torchmanager-diffusion 1.2rc2

Torchmanager Implementation for Diffusion Model (v1.2 Release Candidate 2)

Torchmanager Diffusion Models Plug-in

The torchmanager implementation for diffusion models.

Pre-requisites

• Python >= 3.9
• SciPy >= 1.11.4
• PyTorch >= 2.0.1
• LPIPS
• torchmanager >= 1.2
• einops >= 0.6.1

Installation

• PyPi: pip install torchmanager-diffusion

DDPM Manager Usage

Train DDPM

Direct compile DDPMManager with a model, a beta space, and a number of time steps. Then, use fit method to train the model.

import diffusion
from diffusion import DDPMManager
from torchmanager import callbacks, data, losses

# initialize dataset
dataset: data.Dataset = ...

# initialize model, beta_space, and time_steps
model: torch.nn.Module = ...
beta_space: diffusion.scheduling.BetaSpace = ...
time_steps: int = ...

# initialize optimizer and loss function
optimizer: torch.optim.Optimizer = ...
loss_fn: losses.Loss = ...

# compile the ddpm manager
manager = DDPMManager(model, beta_space, time_steps, optimizer=optimizer, loss_fn=loss_fn)

# initialize callbacks
callback_list: list[callbacks.Callback] = ...

# train the model
trained_model = manager.fit(dataset, epochs=..., callbacks=callback_list)

Evaluate DDPM

Add necessary metrics and use test method with sampling_images as True to evaluate the trained model.

import torch
from diffusion import DDPMManager
from torchmanager import data, metrics
from torchvision import models

# load manager from checkpoints
manager = DDPMManager.from_checkpoint(...)
assert isinstance(manager, DDPMManager), "manager is not a DDPMManager."

# initialize dataset
testing_dataset: data.Dataset = ...

# add neccessary metrics
inception = models.inception_v3(pretrained=True)
inception.fc = torch.nn.Identity()  # type: ignore
inception.eval()
fid = metrics.FID(inception)
manager.metrics.update({"FID": fid})

# evaluate the model
summary = manager.test(testing_dataset, sampling_images=True)

Customize Diffusion Algorithm

Inherit DiffusionManager and implement abstract methods forward_diffusion and sampling_step to customize the diffusion algorithm.

from diffusion import DiffusionManager

class CustomizedManager(DiffusionManager):
    def forward_diffusion(self, data: Any, condition: Optional[torch.Tensor] = None, t: Optional[torch.Tensor] = None) -> tuple[Any, torch.Tensor]:
        ...

    def sampling_step(self, data: DiffusionData, i: int, /, *, return_noise: bool = False) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
        ...