mirror of https://github.com/vladmandic/automatic
264 lines
10 KiB
Python
264 lines
10 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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import math
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from typing import ClassVar, 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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
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from diffusers.utils import logging
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logger = logging.get_logger(__name__)
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class CommonSigmaScheduler(SchedulerMixin, ConfigMixin):
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"""
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Common Sigma scheduler using Exponential Integrator step.
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"""
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_compatibles: ClassVar[list[str]] = [e.name for e in KarrasDiffusionSchedulers]
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order: ClassVar[int] = 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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profile: Literal["sigmoid", "sine", "easing", "arcsine", "smoothstep"] = "sigmoid",
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variant: str = "logistic",
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strength: float = 1.0,
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gain: float = 1.0,
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offset: float = 0.0,
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prediction_type: str = "epsilon",
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timestep_spacing: str = "linspace",
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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} does not exist.")
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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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# Standard deviation of the initial noise distribution
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self.init_noise_sigma = 1.0
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# Setable values
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self.num_inference_steps = None
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self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
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self.sigmas = torch.Tensor([])
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self._step_index = None
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self._begin_index = None
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@property
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def step_index(self) -> int | None:
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return self._step_index
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@property
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def begin_index(self) -> int | None:
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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 set_timesteps(self, num_inference_steps: int, device: str | torch.device = None, mu: float | None = 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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# Derived sigma range from alphas_cumprod
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base_sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
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sigma_max = base_sigmas[-1]
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sigma_min = base_sigmas[0]
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t = torch.linspace(0, 1, num_inference_steps)
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profile = self.config.profile
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variant = self.config.variant
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gain = self.config.gain
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offset = self.config.offset
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if profile == "sigmoid":
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x = gain * (t * 10 - 5 + offset)
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if variant == "logistic":
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result = 1.0 / (1.0 + torch.exp(-x))
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elif variant == "tanh":
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result = (torch.tanh(x) + 1) / 2
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else:
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result = torch.sigmoid(x)
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elif profile == "sine":
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result = torch.sin(t * math.pi / 2)
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elif profile == "easing":
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result = t * t * (3 - 2 * t)
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elif profile == "arcsine":
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result = torch.arcsin(t) / (math.pi / 2)
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else:
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result = t
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# Map profile to sigma range
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sigmas = (sigma_max * (1 - result) + sigma_min * result).cpu().numpy()
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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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sigmas = get_sigmas_flow(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
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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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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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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 scale_model_input(self, sample: torch.Tensor, timestep: 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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sample = sample / ((sigma**2 + 1) ** 0.5)
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return sample
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def step(
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self,
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model_output: torch.Tensor,
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timestep: float | torch.Tensor,
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sample: torch.Tensor,
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return_dict: bool = True,
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) -> 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_index = self._step_index
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sigma = self.sigmas[step_index]
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sigma_next = self.sigmas[step_index + 1]
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# RECONSTRUCT X0
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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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x0 = model_output
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# Exponential Integrator Update
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if sigma_next == 0:
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x_next = x0
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else:
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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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x_next = torch.exp(-h) * sample + (1 - torch.exp(-h)) * x0
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self._step_index += 1
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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 _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 __len__(self):
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return self.config.num_train_timesteps
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