676 lines
29 KiB
Python
676 lines
29 KiB
Python
# Copyright 2025 Katherine Crowson, The HuggingFace Team and hlky. 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 dataclasses import dataclass
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from typing import List, Optional, Tuple, Union
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import numpy as np
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import torch
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import torchsde
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from ..configuration_utils import ConfigMixin, register_to_config
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from ..utils import BaseOutput, is_scipy_available
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from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
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if is_scipy_available():
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import scipy.stats
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@dataclass
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# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DPMSolverSDE
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class DPMSolverSDESchedulerOutput(BaseOutput):
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"""
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Output class for the scheduler's `step` function output.
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Args:
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prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
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Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
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denoising loop.
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pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
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The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
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`pred_original_sample` can be used to preview progress or for guidance.
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"""
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prev_sample: torch.Tensor
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pred_original_sample: Optional[torch.Tensor] = None
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class BatchedBrownianTree:
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"""A wrapper around torchsde.BrownianTree that enables batches of entropy."""
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def __init__(self, x, t0, t1, seed=None, **kwargs):
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t0, t1, self.sign = self.sort(t0, t1)
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w0 = kwargs.get("w0", torch.zeros_like(x))
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if seed is None:
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seed = torch.randint(0, 2**63 - 1, []).item()
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self.batched = True
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try:
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assert len(seed) == x.shape[0]
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w0 = w0[0]
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except TypeError:
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seed = [seed]
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self.batched = False
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self.trees = [
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torchsde.BrownianInterval(
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t0=t0,
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t1=t1,
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size=w0.shape,
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dtype=w0.dtype,
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device=w0.device,
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entropy=s,
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tol=1e-6,
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pool_size=24,
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halfway_tree=True,
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)
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for s in seed
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]
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@staticmethod
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def sort(a, b):
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return (a, b, 1) if a < b else (b, a, -1)
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def __call__(self, t0, t1):
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t0, t1, sign = self.sort(t0, t1)
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w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
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return w if self.batched else w[0]
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class BrownianTreeNoiseSampler:
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"""A noise sampler backed by a torchsde.BrownianTree.
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Args:
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x (Tensor): The tensor whose shape, device and dtype to use to generate
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random samples.
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sigma_min (float): The low end of the valid interval.
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sigma_max (float): The high end of the valid interval.
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seed (int or List[int]): The random seed. If a list of seeds is
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supplied instead of a single integer, then the noise sampler will use one BrownianTree per batch item, each
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with its own seed.
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transform (callable): A function that maps sigma to the sampler's
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internal timestep.
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"""
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def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x):
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self.transform = transform
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t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
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self.tree = BatchedBrownianTree(x, t0, t1, seed)
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def __call__(self, sigma, sigma_next):
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t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
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return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
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# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
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def betas_for_alpha_bar(
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num_diffusion_timesteps,
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max_beta=0.999,
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alpha_transform_type="cosine",
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):
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"""
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Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
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(1-beta) over time from t = [0,1].
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Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
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to that part of the diffusion process.
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Args:
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num_diffusion_timesteps (`int`): the number of betas to produce.
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max_beta (`float`): the maximum beta to use; use values lower than 1 to
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prevent singularities.
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alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
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Choose from `cosine` or `exp`
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Returns:
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betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
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"""
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if alpha_transform_type == "cosine":
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def alpha_bar_fn(t):
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return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
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elif alpha_transform_type == "exp":
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def alpha_bar_fn(t):
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return math.exp(t * -12.0)
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else:
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raise ValueError(f"Unsupported alpha_transform_type: {alpha_transform_type}")
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betas = []
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for i in range(num_diffusion_timesteps):
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t1 = i / num_diffusion_timesteps
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t2 = (i + 1) / num_diffusion_timesteps
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betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
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return torch.tensor(betas, dtype=torch.float32)
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class DPMSolverSDEScheduler(SchedulerMixin, ConfigMixin):
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"""
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DPMSolverSDEScheduler implements the stochastic sampler from the [Elucidating the Design Space of Diffusion-Based
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Generative Models](https://huggingface.co/papers/2206.00364) paper.
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This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
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methods the library implements for all schedulers such as loading and saving.
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Args:
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num_train_timesteps (`int`, defaults to 1000):
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The number of diffusion steps to train the model.
