-
Notifications
You must be signed in to change notification settings - Fork 121
/
templates.py
301 lines (253 loc) · 7.96 KB
/
templates.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
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
from experiment import *
def ddpm():
"""
base configuration for all DDIM-based models.
"""
conf = TrainConfig()
conf.batch_size = 32
conf.beatgans_gen_type = GenerativeType.ddim
conf.beta_scheduler = 'linear'
conf.data_name = 'ffhq'
conf.diffusion_type = 'beatgans'
conf.eval_ema_every_samples = 200_000
conf.eval_every_samples = 200_000
conf.fp16 = True
conf.lr = 1e-4
conf.model_name = ModelName.beatgans_ddpm
conf.net_attn = (16, )
conf.net_beatgans_attn_head = 1
conf.net_beatgans_embed_channels = 512
conf.net_ch_mult = (1, 2, 4, 8)
conf.net_ch = 64
conf.sample_size = 32
conf.T_eval = 20
conf.T = 1000
conf.make_model_conf()
return conf
def autoenc_base():
"""
base configuration for all Diff-AE models.
"""
conf = TrainConfig()
conf.batch_size = 32
conf.beatgans_gen_type = GenerativeType.ddim
conf.beta_scheduler = 'linear'
conf.data_name = 'ffhq'
conf.diffusion_type = 'beatgans'
conf.eval_ema_every_samples = 200_000
conf.eval_every_samples = 200_000
conf.fp16 = True
conf.lr = 1e-4
conf.model_name = ModelName.beatgans_autoenc
conf.net_attn = (16, )
conf.net_beatgans_attn_head = 1
conf.net_beatgans_embed_channels = 512
conf.net_beatgans_resnet_two_cond = True
conf.net_ch_mult = (1, 2, 4, 8)
conf.net_ch = 64
conf.net_enc_channel_mult = (1, 2, 4, 8, 8)
conf.net_enc_pool = 'adaptivenonzero'
conf.sample_size = 32
conf.T_eval = 20
conf.T = 1000
conf.make_model_conf()
return conf
def ffhq64_ddpm():
conf = ddpm()
conf.data_name = 'ffhqlmdb256'
conf.warmup = 0
conf.total_samples = 72_000_000
conf.scale_up_gpus(4)
return conf
def ffhq64_autoenc():
conf = autoenc_base()
conf.data_name = 'ffhqlmdb256'
conf.warmup = 0
conf.total_samples = 72_000_000
conf.net_ch_mult = (1, 2, 4, 8)
conf.net_enc_channel_mult = (1, 2, 4, 8, 8)
conf.eval_every_samples = 1_000_000
conf.eval_ema_every_samples = 1_000_000
conf.scale_up_gpus(4)
conf.make_model_conf()
return conf
def celeba64d2c_ddpm():
conf = ffhq128_ddpm()
conf.data_name = 'celebalmdb'
conf.eval_every_samples = 10_000_000
conf.eval_ema_every_samples = 10_000_000
conf.total_samples = 72_000_000
conf.name = 'celeba64d2c_ddpm'
return conf
def celeba64d2c_autoenc():
conf = ffhq64_autoenc()
conf.data_name = 'celebalmdb'
conf.eval_every_samples = 10_000_000
conf.eval_ema_every_samples = 10_000_000
conf.total_samples = 72_000_000
conf.name = 'celeba64d2c_autoenc'
return conf
def ffhq128_ddpm():
conf = ddpm()
conf.data_name = 'ffhqlmdb256'
conf.warmup = 0
conf.total_samples = 48_000_000
conf.img_size = 128
conf.net_ch = 128
# channels:
# 3 => 128 * 1 => 128 * 1 => 128 * 2 => 128 * 3 => 128 * 4
# sizes:
# 128 => 128 => 64 => 32 => 16 => 8
conf.net_ch_mult = (1, 1, 2, 3, 4)
conf.eval_every_samples = 1_000_000
conf.eval_ema_every_samples = 1_000_000
conf.scale_up_gpus(4)
conf.eval_ema_every_samples = 10_000_000
conf.eval_every_samples = 10_000_000
conf.make_model_conf()
return conf
def ffhq128_autoenc_base():
conf = autoenc_base()
conf.data_name = 'ffhqlmdb256'
conf.scale_up_gpus(4)
conf.img_size = 128
conf.net_ch = 128
# final resolution = 8x8
conf.net_ch_mult = (1, 1, 2, 3, 4)
# final resolution = 4x4
conf.net_enc_channel_mult = (1, 1, 2, 3, 4, 4)
conf.eval_ema_every_samples = 10_000_000
conf.eval_every_samples = 10_000_000
conf.make_model_conf()
return conf
def ffhq256_autoenc():
conf = ffhq128_autoenc_base()
conf.img_size = 256
conf.net_ch = 128
conf.net_ch_mult = (1, 1, 2, 2, 4, 4)
conf.net_enc_channel_mult = (1, 1, 2, 2, 4, 4, 4)
conf.eval_every_samples = 10_000_000
