288 lines
13 KiB
Python
288 lines
13 KiB
Python
# Copyright 2022 Katherine Crowson 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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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 _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS, BaseOutput, logging
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from .scheduling_utils import SchedulerMixin
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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@dataclass
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# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
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class EulerDiscreteSchedulerOutput(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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class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
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"""
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Euler scheduler (Algorithm 2) from Karras et al. (2022) https://arxiv.org/abs/2206.00364. . Based on the original
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k-diffusion implementation by Katherine Crowson:
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https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51
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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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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` or `scaled_linear`.
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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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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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"""
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_compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()
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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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prediction_type: str = "epsilon",
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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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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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sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
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sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32)
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self.sigmas = torch.from_numpy(sigmas)
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# standard deviation of the initial noise distribution
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self.init_noise_sigma = self.sigmas.max()
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# setable values
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self.num_inference_steps = None
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timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy()
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self.timesteps = torch.from_numpy(timesteps)
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self.is_scale_input_called = False
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def scale_model_input(
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self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor]
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) -> torch.FloatTensor:
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"""
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Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.
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Args:
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sample (`torch.FloatTensor`): input sample
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timestep (`float` or `torch.FloatTensor`): the current timestep in the diffusion chain
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Returns:
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`torch.FloatTensor`: scaled input sample
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"""
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if isinstance(timestep, torch.Tensor):
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timestep = timestep.to(self.timesteps.device)
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step_index = (self.timesteps == timestep).nonzero().item()
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sigma = self.sigmas[step_index]
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sample = sample / ((sigma**2 + 1) ** 0.5)
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self.is_scale_input_called = True
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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 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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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.num_inference_steps = num_inference_steps
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timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()
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sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
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sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
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sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
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self.sigmas = torch.from_numpy(sigmas).to(device=device)
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if str(device).startswith("mps"):
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# mps does not support float64
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self.timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32)
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else:
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self.timesteps = torch.from_numpy(timesteps).to(device=device)
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def step(
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self,
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model_output: torch.FloatTensor,
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timestep: Union[float, torch.FloatTensor],
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sample: torch.FloatTensor,
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s_churn: float = 0.0,
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s_tmin: float = 0.0,
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s_tmax: float = float("inf"),
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s_noise: float = 1.0,
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generator: Optional[torch.Generator] = None,
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return_dict: bool = True,
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) -> Union[EulerDiscreteSchedulerOutput, 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 (`float`): current 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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s_churn (`float`)
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s_tmin (`float`)
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s_tmax (`float`)
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s_noise (`float`)
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generator (`torch.Generator`, optional): Random number generator.
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return_dict (`bool`): option for returning tuple rather than EulerDiscreteSchedulerOutput class
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Returns:
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[`~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput`] or `tuple`:
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[`~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput`] if `return_dict` is True, otherwise a
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`tuple`. When returning a tuple, the first element is the sample tensor.
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"""
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if (
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isinstance(timestep, int)
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or isinstance(timestep, torch.IntTensor)
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or isinstance(timestep, torch.LongTensor)
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):
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raise ValueError(
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"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
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" `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
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" one of the `scheduler.timesteps` as a timestep.",
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)
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if not self.is_scale_input_called:
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logger.warning(
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"The `scale_model_input` function should be called before `step` to ensure correct denoising. "
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"See `StableDiffusionPipeline` for a usage example."
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)
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if isinstance(timestep, torch.Tensor):
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timestep = timestep.to(self.timesteps.device)
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step_index = (self.timesteps == timestep).nonzero().item()
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sigma = self.sigmas[step_index]
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gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0
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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, device="cpu", generator=generator).to(
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device
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)
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else:
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noise = torch.randn(model_output.shape, dtype=model_output.dtype, device=device, generator=generator).to(
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device
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)
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eps = noise * s_noise
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sigma_hat = sigma * (gamma + 1)
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if gamma > 0:
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sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5
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# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
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if self.config.prediction_type == "epsilon":
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pred_original_sample = sample - sigma_hat * model_output
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elif self.config.prediction_type == "v_prediction":
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# * c_out + input * c_skip
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pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
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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`, or `v_prediction`"
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)
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# 2. Convert to an ODE derivative
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derivative = (sample - pred_original_sample) / sigma_hat
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dt = self.sigmas[step_index + 1] - sigma_hat
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prev_sample = sample + derivative * dt
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if not return_dict:
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return (prev_sample,)
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return EulerDiscreteSchedulerOutput(prev_sample=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.FloatTensor,
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) -> torch.FloatTensor:
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# Make sure sigmas and timesteps have the same device and dtype as original_samples
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self.sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
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if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
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# mps does not support float64
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self.timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
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timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
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else:
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self.timesteps = self.timesteps.to(original_samples.device)
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timesteps = timesteps.to(original_samples.device)
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schedule_timesteps = self.timesteps
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step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
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sigma = self.sigmas[step_indices].flatten()
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while len(sigma.shape) < len(original_samples.shape):
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sigma = sigma.unsqueeze(-1)
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noisy_samples = original_samples + noise * sigma
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return noisy_samples
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def __len__(self):
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return self.config.num_train_timesteps
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