2 files changed, 6 insertions, 6 deletions

diff --git a/data/dreambooth/csv.py b/data/dreambooth/csv.py
index 4ebdc13..9075979 100644
--- a/data/dreambooth/csv.py
+++ b/data/dreambooth/csv.py
@@ -150,7 +150,7 @@ class CSVDataset(Dataset):
             max_length=self.tokenizer.model_max_length,
         ).input_ids
 
-        if self.class_identifier:
+        if self.class_identifier is not None:
             class_image = Image.open(class_image_path)
             if not class_image.mode == "RGB":
                 class_image = class_image.convert("RGB")
@@ -177,7 +177,7 @@ class CSVDataset(Dataset):
         example["instance_images"] = self.image_transforms(unprocessed_example["instance_images"])
         example["instance_prompt_ids"] = unprocessed_example["instance_prompt_ids"]
 
-        if self.class_identifier:
+        if self.class_identifier is not None:
             example["class_images"] = self.image_transforms(unprocessed_example["class_images"])
             example["class_prompt_ids"] = unprocessed_example["class_prompt_ids"]
 
diff --git a/dreambooth.py b/dreambooth.py
index 1bff414..dd93e09 100644
--- a/dreambooth.py
+++ b/dreambooth.py
@@ -497,12 +497,12 @@ def main():
         pixel_values = [example["instance_images"] for example in examples]
 
         # concat class and instance examples for prior preservation
-        if args.class_identifier and "class_prompt_ids" in examples[0]:
+        if args.class_identifier is not None and "class_prompt_ids" in examples[0]:
             input_ids += [example["class_prompt_ids"] for example in examples]
             pixel_values += [example["class_images"] for example in examples]
 
         pixel_values = torch.stack(pixel_values)
-        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
+        pixel_values = pixel_values.to(dtype=torch.float32, memory_format=torch.contiguous_format)
 
         input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids
 
@@ -529,7 +529,7 @@ def main():
     datamodule.prepare_data()
     datamodule.setup()
 
-    if args.class_identifier:
+    if args.class_identifier is not None:
         missing_data = [item for item in datamodule.data if not item[1].exists()]
 
         if len(missing_data) != 0:
@@ -686,7 +686,7 @@ def main():
                     # Predict the noise residual
                     noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
 
-                    if args.class_identifier:
+                    if args.class_identifier is not None:
                         # Chunk the noise and noise_pred into two parts and compute the loss on each part separately.
                         noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)
                         noise, noise_prior = torch.chunk(noise, 2, dim=0)