[Type Hint] Unet Models (#330)

* add void check

* remove void, add types for params

src/diffusers/models/unet_2d.py

-from typing import Dict, Union
+from typing import Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -13,23 +13,23 @@ class UNet2DModel(ModelMixin, ConfigMixin):
     @register_to_config
     def __init__(
         self,
-        sample_size=None,
-        in_channels=3,
-        out_channels=3,
-        center_input_sample=False,
-        time_embedding_type="positional",
-        freq_shift=0,
-        flip_sin_to_cos=True,
-        down_block_types=("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
-        up_block_types=("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),
-        block_out_channels=(224, 448, 672, 896),
-        layers_per_block=2,
-        mid_block_scale_factor=1,
-        downsample_padding=1,
-        act_fn="silu",
-        attention_head_dim=8,
-        norm_num_groups=32,
-        norm_eps=1e-5,
+        sample_size: Optional[int] = None,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        center_input_sample: bool = False,
+        time_embedding_type: str = "positional",
+        freq_shift: int = 0,
+        flip_sin_to_cos: bool = True,
+        down_block_types: Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
+        up_block_types: Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),
+        block_out_channels: Tuple[int] = (224, 448, 672, 896),
+        layers_per_block: int = 2,
+        mid_block_scale_factor: float = 1,
+        downsample_padding: int = 1,
+        act_fn: str = "silu",
+        attention_head_dim: int = 8,
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-5,
     ):
         super().__init__()
 

src/diffusers/models/unet_2d_condition.py

-from typing import Dict, Union
+from typing import Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -13,23 +13,28 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin):
     @register_to_config
     def __init__(
         self,
-        sample_size=None,
-        in_channels=4,
-        out_channels=4,
-        center_input_sample=False,
-        flip_sin_to_cos=True,
-        freq_shift=0,
-        down_block_types=("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"),
-        up_block_types=("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
-        block_out_channels=(320, 640, 1280, 1280),
-        layers_per_block=2,
-        downsample_padding=1,
-        mid_block_scale_factor=1,
-        act_fn="silu",
-        norm_num_groups=32,
-        norm_eps=1e-5,
-        cross_attention_dim=1280,
-        attention_head_dim=8,
+        sample_size: Optional[int] = None,
+        in_channels: int = 4,
+        out_channels: int = 4,
+        center_input_sample: bool = False,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "DownBlock2D",
+        ),
+        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: int = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        act_fn: str = "silu",
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: int = 1280,
+        attention_head_dim: int = 8,
     ):
         super().__init__()