Refactor WAN loading logic: granular flags & boilerplate cleanup.

Replace  'vae_only' and 'transformer_only' flags in WAN pipelines and
checkpointers with granular 'load_*' boolean flags. Centralize repetitive
loading and instantiation logic (from_pretrained, from_checkpoint, etc.)
into the base classes.

Author: ninatu

src/maxdiffusion/checkpointing/wan_checkpointer.py

@@ -16,26 +16,27 @@
 
 from abc import ABC, abstractmethod
 import json
-from typing import Optional, Tuple
+from typing import Optional, Tuple, Generic, TypeVar, Type
 import jax
 from flax import nnx
 from maxdiffusion.checkpointing.checkpointing_utils import (
     add_sharding_to_struct,
     create_orbax_checkpoint_manager,
     get_cpu_mesh_and_sharding,
 )
-from ..pipelines.wan.wan_pipeline_2_1 import WanPipeline2_1
-from ..pipelines.wan.wan_pipeline_2_2 import WanPipeline2_2
-from ..pipelines.wan.wan_pipeline_i2v_2p1 import WanPipelineI2V_2_1
-from ..pipelines.wan.wan_pipeline_i2v_2p2 import WanPipelineI2V_2_2
+from ..pipelines.wan.wan_pipeline import WanPipeline
 from .. import max_logging, max_utils
 import orbax.checkpoint as ocp
 
 
 WAN_CHECKPOINT = "WAN_CHECKPOINT"
 
 
-class WanCheckpointer(ABC):
+T = TypeVar("T", bound=WanPipeline)
+
+
+class WanCheckpointer(Generic[T], ABC):
+  pipeline_class: Optional[Type[T]] = None
 
   def __init__(self, config, checkpoint_type: str = WAN_CHECKPOINT):
     self.config = config
@@ -176,16 +177,61 @@ def _pretrained_save_items(pipeline, pretrained_state_sources, pretrained_config
   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
+  def load_diffusers_checkpoint(
+      self,
+      vae_only=False,
+      load_vae=None,
+      load_text_encoder=None,
+      load_transformer=None,
+      load_scheduler=None,
+  ) -> T:
+    pipeline = self.pipeline_class.from_pretrained(
+        self.config,
+        vae_only=vae_only,
+        load_vae=load_vae,
+        load_text_encoder=load_text_encoder,
+        load_transformer=load_transformer,
+        load_scheduler=load_scheduler,
+    )
+    return pipeline
 
-  @abstractmethod
   def load_checkpoint(
-      self, step=None
-  ) -> Tuple[
-      Optional[WanPipeline2_1 | WanPipeline2_2 | WanPipelineI2V_2_1 | WanPipelineI2V_2_2], Optional[dict], Optional[int]
-  ]:
+      self,
+      step=None,
+      vae_only=False,
+      load_vae=None,
+      load_text_encoder=None,
+      load_transformer=None,
+      load_scheduler=None,
+  ) -> Tuple[T, 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 = self.pipeline_class.from_checkpoint(
+          self.config,
+          restored_checkpoint,
+          vae_only=vae_only,
+          load_vae=load_vae,
+          load_text_encoder=load_text_encoder,
+          load_transformer=load_transformer,
+          load_scheduler=load_scheduler,
+      )
+      opt_state = self._extract_opt_state(restored_checkpoint)
+    else:
+      max_logging.log("No checkpoint found, loading default pipeline.")
+      pipeline = self.load_diffusers_checkpoint(
+          vae_only=vae_only,
+          load_vae=load_vae,
+          load_text_encoder=load_text_encoder,
+          load_transformer=load_transformer,
+          load_scheduler=load_scheduler,
+      )
+
+    return pipeline, opt_state, step
+
+  @abstractmethod
+  def _extract_opt_state(self, restored_checkpoint):
     raise NotImplementedError
 
   @abstractmethod

src/maxdiffusion/checkpointing/wan_checkpointer_2_1.py

@@ -24,7 +24,8 @@
 from ..pipelines.wan.wan_pipeline_2_1 import WanPipeline2_1
 
 
-class WanCheckpointer2_1(WanCheckpointer):
+class WanCheckpointer2_1(WanCheckpointer[WanPipeline2_1]):
+  pipeline_class = WanPipeline2_1
 
   def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dict], Optional[int]]:
     if step is None:
@@ -58,23 +59,10 @@ def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dic
     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_1.from_pretrained(self.config)
-    return pipeline
-
-  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 = 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 _extract_opt_state(self, restored_checkpoint):
+    if "opt_state" in restored_checkpoint.wan_state.keys():
+      return restored_checkpoint.wan_state["opt_state"]
+    return None
 
   def save_checkpoint(self, train_step, pipeline: WanPipeline2_1, train_states: dict):
     """Saves the training state and model configurations."""

