Added support for WAN 2.2 model

README.md

@@ -17,6 +17,7 @@
 [![Unit Tests](https://github.com/google/maxtext/actions/workflows/UnitTests.yml/badge.svg)](https://github.com/AI-Hypercomputer/maxdiffusion/actions/workflows/UnitTests.yml)
 
 # What's new?
+- **`2025/11/11`**: Wan2.2 txt2vid generation is now supported
 - **`2025/10/10`**: Wan2.1 txt2vid training and generation is now supported.
 - **`2025/10/14`**: NVIDIA DGX Spark Flux support.
 - **`2025/8/14`**: LTX-Video img2vid generation is now supported.
@@ -481,7 +482,23 @@ To generate images, run the following command:
 
   ```bash
   HF_HUB_CACHE=/mnt/disks/external_disk/maxdiffusion_hf_cache/
-  LIBTPU_INIT_ARGS="--xla_tpu_enable_async_collective_fusion=true --xla_tpu_enable_async_collective_fusion_fuse_all_reduce=true --xla_tpu_enable_async_collective_fusion_multiple_steps=true --xla_tpu_overlap_compute_collective_tc=true --xla_enable_async_all_reduce=true" HF_HUB_ENABLE_HF_TRANSFER=1 python src/maxdiffusion/generate_wan.py   src/maxdiffusion/configs/base_wan_14b.yml   attention="flash"   num_inference_steps=50   num_frames=81   width=1280   height=720   jax_cache_dir=gs://jfacevedo-maxdiffusion/jax_cache/   per_device_batch_size=.125   ici_data_parallelism=2   ici_fsdp_parallelism=2   flow_shift=5.0   enable_profiler=True   run_name=wan-inference-testing-720p   output_dir=gs:/jfacevedo-maxdiffusion   fps=16   flash_min_seq_length=0   flash_block_sizes='{"block_q" : 3024, "block_kv_compute" : 1024, "block_kv" : 2048, "block_q_dkv": 3024, "block_kv_dkv" : 2048, "block_kv_dkv_compute" : 2048, "block_q_dq" : 3024, "block_kv_dq" : 2048 }' seed=118445
+  LIBTPU_INIT_ARGS="--xla_tpu_enable_async_collective_fusion=true --xla_tpu_enable_async_collective_fusion_fuse_all_reduce=true --xla_tpu_enable_async_collective_fusion_multiple_steps=true --xla_tpu_overlap_compute_collective_tc=true --xla_enable_async_all_reduce=true" HF_HUB_ENABLE_HF_TRANSFER=1 python src/maxdiffusion/generate_wan.py  src/maxdiffusion/configs/base_wan_14b.yml   attention="flash"   num_inference_steps=50   num_frames=81   width=1280   height=720   jax_cache_dir=gs://jfacevedo-maxdiffusion/jax_cache/   per_device_batch_size=.125   ici_data_parallelism=2   ici_fsdp_parallelism=2   flow_shift=5.0   enable_profiler=True   run_name=wan-inference-testing-720p   output_dir=gs:/jfacevedo-maxdiffusion   fps=16   flash_min_seq_length=0   flash_block_sizes='{"block_q" : 3024, "block_kv_compute" : 1024, "block_kv" : 2048, "block_q_dkv": 3024, "block_kv_dkv" : 2048, "block_kv_dkv_compute" : 2048, "block_q_dq" : 3024, "block_kv_dq" : 2048 }' seed=118445
+  ```
+  ## Wan2.2
+
+  Although not required, attaching an external disk is recommended as weights take up a lot of disk space. [Follow these instructions if you would like to attach an external disk](https://cloud.google.com/tpu/docs/attach-durable-block-storage).
+
+  ```bash
+  HF_HUB_CACHE=/mnt/disks/external_disk/maxdiffusion_hf_cache/
+  LIBTPU_INIT_ARGS="--xla_tpu_enable_async_collective_fusion=true --xla_tpu_enable_async_collective_fusion_fuse_all_reduce=true --xla_tpu_enable_async_collective_fusion_multiple_steps=true --xla_tpu_overlap_compute_collective_tc=true --xla_enable_async_all_reduce=true" HF_HUB_ENABLE_HF_TRANSFER=1 python src/maxdiffusion/generate_wan.py  src/maxdiffusion/configs/base_wan_27b.yml   attention="flash"   num_inference_steps=50   num_frames=81   width=1280   height=720   jax_cache_dir=gs://jfacevedo-maxdiffusion/jax_cache/   per_device_batch_size=.125   ici_data_parallelism=2   ici_fsdp_parallelism=2   flow_shift=5.0   enable_profiler=True   run_name=wan-inference-testing-720p   output_dir=gs:/jfacevedo-maxdiffusion   fps=16   flash_min_seq_length=0   flash_block_sizes='{"block_q" : 3024, "block_kv_compute" : 1024, "block_kv" : 2048, "block_q_dkv": 3024, "block_kv_dkv" : 2048, "block_kv_dkv_compute" : 2048, "block_q_dq" : 3024, "block_kv_dq" : 2048 }' seed=118445
+  ```
+  ## Wan2.2
+
+  Although not required, attaching an external disk is recommended as weights take up a lot of disk space. [Follow these instructions if you would like to attach an external disk](https://cloud.google.com/tpu/docs/attach-durable-block-storage).
+
+  ```bash
+  HF_HUB_CACHE=/mnt/disks/external_disk/maxdiffusion_hf_cache/
+  LIBTPU_INIT_ARGS="--xla_tpu_enable_async_collective_fusion=true --xla_tpu_enable_async_collective_fusion_fuse_all_reduce=true --xla_tpu_enable_async_collective_fusion_multiple_steps=true --xla_tpu_overlap_compute_collective_tc=true --xla_enable_async_all_reduce=true" HF_HUB_ENABLE_HF_TRANSFER=1 python src/maxdiffusion/generate_wan.py   src/maxdiffusion/configs/base_wan_27b.yml   attention="flash"   num_inference_steps=50   num_frames=81   width=1280   height=720   jax_cache_dir=gs://jfacevedo-maxdiffusion/jax_cache/   per_device_batch_size=.125   ici_data_parallelism=2   ici_fsdp_parallelism=2   flow_shift=5.0   enable_profiler=True   run_name=wan-inference-testing-720p   output_dir=gs:/jfacevedo-maxdiffusion   fps=16   flash_min_seq_length=0   flash_block_sizes='{"block_q" : 3024, "block_kv_compute" : 1024, "block_kv" : 2048, "block_q_dkv": 3024, "block_kv_dkv" : 2048, "block_kv_dkv_compute" : 2048, "block_q_dq" : 3024, "block_kv_dq" : 2048 }' seed=118445
   ```
   ## Wan2.2
 

