[MAX] Add Wan T2V diffusion pipeline with MoE support

## Summary

Add the Wan text-to-video (T2V) diffusion pipeline with MoE (Mixture of Experts) dual-transformer support.

## Description

- Implements the full Wan T2V pipeline: text encoding → latent preparation → denoising loop → VAE decode
- Supports **Wan 2.2 MoE models** (A14B) with dual transformers: high-noise expert for early steps, low-noise expert for later steps, with configurable boundary timestep
- Supports **Wan 2.1 single-transformer models** (14B) with the same code path
- LoRA support with automatic download from HuggingFace (e.g. Lightning turbo LoRAs for 4-step generation)
- Classifier-free guidance with batched forward pass (positive + negative in one call)
- On-device UniPC scheduler steps via compiled graphs — no Python-side numpy during denoising
- Architecture registration for `Wan-AI/Wan2.2-T2V-A14B-Diffusers`, `Wan-AI/Wan2.1-T2V-14B-Diffusers`, etc.
- Adds `guidance_scale_2` field to `VideoProviderOptions` for MoE boundary guidance control
- Minimal upstream changes: only `_weight_paths` storage in `DiffusionPipeline.__init__` and Wan registration in `pixel_tokenizer.py` / `registry.py`

## Dependencies

Depends on all previous PRs: modular#6298 (scheduler), modular#6299 (UMT5), modular#6300 (VAE), modular#6301 (transformer).

## Checklist

- [x] PR is small and focused
- [x] I ran `./bazelw run format` to format my changes

Assisted-by: Claude Code

Assisted-by: Claude Code

Author: jglee-sqbits

max/kernels/src/nn/conv/conv.mojo

@@ -5280,12 +5280,13 @@ def _conv3d_cudnn[
     var algo: cudnnConvolutionFwdAlgo_t
     var workspace_size_var: Int
 
-    if ptr_cached := _get_global_or_null(cache_key).bitcast[
-        _Conv3dAlgoCacheEntry
-    ]():
+    if ptr_cached := _get_global_or_null(cache_key):
+        var cached = ptr_cached.unsafe_value().bitcast[
+            _Conv3dAlgoCacheEntry
+        ]()
         # Cache hit — reuse previously selected algorithm.
-        algo = ptr_cached[].algo()
-        workspace_size_var = ptr_cached[].workspace_size
+        algo = cached[].algo()
+        workspace_size_var = cached[].workspace_size
     else:
         # Cache miss — run FindEx to find the fastest algorithm.
         var find_ws = ctx.enqueue_create_buffer[DType.uint8](FIND_WS_CAP)

max/python/max/interfaces/provider_options/modality/video.py

@@ -66,3 +66,9 @@ class VideoProviderOptions(BaseModel):
         ),
         gt=0,
     )
+
+    guidance_scale_2: float | None = Field(
+        None,
+        description="Secondary guidance scale for boundary timestep switching.",
+        gt=0.0,
+    )

max/python/max/pipelines/architectures/__init__.py

@@ -82,6 +82,7 @@ def register_all_models() -> None:
     from .qwen3vl_moe import qwen3vl_arch, qwen3vl_moe_arch
     from .unified_eagle_llama3 import unified_eagle_llama3_arch
     from .unified_mtp_deepseekV3 import unified_mtp_deepseekV3_arch
+    from .wan import wan_arch, wan_i2v_arch
     from .z_image_modulev3 import z_image_arch
 
     architectures = [
@@ -137,6 +138,8 @@ def register_all_models() -> None:
         qwen3vl_moe_arch,
         unified_eagle_llama3_arch,
         unified_mtp_deepseekV3_arch,
+        wan_arch,
+        wan_i2v_arch,
         z_image_arch,
     ]
 

max/python/max/pipelines/architectures/wan/__init__.py

@@ -0,0 +1,25 @@
+# ===----------------------------------------------------------------------=== #
+# Copyright (c) 2026, Modular Inc. All rights reserved.
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions:
+# https://llvm.org/LICENSE.txt
+#
+# 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.
+# ===----------------------------------------------------------------------=== #
+
+from .arch import wan_arch, wan_i2v_arch
+from .model import WanTransformerModel
+from .pipeline_wan import WanPipeline
+from .pipeline_wan_i2v import WanI2VPipeline
+
+__all__ = [
+    "WanI2VPipeline",
+    "WanPipeline",
+    "WanTransformerModel",
+    "wan_arch",
+    "wan_i2v_arch",
+]

max/python/max/pipelines/architectures/wan/arch.py

@@ -0,0 +1,87 @@
+# ===----------------------------------------------------------------------=== #
+# Copyright (c) 2026, Modular Inc. All rights reserved.
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions:
+# https://llvm.org/LICENSE.txt
+#
+# 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.
+# ===----------------------------------------------------------------------=== #
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from max.graph.weights import WeightsFormat
+from max.interfaces import PipelineTask
+from max.pipelines.core import PixelContext
+from max.pipelines.lib import (
+    PixelGenerationTokenizer,
+    SupportedArchitecture,
+)
+from max.pipelines.lib.config import MAXModelConfig, PipelineConfig
+from max.pipelines.lib.interfaces import ArchConfig
+from typing_extensions import Self
+
+from .pipeline_wan import WanPipeline
+from .pipeline_wan_i2v import WanI2VPipeline
+
+
+@dataclass(kw_only=True)
+class WanArchConfig(ArchConfig):
+    """Pipeline-level config for Wan (implements ArchConfig; no KV cache)."""
+
+    pipeline_config: PipelineConfig
+
+    def get_max_seq_len(self) -> int:
+        # Tokenizer padding length — matches diffusers __call__ default.
+        return 512
+
+    @classmethod
+    def initialize(
+        cls,
+        pipeline_config: PipelineConfig,
+        model_config: MAXModelConfig | None = None,
+    ) -> Self:
+        model_config = model_config or pipeline_config.model
+        if len(model_config.device_specs) != 1:
+            raise ValueError("Wan is only supported on a single device")
+        return cls(pipeline_config=pipeline_config)
+
+
+wan_arch = SupportedArchitecture(
+    name="WanPipeline",
+    task=PipelineTask.PIXEL_GENERATION,
+    default_encoding="bfloat16",
+    supported_encodings={"bfloat16", "float32"},
+    example_repo_ids=[
+        "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
+        "Wan-AI/Wan2.1-T2V-14B-Diffusers",
+        "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
+        "yetter-ai/Wan2.2-TI2V-5B-Turbo-Diffusers",
+    ],
+    pipeline_model=WanPipeline,  # type: ignore[arg-type]
+    context_type=PixelContext,
+    default_weights_format=WeightsFormat.safetensors,
+    tokenizer=PixelGenerationTokenizer,
+    config=WanArchConfig,
+)
+
+wan_i2v_arch = SupportedArchitecture(
+    name="WanImageToVideoPipeline",
+    task=PipelineTask.PIXEL_GENERATION,
+    default_encoding="bfloat16",
+    supported_encodings={"bfloat16", "float32"},
+    example_repo_ids=[
+        "Wan-AI/Wan2.2-I2V-A14B-Diffusers",
+        "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers",
+    ],
+    pipeline_model=WanI2VPipeline,
+    context_type=PixelContext,
+    default_weights_format=WeightsFormat.safetensors,
+    tokenizer=PixelGenerationTokenizer,
+    config=WanArchConfig,
+)