3 files changed, 30 insertions, 58 deletions

diff --git a/training/strategy/dreambooth.py b/training/strategy/dreambooth.py
index 28fccff..9808027 100644
--- a/training/strategy/dreambooth.py
+++ b/training/strategy/dreambooth.py
@@ -74,6 +74,7 @@ def dreambooth_strategy_callbacks(
             power=ema_power,
             max_value=ema_max_decay,
         )
+        ema_unet.to(accelerator.device)
     else:
         ema_unet = None
 
@@ -86,14 +87,6 @@ def dreambooth_strategy_callbacks(
     def on_accum_model():
         return unet
 
-    def on_prepare():
-        unet.requires_grad_(True)
-        text_encoder.text_model.encoder.requires_grad_(True)
-        text_encoder.text_model.final_layer_norm.requires_grad_(True)
-
-        if ema_unet is not None:
-            ema_unet.to(accelerator.device)
-
     @contextmanager
     def on_train(epoch: int):
         tokenizer.train()
@@ -181,7 +174,6 @@ def dreambooth_strategy_callbacks(
             torch.cuda.empty_cache()
 
     return TrainingCallbacks(
-        on_prepare=on_prepare,
         on_accum_model=on_accum_model,
         on_train=on_train,
         on_eval=on_eval,
@@ -203,7 +195,12 @@ def dreambooth_prepare(
     lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
     **kwargs
 ):
-    return accelerator.prepare(text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler) + ({},)
+    text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
+        text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler)
+
+    text_encoder.text_model.embeddings.requires_grad_(False)
+
+    return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {}
 
 
 dreambooth_strategy = TrainingStrategy(
diff --git a/training/strategy/lora.py b/training/strategy/lora.py
index 1c8fad6..3971eae 100644
--- a/training/strategy/lora.py
+++ b/training/strategy/lora.py
@@ -10,18 +10,12 @@ from torch.utils.data import DataLoader
 from accelerate import Accelerator
 from transformers import CLIPTextModel
 from diffusers import AutoencoderKL, UNet2DConditionModel, DPMSolverMultistepScheduler
-from peft import LoraConfig, LoraModel, get_peft_model_state_dict
-from peft.tuners.lora import mark_only_lora_as_trainable
+from peft import get_peft_model_state_dict
 
 from models.clip.tokenizer import MultiCLIPTokenizer
 from training.functional import TrainingStrategy, TrainingCallbacks, save_samples
 
 
-# https://github.com/huggingface/peft/blob/main/examples/lora_dreambooth/train_dreambooth.py
-UNET_TARGET_MODULES = ["to_q", "to_v", "query", "value"]
-TEXT_ENCODER_TARGET_MODULES = ["q_proj", "v_proj"]
-
-
 def lora_strategy_callbacks(
     accelerator: Accelerator,
     unet: UNet2DConditionModel,
@@ -61,10 +55,6 @@ def lora_strategy_callbacks(
         image_size=sample_image_size,
     )
 
-    def on_prepare():
-        mark_only_lora_as_trainable(unet.model, unet.peft_config.bias)
-        mark_only_lora_as_trainable(text_encoder.model, text_encoder.peft_config.bias)
-
     def on_accum_model():
         return unet
 
@@ -93,15 +83,15 @@ def lora_strategy_callbacks(
         text_encoder_ = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=False)
 
         lora_config = {}
-        state_dict = get_peft_model_state_dict(unet, state_dict=accelerator.get_state_dict(unet))
-        lora_config["peft_config"] = unet.get_peft_config_as_dict(inference=True)
+        state_dict = get_peft_model_state_dict(unet_, state_dict=accelerator.get_state_dict(unet_))
+        lora_config["peft_config"] = unet_.get_peft_config_as_dict(inference=True)
 
         text_encoder_state_dict = get_peft_model_state_dict(
-            text_encoder, state_dict=accelerator.get_state_dict(text_encoder)
+            text_encoder_, state_dict=accelerator.get_state_dict(text_encoder_)
         )
         text_encoder_state_dict = {f"text_encoder_{k}": v for k, v in text_encoder_state_dict.items()}
         state_dict.update(text_encoder_state_dict)
-        lora_config["text_encoder_peft_config"] = text_encoder.get_peft_config_as_dict(inference=True)
+        lora_config["text_encoder_peft_config"] = text_encoder_.get_peft_config_as_dict(inference=True)
 
         accelerator.print(state_dict)
         accelerator.save(state_dict, checkpoint_output_dir / f"{step}_{postfix}.pt")
@@ -111,11 +101,16 @@ def lora_strategy_callbacks(
 
