109 lines
4.2 KiB
Python
109 lines
4.2 KiB
Python
# Copyright 2025 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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import math
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from typing import Union
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import torch
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from ...configuration_utils import ConfigMixin, register_to_config
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from ...utils.torch_utils import randn_tensor
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from ..scheduling_utils import SchedulerMixin
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class ScoreSdeVpScheduler(SchedulerMixin, ConfigMixin):
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"""
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`ScoreSdeVpScheduler` is a variance preserving stochastic differential equation (SDE) scheduler.
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This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
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methods the library implements for all schedulers such as loading and saving.
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Args:
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num_train_timesteps (`int`, defaults to 2000):
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The number of diffusion steps to train the model.
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beta_min (`int`, defaults to 0.1):
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beta_max (`int`, defaults to 20):
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sampling_eps (`int`, defaults to 1e-3):
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The end value of sampling where timesteps decrease progressively from 1 to epsilon.
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"""
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order = 1
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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):
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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, device: Union[str, torch.device] = None):
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"""
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Sets the continuous timesteps used for the diffusion chain (to be run before inference).
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Args:
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num_inference_steps (`int`):
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The number of diffusion steps used when generating samples with a pre-trained model.
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device (`str` or `torch.device`, *optional*):
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The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
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"""
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self.timesteps = torch.linspace(1, self.config.sampling_eps, num_inference_steps, device=device)
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def step_pred(self, score, x, t, generator=None):
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"""
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Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
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process from the learned model outputs (most often the predicted noise).
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Args:
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score ():
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x ():
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t ():
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generator (`torch.Generator`, *optional*):
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A random number generator.
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"""
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if self.timesteps is None:
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raise ValueError(
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"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
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)
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# TODO(Patrick) better comments + non-PyTorch
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# postprocess model score
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log_mean_coeff = -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
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std = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
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std = std.flatten()
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while len(std.shape) < len(score.shape):
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std = std.unsqueeze(-1)
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score = -score / std
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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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beta_t = beta_t.flatten()
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while len(beta_t.shape) < len(x.shape):
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beta_t = beta_t.unsqueeze(-1)
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drift = -0.5 * beta_t * x
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diffusion = torch.sqrt(beta_t)
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drift = drift - diffusion**2 * score
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x_mean = x + drift * dt
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# add noise
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noise = randn_tensor(x.shape, layout=x.layout, generator=generator, device=x.device, dtype=x.dtype)
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x = x_mean + diffusion * math.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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