Support zeroing out text embeddings with the attention mask.

comfy/sd1_clip.py

@@ -68,7 +68,8 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     ]
     def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
                  freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
-                 special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, return_projected_pooled=True):  # clip-vit-base-patch32
+                 special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
+                 return_projected_pooled=True):  # clip-vit-base-patch32
         super().__init__()
         assert layer in self.LAYERS
 
@@ -90,6 +91,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
         self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
         self.enable_attention_masks = enable_attention_masks
+        self.zero_out_masked = zero_out_masked
 
         self.layer_norm_hidden_state = layer_norm_hidden_state
         self.return_projected_pooled = return_projected_pooled
@@ -179,9 +181,12 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         self.transformer.set_input_embeddings(backup_embeds)
 
         if self.layer == "last":
-            z = outputs[0]
+            z = outputs[0].float()
         else:
-            z = outputs[1]
+            z = outputs[1].float()
+
+        if self.zero_out_masked and attention_mask is not None:
+            z *= attention_mask.unsqueeze(-1).float()
 
         pooled_output = None
         if len(outputs) >= 3:
@@ -190,7 +195,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             elif outputs[2] is not None:
                 pooled_output = outputs[2].float()
 
-        return z.float(), pooled_output
+        return z, pooled_output
 
     def encode(self, tokens):
         return self(tokens)