moving and renaming files to have a cleaner file tree

src/lerobot/policies/fastwam/modeling_fastwam.py

@@ -26,13 +26,13 @@ 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
+from .wan.components import (
-from .wan_components import (
     build_wan_tokenizer,
     load_pretrained_wan_text_encoder,
     load_pretrained_wan_vae,
 )
-from .wan_video_dit import WanVideoDiT
+from .wan.modular import ActionDiT, FastWAM, MoT
+from .wan.video_dit import WanVideoDiT
 
 
 class FastWAMPolicy(PreTrainedPolicy):

src/lerobot/policies/fastwam/wan/README.md

@@ -1,6 +1,10 @@
-# Wan2.2 Upstream Subset
+# FastWAM `wan` package
 
-This directory contains the trimmed subset of the official Wan2.2 source tree used by FastWAM.
+This package holds FastWAM's model implementation. It mixes a small **vendored
+subset of the official Wan2.2 source tree** with FastWAM's own code, kept flat in
+a single directory.
+
+## Vendored from Wan2.2
 
 - Upstream repository: https://github.com/Wan-Video/Wan2.2
 - Upstream commit: `42bf4cfaa384bc21833865abc2f9e6c0e67233dc`
@@ -8,18 +12,22 @@ This directory contains the trimmed subset of the official Wan2.2 source tree us
 
 Copied files:
 
-- `wan/modules/attention.py`
+- `attention.py` (was `wan/modules/attention.py`)
-- `wan/modules/model.py`
+- `model.py` (was `wan/modules/model.py`)
-- `wan/modules/__init__.py`
+- `get_sampling_sigmas` in `video_dit.py` (was `wan/utils/fm_solvers.py`), inlined
-- `wan/utils/fm_solvers.py`
+  next to its only caller.
-- `wan/utils/__init__.py`
 
-This subset now only backs FastWAM's **custom MoT video DiT**. The Wan2.2 VAE,
+This subset only backs FastWAM's **custom MoT video DiT**. The Wan2.2 VAE,
-UMT5 text encoder, and tokenizer are no longer vendored — they come from
+UMT5 text encoder, and tokenizer are no longer vendored - they come from
 `diffusers.AutoencoderKLWan`, `transformers.UMT5EncoderModel`, and
-`transformers.AutoTokenizer` (see `../wan_adapters.py` and `../wan_components.py`).
+`transformers.AutoTokenizer` (see `components.py` and `adapters.py`).
 
-Current FastWAM adapters that directly reuse this vendored subset:
+## FastWAM's own code
 
-- `../wan_video_dit.py` builds on `wan.modules.model` (`sinusoidal_embedding_1d`, `rope_params`, `rope_apply`, …) and `wan.modules.attention.flash_attention`.
+- `video_dit.py` builds on `model` (`sinusoidal_embedding_1d`, `rope_params`,
-- `../modular_fastwam.py` reuses `wan.utils.fm_solvers.get_sampling_sigmas` for Wan-compatible inference timesteps.
+  `rope_apply`, …) and `attention.flash_attention`. Its
+  `WanContinuousFlowMatchScheduler` uses `get_sampling_sigmas` for Wan-compatible
+  inference timesteps.
+- `components.py` / `adapters.py` load the VAE, text encoder, tokenizer, and the
+  custom DiT weights.
+- `modular.py` defines the FastWAM model (`ActionDiT`, `MoT`, `FastWAM`, …).

src/lerobot/policies/fastwam/wan/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

src/lerobot/policies/fastwam/wan/components.py

@@ -36,8 +36,8 @@ if TYPE_CHECKING or _diffusers_available:
 else:
     AutoencoderKLWan = None
 
-from .wan_adapters import WanVideoVAE38
+from .adapters import WanVideoVAE38
-from .wan_video_dit import WanVideoDiT
+from .video_dit import WanVideoDiT
 
 logger = logging.getLogger(__name__)
 

src/lerobot/policies/fastwam/wan/modular.py

@@ -25,7 +25,7 @@ import torch.nn as nn
 import torch.nn.functional as functional
 from PIL import Image
 
