How does DDIM speed up diffusion sampling?
hardAnswer
- DDIM (Song 2020) formulates a deterministic non-Markovian reverse process that shares the same training objective as DDPM but allows sampling with far fewer steps (e.g., 50 instead of 1000) with minimal quality loss.
- Also enables deterministic sampling (identical noise → identical output), which is essential for tasks like image-to-image editing, inversion, and reproducibility.
- Foundation for modern samplers (Euler, Heun, DPM-Solver++).
Check yourself — multiple choice
- DDIM requires more steps than DDPM
- Non-Markovian deterministic sampler → far fewer sampling steps, same training loss; enables editing / inversion
- Removes noise entirely
- Only for VAEs
DDIM: deterministic, few-step sampler with same DDPM training objective.
#diffusion#generative
Practise Deep Learning
214 interview questions in this topic.
Related questions
- Describe the forward and reverse processes in diffusion models.
- What is classifier-free guidance (CFG) in diffusion?
- How does Latent Diffusion (Stable Diffusion) reduce compute?
- What is InstanceNorm and where does it shine?
- What is a transposed convolution ('deconv') and its checkerboard artifact issue?
- What is the standard reconstruction loss for autoencoders?