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# Copyright 2024 NVIDIA CORPORATION & 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.
#
# SPDX-License-Identifier: Apache-2.0
# Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
from diffusion.model.respace import SpacedDiffusion, space_timesteps
from .model import gaussian_diffusion as gd
def Scheduler(
timestep_respacing,
noise_schedule="linear",
use_kl=False,
sigma_small=False,
predict_xstart=False,
predict_v=False,
learn_sigma=True,
pred_sigma=True,
rescale_learned_sigmas=False,
diffusion_steps=1000,
snr=False,
return_startx=False,
flow_shift=1.0,
):
betas = gd.get_named_beta_schedule(noise_schedule, diffusion_steps)
if use_kl:
loss_type = gd.LossType.RESCALED_KL
elif rescale_learned_sigmas:
loss_type = gd.LossType.RESCALED_MSE
else:
loss_type = gd.LossType.MSE
if timestep_respacing is None or timestep_respacing == "":
timestep_respacing = [diffusion_steps]
if predict_xstart:
model_mean_type = gd.ModelMeanType.START_X
elif predict_v:
model_mean_type = gd.ModelMeanType.VELOCITY
else:
model_mean_type = gd.ModelMeanType.EPSILON
return SpacedDiffusion(
use_timesteps=space_timesteps(diffusion_steps, timestep_respacing),
betas=betas,
model_mean_type=model_mean_type,
model_var_type=(
(
(gd.ModelVarType.FIXED_LARGE if not sigma_small else gd.ModelVarType.FIXED_SMALL)
if not learn_sigma
else gd.ModelVarType.LEARNED_RANGE
)
if pred_sigma
else None
),
loss_type=loss_type,
snr=snr,
return_startx=return_startx,
# rescale_timesteps=rescale_timesteps,
flow="flow" in noise_schedule,
flow_shift=flow_shift,
diffusion_steps=diffusion_steps,
)
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