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from .DiffAE_support_config 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
#created from ffhq128_autoenc_130M
def ukbb_autoenc(ds_name="ukbb", n_latents=128):
conf = TrainConfig()
conf.beatgans_gen_type = GenerativeType.ddim
conf.beta_scheduler = 'linear'
conf.diffusion_type = 'beatgans'
conf.fp16 = True
conf.model_name = ModelName.beatgans_autoenc
conf.net_attn = (16, )
conf.net_beatgans_attn_head = 1
conf.net_beatgans_embed_channels = n_latents
conf.style_ch = n_latents
conf.net_beatgans_resnet_two_cond = True
conf.net_enc_pool = 'adaptivenonzero'
conf.sample_size = 32
conf.T_eval = 20
conf.T = 1000
conf.T_inv = 200
conf.T_step = 100
conf.data_name = ds_name
conf.net_ch_mult = (1, 1, 2, 3, 4)
conf.net_enc_channel_mult = (1, 1, 2, 3, 4, 4)
conf.name = 'ukbb_ffhq128_autoenc'
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 |