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beta_start (`float`, defaults to 0.00085):
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The starting `beta` value of inference.
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beta_end (`float`, defaults to 0.012):
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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. Choose from
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`linear` or `scaled_linear`.
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trained_betas (`np.ndarray`, *optional*):
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Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
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prediction_type (`str`, defaults to `epsilon`, *optional*):
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Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
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`sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
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Video](https://imagen.research.google/video/paper.pdf) paper).
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use_karras_sigmas (`bool`, *optional*, defaults to `False`):
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Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,
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the sigmas are determined according to a sequence of noise levels {σi}.
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use_exponential_sigmas (`bool`, *optional*, defaults to `False`):
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Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.
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use_beta_sigmas (`bool`, *optional*, defaults to `False`):
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Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to [Beta
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Sampling is All You Need](https://huggingface.co/papers/2407.12173) for more information.
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noise_sampler_seed (`int`, *optional*, defaults to `None`):
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The random seed to use for the noise sampler. If `None`, a random seed is generated.
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timestep_spacing (`str`, defaults to `"linspace"`):
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The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
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Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
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steps_offset (`int`, defaults to 0):
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An offset added to the inference steps, as required by some model families.
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"""
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_compatibles = [e.name for e in KarrasDiffusionSchedulers]
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order = 2
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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, # sensible defaults
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beta_end: float = 0.012,
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beta_schedule: str = "linear",
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trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
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prediction_type: str = "epsilon",
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use_karras_sigmas: Optional[bool] = False,
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use_exponential_sigmas: Optional[bool] = False,
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use_beta_sigmas: Optional[bool] = False,
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noise_sampler_seed: Optional[int] = None,
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timestep_spacing: str = "linspace",
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steps_offset: int = 0,
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):
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if self.config.use_beta_sigmas and not is_scipy_available():
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raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
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if sum([self.config.use_beta_sigmas, self.config.use_exponential_sigmas, self.config.use_karras_sigmas]) > 1:
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raise ValueError(
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"Only one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used."
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)
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if trained_betas is not None:
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self.betas = torch.tensor(trained_betas, dtype=torch.float32)
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elif 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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# this schedule is very specific to the latent diffusion model.
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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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# Glide cosine schedule
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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 for {self.__class__}")
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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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# set all values
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self.set_timesteps(num_train_timesteps, None, num_train_timesteps)
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self.use_karras_sigmas = use_karras_sigmas
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self.noise_sampler = None
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self.noise_sampler_seed = noise_sampler_seed
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self._step_index = None
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self._begin_index = None
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self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
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# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep
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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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# The sigma index that is taken for the **very** first `step`
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# is always the second index (or the last index if there is only 1)
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# This way we can ensure we don't accidentally skip a sigma in
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# case we start in the middle of the denoising schedule (e.g. for image-to-image)
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pos = 1 if len(indices) > 1 else 0
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return indices[pos].item()
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# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index
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def _init_step_index(self, timestep):
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if 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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@property
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def init_noise_sigma(self):
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# standard deviation of the initial noise distribution
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if self.config.timestep_spacing in ["linspace", "trailing"]:
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return self.sigmas.max()
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return (self.sigmas.max() ** 2 + 1) ** 0.5
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@property
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def step_index(self):
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"""
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The index counter for current timestep. It will increase 1 after each scheduler step.
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"""
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return self._step_index
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@property
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def begin_index(self):
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"""
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The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
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"""
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return self._begin_index
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# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
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def set_begin_index(self, begin_index: int = 0):
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"""
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Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
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Args:
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begin_index (`int`):
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The begin index for the scheduler.
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"""
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self._begin_index = begin_index
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def scale_model_input(
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self,
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sample: torch.Tensor,
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timestep: Union[float, torch.Tensor],
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) -> torch.Tensor:
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"""
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Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
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current timestep.
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Args:
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sample (`torch.Tensor`):
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The input sample.
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timestep (`int`, *optional*):
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The current timestep in the diffusion chain.
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Returns:
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`torch.Tensor`:
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A scaled input sample.
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"""
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if self.step_index is None:
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self._init_step_index(timestep)
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sigma = self.sigmas[self.step_index]
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sigma_input = sigma if self.state_in_first_order else self.mid_point_sigma
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sample = sample / ((sigma_input**2 + 1) ** 0.5)
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return sample
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def set_timesteps(
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self,
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num_inference_steps: int,
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device: Union[str, torch.device] = None,
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num_train_timesteps: Optional[int] = None,
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):
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"""
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Sets the discrete timesteps used for the diffusion chain (to be run before inference).
