mirror of https://github.com/vladmandic/automatic
429 lines
17 KiB
Python
429 lines
17 KiB
Python
# Copyright 2024 Stability AI, Katherine Crowson 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 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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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.schedulers.scheduling_utils import SchedulerMixin
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from diffusers.utils import BaseOutput, is_scipy_available, logging
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from diffusers.utils.torch_utils import randn_tensor
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if is_scipy_available():
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import scipy.stats
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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@dataclass
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class FlashFlowMatchEulerDiscreteSchedulerOutput(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.FloatTensor` 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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"""
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prev_sample: torch.FloatTensor
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class FlashFlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
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"""
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Euler scheduler.
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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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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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shift (`float`, defaults to 1.0):
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The shift value for the timestep schedule.
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"""
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_compatibles = []
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order = 1
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@register_to_config
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def __init__(
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self,
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num_train_timesteps: int = 1000,
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shift: float = 1.0,
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use_dynamic_shifting=False,
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base_shift: Optional[float] = 0.5,
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max_shift: Optional[float] = 1.15,
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base_image_seq_len: Optional[int] = 256,
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max_image_seq_len: Optional[int] = 4096,
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invert_sigmas: bool = False,
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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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):
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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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timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
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timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
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sigmas = timesteps / num_train_timesteps
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if not use_dynamic_shifting:
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# when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution
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sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
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self.timesteps = sigmas * num_train_timesteps
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self._step_index = None
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self._begin_index = None
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self.sigmas = sigmas.to("cpu") # to avoid too much CPU/GPU communication
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self.sigma_min = self.sigmas[-1].item()
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self.sigma_max = self.sigmas[0].item()
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@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_noise(
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self,
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sample: torch.FloatTensor,
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timestep: Union[float, torch.FloatTensor],
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noise: Optional[torch.FloatTensor] = None,
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) -> torch.FloatTensor:
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"""
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Forward process in flow-matching
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Args:
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sample (`torch.FloatTensor`):
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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.FloatTensor`:
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A scaled input sample.
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"""
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# Make sure sigmas and timesteps have the same device and dtype as original_samples
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sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype)
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if sample.device.type == "mps" and torch.is_floating_point(timestep):
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# mps does not support float64
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schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32)
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timestep = timestep.to(sample.device, dtype=torch.float32)
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else:
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schedule_timesteps = self.timesteps.to(sample.device)
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timestep = timestep.to(sample.device)
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# self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index
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if self.begin_index is None:
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step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timestep]
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elif self.step_index is not None:
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# add_noise is called after first denoising step (for inpainting)
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step_indices = [self.step_index] * timestep.shape[0]
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else:
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# add noise is called before first denoising step to create initial latent(img2img)
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step_indices = [self.begin_index] * timestep.shape[0]
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sigma = sigmas[step_indices].flatten()
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while len(sigma.shape) < len(sample.shape):
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sigma = sigma.unsqueeze(-1)
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sample = sigma * noise + (1.0 - sigma) * sample
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return sample
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def _sigma_to_t(self, sigma):
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return sigma * self.config.num_train_timesteps
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def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
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return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
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def set_timesteps(
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self,
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num_inference_steps: int = None,
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device: Union[str, torch.device] = None,
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sigmas: Optional[List[float]] = None,
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mu: Optional[float] = 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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if self.config.use_dynamic_shifting and mu is None:
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raise ValueError(" you have a pass a value for `mu` when `use_dynamic_shifting` is set to be `True`")
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if sigmas is None:
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timesteps = np.linspace(
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self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps
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)
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sigmas = timesteps / self.config.num_train_timesteps
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else:
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sigmas = np.array(sigmas).astype(np.float32)
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num_inference_steps = len(sigmas)
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self.num_inference_steps = num_inference_steps
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if self.config.use_dynamic_shifting:
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sigmas = self.time_shift(mu, 1.0, sigmas)
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else:
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sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas)
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if self.config.use_karras_sigmas:
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sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
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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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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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sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
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timesteps = sigmas * self.config.num_train_timesteps
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if self.config.invert_sigmas:
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sigmas = 1.0 - sigmas
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timesteps = sigmas * self.config.num_train_timesteps
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sigmas = torch.cat([sigmas, torch.ones(1, device=sigmas.device)])
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else:
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sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
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self.timesteps = timesteps.to(device=device)
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self.sigmas = sigmas
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self._step_index = None
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self._begin_index = None
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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 _init_step_index(self, timestep):
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if self.begin_index is None:
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if isinstance(timestep, torch.Tensor):
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timestep = timestep.to(self.timesteps.device)
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self._step_index = self.index_for_timestep(timestep)
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else:
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self._step_index = self._begin_index
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def step(
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self,
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model_output: torch.FloatTensor,
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timestep: Union[float, torch.FloatTensor],
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sample: torch.FloatTensor,
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s_churn: float = 0.0,
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s_tmin: float = 0.0,
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s_tmax: float = float("inf"),
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s_noise: float = 1.0,
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generator: Optional[torch.Generator] = None,
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return_dict: bool = True,
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) -> Union[FlashFlowMatchEulerDiscreteSchedulerOutput, 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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s_churn (`float`):
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s_tmin (`float`):
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s_tmax (`float`):
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s_noise (`float`, defaults to 1.0):
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Scaling factor for noise added to the sample.
