265 lines
11 KiB
Python
265 lines
11 KiB
Python
# # Copyright 2025 Sana-Sprint Authors 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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# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
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# and https://github.com/hojonathanho/diffusion
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from dataclasses import dataclass
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from typing import Optional, Tuple, Union
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import numpy as np
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import torch
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from ..configuration_utils import ConfigMixin, register_to_config
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from ..schedulers.scheduling_utils import SchedulerMixin
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from ..utils import BaseOutput, logging
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from ..utils.torch_utils import randn_tensor
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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@dataclass
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# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->SCM
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class SCMSchedulerOutput(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 SCMScheduler(SchedulerMixin, ConfigMixin):
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"""
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`SCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
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non-Markovian guidance. This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass
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documentation for the generic 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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prediction_type (`str`, defaults to `trigflow`):
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Prediction type of the scheduler function. Currently only supports "trigflow".
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sigma_data (`float`, defaults to 0.5):
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The standard deviation of the noise added during multi-step inference.
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"""
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# _compatibles = [e.name for e in KarrasDiffusionSchedulers]
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order = 1
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@register_to_config
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def __init__(
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self,
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num_train_timesteps: int = 1000,
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prediction_type: str = "trigflow",
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sigma_data: float = 0.5,
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):
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"""
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Initialize the SCM scheduler.
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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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prediction_type (`str`, defaults to `trigflow`):
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Prediction type of the scheduler function. Currently only supports "trigflow".
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sigma_data (`float`, defaults to 0.5):
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The standard deviation of the noise added during multi-step inference.
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"""
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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().astype(np.int64))
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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):
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return self._step_index
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@property
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def begin_index(self):
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return self._begin_index
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# 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 set_timesteps(
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self,
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num_inference_steps: int,
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timesteps: torch.Tensor = None,
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device: Union[str, torch.device] = None,
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max_timesteps: float = 1.57080,
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intermediate_timesteps: float = 1.3,
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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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timesteps (`torch.Tensor`, *optional*):
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Custom timesteps to use for the denoising process.
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max_timesteps (`float`, defaults to 1.57080):
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The maximum timestep value used in the SCM scheduler.
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intermediate_timesteps (`float`, *optional*, defaults to 1.3):
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The intermediate timestep value used in SCM scheduler (only used when num_inference_steps=2).
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"""
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if num_inference_steps > self.config.num_train_timesteps:
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raise ValueError(
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f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
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f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
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f" maximal {self.config.num_train_timesteps} timesteps."
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)
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if timesteps is not None and len(timesteps) != num_inference_steps + 1:
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raise ValueError("If providing custom timesteps, `timesteps` must be of length `num_inference_steps + 1`.")
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if timesteps is not None and max_timesteps is not None:
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raise ValueError("If providing custom timesteps, `max_timesteps` should not be provided.")
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if timesteps is None and max_timesteps is None:
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raise ValueError("Should provide either `timesteps` or `max_timesteps`.")
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if intermediate_timesteps is not None and num_inference_steps != 2:
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raise ValueError("Intermediate timesteps for SCM is not supported when num_inference_steps != 2.")
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self.num_inference_steps = num_inference_steps
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if timesteps is not None:
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if isinstance(timesteps, list):
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self.timesteps = torch.tensor(timesteps, device=device).float()
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elif isinstance(timesteps, torch.Tensor):
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self.timesteps = timesteps.to(device).float()
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else:
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raise ValueError(f"Unsupported timesteps type: {type(timesteps)}")
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elif intermediate_timesteps is not None:
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self.timesteps = torch.tensor([max_timesteps, intermediate_timesteps, 0], device=device).float()
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else:
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# max_timesteps=arctan(80/0.5)=1.56454 is the default from sCM paper, we choose a different value here
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self.timesteps = torch.linspace(max_timesteps, 0, num_inference_steps + 1, device=device).float()
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print(f"Set timesteps: {self.timesteps}")
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self._step_index = None
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self._begin_index = None
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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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# 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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def step(
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self,
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model_output: torch.FloatTensor,
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timestep: float,
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sample: torch.FloatTensor,
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generator: torch.Generator = None,
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return_dict: bool = True,
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) -> Union[SCMSchedulerOutput, Tuple]:
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"""
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Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
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process from the learned model outputs (most often the predicted noise).
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Args:
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model_output (`torch.FloatTensor`):
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The direct output from learned diffusion model.
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timestep (`float`):
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The current discrete timestep in the diffusion chain.
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sample (`torch.FloatTensor`):
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A current instance of a sample created by the diffusion process.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~schedulers.scheduling_scm.SCMSchedulerOutput`] or `tuple`.
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Returns:
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[`~schedulers.scheduling_utils.SCMSchedulerOutput`] or `tuple`:
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If return_dict is `True`, [`~schedulers.scheduling_scm.SCMSchedulerOutput`] is returned, otherwise a
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tuple is returned where the first element is the sample tensor.
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"""
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if self.num_inference_steps is None:
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raise ValueError(
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"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
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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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# 2. compute alphas, betas
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t = self.timesteps[self.step_index + 1]
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s = self.timesteps[self.step_index]
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# 4. Different Parameterization:
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parameterization = self.config.prediction_type
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if parameterization == "trigflow":
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pred_x0 = torch.cos(s) * sample - torch.sin(s) * model_output
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else:
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raise ValueError(f"Unsupported parameterization: {parameterization}")
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# 5. Sample z ~ N(0, I), For MultiStep Inference
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# Noise is not used for one-step sampling.
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if len(self.timesteps) > 1:
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noise = (
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randn_tensor(model_output.shape, device=model_output.device, generator=generator)
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* self.config.sigma_data
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)
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prev_sample = torch.cos(t) * pred_x0 + torch.sin(t) * noise
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else:
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prev_sample = pred_x0
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self._step_index += 1
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if not return_dict:
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return (prev_sample, pred_x0)
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return SCMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_x0)
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def __len__(self):
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return self.config.num_train_timesteps
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