quiz bu lëkkale ak tegtal · Diggante Niveau

Quiz diffusion stabil

Xoolaat pipeline bu nëbbu bu Stable Diffusion v1, bind bi, tegtal ak tànneefi sampler, ak ControlNet ak arithmétique griy bu nëbbu.

Yooni njiit yu jëm ciStable Diffusion
Laaj 1 de 8

Ci niveau bu kawe, naka la modelu diffusion di defaree ab nataal?

Xoolaatal laaj bu nekk8 laaj

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

  1. Ci niveau bu kawe, naka la modelu diffusion di defaree ab nataal?
    • Dafay tàmbalee ci bruit bu bariwul ba noppi di dindi bruit yi lu bari yoon ngir sos nataal
    • Dafay kopie nataal bi gëna méngoo ak limu tàggat
    • Dafay rëdd jëmm yi benn-benn ni nit
    • Dafay boole nataal yi nga fekk ci net bi
  2. Where does the original Stable Diffusion v1 pipeline perform its learned denoising work?
    • Only in an unchanged image filename
    • Only in a single scalar with no spatial features
    • Without any learned neural model
    • In an autoencoder latent representation rather than directly over full-resolution pixels
  3. How does text condition the documented original Stable Diffusion v1 denoiser?
    • The prompt only names the output file
    • Each prompt word must be printed literally into every output
    • CLIP text embeddings provide conditioning through cross-attention
    • The model ignores all text information
  4. What does classifier-free guidance combine in the documented sampling formula?
    • Two unrelated image filenames
    • Conditional and unconditional denoising predictions
    • Only the red and blue display channels
    • A guarantee that every increased scale improves quality
  5. Which statement accurately qualifies a change to sampler step count?
    • Every additional step must improve every model’s image
    • Step count never changes any computation
    • A single step guarantees perfect image fidelity
    • Quality and elapsed time depend on the model, sampler and settings; more steps are not universally better
  6. What does the original Stable Diffusion project’s publication of model artifacts enable?
    • Inspection and supported extension under the stated model and license conditions
    • Unrestricted use of every model without any conditions
    • A guarantee that all devices can run every checkpoint
    • Proof that every possible fine-tune is compatible
  7. What additional input type does the cited ControlNet method provide for diffusion generation?
    • Only the final output filename
    • Spatial conditions such as edges, pose or depth
    • A mandatory list of every training image
    • Only an encryption password
  8. With the documented factor-eight spatial downsampling, what latent height and width correspond to a 512-by-512 v1 image?
    • 512 by 512
    • 128 by 128
    • 64 by 64
    • 8 by 8