wehub-resource-sync
403 lines
19 KiB
Python
403 lines
19 KiB
Python
# Copyright 2023 UC Berkeley Team 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/ermongroup/ddim
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import math
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from dataclasses import dataclass
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from typing import List, 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, randn_tensor
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from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
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@dataclass
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class DDPMSchedulerOutput(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 the current timestep.
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`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: Optional[torch.FloatTensor] = None
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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 DDPMScheduler(SchedulerMixin, ConfigMixin):
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"""
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Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and
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Langevin dynamics sampling.
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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/abs/2006.11239
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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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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 for numerical stability.
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clip_sample_range (`float`, default `1.0`):
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the maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
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prediction_type (`str`, default `epsilon`, optional):
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prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
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process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
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https://imagen.research.google/video/paper.pdf)
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thresholding (`bool`, default `False`):
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whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).
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Note that the thresholding method is unsuitable for latent-space diffusion models (such as
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stable-diffusion).
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dynamic_thresholding_ratio (`float`, default `0.995`):
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the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen
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(https://arxiv.org/abs/2205.11487). Valid only when `thresholding=True`.
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sample_max_value (`float`, default `1.0`):
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the threshold value for dynamic thresholding. Valid only when `thresholding=True`.
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"""
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_compatibles = [e.name for e in KarrasDiffusionSchedulers]
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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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trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
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variance_type: str = "fixed_small",
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clip_sample: bool = True,
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prediction_type: str = "epsilon",
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thresholding: bool = False,
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dynamic_thresholding_ratio: float = 0.995,
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clip_sample_range: float = 1.0,
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sample_max_value: float = 1.0,
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):
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if trained_betas is not None:
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self.betas = torch.tensor(trained_betas, dtype=torch.float32)
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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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# 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.variance_type = variance_type
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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(self, num_inference_steps: int, device: Union[str, torch.device] = None):
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"""
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Sets the discrete timesteps used for the diffusion chain. Supporting function 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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"""
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if num_inference_steps > self.config.num_train_timesteps:
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raise ValueError(
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f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
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f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
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f" maximal {self.config.num_train_timesteps} timesteps."
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)
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self.num_inference_steps = num_inference_steps
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step_ratio = self.config.num_train_timesteps // self.num_inference_steps
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timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)
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# print(timesteps)
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# exit(0)
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self.timesteps = torch.from_numpy(timesteps).to(device)
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def _get_variance(self, t, predicted_variance=None, variance_type=None):
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num_inference_steps = self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps
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prev_t = t - self.config.num_train_timesteps // 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_t] if prev_t >= 0 else self.one
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current_beta_t = 1 - alpha_prod_t / alpha_prod_t_prev
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# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
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# and sample from it to get previous sample
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# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
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variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * current_beta_t
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if variance_type is None:
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variance_type = self.config.variance_type
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# hacks - were probably added for training stability
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if variance_type == "fixed_small":
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variance = torch.clamp(variance, min=1e-20)
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# for rl-diffuser https://arxiv.org/abs/2205.09991
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elif variance_type == "fixed_small_log":
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variance = torch.log(torch.clamp(variance, min=1e-20))
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variance = torch.exp(0.5 * variance)
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elif variance_type == "fixed_large":
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variance = current_beta_t
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elif variance_type == "fixed_large_log":
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# Glide max_log
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variance = torch.log(current_beta_t)
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elif variance_type == "learned":
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return predicted_variance
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elif variance_type == "learned_range":
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min_log = torch.log(variance)
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max_log = torch.log(self.betas[t])
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frac = (predicted_variance + 1) / 2
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variance = frac * max_log + (1 - frac) * min_log
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return variance
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def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
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# Dynamic thresholding in https://arxiv.org/abs/2205.11487
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dynamic_max_val = (
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sample.flatten(1)
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.abs()
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.quantile(self.config.dynamic_thresholding_ratio, dim=1)
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.clamp_min(self.config.sample_max_value)
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.view(-1, *([1] * (sample.ndim - 1)))
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)
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return sample.clamp(-dynamic_max_val, dynamic_max_val) / dynamic_max_val
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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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generator=None,
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return_dict: bool = True,
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) -> Union[DDPMSchedulerOutput, 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 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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generator: random number generator.
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return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class
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Returns:
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[`~schedulers.scheduling_utils.DDPMSchedulerOutput`] or `tuple`:
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[`~schedulers.scheduling_utils.DDPMSchedulerOutput`] 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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num_inference_steps = self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps
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prev_t = timestep - self.config.num_train_timesteps // num_inference_steps
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if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
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model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
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else:
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predicted_variance = None
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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_t] if prev_t >= 0 else self.one
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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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current_alpha_t = alpha_prod_t / alpha_prod_t_prev
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current_beta_t = 1 - current_alpha_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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if self.config.prediction_type == "epsilon":
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pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
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elif self.config.prediction_type == "sample":
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pred_original_sample = model_output
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elif self.config.prediction_type == "v_prediction":
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pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
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else:
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raise ValueError(
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f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or"
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" `v_prediction` for the DDPMScheduler."
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)
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# 3. Clip or threshold "predicted x_0"
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if self.config.clip_sample:
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pred_original_sample = pred_original_sample.clamp(
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-self.config.clip_sample_range, self.config.clip_sample_range
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)
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if self.config.thresholding:
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pred_original_sample = self._threshold_sample(pred_original_sample)
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# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
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# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
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pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * current_beta_t) / beta_prod_t
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current_sample_coeff = current_alpha_t ** (0.5) * beta_prod_t_prev / beta_prod_t
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# 5. Compute predicted previous sample µ_t
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# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
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pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
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# 6. Add noise
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variance = 0
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if t > 0:
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device = model_output.device
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variance_noise = randn_tensor(
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model_output.shape, generator=generator, device=device, dtype=model_output.dtype
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)
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if self.variance_type == "fixed_small_log":
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variance = self._get_variance(t, predicted_variance=predicted_variance) * variance_noise
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elif self.variance_type == "learned_range":
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variance = self._get_variance(t, predicted_variance=predicted_variance)
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variance = torch.exp(0.5 * variance) * variance_noise
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else:
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variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * variance_noise
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pred_prev_sample = pred_prev_sample + variance
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if not return_dict:
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return (pred_prev_sample,)
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return DDPMSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_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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# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
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self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
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timesteps = timesteps.to(original_samples.device)
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sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
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sqrt_alpha_prod = sqrt_alpha_prod.flatten()
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while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
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sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
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sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
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sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
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while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
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sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
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noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
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return noisy_samples
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def get_x0_from_noise(self, noise, t, x_t): # add this function
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self.alphas_cumprod = self.alphas_cumprod.to(device=noise.device, dtype=noise.dtype)
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x_0 = 1 / torch.sqrt(self.alphas_cumprod[t][:,None,None,None]) * x_t - torch.sqrt(1 / self.alphas_cumprod[t][:,None,None,None] - 1) * noise
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return x_0
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def get_velocity(
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self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
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) -> torch.FloatTensor:
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# Make sure alphas_cumprod and timestep have same device and dtype as sample
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self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)
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timesteps = timesteps.to(sample.device)
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sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
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sqrt_alpha_prod = sqrt_alpha_prod.flatten()
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while len(sqrt_alpha_prod.shape) < len(sample.shape):
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sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
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sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
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sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
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while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
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sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
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velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
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return velocity
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def __len__(self):
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return self.config.num_train_timesteps
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