolder('path/to/dataset', transform=transform)
dataloader = DataLoader(dataset, batch_size=16, shuffle=True, num_workers=4)
model = DiffusionModel(1000, torch.linspace(1e-4, 0.1, 1000), Unet()).to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
for epoch in range(10):
train(model, dataloader, optimizer, device)
val_loss = validate(model, dataloader, device)
print(f'Epoch {epoch}: validation loss {val_loss:.4f}')
torch.save(model.state_dict(), 'path/to/model')
if __name__ == '__main__':
main()
```
在训练完成后,可以使用以下代码来融合两张图片:
```python
import torch
from PIL import Image
from torchvision import transforms
def main():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(256),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# 加载模型
model = DiffusionModel(1000, torch.linspace(1e-4, 0.1, 1000), Unet()).to(device)
model.load_state_dict(torch.load('path/to/model', map_location=device))
# 加载图片
image1 = Image.open('path/to/image1').convert('RGB')
image2 = Image.open('path/to/image2').convert('RGB')
x1 = transform(image1).unsqueeze(0).to(device)
x2 = transform(image2).unsqueeze(0).to(device)
# 融合图片
alpha = torch.linspace(0, 1, 11)
for a in alpha:
x = a * x1 + (1 - a) * x2
y = model(x).squeeze(0).detach().cpu()
y = y * 0.5 + 0.5 # 反归一化
y = transforms.ToPILImage()(y)
y.save(f'path/to/result_{a:.1f}.jpg')
if __name__ == '__main__':
main()
```
该代码将两张图片进行线性插值,得到11张融合后的图片,其中`alpha`参数指定了插值的权重。在融合过程中,需要进行反归一化操作,将输出的图片转换为PIL格式,并保存到指定路径。