Update app.py

app.py

@@ -43,21 +43,19 @@ bise_net = BiSeNet(n_classes = 19)
 bise_net.load_state_dict(torch.load(bise_net_cp_path, map_location="cpu")) # device fail
 bise_net.cuda()
 
-import sys
-sys.path.append("./models/LLaVA1.5/LLaVA/")
-from llava_infer.model.builder import load_pretrained_model
-from llava_infer.mm_utils import get_model_name_from_path
-from llava_infer.eval.run_llava import eval_model
-
-# Load Llava for prompt enhancement
-llva_model_path = "liuhaotian/llava-v1.5-7b"  
-llva_tokenizer, llva_model, llva_image_processor, llva_context_len = load_pretrained_model(
-    model_path=llva_model_path,
-    model_base=None,
-    model_name=get_model_name_from_path(llva_model_path),)    
-# llva_tokenizer.cuda()
-llva_model.to(device)
-# llva_image_processor.to(device)
+# import sys
+# sys.path.append("./models/LLaVA1.5/LLaVA/")
+# from llava_infer.model.builder import load_pretrained_model
+# from llava_infer.mm_utils import get_model_name_from_path
+# from llava_infer.eval.run_llava import eval_model
+
+### Load Llava for prompt enhancement
+# llva_model_path = "liuhaotian/llava-v1.5-7b"  
+# llva_tokenizer, llva_model, llva_image_processor, llva_context_len = load_pretrained_model(
+#     model_path=llva_model_path,
+#     model_base=None,
+#     model_name=get_model_name_from_path(llva_model_path),)    
+# llva_model.to(device)
 
 ### Load consistentID_model checkpoint
 pipe.load_ConsistentID_model(
@@ -124,7 +122,8 @@ def process(selected_template_images,costum_image,prompt
         prompt = "A man, with backpack, in a raining tropical forest, adventuring, holding a flashlight, in mist, seeking animals"
         prompt = "A person, in a sowm, wearing santa hat and a scarf, with a cottage behind"
     else:
-        prompt=Enhance_prompt(prompt,Image.new('RGB', (200, 200), color = 'white'))
+        # prompt=Enhance_prompt(prompt,Image.new('RGB', (200, 200), color = 'white'))
+        prompt = "cinematic photo," + prompt + ", 50mm photograph, half-length portrait, film, bokeh, professional, 4k, highly detailed"
         print(prompt)
         pass