562 lines
23 KiB
Python
562 lines
23 KiB
Python
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# Copyright 2025 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 ..configuration_utils import ConfigMixin, register_to_config
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from ..utils import BaseOutput, is_scipy_available, logging
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from ..utils.torch_utils import randn_tensor
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from .scheduling_utils import SchedulerMixin
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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 FlowMatchLCMSchedulerOutput(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 FlowMatchLCMScheduler(SchedulerMixin, ConfigMixin):
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"""
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LCM scheduler for Flow Matching.
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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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shift (`float`, defaults to 1.0):
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The shift value for the timestep schedule.
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use_dynamic_shifting (`bool`, defaults to False):
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Whether to apply timestep shifting on-the-fly based on the image resolution.
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base_shift (`float`, defaults to 0.5):
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Value to stabilize image generation. Increasing `base_shift` reduces variation and image is more consistent
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with desired output.
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max_shift (`float`, defaults to 1.15):
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Value change allowed to latent vectors. Increasing `max_shift` encourages more variation and image may be
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more exaggerated or stylized.
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base_image_seq_len (`int`, defaults to 256):
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The base image sequence length.
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max_image_seq_len (`int`, defaults to 4096):
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The maximum image sequence length.
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invert_sigmas (`bool`, defaults to False):
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Whether to invert the sigmas.
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shift_terminal (`float`, defaults to None):
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The end value of the shifted timestep schedule.
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use_karras_sigmas (`bool`, defaults to False):
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Whether to use Karras sigmas for step sizes in the noise schedule during sampling.
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use_exponential_sigmas (`bool`, defaults to False):
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Whether to use exponential sigmas for step sizes in the noise schedule during sampling.
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use_beta_sigmas (`bool`, defaults to False):
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Whether to use beta sigmas for step sizes in the noise schedule during sampling.
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time_shift_type (`str`, defaults to "exponential"):
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The type of dynamic resolution-dependent timestep shifting to apply. Either "exponential" or "linear".
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scale_factors ('list', defaults to None)
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It defines how to scale the latents at which predictions are made.
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upscale_mode ('str', defaults to 'bicubic')
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Upscaling method, applied if scale-wise generation is considered
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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: bool = 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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shift_terminal: Optional[float] = None,
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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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time_shift_type: str = "exponential",
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scale_factors: Optional[List[float]] = None,
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upscale_mode: Optional[str] = "bicubic",
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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 time_shift_type not in {"exponential", "linear"}:
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raise ValueError("`time_shift_type` must either be 'exponential' or 'linear'.")
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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._shift = shift
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self._init_size = None
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self._scale_factors = scale_factors
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self._upscale_mode = upscale_mode
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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 shift(self):
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"""
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The value used for shifting.
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"""
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return self._shift
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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 set_shift(self, shift: float):
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self._shift = shift
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def set_scale_factors(self, scale_factors: list, upscale_mode):
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"""
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Sets scale factors for a scale-wise generation regime.
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Args:
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scale_factors (`list`):
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The scale factors for each step
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upscale_mode (`str`):
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Upscaling method
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"""
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self._scale_factors = scale_factors
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self._upscale_mode = upscale_mode
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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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if self.config.time_shift_type == "exponential":
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return self._time_shift_exponential(mu, sigma, t)
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elif self.config.time_shift_type == "linear":
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return self._time_shift_linear(mu, sigma, t)
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def stretch_shift_to_terminal(self, t: torch.Tensor) -> torch.Tensor:
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r"""
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Stretches and shifts the timestep schedule to ensure it terminates at the configured `shift_terminal` config
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value.
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Reference:
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https://github.com/Lightricks/LTX-Video/blob/a01a171f8fe3d99dce2728d60a73fecf4d4238ae/ltx_video/schedulers/rf.py#L51
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Args:
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t (`torch.Tensor`):
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A tensor of timesteps to be stretched and shifted.
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Returns:
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`torch.Tensor`:
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A tensor of adjusted timesteps such that the final value equals `self.config.shift_terminal`.
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"""
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one_minus_z = 1 - t
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scale_factor = one_minus_z[-1] / (1 - self.config.shift_terminal)
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stretched_t = 1 - (one_minus_z / scale_factor)
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return stretched_t
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def set_timesteps(
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self,
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num_inference_steps: Optional[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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timesteps: Optional[List[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`, *optional*):
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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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sigmas (`List[float]`, *optional*):
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Custom values for sigmas to be used for each diffusion step. If `None`, the sigmas are computed
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automatically.
