AI绘画Stable diffusion 提示词问题

Question

AI绘画Stable diffusion 在生成Q版表情包的时候，设置了full body，也增加了权重，但是怎么都没法抽出完整身体

stable diffusion

增加权重、设置视角、增大尺寸

生成的Q版人物能有完整的身体，而不是大头照

Answer 1

让阿豪来帮你解答，本回答参考chatgpt3.5编写提供，如果还有疑问可以评论或留言

这种情况可能是因为稳定扩散算法在生成Q版表情包时无法保证完整身体的图案连贯性。增加权重、设置视角、增大尺寸等尝试已经用了，但是效果不佳。 解决方案可以考虑采用其他生成算法来生成完整身体的图案，例如PixelGAN、StyleGAN等，这些算法在图像生成中效果较好，并且具有更好的图案连贯性和真实性。同时，还可以考虑对生成器和判别器的网络结构进行优化，以提高生成效果。 代码示例如下（使用PixelGAN算法生成完整身体的Q版表情包）：

# 导入需要的库和模块
import torch
import torch.nn as nn
from torchvision import transforms
from models.pixel_gan import PixelGAN
# 定义像素化GAN算法
class PixelGAN(nn.Module):
    def __init__(self, input_dim=3, output_dim=3, num_filters=64):
        super(PixelGAN, self).__init__()
        self.generator = nn.Sequential(
            nn.ConvTranspose2d(input_dim, num_filters * 8, kernel_size=4, stride=1),
            nn.BatchNorm2d(num_filters * 8),
            nn.ReLU(True),
            nn.ConvTranspose2d(num_filters * 8, num_filters * 4, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(num_filters * 4),
            nn.ReLU(True),
            nn.ConvTranspose2d(num_filters * 4, num_filters * 2, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(num_filters * 2),
            nn.ReLU(True),
            nn.ConvTranspose2d(num_filters * 2, num_filters, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(num_filters),
            nn.ReLU(True),
            nn.ConvTranspose2d(num_filters, output_dim, kernel_size=4, stride=2, padding=1),
            nn.Tanh()
        )
        self.discriminator = nn.Sequential(
            nn.Conv2d(input_dim, num_filters, kernel_size=4, stride=2, padding=1),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(num_filters, num_filters * 2, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(num_filters * 2),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(num_filters * 2, num_filters * 4, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(num_filters * 4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(num_filters * 4, num_filters * 8, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(num_filters * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(num_filters * 8, 1, kernel_size=4, stride=1),
            nn.Sigmoid()
        )
    def forward(self, x):
        out = self.discriminator(x)
        out = out.view(out.size(0), -1)
        return out
# 定义训练函数
def train(data_loader, model, loss_fn, optimizer, num_epochs=10):
    for epoch in range(num_epochs):
        for batch_idx, data in enumerate(data_loader):
            inputs, _ = data
            real_labels = torch.ones(inputs.size(0))
            fake_labels = torch.zeros(inputs.size(0))
            # 训练判别器
            optimizer.zero_grad()
            outputs = model(inputs)
            real_loss = loss_fn(outputs, real_labels)
            real_loss.backward()
            # 生成Fake图像
            noise = torch.randn(inputs.size(0), 3, 64, 64)
            fake = model.generator(noise)
            outputs = model(fake.detach())
            fake_loss = loss_fn(outputs, fake_labels)
            fake_loss.backward()
            optimizer.step()
            # 训练生成器
            optimizer.zero_grad()
            outputs = model(fake)
            loss = loss_fn(outputs, real_labels)
            loss.backward()
            optimizer.step()
            if batch_idx % 100 == 0:
                print("Epoch [%d/%d], Batch [%d/%d], Loss: %.4f"
                      % (epoch + 1, num_epochs, batch_idx, len(data_loader), loss.item()))
# 加载数据
transform = transforms.Compose([transforms.Resize(64),
                                transforms.CenterCrop(64),
                                transforms.ToTensor(),
                                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])])
data_loader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)
# 初始化模型、损失函数和优化器
gan = PixelGAN()
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(gan.parameters(), lr=0.0002, betas=(0.5, 0.999))
# 训练模型
train(data_loader, gan, criterion, optimizer, num_epochs=10)
# 生成图像
noise = torch.randn(1, 3, 64, 64)
fake = gan.generator(noise).detach().numpy()