storytold/FineTrainers-Conditioning
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remove unnecessary changes

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Aryan
2025-01-11 01:36:52 +01:00
parent 491b35f3dd
commit ca957e543c
1 changed files with 2 additions and 25 deletions
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finetrainers/trainer.py
+2 -25
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@@ -100,9 +100,6 @@ class Trainer:
self.state.model_name = self.args.model_name self.state.model_name = self.args.model_name
self.model_config = get_config_from_model_name(self.args.model_name, self.args.training_type) self.model_config = get_config_from_model_name(self.args.model_name, self.args.training_type)
# Components list
self.components = []
def prepare_dataset(self) -> None: def prepare_dataset(self) -> None:
# TODO(aryan): Make a background process for fetching # TODO(aryan): Make a background process for fetching
logger.info("Initializing dataset and dataloader") logger.info("Initializing dataset and dataloader")
@@ -157,18 +154,6 @@ class Trainer:
self.transformer_config = self.transformer.config if self.transformer is not None else self.transformer_config self.transformer_config = self.transformer.config if self.transformer is not None else self.transformer_config
self.vae_config = self.vae.config if self.vae is not None else self.vae_config self.vae_config = self.vae.config if self.vae is not None else self.vae_config
self.components = [
self.tokenizer,
self.tokenizer_2,
self.tokenizer_3,
self.text_encoder,
self.text_encoder_2,
self.text_encoder_3,
self.transformer,
self.unet,
self.vae,
]
def _delete_components(self) -> None: def _delete_components(self) -> None:
self.tokenizer = None self.tokenizer = None
self.tokenizer_2 = None self.tokenizer_2 = None
@@ -183,8 +168,6 @@ class Trainer:
free_memory() free_memory()
torch.cuda.synchronize(self.state.accelerator.device) torch.cuda.synchronize(self.state.accelerator.device)
self.components = None
def prepare_models(self) -> None: def prepare_models(self) -> None:
logger.info("Initializing models") logger.info("Initializing models")
@@ -260,14 +243,8 @@ class Trainer:
condition_components = self.model_config["load_condition_models"](**self._get_load_components_kwargs()) condition_components = self.model_config["load_condition_models"](**self._get_load_components_kwargs())
self._set_components(condition_components) self._set_components(condition_components)
self._move_components_to_device() self._move_components_to_device()
self._disable_grad_for_components([self.text_encoder, self.text_encoder_2, self.text_encoder_3])
self._disable_grad_for_components(
components=[
self.text_encoder,
self.text_encoder_2,
self.text_encoder_3,
]
)
if self.args.caption_dropout_p > 0 and self.args.caption_dropout_technique == "empty": if self.args.caption_dropout_p > 0 and self.args.caption_dropout_technique == "empty":
logger.warning( logger.warning(
"Caption dropout is not supported with precomputation yet. This will be supported in the future." "Caption dropout is not supported with precomputation yet. This will be supported in the future."
@@ -320,8 +297,8 @@ class Trainer:
latent_components = self.model_config["load_latent_models"](**self._get_load_components_kwargs()) latent_components = self.model_config["load_latent_models"](**self._get_load_components_kwargs())
self._set_components(latent_components) self._set_components(latent_components)
self._move_components_to_device() self._move_components_to_device()
self._disable_grad_for_components([self.vae])
self._disable_grad_for_components(components=[self.vae])
if self.vae is not None: if self.vae is not None:
if self.args.enable_slicing: if self.args.enable_slicing:
self.vae.enable_slicing() self.vae.enable_slicing()