mirror of https://github.com/vladmandic/automatic
452 lines
18 KiB
Python
452 lines
18 KiB
Python
# Copyright 2025 The RES4LYF Team (Clybius) and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import ClassVar, List, Literal, 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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
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from diffusers.utils import logging
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from .phi_functions import Phi, calculate_gamma
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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class RESMultistepScheduler(SchedulerMixin, ConfigMixin):
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"""
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RESMultistepScheduler (Restartable Exponential Integrator) ported from RES4LYF.
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Supports RES 2M, 3M and DEIS 2M, 3M variants.
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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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beta_start (`float`, defaults to 0.0001):
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The starting `beta` value of inference.
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beta_end (`float`, defaults to 0.02):
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The final `beta` value.
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beta_schedule (`str`, defaults to "linear"):
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The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model.
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prediction_type (`str`, defaults to "epsilon"):
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The prediction type of the scheduler function.
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variant (`str`, defaults to "res_2m"):
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The specific RES/DEIS variant to use. Supported: "res_2m", "res_3m", "deis_2m", "deis_3m".
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use_analytic_solution (`bool`, defaults to True):
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Whether to use high-precision analytic solutions for phi functions.
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"""
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_compatibles: ClassVar[List[str]] = [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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beta_start: float = 0.00085,
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beta_end: float = 0.012,
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beta_schedule: str = "linear",
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prediction_type: str = "epsilon",
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variant: Literal["res_2m", "res_3m", "deis_2m", "deis_3m"] = "res_2m",
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use_analytic_solution: bool = True,
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timestep_spacing: str = "linspace",
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steps_offset: int = 0,
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rescale_betas_zero_snr: bool = False,
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use_karras_sigmas: bool = False,
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use_exponential_sigmas: bool = False,
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use_beta_sigmas: bool = False,
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use_flow_sigmas: bool = False,
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shift: float = 1.0,
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use_dynamic_shifting: bool = False,
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base_shift: float = 0.5,
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max_shift: float = 1.15,
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base_image_seq_len: int = 256,
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max_image_seq_len: int = 4096,
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):
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from .scheduler_utils import betas_for_alpha_bar, rescale_zero_terminal_snr
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if beta_schedule == "linear":
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self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
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elif beta_schedule == "scaled_linear":
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self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
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elif beta_schedule == "squaredcos_cap_v2":
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self.betas = betas_for_alpha_bar(num_train_timesteps)
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else:
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raise NotImplementedError(f"{beta_schedule} is not implemented")
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if rescale_betas_zero_snr:
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self.betas = rescale_zero_terminal_snr(self.betas)
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self.alphas = 1.0 - self.betas
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self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
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# Buffer for multistep
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self.model_outputs = []
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self.x0_outputs = []
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self.prev_sigmas = []
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self._step_index = None
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self._begin_index = None
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self.init_noise_sigma = 1.0
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@property
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def step_index(self) -> Optional[int]:
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return self._step_index
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@property
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def begin_index(self) -> Optional[int]:
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return self._begin_index
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def set_begin_index(self, begin_index: int = 0) -> None:
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self._begin_index = begin_index
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def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
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if self._step_index is None:
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self._init_step_index(timestep)
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if self.config.prediction_type == "flow_prediction":
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return sample
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sigma = self.sigmas[self._step_index]
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return sample / ((sigma**2 + 1) ** 0.5)
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def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None, mu: Optional[float] = None, dtype: torch.dtype = torch.float32):
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from .scheduler_utils import (
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apply_shift,
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get_dynamic_shift,
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get_sigmas_beta,
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get_sigmas_exponential,
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get_sigmas_flow,
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get_sigmas_karras,
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)
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self.num_inference_steps = num_inference_steps
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if self.config.timestep_spacing == "linspace":
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timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()
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elif self.config.timestep_spacing == "leading":
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step_ratio = self.config.num_train_timesteps // self.num_inference_steps
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timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy()
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timesteps += self.config.steps_offset
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elif self.config.timestep_spacing == "trailing":
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step_ratio = self.config.num_train_timesteps / self.num_inference_steps
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timesteps = (np.arange(self.config.num_train_timesteps, 0, -step_ratio)).round().copy()
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timesteps -= 1
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else:
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raise ValueError(f"timestep_spacing {self.config.timestep_spacing} is not supported.")