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Args:
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num_inference_steps (`int`):
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The number of diffusion steps used when generating samples with a pre-trained model.
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device (`str` or `torch.device`, *optional*):
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The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
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"""
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self.num_inference_steps = num_inference_steps
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num_train_timesteps = num_train_timesteps or self.config.num_train_timesteps
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# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://huggingface.co/papers/2305.08891
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if self.config.timestep_spacing == "linspace":
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timesteps = np.linspace(0, 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 = num_train_timesteps // self.num_inference_steps
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# creates integer timesteps by multiplying by ratio
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# casting to int to avoid issues when num_inference_step is power of 3
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timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(float)
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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 = num_train_timesteps / self.num_inference_steps
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# creates integer timesteps by multiplying by ratio
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# casting to int to avoid issues when num_inference_step is power of 3
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timesteps = (np.arange(num_train_timesteps, 0, -step_ratio)).round().copy().astype(float)
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timesteps -= 1
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else:
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raise ValueError(
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f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
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)
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sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
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log_sigmas = np.log(sigmas)
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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 = self._convert_to_karras(in_sigmas=sigmas)
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timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
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elif self.config.use_exponential_sigmas:
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sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
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timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
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elif self.config.use_beta_sigmas:
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sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
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timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
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second_order_timesteps = self._second_order_timesteps(sigmas, log_sigmas)
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sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
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sigmas = torch.from_numpy(sigmas).to(device=device)
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self.sigmas = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2), sigmas[-1:]])
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timesteps = torch.from_numpy(timesteps)
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second_order_timesteps = torch.from_numpy(second_order_timesteps)
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timesteps = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2)])
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timesteps[1::2] = second_order_timesteps
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if str(device).startswith("mps"):
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# mps does not support float64
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self.timesteps = timesteps.to(device, dtype=torch.float32)
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else:
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self.timesteps = timesteps.to(device=device)
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# empty first order variables
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self.sample = None
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self.mid_point_sigma = None
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self._step_index = None
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self._begin_index = None
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self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
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self.noise_sampler = None
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def _second_order_timesteps(self, sigmas, log_sigmas):
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def sigma_fn(_t):
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return np.exp(-_t)
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def t_fn(_sigma):
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return -np.log(_sigma)
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midpoint_ratio = 0.5
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t = t_fn(sigmas)
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delta_time = np.diff(t)
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t_proposed = t[:-1] + delta_time * midpoint_ratio
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sig_proposed = sigma_fn(t_proposed)
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timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sig_proposed])
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return timesteps
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# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
|
||
def _sigma_to_t(self, sigma, log_sigmas):
|
||
# get log sigma
|
||
log_sigma = np.log(np.maximum(sigma, 1e-10))
|
||
|
||
# get distribution
|
||
dists = log_sigma - log_sigmas[:, np.newaxis]
|
||
|
||
# get sigmas range
|
||
low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
|
||
high_idx = low_idx + 1
|
||
|
||
low = log_sigmas[low_idx]
|
||
high = log_sigmas[high_idx]
|
||
|
||
# interpolate sigmas
|
||
w = (low - log_sigma) / (low - high)
|
||
w = np.clip(w, 0, 1)
|
||
|
||
# transform interpolation to time range
|
||
t = (1 - w) * low_idx + w * high_idx
|
||
t = t.reshape(sigma.shape)
|
||
return t
|
||
|
||
# copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
|
||
def _convert_to_karras(self, in_sigmas: torch.Tensor) -> torch.Tensor:
|
||
"""Constructs the noise schedule of Karras et al. (2022)."""
|
||
|
||
sigma_min: float = in_sigmas[-1].item()
|
||
sigma_max: float = in_sigmas[0].item()
|
||
|
||
rho = 7.0 # 7.0 is the value used in the paper
|
||
ramp = np.linspace(0, 1, self.num_inference_steps)
|
||
min_inv_rho = sigma_min ** (1 / rho)
|
||
max_inv_rho = sigma_max ** (1 / rho)
|
||
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
|
||
return sigmas
|
||
|
||
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_exponential
|
||
def _convert_to_exponential(self, in_sigmas: torch.Tensor, num_inference_steps: int) -> torch.Tensor:
|
||
"""Constructs an exponential noise schedule."""
