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# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
import inspect
from dataclasses import dataclass
from typing import Any, Callable, Dict, Type
@dataclass
class AttentionProcessorMetadata:
skip_processor_output_fn: Callable[[Any], Any]
@dataclass
class TransformerBlockMetadata:
return_hidden_states_index: int = None
return_encoder_hidden_states_index: int = None
_cls: Type = None
_cached_parameter_indices: Dict[str, int] = None
def _get_parameter_from_args_kwargs(self, identifier: str, args=(), kwargs=None):
kwargs = kwargs or {}
if identifier in kwargs:
return kwargs[identifier]
if self._cached_parameter_indices is not None:
return args[self._cached_parameter_indices[identifier]]
if self._cls is None:
raise ValueError("Model class is not set for metadata.")
parameters = list(inspect.signature(self._cls.forward).parameters.keys())
parameters = parameters[1:] # skip `self`
self._cached_parameter_indices = {param: i for i, param in enumerate(parameters)}
if identifier not in self._cached_parameter_indices:
raise ValueError(f"Parameter '{identifier}' not found in function signature but was requested.")
index = self._cached_parameter_indices[identifier]
if index >= len(args):
raise ValueError(f"Expected {index} arguments but got {len(args)}.")
return args[index]
class AttentionProcessorRegistry:
_registry = {}
# TODO(aryan): this is only required for the time being because we need to do the registrations
# for classes. If we do it eagerly, i.e. call the functions in global scope, we will get circular
# import errors because of the models imported in this file.
_is_registered = False
@classmethod
def register(cls, model_class: Type, metadata: AttentionProcessorMetadata):
cls._register()
cls._registry[model_class] = metadata
@classmethod
def get(cls, model_class: Type) -> AttentionProcessorMetadata:
cls._register()
if model_class not in cls._registry:
raise ValueError(f"Model class {model_class} not registered.")
return cls._registry[model_class]
@classmethod
def _register(cls):
if cls._is_registered:
return
cls._is_registered = True
_register_attention_processors_metadata()
class TransformerBlockRegistry:
_registry = {}
# TODO(aryan): this is only required for the time being because we need to do the registrations
# for classes. If we do it eagerly, i.e. call the functions in global scope, we will get circular
# import errors because of the models imported in this file.
_is_registered = False
@classmethod
def register(cls, model_class: Type, metadata: TransformerBlockMetadata):
cls._register()
metadata._cls = model_class
cls._registry[model_class] = metadata
@classmethod
def get(cls, model_class: Type) -> TransformerBlockMetadata:
cls._register()
if model_class not in cls._registry:
raise ValueError(f"Model class {model_class} not registered.")
return cls._registry[model_class]
@classmethod
def _register(cls):
if cls._is_registered:
return
cls._is_registered = True
_register_transformer_blocks_metadata()
def _register_attention_processors_metadata():
from ..models.attention_processor import AttnProcessor2_0
from ..models.transformers.transformer_cogview4 import CogView4AttnProcessor
from ..models.transformers.transformer_flux import FluxAttnProcessor
from ..models.transformers.transformer_hunyuanimage import HunyuanImageAttnProcessor
from ..models.transformers.transformer_qwenimage import QwenDoubleStreamAttnProcessor2_0
from ..models.transformers.transformer_wan import WanAttnProcessor2_0
from ..models.transformers.transformer_z_image import ZSingleStreamAttnProcessor
# AttnProcessor2_0
AttentionProcessorRegistry.register(
model_class=AttnProcessor2_0,
metadata=AttentionProcessorMetadata(
skip_processor_output_fn=_skip_proc_output_fn_Attention_AttnProcessor2_0,
),
)
# CogView4AttnProcessor
AttentionProcessorRegistry.register(
model_class=CogView4AttnProcessor,
metadata=AttentionProcessorMetadata(
skip_processor_output_fn=_skip_proc_output_fn_Attention_CogView4AttnProcessor,
),
)
# WanAttnProcessor2_0
AttentionProcessorRegistry.register(
model_class=WanAttnProcessor2_0,
metadata=AttentionProcessorMetadata(
skip_processor_output_fn=_skip_proc_output_fn_Attention_WanAttnProcessor2_0,
),
)
# FluxAttnProcessor
AttentionProcessorRegistry.register(
model_class=FluxAttnProcessor,
metadata=AttentionProcessorMetadata(skip_processor_output_fn=_skip_proc_output_fn_Attention_FluxAttnProcessor),
)
# QwenDoubleStreamAttnProcessor2
AttentionProcessorRegistry.register(
model_class=QwenDoubleStreamAttnProcessor2_0,
metadata=AttentionProcessorMetadata(
skip_processor_output_fn=_skip_proc_output_fn_Attention_QwenDoubleStreamAttnProcessor2_0
),
)
# HunyuanImageAttnProcessor
AttentionProcessorRegistry.register(
model_class=HunyuanImageAttnProcessor,
metadata=AttentionProcessorMetadata(
skip_processor_output_fn=_skip_proc_output_fn_Attention_HunyuanImageAttnProcessor,
),
)
