denoising-diffusion-pytorch icon indicating copy to clipboard operation
denoising-diffusion-pytorch copied to clipboard

Did anyone get good CIFAR10 results?

Open IdoZach opened this issue 2 years ago • 8 comments

Hi, thanks for providing this code. I'm trying to reproduce the CIFAR10 results from the original DDPM paper. I use 3x32x32 images, all the CIFAR data (50k frames), 2000 epochs (but I check every 100 epochs how it looks like), and I get some similar results, but not as good as the paper. This is the result that I get: image I'm also attaching the training results that I get (the divergent one is the validation loss): image My training schedule is similar to the original except that I maximize the batch size on my GPUs. I'm using image size of 32, and U-Net options dim=64, dim_mults=(1,2,4,8).

Was anyone more successful and can share their results and tips? I think that this result is far from perfect. Thanks very much, I hope you could help me find what I'm missing.

IdoZach avatar Sep 30 '22 09:09 IdoZach

nop, have been trying all day. MNIST works fine

tcapelle avatar Oct 04 '22 13:10 tcapelle

you can try p2_loss_weight_gamma = 1. , my result with cifar10 wasn't great either with the default setting but I think you can see a big difference with p2 weighting - the original paper used a reweighted loss too

yiyixuxu avatar Oct 04 '22 16:10 yiyixuxu

How do you show the loss on the tensorboard? Could you share the code maybe?

we1cao avatar Oct 28 '22 19:10 we1cao

does someone have example code for mnist/cifar10?

malbergo avatar Nov 05 '22 17:11 malbergo

I do here, but with a different codebase: https://wandb.ai/capecape/train_sd/reports/How-to-Train-a-Conditional-Diffusion-Model-from-Scratch--VmlldzoyNzIzNTQ1

Sent from ProtonMail mobile

-------- Original Message -------- On Nov 5, 2022, 6:30 PM, Michael Albergo < @.***> wrote:

does someone have example code for mnist/cifar10?

— Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.AEMWOAMOBC4DFILSUZCKI6TWG2KT5A5CNFSM6AAAAAAQZTJ7GOWGG33NNVSW45C7OR4XAZNMJFZXG5LFINXW23LFNZ2KUY3PNVWWK3TUL5UWJTSNYKH4M.gifMessage ID: @.***>

tcapelle avatar Nov 05 '22 20:11 tcapelle

similar problem, any solution please? sample-69

Gregory1994 avatar Mar 03 '23 10:03 Gregory1994

What parameter is 'p2_loss_weight_gamma'? and where do we exactly use it? Can anyone highlight it in the denoising_diffusion_pytorch.py file?

pimakshay avatar Apr 24 '23 08:04 pimakshay

you can try p2_loss_weight_gamma = 1. , my result with cifar10 wasn't great either with the default setting but I think you can see a big difference with p2 weighting - the original paper used a reweighted loss too

Can u tell me where is p2_loss_weight_gamma, i did not find it

chengyiqiu1121 avatar Jun 14 '24 12:06 chengyiqiu1121