D3PM_replication

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D3PM_replication

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Implementation of the D3PM algorithm (diffusion with categorical variable)

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# D3PM_replication

Implementation (in pytorch) of the D3PM algorithm (diffusion with categorical variable) : https://arxiv.org/pdf/2107.03006.pdf

In this repository we will implement the D3PM algorithm on discrete MNIST data.

## Forward discrete diffusion process

in the forward discrete we have something like that :

![image](https://github.com/Forbu/D3PM_replication/assets/11457947/c932140e-25a9-4eca-a900-cca43e915aa0)

## Reverse diffusion process after training

Training is currently being done.

Result after training on 10% of the data points :

![image](https://github.com/Forbu/D3PM_replication/assets/11457947/abd70b55-6830-47f3-9f76-28403e002af6)

## Training params

We train using nb_temporal_step = 254, lambda = 0.01 and hidden_dim = 32 (no need to have huge number of latent images).

Also we only have 3 bins (categorical variables) in the above exemple images.

Also as the base model we use ContextUnet architecture coming from the deeplearning.ai course on diffusion process : https://deeplearning.ai/short-courses/how-diffusion-models-work/

## Notes on the current development

The modification is concern the passage here :

I want to point out that there is currently some (tiny) differences with the paper implementation :

In our implementation, we currently directly parametrize <img width="96" alt="Screenshot 2024-01-07 at 22 25 21" src="https://github.com/Forbu/D3PM_replication/assets/11457947/069f0427-78e9-46c2-b830-8e324246586a"> as being the neural network and not <img width="78" alt="Screenshot 2024-01-07 at 22 27 11" src="https://github.com/Forbu/D3PM_replication/assets/11457947/472d17fa-acc3-4531-aedd-215f396864e5">

A second interesting point : in the current code we only use the Uniform Noise and not the absorbing noise setup which seems to give better result in the paper.

> [!CAUTION] This was a repo for an old discrete diffusion method. Today i would use a simpler discrete diffusion set like the one used for diffusion llm like https://arxiv.org/pdf/2508.02193

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