tcapelle commented 1 year ago

Hey, this code has made me play and have so much fun =) I refactored the code to do multiple things:

It uses mixed precision training (2x speed up).
Refactor the modules to make them more readable and not repeat the code
For the moment only refactores the conditional model with argparse and defaults. Added a bunch of params to the script argparse.
Logging to Weights and Biases so you get intermediate results and can follow training. For instance, this is my recent Cifar10 run
Added scripts to download the dataset, Cifar10, Cifar100 and Cifar10_64px from Kaggle.
Refactored the training script to make it modular, also added validation metrics if available.
Added checkpoint loading.

Please take a look, and feel free to contact me.

Added FFCV dataloading option, this makes training dramatically faster.

dome272 commented 1 year ago

Hey this is an awesome addition and thank you so much for the work. The only problem I have is that people might be confused who are coming from the video and suddenly see so much new code. As a result I probably will push your code to a new branch. Im very keen to try all of this out!

tcapelle commented 1 year ago

I know, but this is very cool as it produces a nice wandb.workspace, you can see the logging of the run here: https://wandb.ai/capecape/train_sd?workspace=user-capecape

tcapelle commented 1 year ago

I am writing an article based on your code and video: https://wandb.ai/capecape/train_sd/reports/Training-a-Conditional-Diffusion-model-from-scratch--VmlldzoyODMxNjE3

dome272 commented 1 year ago

Very very cool. Thank you!! I added a reference to your repo and blog in the readme

noushineftekhari commented 11 months ago

I am writing an article based on your code and video: https://wandb.ai/capecape/train_sd/reports/Training-a-Conditional-Diffusion-model-from-scratch--VmlldzoyODMxNjE3

Hello, Can I ask why we have three.PT files after training? Which one is for test and did you used diffusion for feature representation?

tcapelle commented 10 months ago

https://wandb.ai/capecape/train_sd/reports/How-To-Train-a-Conditional-Diffusion-Model-From-Scratch--VmlldzoyNzIzNTQ1

noushineftekhari commented 10 months ago

Hi Thank you for the link.

I have another question. I run the model and I have the following weight: @.***

No I am trying to test the model I used the below script: n = 10 device = "cpu" model = UNet_conditional(num_classes=4).to(device)

ckpt = torch.load(r"models\DDPM_conditional\ckpt.pt")

file_path = r"C:\Users\noueft\Downloads\256x256_classifier.pt" if os.path.exists(file_path): ckpt = torch.load(file_path) else: print("The file does not exist at the specified path.") model.load_state_dict(ckpt) diffusion = Diffusion(img_size=64, device=device) y = torch.Tensor([6] * n).long().to(device) x = diffusion.sample(model, n, y, cfg_scale=3) plot_images(x)

But the following error : n = len(labels) TypeError: object of type 'int' has no len()

I have the classes in separate folder:

@.***

Can you help me with it?

From: Thomas Capelle @.> Sent: Monday, October 23, 2023 8:14 AM To: dome272/Diffusion-Models-pytorch @.> Cc: Eftekhari, Noushin @.>; Comment @.> Subject: Re: [dome272/Diffusion-Models-pytorch] Big Refactor (PR #1)

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dome272 / Diffusion-Models-pytorch

Big Refactor #1

ckpt = torch.load(r"models\DDPM_conditional\ckpt.pt")