802 lines
29 KiB
Python
802 lines
29 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from Helios (https://github.com/BestWishYsh/Helios)
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import math
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from dataclasses import dataclass
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from typing import Literal
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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 SchedulerMixin
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from diffusers.utils import BaseOutput
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@dataclass
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class HeliosSchedulerOutput(BaseOutput):
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prev_sample: torch.FloatTensor
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model_outputs: torch.FloatTensor | None = None
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last_sample: torch.FloatTensor | None = None
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this_order: int | None = None
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class HeliosScheduler(SchedulerMixin, ConfigMixin):
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_compatibles = []
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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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shift: float = 1.0,
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stages: int = 3,
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stage_range: list = [0, 1 / 3, 2 / 3, 1],
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gamma: float = 1 / 3,
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thresholding: bool = False,
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prediction_type: str = "flow_prediction",
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solver_order: int = 2,
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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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use_flow_sigmas: bool = True,
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scheduler_type: str = "unipc",
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use_dynamic_shifting: bool = False,
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time_shift_type: Literal["exponential", "linear"] = "linear",
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):
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self.timestep_ratios = {}
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self.timesteps_per_stage = {}
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self.sigmas_per_stage = {}
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self.start_sigmas = {}
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self.end_sigmas = {}
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self.ori_start_sigmas = {}
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self.init_sigmas_for_each_stage()
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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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self.gamma = gamma
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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(f"{solver_type} is not implemented for {self.__class__}")
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self.predict_x0 = predict_x0
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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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def init_sigmas(self):
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num_train_timesteps = self.config.num_train_timesteps
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shift = self.config.shift
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alphas = np.linspace(1, 1 / num_train_timesteps, num_train_timesteps + 1, dtype=np.float32)
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sigmas = 1.0 - alphas
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sigmas = np.flip(shift * sigmas / (1 + (shift - 1) * sigmas))[:-1].copy()
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sigmas = torch.from_numpy(sigmas)
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timesteps = (sigmas * num_train_timesteps).clone()
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self._step_index = None
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self._begin_index = None
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self.timesteps = timesteps
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self.sigmas = sigmas.to("cpu")
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def init_sigmas_for_each_stage(self):
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self.init_sigmas()
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stage_distance = []
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stages = self.config.stages
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training_steps = self.config.num_train_timesteps
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stage_range = self.config.stage_range
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for i_s in range(stages):
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start_indice = int(stage_range[i_s] * training_steps)
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start_indice = max(start_indice, 0)
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end_indice = int(stage_range[i_s + 1] * training_steps)
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end_indice = min(end_indice, training_steps)
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start_sigma = self.sigmas[start_indice].item()
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end_sigma = self.sigmas[end_indice].item() if end_indice < training_steps else 0.0
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self.ori_start_sigmas[i_s] = start_sigma
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if i_s != 0:
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ori_sigma = 1 - start_sigma
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gamma = self.config.gamma
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corrected_sigma = (1 / (math.sqrt(1 + (1 / gamma)) * (1 - ori_sigma) + ori_sigma)) * ori_sigma
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start_sigma = 1 - corrected_sigma
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stage_distance.append(start_sigma - end_sigma)
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self.start_sigmas[i_s] = start_sigma
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self.end_sigmas[i_s] = end_sigma
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tot_distance = sum(stage_distance)
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for i_s in range(stages):
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if i_s == 0:
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start_ratio = 0.0
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else:
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start_ratio = sum(stage_distance[:i_s]) / tot_distance
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if i_s == stages - 1:
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end_ratio = 0.9999999999999999
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else:
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end_ratio = sum(stage_distance[: i_s + 1]) / tot_distance
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self.timestep_ratios[i_s] = (start_ratio, end_ratio)
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for i_s in range(stages):
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timestep_ratio = self.timestep_ratios[i_s]
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timestep_max = min(self.timesteps[int(timestep_ratio[0] * training_steps)], 999)
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timestep_min = self.timesteps[min(int(timestep_ratio[1] * training_steps), training_steps - 1)]
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timesteps = np.linspace(timestep_max, timestep_min, training_steps + 1, dtype=np.float32)
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self.timesteps_per_stage[i_s] = (
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timesteps[:-1] if isinstance(timesteps, torch.Tensor) else torch.from_numpy(timesteps[:-1])
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)
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stage_sigmas = np.linspace(0.999, 0, training_steps + 1, dtype=np.float32)
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self.sigmas_per_stage[i_s] = torch.from_numpy(stage_sigmas[:-1])
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@property
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def step_index(self):