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mu (`float`, *optional*):
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Determines the amount of shifting applied to sigmas when performing resolution-dependent timestep
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shifting.
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timesteps (`List[float]`, *optional*):
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Custom values for timesteps to be used for each diffusion step. If `None`, the timesteps are computed
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automatically.
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"""
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if self.config.use_dynamic_shifting and mu is None:
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raise ValueError("`mu` must be passed when `use_dynamic_shifting` is set to be `True`")
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if sigmas is not None and timesteps is not None:
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if len(sigmas) != len(timesteps):
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raise ValueError("`sigmas` and `timesteps` should have the same length")
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if num_inference_steps is not None:
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if (sigmas is not None and len(sigmas) != num_inference_steps) or (
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timesteps is not None and len(timesteps) != num_inference_steps
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):
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raise ValueError(
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"`sigmas` and `timesteps` should have the same length as num_inference_steps, if `num_inference_steps` is provided"
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)
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else:
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num_inference_steps = len(sigmas) if sigmas is not None else len(timesteps)
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self.num_inference_steps = num_inference_steps
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# 1. Prepare default sigmas
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is_timesteps_provided = timesteps is not None
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if is_timesteps_provided:
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timesteps = np.array(timesteps).astype(np.float32)
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if sigmas is None:
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if timesteps 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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# 2. Perform timestep shifting. Either no shifting is applied, or resolution-dependent shifting of
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# "exponential" or "linear" type is applied
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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.shift * sigmas / (1 + (self.shift - 1) * sigmas)
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# 3. If required, stretch the sigmas schedule to terminate at the configured `shift_terminal` value
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if self.config.shift_terminal:
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sigmas = self.stretch_shift_to_terminal(sigmas)
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# 4. If required, convert sigmas to one of karras, exponential, or beta sigma schedules
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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)
|
||
|
|
elif self.config.use_beta_sigmas:
|
||
|
|
sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
|
||
|
|
|
||
|
|
# 5. Convert sigmas and timesteps to tensors and move to specified device
|
||
|
|
sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
|
||
|
|
if not is_timesteps_provided:
|
||
|
|
timesteps = sigmas * self.config.num_train_timesteps
|
||
|
|
else:
|
||
|
|
timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32, device=device)
|
||
|
|
|
||
|
|
# 6. Append the terminal sigma value.
|
||
|
|
# If a model requires inverted sigma schedule for denoising but timesteps without inversion, the
|
||
|
|
# `invert_sigmas` flag can be set to `True`. This case is only required in Mochi
|
||
|
|
if self.config.invert_sigmas:
|
||
|
|
sigmas = 1.0 - sigmas
|
||
|
|
timesteps = sigmas * self.config.num_train_timesteps
|
||
|
|
sigmas = torch.cat([sigmas, torch.ones(1, device=sigmas.device)])
|
||
|
|
else:
|
||
|
|
sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
|
||
|
|
|
||
|
|
self.timesteps = timesteps
|
||
|
|
self.sigmas = sigmas
|
||
|
|
self._step_index = None
|
||
|
|
self._begin_index = None
|
||
|
|
|
||
|
|
def index_for_timestep(self, timestep, schedule_timesteps=None):
|
||
|
|
if schedule_timesteps is None:
|
||
|
|
schedule_timesteps = self.timesteps
|
||
|
|
|
||
|
|
indices = (schedule_timesteps == timestep).nonzero()
|
||
|
|
|
||
|
|
# The sigma index that is taken for the **very** first `step`
|
||
|
|
# is always the second index (or the last index if there is only 1)
|
||
|
|
# This way we can ensure we don't accidentally skip a sigma in
|
||
|
|
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
|
||
|
|
pos = 1 if len(indices) > 1 else 0
|
||
|
|
|
||
|
|
return indices[pos].item()
|
||
|
|
|
||
|
|
def _init_step_index(self, timestep):
|
||
|
|
if self.begin_index is None:
|
||
|
|
if isinstance(timestep, torch.Tensor):
|
||
|
|
timestep = timestep.to(self.timesteps.device)
|
||
|
|
self._step_index = self.index_for_timestep(timestep)
|
||
|
|
else:
|
||
|
|
self._step_index = self._begin_index
|
||
|
|
|
||
|
|
def step(
|
||
|
|
self,
|
||
|
|
model_output: torch.FloatTensor,
|
||
|
|
timestep: Union[float, torch.FloatTensor],
|
||
|
|
sample: torch.FloatTensor,
|
||
|
|
generator: Optional[torch.Generator] = None,
|
||
|
|
return_dict: bool = True,
|
||
|
|
) -> Union[FlowMatchLCMSchedulerOutput, 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.FloatTensor`):
|
||
|
|
The direct output from learned diffusion model.
