Fix many type hint errors (#12289)

* fix hidream type hint

* fix hunyuan-video type hint

* fix many type hint

* fix many type hint errors

* fix many type hint errors

* fix many type hint errors

* make stype & make quality

src/diffusers/models/attention.py

@@ -674,7 +674,7 @@ class JointTransformerBlock(nn.Module):
         encoder_hidden_states: torch.FloatTensor,
         temb: torch.FloatTensor,
         joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         joint_attention_kwargs = joint_attention_kwargs or {}
         if self.use_dual_attention:
             norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_hidden_states2, gate_msa2 = self.norm1(

src/diffusers/models/transformers/auraflow_transformer_2d.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 
-from typing import Any, Dict, Optional, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -92,7 +92,7 @@ class AuraFlowPatchEmbed(nn.Module):
 
         return selected_indices
 
-    def forward(self, latent):
+    def forward(self, latent) -> torch.Tensor:
         batch_size, num_channels, height, width = latent.size()
         latent = latent.view(
             batch_size,
@@ -173,7 +173,7 @@ class AuraFlowSingleTransformerBlock(nn.Module):
         hidden_states: torch.FloatTensor,
         temb: torch.FloatTensor,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
+    ) -> torch.Tensor:
         residual = hidden_states
         attention_kwargs = attention_kwargs or {}
 
@@ -242,7 +242,7 @@ class AuraFlowJointTransformerBlock(nn.Module):
         encoder_hidden_states: torch.FloatTensor,
         temb: torch.FloatTensor,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         residual = hidden_states
         residual_context = encoder_hidden_states
         attention_kwargs = attention_kwargs or {}
@@ -472,7 +472,7 @@ class AuraFlowTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, From
         timestep: torch.LongTensor = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
-    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
             lora_scale = attention_kwargs.pop("scale", 1.0)

src/diffusers/models/transformers/cogvideox_transformer_3d.py

@@ -122,7 +122,7 @@ class CogVideoXBlock(nn.Module):
         temb: torch.Tensor,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_length = encoder_hidden_states.size(1)
         attention_kwargs = attention_kwargs or {}
 
@@ -441,7 +441,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, Cac
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
-    ):
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
             lora_scale = attention_kwargs.pop("scale", 1.0)

src/diffusers/models/transformers/consisid_transformer_3d.py

@@ -315,7 +315,7 @@ class ConsisIDBlock(nn.Module):
         encoder_hidden_states: torch.Tensor,
         temb: torch.Tensor,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_length = encoder_hidden_states.size(1)
 
         # norm & modulate
@@ -691,7 +691,7 @@ class ConsisIDTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
         id_cond: Optional[torch.Tensor] = None,
         id_vit_hidden: Optional[torch.Tensor] = None,
         return_dict: bool = True,
-    ):
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
             lora_scale = attention_kwargs.pop("scale", 1.0)

src/diffusers/models/transformers/lumina_nextdit2d.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -124,7 +124,7 @@ class LuminaNextDiTBlock(nn.Module):
         encoder_mask: torch.Tensor,
         temb: torch.Tensor,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
+    ) -> torch.Tensor:
         """
         Perform a forward pass through the LuminaNextDiTBlock.
 
@@ -297,7 +297,7 @@ class LuminaNextDiT2DModel(ModelMixin, ConfigMixin):
         image_rotary_emb: torch.Tensor,
         cross_attention_kwargs: Dict[str, Any] = None,
         return_dict=True,
-    ) -> torch.Tensor:
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         """
         Forward pass of LuminaNextDiT.
 

src/diffusers/models/transformers/transformer_bria.py

@@ -472,7 +472,7 @@ class BriaSingleTransformerBlock(nn.Module):
         temb: torch.Tensor,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_len = encoder_hidden_states.shape[1]
         hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
 
@@ -588,7 +588,7 @@ class BriaTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOrig
         return_dict: bool = True,
         controlnet_block_samples=None,
         controlnet_single_block_samples=None,
-    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         """
         The [`BriaTransformer2DModel`] forward method.
 