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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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return self._begin_index
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def set_begin_index(self, begin_index: int = 0):
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self._begin_index = begin_index
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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 set_timesteps(
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self,
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num_inference_steps: int,
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stage_index: int | None = None,
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device: str | torch.device = None,
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sigmas: bool | None = None,
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mu: bool | None = None,
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is_amplify_first_chunk: bool = False,
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):
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if self.config.scheduler_type == "dmd":
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if is_amplify_first_chunk:
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num_inference_steps = num_inference_steps * 2 + 1
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else:
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num_inference_steps = num_inference_steps + 1
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self.num_inference_steps = num_inference_steps
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self.init_sigmas()
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if self.config.stages == 1 or stage_index is None:
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if sigmas is None:
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sigmas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)[:-1].astype(
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np.float32
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)
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if self.config.shift != 1.0:
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assert not self.config.use_dynamic_shifting
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sigmas = self.time_shift(self.config.shift, 1.0, sigmas)
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timesteps = (sigmas * self.config.num_train_timesteps).copy()
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sigmas = torch.from_numpy(sigmas)
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else:
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stage_timesteps = self.timesteps_per_stage[stage_index]
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timesteps = np.linspace(
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stage_timesteps[0].item(),
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stage_timesteps[-1].item(),
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num_inference_steps,
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dtype=np.float32,
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)
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stage_sigmas = self.sigmas_per_stage[stage_index]
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ratios = np.linspace(stage_sigmas[0].item(), stage_sigmas[-1].item(), num_inference_steps, dtype=np.float32)
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sigmas = torch.from_numpy(ratios)
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self.timesteps = torch.from_numpy(timesteps).to(device=device)
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self.sigmas = torch.cat([sigmas, torch.zeros(1)]).to(device=device)
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self._step_index = None
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self.reset_scheduler_history()
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if self.config.scheduler_type == "dmd":
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self.timesteps = self.timesteps[:-1]
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self.sigmas = torch.cat([self.sigmas[:-2], self.sigmas[-1:]])
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if self.config.use_dynamic_shifting:
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assert self.config.shift == 1.0
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self.sigmas = self.time_shift(mu, 1.0, self.sigmas)
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if self.config.stages == 1 or stage_index is None:
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self.timesteps = self.sigmas[:-1] * self.config.num_train_timesteps
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else:
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self.timesteps = self.timesteps_per_stage[stage_index].min() + self.sigmas[:-1] * (
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self.timesteps_per_stage[stage_index].max() - self.timesteps_per_stage[stage_index].min()
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)
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def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
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if self.config.time_shift_type == "exponential":
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return self._time_shift_exponential(mu, sigma, t)
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elif self.config.time_shift_type == "linear":
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return self._time_shift_linear(mu, sigma, t)
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def _time_shift_exponential(self, mu, sigma, t):
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return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
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def _time_shift_linear(self, mu, sigma, t):
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return mu / (mu + (1 / t - 1) ** sigma)
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# ---------------------------------- Euler ----------------------------------
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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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pos = 1 if len(indices) > 1 else 0
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return indices[pos].item()
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def _init_step_index(self, timestep):
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if self.begin_index is None:
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if isinstance(timestep, torch.Tensor):
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timestep = timestep.to(self.timesteps.device)
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self._step_index = self.index_for_timestep(timestep)
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else:
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self._step_index = self._begin_index
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def step_euler(
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self,
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model_output: torch.FloatTensor,
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timestep: float | torch.FloatTensor = None,
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sample: torch.FloatTensor = None,
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generator: torch.Generator | None = None,
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sigma: torch.FloatTensor | None = None,
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sigma_next: torch.FloatTensor | None = None,
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return_dict: bool = True,
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) -> HeliosSchedulerOutput | tuple:
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assert (sigma is None) == (sigma_next is None)
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if sigma is None and sigma_next is None:
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if (
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isinstance(timestep, int)
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or isinstance(timestep, torch.IntTensor)
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or isinstance(timestep, torch.LongTensor)
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):
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raise ValueError(
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"Passing integer indices as timesteps to EulerDiscreteScheduler.step() is not supported. "
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"Pass one of scheduler.timesteps as a timestep."
