mirror of
https://github.com/ostris/ai-toolkit.git
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90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
from typing import List
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class SaveConfig:
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def __init__(self, **kwargs):
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self.save_every: int = kwargs.get('save_every', 1000)
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self.dtype: str = kwargs.get('save_dtype', 'float16')
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class LogingConfig:
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def __init__(self, **kwargs):
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self.log_every: int = kwargs.get('log_every', 100)
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self.verbose: bool = kwargs.get('verbose', False)
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self.use_wandb: bool = kwargs.get('use_wandb', False)
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class SampleConfig:
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def __init__(self, **kwargs):
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self.sample_every: int = kwargs.get('sample_every', 100)
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self.width: int = kwargs.get('width', 512)
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self.height: int = kwargs.get('height', 512)
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self.prompts: list[str] = kwargs.get('prompts', [])
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self.neg = kwargs.get('neg', False)
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self.seed = kwargs.get('seed', 0)
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self.walk_seed = kwargs.get('walk_seed', False)
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self.guidance_scale = kwargs.get('guidance_scale', 7)
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self.sample_steps = kwargs.get('sample_steps', 20)
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self.network_multiplier = kwargs.get('network_multiplier', 1)
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class NetworkConfig:
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def __init__(self, **kwargs):
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self.type: str = kwargs.get('type', 'lierla')
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self.rank: int = kwargs.get('rank', 4)
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self.alpha: float = kwargs.get('alpha', 1.0)
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class TrainConfig:
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def __init__(self, **kwargs):
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self.noise_scheduler = kwargs.get('noise_scheduler', 'ddpm')
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self.steps: int = kwargs.get('steps', 1000)
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self.lr = kwargs.get('lr', 1e-6)
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self.optimizer = kwargs.get('optimizer', 'adamw')
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self.lr_scheduler = kwargs.get('lr_scheduler', 'constant')
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self.max_denoising_steps: int = kwargs.get('max_denoising_steps', 50)
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self.batch_size: int = kwargs.get('batch_size', 1)
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self.dtype: str = kwargs.get('dtype', 'fp32')
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self.xformers = kwargs.get('xformers', False)
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self.train_unet = kwargs.get('train_unet', True)
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self.train_text_encoder = kwargs.get('train_text_encoder', True)
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self.noise_offset = kwargs.get('noise_offset', 0.0)
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self.optimizer_params = kwargs.get('optimizer_params', {})
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class ModelConfig:
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def __init__(self, **kwargs):
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self.name_or_path: str = kwargs.get('name_or_path', None)
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self.is_v2: bool = kwargs.get('is_v2', False)
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self.is_v_pred: bool = kwargs.get('is_v_pred', False)
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if self.name_or_path is None:
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raise ValueError('name_or_path must be specified')
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class SliderTargetConfig:
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def __init__(self, **kwargs):
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self.target_class: str = kwargs.get('target_class', '')
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self.positive: str = kwargs.get('positive', '')
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self.negative: str = kwargs.get('negative', '')
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self.multiplier: float = kwargs.get('multiplier', 1.0)
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self.weight: float = kwargs.get('weight', 1.0)
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class SliderConfigAnchors:
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def __init__(self, **kwargs):
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self.prompt = kwargs.get('prompt', '')
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self.neg_prompt = kwargs.get('neg_prompt', '')
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self.multiplier = kwargs.get('multiplier', 1.0)
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class SliderConfig:
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def __init__(self, **kwargs):
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targets = kwargs.get('targets', [])
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targets = [SliderTargetConfig(**target) for target in targets]
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self.targets: List[SliderTargetConfig] = targets
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anchors = kwargs.get('anchors', [])
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anchors = [SliderConfigAnchors(**anchor) for anchor in anchors]
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self.anchors: List[SliderConfigAnchors] = anchors
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self.resolutions: List[List[int]] = kwargs.get('resolutions', [[512, 512]])
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