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from dataclasses import dataclass
from typing import Optional, Tuple, Union, List
import math
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import BaseOutput, logging
from diffusers.utils.torch_utils import randn_tensor
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from IPython import embed
@dataclass
class FlowMatchEulerDiscreteSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
"""
prev_sample: torch.FloatTensor
class PyramidFlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
Euler scheduler.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`int`, defaults to 1000):
The number of diffusion steps to train the model.
timestep_spacing (`str`, defaults to `"linspace"`):
The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
shift (`float`, defaults to 1.0):
The shift value for the timestep schedule.
"""
_compatibles = []
order = 1
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
shift: float = 1.0, # Following Stable diffusion 3,
stages: int = 3,
stage_range: List = [0, 1/3, 2/3, 1],
gamma: float = 1/3,
):
self.timestep_ratios = {} # The timestep ratio for each stage
self.timesteps_per_stage = {} # The detailed timesteps per stage
self.sigmas_per_stage = {}
self.start_sigmas = {}
self.end_sigmas = {}
self.ori_start_sigmas = {}
# self.init_sigmas()
self.init_sigmas_for_each_stage()
self.sigma_min = self.sigmas[-1].item()
self.sigma_max = self.sigmas[0].item()
self.gamma = gamma
def init_sigmas(self):
"""
initialize the global timesteps and sigmas
"""
num_train_timesteps = self.config.num_train_timesteps
shift = self.config.shift
timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
sigmas = timesteps / num_train_timesteps
sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
self.timesteps = sigmas * num_train_timesteps
self._step_index = None
self._begin_index = None
self.sigmas = sigmas.to("cpu") # to avoid too much CPU/GPU communication
def init_sigmas_for_each_stage(self):
"""
Init the timesteps for each stage
"""
self.init_sigmas()
stage_distance = []
stages = self.config.stages
training_steps = self.config.num_train_timesteps
stage_range = self.config.stage_range
# Init the start and end point of each stage
for i_s in range(stages):
# To decide the start and ends point
start_indice = int(stage_range[i_s] * training_steps)
start_indice = max(start_indice, 0)
end_indice = int(stage_range[i_s+1] * training_steps)
end_indice = min(end_indice, training_steps)
start_sigma = self.sigmas[start_indice].item()
end_sigma = self.sigmas[end_indice].item() if end_indice < training_steps else 0.0
self.ori_start_sigmas[i_s] = start_sigma
if i_s != 0:
ori_sigma = 1 - start_sigma
gamma = self.config.gamma
corrected_sigma = (1 / (math.sqrt(1 + (1 / gamma)) * (1 - ori_sigma) + ori_sigma)) * ori_sigma
# corrected_sigma = 1 / (2 - ori_sigma) * ori_sigma
start_sigma = 1 - corrected_sigma
stage_distance.append(start_sigma - end_sigma)
self.start_sigmas[i_s] = start_sigma
self.end_sigmas[i_s] = end_sigma
# Determine the ratio of each stage according to flow length
tot_distance = sum(stage_distance)
for i_s in range(stages):
if i_s == 0:
start_ratio = 0.0
else:
start_ratio = sum(stage_distance[:i_s]) / tot_distance
if i_s == stages - 1:
end_ratio = 1.0
else:
end_ratio = sum(stage_distance[:i_s+1]) / tot_distance
self.timestep_ratios[i_s] = (start_ratio, end_ratio)
# Determine the timesteps and sigmas for each stage
for i_s in range(stages):
timestep_ratio = self.timestep_ratios[i_s]
timestep_max = self.timesteps[int(timestep_ratio[0] * training_steps)]
timestep_min = self.timesteps[min(int(timestep_ratio[1] * training_steps), training_steps - 1)]
timesteps = np.linspace(
timestep_max, timestep_min, training_steps + 1,
)
self.timesteps_per_stage[i_s] = torch.from_numpy(timesteps[:-1])
stage_sigmas = np.linspace(
1, 0, training_steps + 1,
)
self.sigmas_per_stage[i_s] = torch.from_numpy(stage_sigmas[:-1])
@property
def step_index(self):
"""
The index counter for current timestep. It will increase 1 after each scheduler step.
"""
return self._step_index
@property
def begin_index(self):
"""
The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
"""
return self._begin_index
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
def set_begin_index(self, begin_index: int = 0):
"""
Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
Args:
begin_index (`int`):
The begin index for the scheduler.
"""
self._begin_index = begin_index
def _sigma_to_t(self, sigma):
return sigma * self.config.num_train_timesteps
def set_timesteps(self, num_inference_steps: int, stage_index: int, device: Union[str, torch.device] = None):
"""
Setting the timesteps and sigmas for each stage
"""
self.num_inference_steps = num_inference_steps
training_steps = self.config.num_train_timesteps
self.init_sigmas()
stage_timesteps = self.timesteps_per_stage[stage_index]
timestep_max = stage_timesteps[0].item()
timestep_min = stage_timesteps[-1].item()
timesteps = np.linspace(
timestep_max, timestep_min, num_inference_steps,
)
self.timesteps = torch.from_numpy(timesteps).to(device=device)
stage_sigmas = self.sigmas_per_stage[stage_index]
sigma_max = stage_sigmas[0].item()
sigma_min = stage_sigmas[-1].item()
ratios = np.linspace(
sigma_max, sigma_min, num_inference_steps
)
sigmas = torch.from_numpy(ratios).to(device=device)
self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
self._step_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[FlowMatchEulerDiscreteSchedulerOutput, 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_euler_discrete.EulerDiscreteSchedulerOutput`] or
tuple.
Returns:
[`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] 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"
" `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
" one of the `scheduler.timesteps` as a timestep."
),
)
if self.step_index is None:
self._step_index = 0
# 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]
prev_sample = sample + (sigma_next - sigma) * model_output
# Cast sample back to model compatible dtype
prev_sample = prev_sample.to(model_output.dtype)
# upon completion increase step index by one
self._step_index += 1
if not return_dict:
return (prev_sample,)
return FlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample)
def __len__(self):
return self.config.num_train_timesteps
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