Add config parsing support in backend

Signed-off-by: Felipe Cardoso <felipe.cardoso@hotmail.it>

backend/app/models/config.py

@@ -0,0 +1,93 @@
+from typing import List, Optional, Dict, Any
+
+from pydantic import BaseModel
+
+
+class SampleConfig(BaseModel):
+    sampler: str
+    sample_every: int
+    width: int
+    height: int
+    prompts: List[str]
+    neg: str
+    seed: int
+    walk_seed: bool
+    guidance_scale: float
+    sample_steps: int
+
+
+class DatasetConfig(BaseModel):
+    folder_path: str
+    caption_ext: Optional[str] = None
+    caption_dropout_rate: Optional[float] = None
+    shuffle_tokens: Optional[bool] = False
+    resolution: Optional[List[int]] = None
+
+
+class EMAConfig(BaseModel):
+    use_ema: Optional[bool] = False
+    ema_decay: Optional[float] = None
+
+
+class TrainConfig(BaseModel):
+    batch_size: int
+    bypass_guidance_embedding: Optional[bool] = False
+    timestep_type: Optional[str] = None
+    steps: int
+    gradient_accumulation: Optional[int] = 1
+    train_unet: Optional[bool] = True
+    train_text_encoder: Optional[bool] = False
+    gradient_checkpointing: Optional[bool] = False
+    noise_scheduler: Optional[str] = None
+    optimizer: Optional[str] = None
+    lr: Optional[float] = None
+    ema_config: Optional[EMAConfig] = None
+    dtype: Optional[str] = None
+    do_paramiter_swapping: Optional[bool] = False
+    paramiter_swapping_factor: Optional[float] = None
+    skip_first_sample: Optional[bool] = False
+    disable_sampling: Optional[bool] = False
+
+
+class ModelConfig(BaseModel):
+    name_or_path: str
+    is_flux: Optional[bool] = False
+    quantize: Optional[bool] = False
+    quantize_te: Optional[bool] = False
+
+
+class SaveConfig(BaseModel):
+    dtype: Optional[str] = None
+    save_every: Optional[int] = None
+    max_step_saves_to_keep: Optional[int] = None
+    save_format: Optional[str] = None
+
+
+class ProcessConfig(BaseModel):
+    type: str
+    training_folder: str
+    performance_log_every: Optional[int] = None
+    device: Optional[str] = None
+    trigger_word: Optional[str] = None
+    save: Optional[SaveConfig] = None
+    datasets: List[DatasetConfig]
+    train: TrainConfig
+    model: ModelConfig
+    sample: SampleConfig
+
+
+class MetaConfig(BaseModel):
+    name: Optional[str] = None
+    version: Optional[str] = None
+
+
+class TrainingConfig(BaseModel):
+    job: str
+    config: Dict[str, Any]  # This will contain 'name' and 'process'
+    meta: MetaConfig
+
+
+# And a Config class to represent the middle layer:
+class Config(BaseModel):
+    name: str
+    process: List[ProcessConfig]