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generator (`torch.Generator`, *optional*):
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A random number generator.
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return_dict (`bool`):
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Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or
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tuple.
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Returns:
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[`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`:
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If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is
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returned, otherwise a tuple is returned where the first element is the sample tensor.
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"""
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if (
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isinstance(timestep, int)
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or isinstance(timestep, torch.IntTensor)
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or isinstance(timestep, torch.LongTensor)
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):
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raise ValueError(
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(
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"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
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" `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
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" one of the `scheduler.timesteps` as a timestep."
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),
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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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# Upcast to avoid precision issues when computing prev_sample
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sigma = self.sigmas[self.step_index]
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# Upcast to avoid precision issues when computing prev_sample
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sample = sample.to(torch.float32)
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denoised = sample - model_output * sigma
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if self.step_index < self.num_inference_steps - 1:
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sigma_next = self.sigmas[self.step_index + 1]
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noise = randn_tensor(
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model_output.shape,
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generator=generator,
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device=model_output.device,
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dtype=denoised.dtype,
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)
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sample = sigma_next * noise + (1.0 - sigma_next) * denoised
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self._step_index += 1
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sample = sample.to(model_output.dtype)
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if not return_dict:
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return (sample,)
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return FlashFlowMatchEulerDiscreteSchedulerOutput(prev_sample=sample)
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# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
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def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:
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"""Constructs the noise schedule of Karras et al. (2022)."""
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# Hack to make sure that other schedulers which copy this function don't break
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# TODO: Add this logic to the other schedulers
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if hasattr(self.config, "sigma_min"):
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sigma_min = self.config.sigma_min
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else:
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sigma_min = None
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if hasattr(self.config, "sigma_max"):
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sigma_max = self.config.sigma_max
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else:
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sigma_max = None
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sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()
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sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()
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rho = 7.0 # 7.0 is the value used in the paper
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ramp = np.linspace(0, 1, num_inference_steps)
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min_inv_rho = sigma_min ** (1 / rho)
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max_inv_rho = sigma_max ** (1 / rho)
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sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
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return sigmas
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# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_exponential
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def _convert_to_exponential(self, in_sigmas: torch.Tensor, num_inference_steps: int) -> torch.Tensor:
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"""Constructs an exponential noise schedule."""
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# Hack to make sure that other schedulers which copy this function don't break
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# TODO: Add this logic to the other schedulers
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if hasattr(self.config, "sigma_min"):
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sigma_min = self.config.sigma_min
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else:
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sigma_min = None
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if hasattr(self.config, "sigma_max"):
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sigma_max = self.config.sigma_max
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else:
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sigma_max = None
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sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()
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sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()
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sigmas = np.exp(np.linspace(math.log(sigma_max), math.log(sigma_min), num_inference_steps))
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return sigmas
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# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_beta
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def _convert_to_beta(
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self, in_sigmas: torch.Tensor, num_inference_steps: int, alpha: float = 0.6, beta: float = 0.6
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) -> torch.Tensor:
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"""From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024)"""
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# Hack to make sure that other schedulers which copy this function don't break
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# TODO: Add this logic to the other schedulers
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if hasattr(self.config, "sigma_min"):
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sigma_min = self.config.sigma_min
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else:
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sigma_min = None
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if hasattr(self.config, "sigma_max"):
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sigma_max = self.config.sigma_max
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else:
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sigma_max = None
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sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item()
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sigma_max = sigma_max if sigma_max is not None else in_sigmas[0].item()
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sigmas = np.array(
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[
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sigma_min + (ppf * (sigma_max - sigma_min))
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for ppf in [
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scipy.stats.beta.ppf(timestep, alpha, beta)
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for timestep in 1 - np.linspace(0, 1, num_inference_steps)
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]
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]
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)
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return sigmas
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def __len__(self):
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return self.config.num_train_timesteps
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