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https://github.com/Wan-Video/Wan2.1.git
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76e9427657
* isort the code * format the code * Add yapf config file * Remove torch cuda memory profiler
803 lines
32 KiB
Python
803 lines
32 KiB
Python
# Copied from https://github.com/huggingface/diffusers/blob/v0.31.0/src/diffusers/schedulers/scheduling_unipc_multistep.py
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# Convert unipc for flow matching
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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import math
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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.schedulers.scheduling_utils import (
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KarrasDiffusionSchedulers,
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SchedulerMixin,
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SchedulerOutput,
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)
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from diffusers.utils import deprecate, is_scipy_available
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if is_scipy_available():
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import scipy.stats
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class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
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"""
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`UniPCMultistepScheduler` is a training-free framework designed for the fast sampling of diffusion 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 1000):
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The number of diffusion steps to train the model.
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solver_order (`int`, default `2`):
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The UniPC order which can be any positive integer. The effective order of accuracy is `solver_order + 1`
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due to the UniC. It is recommended to use `solver_order=2` for guided sampling, and `solver_order=3` for
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unconditional sampling.
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prediction_type (`str`, defaults to "flow_prediction"):
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Prediction type of the scheduler function; must be `flow_prediction` for this scheduler, which predicts
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the flow of the diffusion process.
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thresholding (`bool`, defaults to `False`):
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Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
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as Stable Diffusion.
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dynamic_thresholding_ratio (`float`, defaults to 0.995):
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The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
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sample_max_value (`float`, defaults to 1.0):
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The threshold value for dynamic thresholding. Valid only when `thresholding=True` and `predict_x0=True`.
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predict_x0 (`bool`, defaults to `True`):
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Whether to use the updating algorithm on the predicted x0.
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solver_type (`str`, default `bh2`):
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Solver type for UniPC. It is recommended to use `bh1` for unconditional sampling when steps < 10, and `bh2`
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otherwise.
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lower_order_final (`bool`, default `True`):
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Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can
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stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
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disable_corrector (`list`, default `[]`):
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Decides which step to disable the corrector to mitigate the misalignment between `epsilon_theta(x_t, c)`
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and `epsilon_theta(x_t^c, c)` which can influence convergence for a large guidance scale. Corrector is
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usually disabled during the first few steps.
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solver_p (`SchedulerMixin`, default `None`):
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Any other scheduler that if specified, the algorithm becomes `solver_p + UniC`.
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use_karras_sigmas (`bool`, *optional*, defaults to `False`):
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Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,
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the sigmas are determined according to a sequence of noise levels {σi}.
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use_exponential_sigmas (`bool`, *optional*, defaults to `False`):
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Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.
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timestep_spacing (`str`, defaults to `"linspace"`):
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The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
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Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
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steps_offset (`int`, defaults to 0):
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An offset added to the inference steps, as required by some model families.
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final_sigmas_type (`str`, defaults to `"zero"`):
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The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final
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sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.
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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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solver_order: int = 2,
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prediction_type: str = "flow_prediction",
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shift: Optional[float] = 1.0,
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use_dynamic_shifting=False,
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thresholding: bool = False,
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dynamic_thresholding_ratio: float = 0.995,
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sample_max_value: float = 1.0,
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predict_x0: bool = True,
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solver_type: str = "bh2",
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lower_order_final: bool = True,
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disable_corrector: List[int] = [],
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solver_p: SchedulerMixin = None,
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timestep_spacing: str = "linspace",
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steps_offset: int = 0,
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final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min"
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):
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if solver_type not in ["bh1", "bh2"]:
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if solver_type in ["midpoint", "heun", "logrho"]:
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self.register_to_config(solver_type="bh2")
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else:
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raise NotImplementedError(
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f"{solver_type} is not implemented for {self.__class__}")
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self.predict_x0 = predict_x0
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# setable values
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self.num_inference_steps = None
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alphas = np.linspace(1, 1 / num_train_timesteps,
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num_train_timesteps)[::-1].copy()
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sigmas = 1.0 - alphas
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sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32)
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if not use_dynamic_shifting:
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# when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution
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sigmas = shift * sigmas / (1 +
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(shift - 1) * sigmas) # pyright: ignore
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self.sigmas = sigmas
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self.timesteps = sigmas * num_train_timesteps
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self.model_outputs = [None] * solver_order
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self.timestep_list = [None] * solver_order
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self.lower_order_nums = 0
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self.disable_corrector = disable_corrector
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self.solver_p = solver_p
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self.last_sample = None
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self._step_index = None
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self._begin_index = None
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self.sigmas = self.sigmas.to(
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"cpu") # to avoid too much CPU/GPU communication
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self.sigma_min = self.sigmas[-1].item()
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self.sigma_max = self.sigmas[0].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 increase 1 after each scheduler step.
