File size: 9,064 Bytes
c81908d
 
 
06c5f0c
18764bb
 
 
 
 
 
 
 
 
 
c81908d
 
 
 
 
 
 
 
27911d6
 
 
c81908d
27911d6
 
 
 
c81908d
27911d6
 
c81908d
27911d6
 
 
 
 
 
 
 
 
 
 
c81908d
27911d6
 
 
 
 
c81908d
27911d6
 
 
 
 
c81908d
18764bb
 
c81908d
 
 
 
 
 
18764bb
c81908d
 
 
 
 
18764bb
c81908d
 
 
 
 
 
 
18764bb
c81908d
 
 
 
 
 
18764bb
c81908d
 
 
 
 
 
 
 
 
18764bb
c81908d
 
 
 
27911d6
c81908d
 
18764bb
06c5f0c
 
 
 
 
 
18764bb
06c5f0c
 
 
 
 
18764bb
06c5f0c
 
 
 
 
18764bb
ae26d48
 
169bc4a
 
ae26d48
06c5f0c
18764bb
06c5f0c
 
 
 
 
27911d6
06c5f0c
 
 
c81908d
18764bb
06c5f0c
 
 
 
 
18764bb
ae26d48
 
 
169bc4a
 
ae26d48
 
18764bb
06c5f0c
 
 
 
 
18764bb
06c5f0c
 
 
 
 
18764bb
06c5f0c
 
 
169bc4a
 
06c5f0c
 
18764bb
ae26d48
 
 
 
 
 
18764bb
ae26d48
 
169bc4a
 
ae26d48
06c5f0c
be61cf2
 
18764bb
be61cf2
 
169bc4a
 
be61cf2
 
 
 
 
 
18764bb
 
be61cf2
 
 
 
 
 
 
18764bb
be61cf2
 
 
 
 
 
18764bb
be61cf2
 
 
 
 
18764bb
be61cf2
 
 
 
 
18764bb
be61cf2
 
 
18764bb
 
 
be61cf2
 
 
18764bb
 
 
be61cf2
 
 
18764bb
 
 
be61cf2
 
 
18764bb
 
 
be61cf2
 
 
 
18764bb
 
 
be61cf2
 
 
 
18764bb
 
be61cf2
 
 
18764bb
 
be61cf2
 
 
 
18764bb
 
 
 
be61cf2
 
 
18764bb
 
be61cf2
 
 
 
 
18764bb
 
be61cf2
 
 
 
18764bb
 
 
be61cf2
 
 
169bc4a
be61cf2
 
18764bb
7ac3881
 
 
 
 
18764bb
7ac3881
 
 
 
18764bb
a45817e
7060b15
a45817e
 
18764bb
7ac3881
 
 
 
 
18764bb
7060b15
 
 
 
 
18764bb
7060b15
 
 
 
 
 
18764bb
e96a195
 
 
 
 
 
18764bb
e96a195
 
 
 
18764bb
e96a195
 
 
 
18764bb
e96a195
 
 
 
18764bb
3dcdf92
 
27911d6
3dcdf92
 
18764bb
e96a195
 
 
 
18764bb
3dcdf92
 
 
 
18764bb
3dcdf92
 
 
 
 
18764bb
3dcdf92
 
 
 
 
18764bb
 
 
 
 
3dcdf92
18764bb
 
 
 
 
06c5f0c
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
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
import os
from glob import glob
from subprocess import call
import json

models = {}

def register(func):
    models[func.__name__] = func
    return func

def get_model_config(model_name):
    return models[model_name]()["model"]

def base():
    return {
        "slurm":{
            "t": 360,
            "N": 2,
            "n": 8,
        },
        "model":{
            "dataset": "wds",
            "seed": 0,
            "cross_attention": False,
            "num_channels": 3,
            "centered": True,
            "use_geometric": False,
            "beta_min": 0.1,
            "beta_max": 20.0,
            "num_channels_dae": 128,
            "n_mlp": 3,
            "ch_mult": [1, 1, 2, 2, 4, 4],
            "num_res_blocks": 2,
            "attn_resolutions": (16,),
            "dropout": 0.0,
            "resamp_with_conv": True,
            "conditional": True,
            "fir": True,
            "fir_kernel": [1, 3, 3, 1],
            "skip_rescale": True,
            "resblock_type": "biggan",
            "progressive": "none",
            "progressive_input": "residual",
            "progressive_combine": "sum",
            "embedding_type": "positional",
            "fourier_scale": 16.0,
            "not_use_tanh": False,
            "image_size": 256,
            "nz": 100,
            "num_timesteps": 4,
            "z_emb_dim": 256,
            "t_emb_dim": 256,
            "text_encoder": "google/t5-v1_1-base",
            "masked_mean": True,
            "cross_attention_block": "basic",
        }
    }

@register
def ddgan_cc12m_v2():
    cfg =  base()
    cfg['slurm']['N'] = 2
    cfg['slurm']['n'] = 8
    return cfg

