lucidrains / lucidrains/bit-diffusion
Which formulation used in ddpm_sample() implementation?
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- Python
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Description
Hi,
thanks for the implementation!
I am trying to understand how you implemented method ddpm_sample, in particular, how you incorporated "time_next" into into the denoising step. Which paper did you use as guidance? I was comparing your ddpm_sample with Algorithm 2 of paper "Denoising diffusion probabilistic models" but can't match your use of expm1, for instance.
Thanks a lot.
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Research direction
Start at the ddpm_sample() implementation and trace how time_next and expm1 are used in the denoising step. Compare that flow with Algorithm 2 of the cited Denoising diffusion probabilistic models paper; the work is done when the formulation and paper or derivation guiding it are clearly explained.
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Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100