rename config param for multiple vision encoders

docs/source/multitask_dit.mdx

@@ -157,7 +157,7 @@ The model supports two positional encoding methods for action sequences:
 # Use separate vision encoder per camera
 # This may be useful when cameras have significantly different characteristics, but
 # be wary of increased VRAM footprint.
---policy.use_separate_encoder_per_camera=true
+--policy.use_separate_rgb_encoder_per_camera=true
 
 # Image preprocessing
 --policy.image_resize_shape=[XXX,YYY] \ # you may need to resize your images for inference speed ups

src/lerobot/policies/multi_task_dit/configuration_multi_task_dit.py

@@ -71,7 +71,7 @@ class MultiTaskDiTConfig(PreTrainedConfig):
 
     # Vision Encoder (CLIP)
     vision_encoder_name: str = "openai/clip-vit-base-patch16"  # HuggingFace CLIP model
-    use_separate_encoder_per_camera: bool = False  # Separate encoder per camera view
+    use_separate_rgb_encoder_per_camera: bool = False  # Separate encoder per camera view
     vision_encoder_lr_multiplier: float = 0.1  # LR multiplier for vision encoder
     image_resize_shape: tuple[int, int] | None = None  # Resize images before crop
     image_crop_shape: tuple[int, int] | None = (224, 224)  # Crop shape (CLIP default)
@@ -113,9 +113,7 @@ class MultiTaskDiTConfig(PreTrainedConfig):
         """Validate configuration parameters."""
         # Objective validation
         if self.objective not in ["diffusion", "flow_matching"]:
-            raise ValueError(
+            raise ValueError(f"objective must be 'diffusion' or 'flow_matching', got '{self.objective}'")
-                f"objective must be 'diffusion' or 'flow_matching', got '{self.objective}'"
-            )
 
         # Transformer validation
         if self.hidden_dim <= 0:

src/lerobot/policies/multi_task_dit/modeling_multi_task_dit.py

@@ -246,7 +246,7 @@ class ObservationEncoder(nn.Module):
             self.num_cameras = len(config.image_features)
             self.camera_names = list(config.image_features.keys())
 
-            if config.use_separate_encoder_per_camera:
+            if config.use_separate_rgb_encoder_per_camera:
                 self.vision_encoders = nn.ModuleList(
                     [CLIPVisionEncoder(model_name=config.vision_encoder_name) for _ in self.camera_names]
                 )
@@ -326,7 +326,7 @@ class ObservationEncoder(nn.Module):
             if len(images.shape) == 5:
                 images = images.unsqueeze(1)
 
-            if self.config.use_separate_encoder_per_camera:
+            if self.config.use_separate_rgb_encoder_per_camera:
                 camera_features = []
                 for cam_idx in range(self.num_cameras):
                     cam_images = images[:, :, cam_idx]