@register
def ddgan_cc12m_v6():
    cfg = base()
    cfg['model']['text_encoder'] = "google/t5-v1_1-large"
    return cfg

@register
def ddgan_cc12m_v7():
    cfg = base()
    cfg['model']['classifier_free_guidance_proba'] = 0.2
    cfg['slurm']['N'] = 2
    cfg['slurm']['n'] = 8
    return cfg

@register
def ddgan_cc12m_v8():
    cfg = base()
    cfg['model']['text_encoder'] = "google/t5-v1_1-large"    
    cfg['model']['classifier_free_guidance_proba'] = 0.2
    return cfg

@register
def ddgan_cc12m_v9():
    cfg = base()
    cfg['model']['text_encoder'] = "google/t5-v1_1-large"    
    cfg['model']['classifier_free_guidance_proba'] = 0.2
    cfg['model']['num_channels_dae'] = 320
    cfg['model']['image_size'] = 64
    cfg['model']['batch_size'] = 1
    return cfg

@register
def ddgan_cc12m_v11():
    cfg = base()
    cfg['model']['text_encoder'] = "google/t5-v1_1-large"    
    cfg['model']['classifier_free_guidance_proba'] = 0.2
    cfg['model']['cross_attention'] = True
    return cfg

@register
def ddgan_cc12m_v12():
    cfg = ddgan_cc12m_v11()
    cfg['model']['text_encoder'] = "google/t5-v1_1-xl"    
    cfg['model']['preprocessing'] = 'random_resized_crop_v1'
    return cfg

@register
def ddgan_cc12m_v13():
    cfg = ddgan_cc12m_v12()
    cfg['model']['discr_type'] = "large_cond_attn"
    return cfg

@register
def ddgan_cc12m_v14():
    cfg = ddgan_cc12m_v12()
    cfg['model']['num_channels_dae'] = 192
    return cfg

@register
def ddgan_cc12m_v15():
    cfg = ddgan_cc12m_v11()
    cfg['model']['mismatch_loss'] = True
    cfg['model']['grad_penalty_cond'] = True
    return cfg

@register
def ddgan_cifar10_cond17():
    cfg = base()
    cfg['model']['image_size'] = 32    
    cfg['model']['classifier_free_guidance_proba'] = 0.2
    cfg['model']['ch_mult'] = "1 2 2 2"
    cfg['model']['cross_attention'] = True
    cfg['model']['dataset'] = "cifar10"
    cfg['model']['n_mlp'] = 4
    return cfg

@register
def ddgan_cifar10_cond18():
    cfg = ddgan_cifar10_cond17()
    cfg['model']['text_encoder'] = "google/t5-v1_1-xl"    
    return cfg

@register
def ddgan_cifar10_cond19():
    cfg = ddgan_cifar10_cond17()
    cfg['model']['discr_type'] = 'small_cond_attn'
    cfg['model']['mismatch_loss'] = True
    cfg['model']['grad_penalty_cond'] =True
    return cfg

@register
def ddgan_laion_aesthetic_v1():
    cfg = ddgan_cc12m_v11()
    cfg['model']['dataset_root'] = '"/p/scratch/ccstdl/cherti1/LAION-aesthetic/output/{00000..05038}.tar"'
    return cfg

@register
def ddgan_laion_aesthetic_v2():
    cfg = ddgan_laion_aesthetic_v1()
    cfg['model']['discr_type'] = "large_cond_attn"
    return cfg

@register
def ddgan_laion_aesthetic_v3():
    cfg = ddgan_laion_aesthetic_v1()
    cfg['model']['text_encoder'] = "google/t5-v1_1-xl" 
    cfg['model']['mismatch_loss'] = True
    cfg['model']['grad_penalty_cond'] = True
    return cfg

@register
def ddgan_laion_aesthetic_v4():
    cfg = ddgan_laion_aesthetic_v1()
    cfg['model']['text_encoder'] = "openclip/ViT-L-14-336/openai" 
    return cfg


@register
def ddgan_laion_aesthetic_v5():
    cfg = ddgan_laion_aesthetic_v1()
    cfg['model']['mismatch_loss'] = True
    cfg['model']['grad_penalty_cond'] = True
    return cfg



@register
def ddgan_laion2b_v1():
    cfg = ddgan_laion_aesthetic_v3()
    cfg['model']['mismatch_loss'] = True
    cfg['model']['grad_penalty_cond'] = True
    cfg['model']['num_channels_dae'] = 224
    cfg['model']['batch_size'] = 2
    cfg['model']['discr_type'] = "large_cond_attn"
    cfg['model']['preprocessing'] = 'random_resized_crop_v1'
    return cfg


@register
def ddgan_laion_aesthetic_v6():
    cfg = ddgan_laion_aesthetic_v3()
    cfg['model']['no_lr_decay'] = ''
    return cfg



@register
def ddgan_laion_aesthetic_v7():
    cfg = ddgan_laion_aesthetic_v6()
    cfg['model']['r1_gamma'] = 5
    return cfg


