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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from dataclasses import dataclass
from typing import Literal
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..schedulers.scheduling_utils import SchedulerMixin
from ..utils import BaseOutput
@dataclass
class HeliosDMDSchedulerOutput(BaseOutput):
prev_sample: torch.FloatTensor
model_outputs: torch.FloatTensor | None = None
last_sample: torch.FloatTensor | None = None
this_order: int | None = None
class HeliosDMDScheduler(SchedulerMixin, ConfigMixin):
_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,
prediction_type: str = "flow_prediction",
use_flow_sigmas: bool = True,
use_dynamic_shifting: bool = False,
time_shift_type: Literal["exponential", "linear"] = "linear",
):
self.timestep_ratios = {} # The timestep ratio for each stage
self.timesteps_per_stage = {} # The detailed timesteps per stage (fix max and min per stage)
self.sigmas_per_stage = {} # always uniform [1000, 0]
self.start_sigmas = {} # for start point / upsample renoise
self.end_sigmas = {} # for end point
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
self.last_sample = None
self._step_index = None
self._begin_index = None
def init_sigmas(self):
"""
initialize the global timesteps and sigmas
"""
num_train_timesteps = self.config.num_train_timesteps
shift = self.config.shift
alphas = np.linspace(1, 1 / num_train_timesteps, num_train_timesteps + 1)
sigmas = 1.0 - alphas
sigmas = np.flip(shift * sigmas / (1 + (shift - 1) * sigmas))[:-1].copy()
sigmas = torch.from_numpy(sigmas)
timesteps = (sigmas * num_train_timesteps).clone()
self._step_index = None
self._begin_index = None
self.timesteps = timesteps
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 = 0.9999999999999999
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_max = min(self.timesteps[int(timestep_ratio[0] * training_steps)], 999)
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] = (
timesteps[:-1] if isinstance(timesteps, torch.Tensor) else torch.from_numpy(timesteps[:-1])
)
stage_sigmas = np.linspace(0.999, 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
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 | None = None,
device: str | torch.device = None,
sigmas: bool | None = None,
mu: bool | None = None,
is_amplify_first_chunk: bool = False,
):
"""
Setting the timesteps and sigmas for each stage
"""
if is_amplify_first_chunk:
num_inference_steps = num_inference_steps * 2 + 1
else:
num_inference_steps = num_inference_steps + 1
self.num_inference_steps = num_inference_steps
self.init_sigmas()
if self.config.stages == 1:
if sigmas is None:
sigmas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)[:-1].astype(
np.float32
)
if self.config.shift != 1.0:
assert not self.config.use_dynamic_shifting
sigmas = self.time_shift(self.config.shift, 1.0, sigmas)
timesteps = (sigmas * self.config.num_train_timesteps).copy()
sigmas = torch.from_numpy(sigmas)
else:
stage_timesteps = self.timesteps_per_stage[stage_index]
timesteps = np.linspace(
stage_timesteps[0].item(),
stage_timesteps[-1].item(),
num_inference_steps,
)
stage_sigmas = self.sigmas_per_stage[stage_index]
ratios = np.linspace(stage_sigmas[0].item(), stage_sigmas[-1].item(), num_inference_steps)
sigmas = torch.from_numpy(ratios)
self.timesteps = torch.from_numpy(timesteps).to(device=device)
self.sigmas = torch.cat([sigmas, torch.zeros(1)]).to(device=device)
self._step_index = None
self.reset_scheduler_history()
self.timesteps = self.timesteps[:-1]
self.sigmas = torch.cat([self.sigmas[:-2], self.sigmas[-1:]])
if self.config.use_dynamic_shifting:
assert self.config.shift == 1.0
self.sigmas = self.time_shift(mu, 1.0, self.sigmas)
if self.config.stages == 1:
self.timesteps = self.sigmas[:-1] * self.config.num_train_timesteps
else:
self.timesteps = self.timesteps_per_stage[stage_index].min() + self.sigmas[:-1] * (
self.timesteps_per_stage[stage_index].max() - self.timesteps_per_stage[stage_index].min()
)
# Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.time_shift
def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
"""
Apply time shifting to the sigmas.
Args:
mu (`float`):
The mu parameter for the time shift.
sigma (`float`):
The sigma parameter for the time shift.
t (`torch.Tensor`):
The input timesteps.
Returns:
`torch.Tensor`:
The time-shifted timesteps.
"""
if self.config.time_shift_type == "exponential":
return self._time_shift_exponential(mu, sigma, t)
elif self.config.time_shift_type == "linear":
return self._time_shift_linear(mu, sigma, t)
# Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._time_shift_exponential
def _time_shift_exponential(self, mu, sigma, t):
return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
# Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._time_shift_linear
def _time_shift_linear(self, mu, sigma, t):
return mu / (mu + (1 / t - 1) ** sigma)
# ---------------------------------- For DMD ----------------------------------
def add_noise(self, original_samples, noise, timestep, sigmas, timesteps):
sigmas = sigmas.to(noise.device)
timesteps = timesteps.to(noise.device)
timestep_id = torch.argmin((timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
sigma = sigmas[timestep_id].reshape(-1, 1, 1, 1, 1)
sample = (1 - sigma) * original_samples + sigma * noise
return sample.type_as(noise)
def convert_flow_pred_to_x0(self, flow_pred, xt, timestep, sigmas, timesteps):
# use higher precision for calculations
original_dtype = flow_pred.dtype
device = flow_pred.device
flow_pred, xt, sigmas, timesteps = (x.double().to(device) for x in (flow_pred, xt, sigmas, timesteps))
timestep_id = torch.argmin((timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
sigma_t = sigmas[timestep_id].reshape(-1, 1, 1, 1, 1)
x0_pred = xt - sigma_t * flow_pred
return x0_pred.to(original_dtype)
def step(
self,
model_output: torch.FloatTensor,
timestep: float | torch.FloatTensor = None,
sample: torch.FloatTensor = None,
generator: torch.Generator | None = None,
return_dict: bool = True,
cur_sampling_step: int = 0,
dmd_noisy_tensor: torch.FloatTensor | None = None,
dmd_sigmas: torch.FloatTensor | None = None,
dmd_timesteps: torch.FloatTensor | None = None,
all_timesteps: torch.FloatTensor | None = None,
) -> HeliosDMDSchedulerOutput | tuple:
pred_image_or_video = self.convert_flow_pred_to_x0(
flow_pred=model_output,
xt=sample,
timestep=torch.full((model_output.shape[0],), timestep, dtype=torch.long, device=model_output.device),
sigmas=dmd_sigmas,
timesteps=dmd_timesteps,
)
if cur_sampling_step < len(all_timesteps) - 1:
prev_sample = self.add_noise(
pred_image_or_video,
dmd_noisy_tensor,
torch.full(
(model_output.shape[0],),
all_timesteps[cur_sampling_step + 1],
dtype=torch.long,
device=model_output.device,
),
sigmas=dmd_sigmas,
timesteps=dmd_timesteps,
)
else:
prev_sample = pred_image_or_video
if not return_dict:
return (prev_sample,)
return HeliosDMDSchedulerOutput(prev_sample=prev_sample)
def reset_scheduler_history(self):
self._step_index = None
self._begin_index = None
def __len__(self):
return self.config.num_train_timesteps
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