334 lines
14 KiB
Python
334 lines
14 KiB
Python
# Copyright 2022 ETH Zurich Computer Vision Lab 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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import math
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from dataclasses import dataclass
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from typing import Optional, Tuple, Union
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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 ..utils import BaseOutput
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from .scheduling_utils import SchedulerMixin
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@dataclass
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class RePaintSchedulerOutput(BaseOutput):
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"""
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Output class for the scheduler's step function output.
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Args:
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prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
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Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
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denoising loop.
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pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
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The predicted denoised sample (x_{0}) based on the model output from
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the current timestep. `pred_original_sample` can be used to preview progress or for guidance.
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"""
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prev_sample: torch.FloatTensor
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pred_original_sample: torch.FloatTensor
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def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
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"""
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Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
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(1-beta) over time from t = [0,1].
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Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
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to that part of the diffusion process.
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Args:
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num_diffusion_timesteps (`int`): the number of betas to produce.
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max_beta (`float`): the maximum beta to use; use values lower than 1 to
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prevent singularities.
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Returns:
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betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
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"""
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def alpha_bar(time_step):
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return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
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betas = []
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for i in range(num_diffusion_timesteps):
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t1 = i / num_diffusion_timesteps
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t2 = (i + 1) / num_diffusion_timesteps
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betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
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return torch.tensor(betas, dtype=torch.float32)
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class RePaintScheduler(SchedulerMixin, ConfigMixin):
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"""
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RePaint is a schedule for DDPM inpainting inside a given mask.
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[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
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function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
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[`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
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[`~SchedulerMixin.from_pretrained`] functions.
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For more details, see the original paper: https://arxiv.org/pdf/2201.09865.pdf
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Args:
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num_train_timesteps (`int`): number of diffusion steps used to train the model.
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beta_start (`float`): the starting `beta` value of inference.
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beta_end (`float`): the final `beta` value.
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beta_schedule (`str`):
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the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
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`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
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eta (`float`):
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The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 -0.0 is DDIM and
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1.0 is DDPM scheduler respectively.
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trained_betas (`np.ndarray`, optional):
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option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
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variance_type (`str`):
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options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
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`fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
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clip_sample (`bool`, default `True`):
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option to clip predicted sample between -1 and 1 for numerical stability.
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"""
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order = 1
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@register_to_config
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def __init__(
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self,
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num_train_timesteps: int = 1000,
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beta_start: float = 0.0001,
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beta_end: float = 0.02,
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beta_schedule: str = "linear",
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eta: float = 0.0,
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trained_betas: Optional[np.ndarray] = None,
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clip_sample: bool = True,
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):
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if trained_betas is not None:
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self.betas = torch.from_numpy(trained_betas)
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elif beta_schedule == "linear":
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self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
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elif beta_schedule == "scaled_linear":
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# this schedule is very specific to the latent diffusion model.
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self.betas = (
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torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
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)
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elif beta_schedule == "squaredcos_cap_v2":
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# Glide cosine schedule
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self.betas = betas_for_alpha_bar(num_train_timesteps)
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elif beta_schedule == "sigmoid":
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# GeoDiff sigmoid schedule
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betas = torch.linspace(-6, 6, num_train_timesteps)
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self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
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else:
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raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
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self.alphas = 1.0 - self.betas
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self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
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self.one = torch.tensor(1.0)
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self.final_alpha_cumprod = torch.tensor(1.0)
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# standard deviation of the initial noise distribution
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self.init_noise_sigma = 1.0
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# setable values
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self.num_inference_steps = None
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self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
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self.eta = eta
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def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
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"""
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Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
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current timestep.
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Args:
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sample (`torch.FloatTensor`): input sample
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timestep (`int`, optional): current timestep
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Returns:
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`torch.FloatTensor`: scaled input sample
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"""
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return sample
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def set_timesteps(
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self,
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num_inference_steps: int,
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jump_length: int = 10,
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jump_n_sample: int = 10,
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device: Union[str, torch.device] = None,
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):
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num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)
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self.num_inference_steps = num_inference_steps
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timesteps = []
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jumps = {}
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for j in range(0, num_inference_steps - jump_length, jump_length):
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jumps[j] = jump_n_sample - 1
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t = num_inference_steps
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while t >= 1:
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t = t - 1
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timesteps.append(t)
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if jumps.get(t, 0) > 0:
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jumps[t] = jumps[t] - 1
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for _ in range(jump_length):
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t = t + 1
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timesteps.append(t)
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timesteps = np.array(timesteps) * (self.config.num_train_timesteps // self.num_inference_steps)
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self.timesteps = torch.from_numpy(timesteps).to(device)
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def _get_variance(self, t):
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prev_timestep = t - self.config.num_train_timesteps // self.num_inference_steps
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alpha_prod_t = self.alphas_cumprod[t]
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alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
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beta_prod_t = 1 - alpha_prod_t
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beta_prod_t_prev = 1 - alpha_prod_t_prev
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# For t > 0, compute predicted variance βt (see formula (6) and (7) from
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# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
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# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
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# variance to pred_sample
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# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
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# without eta.
