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| # Copyright 2022 NVIDIA and The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from dataclasses import dataclass | |
| from typing import Optional, Tuple, Union | |
| import numpy as np | |
| import torch | |
| from ..configuration_utils import ConfigMixin, register_to_config | |
| from ..utils import BaseOutput | |
| from .scheduling_utils import SchedulerMixin | |
| class KarrasVeOutput(BaseOutput): | |
| """ | |
| Output class for the scheduler's step function output. | |
| Args: | |
| prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): | |
| Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the | |
| denoising loop. | |
| derivative (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): | |
| Derivate of predicted original image sample (x_0). | |
| """ | |
| prev_sample: torch.FloatTensor | |
| derivative: torch.FloatTensor | |
| class KarrasVeScheduler(SchedulerMixin, ConfigMixin): | |
| """ | |
| Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and | |
| the VE column of Table 1 from [1] for reference. | |
| [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models." | |
| https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic | |
| differential equations." https://arxiv.org/abs/2011.13456 | |
| [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` | |
| function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. | |
| [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and | |
| [`~ConfigMixin.from_config`] functios. | |
| For more details on the parameters, see the original paper's Appendix E.: "Elucidating the Design Space of | |
| Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364. The grid search values used to find the | |
| optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the paper. | |
| Args: | |
| sigma_min (`float`): minimum noise magnitude | |
| sigma_max (`float`): maximum noise magnitude | |
| s_noise (`float`): the amount of additional noise to counteract loss of detail during sampling. | |
| A reasonable range is [1.000, 1.011]. | |
| s_churn (`float`): the parameter controlling the overall amount of stochasticity. | |
| A reasonable range is [0, 100]. | |
| s_min (`float`): the start value of the sigma range where we add noise (enable stochasticity). | |
| A reasonable range is [0, 10]. | |
| s_max (`float`): the end value of the sigma range where we add noise. | |
| A reasonable range is [0.2, 80]. | |
| tensor_format (`str`): whether the scheduler expects pytorch or numpy arrays. | |
| """ | |
| def __init__( | |
| self, | |
| sigma_min: float = 0.02, | |
| sigma_max: float = 100, | |
| s_noise: float = 1.007, | |
| s_churn: float = 80, | |
| s_min: float = 0.05, | |
| s_max: float = 50, | |
| tensor_format: str = "pt", | |
| ): | |
| # setable values | |
| self.num_inference_steps = None | |
| self.timesteps = None | |
| self.schedule = None # sigma(t_i) | |
| self.tensor_format = tensor_format | |
| self.set_format(tensor_format=tensor_format) | |
| def set_timesteps(self, num_inference_steps: int): | |
| """ | |
| Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference. | |
| Args: | |
| num_inference_steps (`int`): | |
| the number of diffusion steps used when generating samples with a pre-trained model. | |
| """ | |
| self.num_inference_steps = num_inference_steps | |
| self.timesteps = np.arange(0, self.num_inference_steps)[::-1].copy() | |
| self.schedule = [ | |
| (self.sigma_max * (self.sigma_min**2 / self.sigma_max**2) ** (i / (num_inference_steps - 1))) | |
| for i in self.timesteps | |
| ] | |
| self.schedule = np.array(self.schedule, dtype=np.float32) | |
| self.set_format(tensor_format=self.tensor_format) | |
| def add_noise_to_input( | |
| self, sample: Union[torch.FloatTensor, np.ndarray], sigma: float, generator: Optional[torch.Generator] = None | |
| ) -> Tuple[Union[torch.FloatTensor, np.ndarray], float]: | |
| """ | |
| Explicit Langevin-like "churn" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a | |
| higher noise level sigma_hat = sigma_i + gamma_i*sigma_i. | |
| TODO Args: | |
| """ | |
| if self.s_min <= sigma <= self.s_max: | |
| gamma = min(self.s_churn / self.num_inference_steps, 2**0.5 - 1) | |
| else: | |
| gamma = 0 | |
| # sample eps ~ N(0, S_noise^2 * I) | |
| eps = self.s_noise * torch.randn(sample.shape, generator=generator).to(sample.device) | |
| sigma_hat = sigma + gamma * sigma | |
| sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps) | |
| return sample_hat, sigma_hat | |
| def step( | |
| self, | |
| model_output: Union[torch.FloatTensor, np.ndarray], | |
| sigma_hat: float, | |
| sigma_prev: float, | |
| sample_hat: Union[torch.FloatTensor, np.ndarray], | |
| return_dict: bool = True, | |
| ) -> Union[KarrasVeOutput, Tuple]: | |
| """ | |
| Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion | |
| process from the learned model outputs (most often the predicted noise). | |
| Args: | |
| model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model. | |
| sigma_hat (`float`): TODO | |
| sigma_prev (`float`): TODO | |
| sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO | |
| return_dict (`bool`): option for returning tuple rather than SchedulerOutput class | |
| KarrasVeOutput: updated sample in the diffusion chain and derivative (TODO double check). | |
| Returns: | |
| [`~schedulers.scheduling_karras_ve.KarrasVeOutput`] or `tuple`: | |
| [`~schedulers.scheduling_karras_ve.KarrasVeOutput`] if `return_dict` is True, otherwise a `tuple`. When | |
| returning a tuple, the first element is the sample tensor. | |
| """ | |
| pred_original_sample = sample_hat + sigma_hat * model_output | |
| derivative = (sample_hat - pred_original_sample) / sigma_hat | |
| sample_prev = sample_hat + (sigma_prev - sigma_hat) * derivative | |
| if not return_dict: | |
| return (sample_prev, derivative) | |
| return KarrasVeOutput(prev_sample=sample_prev, derivative=derivative) | |
| def step_correct( | |
| self, | |
| model_output: Union[torch.FloatTensor, np.ndarray], | |
| sigma_hat: float, | |
| sigma_prev: float, | |
| sample_hat: Union[torch.FloatTensor, np.ndarray], | |
| sample_prev: Union[torch.FloatTensor, np.ndarray], | |
| derivative: Union[torch.FloatTensor, np.ndarray], | |
| return_dict: bool = True, | |
| ) -> Union[KarrasVeOutput, Tuple]: | |
| """ | |
| Correct the predicted sample based on the output model_output of the network. TODO complete description | |
| Args: | |
| model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model. | |
| sigma_hat (`float`): TODO | |
| sigma_prev (`float`): TODO | |
| sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO | |
| sample_prev (`torch.FloatTensor` or `np.ndarray`): TODO | |
| derivative (`torch.FloatTensor` or `np.ndarray`): TODO | |
| return_dict (`bool`): option for returning tuple rather than SchedulerOutput class | |
| Returns: | |
| prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO | |
| """ | |
| pred_original_sample = sample_prev + sigma_prev * model_output | |
| derivative_corr = (sample_prev - pred_original_sample) / sigma_prev | |
| sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr) | |
| if not return_dict: | |
| return (sample_prev, derivative) | |
| return KarrasVeOutput(prev_sample=sample_prev, derivative=derivative) | |
| def add_noise(self, original_samples, noise, timesteps): | |
| raise NotImplementedError() | |