remove void, add types for params

src/diffusers/models/unet_2d.py

@@ -1,4 +1,4 @@
-from typing import Dict, Union
+from typing import Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -13,24 +13,24 @@ 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,
- ) -> None:
+ 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__()
 
  self.sample_size = sample_size

src/diffusers/models/unet_2d_condition.py

@@ -1,4 +1,4 @@
-from typing import Dict, Union
+from typing import Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -13,24 +13,29 @@ 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,
- ) -> None:
+ 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__()
 
  self.sample_size = sample_size