simplify dataloader

trainer/dataset_and_utils.py

@@ -196,9 +196,7 @@ class PreprocessedDataset(Dataset):
     ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
         image_path = self.image_path[idx]
         image_path = os.path.join(os.path.dirname(self.csv_path), image_path)
-
         image = PIL.Image.open(image_path).convert("RGB")
-
         image = prepare_image(image, self.size, self.size).to(
             dtype=self.vae_encoder.dtype, device=self.vae_encoder.device
         )
@@ -210,7 +208,7 @@ class PreprocessedDataset(Dataset):
         ti1 = self.tokenizer_1(
             caption,
             padding="max_length",
-            max_length=77,
+            max_length=self.tokenizer_1.model_max_length,
             truncation=True,
             add_special_tokens=True,
             return_tensors="pt",
@@ -222,17 +220,13 @@ class PreprocessedDataset(Dataset):
             ti2 = self.tokenizer_2(
                 caption,
                 padding="max_length",
-                max_length=77,
+                max_length=self.tokenizer_2.model_max_length,
                 truncation=True,
                 add_special_tokens=True,
                 return_tensors="pt",
             ).input_ids.squeeze()
 
         vae_latent = self.vae_encoder.encode(image).latent_dist#.sample()
-
-        #if self.scale_vae_latents:
-        #    vae_latent = vae_latent * self.vae_encoder.config.scaling_factor
-
         dummy_vae_latent = vae_latent.sample()
 
         if self.mask_path is None:
@@ -243,9 +237,7 @@ class PreprocessedDataset(Dataset):
         else:
             mask_path = self.mask_path[idx]
             mask_path = os.path.join(os.path.dirname(self.csv_path), mask_path)
-
             mask = PIL.Image.open(mask_path)
-
             mask = prepare_mask(mask, self.size, self.size).to(
                 dtype=self.vae_encoder.dtype, device=self.vae_encoder.device
             )
@@ -265,26 +257,20 @@ class PreprocessedDataset(Dataset):
         else: # sdxl
             return (ti1, ti2), vae_latent, mask.squeeze()
 
-    def atidx(
+    def __getitem__(
         self, idx: int
     ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
         if self.do_cache:
             vae_latent = self.vae_latents[idx].sample()
             if self.scale_vae_latents:
                 vae_latent *= self.vae_scaling_factor
-            return self.tokens_tuple[idx], vae_latent, self.masks[idx]
+            return self.tokens_tuple[idx], vae_latent.squeeze(), self.masks[idx]
         else:
             tokens, vae_latent, mask = self._process(idx)
             vae_latent = vae_latent.sample()
             if self.scale_vae_latents:
                 vae_latent *= self.vae_scaling_factor
-            return tokens, vae_latent, mask
-
-    def __getitem__(
-        self, idx: int
-    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
-        token, vae_latent, mask = self.atidx(idx)
-        return token, vae_latent.squeeze(), mask
+            return tokens, vae_latent.squeeze(), mask
 
 
 def import_model_class_from_model_name_or_path(