Fix typo in AttnProcessor2_0 symbol (#2404)

Fix typo in AttnProcessor2_0 symbol.

docs/source/en/optimization/torch2.0.mdx

@@ -50,10 +50,10 @@ pip install --pre torch torchvision --index-url https://download.pytorch.org/whl
     ```Python
     import torch
     from diffusers import StableDiffusionPipeline
-    from diffusers.models.cross_attention import AttnProccesor2_0
+    from diffusers.models.cross_attention import AttnProcessor2_0
 
     pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
-    pipe.unet.set_attn_processor(AttnProccesor2_0())
+    pipe.unet.set_attn_processor(AttnProcessor2_0())
 
     prompt = "a photo of an astronaut riding a horse on mars"
     image = pipe(prompt).images[0]

src/diffusers/models/cross_attention.py

@@ -99,10 +99,10 @@ class CrossAttention(nn.Module):
         self.to_out.append(nn.Dropout(dropout))
 
         # set attention processor
-        # We use the AttnProccesor2_0 by default when torch2.x is used which uses
+        # We use the AttnProcessor2_0 by default when torch2.x is used which uses
         # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
         if processor is None:
-            processor = AttnProccesor2_0() if hasattr(F, "scaled_dot_product_attention") else CrossAttnProcessor()
+            processor = AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else CrossAttnProcessor()
         self.set_processor(processor)
 
     def set_use_memory_efficient_attention_xformers(
@@ -466,10 +466,10 @@ class XFormersCrossAttnProcessor:
         return hidden_states
 
 
-class AttnProccesor2_0:
+class AttnProcessor2_0:
     def __init__(self):
         if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProccesor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
 
     def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None):
         batch_size, sequence_length, inner_dim = hidden_states.shape