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