Idanwo ControlNet

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

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. Kini ipa ti awọn fẹlẹfẹlẹ 'odo-convolution' ni ControlNet?
   • Wọn pa awọn iwuwo awoṣe atilẹba rẹ
   • Wọn bẹrẹ pẹlu awọn iwuwo odo nitorina ikẹkọ bẹrẹ lati ihuwasi awoṣe atilẹba
   • Wọn yi awọn aworan pada si iwọn grẹy
   • Wọn ṣafikun ariwo laileto si tọ
3. Ewo ninu iwọnyi jẹ igbewọle isọdọtun IṣakosoNet to wulo?
   • A lẹja ti awọn nọmba
   • Fọọmu igbi ohun
   • Egungun OpenPose tabi maapu ijinle
   • Eto ipilẹ data
4. Bawo ni ControlNet ṣe ni ibatan si awoṣe itọka ipilẹ bi Itankale Iduroṣinṣin?
   • O rọpo awoṣe ipilẹ patapata
   • O ṣiṣẹ nikan laisi awoṣe ipilẹ eyikeyi
   • O nṣiṣẹ lori kọnputa lọtọ lori intanẹẹti
   • O so bi ẹka ikẹkọ lakoko ti awoṣe ipilẹ duro didi
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