what changes would we need to do if we used our own dataset?

[awais00012]

Thanks for the awesome explanation. Could you tell me which changes we need before training the model on our data?

[explainingai-code]

Hello,

Thanks for the appreciation. I apologize that should have been part of the README , I have updated it now.
Can you take a look - https://github.com/explainingai-code/DDPM-Pytorch/blob/main/README.md#training-on-your-own-images and let me know in case you face any issues

[awais00012]

thanks for the modification in the repo for training the model on custom dataset. however i am facing this issue when i triad the model on my own data. my dataset with the name of ultrasound256CH1 contain train and test images. all the images have 256*256 size, channel 1.

[explainingai-code]

Can you tell me the im_path value you used in the config ?
And also the directory structure of your dataset. Is it $REPO_ROOT/ultrasound256CH1/train/*.png ?

The error basically means that the code wasn't able to find any png files in the location it was searching.

[awais00012]

Can you tell me the im_path value you used in the config ? And also the directory structure of your dataset. Is it $REPO_ROOT/ultrasound256CH1/train/*.png ?

The error basically means that the code wasn't able to find any png files in the location it was searching.

yes the dataset is in the repo root, still getting this error, kindly how can i solve it.

[explainingai-code]

Got it. Create a subfolder 'images' inside train directory and put all training png files in there.
So $REPO_ROOT/ultrasound256CH1/train/images/*.png

Leave the config as it is to point to "ultrasound256CH1/train" .
Can you try that and let me know if it works?

[awais00012]

yes i tried, unfortunately it does not work.

[explainingai-code]

Can you print the directory and path the code is searching at https://github.com/explainingai-code/DDPM-Pytorch/blob/main/dataset/mnist_dataset.py#L40 and share that.

print(d_name, os.path.join(im_path, d_name, '*.{}'.format(self.im_ext)))
Also comment line https://github.com/explainingai-code/DDPM-Pytorch/blob/main/dataset/mnist_dataset.py#L42

[awais00012]

that error has been resolved. that error was occurring because of the arrangement of the dataset. i created 5 classes of the different images for the train data and used "data/train/" as a path and its worked.
now encountering this error:

[explainingai-code]

You are training on cpu as of now right ?
Also can you confirm if your conda environment has python3.8 and have the requirements installed as mentioned in https://github.com/explainingai-code/DDPM-Pytorch/tree/main?tab=readme-ov-file#quickstart

[awais00012]

hi sir, i kept the batch size 10, just want to run for 40 epochs and the total images are only 828. could you pleases tell me why the model required so heavy computational power(memory) and how can i handle this issue?

RuntimeError: CUDA out of memory. Tried to allocate 640.00 GiB (GPU 0; 14.75 GiB total capacity; 2.16 GiB already allocated; 11.63 GiB free; 2.17 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF.

[explainingai-code]

Its because the images are 256x256 and by default the model config does downsampling only twice.
Few things you can try to ensure you are able to train.

1. Resize the images to 64x64 in the loader and train diffusion model on these 64x64 images
2. Have all three down blocks to downsample by placing down_sample : [True, True, True] in the config
3. Try with num_mid_layers : 1 in the config
4. Reduce number of Midblocks by changing mid_channels : [256, 128] in config

I think that should reduce the model size considerably and should allow you to train.

[awais00012]

Thanks, the model works very well i trained on my datasets.
I would request you to add a few more things to the repo for better results and comparative analytical analysis between the original and the generated images like classifier free guidance, exponential moving average, IS and FID score. looking forward to your outstanding and easy implementation!

[explainingai-code]

Yes, I wanted to have this repo as a intro to diffusion which is why didn't want to add those and leave this as a bare minimum diffusion repo. I do plan to create a stable diffusion repo which should have some of these incorporated. Once that is done I will try to put the parts you mentioned here as well (if I am able to do that without adding too much complexity to the current implementation ).

[thatdev6]

Hello,

I made all the relevant changes mentioned in the readme and in this thread but after my images are loaded I get an AttributeError.