ControlNet Quiz

Answers and explanations appear as you take the quiz.

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. What is the role of the 'zero-convolution' layers in ControlNet?
   • They delete the original model weights
   • They start with zero weights so training begins from the original model's behavior
   • They convert images to grayscale
   • They add random noise to the prompt
3. Which of these is a valid ControlNet conditioning input?
   • A spreadsheet of numbers
   • An audio waveform
   • An OpenPose skeleton or depth map
   • A database schema
4. How does ControlNet relate to the base diffusion model like Stable Diffusion?
   • It replaces the base model entirely
   • It only works without any base model
   • It runs on a separate computer over the internet
   • It attaches as a trainable branch while the base model stays frozen
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