|
||
|
||
# Hack to make sure that other schedulers which copy this function don't break
|
||
# TODO: Add this logic to the other schedulers
|
||
if hasattr(self.config, "sigma_min"):
|
||
sigma_min = self.config.sigma_min
|
||
else:
|
||
sigma_min = None
|
||
|
||
if hasattr(self.config, "sigma_max"):
|
||
sigma_max = self.config.sigma_max
|
||
else:
|
||
sigma_max = None
|
||
|
||
sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()
|
||
sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()
|
||
|
||
sigmas = np.exp(np.linspace(math.log(sigma_max), math.log(sigma_min), num_inference_steps))
|
||
return sigmas
|
||
|
||
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_beta
|
||
def _convert_to_beta(
|
||
self, in_sigmas: torch.Tensor, num_inference_steps: int, alpha: float = 0.6, beta: float = 0.6
|
||
) -> torch.Tensor:
|
||
"""From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024)"""
|
||
|
||
# Hack to make sure that other schedulers which copy this function don't break
|
||
# TODO: Add this logic to the other schedulers
|
||
if hasattr(self.config, "sigma_min"):
|
||
sigma_min = self.config.sigma_min
|
||
else:
|
||
sigma_min = None
|
||
|
||
if hasattr(self.config, "sigma_max"):
|
||
sigma_max = self.config.sigma_max
|
||
else:
|
||
sigma_max = None
|
||
|
||
sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()
|
||
sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()
|
||
|
||
sigmas = np.array(
|
||
[
|
||
sigma_min + (ppf * (sigma_max - sigma_min))
|
||
for ppf in [
|
||
scipy.stats.beta.ppf(timestep, alpha, beta)
|
||
for timestep in 1 - np.linspace(0, 1, num_inference_steps)
|
||
]
|
||
]
|
||
)
|
||
return sigmas
|
||
|
||
@property
|
||
def state_in_first_order(self):
|
||
return self.sample is None
|
||
|
||
def step(
|
||
self,
|
||
model_output: Union[torch.Tensor, np.ndarray],
|
||
timestep: Union[float, torch.Tensor],
|
||
sample: Union[torch.Tensor, np.ndarray],
|
||
return_dict: bool = True,
|
||
s_noise: float = 1.0,
|
||
) -> Union[DPMSolverSDESchedulerOutput, Tuple]:
|
||
"""
|
||
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
|
||
process from the learned model outputs (most often the predicted noise).
|
||
|
||
Args:
|
||
model_output (`torch.Tensor` or `np.ndarray`):
|
||
The direct output from learned diffusion model.
|
||
timestep (`float` or `torch.Tensor`):
|
||
The current discrete timestep in the diffusion chain.
|
||
sample (`torch.Tensor` or `np.ndarray`):
|
||
A current instance of a sample created by the diffusion process.
|
||
return_dict (`bool`):
|
||
Whether or not to return a [`~schedulers.scheduling_dpmsolver_sde.DPMSolverSDESchedulerOutput`] or
|
||
tuple.
|
||
s_noise (`float`, *optional*, defaults to 1.0):
|
||
Scaling factor for noise added to the sample.
|
||
|
||
Returns:
|
||
[`~schedulers.scheduling_dpmsolver_sde.DPMSolverSDESchedulerOutput`] or `tuple`:
|
||
If return_dict is `True`, [`~schedulers.scheduling_dpmsolver_sde.DPMSolverSDESchedulerOutput`] is
|
||
returned, otherwise a tuple is returned where the first element is the sample tensor.