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"""
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return self._step_index
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@property
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def begin_index(self):
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"""
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The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
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"""
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return self._begin_index
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# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
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def set_begin_index(self, begin_index: int = 0):
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"""
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Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
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Args:
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begin_index (`int`):
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The begin index for the scheduler.
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"""
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self._begin_index = begin_index
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# Modified from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.set_timesteps
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def set_timesteps(
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self,
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num_inference_steps: Union[int, None] = None,
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device: Union[str, torch.device] = None,
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sigmas: Optional[List[float]] = None,
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mu: Optional[Union[float, None]] = None,
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shift: Optional[Union[float, None]] = None,
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):
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"""
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Sets the discrete 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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Total number of the spacing of the time steps.
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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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if self.config.use_dynamic_shifting and mu is None:
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raise ValueError(
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" you have to pass a value for `mu` when `use_dynamic_shifting` is set to be `True`"
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)
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if sigmas is None:
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sigmas = np.linspace(self.sigma_max, self.sigma_min,
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num_inference_steps +
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1).copy()[:-1] # pyright: ignore
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if self.config.use_dynamic_shifting:
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sigmas = self.time_shift(mu, 1.0, sigmas) # pyright: ignore
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else:
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if shift is None:
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shift = self.config.shift
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sigmas = shift * sigmas / (1 +
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(shift - 1) * sigmas) # pyright: ignore
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if self.config.final_sigmas_type == "sigma_min":
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sigma_last = ((1 - self.alphas_cumprod[0]) /
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self.alphas_cumprod[0])**0.5
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elif self.config.final_sigmas_type == "zero":
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sigma_last = 0
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else:
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raise ValueError(
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f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}"
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)
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timesteps = sigmas * self.config.num_train_timesteps
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sigmas = np.concatenate([sigmas, [sigma_last]
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]).astype(np.float32) # pyright: ignore
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self.sigmas = torch.from_numpy(sigmas)
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self.timesteps = torch.from_numpy(timesteps).to(
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device=device, dtype=torch.int64)
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self.num_inference_steps = len(timesteps)
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self.model_outputs = [
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None,
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] * self.config.solver_order
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self.lower_order_nums = 0
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self.last_sample = None
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if self.solver_p:
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self.solver_p.set_timesteps(self.num_inference_steps, device=device)
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# add an index counter for schedulers that allow duplicated timesteps
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self._step_index = None
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self._begin_index = None
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self.sigmas = self.sigmas.to(
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"cpu") # to avoid too much CPU/GPU communication
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# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
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def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
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"""
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"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
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prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
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s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
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pixels from saturation at each step. We find that dynamic thresholding results in significantly better
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photorealism as well as better image-text alignment, especially when using very large guidance weights."
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https://arxiv.org/abs/2205.11487
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"""
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dtype = sample.dtype
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batch_size, channels, *remaining_dims = sample.shape
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if dtype not in (torch.float32, torch.float64):
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sample = sample.float(
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) # upcast for quantile calculation, and clamp not implemented for cpu half
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# Flatten sample for doing quantile calculation along each image
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sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))
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abs_sample = sample.abs() # "a certain percentile absolute pixel value"
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s = torch.quantile(
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abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
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s = torch.clamp(
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s, min=1, max=self.config.sample_max_value
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) # When clamped to min=1, equivalent to standard clipping to [-1, 1]
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s = s.unsqueeze(
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1) # (batch_size, 1) because clamp will broadcast along dim=0
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sample = torch.clamp(
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sample, -s, s
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) / s # "we threshold xt0 to the range [-s, s] and then divide by s"
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sample = sample.reshape(batch_size, channels, *remaining_dims)
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sample = sample.to(dtype)
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return sample
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# Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._sigma_to_t
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def _sigma_to_t(self, sigma):
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return sigma * self.config.num_train_timesteps
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def _sigma_to_alpha_sigma_t(self, sigma):
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return 1 - sigma, sigma
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# Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.set_timesteps
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def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
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return math.exp(mu) / (math.exp(mu) + (1 / t - 1)**sigma)
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def convert_model_output(
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self,
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model_output: torch.Tensor,
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*args,
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sample: torch.Tensor = None,
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**kwargs,
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) -> torch.Tensor:
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r"""
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Convert the model output to the corresponding type the UniPC algorithm needs.