# ZSingleStreamAttnProcessor
AttentionProcessorRegistry.register(
model_class=ZSingleStreamAttnProcessor,
metadata=AttentionProcessorMetadata(
skip_processor_output_fn=_skip_proc_output_fn_Attention_ZSingleStreamAttnProcessor,
),
)
def _register_transformer_blocks_metadata():
from ..models.attention import BasicTransformerBlock
from ..models.transformers.cogvideox_transformer_3d import CogVideoXBlock
from ..models.transformers.transformer_bria import BriaTransformerBlock
from ..models.transformers.transformer_cogview4 import CogView4TransformerBlock
from ..models.transformers.transformer_flux import FluxSingleTransformerBlock, FluxTransformerBlock
from ..models.transformers.transformer_hunyuan_video import (
HunyuanVideoSingleTransformerBlock,
HunyuanVideoTokenReplaceSingleTransformerBlock,
HunyuanVideoTokenReplaceTransformerBlock,
HunyuanVideoTransformerBlock,
)
from ..models.transformers.transformer_hunyuanimage import (
HunyuanImageSingleTransformerBlock,
HunyuanImageTransformerBlock,
)
from ..models.transformers.transformer_ltx import LTXVideoTransformerBlock
from ..models.transformers.transformer_mochi import MochiTransformerBlock
from ..models.transformers.transformer_qwenimage import QwenImageTransformerBlock
from ..models.transformers.transformer_wan import WanTransformerBlock
from ..models.transformers.transformer_z_image import ZImageTransformerBlock
# BasicTransformerBlock
TransformerBlockRegistry.register(
model_class=BasicTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=None,
),
)
TransformerBlockRegistry.register(
model_class=BriaTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=None,
),
)
# CogVideoX
TransformerBlockRegistry.register(
model_class=CogVideoXBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=1,
),
)
# CogView4
TransformerBlockRegistry.register(
model_class=CogView4TransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=1,
),
)
# Flux
TransformerBlockRegistry.register(
model_class=FluxTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=1,
return_encoder_hidden_states_index=0,
),
)
TransformerBlockRegistry.register(
model_class=FluxSingleTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=1,
return_encoder_hidden_states_index=0,
),
)
# HunyuanVideo
TransformerBlockRegistry.register(
model_class=HunyuanVideoTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=1,
),
)
TransformerBlockRegistry.register(
model_class=HunyuanVideoSingleTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=1,
),
)
TransformerBlockRegistry.register(
model_class=HunyuanVideoTokenReplaceTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=1,
),
)
TransformerBlockRegistry.register(
model_class=HunyuanVideoTokenReplaceSingleTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=1,
),
)
# LTXVideo
TransformerBlockRegistry.register(
model_class=LTXVideoTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=None,
),
)
# Mochi
TransformerBlockRegistry.register(
model_class=MochiTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=1,
),
)
# Wan
TransformerBlockRegistry.register(
model_class=WanTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=None,
),
)
# QwenImage
TransformerBlockRegistry.register(
model_class=QwenImageTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=1,
return_encoder_hidden_states_index=0,
),
)
# HunyuanImage2.1
TransformerBlockRegistry.register(
model_class=HunyuanImageTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=1,
),
)
TransformerBlockRegistry.register(
model_class=HunyuanImageSingleTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=1,
),
)
# ZImage
TransformerBlockRegistry.register(
model_class=ZImageTransformerBlock,
metadata=TransformerBlockMetadata(
return_hidden_states_index=0,
return_encoder_hidden_states_index=None,
),
)
# fmt: off
def _skip_attention___ret___hidden_states(self, *args, **kwargs):
hidden_states = kwargs.get("hidden_states", None)
if hidden_states is None and len(args) > 0:
hidden_states = args[0]
return hidden_states
def _skip_attention___ret___hidden_states___encoder_hidden_states(self, *args, **kwargs):
hidden_states = kwargs.get("hidden_states", None)
encoder_hidden_states = kwargs.get("encoder_hidden_states", None)
if hidden_states is None and len(args) > 0:
hidden_states = args[0]
if encoder_hidden_states is None and len(args) > 1:
encoder_hidden_states = args[1]
return hidden_states, encoder_hidden_states
_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
# not sure what this is yet.
_skip_proc_output_fn_Attention_FluxAttnProcessor = _skip_attention___ret___hidden_states
_skip_proc_output_fn_Attention_QwenDoubleStreamAttnProcessor2_0 = _skip_attention___ret___hidden_states
_skip_proc_output_fn_Attention_HunyuanImageAttnProcessor = _skip_attention___ret___hidden_states
_skip_proc_output_fn_Attention_ZSingleStreamAttnProcessor = _skip_attention___ret___hidden_states
# fmt: on
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