Guide-linked quiz · Medium Level

GLIGEN Grounded Generation Quiz

Explore how GLIGEN adds spatial grounding to text-to-image diffusion while retaining the knowledge in a frozen pretrained model.

Related guide pathsGligen Grounded Generation
Question 1 of 8

What problem does GLIGEN primarily solve?

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  1. What problem does GLIGEN primarily solve?
    • Lack of precise spatial control over where objects appear
    • Slow generation speed
    • Inability to generate color images
    • Missing audio in generated videos
  2. How does GLIGEN preserve the knowledge of the pretrained diffusion model?
    • It retrains all weights from scratch
    • It freezes the original weights and adds new trainable gated self-attention layers
    • It deletes the text encoder
    • It uses a GAN instead of diffusion
  3. What is a typical grounding input GLIGEN accepts?
    • A musical score
    • A list of hyperparameters
    • A bounding box paired with a text or image entity
    • A spreadsheet of pixel values
  4. Why is the gate in GLIGEN's new attention layers initialized to zero?
    • To make images grayscale at first
    • To disable the text prompt
    • To reduce the image resolution
    • So the base model's behavior is preserved early in training and control is added gradually
  5. What does 'open-world' grounded generation mean in GLIGEN?
    • It can ground arbitrary described concepts and layouts not seen during grounding training
    • It only works on indoor scenes
    • It requires a 3D world model
    • It generates only outdoor landscapes
  6. Where are GLIGEN's new gated self-attention layers inserted in the U-Net?
    • Only at the output VAE
    • Between the existing self-attention and cross-attention blocks
    • Inside the text encoder only
    • Before the noise scheduler
  7. What does a GLIGEN grounding token combine?
    • A denoising timestep with a VAE decoder output
    • A color histogram with the final image resolution
    • A text or image embedding with spatial information such as a bounding box
    • A training dataset name with a classifier label
  8. Besides bounding boxes, which inputs can GLIGEN use to ground objects?
    • Only unstructured audio waveforms
    • A crop-size label without any object information
    • A list of model checkpoint filenames
    • Keypoints or reference images

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