Training update

3 files changed, 15 insertions, 12 deletions

diff --git a/training/functional.py b/training/functional.py
index 1548784..3d27380 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -87,8 +87,6 @@ def save_samples(
 ):
     print(f"Saving samples for step {step}...")
 
-    samples_path = output_dir.joinpath("samples")
-
     grid_cols = min(batch_size, 4)
     grid_rows = (num_batches * batch_size) // grid_cols
 
@@ -120,7 +118,7 @@ def save_samples(
 
     for pool, data, gen in datasets:
         all_samples = []
-        file_path = samples_path.joinpath(pool, f"step_{step}.jpg")
+        file_path = output_dir.joinpath(pool, f"step_{step}.jpg")
         file_path.parent.mkdir(parents=True, exist_ok=True)
 
         batches = list(itertools.islice(itertools.cycle(data), batch_size * num_batches))
diff --git a/training/strategy/dreambooth.py b/training/strategy/dreambooth.py
index aeaa828..93c81cb 100644
--- a/training/strategy/dreambooth.py
+++ b/training/strategy/dreambooth.py
@@ -27,7 +27,8 @@ def dreambooth_strategy(
     sample_scheduler: DPMSolverMultistepScheduler,
     train_dataloader: DataLoader,
     val_dataloader: Optional[DataLoader],
-    output_dir: Path,
+    sample_output_dir: Path,
+    checkpoint_output_dir: Path,
     seed: int,
     train_text_encoder_epochs: int,
     max_grad_norm: float = 1.0,
@@ -47,6 +48,9 @@ def dreambooth_strategy(
             "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
         )
 
+    sample_output_dir.mkdir(parents=True, exist_ok=True)
+    checkpoint_output_dir.mkdir(parents=True, exist_ok=True)
+
     weight_dtype = torch.float32
     if accelerator.state.mixed_precision == "fp16":
         weight_dtype = torch.float16
@@ -64,7 +68,7 @@ def dreambooth_strategy(
         train_dataloader=train_dataloader,
         val_dataloader=val_dataloader,
         dtype=weight_dtype,
-        output_dir=output_dir,
+        output_dir=sample_output_dir,
         seed=seed,
         batch_size=sample_batch_size,
         num_batches=sample_num_batches,
@@ -156,7 +160,7 @@ def dreambooth_strategy(
                 tokenizer=tokenizer,
                 scheduler=sample_scheduler,
             )
-            pipeline.save_pretrained(output_dir.joinpath("model"))
+            pipeline.save_pretrained(checkpoint_output_dir)
 
         del unet_
         del text_encoder_
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index 9d39e15..00f3529 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -27,7 +27,8 @@ def textual_inversion_strategy(
     sample_scheduler: DPMSolverMultistepScheduler,
     train_dataloader: DataLoader,
     val_dataloader: Optional[DataLoader],
-    output_dir: Path,
+    sample_output_dir: Path,
+    checkpoint_output_dir: Path,
     seed: int,
     placeholder_tokens: list[str],
     placeholder_token_ids: list[list[int]],
@@ -47,6 +48,9 @@ def textual_inversion_strategy(
     sample_guidance_scale: float = 7.5,
     sample_image_size: Optional[int] = None,
 ):
+    sample_output_dir.mkdir(parents=True, exist_ok=True)
+    checkpoint_output_dir.mkdir(parents=True, exist_ok=True)
+
     weight_dtype = torch.float32
     if accelerator.state.mixed_precision == "fp16":
         weight_dtype = torch.float16
@@ -64,7 +68,7 @@ def textual_inversion_strategy(
         train_dataloader=train_dataloader,
         val_dataloader=val_dataloader,
         dtype=weight_dtype,
-        output_dir=output_dir,
+        output_dir=sample_output_dir,
         seed=seed,
         batch_size=sample_batch_size,
         num_batches=sample_num_batches,
@@ -135,14 +139,11 @@ def textual_inversion_strategy(
     def on_checkpoint(step, postfix):
         print(f"Saving checkpoint for step {step}...")
 
-        checkpoints_path = output_dir.joinpath("checkpoints")
-        checkpoints_path.mkdir(parents=True, exist_ok=True)
-
         with ema_context():
             for (token, ids) in zip(placeholder_tokens, placeholder_token_ids):
                 text_encoder.text_model.embeddings.save_embed(
                     ids,
-                    checkpoints_path.joinpath(f"{slugify(token)}_{step}_{postfix}.bin")
+                    checkpoint_output_dir.joinpath(f"{slugify(token)}_{step}_{postfix}.bin")
                 )
 
     @torch.no_grad()