Latent Diffusion Models Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Ndechipi chikuru kuvandudzwa kwemamodhi akasarudzika ekusiyanisa achienzaniswa nepixel-nzvimbo diffusion?
   • Ivo vanomhanyisa nzira yekuparadzira munzvimbo yakamanikidzwa yakadzikama panzvimbo pemapikisi akaomeswa
   • Vanobvisa kudiwa kwechero neural network
   • Vanongoshanda pamifananidzo yakasviba-uye-chena
   • Ivo havadi data yekudzidziswa zvachose
2. In latent diffusion, what does the first-stage autoencoder do?
   • Predicts text tokens without processing images
   • Encodes an image into a latent representation and decodes latents back into images
   • Replaces the diffusion model at every denoising step
   • Classifies a prompt as safe or unsafe
3. Ko mameseji anowanzo kuisirwa sei mune denoising U-Net mukupararira kwakadzikama?
   • Nekubatanidza tambo yemavara akasvibira kumufananidzo
   • Nekugadzirisa mapixels
   • Kuburikidza nemharadzano-yekutarisa maseru uchishandisa mavara embeddings
   • Mavara haagone kushandiswa zvachose
4. Why can denoising in a latent diffusion model reduce computation compared with pixel-space diffusion?
   • The model skips all image decoding
   • The diffusion network uses no neural layers
   • The model removes every denoising step
   • The denoiser operates on a smaller spatial latent grid, though decoding is still needed at the end
5. Which first-stage autoencoder regularization variants does the original latent diffusion paper investigate?
   • KL-regularized and vector-quantized autoencoders
   • Only a classifier-free guidance model
   • Only a causal language model
   • A decoder that has no learned parameters
6. Chii chinonzi diffusion modhi chinodzidza kuita panguva yekudzidziswa?
   • Kuisa mifananidzo muzvikamu
   • Kudzoreredza maitiro eruzha zvishoma nezvishoma uye denoise nhanho nhanho
   • Kudzvanya zvinyorwa kuita odhiyo
   • Kushandura mitauro
7. In the paper’s f=8 autoencoder configuration, what does the downsampling factor describe?
   • The model uses eight separate text encoders
   • Every input is reduced to exactly eight pixels
   • Each spatial dimension of the latent grid is reduced by a factor of eight
   • The diffusion model performs eight total training updates
8. How does the latent diffusion paper inject spatial conditions such as semantic layouts into the denoiser?
   • It converts the layout into an English sentence only
   • It removes spatial coordinates before denoising
   • It uses the condition only after decoding to pixels
   • It concatenates the spatial condition with the latent representation along channels