Distillation Diffusion Adversarial iyo Turbo Models Quiz

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa kuwee labada khasaare ee ADD isku daraa?
   • An FID loss and a CLIP loss
   • An adversarial loss and a distillation loss from a teacher
   • A perceptual loss and a pixel loss only
   • A contrastive loss and cross-entropy
2. Qaabkee ayaa lagu siidaayay warqada ADD?
   • Flux.1 dev
   • DALL-E 3
   • SDXL Turbo
   • Faafinta Deggan 1.5
3. Waa maxay laf dhabarta uu isticmaalo takoorka ADD?
   • A frozen DINOv2 feature network with trainable heads
   • A fully trainable Inception network
   • The teacher's text encoder
   • No backbone; it compares raw pixels
4. Sidee LADD uga duwan yahay ADD?
   • It uses more denoising steps
   • It removes the teacher
   • It only works for video
   • It runs the discriminator in latent space using the teacher's features
5. Waa maxay sababta soo-jeedinta taban ay inta badan saameyn yar ugu yeelato moodooyinka turbo?
   • The tokenizer filters them out
   • The models have no text encoder
   • Classifier-free guidance is typically built in during distillation
   • Negative prompts only work at 1024x1024
6. What happens if you run a normal, undistilled diffusion model at one step?
   • It matches SDXL Turbo
   • It usually produces a blurry, unfinished image
   • It generates video
   • It refuses to run
7. Which quality trade-off is common in distilled few-step models?
   • Higher diversity than the teacher
   • Better text rendering in every case
   • Support for unlimited resolution
   • Less variety across different seeds
8. In ADD's distillation loss, what is the student's target?
   • The teacher's denoised prediction of the re-noised student output
   • A random real image
   • The discriminator's logits
   • The CLIP score of the prompt