simpler in-place expressions

Author: bm-synth

src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py

@@ -303,7 +303,7 @@ def forward(
         hidden_states, new_conv_cache["conv1"] = self.conv1(hidden_states, conv_cache=conv_cache.get("conv1"))
 
         if temb is not None:
-            hidden_states.add_(self.temb_proj(self.nonlinearity(temb))[:, :, None, None, None])
+            hidden_states += self.temb_proj(self.nonlinearity(temb))[:, :, None, None, None]
 
         if zq is not None:
             hidden_states, new_conv_cache["norm2"] = self.norm2(hidden_states, zq, conv_cache=conv_cache.get("norm2"))
@@ -322,7 +322,7 @@ def forward(
             else:
                 inputs = self.conv_shortcut(inputs)
 
-        hidden_states.add_(inputs)
+        hidden_states += inputs
         return hidden_states, new_conv_cache
 
 

src/diffusers/models/transformers/cogvideox_transformer_3d.py

@@ -138,8 +138,8 @@ def forward(
             **attention_kwargs,
         )
 
-        hidden_states.add_(gate_msa * attn_hidden_states)
-        encoder_hidden_states.add_(enc_gate_msa * attn_encoder_hidden_states)
+        hidden_states += gate_msa * attn_hidden_states
+        encoder_hidden_states += enc_gate_msa * attn_encoder_hidden_states
 
         # norm & modulate
         norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2(
@@ -150,8 +150,8 @@ def forward(
         norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
         ff_output = self.ff(norm_hidden_states)
 
-        hidden_states.add_(gate_ff * ff_output[:, text_seq_length:])
-        encoder_hidden_states.add_(enc_gate_ff * ff_output[:, :text_seq_length])
+        hidden_states += gate_ff * ff_output[:, text_seq_length:]
+        encoder_hidden_states += enc_gate_ff * ff_output[:, :text_seq_length]
 
         return hidden_states, encoder_hidden_states
 

src/diffusers/models/transformers/transformer_cogview3plus.py

@@ -106,8 +106,8 @@ def forward(
             hidden_states=norm_hidden_states, encoder_hidden_states=norm_encoder_hidden_states
         )
 
-        hidden_states.add_(gate_msa.unsqueeze(1) * attn_hidden_states)
-        encoder_hidden_states.add_(c_gate_msa.unsqueeze(1) * attn_encoder_hidden_states)
+        hidden_states += gate_msa.unsqueeze(1) * attn_hidden_states
+        encoder_hidden_states += c_gate_msa.unsqueeze(1) * attn_encoder_hidden_states
 
         # norm & modulate
         norm_hidden_states = self.norm2(hidden_states)
@@ -120,8 +120,8 @@ def forward(
         norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
         ff_output = self.ff(norm_hidden_states)
 
-        hidden_states.add_(gate_mlp.unsqueeze(1) * ff_output[:, text_seq_length:])
-        encoder_hidden_states.add_(c_gate_mlp.unsqueeze(1) * ff_output[:, :text_seq_length])
+        hidden_states += gate_mlp.unsqueeze(1) * ff_output[:, text_seq_length:]
+        encoder_hidden_states += c_gate_mlp.unsqueeze(1) * ff_output[:, :text_seq_length]
 
         if hidden_states.dtype == torch.float16:
             hidden_states = hidden_states.clip(-65504, 65504)