Maswali ya ControlNet

Majibu na maelezo huonekana unapojibu maswali.

1. What does ControlNet add to a pretrained text-to-image diffusion model?
   • A spatial conditioning path that can guide structure using inputs such as edges or pose maps
   • A guarantee that every generated pixel matches the input map exactly
   • A replacement for the text encoder and denoiser
   • A tool that predicts the original training image
2. Je, ni nini jukumu la tabaka za 'zero-convolution' katika ControlNet?
   • Wanafuta uzani wa mfano wa asili
   • Wanaanza na uzani sifuri kwa hivyo mafunzo huanza kutoka kwa tabia ya mfano wa asili
   • Wanabadilisha picha kuwa kijivu
   • Wanaongeza kelele nasibu kwa haraka
3. Ni ipi kati ya hizi ni ingizo halali la uwekaji hali ya ControlNet?
   • Lahajedwali ya nambari
   • Muundo wa wimbi la sauti
   • Mifupa ya OpenPose au ramani ya kina
   • Mchoro wa hifadhidata
4. Je, ControlNet inahusiana vipi na modeli ya uenezaji wa msingi kama Usambazaji Imara?
   • Inachukua nafasi ya mfano wa msingi kabisa
   • Inafanya kazi tu bila mfano wowote wa msingi
   • Inaendesha kwenye kompyuta tofauti juu ya mtandao
   • Inashikamana kama tawi linaloweza kufunzwa wakati muundo wa msingi hubakia kuganda
5. In a ControlNet workflow, what roles can text and spatial conditioning play?
   • Text can describe semantic/style content while a spatial map supplies structural guidance
   • The spatial map proves the text prompt is factually true
   • Text always supplies exact pixel positions and the map supplies the object name
   • The text prompt is ignored whenever a map is present
6. What training-data scale does the ControlNet paper report as robust for training its controls?
   • Only datasets with exactly 50 examples
   • Both small datasets under 50,000 examples and large datasets over 1 million examples
   • Only datasets larger than 100 million examples
   • No training data is used
7. What does the ControlNet paper report about combining conditioning controls?
   • It permits only one edge map and requires no prompt in every case
   • It accepts text prompts but no spatial controls
   • It tests single or multiple conditions, with or without a text prompt
   • It requires exactly two depth maps for each image
8. Which pretrained part does ControlNet reuse as a strong backbone for learning spatial controls?
   • A database of user prompts
   • A language-model tokenizer only
   • A separately trained classifier for every edge map
   • The deep encoding layers of a pretrained diffusion model