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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
import torch
from .model import gaussian_diffusion as gd
from .model.dpm_solver import DPM_Solver, NoiseScheduleFlow, NoiseScheduleVP, model_wrapper
def DPMS(
model,
condition,
uncondition,
cfg_scale,
pag_scale=1.0,
pag_applied_layers=None,
model_type="noise", # or "x_start" or "v" or "score", "flow"
noise_schedule="linear",
guidance_type="classifier-free",
model_kwargs=None,
diffusion_steps=1000,
schedule="VP",
interval_guidance=None,
):
if pag_applied_layers is None:
pag_applied_layers = []
if model_kwargs is None:
model_kwargs = {}
if interval_guidance is None:
interval_guidance = [0, 1.0]
betas = torch.tensor(gd.get_named_beta_schedule(noise_schedule, diffusion_steps))
## 1. Define the noise schedule.
if schedule == "VP":
noise_schedule = NoiseScheduleVP(schedule="discrete", betas=betas)
elif schedule == "FLOW":
noise_schedule = NoiseScheduleFlow(schedule="discrete_flow")
## 2. Convert your discrete-time `model` to the continuous-time
## noise prediction model. Here is an example for a diffusion model
## `model` with the noise prediction type ("noise") .
model_fn = model_wrapper(
model,
noise_schedule,
model_type=model_type,
model_kwargs=model_kwargs,
guidance_type=guidance_type,
pag_scale=pag_scale,
pag_applied_layers=pag_applied_layers,
condition=condition,
unconditional_condition=uncondition,
guidance_scale=cfg_scale,
interval_guidance=interval_guidance,
)
## 3. Define dpm-solver and sample by multistep DPM-Solver.
return DPM_Solver(model_fn, noise_schedule, algorithm_type="dpmsolver++")
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