Fix: update type hints for Tuple parameters across multiple files to support...

Fix: update type hints for Tuple parameters across multiple files to support variable-length tuples (#12544)

* Fix: update type hints for Tuple parameters across multiple files to support variable-length tuples

* Apply style fixes

---------
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

src/diffusers/models/unets/unet_2d_condition.py

@@ -177,16 +177,21 @@ class UNet2DConditionModel(
         center_input_sample: bool = False,
         flip_sin_to_cos: bool = True,
         freq_shift: int = 0,
-        down_block_types: Tuple[str] = (
+        down_block_types: Tuple[str, ...] = (
             "CrossAttnDownBlock2D",
             "CrossAttnDownBlock2D",
             "CrossAttnDownBlock2D",
             "DownBlock2D",
         ),
         mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
-        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+        up_block_types: Tuple[str, ...] = (
+            "UpBlock2D",
+            "CrossAttnUpBlock2D",
+            "CrossAttnUpBlock2D",
+            "CrossAttnUpBlock2D",
+        ),
         only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
         layers_per_block: Union[int, Tuple[int]] = 2,
         downsample_padding: int = 1,
         mid_block_scale_factor: float = 1,
@@ -486,10 +491,10 @@ class UNet2DConditionModel(
 
     def _check_config(
         self,
-        down_block_types: Tuple[str],
-        up_block_types: Tuple[str],
+        down_block_types: Tuple[str, ...],
+        up_block_types: Tuple[str, ...],
         only_cross_attention: Union[bool, Tuple[bool]],
-        block_out_channels: Tuple[int],
+        block_out_channels: Tuple[int, ...],
         layers_per_block: Union[int, Tuple[int]],
         cross_attention_dim: Union[int, Tuple[int]],
         transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]],

src/diffusers/models/unets/unet_kandinsky3.py

@@ -54,7 +54,7 @@ class Kandinsky3UNet(ModelMixin, ConfigMixin):
         groups: int = 32,
         attention_head_dim: int = 64,
         layers_per_block: Union[int, Tuple[int]] = 3,
-        block_out_channels: Tuple[int] = (384, 768, 1536, 3072),
+        block_out_channels: Tuple[int, ...] = (384, 768, 1536, 3072),
         cross_attention_dim: Union[int, Tuple[int]] = 4096,
         encoder_hid_dim: int = 4096,
     ):

src/diffusers/models/unets/unet_spatio_temporal_condition.py

@@ -73,25 +73,25 @@ class UNetSpatioTemporalConditionModel(ModelMixin, ConfigMixin, UNet2DConditionL
         sample_size: Optional[int] = None,
         in_channels: int = 8,
         out_channels: int = 4,
-        down_block_types: Tuple[str] = (
+        down_block_types: Tuple[str, ...] = (
             "CrossAttnDownBlockSpatioTemporal",
             "CrossAttnDownBlockSpatioTemporal",
             "CrossAttnDownBlockSpatioTemporal",
             "DownBlockSpatioTemporal",
         ),
-        up_block_types: Tuple[str] = (
+        up_block_types: Tuple[str, ...] = (
             "UpBlockSpatioTemporal",
             "CrossAttnUpBlockSpatioTemporal",
             "CrossAttnUpBlockSpatioTemporal",
             "CrossAttnUpBlockSpatioTemporal",
         ),
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
         addition_time_embed_dim: int = 256,
         projection_class_embeddings_input_dim: int = 768,
         layers_per_block: Union[int, Tuple[int]] = 2,
         cross_attention_dim: Union[int, Tuple[int]] = 1024,
         transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
-        num_attention_heads: Union[int, Tuple[int]] = (5, 10, 20, 20),
+        num_attention_heads: Union[int, Tuple[int, ...]] = (5, 10, 20, 20),
         num_frames: int = 25,
     ):
         super().__init__()

src/diffusers/models/unets/unet_stable_cascade.py

@@ -145,10 +145,10 @@ class StableCascadeUNet(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         timestep_ratio_embedding_dim: int = 64,
         patch_size: int = 1,
         conditioning_dim: int = 2048,
-        block_out_channels: Tuple[int] = (2048, 2048),
-        num_attention_heads: Tuple[int] = (32, 32),
-        down_num_layers_per_block: Tuple[int] = (8, 24),
-        up_num_layers_per_block: Tuple[int] = (24, 8),
+        block_out_channels: Tuple[int, ...] = (2048, 2048),
+        num_attention_heads: Tuple[int, ...] = (32, 32),
+        down_num_layers_per_block: Tuple[int, ...] = (8, 24),
+        up_num_layers_per_block: Tuple[int, ...] = (24, 8),
         down_blocks_repeat_mappers: Optional[Tuple[int]] = (
             1,
             1,
@@ -167,7 +167,7 @@ class StableCascadeUNet(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         kernel_size=3,
         dropout: Union[float, Tuple[float]] = (0.1, 0.1),
         self_attn: Union[bool, Tuple[bool]] = True,
-        timestep_conditioning_type: Tuple[str] = ("sca", "crp"),
+        timestep_conditioning_type: Tuple[str, ...] = ("sca", "crp"),
         switch_level: Optional[Tuple[bool]] = None,
     ):
         """