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)
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if self.step_index is None:
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self._step_index = 0
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sample = sample.to(torch.float32)
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if sigma is None and sigma_next is None:
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sigma = self.sigmas[self.step_index]
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sigma_next = self.sigmas[self.step_index + 1]
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prev_sample = sample + (sigma_next - sigma) * model_output
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prev_sample = prev_sample.to(model_output.dtype)
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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 HeliosSchedulerOutput(prev_sample=prev_sample)
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# ---------------------------------- UniPC ----------------------------------
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def _sigma_to_alpha_sigma_t(self, sigma):
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if self.config.use_flow_sigmas:
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alpha_t = 1 - sigma
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sigma_t = torch.clamp(sigma, min=1e-8)
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else:
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alpha_t = 1 / ((sigma**2 + 1) ** 0.5)
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sigma_t = sigma * alpha_t
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return alpha_t, sigma_t
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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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sigma: torch.Tensor = None,
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**kwargs,
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) -> torch.Tensor:
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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("missing `sample` as a required keyword argument")
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flag = False
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if sigma is None:
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flag = True
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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 == "epsilon":
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x0_pred = (sample - sigma_t * model_output) / alpha_t
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elif self.config.prediction_type == "sample":
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x0_pred = model_output
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elif self.config.prediction_type == "v_prediction":
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x0_pred = alpha_t * sample - sigma_t * model_output
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elif self.config.prediction_type == "flow_prediction":
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if flag:
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sigma_t = self.sigmas[self.step_index]
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else:
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sigma_t = sigma
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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`."
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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 == "epsilon":
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return model_output
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elif self.config.prediction_type == "sample":
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epsilon = (sample - alpha_t * model_output) / sigma_t
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return epsilon
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elif self.config.prediction_type == "v_prediction":
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epsilon = alpha_t * model_output + sigma_t * sample
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return epsilon
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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`, or"
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" `v_prediction`."
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)
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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,
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sigma: torch.Tensor = None,
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sigma_next: torch.Tensor = None,
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**kwargs,
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) -> torch.Tensor:
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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("missing `sample` as a required keyword 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("missing `order` as a required keyword argument")
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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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if sigma_next is None and sigma is None:
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sigma_t, sigma_s0 = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]
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else:
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sigma_t, sigma_s0 = sigma_next, sigma
