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관련 가이드 경로확산 모델
질문 1 의 8

How does classic DDPM sampling produce a generated image from an initial noise sample?

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  1. How does classic DDPM sampling produce a generated image from an initial noise sample?
    • By copying a required identical training image
    • By assigning the output filename as its pixel values
    • By performing no model computation
    • By following successive learned reverse-process steps
  2. In the common noise-prediction DDPM parameterization, what training target does the network estimate?
    • The market price of the image
    • The Gaussian noise added to the training example
    • The image's copyright owner
    • A required final file extension
  3. In the text-conditioned latent diffusion formulation, how can prompt embeddings influence denoising?
    • Through cross-attention conditioning
    • Only by changing the file extension
    • By removing every learned layer
    • By guaranteeing an exact training-image copy
  4. Why does latent diffusion perform denoising in a compressed representation?
    • To guarantee identical reconstruction to every pixel-space model
    • To eliminate all neural computation
    • To reduce the dimensionality of the heavy denoising computation
    • To make every output necessarily monochrome
  5. Which component is not required by the classic DDPM denoising training objective?
    • Training examples
    • An adversarial discriminator trained to distinguish real from generated samples
    • A defined corruption process
    • A learned reverse-process model
  6. What does progressive distillation of a diffusion sampler aim to reduce?
    • The need to evaluate generation quality
    • Every condition supplied to the model
    • The number of pixels in every possible output by definition
    • The number of sampling steps or model evaluations
  7. After denoising in a latent diffusion image model, which component reconstructs image pixels from the latent representation?
    • A filename formatter
    • The original training-data shuffle
    • The autoencoder decoder
    • An adversarial label threshold in every model
  8. What does a DDPM forward noise schedule specify across successive steps?
    • How much noise variance is introduced
    • A guarantee that every sample is factually true
    • The copyright license for the training data
    • The alphabetical ordering of prompt words