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Args:
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model_output (`torch.Tensor`):
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The direct output from the learned diffusion model.
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timestep (`int`):
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The current discrete timestep in the diffusion chain.
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sample (`torch.Tensor`):
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A current instance of a sample created by the diffusion process.
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Returns:
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`torch.Tensor`:
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The converted model output.
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"""
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timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None)
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if sample is None:
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if len(args) > 1:
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sample = args[1]
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else:
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raise ValueError(
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"missing `sample` as a required keyward argument")
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if timestep is not None:
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deprecate(
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"timesteps",
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"1.0.0",
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"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
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)
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sigma = self.sigmas[self.step_index]
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alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
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if self.predict_x0:
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if self.config.prediction_type == "flow_prediction":
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sigma_t = self.sigmas[self.step_index]
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x0_pred = sample - sigma_t * 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`,"
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" `v_prediction` or `flow_prediction` for the UniPCMultistepScheduler."
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)
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if self.config.thresholding:
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x0_pred = self._threshold_sample(x0_pred)
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return x0_pred
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else:
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if self.config.prediction_type == "flow_prediction":
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sigma_t = self.sigmas[self.step_index]
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epsilon = sample - (1 - sigma_t) * 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`,"
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" `v_prediction` or `flow_prediction` for the UniPCMultistepScheduler."
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)
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if self.config.thresholding:
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sigma_t = self.sigmas[self.step_index]
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x0_pred = sample - sigma_t * model_output
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x0_pred = self._threshold_sample(x0_pred)
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epsilon = model_output + x0_pred
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return epsilon
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def multistep_uni_p_bh_update(
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self,
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model_output: torch.Tensor,
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*args,
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sample: torch.Tensor = None,
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order: int = None, # pyright: ignore
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**kwargs,
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) -> torch.Tensor:
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"""
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One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified.
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Args:
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model_output (`torch.Tensor`):
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The direct output from the learned diffusion model at the current timestep.
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prev_timestep (`int`):
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The previous discrete timestep in the diffusion chain.
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sample (`torch.Tensor`):
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A current instance of a sample created by the diffusion process.
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order (`int`):
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The order of UniP at this timestep (corresponds to the *p* in UniPC-p).
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Returns:
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`torch.Tensor`:
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The sample tensor at the previous timestep.
