Spaces:
Running
on
Zero
Running
on
Zero
| # Copyright 2023 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 List, Optional, Tuple, Union | |
| import numpy as np | |
| import torch | |
| from diffusers.configuration_utils import ConfigMixin, register_to_config | |
| from diffusers.utils import BaseOutput, logging | |
| from diffusers.utils.torch_utils import randn_tensor | |
| from diffusers.schedulers.scheduling_utils import SchedulerMixin | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| class AnimateLCMSVDStochasticIterativeSchedulerOutput(BaseOutput): | |
| """ | |
| Output class for the scheduler's `step` function. | |
| 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. | |
| """ | |
| prev_sample: torch.FloatTensor | |
| class AnimateLCMSVDStochasticIterativeScheduler(SchedulerMixin, ConfigMixin): | |
| """ | |
| Multistep and onestep sampling for consistency models. | |
| This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic | |
| methods the library implements for all schedulers such as loading and saving. | |
| Args: | |
| num_train_timesteps (`int`, defaults to 40): | |
| The number of diffusion steps to train the model. | |
| sigma_min (`float`, defaults to 0.002): | |
| Minimum noise magnitude in the sigma schedule. Defaults to 0.002 from the original implementation. | |
| sigma_max (`float`, defaults to 80.0): | |
| Maximum noise magnitude in the sigma schedule. Defaults to 80.0 from the original implementation. | |
| sigma_data (`float`, defaults to 0.5): | |
| The standard deviation of the data distribution from the EDM | |
| [paper](https://huggingface.co/papers/2206.00364). Defaults to 0.5 from the original implementation. | |
| s_noise (`float`, defaults to 1.0): | |
| The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000, | |
| 1.011]. Defaults to 1.0 from the original implementation. | |
| rho (`float`, defaults to 7.0): | |
| The parameter for calculating the Karras sigma schedule from the EDM | |
| [paper](https://huggingface.co/papers/2206.00364). Defaults to 7.0 from the original implementation. | |
| clip_denoised (`bool`, defaults to `True`): | |
| Whether to clip the denoised outputs to `(-1, 1)`. | |
| timesteps (`List` or `np.ndarray` or `torch.Tensor`, *optional*): | |
| An explicit timestep schedule that can be optionally specified. The timesteps are expected to be in | |
| increasing order. | |
| """ | |
| order = 1 | |
| def __init__( | |
| self, | |
| num_train_timesteps: int = 40, | |
| sigma_min: float = 0.002, | |
| sigma_max: float = 80.0, | |
| sigma_data: float = 0.5, | |
| s_noise: float = 1.0, | |
| rho: float = 7.0, | |
| clip_denoised: bool = True, | |
| ): | |
| # standard deviation of the initial noise distribution | |
| self.init_noise_sigma = (sigma_max**2 + 1) ** 0.5 | |
| # self.init_noise_sigma = sigma_max | |
| ramp = np.linspace(0, 1, num_train_timesteps) | |
| sigmas = self._convert_to_karras(ramp) | |
| sigmas = np.concatenate([sigmas, np.array([0])]) | |
| timesteps = self.sigma_to_t(sigmas) | |
| # setable values | |
| self.num_inference_steps = None | |
| self.sigmas = torch.from_numpy(sigmas) | |
| self.timesteps = torch.from_numpy(timesteps) | |
| self.custom_timesteps = False | |
| self.is_scale_input_called = False | |
| self._step_index = None | |
| self.sigmas.to("cpu") # to avoid too much CPU/GPU communication | |
| def index_for_timestep(self, timestep, schedule_timesteps=None): | |
| if schedule_timesteps is None: | |
| schedule_timesteps = self.timesteps | |
| indices = (schedule_timesteps == timestep).nonzero() | |
| return indices.item() | |
| def step_index(self): | |
| """ | |
| The index counter for current timestep. It will increae 1 after each scheduler step. | |
| """ | |
| return self._step_index | |
| def scale_model_input( | |
| self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor] | |
| ) -> torch.FloatTensor: | |
| """ | |
| Scales the consistency model input by `(sigma**2 + sigma_data**2) ** 0.5`. | |
