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Running
on
Zero
File size: 1,624 Bytes
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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.
import torch
from ...types import SamplingDirection
from ..base import SamplingTimesteps
class UniformTrailingSamplingTimesteps(SamplingTimesteps):
"""
Uniform trailing sampling timesteps.
Defined in (https://arxiv.org/abs/2305.08891)
Shift is proposed in SD3 for RF schedule.
Defined in (https://arxiv.org/pdf/2403.03206) eq.23
"""
def __init__(
self,
T: int,
steps: int,
shift: float = 1.0,
device: torch.device = "cpu",
):
# Create trailing timesteps.
timesteps = torch.arange(1.0, 0.0, -1.0 / steps, device=device)
# Shift timesteps.
timesteps = shift * timesteps / (1 + (shift - 1) * timesteps)
# Scale to T range.
if isinstance(T, float):
timesteps = timesteps * T
else:
timesteps = timesteps.mul(T + 1).sub(1).round().int()
super().__init__(T=T, timesteps=timesteps, direction=SamplingDirection.backward)
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