Diffusion Models MCQ
Gradually destroy structure with noise, then learn to reverse the process to synthesize images.
Forward
Add noise
Reverse
Denoise
Steps
T timesteps
Sample
Generate
Diffusion for generation
Diffusion models define a forward Markov process that adds Gaussian noise until data becomes pure noise, then learn a reverse process (neural denoiser) to sample new data. DDPM and score-based formulations connect denoising to learning gradients of log density. Modern text-to-image systems build on these ideas with large U-Nets and conditioning.
Why denoise step-by-step
Iterative refinement from noise allows modeling complex high-dimensional distributions more stably than single-shot generators.
Key ideas
Forward process
q(x_t|x_{t-1}) adds noise per timestep.
Reverse model
pθ(x_{t-1}|x_t) learns to remove noise.
Schedule
β_t controls how much noise per step across T.
Conditioning
Text or class embeddings guide the denoiser (classifier-free guidance).
Sampling
Start from Gaussian noise → T reverse steps with learned denoiser → image