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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
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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)
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rks.append(1.0)
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rks = torch.tensor(rks, device=device, dtype=torch.float32)
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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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hh = hh.float()
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h_phi_1 = torch.expm1(hh)
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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))
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b.append(h_phi_k * factorial_i / B_h)
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factorial_i *= i + 1
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h_phi_k = h_phi_k / hh - 1 / factorial_i
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R = torch.stack(R)
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b = torch.tensor(b, device=device, dtype=torch.float32)
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if len(D1s) > 0:
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D1s = torch.stack(D1s, dim=1)
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if order == 2:
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rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device)
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else:
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rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]).to(device).to(x.dtype)
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else:
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D1s = None
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if self.predict_x0:
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x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
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if D1s is not None:
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pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s)
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else:
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pred_res = 0
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x_t = x_t_ - alpha_t * B_h * pred_res
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else:
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x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
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if D1s is not None:
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pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s)
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else:
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pred_res = 0
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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,
|
|
sigma_before: torch.Tensor = None,
|
|
sigma: torch.Tensor = None,
|
|
**kwargs,
|
|
) -> torch.Tensor:
|
|
if last_sample is None:
|
|
if len(args) > 1:
|
|
last_sample = args[1]
|
|
else:
|
|
raise ValueError("missing `last_sample` as a required keyword argument")
|
|
if this_sample is None:
|
|
if len(args) > 2:
|
|
this_sample = args[2]
|
|
else:
|
|
raise ValueError("missing `this_sample` as a required keyword argument")
|
|
if order is None:
|
|
if len(args) > 3:
|
|
order = args[3]
|
|
else:
|
|
raise ValueError("missing `order` as a required keyword argument")
|
|
|
|
model_output_list = self.model_outputs
|
|
|
|
m0 = model_output_list[-1]
|
|
x = last_sample
|
|
x_t = this_sample
|
|
model_t = this_model_output
|
|
|
|
if sigma_before is None and sigma is None:
|
|
sigma_t, sigma_s0 = self.sigmas[self.step_index], self.sigmas[self.step_index - 1]
|
|
else:
|
|
sigma_t, sigma_s0 = sigma, sigma_before
|
|
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)
|
|
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)
|
|
|
|
rks.append(1.0)
|
|
rks = torch.tensor(rks, device=device, dtype=torch.float32)
|
|
|
|
R = []
|
|
b = []
|
|
|
|
hh = -h if self.predict_x0 else h
|
|
hh = hh.float()
|
|
h_phi_1 = torch.expm1(hh)
|
|
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, dtype=torch.float32)
|
|
|
|
if len(D1s) > 0:
|
|
D1s = torch.stack(D1s, dim=1)
|
|
else:
|
|
D1s = None
|
|
|
|
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 step_unipc(
|
|
self,
|
|
model_output: torch.Tensor,
|
|
timestep: int | torch.Tensor = None,
|
|
sample: torch.Tensor = None,
|
|
return_dict: bool = True,
|
|
model_outputs: list = None,
|
|
timestep_list: list = None,
|
|
sigma_before: torch.Tensor = None,
|
|
sigma: torch.Tensor = None,
|
|
sigma_next: torch.Tensor = None,
|
|
cus_step_index: int = None,
|
|
cus_lower_order_num: int = None,
|
|
cus_this_order: int = None,
|
|
cus_last_sample: torch.Tensor = None,
|
|
) -> HeliosSchedulerOutput | tuple:
|
|
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 cus_step_index is None:
|
|
if self.step_index is None:
|
|
self._step_index = 0
|
|
else:
|
|
self._step_index = cus_step_index
|
|
|
|
if cus_lower_order_num is not None:
|
|
self.lower_order_nums = cus_lower_order_num
|
|
|
|
if cus_this_order is not None:
|
|
self.this_order = cus_this_order
|
|
|
|
if cus_last_sample is not None:
|
|
self.last_sample = cus_last_sample
|
|
|
|
use_corrector = (
|
|
self.step_index > 0 and self.step_index - 1 not in self.disable_corrector and self.last_sample is not None
|
|
)
|
|
|
|