conf.eval_ema_every_samples = 10_000_000
conf.total_samples = 200_000_000
conf.batch_size = 64
conf.make_model_conf()
conf.name = 'ffhq256_autoenc'
return conf
def ffhq256_autoenc_eco():
conf = ffhq128_autoenc_base()
conf.img_size = 256
conf.net_ch = 128
conf.net_ch_mult = (1, 1, 2, 2, 4, 4)
conf.net_enc_channel_mult = (1, 1, 2, 2, 4, 4, 4)
conf.eval_every_samples = 10_000_000
conf.eval_ema_every_samples = 10_000_000
conf.total_samples = 200_000_000
conf.batch_size = 64
conf.make_model_conf()
conf.name = 'ffhq256_autoenc_eco'
return conf
def ffhq128_ddpm_72M():
conf = ffhq128_ddpm()
conf.total_samples = 72_000_000
conf.name = 'ffhq128_ddpm_72M'
return conf
def ffhq128_autoenc_72M():
conf = ffhq128_autoenc_base()
conf.total_samples = 72_000_000
conf.name = 'ffhq128_autoenc_72M'
return conf
def ffhq128_ddpm_130M():
conf = ffhq128_ddpm()
conf.total_samples = 130_000_000
conf.eval_ema_every_samples = 10_000_000
conf.eval_every_samples = 10_000_000
conf.name = 'ffhq128_ddpm_130M'
return conf
def ffhq128_autoenc_130M():
conf = ffhq128_autoenc_base()
conf.total_samples = 130_000_000
conf.eval_ema_every_samples = 10_000_000
conf.eval_every_samples = 10_000_000
conf.name = 'ffhq128_autoenc_130M'
return conf
def horse128_ddpm():
conf = ffhq128_ddpm()
conf.data_name = 'horse256'
conf.total_samples = 130_000_000
conf.eval_ema_every_samples = 10_000_000
conf.eval_every_samples = 10_000_000
conf.name = 'horse128_ddpm'
return conf
def horse128_autoenc():
conf = ffhq128_autoenc_base()
conf.data_name = 'horse256'
conf.total_samples = 130_000_000
conf.eval_ema_every_samples = 10_000_000
conf.eval_every_samples = 10_000_000
conf.name = 'horse128_autoenc'
return conf
def bedroom128_ddpm():
conf = ffhq128_ddpm()
conf.data_name = 'bedroom256'
conf.eval_ema_every_samples = 10_000_000
conf.eval_every_samples = 10_000_000
conf.total_samples = 120_000_000
conf.name = 'bedroom128_ddpm'
return conf
def bedroom128_autoenc():
conf = ffhq128_autoenc_base()
conf.data_name = 'bedroom256'
conf.eval_ema_every_samples = 10_000_000
conf.eval_every_samples = 10_000_000
conf.total_samples = 120_000_000
conf.name = 'bedroom128_autoenc'
return conf
def pretrain_celeba64d2c_72M():
conf = celeba64d2c_autoenc()
conf.pretrain = PretrainConfig(
name='72M',
path=f'checkpoints/{celeba64d2c_autoenc().name}/last.ckpt',
)
conf.latent_infer_path = f'checkpoints/{celeba64d2c_autoenc().name}/latent.pkl'
return conf
def pretrain_ffhq128_autoenc72M():
conf = ffhq128_autoenc_base()
conf.postfix = ''
conf.pretrain = PretrainConfig(
name='72M',
path=f'checkpoints/{ffhq128_autoenc_72M().name}/last.ckpt',
)
conf.latent_infer_path = f'checkpoints/{ffhq128_autoenc_72M().name}/latent.pkl'
return conf
def pretrain_ffhq128_autoenc130M():
conf = ffhq128_autoenc_base()
conf.pretrain = PretrainConfig(
name='130M',
path=f'checkpoints/{ffhq128_autoenc_130M().name}/last.ckpt',
)
conf.latent_infer_path = f'checkpoints/{ffhq128_autoenc_130M().name}/latent.pkl'
return conf
def pretrain_ffhq256_autoenc():
conf = ffhq256_autoenc()
conf.pretrain = PretrainConfig(
name='90M',
path=f'checkpoints/{ffhq256_autoenc().name}/last.ckpt',
)
conf.latent_infer_path = f'checkpoints/{ffhq256_autoenc().name}/latent.pkl'
return conf
def pretrain_horse128():
conf = horse128_autoenc()
conf.pretrain = PretrainConfig(
name='82M',
path=f'checkpoints/{horse128_autoenc().name}/last.ckpt',
)
conf.latent_infer_path = f'checkpoints/{horse128_autoenc().name}/latent.pkl'
return conf
def pretrain_bedroom128():
conf = bedroom128_autoenc()
conf.pretrain = PretrainConfig(
name='120M',
path=f'checkpoints/{bedroom128_autoenc().name}/last.ckpt',
)
conf.latent_infer_path = f'checkpoints/{bedroom128_autoenc().name}/latent.pkl'
return conf