안정 확산 퀴즈

퀴즈를 풀면 정답과 설명이 나타납니다.

1. 높은 수준에서 확산 모델은 어떻게 이미지를 생성합니까?
   • 무작위적인 노이즈에서 시작하여 반복적으로 노이즈를 제거하여 이미지를 형성합니다.
   • 훈련 세트에서 가장 가깝게 일치하는 이미지를 복사합니다.
   • 사람처럼 한 획씩 모양을 그립니다.
   • 온라인에서 찾은 사진을 연결합니다.
2. 원래 Stable Diffusion v1 파이프라인은 학습된 노이즈 제거 작업을 어디에서 수행합니까?
   • 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. 문서화된 원본 Stable Diffusion v1 디노이저의 텍스트 조건은 어떻게 되나요?
   • 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. 분류자가 없는 지침은 문서화된 샘플링 공식에 무엇을 결합합니까?
   • 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. 샘플러 단계 수 변경을 정확하게 설명하는 설명은 무엇입니까?
   • 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