src/maxdiffusion/checkpointing/wan_checkpointer.py

@@ -14,34 +14,50 @@
  limitations under the License.
 """
 
-from abc import ABC
+from abc import ABC, abstractmethod
 import json
 
 import jax
 import numpy as np
-from typing import Optional, Tuple
+from typing import Optional, Tuple, Type
 from maxdiffusion.checkpointing.checkpointing_utils import (create_orbax_checkpoint_manager)
-from ..pipelines.wan.wan_pipeline import WanPipeline
+from ..pipelines.wan.wan_pipeline import WanPipeline2_1, WanPipeline2_2
 from .. import max_logging, max_utils
 import orbax.checkpoint as ocp
 from etils import epath
 
+
 WAN_CHECKPOINT = "WAN_CHECKPOINT"
 
 
 class WanCheckpointer(ABC):
+  _SUBCLASS_MAP: dict[str, Type['WanCheckpointer']] = {}
+
+  def __new__(cls, model_key: str, config, checkpoint_type: str = WAN_CHECKPOINT):
+    if cls is WanCheckpointer:
+      subclass = cls._SUBCLASS_MAP.get(model_key)
+      if subclass is None:
+          raise ValueError(
+              f"Unknown model_key: '{model_key}'. "
+              f"Supported keys are: {list(cls._SUBCLASS_MAP.keys())}"
+          )
+      return super().__new__(subclass)
+    else:
+      return super().__new__(cls)
 
-  def __init__(self, config, checkpoint_type):
+  def __init__(self, model_key, config, checkpoint_type: str = WAN_CHECKPOINT):
     self.config = config
     self.checkpoint_type = checkpoint_type
     self.opt_state = None
 
