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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from typing import Protocol
import torch
from mattergen.diffusion.corruption.sde_lib import SDE
class TimestepSampler(Protocol):
min_t: float
max_t: float
def __call__(self, batch_size: int, device: torch.device) -> torch.FloatTensor:
raise NotImplementedError
class UniformTimestepSampler:
"""Samples diffusion timesteps uniformly over the training time."""
def __init__(
self,
*,
min_t: float,
max_t: float,
):
"""Initializes the sampler.
Args:
min_t (float): Smallest timestep that will be seen during training.
max_t (float): Largest timestep that will be seen during training.
"""
super().__init__()
self.min_t = min_t
self.max_t = max_t
def __call__(self, batch_size: int, device: torch.device) -> torch.FloatTensor:
return torch.rand(batch_size, device=device) * (self.max_t - self.min_t) + self.min_t
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