| Args: | |
| sample (`torch.FloatTensor`): | |
| The input sample. | |
| timestep (`float` or `torch.FloatTensor`): | |
| The current timestep in the diffusion chain. | |
| Returns: | |
| `torch.FloatTensor`: | |
| A scaled input sample. | |
| """ | |
| # Get sigma corresponding to timestep | |
| if self.step_index is None: | |
| self._init_step_index(timestep) | |
| sigma = self.sigmas[self.step_index] | |
| sample = sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5) | |
| self.is_scale_input_called = True | |
| return sample | |
| # def _sigma_to_t(self, sigma, log_sigmas): | |
| # # get log sigma | |
| # log_sigma = np.log(np.maximum(sigma, 1e-10)) | |
| # # get distribution | |
| # dists = log_sigma - log_sigmas[:, np.newaxis] | |
| # # get sigmas range | |
| # low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2) | |
| # high_idx = low_idx + 1 | |
| # low = log_sigmas[low_idx] | |
| # high = log_sigmas[high_idx] | |
| # # interpolate sigmas | |
| # w = (low - log_sigma) / (low - high) | |
| # w = np.clip(w, 0, 1) | |
| # # transform interpolation to time range | |
| # t = (1 - w) * low_idx + w * high_idx | |
| # t = t.reshape(sigma.shape) | |
| # return t | |
| def sigma_to_t(self, sigmas: Union[float, np.ndarray]): | |
| """ | |
| Gets scaled timesteps from the Karras sigmas for input to the consistency model. | |
| Args: | |
| sigmas (`float` or `np.ndarray`): | |
| A single Karras sigma or an array of Karras sigmas. | |
| Returns: | |
| `float` or `np.ndarray`: | |
| A scaled input timestep or scaled input timestep array. | |
| """ | |
| if not isinstance(sigmas, np.ndarray): | |
| sigmas = np.array(sigmas, dtype=np.float64) | |
| timesteps = 0.25 * np.log(sigmas + 1e-44) | |
| return timesteps | |
| def set_timesteps( | |
| self, | |
| num_inference_steps: Optional[int] = None, | |
| device: Union[str, torch.device] = None, | |
| timesteps: Optional[List[int]] = None, | |
| ): | |
| """ | |
| Sets the timesteps used for the diffusion chain (to be run before inference). | |
| Args: | |
| num_inference_steps (`int`): | |
| The number of diffusion steps used when generating samples with a pre-trained model. | |
| device (`str` or `torch.device`, *optional*): | |
| The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. | |
| timesteps (`List[int]`, *optional*): | |
| Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default | |
| timestep spacing strategy of equal spacing between timesteps is used. If `timesteps` is passed, | |
| `num_inference_steps` must be `None`. | |
| """ | |
| if num_inference_steps is None and timesteps is None: | |
| raise ValueError( | |
| "Exactly one of `num_inference_steps` or `timesteps` must be supplied." | |
| ) | |
| if num_inference_steps is not None and timesteps is not None: | |
| raise ValueError( | |
| "Can only pass one of `num_inference_steps` or `timesteps`." | |
| ) | |
| # Follow DDPMScheduler custom timesteps logic | |
| if timesteps is not None: | |
| for i in range(1, len(timesteps)): | |
| if timesteps[i] >= timesteps[i - 1]: | |
| raise ValueError("`timesteps` must be in descending order.") | |
| if timesteps[0] >= self.config.num_train_timesteps: | |
| raise ValueError( | |
| f"`timesteps` must start before `self.config.train_timesteps`:" | |
| f" {self.config.num_train_timesteps}." | |
| ) | |
| timesteps = np.array(timesteps, dtype=np.int64) | |
| self.custom_timesteps = True | |
| else: | |
| if num_inference_steps > self.config.num_train_timesteps: | |
| raise ValueError( | |
| f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:" | |
| f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" | |
| f" maximal {self.config.num_train_timesteps} timesteps." | |
| ) | |
| self.num_inference_steps = num_inference_steps | |
| step_ratio = self.config.num_train_timesteps // self.num_inference_steps | |
| timesteps = ( | |
| (np.arange(0, num_inference_steps) * step_ratio) | |
| .round()[::-1] | |
| .copy() | |
| .astype(np.int64) | |
| ) | |