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sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
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# Linear remapping for Flow Matching
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if self.config.use_flow_sigmas:
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# Standardize linear spacing
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sigmas = np.linspace(1.0, 1 / 1000, num_inference_steps)
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else:
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sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
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if self.config.use_karras_sigmas:
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sigmas = get_sigmas_karras(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
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elif self.config.use_exponential_sigmas:
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sigmas = get_sigmas_exponential(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
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elif self.config.use_beta_sigmas:
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sigmas = get_sigmas_beta(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
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elif self.config.use_flow_sigmas:
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# Already handled above, ensuring variable consistency
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sigmas = np.linspace(1.0, 1 / 1000, num_inference_steps)
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if self.config.shift != 1.0 or self.config.use_dynamic_shifting:
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shift = self.config.shift
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if self.config.use_dynamic_shifting and mu is not None:
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shift = get_dynamic_shift(
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mu,
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self.config.base_shift,
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self.config.max_shift,
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self.config.base_image_seq_len,
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self.config.max_image_seq_len,
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)
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sigmas = apply_shift(torch.from_numpy(sigmas), shift).numpy()
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if self.config.use_flow_sigmas:
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timesteps = sigmas * self.config.num_train_timesteps
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self.sigmas = torch.from_numpy(np.concatenate([sigmas, [0.0]])).to(device=device, dtype=dtype)
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self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=dtype)
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self.init_noise_sigma = self.sigmas.max().item() if self.sigmas.numel() > 0 else 1.0
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self._step_index = None
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self._begin_index = None
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self.model_outputs = []
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self.x0_outputs = []
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self.prev_sigmas = []
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self.lower_order_nums = 0
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def index_for_timestep(self, timestep, schedule_timesteps=None):
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from .scheduler_utils import index_for_timestep
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if schedule_timesteps is None:
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schedule_timesteps = self.timesteps
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return index_for_timestep(timestep, schedule_timesteps)
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def add_noise(
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self,
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original_samples: torch.Tensor,
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noise: torch.Tensor,
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timesteps: torch.Tensor,
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) -> torch.Tensor:
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from .scheduler_utils import add_noise_to_sample
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return add_noise_to_sample(original_samples, noise, self.sigmas, timesteps, self.timesteps)
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def step(
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self,
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model_output: torch.Tensor,
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timestep: Union[float, torch.Tensor],
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sample: torch.Tensor,
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return_dict: bool = True,
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) -> Union[SchedulerOutput, Tuple]:
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if self._step_index is None:
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self._init_step_index(timestep)
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step = self._step_index
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sigma = self.sigmas[step]
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sigma_next = self.sigmas[step + 1]
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h = -torch.log(sigma_next / sigma) if sigma > 0 and sigma_next > 0 else torch.zeros_like(sigma)
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# RECONSTRUCT X0 (Matching PEC pattern)
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if self.config.prediction_type == "epsilon":
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x0 = sample - sigma * model_output
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elif self.config.prediction_type == "sample":
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x0 = model_output
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elif self.config.prediction_type == "v_prediction":
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alpha_t = 1.0 / (sigma**2 + 1) ** 0.5
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sigma_t = sigma * alpha_t
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x0 = alpha_t * sample - sigma_t * model_output
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elif self.config.prediction_type == "flow_prediction":
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x0 = sample - sigma * model_output
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else:
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raise ValueError(f"prediction_type {self.config.prediction_type} is not supported.")
