74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
# // 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.
|
|
|
|
"""
|
|
Utility functions for creating schedules and samplers from config.
|
|
"""
|
|
|
|
import torch
|
|
from omegaconf import DictConfig
|
|
|
|
from .samplers.base import Sampler
|
|
from .samplers.euler import EulerSampler
|
|
from .schedules.base import Schedule
|
|
from .schedules.lerp import LinearInterpolationSchedule
|
|
from .timesteps.base import SamplingTimesteps
|
|
from .timesteps.sampling.trailing import UniformTrailingSamplingTimesteps
|
|
|
|
|
|
def create_schedule_from_config(
|
|
config: DictConfig,
|
|
device: torch.device,
|
|
dtype: torch.dtype = torch.float32,
|
|
) -> Schedule:
|
|
"""
|
|
Create a schedule from configuration.
|
|
"""
|
|
if config.type == "lerp":
|
|
return LinearInterpolationSchedule(T=config.get("T", 1.0))
|
|
|
|
raise NotImplementedError
|
|
|
|
|
|
def create_sampler_from_config(
|
|
config: DictConfig,
|
|
schedule: Schedule,
|
|
timesteps: SamplingTimesteps,
|
|
) -> Sampler:
|
|
"""
|
|
Create a sampler from configuration.
|
|
"""
|
|
if config.type == "euler":
|
|
return EulerSampler(
|
|
schedule=schedule,
|
|
timesteps=timesteps,
|
|
prediction_type=config.prediction_type,
|
|
)
|
|
raise NotImplementedError
|
|
|
|
|
|
def create_sampling_timesteps_from_config(
|
|
config: DictConfig,
|
|
schedule: Schedule,
|
|
device: torch.device,
|
|
dtype: torch.dtype = torch.float32,
|
|
) -> SamplingTimesteps:
|
|
if config.type == "uniform_trailing":
|
|
return UniformTrailingSamplingTimesteps(
|
|
T=schedule.T,
|
|
steps=config.steps,
|
|
shift=config.get("shift", 1.0),
|
|
device=device,
|
|
)
|
|
raise NotImplementedError |