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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
"""SAMPLING ONLY."""
import numpy as np
import torch
from diffusion.model.sa_solver import NoiseScheduleVP, SASolver, model_wrapper
from .model import gaussian_diffusion as gd
class SASolverSampler:
def __init__(
self,
model,
noise_schedule="linear",
diffusion_steps=1000,
device="cpu",
):
super().__init__()
self.model = model
self.device = device
to_torch = lambda x: x.clone().detach().to(torch.float32).to(device)
betas = torch.tensor(gd.get_named_beta_schedule(noise_schedule, diffusion_steps))
alphas = 1.0 - betas
self.register_buffer("alphas_cumprod", to_torch(np.cumprod(alphas, axis=0)))
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
setattr(self, name, attr)
@torch.no_grad()
def sample(
self,
S,
batch_size,
shape,
conditioning=None,
callback=None,
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.0,
mask=None,
x0=None,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
model_kwargs={},
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs,
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
device = self.device
if x_T is None:
img = torch.randn(size, device=device)
else:
img = x_T
ns = NoiseScheduleVP("discrete", alphas_cumprod=self.alphas_cumprod)
model_fn = model_wrapper(
self.model,
ns,
model_type="noise",
guidance_type="classifier-free",
condition=conditioning,
unconditional_condition=unconditional_conditioning,
guidance_scale=unconditional_guidance_scale,
model_kwargs=model_kwargs,
)
sasolver = SASolver(model_fn, ns, algorithm_type="data_prediction")
tau_t = lambda t: eta if 0.2 <= t <= 0.8 else 0
x = sasolver.sample(
mode="few_steps",
x=img,
tau=tau_t,
steps=S,
skip_type="time",
skip_order=1,
predictor_order=2,
corrector_order=2,
pc_mode="PEC",
return_intermediate=False,
)
return x.to(device), None
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