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"""
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prev_timestep = args[0] if len(args) > 0 else kwargs.pop(
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"prev_timestep", None)
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if sample is None:
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if len(args) > 1:
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sample = args[1]
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else:
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raise ValueError(
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" missing `sample` as a required keyward argument")
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if order is None:
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if len(args) > 2:
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order = args[2]
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else:
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raise ValueError(
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" missing `order` as a required keyward argument")
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if prev_timestep is not None:
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deprecate(
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"prev_timestep",
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"1.0.0",
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"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
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)
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model_output_list = self.model_outputs
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s0 = self.timestep_list[-1]
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m0 = model_output_list[-1]
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x = sample
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if self.solver_p:
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x_t = self.solver_p.step(model_output, s0, x).prev_sample
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return x_t
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sigma_t, sigma_s0 = self.sigmas[self.step_index + 1], self.sigmas[
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self.step_index] # pyright: ignore
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alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
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alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
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lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
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lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
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h = lambda_t - lambda_s0
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device = sample.device
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rks = []
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D1s = []
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for i in range(1, order):
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si = self.step_index - i # pyright: ignore
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mi = model_output_list[-(i + 1)]
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alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
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lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
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rk = (lambda_si - lambda_s0) / h
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rks.append(rk)
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D1s.append((mi - m0) / rk) # pyright: ignore
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rks.append(1.0)
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rks = torch.tensor(rks, device=device)
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R = []
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b = []
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hh = -h if self.predict_x0 else h
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h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
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h_phi_k = h_phi_1 / hh - 1
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factorial_i = 1
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if self.config.solver_type == "bh1":
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B_h = hh
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elif self.config.solver_type == "bh2":
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B_h = torch.expm1(hh)
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else:
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raise NotImplementedError()
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for i in range(1, order + 1):
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R.append(torch.pow(rks, i - 1))
|
||
b.append(h_phi_k * factorial_i / B_h)
|
||
factorial_i *= i + 1
|
||
h_phi_k = h_phi_k / hh - 1 / factorial_i
|
||
|
||
R = torch.stack(R)
|
||
b = torch.tensor(b, device=device)
|
||
|
||
if len(D1s) > 0:
|
||
D1s = torch.stack(D1s, dim=1) # (B, K)
|
||
# for order 2, we use a simplified version
|
||
if order == 2:
|
||
rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device)
|
||
else:
|
||
rhos_p = torch.linalg.solve(R[:-1, :-1],
|
||
b[:-1]).to(device).to(x.dtype)
|
||
else:
|
||
D1s = None
|
||
|
||
if self.predict_x0:
|
||
x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
|
||
if D1s is not None:
|
||
pred_res = torch.einsum("k,bkc...->bc...", rhos_p,
|
||
D1s) # pyright: ignore
|
||
else:
|
||
pred_res = 0
|
||
x_t = x_t_ - alpha_t * B_h * pred_res
|
||
else:
|
||
x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
|
||
if D1s is not None:
|
||
pred_res = torch.einsum("k,bkc...->bc...", rhos_p,
|
||
D1s) # pyright: ignore
|
||
else:
|
||
pred_res = 0
|
||
x_t = x_t_ - sigma_t * B_h * pred_res
|
||
|
||
x_t = x_t.to(x.dtype)
|
||
return x_t
|
||
|
||
def multistep_uni_c_bh_update(
|
||
self,
|
||
this_model_output: torch.Tensor,
|
||
*args,
|
||
last_sample: torch.Tensor = None,
|
||
this_sample: torch.Tensor = None,
|
||
order: int = None, # pyright: ignore
|
||
**kwargs,
|
||
) -> torch.Tensor:
|
||
"""
|
||
One step for the UniC (B(h) version).
|
||
|
||
Args:
|
||
this_model_output (`torch.Tensor`):
|
||
The model outputs at `x_t`.
|
||
this_timestep (`int`):
|
||
The current timestep `t`.
|
||
last_sample (`torch.Tensor`):
|
||
The generated sample before the last predictor `x_{t-1}`.
|
||
this_sample (`torch.Tensor`):
|
||
The generated sample after the last predictor `x_{t}`.
|
||
order (`int`):
|
||
The `p` of UniC-p at this step. The effective order of accuracy should be `order + 1`.
|
||
|
||
Returns:
|
||
`torch.Tensor`:
|
||
The corrected sample tensor at the current timestep.
