Comments (2)
Hi there! It seems that the example command you were using is the one with a DDIM sampler, which is a fast (10x faster in this case; 100 sampling steps vs. 1000) sampler proposed by another work Denoising Diffusion Implicit Models (DDIM) 1. If you would like to reproduce the results of DDPM reported in this repo, please consider removing the use-ddim
flag.
By the way, your DDIM result (
and also the author's reply (implying a FID of 4.26 with a different seed in the same setting) on GitHub
https://github.com/ermongroup/ddim/issues/3#issuecomment-960431667
Footnotes
-
Song, Jiaming, Chenlin Meng, and Stefano Ermon. "Denoising Diffusion Implicit Models." In International Conference on Learning Representations. 2020. ↩
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Thank you for your clarification!
Edit: I reran with DDPM at 1000 subseq-size and got an FID of ~3.2 as reported in the table
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Related Issues (16)
- Vector-conditioning using classifier-free guidance HOT 4
- DDIM sampler training parameters HOT 2
- Question about FID calculation HOT 2
- Bug in ddim.py HOT 1
- FID & Loss diverge on CIFAR10? HOT 2
- what changes would be needed if we want to use our own custom greyscale(channel=1) dataset having size of 256*256. HOT 1
- some checkpoint HOT 2
- Bug in target noise HOT 1
- CIFAR 10 config HOT 1
- Comment in code is wrong. Upsample supports align_corners=True HOT 2
- Meet bugs when adopting the --eval choice for training. HOT 2
- train my own image datasets HOT 1
- pretrain HOT 1
- Have you tested the FID for CelebaHQ? HOT 2
- Minor bug in Gaussian Diffusion implementation (q_mean_var)? HOT 2
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