slow port to a standard config

predict.py

@@ -186,33 +186,72 @@ class Predictor(BasePredictor):
         bs=6: 8.0 imgs/s,
         """
 
+        from trainer.config import TrainingConfig
+
+        config = TrainingConfig(
+            name=name,
+            lora_training_urls=lora_training_urls,
+            concept_mode=concept_mode,
+            sd_model_version=sd_model_version,
+            seed=seed,
+            resolution=resolution,
+            train_batch_size=train_batch_size,
+            num_train_epochs=num_train_epochs,
+            max_train_steps=max_train_steps,
+            checkpointing_steps=checkpointing_steps,
+            gradient_accumulation_steps=gradient_accumulation_steps,
+            is_lora=is_lora,
+            prodigy_d_coef=prodigy_d_coef,
+            ti_lr=ti_lr,
+            ti_weight_decay=ti_weight_decay,
+            lora_weight_decay=lora_weight_decay,
+            l1_penalty=l1_penalty,
+            lora_param_scaler=lora_param_scaler,
+            snr_gamma=snr_gamma,
+            lora_rank=lora_rank,
+            caption_prefix=caption_prefix,
+            caption_model=caption_model,
+            left_right_flip_augmentation=left_right_flip_augmentation,
+            augment_imgs_up_to_n=augment_imgs_up_to_n,
+            n_tokens=n_tokens,
+            mask_target_prompts=mask_target_prompts,
+            crop_based_on_salience=crop_based_on_salience,
+            use_face_detection_instead=use_face_detection_instead,
+            clipseg_temperature=clipseg_temperature,
+            verbose=verbose,
+            run_name=run_name,
+            debug=debug,
+            hard_pivot=hard_pivot,
+            off_ratio_power=off_ratio_power,
+        )
+
         start_time = time.time()
         out_root_dir = "lora_models"
 
-        if seed is None:
-            seed = np.random.randint(0, 2**32 - 1)
+        if config.seed is None:
+            config.seed = np.random.randint(0, 2**32 - 1)
 
         # Try to make the training reproducible:
-        random.seed(seed)
-        np.random.seed(seed)
-        torch.manual_seed(seed)
-        torch.cuda.manual_seed_all(seed)
+        random.seed(config.seed)
+        np.random.seed(config.seed)
+        torch.manual_seed(config.seed)
+        torch.cuda.manual_seed_all(config.seed)
 
-        if concept_mode == "face":
-            left_right_flip_augmentation = False  # always disable lr flips for face mode!
-            mask_target_prompts = "face"
+        if config.concept_mode == "face":
+            config.left_right_flip_augmentation = False  # always disable lr flips for face mode!
+            config.mask_target_prompts = "face"
             clipseg_temperature = 0.4
 
-        if concept_mode == "concept": # gracefully catch any old versions of concept_mode
-            concept_mode = "object"
+        if config.concept_mode == "concept": # gracefully catch any old versions of concept_mode
+            config.concept_mode = "object"
 
-        if concept_mode == "style": # for styles you usually want the LoRA matrices to absorb a lot (instead of just the token embedding)
-            l1_penalty = 0.05
+        if config.concept_mode == "style": # for styles you usually want the LoRA matrices to absorb a lot (instead of just the token embedding)
+            config.l1_penalty = 0.05
 
-        print(f"cog:predict:train_lora:{concept_mode}")
+        print(f"cog:predict:train_lora:{config.concept_mode}")
 
         if not debug:
-            yield CogOutput(name=name, progress=0.0)
+            yield CogOutput(name=config.name, progress=0.0)
 
         # Initialize pretrained_model dictionary
         pretrained_model = {"version": sd_model_version}

trainer/config.py

@@ -0,0 +1,42 @@
+from typing import Union
+from pydantic import BaseModel
+import random
+import json
+from typing import Literal
+import torch
+
+class TrainingConfig(BaseModel, extra = "forbid"):
+    name: str = "unnamed"
+    lora_training_urls: str
+    concept_mode: Literal["face", "style", "object"]
+    sd_model_version: Literal["sdxl", "sd15"]
+    seed: Union[int, None] = None
+    resolution: int = 960
+    train_batch_size: int = 4
+    num_train_epochs: int = 10_000
+    max_train_steps: int = 600
+    checkpointing_steps: int
+    gradient_accumulation_steps: int = 1
+    is_lora: bool = True
+    prodigy_d_coef: float = 0.5
+    ti_lr: float = 1e-3
+    ti_weight_decay: float = 3e-4
+    lora_weight_decay: float = 0.002
+    l1_penalty: float = 0.1
+    lora_param_scaler: float = 0.5
+    snr_gamma: float = 5.0
+    lora_rank: int = 12
+    caption_prefix: str = ""
+    caption_model: Literal["gpt4-v", "blip"] = "blip"
+    left_right_flip_augmentation: bool = True
+    augment_imgs_up_to_n: int = 20
+    n_tokens: int = 2
+    mask_target_prompts: Union[None, str]
+    crop_based_on_salience: bool = True
+    use_face_detection_instead: bool = False
+    clipseg_temperature: float = 0.7
+    verbose: bool = False
+    run_name: str = "default_run_name"
+    debug: bool = False
+    hard_pivot: bool = False
+    off_ratio_power: float = False