expand the observation encoder to support differnt size encoders for vision and text

src/lerobot/policies/multi_task_dit/configuration_multi_task_dit.py

@@ -232,20 +232,18 @@ class DinoV3EncoderConfig(VisionEncoderConfig):
     DinoV3 is a self-supervised Vision Transformer trained by Meta.
     CLS token usage and spatial feature extraction are handled automatically.
 
-    Available backbones:
+    Any timm model with "dinov3" in the name can be used. Examples:
     - vit_base_patch16_dinov3.lvd1689m (768 dims)
+    - vit_large_patch14_dinov3.lvd142m (1024 dims)
     """
 
     backbone: str = "vit_base_patch16_dinov3.lvd1689m"
 
     def __post_init__(self):
         super().__post_init__()
-        # Validate backbone name
-        valid_backbones = [
-            "vit_base_patch16_dinov3.lvd1689m",
-        ]
-        if self.backbone not in valid_backbones:
-            raise ValueError(f"backbone must be one of {valid_backbones}, got '{self.backbone}'")
+        # Validate that backbone name contains "dinov3" to ensure correct encoder type
+        if "dinov3" not in self.backbone.lower():
+            raise ValueError(f"backbone must be a DinoV3 model (contain 'dinov3'), got '{self.backbone}'")
 
 
 @VisionEncoderConfig.register_subclass("clip")
@@ -258,17 +256,18 @@ class CLIPVisionEncoderConfig(VisionEncoderConfig):
     CLIP's internal preprocessing (resize to 224x224) can be overridden
     by setting resize_shape and crop_shape.
 
-    Available backbones:
+    Any timm model with "clip" in the name can be used. Examples:
     - vit_base_patch16_clip_224.openai (default, 768 dims, 14x14 patches for 224x224)
+    - vit_large_patch14_clip_224.openai (1024 dims)
     """
 
     backbone: str = "vit_base_patch16_clip_224.openai"
 
     def __post_init__(self):
         super().__post_init__()
-        # Validate backbone name
+        # Validate that backbone name contains "clip" to ensure correct encoder type
         if "clip" not in self.backbone.lower():
-            raise ValueError(f"backbone must be a CLIP model, got '{self.backbone}'")
+            raise ValueError(f"backbone must be a CLIP model (contain 'clip'), got '{self.backbone}'")
 
 
 @dataclass
@@ -294,14 +293,19 @@ class CLIPTextEncoderConfig(TextEncoderConfig):
     used to condition the policy. The text embeddings are processed by
     a learnable projection layer before being concatenated into the
     conditioning vector.
+
+    Any HuggingFace CLIP model can be used. Examples:
+    - openai/clip-vit-base-patch16 (default)
+    - openai/clip-vit-large-patch14
     """
 
     model: str = "openai/clip-vit-base-patch16"
 
     def __post_init__(self):
         super().__post_init__()
+        # Validate that model name contains "clip" to ensure correct encoder type
         if "clip" not in self.model.lower():
-            raise ValueError(f"CLIP text encoder requires a CLIP model. Got '{self.model}'")
+            raise ValueError(f"CLIP text encoder requires a CLIP model (contain 'clip'). Got '{self.model}'")
 
 
 @dataclass

src/lerobot/policies/multi_task_dit/modules/observation_encoder.py

@@ -123,6 +123,11 @@ class CLIPEncoder(nn.Module, BaseVisionEncoder):
 
 
 def create_vision_encoder(config) -> BaseVisionEncoder:
+    """Create a vision encoder from config.
+
+    Supports any timm model with "clip" or "dinov3" in the backbone name.
+    The encoder type is automatically detected based on the backbone name.
+    """
     backbone_name = config.backbone.lower()
 
     # Check if it's a CLIP model
@@ -136,7 +141,7 @@ def create_vision_encoder(config) -> BaseVisionEncoder:
     else:
         raise ValueError(
             f"Unsupported vision backbone: {config.backbone}. "
-            f"Currently supported: DinoV3 models and CLIP models"
+            f"Currently supported: any timm model with 'dinov3' or 'clip' in the name"
         )
 
 
@@ -148,26 +153,19 @@ VISION_ENCODER_REGISTRY: dict[str, type] = {
 
 
 def register_vision_encoder(name: str, encoder_class: type):
-    """Register a new vision encoder type.
-
-    Args:
-        name: Identifier for the encoder type
-        encoder_class: Class implementing BaseVisionEncoder interface
-    """
+    """Register a new vision encoder type."""
     VISION_ENCODER_REGISTRY[name] = encoder_class
 
 
 def get_registered_encoders() -> dict[str, type]:
-    """Get all registered vision encoder types.
-
-    Returns:
-        Dictionary mapping encoder names to classes
-    """
+    """Get all registered vision encoder types."""
     return VISION_ENCODER_REGISTRY.copy()
 
 
 class CLIPTextEncoder(nn.Module):
-    """CLIP text encoder with frozen weights and learnable projection."""
+    """Supports any HuggingFace CLIP model. The encoder weights are frozen,
+    and a learnable projection layer maps the CLIP embeddings to the desired dimension.
+    """
 
     def __init__(self, model_name: str = "openai/clip-vit-base-patch16", projection_dim: int = 512):
         super().__init__()