model_output_convert = self.convert_model_output(model_output, sample=sample, sigma=sigma)
|
|
|
|
if model_outputs is not None and timestep_list is not None:
|
|
self.model_outputs = model_outputs[:-1]
|
|
self.timestep_list = timestep_list[:-1]
|
|
|
|
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,
|
|
sigma_before=sigma_before,
|
|
sigma=sigma,
|
|
)
|
|
|
|
if model_outputs is not None and timestep_list is not None:
|
|
model_outputs[-1] = model_output_convert
|
|
self.model_outputs = model_outputs[1:]
|
|
self.timestep_list = timestep_list[1:]
|
|
else:
|
|
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
|
|
|
|
if self.config.lower_order_final:
|
|
this_order = min(self.config.solver_order, len(self.timesteps) - self.step_index)
|
|
else:
|
|
this_order = self.config.solver_order
|
|
self.this_order = min(this_order, self.lower_order_nums + 1)
|
|
assert self.this_order > 0
|
|
|
|
self.last_sample = sample
|
|
prev_sample = self.multistep_uni_p_bh_update(
|
|
model_output=model_output,
|
|
sample=sample,
|
|
order=self.this_order,
|
|
sigma=sigma,
|
|
sigma_next=sigma_next,
|
|
)
|
|
|
|
if cus_lower_order_num is None:
|
|
if self.lower_order_nums < self.config.solver_order:
|
|
self.lower_order_nums += 1
|
|
|
|
if cus_step_index is None:
|
|
self._step_index += 1
|
|
|
|
if not return_dict:
|
|
return (prev_sample, model_outputs, self.last_sample, self.this_order)
|
|
|
|
return HeliosSchedulerOutput(
|
|
prev_sample=prev_sample,
|
|
model_outputs=model_outputs,
|
|
last_sample=self.last_sample,
|
|
this_order=self.this_order,
|
|
)
|
|
|
|
# ---------------------------------- DMD ----------------------------------
|
|
def add_noise(self, original_samples, noise, timestep, sigmas, timesteps):
|
|
sigmas = sigmas.to(noise.device)
|
|
timesteps = timesteps.to(noise.device)
|
|
timestep_id = torch.argmin((timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
|
|
sigma = sigmas[timestep_id].reshape(-1, 1, 1, 1, 1)
|
|
sample = (1 - sigma) * original_samples + sigma * noise
|
|
return sample.type_as(noise)
|
|
|
|
def convert_flow_pred_to_x0(self, flow_pred, xt, timestep, sigmas, timesteps):
|
|
original_dtype = flow_pred.dtype
|
|
device = flow_pred.device
|
|
flow_pred, xt, sigmas, timesteps = (x.double().to(device) for x in (flow_pred, xt, sigmas, timesteps))
|
|
|
|
timestep_id = torch.argmin((timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
|
|
sigma_t = sigmas[timestep_id].reshape(-1, 1, 1, 1, 1)
|
|
x0_pred = xt - sigma_t * flow_pred
|
|
return x0_pred.to(original_dtype)
|
|
|
|
def step_dmd(
|
|
self,
|
|
model_output: torch.FloatTensor,
|
|
timestep: float | torch.FloatTensor = None,
|
|
sample: torch.FloatTensor = None,
|
|
generator: torch.Generator | None = None,
|
|
return_dict: bool = True,
|
|
cur_sampling_step: int = 0,
|
|
dmd_noisy_tensor: torch.FloatTensor | None = None,
|
|
dmd_sigmas: torch.FloatTensor | None = None,
|
|
dmd_timesteps: torch.FloatTensor | None = None,
|
|
all_timesteps: torch.FloatTensor | None = None,
|
|
):
|
|
pred_image_or_video = self.convert_flow_pred_to_x0(
|
|
flow_pred=model_output,
|
|
xt=sample,
|
|
timestep=torch.full((model_output.shape[0],), timestep, dtype=torch.long, device=model_output.device),
|
|
sigmas=dmd_sigmas,
|
|
timesteps=dmd_timesteps,
|
|
)
|
|
if cur_sampling_step < len(all_timesteps) - 1:
|
|
prev_sample = self.add_noise(
|
|
pred_image_or_video,
|
|
dmd_noisy_tensor,
|
|
torch.full(
|
|
(model_output.shape[0],),
|
|
all_timesteps[cur_sampling_step + 1],
|
|
dtype=torch.long,
|
|
device=model_output.device,
|
|
),
|
|
sigmas=dmd_sigmas,
|
|
timesteps=dmd_timesteps,
|
|
)
|
|
else:
|
|
prev_sample = pred_image_or_video
|
|
|
|
if not return_dict:
|
|
return (prev_sample,)
|
|
|
|
return HeliosSchedulerOutput(prev_sample=prev_sample)
|
|
|
|
# ---------------------------------- Merge ----------------------------------
|
|
def step(
|
|
self,
|
|
model_output: torch.FloatTensor,
|
|
timestep: float | torch.FloatTensor = None,
|
|
sample: torch.FloatTensor = None,
|
|
generator: torch.Generator | None = None,
|
|
return_dict: bool = True,
|
|
cur_sampling_step: int = 0,
|
|
dmd_noisy_tensor: torch.FloatTensor | None = None,
|
|
dmd_sigmas: torch.FloatTensor | None = None,
|
|
dmd_timesteps: torch.FloatTensor | None = None,
|
|
all_timesteps: torch.FloatTensor | None = None,
|
|
) -> HeliosSchedulerOutput | tuple:
|
|
if self.config.scheduler_type == "euler":
|
|
return self.step_euler(
|
|
model_output=model_output,
|
|
timestep=timestep,
|
|
sample=sample,
|
|
generator=generator,
|
|
return_dict=return_dict,
|
|
)
|
|
elif self.config.scheduler_type == "unipc":
|
|
return self.step_unipc(
|
|
model_output=model_output,
|
|
timestep=timestep,
|
|
sample=sample,
|
|
return_dict=return_dict,
|
|
)
|
|
elif self.config.scheduler_type == "dmd":
|
|
return self.step_dmd(
|
|
model_output=model_output,
|
|
timestep=timestep,
|
|
sample=sample,
|
|
generator=generator,
|
|
return_dict=return_dict,
|
|
cur_sampling_step=cur_sampling_step,
|
|
dmd_noisy_tensor=dmd_noisy_tensor,
|
|
dmd_sigmas=dmd_sigmas,
|
|
dmd_timesteps=dmd_timesteps,
|
|
all_timesteps=all_timesteps,
|
|
)
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
def reset_scheduler_history(self):
|
|
self.model_outputs = [None] * self.config.solver_order
|
|
self.timestep_list = [None] * self.config.solver_order
|
|
self.lower_order_nums = 0
|
|
self.disable_corrector = self.config.disable_corrector
|
|
self.solver_p = self.config.solver_p
|
|
self.last_sample = None
|
|
self._step_index = None
|
|
self._begin_index = None
|
|
|
|
def __len__(self):
|
|
return self.config.num_train_timesteps
|