[refactor] Flux/Chroma single file implementation + Attention Dispatcher

src/diffusers/__init__.py

@@ -163,6 +163,7 @@
         [
             "AllegroTransformer3DModel",
             "AsymmetricAutoencoderKL",
+            "AttentionBackendName",
             "AuraFlowTransformer2DModel",
             "AutoencoderDC",
             "AutoencoderKL",
@@ -238,6 +239,7 @@
             "VQModel",
             "WanTransformer3DModel",
             "WanVACETransformer3DModel",
+            "attention_backend",
         ]
     )
     _import_structure["modular_pipelines"].extend(
@@ -815,6 +817,7 @@
         from .models import (
             AllegroTransformer3DModel,
             AsymmetricAutoencoderKL,
+            AttentionBackendName,
             AuraFlowTransformer2DModel,
             AutoencoderDC,
             AutoencoderKL,
@@ -889,6 +892,7 @@
             VQModel,
             WanTransformer3DModel,
             WanVACETransformer3DModel,
+            attention_backend,
         )
         from .modular_pipelines import (
             ComponentsManager,

src/diffusers/hooks/faster_cache.py

@@ -18,6 +18,7 @@
 
 import torch
 
+from ..models.attention import AttentionModuleMixin
 from ..models.attention_processor import Attention, MochiAttention
 from ..models.modeling_outputs import Transformer2DModelOutput
 from ..utils import logging
@@ -567,7 +568,7 @@ def high_frequency_weight_callback(module: torch.nn.Module) -> float:
     _apply_faster_cache_on_denoiser(module, config)
 
     for name, submodule in module.named_modules():
-        if not isinstance(submodule, _ATTENTION_CLASSES):
+        if not isinstance(submodule, (*_ATTENTION_CLASSES, AttentionModuleMixin)):
             continue
         if any(re.search(identifier, name) is not None for identifier in _TRANSFORMER_BLOCK_IDENTIFIERS):
             _apply_faster_cache_on_attention_class(name, submodule, config)

src/diffusers/hooks/pyramid_attention_broadcast.py

@@ -18,6 +18,7 @@
 
 import torch
 
+from ..models.attention import AttentionModuleMixin
 from ..models.attention_processor import Attention, MochiAttention
 from ..utils import logging
 from .hooks import HookRegistry, ModelHook
@@ -227,7 +228,7 @@ def apply_pyramid_attention_broadcast(module: torch.nn.Module, config: PyramidAt
         config.spatial_attention_block_skip_range = 2
 
     for name, submodule in module.named_modules():
-        if not isinstance(submodule, _ATTENTION_CLASSES):
+        if not isinstance(submodule, (*_ATTENTION_CLASSES, AttentionModuleMixin)):
             # PAB has been implemented specific to Diffusers' Attention classes. However, this does not mean that PAB
             # cannot be applied to this layer. For custom layers, users can extend this functionality and implement
             # their own PAB logic similar to `_apply_pyramid_attention_broadcast_on_attention_class`.

src/diffusers/loaders/ip_adapter.py

@@ -40,8 +40,6 @@
 from ..models.attention_processor import (
     AttnProcessor,
     AttnProcessor2_0,
-    FluxAttnProcessor2_0,
-    FluxIPAdapterJointAttnProcessor2_0,
     IPAdapterAttnProcessor,
     IPAdapterAttnProcessor2_0,
     IPAdapterXFormersAttnProcessor,
@@ -867,6 +865,9 @@ def unload_ip_adapter(self):
         >>> ...
         ```
         """
+        # TODO: once the 1.0.0 deprecations are in, we can move the imports to top-level
+        from ..models.transformers.transformer_flux import FluxAttnProcessor, FluxIPAdapterAttnProcessor
+
         # remove CLIP image encoder
         if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is not None:
             self.image_encoder = None
@@ -886,9 +887,9 @@ def unload_ip_adapter(self):
         # restore original Transformer attention processors layers
         attn_procs = {}
         for name, value in self.transformer.attn_processors.items():
-            attn_processor_class = FluxAttnProcessor2_0()
+            attn_processor_class = FluxAttnProcessor()
             attn_procs[name] = (
-                attn_processor_class if isinstance(value, (FluxIPAdapterJointAttnProcessor2_0)) else value.__class__()
+                attn_processor_class if isinstance(value, FluxIPAdapterAttnProcessor) else value.__class__()
             )
         self.transformer.set_attn_processor(attn_procs)
 

src/diffusers/loaders/transformer_flux.py

@@ -86,9 +86,7 @@ def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict, low_cpu_mem_us
         return image_projection
 
     def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=_LOW_CPU_MEM_USAGE_DEFAULT):
-        from ..models.attention_processor import (
-            FluxIPAdapterJointAttnProcessor2_0,
-        )
+        from ..models.transformers.transformer_flux import FluxIPAdapterAttnProcessor
 
         if low_cpu_mem_usage:
             if is_accelerate_available():
@@ -120,7 +118,7 @@ def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=_
             else:
                 cross_attention_dim = self.config.joint_attention_dim
                 hidden_size = self.inner_dim
-                attn_processor_class = FluxIPAdapterJointAttnProcessor2_0
+                attn_processor_class = FluxIPAdapterAttnProcessor
                 num_image_text_embeds = []
                 for state_dict in state_dicts:
                     if "proj.weight" in state_dict["image_proj"]:

src/diffusers/models/__init__.py

@@ -26,6 +26,7 @@
 
 if is_torch_available():
     _import_structure["adapter"] = ["MultiAdapter", "T2IAdapter"]
+    _import_structure["attention_dispatch"] = ["AttentionBackendName", "attention_backend"]
     _import_structure["auto_model"] = ["AutoModel"]
     _import_structure["autoencoders.autoencoder_asym_kl"] = ["AsymmetricAutoencoderKL"]
     _import_structure["autoencoders.autoencoder_dc"] = ["AutoencoderDC"]
@@ -112,6 +113,7 @@
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     if is_torch_available():
         from .adapter import MultiAdapter, T2IAdapter
+        from .attention_dispatch import AttentionBackendName, attention_backend
         from .auto_model import AutoModel
         from .autoencoders import (
             AsymmetricAutoencoderKL,