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# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
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variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
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return variance
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def step(
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self,
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model_output: torch.FloatTensor,
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timestep: int,
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sample: torch.FloatTensor,
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original_image: torch.FloatTensor,
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mask: torch.FloatTensor,
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generator: Optional[torch.Generator] = None,
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return_dict: bool = True,
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) -> Union[RePaintSchedulerOutput, Tuple]:
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"""
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Predict the sample at the previous timestep by reversing the SDE. Core function to propagate 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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model_output (`torch.FloatTensor`): direct output from learned
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diffusion model.
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timestep (`int`): current discrete timestep in the diffusion chain.
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sample (`torch.FloatTensor`):
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current instance of sample being created by diffusion process.
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original_image (`torch.FloatTensor`):
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the original image to inpaint on.
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mask (`torch.FloatTensor`):
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the mask where 0.0 values define which part of the original image to inpaint (change).
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generator (`torch.Generator`, *optional*): random number generator.
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return_dict (`bool`): option for returning tuple rather than
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DDPMSchedulerOutput class
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Returns:
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[`~schedulers.scheduling_utils.RePaintSchedulerOutput`] or `tuple`:
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[`~schedulers.scheduling_utils.RePaintSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
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returning a tuple, the first element is the sample tensor.
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"""
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t = timestep
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prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
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# 1. compute alphas, betas
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alpha_prod_t = self.alphas_cumprod[t]
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alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
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beta_prod_t = 1 - alpha_prod_t
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# 2. compute predicted original sample from predicted noise also called
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# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
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pred_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
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# 3. Clip "predicted x_0"
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if self.config.clip_sample:
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pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
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# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
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# substitute formula (7) in the algorithm coming from DDPM paper
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# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
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# DDIM schedule gives the same results as DDPM with eta = 1.0
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# Noise is being reused in 7. and 8., but no impact on quality has
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# been observed.
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# 5. Add noise
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device = model_output.device
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if device.type == "mps":
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# randn does not work reproducibly on mps
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noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator)
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noise = noise.to(device)
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else:
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noise = torch.randn(model_output.shape, generator=generator, device=device, dtype=model_output.dtype)
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std_dev_t = self.eta * self._get_variance(timestep) ** 0.5
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variance = 0
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if t > 0 and self.eta > 0:
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variance = std_dev_t * noise
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# 6. compute "direction pointing to x_t" of formula (12)
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# from https://arxiv.org/pdf/2010.02502.pdf
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pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
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# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
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prev_unknown_part = alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
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# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
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prev_known_part = (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise
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# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
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pred_prev_sample = mask * prev_known_part + (1.0 - mask) * prev_unknown_part
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if not return_dict:
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return (
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pred_prev_sample,
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pred_original_sample,
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)
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return RePaintSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
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def undo_step(self, sample, timestep, generator=None):
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n = self.config.num_train_timesteps // self.num_inference_steps
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for i in range(n):
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beta = self.betas[timestep + i]
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if sample.device.type == "mps":
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# randn does not work reproducibly on mps
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noise = torch.randn(sample.shape, dtype=sample.dtype, generator=generator)
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noise = noise.to(sample.device)
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else:
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noise = torch.randn(sample.shape, generator=generator, device=sample.device, dtype=sample.dtype)
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# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
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sample = (1 - beta) ** 0.5 * sample + beta**0.5 * noise
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return sample
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def add_noise(
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self,
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original_samples: torch.FloatTensor,
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noise: torch.FloatTensor,
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timesteps: torch.IntTensor,
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) -> torch.FloatTensor:
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raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.")
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def __len__(self):
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return self.config.num_train_timesteps
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