Support attention masking in CLIP implementation.

comfy/clip_model.py

@@ -100,8 +100,12 @@ class CLIPTextModel_(torch.nn.Module):
 
     def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
         x = self.embeddings(input_tokens)
-        #TODO: attention_mask
-        x, i = self.encoder(x, intermediate_output=intermediate_output)
+        mask = None
+        if attention_mask is not None:
+            mask = 1.0 - attention_mask.to(x.dtype).unsqueeze(1).unsqueeze(1).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
+            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
+
+        x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output)
         x = self.final_layer_norm(x)
         if i is not None and final_layer_norm_intermediate:
             i = self.final_layer_norm(i)