Kuis Model Difusi Video

Jawaban dan penjelasan muncul saat Anda mengikuti kuis.

1. Apa ide mendasar di balik model difusi?
   • Belajar membalikkan proses yang secara bertahap menambah kebisingan, mencela langkah demi langkah
   • Menyalin bingkai dari video yang ada
   • Mengompresi audio menjadi gambar
   • Mengenkripsi data untuk keamanan
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. Teknik apa yang membantu model difusi video mengikuti perintah teks dengan kuat?
   • Panduan bebas pengklasifikasi
   • Pengacakan bingkai secara acak
   • Menurunkan resolusi
   • Menghapus prompt seluruhnya
6. Mengapa teknik seperti distilasi dan pemecah yang lebih cepat diterapkan pada difusi video?
   • Untuk membuat model melupakan data
   • Karena pengambilan sampel denoising banyak langkah lambat dan mengurangi jumlah langkah
   • Untuk menambahkan lebih banyak kebisingan secara permanen
   • Untuk menonaktifkan prompt teks
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