|
||
"""
|
||
this_timestep = args[0] if len(args) > 0 else kwargs.pop(
|
||
"this_timestep", None)
|
||
if last_sample is None:
|
||
if len(args) > 1:
|
||
last_sample = args[1]
|
||
else:
|
||
raise ValueError(
|
||
" missing`last_sample` as a required keyward argument")
|
||
if this_sample is None:
|
||
if len(args) > 2:
|
||
this_sample = args[2]
|
||
else:
|
||
raise ValueError(
|
||
" missing`this_sample` as a required keyward argument")
|
||
if order is None:
|
||
if len(args) > 3:
|
||
order = args[3]
|
||
else:
|
||
raise ValueError(
|
||
" missing`order` as a required keyward argument")
|
||
if this_timestep is not None:
|
||
deprecate(
|
||
"this_timestep",
|
||
"1.0.0",
|
||
"Passing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
|
||
)
|
||
|
||
model_output_list = self.model_outputs
|
||
|
||
m0 = model_output_list[-1]
|
||
x = last_sample
|
||
x_t = this_sample
|
||
model_t = this_model_output
|
||
|
||
sigma_t, sigma_s0 = self.sigmas[self.step_index], self.sigmas[
|
||
self.step_index - 1] # pyright: ignore
|
||
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
|
||
alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
|
||
|
||
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
|
||
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
|
||
|
||
h = lambda_t - lambda_s0
|
||
device = this_sample.device
|
||
|
||
rks = []
|
||
D1s = []
|
||
for i in range(1, order):
|
||
si = self.step_index - (i + 1) # pyright: ignore
|
||
mi = model_output_list[-(i + 1)]
|
||
alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
|
||
lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
|
||
rk = (lambda_si - lambda_s0) / h
|
||
rks.append(rk)
|
||
D1s.append((mi - m0) / rk) # pyright: ignore
|
||
|
||
rks.append(1.0)
|
||
rks = torch.tensor(rks, device=device)
|
||
|
||
R = []
|
||
b = []
|
||
|
||
hh = -h if self.predict_x0 else h
|
||
h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
|
||
h_phi_k = h_phi_1 / hh - 1
|
||
|
||
factorial_i = 1
|
||
|
||
if self.config.solver_type == "bh1":
|
||
B_h = hh
|
||
elif self.config.solver_type == "bh2":
|
||
B_h = torch.expm1(hh)
|
||
else:
|
||
raise NotImplementedError()
|
||
|
||
for i in range(1, order + 1):
|
||
R.append(torch.pow(rks, i - 1))
|
||
b.append(h_phi_k * factorial_i / B_h)
|
||
factorial_i *= i + 1
|
||
h_phi_k = h_phi_k / hh - 1 / factorial_i
|
||
|
||
R = torch.stack(R)
|
||
b = torch.tensor(b, device=device)
|
||
|
||
if len(D1s) > 0:
|
||
D1s = torch.stack(D1s, dim=1)
|
||
else:
|
||
D1s = None
|
||
|
||
# for order 1, we use a simplified version
|
||
if order == 1:
|
||
rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device)
|
||
else:
|
||
rhos_c = torch.linalg.solve(R, b).to(device).to(x.dtype)
|
||
|
||
if self.predict_x0:
|
||
x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
|
||
if D1s is not None:
|
||
corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s)
|
||
else:
|
||
corr_res = 0
|
||
D1_t = model_t - m0
|
||
x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t)
|
||
else:
|
||
x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
|
||
if D1s is not None:
|
||
corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s)
|
||
else:
|
||
corr_res = 0
|
||
D1_t = model_t - m0
|
||
x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t)
|
||
x_t = x_t.to(x.dtype)
|
||
return x_t
|
||
|
||
def index_for_timestep(self, timestep, schedule_timesteps=None):
|
||
if schedule_timesteps is None:
|
||
schedule_timesteps = self.timesteps
|
||
|
||
indices = (schedule_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)
|
||
pos = 1 if len(indices) > 1 else 0
|
||
|
||
return indices[pos].item()
|
||
|
||
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._init_step_index
|
||
def _init_step_index(self, timestep):
|
||
"""
|
||
Initialize the step_index counter for the scheduler.
|
||
"""
|
||
|
||
if self.begin_index is None:
|
||
if isinstance(timestep, torch.Tensor):
|
||
timestep = timestep.to(self.timesteps.device)
|
||
self._step_index = self.index_for_timestep(timestep)
|
||
else:
|
||
self._step_index = self._begin_index
|
||
|
||
def step(self,
|
||
model_output: torch.Tensor,
|
||
timestep: Union[int, torch.Tensor],
|
||
sample: torch.Tensor,
|
||
return_dict: bool = True,
|
||
generator=None) -> Union[SchedulerOutput, Tuple]:
|
||
"""
|
||
Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with
|
||
the multistep UniPC.
|
||
|
||
Args:
|
||
model_output (`torch.Tensor`):
|
||
The direct output from learned diffusion model.
|
||
timestep (`int`):
|
||
The current discrete timestep in the diffusion chain.