|
||
"""
|
||
if self.step_index is None:
|
||
self._init_step_index(timestep)
|
||
|
||
# Create a noise sampler if it hasn't been created yet
|
||
if self.noise_sampler is None:
|
||
min_sigma, max_sigma = self.sigmas[self.sigmas > 0].min(), self.sigmas.max()
|
||
self.noise_sampler = BrownianTreeNoiseSampler(sample, min_sigma, max_sigma, self.noise_sampler_seed)
|
||
|
||
# Define functions to compute sigma and t from each other
|
||
def sigma_fn(_t: torch.Tensor) -> torch.Tensor:
|
||
return _t.neg().exp()
|
||
|
||
def t_fn(_sigma: torch.Tensor) -> torch.Tensor:
|
||
return _sigma.log().neg()
|
||
|
||
if self.state_in_first_order:
|
||
sigma = self.sigmas[self.step_index]
|
||
sigma_next = self.sigmas[self.step_index + 1]
|
||
else:
|
||
# 2nd order
|
||
sigma = self.sigmas[self.step_index - 1]
|
||
sigma_next = self.sigmas[self.step_index]
|
||
|
||
# Set the midpoint and step size for the current step
|
||
midpoint_ratio = 0.5
|
||
t, t_next = t_fn(sigma), t_fn(sigma_next)
|
||
delta_time = t_next - t
|
||
t_proposed = t + delta_time * midpoint_ratio
|
||
|
||
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
|
||
if self.config.prediction_type == "epsilon":
|
||
sigma_input = sigma if self.state_in_first_order else sigma_fn(t_proposed)
|
||
pred_original_sample = sample - sigma_input * model_output
|
||
elif self.config.prediction_type == "v_prediction":
|
||
sigma_input = sigma if self.state_in_first_order else sigma_fn(t_proposed)
|
||
pred_original_sample = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
|
||
sample / (sigma_input**2 + 1)
|
||
)
|
||
elif self.config.prediction_type == "sample":
|
||
raise NotImplementedError("prediction_type not implemented yet: sample")
|
||
else:
|
||
raise ValueError(
|
||
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"
|
||
)
|
||
|
||
if sigma_next == 0:
|
||
derivative = (sample - pred_original_sample) / sigma
|
||
dt = sigma_next - sigma
|
||
prev_sample = sample + derivative * dt
|
||
else:
|
||
if self.state_in_first_order:
|
||
t_next = t_proposed
|
||
else:
|
||
sample = self.sample
|
||
|
||
sigma_from = sigma_fn(t)
|
||
sigma_to = sigma_fn(t_next)
|
||
sigma_up = min(sigma_to, (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5)
|
||
sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
|
||
ancestral_t = t_fn(sigma_down)
|
||
prev_sample = (sigma_fn(ancestral_t) / sigma_fn(t)) * sample - (
|
||
t - ancestral_t
|
||
).expm1() * pred_original_sample
|
||
prev_sample = prev_sample + self.noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * sigma_up
|
||
|
||
if self.state_in_first_order:
|
||
# store for 2nd order step
|
||
self.sample = sample
|
||
self.mid_point_sigma = sigma_fn(t_next)
|
||
else:
|
||
# free for "first order mode"
|
||
self.sample = None
|
||
self.mid_point_sigma = None
|
||
|
||
# upon completion increase step index by one
|
||
self._step_index += 1
|
||
|
||
if not return_dict:
|
||
return (
|
||
prev_sample,
|
||
pred_original_sample,
|
||
)
|
||
|
||
return DPMSolverSDESchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
|
||
|
||
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise
|
||
def add_noise(
|
||
self,
|
||
original_samples: torch.Tensor,
|
||
noise: torch.Tensor,
|
||
timesteps: torch.Tensor,
|
||
) -> torch.Tensor:
|
||
# Make sure sigmas and timesteps have the same device and dtype as original_samples
|
||
sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
|
||
if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
|
||
# mps does not support float64
|
||
schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
|
||
timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
|
||
else:
|
||
schedule_timesteps = self.timesteps.to(original_samples.device)
|
||
timesteps = timesteps.to(original_samples.device)
|
||
|
||
# self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index
|
||
if self.begin_index is None:
|
||
step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]
|
||
elif self.step_index is not None:
|
||
# add_noise is called after first denoising step (for inpainting)
|
||
step_indices = [self.step_index] * timesteps.shape[0]
|
||
else:
|
||
# add noise is called before first denoising step to create initial latent(img2img)
|
||
step_indices = [self.begin_index] * timesteps.shape[0]
|
||
|
||
sigma = sigmas[step_indices].flatten()
|
||
while len(sigma.shape) < len(original_samples.shape):
|
||
sigma = sigma.unsqueeze(-1)
|
||
|
||
noisy_samples = original_samples + noise * sigma
|
||
return noisy_samples
|
||
|
||
def __len__(self):
|
||
return self.config.num_train_timesteps
|