-
Notifications
You must be signed in to change notification settings - Fork 15
/
run_img_sampling.py
235 lines (184 loc) · 6.7 KB
/
run_img_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
from pathlib import Path
import numpy as np
import torch
from misc import torch_samps_to_imgs
from adapt import Karras, ScoreAdapter, power_schedule
from adapt_gddpm import GuidedDDPM
from adapt_ncsn import NCSN as _NCSN
# from adapt_vesde import VESDE # not included to prevent import conflicts
from adapt_sd import StableDiffusion
from my.utils import tqdm, EventStorage, HeartBeat, EarlyLoopBreak
from my.config import BaseConf, dispatch
from my.utils.seed import seed_everything
class GDDPM(BaseConf):
"""Guided DDPM from OpenAI"""
model: str = "m_lsun_256"
lsun_cat: str = "bedroom"
imgnet_cat: int = -1
def make(self):
args = self.dict()
model = GuidedDDPM(**args)
return model
class SD(BaseConf):
"""Stable Diffusion"""
variant: str = "v1"
v2_highres: bool = False
prompt: str = "a photograph of an astronaut riding a horse"
scale: float = 3.0 # classifier free guidance scale
precision: str = 'autocast'
def make(self):
args = self.dict()
model = StableDiffusion(**args)
return model
class SDE(BaseConf):
def make(self):
args = self.dict()
model = VESDE(**args)
return model
class NCSN(BaseConf):
def make(self):
args = self.dict()
model = _NCSN(**args)
return model
class KarrasGen(BaseConf):
family: str = "gddpm"
gddpm: GDDPM = GDDPM()
sd: SD = SD()
# sde: SDE = SDE()
ncsn: NCSN = NCSN()
batch_size: int = 10
num_images: int = 1250
num_t: int = 40
σ_max: float = 80.0
heun: bool = True
langevin: bool = False
cls_scaling: float = 1.0 # classifier guidance scaling
def run(self):
args = self.dict()
family = args.pop("family")
model = getattr(self, family).make()
self.karras_generate(model, **args)
@staticmethod
def karras_generate(
model: ScoreAdapter,
batch_size, num_images, σ_max, num_t, langevin, heun, cls_scaling,
**kwargs
):
del kwargs # removed extra args
num_batches = num_images // batch_size
fuse = EarlyLoopBreak(5)
with tqdm(total=num_batches) as pbar, \
HeartBeat(pbar) as hbeat, \
EventStorage() as metric:
all_imgs = []
for _ in range(num_batches):
if fuse.on_break():
break
pipeline = Karras.inference(
model, batch_size, num_t,
init_xs=None, heun=heun, σ_max=σ_max,
langevin=langevin, cls_scaling=cls_scaling
)
for imgs in tqdm(pipeline, total=num_t+1, disable=False):
# _std = imgs.std().item()
# print(_std)
hbeat.beat()
pass
if isinstance(model, StableDiffusion):
imgs = model.decode(imgs)
imgs = torch_samps_to_imgs(imgs, uncenter=model.samps_centered())
all_imgs.append(imgs)
pbar.update()
all_imgs = np.concatenate(all_imgs, axis=0)
metric.put_artifact("imgs", ".npy", lambda fn: np.save(fn, all_imgs))
metric.step()
hbeat.done()
class SMLDGen(BaseConf):
family: str = "ncsn"
gddpm: GDDPM = GDDPM()
# sde: SDE = SDE()
ncsn: NCSN = NCSN()
batch_size: int = 16
num_images: int = 16
num_stages: int = 80
num_steps: int = 15
σ_max: float = 80.0
ε: float = 1e-5
def run(self):
args = self.dict()
family = args.pop("family")
model = getattr(self, family).make()
self.smld_generate(model, **args)
@staticmethod
def smld_generate(
model: ScoreAdapter,
batch_size, num_images, num_stages, num_steps, σ_max, ε,
**kwargs
):
num_batches = num_images // batch_size
σs = power_schedule(σ_max, model.σ_min, num_stages)
σs = [model.snap_t_to_nearest_tick(σ)[0] for σ in σs]
fuse = EarlyLoopBreak(5)
with tqdm(total=num_batches) as pbar, \
HeartBeat(pbar) as hbeat, \
EventStorage() as metric:
all_imgs = []
for _ in range(num_batches):
if fuse.on_break():
break
init_xs = torch.rand(batch_size, *model.data_shape(), device=model.device)
if model.samps_centered():
init_xs = init_xs * 2 - 1 # [0, 1] -> [-1, 1]
pipeline = smld_inference(
model, σs, num_steps, ε, init_xs
)
for imgs in tqdm(pipeline, total=(num_stages * num_steps)+1, disable=False):
pbar.set_description(f"{imgs.max().item():.3f}")
metric.put_scalars(
max=imgs.max().item(), min=imgs.min().item(), std=imgs.std().item()
)
metric.step()
hbeat.beat()
pbar.update()
imgs = torch_samps_to_imgs(imgs, uncenter=model.samps_centered())
all_imgs.append(imgs)
all_imgs = np.concatenate(all_imgs, axis=0)
metric.put_artifact("imgs", ".npy", lambda fn: np.save(fn, all_imgs))
metric.step()
hbeat.done()
def smld_inference(model, σs, num_steps, ε, init_xs):
from math import sqrt
# not doing conditioning or cls guidance; for gddpm only lsun works; fine.
xs = init_xs
yield xs
for i in range(len(σs)):
α_i = ε * ((σs[i] / σs[-1]) ** 2)
for _ in range(num_steps):
grad = model.score(xs, σs[i])
z = torch.randn_like(xs)
xs = xs + α_i * grad + sqrt(2 * α_i) * z
yield xs
def load_np_imgs(fname):
fname = Path(fname)
data = np.load(fname)
if fname.suffix == ".npz":
imgs = data['arr_0']
else:
imgs = data
return imgs
def visualize(max_n_imgs=16):
import torchvision.utils as vutils
from imageio import imwrite
from einops import rearrange
all_imgs = load_np_imgs("imgs/step_0.npy")
imgs = all_imgs[:max_n_imgs]
imgs = rearrange(imgs, "N H W C -> N C H W", C=3)
imgs = torch.from_numpy(imgs)
pane = vutils.make_grid(imgs, padding=2, nrow=4)
pane = rearrange(pane, "C H W -> H W C", C=3)
pane = pane.numpy()
imwrite("preview.jpg", pane)
if __name__ == "__main__":
seed_everything(0)
dispatch(KarrasGen)
visualize(16)