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