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from functools import partial
import json
import math
import warnings
from jsonmerge import merge
from . import augmentation, layers, models, utils
def load_config(file):
defaults = {
'model': {
'sigma_data': 1.,
'patch_size': 1,
'dropout_rate': 0.,
'augment_wrapper': True,
'augment_prob': 0.,
'mapping_cond_dim': 0,
'unet_cond_dim': 0,
'cross_cond_dim': 0,
'cross_attn_depths': None,
'skip_stages': 0,
'has_variance': False,
},
'dataset': {
'type': 'imagefolder',
},
'optimizer': {
'type': 'adamw',
'lr': 1e-4,
'betas': [0.95, 0.999],
'eps': 1e-6,
'weight_decay': 1e-3,
},
'lr_sched': {
'type': 'inverse',
'inv_gamma': 20000.,
'power': 1.,
'warmup': 0.99,
},
'ema_sched': {
'type': 'inverse',
'power': 0.6667,
'max_value': 0.9999
},
}
config = json.load(file)
return merge(defaults, config)
def make_model(config):
config = config['model']
assert config['type'] == 'image_v1'
model = models.ImageDenoiserModelV1(
config['input_channels'],
config['mapping_out'],
config['depths'],
config['channels'],
config['self_attn_depths'],
config['cross_attn_depths'],
patch_size=config['patch_size'],
dropout_rate=config['dropout_rate'],
mapping_cond_dim=config['mapping_cond_dim'] + (9 if config['augment_wrapper'] else 0),
unet_cond_dim=config['unet_cond_dim'],
cross_cond_dim=config['cross_cond_dim'],
skip_stages=config['skip_stages'],
has_variance=config['has_variance'],
)
if config['augment_wrapper']:
model = augmentation.KarrasAugmentWrapper(model)
return model
def make_denoiser_wrapper(config):
config = config['model']
sigma_data = config.get('sigma_data', 1.)
has_variance = config.get('has_variance', False)
if not has_variance:
return partial(layers.Denoiser, sigma_data=sigma_data)
return partial(layers.DenoiserWithVariance, sigma_data=sigma_data)
def make_sample_density(config):
sd_config = config['sigma_sample_density']
sigma_data = config['sigma_data']
if sd_config['type'] == 'lognormal':
loc = sd_config['mean'] if 'mean' in sd_config else sd_config['loc']
scale = sd_config['std'] if 'std' in sd_config else sd_config['scale']
return partial(utils.rand_log_normal, loc=loc, scale=scale)
if sd_config['type'] == 'loglogistic':
loc = sd_config['loc'] if 'loc' in sd_config else math.log(sigma_data)
scale = sd_config['scale'] if 'scale' in sd_config else 0.5
min_value = sd_config['min_value'] if 'min_value' in sd_config else 0.
max_value = sd_config['max_value'] if 'max_value' in sd_config else float('inf')
return partial(utils.rand_log_logistic, loc=loc, scale=scale, min_value=min_value, max_value=max_value)
if sd_config['type'] == 'loguniform':
min_value = sd_config['min_value'] if 'min_value' in sd_config else config['sigma_min']
max_value = sd_config['max_value'] if 'max_value' in sd_config else config['sigma_max']
return partial(utils.rand_log_uniform, min_value=min_value, max_value=max_value)
if sd_config['type'] == 'v-diffusion':
min_value = sd_config['min_value'] if 'min_value' in sd_config else 0.
max_value = sd_config['max_value'] if 'max_value' in sd_config else float('inf')
return partial(utils.rand_v_diffusion, sigma_data=sigma_data, min_value=min_value, max_value=max_value)
if sd_config['type'] == 'split-lognormal':
loc = sd_config['mean'] if 'mean' in sd_config else sd_config['loc']
scale_1 = sd_config['std_1'] if 'std_1' in sd_config else sd_config['scale_1']
scale_2 = sd_config['std_2'] if 'std_2' in sd_config else sd_config['scale_2']
return partial(utils.rand_split_log_normal, loc=loc, scale_1=scale_1, scale_2=scale_2)
raise ValueError('Unknown sample density type')
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