视频扩散模型测验

当您参加测验时，就会出现答案和解释。

1. 扩散模型背后的基本思想是什么？
   • 学习逆转一个逐渐增加噪音的过程，一步步去噪
   • 从现有视频复制帧
   • 将音频压缩为图像
   • 加密数据以确保安全
2. For a video diffusion model, what does temporal modeling add to the denoising process?
   • It guarantees every object remains physically correct
   • It lets the model represent dependencies among frames over time
   • It removes the image decoder from the pipeline
   • It turns each video into one text token
3. Why does the cited Stable Video Diffusion model use a latent representation?
   • To avoid using any learned decoder
   • To store all frames as discrete text tokens
   • To perform video diffusion on compressed latents before decoding frames
   • To guarantee there will be no flicker
4. How did the Stable Video Diffusion paper adapt an image diffusion model for video?
   • It replaced every image layer with a speech recognizer
   • It trained only a language model on video captions
   • It removed the image model and used optical flow alone
   • It inserted temporal layers into a pretrained image model and fine-tuned them on video data
5. 什么技术可以帮助视频传播模型强烈遵循文本提示？
   • 无分类器指导
   • 随机打乱帧
   • 降低分辨率
   • 完全删除提示
6. 为什么蒸馏和更快的求解器等技术被应用于视频扩散？
   • 让模型忘记数据
   • 因为多步去噪采样速度慢并且减少了步数
   • 永久添加更多噪音
   • 禁用文本提示
7. In the Stable Video Diffusion paper, what distinguishes the SVD-XT model from the 14-frame SVD model?
   • It replaces diffusion with autoregressive text generation
   • It removes temporal layers and generates only a still image
   • SVD-XT is fine-tuned to generate 25 frames instead of 14
   • It uses audio tokens as the only conditioning input
8. What is one tradeoff when using fewer denoising steps through distillation or a faster solver?
   • Zero compute is required
   • The video no longer needs conditioning
   • Every result becomes temporally perfect
   • Faster generation may change fidelity or motion quality and must be evaluated