adding lazy imports

src/lerobot/policies/fastwam/modeling_fastwam.py

@@ -23,6 +23,7 @@ from torch import Tensor
 
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.utils.constants import OBS_STATE
+from lerobot.utils.import_utils import require_package
 
 from .configuration_fastwam import FastWAMConfig
 from .modular_fastwam import ActionDiT, FastWAM, MoT
@@ -52,6 +53,11 @@ class FastWAMPolicy(PreTrainedPolicy):
         dataset_stats: dict[str, dict[str, Tensor]] | None = None,
         **kwargs: Any,
     ):
+        # FastWAM's Wan2.2 backbone needs transformers (UMT5 text encoder/tokenizer) and
+        # diffusers (Wan VAE), both behind the `fastwam` extra. Fail fast with an actionable
+        # message in base installs rather than deep in Wan component construction.
+        require_package("transformers", extra="fastwam")
+        require_package("diffusers", extra="fastwam")
         # `make_policy`/`from_pretrained` forward extra kwargs (e.g. `dataset_meta`); the
         # dataset feature metadata is already applied to `config` by make_policy upstream,
         # so we accept and ignore them, matching the other LeRobot policies.

src/lerobot/policies/fastwam/wan/modules/model.py

@@ -1,10 +1,24 @@
 # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
 import math
+from typing import TYPE_CHECKING
 
 import torch
 import torch.nn as nn
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.models.modeling_utils import ModelMixin
+
+from lerobot.utils.import_utils import _diffusers_available
+
+if TYPE_CHECKING or _diffusers_available:
+    from diffusers.configuration_utils import ConfigMixin, register_to_config
+    from diffusers.models.modeling_utils import ModelMixin
+else:
+    class ModelMixin:
+        pass
+
+    class ConfigMixin:
+        pass
+
+    def register_to_config(init):
+        return init
 
 from .attention import flash_attention
 

src/lerobot/policies/fastwam/wan_components.py

@@ -22,14 +22,24 @@ from typing import TYPE_CHECKING, Any
 import torch
 from huggingface_hub import snapshot_download
 from safetensors.torch import load_file
-from transformers import AutoTokenizer, UMT5EncoderModel
+
+from lerobot.utils.import_utils import _diffusers_available, _transformers_available, require_package
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import AutoTokenizer, UMT5EncoderModel
+else:
+    AutoTokenizer = None
+    UMT5EncoderModel = None
+
+if TYPE_CHECKING or _diffusers_available:
+    from diffusers import AutoencoderKLWan
+else:
+    AutoencoderKLWan = None
 
 if TYPE_CHECKING:
     from .wan_adapters import WanVideoVAE38
     from .wan_video_dit import WanVideoDiT
 
-from diffusers import AutoencoderKLWan
-
 from .wan_adapters import WanVideoVAE38
 from .wan_video_dit import WanVideoDiT
 
@@ -73,6 +83,7 @@ class WanTokenizer:
     FastWAM call site expects."""
 
     def __init__(self, name: str = WAN_T5_TOKENIZER, seq_len: int = 512) -> None:
+        require_package("transformers", extra="fastwam")
         self.tokenizer = AutoTokenizer.from_pretrained(name)
         self.seq_len = int(seq_len)
 
@@ -104,12 +115,14 @@ def build_wan_tokenizer(*, tokenizer_max_len: int) -> WanTokenizer:
 
 def load_pretrained_wan_vae(*, torch_dtype: torch.dtype, device: str) -> WanVideoVAE38:
     """Load real Wan2.2 VAE weights from the diffusers repo (offline base creation)."""
+    require_package("diffusers", extra="fastwam")
     vae = AutoencoderKLWan.from_pretrained(WAN22_DIFFUSERS_MODEL_ID, subfolder="vae", torch_dtype=torch_dtype)
     return WanVideoVAE38(dtype=torch_dtype, device=device, pretrained=vae)
 
 
 def load_pretrained_wan_text_encoder(*, torch_dtype: torch.dtype, device: str) -> WanTextEncoder:
     """Load real UMT5-XXL encoder weights from the diffusers repo (offline base creation)."""
+    require_package("transformers", extra="fastwam")
     encoder = UMT5EncoderModel.from_pretrained(
         WAN22_DIFFUSERS_MODEL_ID, subfolder="text_encoder", torch_dtype=torch_dtype
     )

tests/policies/fastwam/test_fastwam_policy.py

@@ -21,6 +21,9 @@ import torch
 from safetensors import safe_open
 from torch import nn
 
+pytest.importorskip("transformers", reason="fastwam requires the `fastwam` extra (transformers)")
+pytest.importorskip("diffusers", reason="fastwam requires the `fastwam` extra (diffusers)")
+
 from lerobot.configs import FeatureType, PolicyFeature, PreTrainedConfig
 from lerobot.policies import FastWAMConfig, get_policy_class, make_policy_config, make_pre_post_processors
 from lerobot.policies.fastwam.modeling_fastwam import FastWAMPolicy