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self.model_outputs.append(model_output)
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self.x0_outputs.append(x0)
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self.prev_sigmas.append(sigma)
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# Order logic
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variant = self.config.variant
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order = int(variant[-2]) if variant.endswith("m") else 1
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# Effective order for current step
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curr_order = min(len(self.prev_sigmas), order) if sigma > 0 else 1
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if self.config.prediction_type == "flow_prediction":
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# Variable Step Adams-Bashforth for Flow Matching
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dt = sigma_next - sigma
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v_n = model_output
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if curr_order == 1:
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x_next = sample + dt * v_n
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elif curr_order == 2:
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# AB2
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sigma_prev = self.prev_sigmas[-2]
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dt_prev = sigma - sigma_prev
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r = dt / dt_prev if abs(dt_prev) > 1e-8 else 0.0
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# Stability check
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if dt_prev == 0 or r < -0.9 or r > 2.0: # Fallback
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x_next = sample + dt * v_n
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else:
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c0 = 1 + 0.5 * r
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c1 = -0.5 * r
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x_next = sample + dt * (c0 * v_n + c1 * self.model_outputs[-2])
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elif curr_order >= 3:
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# Re-implement AB2 logic
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sigma_prev = self.prev_sigmas[-2]
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dt_prev = sigma - sigma_prev
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r = dt / dt_prev if abs(dt_prev) > 1e-8 else 0.0
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c0 = 1 + 0.5 * r
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c1 = -0.5 * r
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x_next = sample + dt * (c0 * v_n + c1 * self.model_outputs[-2])
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self._step_index += 1
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if len(self.model_outputs) > order:
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self.model_outputs.pop(0)
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self.x0_outputs.pop(0)
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self.prev_sigmas.pop(0)
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if not return_dict:
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return (x_next,)
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return SchedulerOutput(prev_sample=x_next)
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# Exponential Integrator Setup
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phi = Phi(h, [0], getattr(self.config, "use_analytic_solution", True))
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phi_1 = phi(1)
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if variant.startswith("res"):
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# Force Order 1 at the end of schedule
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if self.num_inference_steps is not None and self._step_index >= self.num_inference_steps - 3:
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curr_order = 1
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if curr_order == 2:
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h_prev = -torch.log(self.prev_sigmas[-1] / self.prev_sigmas[-2])
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elif curr_order == 3:
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pass
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else:
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pass
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# Exponential Integrator Update in x-space
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if curr_order == 1:
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res = phi_1 * x0
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elif curr_order == 2:
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# b2 = -phi_2 / r
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# b2 = -phi_2 / r = -phi(2) / (h_prev/h)
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# Here we use: b2 = phi(2) / ((-h_prev / h) + 1e-9)
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# Since (-h_prev/h) is negative (-r), this gives correct negative sign for b2.
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# Stability check
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r_check = h_prev / (h + 1e-9) # This is effectively -r if using h_prev definition above?
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# Wait, h_prev above is -log(). Positive.
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# h is positive.
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# So h_prev/h is positive. defined as r in other files.
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# But here code uses -h_prev / h in denominator.
