What is the goal of diffusion models?To encode images to a compressed size, then decode back to the original sizeTo generate images by treating an image as a sequence of vectorsTo pit two neural networks against each otherTo learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space

What are some challenges of diffusion models?They can generate images that are not realistic.They can be computationally expensive to train.They can be difficult to control.All of the above

Which process involves a model learning to remove noise from images?SamplingForward diffusionReverse diffusionGANs

t-to-image diffusion models. In-stanceDiffusion supports free-form language conditions perinstance and allows flexible ways to specify instance lo-cations such as simple single points, scribbles, boundingboxes or intricate instance segmentation masks, and com-binations thereof. We propo

What is the process of forward diffusion?Start with a noisy image and remove noise iterativelyStart with a clean image and add noise iterativelyStart with a clean image and add noise randomlyStart with a noisy image and remove noise randomly

What is the name of the model family that draws inspiration from physics and thermodynamics?Diffusion modelsAutoregressive modelsGenerative adversarial networksVariational autoencoders