潜在扩散模型测验

当您参加测验时，就会出现答案和解释。

1. 与像素空间扩散相比，潜在扩散模型的主要创新是什么？
   • 他们在压缩的潜在空间而不是原始像素上运行扩散过程
   • 它们消除了对任何神经网络的需要
   • 它们仅适用于黑白图像
   • 他们根本不需要训练数据
2. 在潜在扩散中，第一阶段自动编码器的作用是什么？
   • Predicts text tokens without processing images
   • Encodes an image into a latent representation and decodes latents back into images
   • Replaces the diffusion model at every denoising step
   • Classifies a prompt as safe or unsafe
3. 文本通常如何在潜在扩散中注入去噪 U-Net 中？
   • 通过将原始文本字符串连接到图像
   • 通过手动编辑像素
   • 通过使用文本嵌入的交叉注意力层
   • 根本无法使用文字
4. 为什么与像素空间扩散相比，潜在扩散模型中的去噪可以减少计算量？
   • The model skips all image decoding
   • The diffusion network uses no neural layers
   • The model removes every denoising step
   • The denoiser operates on a smaller spatial latent grid, though decoding is still needed at the end
5. 原始潜在扩散论文研究了哪些第一阶段自动编码器正则化变体？
   • KL-regularized and vector-quantized autoencoders
   • Only a classifier-free guidance model
   • Only a causal language model
   • A decoder that has no learned parameters
6. 扩散模型在训练过程中学会做什么？
   • 将图像分类
   • 逆转渐进的噪声过程并逐步去噪
   • 将文本压缩为音频
   • 翻译语言
7. In the paper’s f=8 autoencoder configuration, what does the downsampling factor describe?
   • The model uses eight separate text encoders
   • Every input is reduced to exactly eight pixels
   • Each spatial dimension of the latent grid is reduced by a factor of eight
   • The diffusion model performs eight total training updates
8. How does the latent diffusion paper inject spatial conditions such as semantic layouts into the denoiser?
   • It converts the layout into an English sentence only
   • It removes spatial coordinates before denoising
   • It uses the condition only after decoding to pixels
   • It concatenates the spatial condition with the latent representation along channels