src/maxdiffusion/checkpointing/wan_checkpointer_2_2.py

@@ -24,7 +24,8 @@
 from maxdiffusion.checkpointing.wan_checkpointer import WanCheckpointer
 
 
-class WanCheckpointer2_2(WanCheckpointer):
+class WanCheckpointer2_2(WanCheckpointer[WanPipeline2_2]):
+  pipeline_class = WanPipeline2_2
 
   def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dict], Optional[int]]:
     if step is None:
@@ -79,26 +80,12 @@ def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dic
     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 _extract_opt_state(self, restored_checkpoint):
+    if "opt_state" in restored_checkpoint.low_noise_transformer_state.keys():
+      return restored_checkpoint.low_noise_transformer_state["opt_state"]
+    elif "opt_state" in restored_checkpoint.high_noise_transformer_state.keys():
+      return restored_checkpoint.high_noise_transformer_state["opt_state"]
+    return None
 
   def save_checkpoint(self, train_step, pipeline: WanPipeline2_2, train_states: dict):
     """Saves the training state and model configurations."""

src/maxdiffusion/checkpointing/wan_checkpointer_i2v_2p1.py

@@ -24,7 +24,8 @@
 from ..pipelines.wan.wan_pipeline_i2v_2p1 import WanPipelineI2V_2_1
 
 
-class WanCheckpointerI2V_2_1(WanCheckpointer):
+class WanCheckpointerI2V_2_1(WanCheckpointer[WanPipelineI2V_2_1]):
+  pipeline_class = WanPipelineI2V_2_1
 
   def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dict], Optional[int]]:
     if step is None:
@@ -58,23 +59,10 @@ def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dic
     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 = WanPipelineI2V_2_1.from_pretrained(self.config)
-    return pipeline
-
-  def load_checkpoint(self, step=None) -> Tuple[WanPipelineI2V_2_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 = WanPipelineI2V_2_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 _extract_opt_state(self, restored_checkpoint):
+    if "opt_state" in restored_checkpoint.wan_state.keys():
+      return restored_checkpoint.wan_state["opt_state"]
+    return None
 
   def save_checkpoint(self, train_step, pipeline: WanPipelineI2V_2_1, train_states: dict):
     """Saves the training state and model configurations."""

src/maxdiffusion/checkpointing/wan_checkpointer_i2v_2p2.py

@@ -24,7 +24,8 @@
 from maxdiffusion.checkpointing.wan_checkpointer import WanCheckpointer
 
 
-class WanCheckpointerI2V_2_2(WanCheckpointer):
+class WanCheckpointerI2V_2_2(WanCheckpointer[WanPipelineI2V_2_2]):
+  pipeline_class = WanPipelineI2V_2_2
 
   def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dict], Optional[int]]:
     if step is None:
@@ -79,26 +80,12 @@ def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dic
     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 = WanPipelineI2V_2_2.from_pretrained(self.config)
-    return pipeline
-
-  def load_checkpoint(self, step=None) -> Tuple[WanPipelineI2V_2_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 = WanPipelineI2V_2_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 _extract_opt_state(self, restored_checkpoint):
+    if "opt_state" in restored_checkpoint.low_noise_transformer_state.keys():
+      return restored_checkpoint.low_noise_transformer_state["opt_state"]
+    elif "opt_state" in restored_checkpoint.high_noise_transformer_state.keys():
+      return restored_checkpoint.high_noise_transformer_state["opt_state"]
+    return None
 
   def save_checkpoint(self, train_step, pipeline: WanPipelineI2V_2_2, train_states: dict):
     """Saves the training state and model configurations."""

src/maxdiffusion/checkpointing/wan_vace_checkpointer_2_1.py

@@ -23,7 +23,8 @@
 from ..pipelines.wan.wan_vace_pipeline_2_1 import VaceWanPipeline2_1
 
 
-class WanVaceCheckpointer2_1(WanCheckpointer):
+class WanVaceCheckpointer2_1(WanCheckpointer[VaceWanPipeline2_1]):
+  pipeline_class = VaceWanPipeline2_1
 
   def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dict], Optional[int]]:
     if step is None:
@@ -57,23 +58,10 @@ def load_wan_configs_from_orbax(self, step: Optional[int]) -> Tuple[Optional[dic
     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 = VaceWanPipeline2_1.from_pretrained(self.config)
-    return pipeline
-
-  def load_checkpoint(self, step=None) -> Tuple[VaceWanPipeline2_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 = VaceWanPipeline2_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 _extract_opt_state(self, restored_checkpoint):
+    if "opt_state" in restored_checkpoint.wan_state.keys():
+      return restored_checkpoint.wan_state["opt_state"]
+    return None
 
   def save_checkpoint(self, train_step, pipeline: VaceWanPipeline2_1, train_states: dict):
     """Saves the training state and model configurations."""