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from typing import Literal, Optional
import yaml
from pydantic import BaseModel
import torch
from lora import TRAINING_METHODS
PRECISION_TYPES = Literal["fp32", "fp16", "bf16", "float32", "float16", "bfloat16"]
NETWORK_TYPES = Literal["lierla", "c3lier"]
class PretrainedModelConfig(BaseModel):
name_or_path: str
v2: bool = False
v_pred: bool = False
clip_skip: Optional[int] = None
class NetworkConfig(BaseModel):
type: NETWORK_TYPES = "lierla"
rank: int = 4
alpha: float = 1.0
training_method: TRAINING_METHODS = "full"
class TrainConfig(BaseModel):
precision: PRECISION_TYPES = "bfloat16"
noise_scheduler: Literal["ddim", "ddpm", "lms", "euler_a"] = "ddim"
iterations: int = 500
lr: float = 1e-4
optimizer: str = "adamw"
optimizer_args: str = ""
lr_scheduler: str = "constant"
max_denoising_steps: int = 50
class SaveConfig(BaseModel):
name: str = "untitled"
path: str = "./output"
per_steps: int = 200
precision: PRECISION_TYPES = "float32"
class LoggingConfig(BaseModel):
use_wandb: bool = False
verbose: bool = False
class OtherConfig(BaseModel):
use_xformers: bool = False
class RootConfig(BaseModel):
prompts_file: str
pretrained_model: PretrainedModelConfig
network: NetworkConfig
train: Optional[TrainConfig]
save: Optional[SaveConfig]
logging: Optional[LoggingConfig]
other: Optional[OtherConfig]
def parse_precision(precision: str) -> torch.dtype:
if precision == "fp32" or precision == "float32":
return torch.float32
elif precision == "fp16" or precision == "float16":
return torch.float16
elif precision == "bf16" or precision == "bfloat16":
return torch.bfloat16
raise ValueError(f"Invalid precision type: {precision}")
def load_config_from_yaml(config_path: str) -> RootConfig:
with open(config_path, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
root = RootConfig(**config)
if root.train is None:
root.train = TrainConfig()
if root.save is None:
root.save = SaveConfig()
if root.logging is None:
root.logging = LoggingConfig()
if root.other is None:
root.other = OtherConfig()
return root
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