src/diffusers/models/transformers/transformer_cogview3plus.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 
-from typing import Dict, Union
+from typing import Dict, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -79,7 +79,7 @@ class CogView3PlusTransformerBlock(nn.Module):
         hidden_states: torch.Tensor,
         encoder_hidden_states: torch.Tensor,
         emb: torch.Tensor,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_length = encoder_hidden_states.size(1)
 
         # norm & modulate
@@ -293,7 +293,7 @@ class CogView3PlusTransformer2DModel(ModelMixin, ConfigMixin):
         target_size: torch.Tensor,
         crop_coords: torch.Tensor,
         return_dict: bool = True,
-    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         """
         The [`CogView3PlusTransformer2DModel`] forward method.
 

src/diffusers/models/transformers/transformer_cogview4.py

@@ -494,7 +494,7 @@ class CogView4TransformerBlock(nn.Module):
         ] = None,
         attention_mask: Optional[Dict[str, torch.Tensor]] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         # 1. Timestep conditioning
         (
             norm_hidden_states,
@@ -717,7 +717,7 @@ class CogView4Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, Cach
         image_rotary_emb: Optional[
             Union[Tuple[torch.Tensor, torch.Tensor], List[Tuple[torch.Tensor, torch.Tensor]]]
         ] = None,
-    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
             lora_scale = attention_kwargs.pop("scale", 1.0)

src/diffusers/models/transformers/transformer_hidream_image.py

@@ -55,7 +55,7 @@ class HiDreamImageTimestepEmbed(nn.Module):
         self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0)
         self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size)
 
-    def forward(self, timesteps: torch.Tensor, wdtype: Optional[torch.dtype] = None):
+    def forward(self, timesteps: torch.Tensor, wdtype: Optional[torch.dtype] = None) -> torch.Tensor:
         t_emb = self.time_proj(timesteps).to(dtype=wdtype)
         t_emb = self.timestep_embedder(t_emb)
         return t_emb
@@ -87,7 +87,7 @@ class HiDreamImagePatchEmbed(nn.Module):
         self.out_channels = out_channels
         self.proj = nn.Linear(in_channels * patch_size * patch_size, out_channels, bias=True)
 
-    def forward(self, latent):
+    def forward(self, latent) -> torch.Tensor:
         latent = self.proj(latent)
         return latent
 
@@ -534,7 +534,7 @@ class HiDreamImageTransformerBlock(nn.Module):
         encoder_hidden_states: Optional[torch.Tensor] = None,
         temb: Optional[torch.Tensor] = None,
         image_rotary_emb: torch.Tensor = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         wtype = hidden_states.dtype
         (
             shift_msa_i,
@@ -592,7 +592,7 @@ class HiDreamBlock(nn.Module):
         encoder_hidden_states: Optional[torch.Tensor] = None,
         temb: Optional[torch.Tensor] = None,
         image_rotary_emb: torch.Tensor = None,
-    ) -> torch.Tensor:
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         return self.block(
             hidden_states=hidden_states,
             hidden_states_masks=hidden_states_masks,
@@ -786,7 +786,7 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
         **kwargs,
-    ):
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         encoder_hidden_states = kwargs.get("encoder_hidden_states", None)
 
         if encoder_hidden_states is not None:

src/diffusers/models/transformers/transformer_hunyuan_video.py

@@ -529,7 +529,7 @@ class HunyuanVideoSingleTransformerBlock(nn.Module):
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         *args,
         **kwargs,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_length = encoder_hidden_states.shape[1]
         hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1)
 
@@ -684,7 +684,7 @@ class HunyuanVideoTokenReplaceSingleTransformerBlock(nn.Module):
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         token_replace_emb: torch.Tensor = None,
         num_tokens: int = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_length = encoder_hidden_states.shape[1]
         hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1)
 
@@ -1038,7 +1038,7 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
         guidance: torch.Tensor = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
-    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
             lora_scale = attention_kwargs.pop("scale", 1.0)

src/diffusers/models/transformers/transformer_hunyuan_video_framepack.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -216,7 +216,7 @@ class HunyuanVideoFramepackTransformer3DModel(
         indices_latents_history_4x: Optional[torch.Tensor] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
-    ):
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
             lora_scale = attention_kwargs.pop("scale", 1.0)