| self.custom_timesteps = False | |
| # Map timesteps to Karras sigmas directly for multistep sampling | |
| # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675 | |
| num_train_timesteps = self.config.num_train_timesteps | |
| ramp = timesteps[::-1].copy() | |
| ramp = ramp / (num_train_timesteps - 1) | |
| sigmas = self._convert_to_karras(ramp) | |
| timesteps = self.sigma_to_t(sigmas) | |
| sigmas = np.concatenate([sigmas, [0]]).astype(np.float32) | |
| self.sigmas = torch.from_numpy(sigmas).to(device=device) | |
| if str(device).startswith("mps"): | |
| # mps does not support float64 | |
| self.timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32) | |
| else: | |
| self.timesteps = torch.from_numpy(timesteps).to(device=device) | |
| self._step_index = None | |
| self.sigmas.to("cpu") # to avoid too much CPU/GPU communication | |
| # Modified _convert_to_karras implementation that takes in ramp as argument | |
| def _convert_to_karras(self, ramp): | |
| """Constructs the noise schedule of Karras et al. (2022).""" | |
| sigma_min: float = self.config.sigma_min | |
| sigma_max: float = self.config.sigma_max | |
| rho = self.config.rho | |
| min_inv_rho = sigma_min ** (1 / rho) | |
| max_inv_rho = sigma_max ** (1 / rho) | |
| sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho | |
| return sigmas | |
| def get_scalings(self, sigma): | |
| sigma_data = self.config.sigma_data | |
| c_skip = sigma_data**2 / (sigma**2 + sigma_data**2) | |
| c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5 | |
| return c_skip, c_out | |
| def get_scalings_for_boundary_condition(self, sigma): | |
| """ | |
| Gets the scalings used in the consistency model parameterization (from Appendix C of the | |
| [paper](https://huggingface.co/papers/2303.01469)) to enforce boundary condition. | |
| <Tip> | |
| `epsilon` in the equations for `c_skip` and `c_out` is set to `sigma_min`. | |
| </Tip> | |
| Args: | |
| sigma (`torch.FloatTensor`): | |
| The current sigma in the Karras sigma schedule. | |
| Returns: | |
| `tuple`: | |
| A two-element tuple where `c_skip` (which weights the current sample) is the first element and `c_out` | |
| (which weights the consistency model output) is the second element. | |
| """ | |
| sigma_min = self.config.sigma_min | |
| sigma_data = self.config.sigma_data | |
| c_skip = sigma_data**2 / ((sigma) ** 2 + sigma_data**2) | |
| c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5 | |
| return c_skip, c_out | |
| # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index | |
| def _init_step_index(self, timestep): | |
| if isinstance(timestep, torch.Tensor): | |
| timestep = timestep.to(self.timesteps.device) | |
| index_candidates = (self.timesteps == timestep).nonzero() | |
| # The sigma index that is taken for the **very** first `step` | |
| # is always the second index (or the last index if there is only 1) | |
| # This way we can ensure we don't accidentally skip a sigma in | |
| # case we start in the middle of the denoising schedule (e.g. for image-to-image) | |
| if len(index_candidates) > 1: | |
| step_index = index_candidates[1] | |
| else: | |
| step_index = index_candidates[0] | |
| self._step_index = step_index.item() | |
| def step( | |
| self, | |
| model_output: torch.FloatTensor, | |
| timestep: Union[float, torch.FloatTensor], | |
| sample: torch.FloatTensor, | |
| generator: Optional[torch.Generator] = None, | |
| return_dict: bool = True, | |
| ) -> Union[AnimateLCMSVDStochasticIterativeSchedulerOutput, Tuple]: | |
| """ | |
| Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion | |
| process from the learned model outputs (most often the predicted noise). | |
| Args: | |
| model_output (`torch.FloatTensor`): | |
| The direct output from the learned diffusion model. | |
| timestep (`float`): | |
| The current timestep in the diffusion chain. | |
| sample (`torch.FloatTensor`): | |
| A current instance of a sample created by the diffusion process. | |
| generator (`torch.Generator`, *optional*): | |
