Spaces:
Running on Zero
Running on Zero
File size: 9,592 Bytes
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 | # Copyright (c) 2025 Hansheng Chen
import inspect
import numpy as np
import torch
import diffusers
from dataclasses import dataclass
from typing import Optional, Tuple, Union
from diffusers.configuration_utils import register_to_config
from diffusers.utils import BaseOutput
from diffusers.schedulers import SchedulerMixin
from diffusers.configuration_utils import ConfigMixin
@dataclass
class FlowWrapperSchedulerOutput(BaseOutput):
prev_sample: torch.FloatTensor
class FlowAdapterScheduler(SchedulerMixin, ConfigMixin):
order = 1
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
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,
base_scheduler='UniPCMultistep',
eps=1e-4,
**kwargs):
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
alphas = 1 - self.sigmas
base_scheduler_class = getattr(diffusers.schedulers, base_scheduler + 'Scheduler', None)
if base_scheduler_class is None:
raise AttributeError(f'Cannot find base_scheduler [{base_scheduler}].')
if base_scheduler in ['EulerDiscrete', 'EulerAncestralDiscrete']:
assert kwargs.get('prediction_type', 'epsilon') == 'epsilon'
kwargs['prediction_type'] = 'epsilon'
self.scales = ((alphas ** 2 + self.sigmas ** 2) / (
1 + (self.sigmas / alphas.clamp(min=self.config.eps)) ** 2)).sqrt()
elif base_scheduler in [
'DPMSolverSinglestep', 'DPMSolverMultistep', 'DEISMultistep', 'SASolver']:
assert kwargs.get('prediction_type', 'epsilon') == 'epsilon'
kwargs['prediction_type'] = 'epsilon'
self.scales = (alphas ** 2 + self.sigmas ** 2).sqrt()
elif base_scheduler in ['UniPCMultistep']:
self.scales = torch.ones_like(alphas)
assert kwargs.get('prediction_type', 'flow_prediction') == 'flow_prediction'
kwargs['prediction_type'] = 'flow_prediction'
kwargs['use_flow_sigmas'] = True
else:
raise AttributeError(f'Unsupported base_scheduler [{base_scheduler}].')
signatures = inspect.signature(base_scheduler_class).parameters.keys()
if 'final_sigmas_type' in signatures:
kwargs['final_sigmas_type'] = 'zero'
if 'lower_order_final' in signatures:
kwargs['lower_order_final'] = True
self.base_scheduler = base_scheduler_class(
num_train_timesteps=num_train_timesteps,
**kwargs)
self.base_scheduler.timesteps = self.timesteps
if self.config.base_scheduler in ['EulerDiscrete', 'EulerAncestralDiscrete']:
self.base_scheduler.sigmas = self.sigmas / alphas.clamp(min=self.config.eps)
elif self.config.base_scheduler in [
'DPMSolverSinglestep', 'DPMSolverMultistep', 'DEISMultistep', 'SASolver']:
self.base_scheduler.sigmas = self.sigmas / alphas.clamp(min=self.config.eps)
elif self.config.base_scheduler in ['UniPCMultistep']:
self.base_scheduler.sigmas = self.sigmas
else:
raise AttributeError(f'Unsupported base_scheduler [{self.config.base_scheduler}].')
self._step_index = None
self._begin_index = None
@property
def step_index(self):
return self._step_index
@property
def begin_index(self):
return self._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)
if self.config.base_scheduler in ['DEISMultistep', 'SASolver']:
self.sigmas = torch.cat(
[sigmas, torch.tensor([self.config.eps], dtype=torch.float32, device=sigmas.device)])
else:
self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
alphas = 1 - self.sigmas
self.base_scheduler.set_timesteps(num_inference_steps, device=device)
self.base_scheduler.timesteps = self.timesteps
if self.config.base_scheduler in ['EulerDiscrete', 'EulerAncestralDiscrete']:
self.base_scheduler.sigmas = self.sigmas / alphas.clamp(min=self.config.eps)
self.scales = ((alphas ** 2 + self.sigmas ** 2) / (
1 + (self.sigmas / alphas.clamp(min=self.config.eps)) ** 2)).sqrt()
elif self.config.base_scheduler in [
'DPMSolverSinglestep', 'DPMSolverMultistep', 'DEISMultistep', 'SASolver']:
self.base_scheduler.sigmas = self.sigmas / alphas.clamp(min=self.config.eps)
self.scales = (alphas**2 + self.sigmas**2).sqrt()
elif self.config.base_scheduler in ['UniPCMultistep']:
self.base_scheduler.sigmas = self.sigmas.clamp(max=1 - self.config.eps)
self.scales = torch.ones_like(alphas)
else:
raise AttributeError(f'Unsupported base_scheduler [{self.config.base_scheduler}].')
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,
prediction_type='u',
eps=1e-6) -> Union[FlowWrapperSchedulerOutput, Tuple]:
assert prediction_type in ['u', 'x0']
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]
alpha = 1 - sigma
scale = self.scales[self.step_index]
next_scale = self.scales[self.step_index + 1]
if hasattr(self.base_scheduler, 'is_scale_input_called'):
self.base_scheduler.is_scale_input_called = True
kwargs = dict(return_dict=False)
if generator is not None:
kwargs.update(generator=generator)
if self.config.base_scheduler in ['UniPCMultistep']: # to u
if prediction_type == 'u':
model_output = model_output
else:
model_output = (sample - model_output) / sigma.clamp(min=eps)
else: # to epsilon
if prediction_type == 'u':
model_output = sample + alpha * model_output
else:
model_output = (sample - alpha * model_output) / sigma.clamp(min=eps)
prev_sample = self.base_scheduler.step(
model_output,
timestep,
sample / scale,
**kwargs
)[0] * next_scale
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 FlowWrapperSchedulerOutput(prev_sample=prev_sample)
|