src/diffusers/models/vae_flax.py

@@ -532,8 +532,8 @@ class FlaxEncoder(nn.Module):
 
     in_channels: int = 3
     out_channels: int = 3
-    down_block_types: Tuple[str] = ("DownEncoderBlock2D",)
-    block_out_channels: Tuple[int] = (64,)
+    down_block_types: Tuple[str, ...] = ("DownEncoderBlock2D",)
+    block_out_channels: Tuple[int, ...] = (64,)
     layers_per_block: int = 2
     norm_num_groups: int = 32
     act_fn: str = "silu"
@@ -650,8 +650,8 @@ class FlaxDecoder(nn.Module):
 
     in_channels: int = 3
     out_channels: int = 3
-    up_block_types: Tuple[str] = ("UpDecoderBlock2D",)
-    block_out_channels: int = (64,)
+    up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",)
+    block_out_channels: Tuple[int, ...] = (64,)
     layers_per_block: int = 2
     norm_num_groups: int = 32
     act_fn: str = "silu"
@@ -823,9 +823,9 @@ class FlaxAutoencoderKL(nn.Module, FlaxModelMixin, ConfigMixin):
 
     in_channels: int = 3
     out_channels: int = 3
-    down_block_types: Tuple[str] = ("DownEncoderBlock2D",)
-    up_block_types: Tuple[str] = ("UpDecoderBlock2D",)
-    block_out_channels: Tuple[int] = (64,)
+    down_block_types: Tuple[str, ...] = ("DownEncoderBlock2D",)
+    up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",)
+    block_out_channels: Tuple[int, ...] = (64,)
     layers_per_block: int = 1
     act_fn: str = "silu"
     latent_channels: int = 4

src/diffusers/pipelines/audioldm2/modeling_audioldm2.py

@@ -245,16 +245,21 @@ class AudioLDM2UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoad
         out_channels: int = 4,
         flip_sin_to_cos: bool = True,
         freq_shift: int = 0,
-        down_block_types: Tuple[str] = (
+        down_block_types: Tuple[str, ...] = (
             "CrossAttnDownBlock2D",
             "CrossAttnDownBlock2D",
             "CrossAttnDownBlock2D",
             "DownBlock2D",
         ),
         mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
-        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+        up_block_types: Tuple[str, ...] = (
+            "UpBlock2D",
+            "CrossAttnUpBlock2D",
+            "CrossAttnUpBlock2D",
+            "CrossAttnUpBlock2D",
+        ),
         only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
         layers_per_block: Union[int, Tuple[int]] = 2,
         downsample_padding: int = 1,
         mid_block_scale_factor: float = 1,

src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py

@@ -374,21 +374,21 @@ class UNetFlatConditionModel(ModelMixin, ConfigMixin):
         center_input_sample: bool = False,
         flip_sin_to_cos: bool = True,
         freq_shift: int = 0,
-        down_block_types: Tuple[str] = (
+        down_block_types: Tuple[str, ...] = (
             "CrossAttnDownBlockFlat",
             "CrossAttnDownBlockFlat",
             "CrossAttnDownBlockFlat",
             "DownBlockFlat",
         ),
         mid_block_type: Optional[str] = "UNetMidBlockFlatCrossAttn",
-        up_block_types: Tuple[str] = (
+        up_block_types: Tuple[str, ...] = (
             "UpBlockFlat",
             "CrossAttnUpBlockFlat",
             "CrossAttnUpBlockFlat",
             "CrossAttnUpBlockFlat",
         ),
         only_cross_attention: Union[bool, Tuple[bool]] = False,
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
         layers_per_block: Union[int, Tuple[int]] = 2,
         downsample_padding: int = 1,
         mid_block_scale_factor: float = 1,

src/diffusers/pipelines/shap_e/renderer.py

@@ -742,7 +742,7 @@ class ShapEParamsProjModel(ModelMixin, ConfigMixin):
     def __init__(
         self,
         *,
-        param_names: Tuple[str] = (
+        param_names: Tuple[str, ...] = (
             "nerstf.mlp.0.weight",
             "nerstf.mlp.1.weight",
             "nerstf.mlp.2.weight",
@@ -786,13 +786,13 @@ class ShapERenderer(ModelMixin, ConfigMixin):
     def __init__(
         self,
         *,
-        param_names: Tuple[str] = (
+        param_names: Tuple[str, ...] = (
             "nerstf.mlp.0.weight",
             "nerstf.mlp.1.weight",
             "nerstf.mlp.2.weight",
             "nerstf.mlp.3.weight",
         ),
-        param_shapes: Tuple[Tuple[int]] = (
+        param_shapes: Tuple[Tuple[int, int], ...] = (
             (256, 93),
             (256, 256),
             (256, 256),
@@ -804,7 +804,7 @@ class ShapERenderer(ModelMixin, ConfigMixin):
         n_hidden_layers: int = 6,
         act_fn: str = "swish",
         insert_direction_at: int = 4,
-        background: Tuple[float] = (
+        background: Tuple[float, ...] = (
             255.0,
             255.0,
             255.0,