Fix potential issue with non clip text embeddings.

comfy/clip_config_bigg.json

@@ -5,7 +5,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "gelu",
   "hidden_size": 1280,
   "initializer_factor": 1.0,

comfy/clip_model.py

@@ -87,6 +87,7 @@ class CLIPTextModel_(torch.nn.Module):
         heads = config_dict["num_attention_heads"]
         intermediate_size = config_dict["intermediate_size"]
         intermediate_activation = config_dict["hidden_act"]
+        self.eos_token_id = config_dict["eos_token_id"]
 
         super().__init__()
         self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device)
@@ -111,7 +112,7 @@ class CLIPTextModel_(torch.nn.Module):
         if i is not None and final_layer_norm_intermediate:
             i = self.final_layer_norm(i)
 
-        pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),]
+        pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),]
         return x, i, pooled_output
 
 class CLIPTextModel(torch.nn.Module):

comfy/sd1_clip.py

@@ -140,15 +140,13 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
     def set_up_textual_embeddings(self, tokens, current_embeds):
         out_tokens = []
-        next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1
+        next_new_token = token_dict_size = current_embeds.weight.shape[0]
         embedding_weights = []
 
         for x in tokens:
             tokens_temp = []
             for y in x:
                 if isinstance(y, numbers.Integral):
-                    if y == token_dict_size: #EOS token
-                        y = -1
                     tokens_temp += [int(y)]
                 else:
                     if y.shape[0] == current_embeds.weight.shape[1]:
@@ -164,11 +162,10 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         n = token_dict_size
         if len(embedding_weights) > 0:
             new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
-            new_embedding.weight[:token_dict_size] = current_embeds.weight[:-1]
+            new_embedding.weight[:token_dict_size] = current_embeds.weight
             for x in embedding_weights:
                 new_embedding.weight[n] = x
                 n += 1
-            new_embedding.weight[n] = current_embeds.weight[-1] #EOS embedding
             self.transformer.set_input_embeddings(new_embedding)
 
         processed_tokens = []

comfy/sd1_clip_config.json

@@ -6,7 +6,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "quick_gelu",
   "hidden_size": 768,
   "initializer_factor": 1.0,

comfy/text_encoders/sd2_clip_config.json

@@ -5,7 +5,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "gelu",
   "hidden_size": 1024,
   "initializer_factor": 1.0,