Update app.py

app.py

@@ -262,15 +262,12 @@ def generate_image(prompt, negative, face_emb, face_image, face_kps, image_stren
             text_embedding_name = lora["text_embedding_weights"]
             embedding_path = hf_hub_download(repo_id=lora["repo"], filename=text_embedding_name, repo_type="model")
             state_dict_embedding = load_file(embedding_path)
-            try:
-                pipe.unload_textual_inversion()
-                pipe.load_textual_inversion(state_dict_embedding["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
-                pipe.load_textual_inversion(state_dict_embedding["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
-            except:
-                pipe.unload_textual_inversion()
-                pipe.load_textual_inversion(state_dict_embedding["text_encoders_0"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
-                pipe.load_textual_inversion(state_dict_embedding["text_encoders_1"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
+            pipe.load_textual_inversion(state_dict_embedding["clip_l" if "clip_l" in state_dict_embedding else "text_encoders_0"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
+            pipe.load_textual_inversion(state_dict_embedding["clip_g" if "clip_g" in state_dict_embedding else "text_encoders_1"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
 
+    print("Processing prompt...")
+    st = time.time()
+    conditioning, pooled = compel(prompt)
     print("Processing prompt...")
     st = time.time()
     conditioning, pooled = compel(prompt)