Diffusion Models: Learning to Undo Noise
Training adds noise to a sample in small steps and asks the network to predict the noise that was added. Generation runs that chain backwards from pure noise, so one hard sampling problem becomes a long series of easy denoising ones.
What a learner can do afterwards
- Describe the forward corruption process and say why it needs no training
- State what the network predicts at each step and how repeating it yields a sample
- Say what more sampling steps buy and what they cost
1 · Read
Training starts by wrecking a sample on purpose. Noise is added in small steps until nothing but static remains. This forward corruption is a fixed recipe, so it needs no training at all.
At each noise level the network predicts the noise that was just added. Generation runs the chain backwards: start from pure noise, subtract the predicted noise, and repeat until a clean sample stands. One hard sampling problem becomes a long series of easy denoising ones.
More sampling steps buy a cleaner sample and cost more waiting. Fewer steps run fast but leave rough edges and odd textures.
When results look noisy, add steps before touching anything else. When results look fine but slow, trim steps first.
Learn each denoising step, then walk backwards from noise to sample.
2 · Watch
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8 questions wait behind this lesson, each with its answer explained. Every answer feeds the sky: stars light as they are learned, and dim when it is time to come back.