-    self.checkpoint_manager: ocp.CheckpointManager = create_orbax_checkpoint_manager(
-        self.config.checkpoint_dir,
-        enable_checkpointing=True,
-        save_interval_steps=1,
-        checkpoint_type=checkpoint_type,
-        dataset_type=config.dataset_type,
+    self.checkpoint_manager: ocp.CheckpointManager = (
+        create_orbax_checkpoint_manager(
+            self.config.checkpoint_dir,
+            enable_checkpointing=True,
+            save_interval_steps=1,
+            checkpoint_type=checkpoint_type,
+            dataset_type=config.dataset_type,
+        )
     )
 
   def _create_optimizer(self, model, config, learning_rate):
@@ -51,6 +67,25 @@ def _create_optimizer(self, model, config, learning_rate):
     tx = max_utils.create_optimizer(config, learning_rate_scheduler)
     return tx, learning_rate_scheduler
 
+  @abstractmethod
+  def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dict], Optional[int]]:
+    raise NotImplementedError
+
+  @abstractmethod
+  def load_diffusers_checkpoint(self):
+    raise NotImplementedError
+
+  @abstractmethod
+  def load_checkpoint(self, step=None) -> Tuple[Optional[WanPipeline2_1 | WanPipeline2_2], Optional[dict], Optional[int]]:
+    raise NotImplementedError
+
+  @abstractmethod
+  def save_checkpoint(self, train_step, pipeline, train_states: dict):
+    raise NotImplementedError
+
+
+class WanCheckpointer2_1(WanCheckpointer):
+
   def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dict], Optional[int]]:
     if step is None:
       step = self.checkpoint_manager.latest_step()
@@ -85,24 +120,24 @@ def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dic
     return restored_checkpoint, step
 
   def load_diffusers_checkpoint(self):
-    pipeline = WanPipeline.from_pretrained(self.config)
+    pipeline = WanPipeline2_1.from_pretrained(self.config)
     return pipeline
 
-  def load_checkpoint(self, step=None) -> Tuple[WanPipeline, Optional[dict], Optional[int]]:
+  def load_checkpoint(self, step=None) -> Tuple[WanPipeline2_1, Optional[dict], Optional[int]]:
     restored_checkpoint, step = self.load_wan_configs_from_orbax(step)
     opt_state = None
     if restored_checkpoint:
       max_logging.log("Loading WAN pipeline from checkpoint")
-      pipeline = WanPipeline.from_checkpoint(self.config, restored_checkpoint)
-      if "opt_state" in restored_checkpoint["wan_state"].keys():
-        opt_state = restored_checkpoint["wan_state"]["opt_state"]
+      pipeline = WanPipeline2_1.from_checkpoint(self.config, restored_checkpoint)
+      if "opt_state" in restored_checkpoint.wan_state.keys():
+        opt_state = restored_checkpoint.wan_state["opt_state"]
     else:
       max_logging.log("No checkpoint found, loading default pipeline.")
       pipeline = self.load_diffusers_checkpoint()
 
     return pipeline, opt_state, step
 
-  def save_checkpoint(self, train_step, pipeline: WanPipeline, train_states: dict):
+  def save_checkpoint(self, train_step, pipeline: WanPipeline2_1, train_states: dict):
     """Saves the training state and model configurations."""
 
     def config_to_json(model_or_config):
@@ -120,7 +155,99 @@ def config_to_json(model_or_config):
     max_logging.log(f"Checkpoint for step {train_step} saved.")
 
