Torchmanager Implementation for Diffusion Model (v1.0.3)
Project description
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]]:
...
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