-from .wan_components import (
+from .components import (
     WAN22_DIFFUSERS_MODEL_ID,
     WAN_T5_TOKENIZER,
     build_wan_tokenizer,
@@ -34,7 +34,7 @@ from .wan_components import (
     load_wan_video_dit,
     resolve_wan_dit_paths,
 )
-from .wan_video_dit import (
+from .video_dit import (
     FastWAMAttentionBlock,
     WanContinuousFlowMatchScheduler,
     fastwam_masked_attention,

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

@@ -1,8 +0,0 @@
-# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
-from .attention import flash_attention
-from .model import WanModel
-
-__all__ = [
-    "WanModel",
-    "flash_attention",
-]

src/lerobot/policies/fastwam/wan/utils/__init__.py

@@ -1,6 +0,0 @@
-# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
-from .fm_solvers import get_sampling_sigmas
-
-__all__ = [
-    "get_sampling_sigmas",
-]

src/lerobot/policies/fastwam/wan/utils/fm_solvers.py

@@ -1,9 +0,0 @@
-# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
-
-import numpy as np
-
-
-def get_sampling_sigmas(sampling_steps, shift):
-    sigma = np.linspace(1, 0, sampling_steps + 1)[:sampling_steps]
-    sigma = shift * sigma / (1 + (shift - 1) * sigma)
-    return sigma

src/lerobot/policies/fastwam/wan/video_dit.py

@@ -15,12 +15,13 @@
 import logging
 from typing import Any
 
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as functional
 from einops import rearrange
 
-from .wan.modules.model import (
+from .model import (
     WanAttentionBlock,
     WanLayerNorm,
     WanModel,
@@ -29,11 +30,18 @@ from .wan.modules.model import (
     rope_params,
     sinusoidal_embedding_1d,
 )
-from .wan.utils.fm_solvers import get_sampling_sigmas
 
 logger = logging.getLogger(__name__)
 
 
+def get_sampling_sigmas(sampling_steps, shift):
+    # Vendored from Wan2.2 (formerly wan/utils/fm_solvers.py); computes the
+    # noise-level (sigma) schedule for Wan-compatible flow-matching inference.
+    sigma = np.linspace(1, 0, sampling_steps + 1)[:sampling_steps]
+    sigma = shift * sigma / (1 + (shift - 1) * sigma)
+    return sigma
+
+
 def create_custom_forward(module):
     def custom_forward(*inputs, **kwargs):
         return module(*inputs, **kwargs)
@@ -94,10 +102,6 @@ def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor):
     return x * (1 + scale) + shift
 
 
-def _get_wan_sampling_sigmas(num_inference_steps: int, shift: float) -> list[float]:
-    return get_sampling_sigmas(num_inference_steps, shift)
-
-
 class WanContinuousFlowMatchScheduler:
     """Continuous-time Flow-Matching scheduler with shift-based Wan sampling."""
 
@@ -173,7 +177,7 @@ class WanContinuousFlowMatchScheduler:
             raise ValueError(f"`shift` must be positive, got {shift}")
 
         sigma_steps = torch.as_tensor(
-            _get_wan_sampling_sigmas(num_inference_steps, shift),
+            get_sampling_sigmas(num_inference_steps, shift),
             device=device,
             dtype=torch.float32,
         )

tests/policies/fastwam/test_fastwam_policy.py

@@ -257,7 +257,7 @@ def test_from_pretrained_uses_base_loader_and_skips_wan_backbone(monkeypatch, tm
         raise AssertionError("from_pretrained must not initialize or download the Wan2.2 backbone")
 
     monkeypatch.setattr(
-        "lerobot.policies.fastwam.modular_fastwam.FastWAM.from_wan22_pretrained",
+        "lerobot.policies.fastwam.wan.modular.FastWAM.from_wan22_pretrained",
         fail_if_wan_pretrained_is_loaded,
     )
 
@@ -348,7 +348,7 @@ def test_vae_adapter_empty_build_encode_decode_shapes():
     pytest.importorskip("diffusers")
     from diffusers import AutoencoderKLWan
 
-    from lerobot.policies.fastwam.wan_adapters import WanVideoVAE38
+    from lerobot.policies.fastwam.wan.adapters import WanVideoVAE38
 
     # Production always loads a real pretrained VAE from the diffusers repo; here we
     # build the same architecture with random weights and dummy standardization stats