@register
def ddgan_laion_aesthetic_v8():
    cfg = ddgan_laion_aesthetic_v6()
    cfg['model']['num_timesteps'] = 8
    return cfg

@register
def ddgan_laion_aesthetic_v9():
    cfg = ddgan_laion_aesthetic_v3()
    cfg['model']['num_channels_dae'] = 384
    return cfg

@register
def ddgan_sd_v1():
    cfg = ddgan_laion_aesthetic_v3()
    return cfg


@register
def ddgan_sd_v2():
    cfg = ddgan_laion_aesthetic_v3()
    return cfg


@register
def ddgan_sd_v3():
    cfg = ddgan_laion_aesthetic_v3()
    return cfg


@register
def ddgan_sd_v4():
    cfg = ddgan_laion_aesthetic_v3()
    return cfg


@register
def ddgan_sd_v5():
    cfg = ddgan_laion_aesthetic_v3()
    cfg['model']['num_timesteps'] = 8
    return cfg


@register
def ddgan_sd_v6():
    cfg = ddgan_laion_aesthetic_v3()
    cfg['model']['num_channels_dae'] = 192
    return cfg

@register
def ddgan_sd_v7():
    cfg = ddgan_laion_aesthetic_v3()
    return cfg

@register
def ddgan_sd_v8():
    cfg = ddgan_laion_aesthetic_v3()
    cfg['model']['image_size'] = 512
    return cfg



@register
def ddgan_laion_aesthetic_v12():
    cfg = ddgan_laion_aesthetic_v3()
    return cfg

@register
def ddgan_laion_aesthetic_v13():
    cfg = ddgan_laion_aesthetic_v3()
    cfg['model']['text_encoder'] = "openclip/ViT-H-14/laion2b_s32b_b79k" 
    return cfg


@register
def ddgan_laion_aesthetic_v14():
    cfg = ddgan_laion_aesthetic_v3()
    cfg['model']['text_encoder'] = "openclip/ViT-H-14/laion2b_s32b_b79k" 
    return cfg


@register
def ddgan_sd_v9():
    cfg = ddgan_laion_aesthetic_v3()
    cfg['model']['text_encoder'] = "openclip/ViT-H-14/laion2b_s32b_b79k" 
    cfg['model']['classifier_free_guidance_proba'] = 0.0
    return cfg

@register
def ddgan_sd_v10():
    cfg = ddgan_sd_v9()
    cfg['model']['num_timesteps'] = 2
    return cfg

@register
def ddgan_laion2b_v2():
    cfg = ddgan_sd_v9()
    return cfg

@register
def ddgan_ddb_v1():
    cfg = ddgan_sd_v10()
    return cfg

@register
def ddgan_sd_v11():
    cfg = ddgan_sd_v10()
    cfg['model']['image_size'] = 512
    return cfg

@register
def ddgan_ddb_v2():
    cfg = ddgan_ddb_v1()
    cfg['model']['num_timesteps'] = 1
    return cfg

@register
def ddgan_ddb_v3():
    cfg = ddgan_ddb_v1()
    cfg['model']['num_channels_dae'] = 192
    cfg['model']['num_timesteps'] = 2
    return cfg

@register
def ddgan_ddb_v4():
    cfg = ddgan_ddb_v1()
    cfg['model']['num_channels_dae'] = 256
    cfg['model']['num_timesteps'] = 2
    return cfg

@register
def ddgan_ddb_v5():
    cfg = ddgan_ddb_v2()
    return cfg

@register
def ddgan_ddb_v6():
    cfg = ddgan_ddb_v3()
    return cfg

@register
def ddgan_ddb_v7():
    cfg = ddgan_ddb_v1()
    return cfg

@register
def ddgan_ddb_v9():
    cfg = ddgan_ddb_v3()
    cfg['model']['attn_resolutions'] = [4, 8, 16, 32]
    return cfg

@register
def ddgan_laion_aesthetic_v15():
    cfg = ddgan_ddb_v3()
    return cfg

@register
def ddgan_ddb_v10():
    cfg = ddgan_ddb_v9()
    return cfg

@register
def ddgan_ddb_v11():
    cfg = ddgan_ddb_v3()
    cfg['model']['text_encoder'] = "openclip/ViT-g-14/laion2B-s12B-b42K" 
    return cfg

@register
def ddgan_ddb_v12():
    cfg = ddgan_ddb_v3()
    cfg['model']['text_encoder'] = "openclip/ViT-bigG-14/laion2b_s39b_b160k"
    return cfg

@register
def ddgan_ddb_v13():
    cfg = ddgan_ddb_v3()
    cfg['model']['num_channels_dae'] = 320 # 1B model
    return cfg

@register
def ddgan_ddb_v14():
    cfg = ddgan_ddb_v3()
    cfg['model']['cross_attention_block'] = "cross_and_global_attention"
    return cfg