remove old comments

nerfstudio/data/datamanagers/random_cameras_datamanager.py

@@ -247,18 +247,6 @@ class RandomCamerasDataManager(DataManager):  # pylint: disable=abstract-method
         """Returns the next batch of data from the train dataloader."""
 
         self.train_count += 1
-
-        # # TODO Reimplement when cameras are fully working
-        # if step > 2000:
-        #     cameras, _, _ = random_train_pose(
-        #         self.config.train_images_per_batch, self.config.train_resolution, device=self.device
-        #     )
-
-        #     ray_bundle = cameras.generate_rays(
-        #         torch.tensor(list(range(self.config.train_images_per_batch)))
-        #     ).flatten()
-        #     return ray_bundle, {"initialization": False}
-
         horizontal_range = min((step / max(1, self.config.horizontal_rotation_warmup)), 1) * 180
 
         cameras, vertical_rotation, central_rotation = random_train_pose(

nerfstudio/engine/trainer.py

@@ -231,6 +231,7 @@ class Trainer:
         """Train the model."""
         assert self.pipeline.datamanager.train_dataset is not None, "Missing DatsetInputs"
 
+        # don't want to call save_dataparser_transform if pipeline's datamanager does not have a dataparser
         if isinstance(self.pipeline.datamanager, VanillaDataManager):
             self.pipeline.datamanager.train_dataparser_outputs.save_dataparser_transform(
                 self.base_dir / "dataparser_transforms.json"

nerfstudio/models/dreamfusion.py

@@ -37,7 +37,7 @@ from nerfstudio.fields.density_fields import HashMLPDensityField
 from nerfstudio.fields.dreamfusion_field import DreamFusionField
 from nerfstudio.generative.stable_diffusion import StableDiffusion
 from nerfstudio.generative.deepfloyd import DeepFloyd
-from nerfstudio.generative.dreamfusion_utils import PositionalTextEmbeddings
+from nerfstudio.generative.positional_text_embeddings import PositionalTextEmbeddings
 from nerfstudio.model_components.losses import (
     MSELoss,
     distortion_loss,