Gizli Tutarlılık Modelleri Testi

Testi çözdüğünüzde cevaplar ve açıklamalar görünür.

1. Alıntılanan LCM araştırması hangi örnekleme hedefini inceliyor?
   • A guarantee of identical quality and latency on every phone
   • Useful image generation with substantially fewer sampling steps
   • Removal of all neural computation from generation
   • A mandatory increase to thousands of denoising steps
2. Aynı belirtilen olasılık‑akışı trajektöründeki noktalara hangi tutarlılık ilişkisi uygulanır?
   • Every unrelated image in the dataset must become identical
   • The function must ignore every input time
   • They map to the same near-clean endpoint
   • Each point must map to a different random filename
3. LCM'ler damıtma işlemini hangi alanda gerçekleştirir?
   • Önceden eğitilmiş bir yayılma modelinin sıkıştırılmış gizli alanı
   • Ham piksel alanı
   • Yalnızca ayrı bir metin yerleştirme alanı
   • Sesin frekans alanı
4. LCM‑LoRA raporu, incelenen uyumlu Stable Diffusion aileleri için ne sağlar?
   • A guarantee that every arbitrary adapter matches every checkpoint
   • A replacement for all image decoding
   • A model trained from scratch by a single inference call
   • Low-rank acceleration parameters usable with compatible models and fine-tunes
5. Orijinal LCM damıtma prosedüründe önceden eğitilmiş yönlendirilmiş difüzyon modeli ne rol oynar?
   • It is merely an unrelated image filename list
   • It proves no training operation is needed
   • It supplies the teacher behavior used to train the consistency student
   • It replaces the associated autoencoder decoder entirely
6. Which cited related few-step method combines a teacher signal with an adversarial loss?
   • A JPEG compression setting
   • Adversarial Diffusion Distillation
   • An HTTP browser cache
   • A keyboard layout
7. What value should the consistency function f(x, epsilon) return at its stated near-clean boundary?
   • A required zero vector for every x
   • An unrelated random image identifier
   • The token count of a text prompt
   • x
8. What component turns the sampled latent representation into image pixels in the associated latent-diffusion setup?
   • The autoencoder decoder
   • Only the text tokenizer
   • Only a filename sorter
   • Only a data-split index