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Running
on
Zero
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# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# //
# // Licensed under the Apache License, Version 2.0 (the "License");
# // you may not use this file except in compliance with the License.
# // You may obtain a copy of the License at
# //
# // http://www.apache.org/licenses/LICENSE-2.0
# //
# // Unless required by applicable law or agreed to in writing, software
# // distributed under the License is distributed on an "AS IS" BASIS,
# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# // See the License for the specific language governing permissions and
# // limitations under the License.
"""
Linear interpolation schedule (lerp).
"""
from typing import Union
import torch
from .base import Schedule
class LinearInterpolationSchedule(Schedule):
"""
Linear interpolation schedule (lerp) is proposed by flow matching and rectified flow.
It leads to straighter probability flow theoretically. It is also used by Stable Diffusion 3.
<https://arxiv.org/abs/2209.03003>
<https://arxiv.org/abs/2210.02747>
x_t = (1 - t) * x_0 + t * x_T
Can be either continuous or discrete.
"""
def __init__(self, T: Union[int, float] = 1.0):
self._T = T
@property
def T(self) -> Union[int, float]:
return self._T
def A(self, t: torch.Tensor) -> torch.Tensor:
return 1 - (t / self.T)
def B(self, t: torch.Tensor) -> torch.Tensor:
return t / self.T
# ----------------------------------------------------
def isnr(self, snr: torch.Tensor) -> torch.Tensor:
t = self.T / (1 + snr**0.5)
t = t if self.is_continuous() else t.round().int()
return t
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