Renamed variables from single letter to better naming

src/diffusers/models/attention.py

@@ -137,18 +137,18 @@ def _set_attention_slice(self, slice_size):
         for block in self.transformer_blocks:
             block._set_attention_slice(slice_size)
 
-    def forward(self, x, context=None):
+    def forward(self, hidden_states, context=None):
         # note: if no context is given, cross-attention defaults to self-attention
-        b, c, h, w = x.shape
-        x_in = x
-        x = self.norm(x)
-        x = self.proj_in(x)
-        x = x.permute(0, 2, 3, 1).reshape(b, h * w, c)
+        batch, channel, height, weight = hidden_states.shape
+        residual = hidden_states
+        hidden_states = self.norm(hidden_states)
+        hidden_states = self.proj_in(hidden_states)
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, channel)
         for block in self.transformer_blocks:
-            x = block(x, context=context)
-        x = x.reshape(b, h, w, c).permute(0, 3, 1, 2)
-        x = self.proj_out(x)
-        return x + x_in
+            hidden_states = block(hidden_states, context=context)
+        hidden_states = hidden_states.reshape(batch, height, weight, channel).permute(0, 3, 1, 2)
+        hidden_states = self.proj_out(hidden_states)
+        return hidden_states + residual
 
 
 class BasicTransformerBlock(nn.Module):
@@ -192,12 +192,12 @@ def _set_attention_slice(self, slice_size):
         self.attn1._slice_size = slice_size
         self.attn2._slice_size = slice_size
 
-    def forward(self, x, context=None):
-        x = x.contiguous() if x.device.type == "mps" else x
-        x = self.attn1(self.norm1(x)) + x
-        x = self.attn2(self.norm2(x), context=context) + x
-        x = self.ff(self.norm3(x)) + x
-        return x
+    def forward(self, hidden_states, context=None):
+        hidden_states = hidden_states.contiguous() if hidden_states.device.type == "mps" else hidden_states
+        hidden_states = self.attn1(self.norm1(hidden_states)) + hidden_states
+        hidden_states = self.attn2(self.norm2(hidden_states), context=context) + hidden_states
+        hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
+        return hidden_states
 
 
 class CrossAttention(nn.Module):
@@ -247,22 +247,22 @@ def reshape_batch_dim_to_heads(self, tensor):
         tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
         return tensor
 
-    def forward(self, x, context=None, mask=None):
-        batch_size, sequence_length, dim = x.shape
+    def forward(self, hidden_states, context=None, mask=None):
+        batch_size, sequence_length, dim = hidden_states.shape
 
-        q = self.to_q(x)
-        context = context if context is not None else x
-        k = self.to_k(context)
-        v = self.to_v(context)
+        query = self.to_q(hidden_states)
+        context = context if context is not None else hidden_states
+        key = self.to_k(context)
+        value = self.to_v(context)
 
-        q = self.reshape_heads_to_batch_dim(q)
-        k = self.reshape_heads_to_batch_dim(k)
-        v = self.reshape_heads_to_batch_dim(v)
+        query = self.reshape_heads_to_batch_dim(query)
+        key = self.reshape_heads_to_batch_dim(key)
+        value = self.reshape_heads_to_batch_dim(value)
 
         # TODO(PVP) - mask is currently never used. Remember to re-implement when used
 
         # attention, what we cannot get enough of
-        hidden_states = self._attention(q, k, v, sequence_length, dim)
+        hidden_states = self._attention(query, key, value, sequence_length, dim)
 
         return self.to_out(hidden_states)
 
@@ -308,8 +308,8 @@ def __init__(
 
         self.net = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
 
-    def forward(self, x):
-        return self.net(x)
+    def forward(self, hidden_states):
+        return self.net(hidden_states)
 
 
 # feedforward
@@ -326,6 +326,6 @@ def __init__(self, dim_in: int, dim_out: int):
         super().__init__()
         self.proj = nn.Linear(dim_in, dim_out * 2)
 
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
+    def forward(self, hidden_states):
+        hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1)
+        return hidden_states * F.gelu(gate)