Merge branch 'main' into to-single-file/wan

Author: a-r-r-o-w

src/diffusers/__init__.py

@@ -366,6 +366,8 @@
         [
             "StableDiffusionXLAutoBlocks",
             "StableDiffusionXLModularPipeline",
+            "WanAutoBlocks",
+            "WanModularPipeline",
         ]
     )
     _import_structure["pipelines"].extend(
@@ -999,6 +1001,8 @@
         from .modular_pipelines import (
             StableDiffusionXLAutoBlocks,
             StableDiffusionXLModularPipeline,
+            WanAutoBlocks,
+            WanModularPipeline,
         )
         from .pipelines import (
             AllegroPipeline,

src/diffusers/hooks/_helpers.py

@@ -107,6 +107,7 @@ def _register(cls):
 def _register_attention_processors_metadata():
     from ..models.attention_processor import AttnProcessor2_0
     from ..models.transformers.transformer_cogview4 import CogView4AttnProcessor
+    from ..models.transformers.transformer_wan import WanAttnProcessor2_0
 
     # AttnProcessor2_0
     AttentionProcessorRegistry.register(
@@ -124,6 +125,14 @@ def _register_attention_processors_metadata():
         ),
     )
 
+    # WanAttnProcessor2_0
+    AttentionProcessorRegistry.register(
+        model_class=WanAttnProcessor2_0,
+        metadata=AttentionProcessorMetadata(
+            skip_processor_output_fn=_skip_proc_output_fn_Attention_WanAttnProcessor2_0,
+        ),
+    )
+
 
 def _register_transformer_blocks_metadata():
     from ..models.attention import BasicTransformerBlock
@@ -261,4 +270,5 @@ def _skip_attention___ret___hidden_states___encoder_hidden_states(self, *args, *
 
 _skip_proc_output_fn_Attention_AttnProcessor2_0 = _skip_attention___ret___hidden_states
 _skip_proc_output_fn_Attention_CogView4AttnProcessor = _skip_attention___ret___hidden_states___encoder_hidden_states
+_skip_proc_output_fn_Attention_WanAttnProcessor2_0 = _skip_attention___ret___hidden_states
 # fmt: on

src/diffusers/hooks/layer_skip.py

@@ -91,10 +91,19 @@ def __torch_function__(self, func, types, args=(), kwargs=None):
         if kwargs is None:
             kwargs = {}
         if func is torch.nn.functional.scaled_dot_product_attention:
+            query = kwargs.get("query", None)
+            key = kwargs.get("key", None)
             value = kwargs.get("value", None)
-            if value is None:
-                value = args[2]
-            return value
+            query = query if query is not None else args[0]
+            key = key if key is not None else args[1]
+            value = value if value is not None else args[2]
+            # If the Q sequence length does not match KV sequence length, methods like
+            # Perturbed Attention Guidance cannot be used (because the caller expects
+            # the same sequence length as Q, but if we return V here, it will not match).
+            # When Q.shape[2] != V.shape[2], PAG will essentially not be applied and
+            # the overall effect would that be of normal CFG with a scale of (guidance_scale + perturbed_guidance_scale).
+            if query.shape[2] == value.shape[2]:
+                return value
         return func(*args, **kwargs)
 
 

src/diffusers/modular_pipelines/__init__.py

@@ -40,6 +40,7 @@
         "InsertableDict",
     ]
     _import_structure["stable_diffusion_xl"] = ["StableDiffusionXLAutoBlocks", "StableDiffusionXLModularPipeline"]
+    _import_structure["wan"] = ["WanAutoBlocks", "WanModularPipeline"]
     _import_structure["components_manager"] = ["ComponentsManager"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
@@ -71,6 +72,7 @@
             StableDiffusionXLAutoBlocks,
             StableDiffusionXLModularPipeline,
         )
+        from .wan import WanAutoBlocks, WanModularPipeline
 else:
     import sys
 

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -60,12 +60,14 @@
 MODULAR_PIPELINE_MAPPING = OrderedDict(
     [
         ("stable-diffusion-xl", "StableDiffusionXLModularPipeline"),
+        ("wan", "WanModularPipeline"),
     ]
 )
 
 MODULAR_PIPELINE_BLOCKS_MAPPING = OrderedDict(
     [
         ("StableDiffusionXLModularPipeline", "StableDiffusionXLAutoBlocks"),
+        ("WanModularPipeline", "WanAutoBlocks"),
     ]
 )
 

src/diffusers/modular_pipelines/wan/__init__.py

@@ -0,0 +1,66 @@
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["encoders"] = ["WanTextEncoderStep"]
+    _import_structure["modular_blocks"] = [
+        "ALL_BLOCKS",
+        "AUTO_BLOCKS",
+        "TEXT2VIDEO_BLOCKS",
+        "WanAutoBeforeDenoiseStep",
+        "WanAutoBlocks",
+        "WanAutoBlocks",
+        "WanAutoDecodeStep",
+        "WanAutoDenoiseStep",
+    ]
+    _import_structure["modular_pipeline"] = ["WanModularPipeline"]
+
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
+    else:
+        from .encoders import WanTextEncoderStep
+        from .modular_blocks import (
+            ALL_BLOCKS,
+            AUTO_BLOCKS,
+            TEXT2VIDEO_BLOCKS,
+            WanAutoBeforeDenoiseStep,
+            WanAutoBlocks,
+            WanAutoDecodeStep,
+            WanAutoDenoiseStep,
+        )
+        from .modular_pipeline import WanModularPipeline
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)