 
-def save_checkpoint_orig(self, train_step, pipeline: WanPipeline, train_states: dict):
+class WanCheckpointer2_2(WanCheckpointer):
+
+  def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dict], Optional[int]]:
+    if step is None:
+      step = self.checkpoint_manager.latest_step()
+      max_logging.log(f"Latest WAN checkpoint step: {step}")
+      if step is None:
+        max_logging.log("No WAN checkpoint found.")
+        return None, None
+    max_logging.log(f"Loading WAN checkpoint from step {step}")
+    metadatas = self.checkpoint_manager.item_metadata(step)
+
+    # Handle low_noise_transformer
+    low_noise_transformer_metadata = metadatas.low_noise_transformer_state
+    abstract_tree_structure_low_params = jax.tree_util.tree_map(ocp.utils.to_shape_dtype_struct, low_noise_transformer_metadata)
+    low_params_restore = ocp.args.PyTreeRestore(
+        restore_args=jax.tree.map(
+            lambda _: ocp.RestoreArgs(restore_type=np.ndarray),
+            abstract_tree_structure_low_params,
+        )
+    )
+
+    # Handle high_noise_transformer
+    high_noise_transformer_metadata = metadatas.high_noise_transformer_state
+    abstract_tree_structure_high_params = jax.tree_util.tree_map(ocp.utils.to_shape_dtype_struct, high_noise_transformer_metadata)
+    high_params_restore = ocp.args.PyTreeRestore(
+        restore_args=jax.tree.map(
+            lambda _: ocp.RestoreArgs(restore_type=np.ndarray),
+            abstract_tree_structure_high_params,
+        )
+    )
+
+    max_logging.log("Restoring WAN 2.2 checkpoint")
+    restored_checkpoint = self.checkpoint_manager.restore(
+        directory=epath.Path(self.config.checkpoint_dir),
+        step=step,
+        args=ocp.args.Composite(
+            low_noise_transformer_state=low_params_restore,
+            high_noise_transformer_state=high_params_restore,
+            wan_config=ocp.args.JsonRestore(),
+        ),
+    )
+    max_logging.log(f"restored checkpoint {restored_checkpoint.keys()}")
+    max_logging.log(f"restored checkpoint low_noise_transformer_state {restored_checkpoint.low_noise_transformer_state.keys()}")
+    max_logging.log(f"restored checkpoint high_noise_transformer_state {restored_checkpoint.high_noise_transformer_state.keys()}")
+    max_logging.log(f"optimizer found in low_noise checkpoint {'opt_state' in restored_checkpoint.low_noise_transformer_state.keys()}")
+    max_logging.log(f"optimizer found in high_noise checkpoint {'opt_state' in restored_checkpoint.high_noise_transformer_state.keys()}")
+    max_logging.log(f"optimizer state saved in attribute self.opt_state {self.opt_state}")
+    return restored_checkpoint, step
+
+  def load_diffusers_checkpoint(self):
+    pipeline = WanPipeline2_2.from_pretrained(self.config)
+    return pipeline
+
+  def load_checkpoint(self, step=None) -> Tuple[WanPipeline2_2, Optional[dict], Optional[int]]:
+    restored_checkpoint, step = self.load_wan_configs_from_orbax(step)
+    opt_state = None
+    if restored_checkpoint:
+      max_logging.log("Loading WAN pipeline from checkpoint")
+      pipeline = WanPipeline2_2.from_checkpoint(self.config, restored_checkpoint)
+      # Check for optimizer state in either transformer
+      if "opt_state" in restored_checkpoint.low_noise_transformer_state.keys():
+        opt_state = restored_checkpoint.low_noise_transformer_state["opt_state"]
+      elif "opt_state" in restored_checkpoint.high_noise_transformer_state.keys():
+        opt_state = restored_checkpoint.high_noise_transformer_state["opt_state"]
+    else:
+      max_logging.log("No checkpoint found, loading default pipeline.")
+      pipeline = self.load_diffusers_checkpoint()
+
+    return pipeline, opt_state, step
+
+  def save_checkpoint(self, train_step, pipeline: WanPipeline2_2, train_states: dict):
+    """Saves the training state and model configurations."""
+
+    def config_to_json(model_or_config):
+      return json.loads(model_or_config.to_json_string())
+
+    max_logging.log(f"Saving checkpoint for step {train_step}")
+    items = {
+        "wan_config": ocp.args.JsonSave(config_to_json(pipeline.low_noise_transformer)),
+    }
+
+    items["low_noise_transformer_state"] = ocp.args.PyTreeSave(train_states["low_noise_transformer"])
+    items["high_noise_transformer_state"] = ocp.args.PyTreeSave(train_states["high_noise_transformer"])
+
+    # Save the checkpoint
+    self.checkpoint_manager.save(train_step, args=ocp.args.Composite(**items))
+    max_logging.log(f"Checkpoint for step {train_step} saved.")
+
+WanCheckpointer._SUBCLASS_MAP["wan2.1"] = WanCheckpointer2_1
+WanCheckpointer._SUBCLASS_MAP["wan2.2"] = WanCheckpointer2_2
+
+def save_checkpoint_orig(self, train_step, pipeline, train_states: dict):
   """Saves the training state and model configurations."""
 
   def config_to_json(model_or_config):