Cleanup

1 files changed, 11 insertions, 15 deletions

diff --git a/training/util.py b/training/util.py
index cc4cdee..1008021 100644
--- a/training/util.py
+++ b/training/util.py
@@ -44,32 +44,29 @@ class CheckpointerBase:
         train_dataloader,
         val_dataloader,
         output_dir: Path,
-        sample_image_size: int,
-        sample_batches: int,
-        sample_batch_size: int,
+        sample_steps: int = 20,
+        sample_guidance_scale: float = 7.5,
+        sample_image_size: int = 768,
+        sample_batches: int = 1,
+        sample_batch_size: int = 1,
         seed: Optional[int] = None
     ):
         self.train_dataloader = train_dataloader
         self.val_dataloader = val_dataloader
         self.output_dir = output_dir
         self.sample_image_size = sample_image_size
-        self.seed = seed if seed is not None else torch.random.seed()
+        self.sample_steps = sample_steps
+        self.sample_guidance_scale = sample_guidance_scale
         self.sample_batches = sample_batches
         self.sample_batch_size = sample_batch_size
+        self.seed = seed if seed is not None else torch.random.seed()
 
     @torch.no_grad()
     def checkpoint(self, step: int, postfix: str):
         pass
 
     @torch.inference_mode()
-    def save_samples(
-        self,
-        pipeline,
-        step: int,
-        num_inference_steps: int,
-        guidance_scale: float = 7.5,
-        eta: float = 0.0
-    ):
+    def save_samples(self, pipeline, step: int):
         samples_path = Path(self.output_dir).joinpath("samples")
 
         generator = torch.Generator(device=pipeline.device).manual_seed(self.seed)
@@ -110,9 +107,8 @@ class CheckpointerBase:
                     height=self.sample_image_size,
                     width=self.sample_image_size,
                     generator=gen,
-                    guidance_scale=guidance_scale,
-                    eta=eta,
-                    num_inference_steps=num_inference_steps,
+                    guidance_scale=self.sample_guidance_scale,
+                    num_inference_steps=self.sample_steps,
                     output_type='pil'
                 ).images