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# Stability check
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r_check = h_prev / (h + 1e-9)
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# Hard Restart
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if r_check < 0.5 or r_check > 2.0:
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res = phi_1 * x0
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else:
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b2 = phi(2) / ((-h_prev / h) + 1e-9)
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b1 = phi_1 - b2
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res = b1 * self.x0_outputs[-1] + b2 * self.x0_outputs[-2]
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elif curr_order == 3:
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# Generalized AB3 for Exponential Integrators
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h_p1 = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-2] + 1e-9))
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h_p2 = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-3] + 1e-9))
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r1 = h_p1 / (h + 1e-9)
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r2 = h_p2 / (h + 1e-9)
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if r1 < 0.5 or r1 > 2.0 or r2 < 0.5 or r2 > 2.0:
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res = phi_1 * x0
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else:
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phi_2, phi_3 = phi(2), phi(3)
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denom = r2 - r1 + 1e-9
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b3 = (phi_3 + r1 * phi_2) / (r2 * denom)
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b2 = -(phi_3 + r2 * phi_2) / (r1 * denom)
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b1 = phi_1 - b2 - b3
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res = b1 * self.x0_outputs[-1] + b2 * self.x0_outputs[-2] + b3 * self.x0_outputs[-3]
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else:
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res = phi_1 * x0
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if sigma_next == 0:
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x_next = x0
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else:
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x_next = torch.exp(-h) * sample + h * res
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else:
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# DEIS logic (Linear multistep in log-sigma space)
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b = self._get_deis_coefficients(curr_order, sigma, sigma_next)
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# For DEIS, we apply b to the denoised estimates
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res = torch.zeros_like(sample)
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for i, b_val in enumerate(b[0]):
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idx = len(self.x0_outputs) - 1 - i
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if idx >= 0:
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res += b_val * self.x0_outputs[idx]
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# DEIS update in x-space
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if sigma_next == 0:
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x_next = x0
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else:
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x_next = torch.exp(-h) * sample + h * res
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self._step_index += 1
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if len(self.model_outputs) > order:
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self.model_outputs.pop(0)
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self.x0_outputs.pop(0)
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self.prev_sigmas.pop(0)
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if not return_dict:
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return (x_next,)
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return SchedulerOutput(prev_sample=x_next)
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def _get_res_coefficients(self, rk_type, h, c2, c3):
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ci = [0, c2, c3]
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phi = Phi(h, ci, getattr(self.config, "use_analytic_solution", True))
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if rk_type == "res_2s":
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b2 = phi(2) / (c2 + 1e-9)
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b = [[phi(1) - b2, b2]]
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a = [[0, 0], [c2 * phi(1, 2), 0]]
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elif rk_type == "res_3s":
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gamma_val = calculate_gamma(c2, c3)
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b3 = phi(2) / (gamma_val * c2 + c3 + 1e-9)
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b2 = gamma_val * b3
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b = [[phi(1) - (b2 + b3), b2, b3]]
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a = [] # Simplified
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else:
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b = [[phi(1)]]
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a = [[0]]
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return a, b, ci
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def _get_deis_coefficients(self, order, sigma, sigma_next):
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h = -torch.log(sigma_next / sigma) if sigma > 0 and sigma_next > 0 else torch.zeros_like(sigma)
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phi = Phi(h, [0], getattr(self.config, "use_analytic_solution", True))
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phi_1 = phi(1)
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if order == 1:
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return [[phi_1]]
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elif order == 2:
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h_prev = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-2] + 1e-9))
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r = h_prev / (h + 1e-9)
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# Correct Adams-Bashforth-like coefficients for Exponential Integrators
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# Hard Restart for stability
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if r < 0.5 or r > 2.0:
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return [[phi_1]]
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phi_2 = phi(2)
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b2 = -phi_2 / (r + 1e-9)
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b1 = phi_1 - b2
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return [[b1, b2]]
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elif order == 3:
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h_prev1 = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-2] + 1e-9))
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h_prev2 = -torch.log(self.prev_sigmas[-1] / (self.prev_sigmas[-3] + 1e-9))
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r1 = h_prev1 / (h + 1e-9)
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r2 = h_prev2 / (h + 1e-9)
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if r1 < 0.5 or r1 > 2.0 or r2 < 0.5 or r2 > 2.0:
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return [[phi_1]]
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phi_2 = phi(2)
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phi_3 = phi(3)
|
|
|
|
# Generalized AB3 for Exponential Integrators (Varying steps)
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|
denom = r2 - r1 + 1e-9
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|
b3 = (phi_3 + r1 * phi_2) / (r2 * denom)
|
|
b2 = -(phi_3 + r2 * phi_2) / (r1 * denom)
|
|
b1 = phi_1 - (b2 + b3)
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|
return [[b1, b2, b3]]
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|
else:
|
|
return [[phi_1]]
|
|
|
|
def _init_step_index(self, timestep):
|
|
if self.begin_index is None:
|
|
self._step_index = self.index_for_timestep(timestep)
|
|
else:
|
|
self._step_index = self._begin_index
|
|
|
|
def __len__(self):
|
|
return self.config.num_train_timesteps
|