|
||
|
|
timestep (`float`):
|
||
|
|
The current discrete timestep in the diffusion chain.
|
||
|
|
sample (`torch.FloatTensor`):
|
||
|
|
A current instance of a sample created by the diffusion process.
|
||
|
|
generator (`torch.Generator`, *optional*):
|
||
|
|
A random number generator.
|
||
|
|
return_dict (`bool`):
|
||
|
|
Whether or not to return a [`~schedulers.scheduling_flow_match_lcm.FlowMatchLCMSchedulerOutput`] or
|
||
|
|
tuple.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
[`~schedulers.scheduling_flow_match_lcm.FlowMatchLCMSchedulerOutput`] or `tuple`:
|
||
|
|
If return_dict is `True`, [`~schedulers.scheduling_flow_match_lcm.FlowMatchLCMSchedulerOutput`] is
|
||
|
|
returned, otherwise a tuple is returned where the first element is the sample tensor.
|
||
|
|
"""
|
||
|
|
|
||
|
|
if (
|
||
|
|
isinstance(timestep, int)
|
||
|
|
or isinstance(timestep, torch.IntTensor)
|
||
|
|
or isinstance(timestep, torch.LongTensor)
|
||
|
|
):
|
||
|
|
raise ValueError(
|
||
|
|
(
|
||
|
|
"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
|
||
|
|
" `FlowMatchLCMScheduler.step()` is not supported. Make sure to pass"
|
||
|
|
" one of the `scheduler.timesteps` as a timestep."
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
if self._scale_factors and self._upscale_mode and len(self.timesteps) != len(self._scale_factors) + 1:
|
||
|
|
raise ValueError(
|
||
|
|
"`_scale_factors` should have the same length as `timesteps` - 1, if `_scale_factors` are set."
|
||
|
|
)
|
||
|
|
|
||
|
|
if self._init_size is None or self.step_index is None:
|
||
|
|
self._init_size = model_output.size()[2:]
|
||
|
|
|
||
|
|
if self.step_index is None:
|
||
|
|
self._init_step_index(timestep)
|
||
|
|
|
||
|
|
# Upcast to avoid precision issues when computing prev_sample
|
||
|
|
sample = sample.to(torch.float32)
|
||
|
|
|
||
|
|
sigma = self.sigmas[self.step_index]
|
||
|
|
sigma_next = self.sigmas[self.step_index + 1]
|
||
|
|
x0_pred = sample - sigma * model_output
|
||
|
|
|
||
|
|
if self._scale_factors and self._upscale_mode:
|
||
|
|
if self._step_index < len(self._scale_factors):
|
||
|
|
size = [round(self._scale_factors[self._step_index] * size) for size in self._init_size]
|
||
|
|
x0_pred = torch.nn.functional.interpolate(x0_pred, size=size, mode=self._upscale_mode)
|
||
|
|
|
||
|
|
noise = randn_tensor(x0_pred.shape, generator=generator, device=x0_pred.device, dtype=x0_pred.dtype)
|
||
|
|
prev_sample = (1 - sigma_next) * x0_pred + sigma_next * noise
|
||
|
|
|
||
|
|
# upon completion increase step index by one
|
||
|
|
self._step_index += 1
|
||
|
|
# Cast sample back to model compatible dtype
|
||
|
|
prev_sample = prev_sample.to(model_output.dtype)
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
return (prev_sample,)
|
||
|
|
|
||
|
|
return FlowMatchLCMSchedulerOutput(prev_sample=prev_sample)
|
||
|
|
|
||
|
|
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
|
||
|
|
def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:
|
||
|
|
"""Constructs the noise schedule of Karras et al. (2022)."""
|
||
|
|
|
||
|
|
# 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()
|
||
|
|
|
||
|
|
rho = 7.0 # 7.0 is the value used in the paper
|
||
|
|
ramp = np.linspace(0, 1, 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
|
||
|
|
|
||
|
|
def _time_shift_exponential(self, mu, sigma, t):
|
||
|
|
return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
|
||
|
|
|
||
|
|
def _time_shift_linear(self, mu, sigma, t):
|
||
|
|
return mu / (mu + (1 / t - 1) ** sigma)
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
return self.config.num_train_timesteps
|