forked from comfyanonymous/ComfyUI
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmodel_sampling.py
272 lines (210 loc) · 9.51 KB
/
model_sampling.py
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
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
import torch
from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
import math
class EPS:
def calculate_input(self, sigma, noise):
sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
return model_input - model_output * sigma
def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
if max_denoise:
noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
else:
noise = noise * sigma
noise += latent_image
return noise
def inverse_noise_scaling(self, sigma, latent):
return latent
class V_PREDICTION(EPS):
def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
class EDM(V_PREDICTION):
def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
class CONST:
def calculate_input(self, sigma, noise):
return noise
def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
return model_input - model_output * sigma
def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
return sigma * noise + (1.0 - sigma) * latent_image
def inverse_noise_scaling(self, sigma, latent):
return latent / (1.0 - sigma)
class ModelSamplingDiscrete(torch.nn.Module):
def __init__(self, model_config=None):
super().__init__()
if model_config is not None:
sampling_settings = model_config.sampling_settings
else:
sampling_settings = {}
beta_schedule = sampling_settings.get("beta_schedule", "linear")
linear_start = sampling_settings.get("linear_start", 0.00085)
linear_end = sampling_settings.get("linear_end", 0.012)
self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3)
self.sigma_data = 1.0
def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if given_betas is not None:
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = torch.cumprod(alphas, dim=0)
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
# self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
# self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
# self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
self.set_sigmas(sigmas)
def set_sigmas(self, sigmas):
self.register_buffer('sigmas', sigmas.float())
self.register_buffer('log_sigmas', sigmas.log().float())
@property
def sigma_min(self):
return self.sigmas[0]
@property
def sigma_max(self):
return self.sigmas[-1]
def timestep(self, sigma):
log_sigma = sigma.log()
dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device)
def sigma(self, timestep):
t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1))
low_idx = t.floor().long()
high_idx = t.ceil().long()
w = t.frac()
log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
return log_sigma.exp().to(timestep.device)
def percent_to_sigma(self, percent):
if percent <= 0.0:
return 999999999.9
if percent >= 1.0:
return 0.0
percent = 1.0 - percent
return self.sigma(torch.tensor(percent * 999.0)).item()
class ModelSamplingDiscreteEDM(ModelSamplingDiscrete):
def timestep(self, sigma):
return 0.25 * sigma.log()
def sigma(self, timestep):
return (timestep / 0.25).exp()
class ModelSamplingContinuousEDM(torch.nn.Module):
def __init__(self, model_config=None):
super().__init__()
if model_config is not None:
sampling_settings = model_config.sampling_settings
else:
sampling_settings = {}
sigma_min = sampling_settings.get("sigma_min", 0.002)
sigma_max = sampling_settings.get("sigma_max", 120.0)
sigma_data = sampling_settings.get("sigma_data", 1.0)
self.set_parameters(sigma_min, sigma_max, sigma_data)
def set_parameters(self, sigma_min, sigma_max, sigma_data):
self.sigma_data = sigma_data
sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()
self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
self.register_buffer('log_sigmas', sigmas.log())
@property
def sigma_min(self):
return self.sigmas[0]
@property
def sigma_max(self):
return self.sigmas[-1]
def timestep(self, sigma):
return 0.25 * sigma.log()
def sigma(self, timestep):
return (timestep / 0.25).exp()
def percent_to_sigma(self, percent):
if percent <= 0.0:
return 999999999.9
if percent >= 1.0:
return 0.0
percent = 1.0 - percent
log_sigma_min = math.log(self.sigma_min)
return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)
class ModelSamplingContinuousV(ModelSamplingContinuousEDM):
def timestep(self, sigma):
return sigma.atan() / math.pi * 2
def sigma(self, timestep):
return (timestep * math.pi / 2).tan()
def time_snr_shift(alpha, t):
if alpha == 1.0:
return t
return alpha * t / (1 + (alpha - 1) * t)
class ModelSamplingDiscreteFlow(torch.nn.Module):
def __init__(self, model_config=None):
super().__init__()
if model_config is not None:
sampling_settings = model_config.sampling_settings
else:
sampling_settings = {}
self.set_parameters(shift=sampling_settings.get("shift", 1.0))
def set_parameters(self, shift=1.0, timesteps=1000):
self.shift = shift
ts = self.sigma(torch.arange(1, timesteps + 1, 1))
self.register_buffer('sigmas', ts)
@property
def sigma_min(self):
return self.sigmas[0]
@property
def sigma_max(self):
return self.sigmas[-1]
def timestep(self, sigma):
return sigma * 1000
def sigma(self, timestep):
return time_snr_shift(self.shift, timestep / 1000)
def percent_to_sigma(self, percent):
if percent <= 0.0:
return 1.0
if percent >= 1.0:
return 0.0
return 1.0 - percent
class StableCascadeSampling(ModelSamplingDiscrete):
def __init__(self, model_config=None):
super().__init__()
if model_config is not None:
sampling_settings = model_config.sampling_settings
else:
sampling_settings = {}
self.set_parameters(sampling_settings.get("shift", 1.0))
def set_parameters(self, shift=1.0, cosine_s=8e-3):
self.shift = shift
self.cosine_s = torch.tensor(cosine_s)
self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2
#This part is just for compatibility with some schedulers in the codebase
self.num_timesteps = 10000
sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
for x in range(self.num_timesteps):
t = (x + 1) / self.num_timesteps
sigmas[x] = self.sigma(t)
self.set_sigmas(sigmas)
def sigma(self, timestep):
alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod)
if self.shift != 1.0:
var = alpha_cumprod
logSNR = (var/(1-var)).log()
logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift))
alpha_cumprod = logSNR.sigmoid()
alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999)
return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5
def timestep(self, sigma):
var = 1 / ((sigma * sigma) + 1)
var = var.clamp(0, 1.0)
s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device)
t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s
return t
def percent_to_sigma(self, percent):
if percent <= 0.0:
return 999999999.9
if percent >= 1.0:
return 0.0
percent = 1.0 - percent
return self.sigma(torch.tensor(percent))