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Running on Zero
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f0395ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | # Copyright (c) 2025 Hansheng Chen
import numpy as np
import torch
from dataclasses import dataclass
from typing import Optional, Tuple, Union
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
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class FlowSDESchedulerOutput(BaseOutput):
prev_sample: torch.FloatTensor
class FlowSDEScheduler(SchedulerMixin, ConfigMixin):
_compatibles = []
order = 1
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
h: Union[float, str] = 1.0,
shift: float = 1.0,
use_dynamic_shifting=False,
base_seq_len=256,
max_seq_len=4096,
base_logshift=0.5,
max_logshift=1.15,
terminal_sigma=None):
sigmas = torch.from_numpy(1 - np.linspace(
0, 1, num_train_timesteps, dtype=np.float32, endpoint=False))
self.sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
self.timesteps = self.sigmas * num_train_timesteps
self._step_index = None
self._begin_index = None
self.sigma_min = self.sigmas[-1].item()
self.sigma_max = self.sigmas[0].item()
@property
def step_index(self):
return self._step_index
@property
def begin_index(self):
return self._begin_index
def set_begin_index(self, begin_index: int = 0):
self._begin_index = begin_index
def get_shift(self, seq_len=None):
if self.config.use_dynamic_shifting and seq_len is not None:
m = (self.config.max_logshift - self.config.base_logshift
) / (self.config.max_seq_len - self.config.base_seq_len)
logshift = (seq_len - self.config.base_seq_len) * m + self.config.base_logshift
if isinstance(logshift, torch.Tensor):
shift = torch.exp(logshift)
else:
shift = np.exp(logshift)
else:
shift = self.config.shift
return shift
def stretch_to_terminal(self, sigma):
one_minus_sigma = 1 - sigma
stretched_sigma = 1 - (one_minus_sigma * (1 - self.config.terminal_sigma) / one_minus_sigma[-1])
return stretched_sigma
def set_timesteps(self, num_inference_steps: int, seq_len=None, device=None):
self.num_inference_steps = num_inference_steps
sigmas = torch.from_numpy(np.linspace(
1, 0, num_inference_steps, dtype=np.float32, endpoint=False))
shift = self.get_shift(seq_len=seq_len)
sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
if self.config.terminal_sigma is not None:
sigmas = self.stretch_to_terminal(sigmas)
self.timesteps = (sigmas * self.config.num_train_timesteps).to(device)
self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
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()
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,
prediction_type='u',
eps=1e-6) -> Union[FlowSDESchedulerOutput, Tuple]:
assert prediction_type in ['u', 'x0']
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._init_step_index(timestep)
# Upcast to avoid precision issues when computing prev_sample
ori_dtype = model_output.dtype
sample = sample.to(torch.float32)
model_output = model_output.to(torch.float32)
sigma = self.sigmas[self.step_index]
sigma_to = self.sigmas[self.step_index + 1]
alpha = 1 - sigma
alpha_to = 1 - sigma_to
if prediction_type == 'u':
x0 = sample - sigma * model_output
epsilon = sample + alpha * model_output
else:
x0 = model_output
epsilon = (sample - alpha * x0) / sigma.clamp(min=eps)
noise = randn_tensor(
model_output.shape, dtype=torch.float32, device=model_output.device, generator=generator)
if self.config.h == 'inf':
m = torch.zeros_like(sigma)
elif self.config.h == 0.0:
m = torch.ones_like(sigma)
else:
assert self.config.h > 0.0
h2 = self.config.h * self.config.h
m = (sigma_to * alpha / (sigma * alpha_to).clamp(min=eps)) ** h2
prev_sample = alpha_to * x0 + sigma_to * (m * epsilon + (1 - m.square()).clamp(min=0).sqrt() * noise)
# Cast sample back to model compatible dtype
prev_sample = prev_sample.to(ori_dtype)
# upon completion increase step index by one
self._step_index += 1
if not return_dict:
return (prev_sample,)
return FlowSDESchedulerOutput(prev_sample=prev_sample)
def __len__(self):
return self.config.num_train_timesteps
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