Adversarial Diffusion Distillation and Turbo Models Quiz

Answers and explanations appear as you take the quiz.

1. Which two losses does ADD combine?
   • 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. Which model was released alongside the ADD paper?
   • Flux.1 dev
   • DALL-E 3
   • SDXL Turbo
   • Stable Diffusion 1.5
3. What backbone does ADD's discriminator use?
   • 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. How does LADD differ from 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. Why do negative prompts often have little effect on turbo models?
   • 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