| A random number generator. | |
| return_dict (`bool`, *optional*, defaults to `True`): | |
| Whether or not to return a | |
| [`~schedulers.scheduling_consistency_models.AnimateLCMSVDStochasticIterativeSchedulerOutput`] or `tuple`. | |
| Returns: | |
| [`~schedulers.scheduling_consistency_models.AnimateLCMSVDStochasticIterativeSchedulerOutput`] or `tuple`: | |
| If return_dict is `True`, | |
| [`~schedulers.scheduling_consistency_models.AnimateLCMSVDStochasticIterativeSchedulerOutput`] is returned, | |
| otherwise a tuple is returned where the first element is the sample tensor. | |
| """ | |
| if ( | |
| isinstance(timestep, int) | |
| or isinstance(timestep, torch.IntTensor) | |
| or isinstance(timestep, torch.LongTensor) | |
| ): | |
| raise ValueError( | |
| ( | |
| "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" | |
| f" `{self.__class__}.step()` is not supported. Make sure to pass" | |
| " one of the `scheduler.timesteps` as a timestep." | |
| ), | |
| ) | |
| if not self.is_scale_input_called: | |
| logger.warning( | |
| "The `scale_model_input` function should be called before `step` to ensure correct denoising. " | |
| "See `StableDiffusionPipeline` for a usage example." | |
| ) | |
| sigma_min = self.config.sigma_min | |
| sigma_max = self.config.sigma_max | |
| if self.step_index is None: | |
| self._init_step_index(timestep) | |
| # sigma_next corresponds to next_t in original implementation | |
| sigma = self.sigmas[self.step_index] | |
| if self.step_index + 1 < self.config.num_train_timesteps: | |
| sigma_next = self.sigmas[self.step_index + 1] | |
| else: | |
| # Set sigma_next to sigma_min | |
| sigma_next = self.sigmas[-1] | |
| # Get scalings for boundary conditions | |
| c_skip, c_out = self.get_scalings_for_boundary_condition(sigma) | |
| # 1. Denoise model output using boundary conditions | |
| denoised = c_out * model_output + c_skip * sample | |
| if self.config.clip_denoised: | |
| denoised = denoised.clamp(-1, 1) | |
| # 2. Sample z ~ N(0, s_noise^2 * I) | |
| # Noise is not used for onestep sampling. | |
| if len(self.timesteps) > 1: | |
| noise = randn_tensor( | |
| model_output.shape, | |
| dtype=model_output.dtype, | |
| device=model_output.device, | |
| generator=generator, | |
| ) | |
| else: | |
| noise = torch.zeros_like(model_output) | |
| z = noise * self.config.s_noise | |
| sigma_hat = sigma_next.clamp(min=0, max=sigma_max) | |
| print("denoise currently") | |
| print(sigma_hat) | |
| # origin | |
| prev_sample = denoised + z * sigma_hat | |
| # upon completion increase step index by one | |
| self._step_index += 1 | |
| if not return_dict: | |
| return (prev_sample,) | |
| return AnimateLCMSVDStochasticIterativeSchedulerOutput(prev_sample=prev_sample) | |
| # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise | |
| def add_noise( | |
| self, | |
| original_samples: torch.FloatTensor, | |
| noise: torch.FloatTensor, | |
| timesteps: torch.FloatTensor, | |
| ) -> torch.FloatTensor: | |
| # Make sure sigmas and timesteps have the same device and dtype as original_samples | |
| sigmas = self.sigmas.to( | |
| device=original_samples.device, dtype=original_samples.dtype | |
| ) | |
| if original_samples.device.type == "mps" and torch.is_floating_point(timesteps): | |
| # mps does not support float64 | |
| schedule_timesteps = self.timesteps.to( | |
| original_samples.device, dtype=torch.float32 | |
| ) | |
| timesteps = timesteps.to(original_samples.device, dtype=torch.float32) | |
| else: | |
| schedule_timesteps = self.timesteps.to(original_samples.device) | |
| timesteps = timesteps.to(original_samples.device) | |
| step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] | |
| sigma = sigmas[step_indices].flatten() | |
| while len(sigma.shape) < len(original_samples.shape): | |
| sigma = sigma.unsqueeze(-1) | |
| noisy_samples = original_samples + noise * sigma | |
| return noisy_samples | |
| def __len__(self): | |
| return self.config.num_train_timesteps | |