|
||
sample (`torch.Tensor`):
|
||
A current instance of a sample created by the diffusion process.
|
||
return_dict (`bool`):
|
||
Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.
|
||
|
||
Returns:
|
||
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
|
||
If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
|
||
tuple is returned where the first element is the sample tensor.
|
||
|
||
"""
|
||
if self.num_inference_steps is None:
|
||
raise ValueError(
|
||
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
|
||
)
|
||
|
||
if self.step_index is None:
|
||
self._init_step_index(timestep)
|
||
|
||
use_corrector = (
|
||
self.step_index > 0 and
|
||
self.step_index - 1 not in self.disable_corrector and
|
||
self.last_sample is not None # pyright: ignore
|
||
)
|
||
|
||
model_output_convert = self.convert_model_output(
|
||
model_output, sample=sample)
|
||
if use_corrector:
|
||
sample = self.multistep_uni_c_bh_update(
|
||
this_model_output=model_output_convert,
|
||
last_sample=self.last_sample,
|
||
this_sample=sample,
|
||
order=self.this_order,
|
||
)
|
||
|
||
for i in range(self.config.solver_order - 1):
|
||
self.model_outputs[i] = self.model_outputs[i + 1]
|
||
self.timestep_list[i] = self.timestep_list[i + 1]
|
||
|
||
self.model_outputs[-1] = model_output_convert
|
||
self.timestep_list[-1] = timestep # pyright: ignore
|
||
|
||
if self.config.lower_order_final:
|
||
this_order = min(self.config.solver_order,
|
||
len(self.timesteps) -
|
||
self.step_index) # pyright: ignore
|
||
else:
|
||
this_order = self.config.solver_order
|
||
|
||
self.this_order = min(this_order,
|
||
self.lower_order_nums + 1) # warmup for multistep
|
||
assert self.this_order > 0
|
||
|
||
self.last_sample = sample
|
||
prev_sample = self.multistep_uni_p_bh_update(
|
||
model_output=model_output, # pass the original non-converted model output, in case solver-p is used
|
||
sample=sample,
|
||
order=self.this_order,
|
||
)
|
||
|
||
if self.lower_order_nums < self.config.solver_order:
|
||
self.lower_order_nums += 1
|
||
|
||
# upon completion increase step index by one
|
||
self._step_index += 1 # pyright: ignore
|
||
|
||
if not return_dict:
|
||
return (prev_sample,)
|
||
|
||
return SchedulerOutput(prev_sample=prev_sample)
|
||
|
||
def scale_model_input(self, sample: torch.Tensor, *args,
|
||
**kwargs) -> torch.Tensor:
|
||
"""
|
||
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
|
||
current timestep.
|
||
|
||
Args:
|
||
sample (`torch.Tensor`):
|
||
The input sample.
|
||
|
||
Returns:
|
||
`torch.Tensor`:
|
||
A scaled input sample.
|
||
"""
|
||
return sample
|
||
|
||
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.add_noise
|
||
def add_noise(
|
||
self,
|
||
original_samples: torch.Tensor,
|
||
noise: torch.Tensor,
|
||
timesteps: torch.IntTensor,
|
||
) -> torch.Tensor:
|
||
# 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)
|
||
|
||
# begin_index is None when the scheduler is used for training or pipeline does not implement set_begin_index
|
||
if self.begin_index is None:
|
||
step_indices = [
|
||
self.index_for_timestep(t, schedule_timesteps)
|
||
for t in timesteps
|
||
]
|
||
elif self.step_index is not None:
|
||
# add_noise is called after first denoising step (for inpainting)
|
||
step_indices = [self.step_index] * timesteps.shape[0]
|
||
else:
|
||
# add noise is called before first denoising step to create initial latent(img2img)
|
||
step_indices = [self.begin_index] * timesteps.shape[0]
|
||
|
||
sigma = sigmas[step_indices].flatten()
|
||
while len(sigma.shape) < len(original_samples.shape):
|
||
sigma = sigma.unsqueeze(-1)
|
||
|
||
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
|
||
noisy_samples = alpha_t * original_samples + sigma_t * noise
|
||
return noisy_samples
|
||
|
||
def __len__(self):
|
||
return self.config.num_train_timesteps
|