62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
# Copyright 2022 Google Brain and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
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# TODO(Patrick, Anton, Suraj) - make scheduler framework indepedent and clean-up a bit
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import numpy as np
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import torch
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from ..configuration_utils import ConfigMixin, register_to_config
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from .scheduling_utils import SchedulerMixin
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class ScoreSdeVpScheduler(SchedulerMixin, ConfigMixin):
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@register_to_config
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def __init__(self, num_train_timesteps=2000, beta_min=0.1, beta_max=20, sampling_eps=1e-3, tensor_format="np"):
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self.sigmas = None
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self.discrete_sigmas = None
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self.timesteps = None
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def set_timesteps(self, num_inference_steps):
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self.timesteps = torch.linspace(1, self.config.sampling_eps, num_inference_steps)
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def step_pred(self, score, x, t):
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# TODO(Patrick) better comments + non-PyTorch
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# postprocess model score
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log_mean_coeff = (
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-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
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)
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std = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
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score = -score / std[:, None, None, None]
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# compute
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dt = -1.0 / len(self.timesteps)
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beta_t = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
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drift = -0.5 * beta_t[:, None, None, None] * x
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diffusion = torch.sqrt(beta_t)
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drift = drift - diffusion[:, None, None, None] ** 2 * score
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x_mean = x + drift * dt
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# add noise
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noise = torch.randn_like(x)
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x = x_mean + diffusion[:, None, None, None] * np.sqrt(-dt) * noise
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return x, x_mean
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def __len__(self):
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return self.config.num_train_timesteps
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