     @torch.no_grad()
     def on_sample(step):
+        vae_dtype = vae.dtype
+        vae.to(dtype=text_encoder.dtype)
+
         unet_ = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
         text_encoder_ = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
 
         save_samples_(step=step, unet=unet_, text_encoder=text_encoder_)
 
+        vae.to(dtype=vae_dtype)
+
         del unet_
         del text_encoder_
 
@@ -123,7 +118,6 @@ def lora_strategy_callbacks(
             torch.cuda.empty_cache()
 
     return TrainingCallbacks(
-        on_prepare=on_prepare,
         on_accum_model=on_accum_model,
         on_train=on_train,
         on_eval=on_eval,
@@ -147,28 +141,7 @@ def lora_prepare(
     lora_bias: str = "none",
     **kwargs
 ):
-    unet_config = LoraConfig(
-        r=lora_rank,
-        lora_alpha=lora_alpha,
-        target_modules=UNET_TARGET_MODULES,
-        lora_dropout=lora_dropout,
-        bias=lora_bias,
-    )
-    unet = LoraModel(unet_config, unet)
-
-    text_encoder_config = LoraConfig(
-        r=lora_rank,
-        lora_alpha=lora_alpha,
-        target_modules=TEXT_ENCODER_TARGET_MODULES,
-        lora_dropout=lora_dropout,
-        bias=lora_bias,
-    )
-    text_encoder = LoraModel(text_encoder_config, text_encoder)
-
-    text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
-        text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler)
-
-    return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {}
+    return accelerator.prepare(text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler) + ({},)
 
 
 lora_strategy = TrainingStrategy(
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index 2038e34..10bc6d7 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -78,6 +78,7 @@ def textual_inversion_strategy_callbacks(
             power=ema_power,
             max_value=ema_max_decay,
         )
+        ema_embeddings.to(accelerator.device)
     else:
         ema_embeddings = None
 
@@ -92,15 +93,6 @@ def textual_inversion_strategy_callbacks(
     def on_accum_model():
         return text_encoder.text_model.embeddings.temp_token_embedding
 
-    def on_prepare():
-        text_encoder.text_model.embeddings.temp_token_embedding.requires_grad_(True)
-
-        if ema_embeddings is not None:
-            ema_embeddings.to(accelerator.device)
-
-        if gradient_checkpointing:
-            unet.train()
-
     @contextmanager
     def on_train(epoch: int):
         tokenizer.train()
@@ -177,7 +169,6 @@ def textual_inversion_strategy_callbacks(
             torch.cuda.empty_cache()
 
     return TrainingCallbacks(
-        on_prepare=on_prepare,
         on_accum_model=on_accum_model,
         on_train=on_train,
         on_eval=on_eval,
@@ -197,6 +188,7 @@ def textual_inversion_prepare(
     train_dataloader: DataLoader,
     val_dataloader: Optional[DataLoader],
     lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
+    gradient_checkpointing: bool = False,
     **kwargs
 ):
     weight_dtype = torch.float32
@@ -207,7 +199,17 @@ def textual_inversion_prepare(
 
     text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
         text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler)
+
     unet.to(accelerator.device, dtype=weight_dtype)
+    unet.requires_grad_(False)
+    unet.eval()
+    if gradient_checkpointing:
+        unet.train()
+
+    text_encoder.text_model.encoder.requires_grad_(False)
+    text_encoder.text_model.final_layer_norm.requires_grad_(False)
+    text_encoder.eval()
+
     return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {}