delete process

Author: bubbliiiing

src/diffusers/models/attention_processor.py

@@ -3613,109 +3613,6 @@ def __call__(
         return hidden_states
 
 
-class EasyAnimateAttnProcessor2_0:
-    r"""
-    Attention processor used in EasyAnimate.
-    """
-
-    def __init__(self):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-    def __call__(
-        self,
-        attn: Attention,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-        attn2: Attention = None,
-    ) -> torch.Tensor:
-        text_seq_length = encoder_hidden_states.size(1)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        if attn2 is None:
-            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(hidden_states)
-        value = attn.to_v(hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        if attn.norm_q is not None:
-            query = attn.norm_q(query)
-        if attn.norm_k is not None:
-            key = attn.norm_k(key)
-        
-        if attn2 is not None:
-            query_txt = attn2.to_q(encoder_hidden_states)
-            key_txt = attn2.to_k(encoder_hidden_states)
-            value_txt = attn2.to_v(encoder_hidden_states)
-            
-            inner_dim = key_txt.shape[-1]
-            head_dim = inner_dim // attn.heads
-
-            query_txt = query_txt.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-            key_txt = key_txt.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-            value_txt = value_txt.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-            if attn2.norm_q is not None:
-                query_txt = attn2.norm_q(query_txt)
-            if attn2.norm_k is not None:
-                key_txt = attn2.norm_k(key_txt)
-
-            query = torch.cat([query_txt, query], dim=2)
-            key = torch.cat([key_txt, key], dim=2)
-            value = torch.cat([value_txt, value], dim=2)
-
-        # Apply RoPE if needed
-        if image_rotary_emb is not None:
-            from .embeddings import apply_rotary_emb
-            query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
-            if not attn.is_cross_attention:
-                key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
-
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-
-        if attn2 is None:
-            # linear proj
-            hidden_states = attn.to_out[0](hidden_states)
-            # dropout
-            hidden_states = attn.to_out[1](hidden_states)
-
-            encoder_hidden_states, hidden_states = hidden_states.split(
-                [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
-            )
-        else:
-            encoder_hidden_states, hidden_states = hidden_states.split(
-                [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
-            )
-            # linear proj
-            hidden_states = attn.to_out[0](hidden_states)
-            encoder_hidden_states = attn2.to_out[0](encoder_hidden_states)
-            # dropout
-            hidden_states = attn.to_out[1](hidden_states)
-            encoder_hidden_states = attn2.to_out[1](encoder_hidden_states)
-        return hidden_states, encoder_hidden_states
-
-
 class StableAudioAttnProcessor2_0:
     r"""
     Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is