Kediska Models Fidiyaha Fiidiyowga

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay fikradda aasaasiga ah ee ka dambeysa qaabka fidinta?
   • Barashada dib u noqoshada habka si tartiib tartiib ah ugu daraya buuqa, diida tallaabo tallaabo
   • Ka koobiyaynta fareemada fiidiyowyada jira
   • Ku cadaadi maqalka sawirada
   • Sirin xogta amniga
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. Farsamo noocee ah ayaa ka caawisa qaabka fidinta fiidyowga in uu si adag u raaco qoraalka?
   • Tilmaame-free-classifier
   • Isku shaandhaynta bakhtiyaanasiibka ah
   • Hoos u dhigista xallinta
   • Ka saarida isla markiiba
6. Waa maxay sababta farsamooyinka sida distillation iyo xaliyeyaasha degdega ah loogu dabaqay fidinta fiidyowga?
   • Si moodelku u iloobo xogta
   • Sababtoo ah tillaabooyinka badan ee diidmada muunadku waa mid gaabis ah waxayna yareeyaan tirada tillaabooyinka
   • Si loogu daro buuq badan si joogto ah
   • Si aad u joojiso qoraalka degdega ah
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