Merge branch 'main' into fix/transformers2d-config

src/diffusers/models/attention.py

@@ -143,7 +143,7 @@ class BasicTransformerBlock(nn.Module):
         double_self_attention: bool = False,
         upcast_attention: bool = False,
         norm_elementwise_affine: bool = True,
-        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'layer_norm_i2vgen'
+        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen'
         norm_eps: float = 1e-5,
         final_dropout: bool = False,
         attention_type: str = "default",

src/diffusers/models/transformers/transformer_2d.py

@@ -92,7 +92,7 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
         only_cross_attention: bool = False,
         double_self_attention: bool = False,
         upcast_attention: bool = False,
-        norm_type: str = "layer_norm",
+        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen'
         norm_elementwise_affine: bool = True,
         norm_eps: float = 1e-5,
         attention_type: str = "default",
@@ -100,6 +100,16 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
         interpolation_scale: float = None,
     ):
         super().__init__()
+        if patch_size is not None:
+            if norm_type not in ["ada_norm", "ada_norm_zero", "ada_norm_single"]:
+                raise NotImplementedError(
+                    f"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'."
+                )
+            elif norm_type in ["ada_norm", "ada_norm_zero"] and num_embeds_ada_norm is None:
+                raise ValueError(
+                    f"When using a `patch_size` and this `norm_type` ({norm_type}), `num_embeds_ada_norm` cannot be None."
+                )
+
         self.use_linear_projection = use_linear_projection
         self.num_attention_heads = num_attention_heads
         self.attention_head_dim = attention_head_dim