Update app.py

app.py

@@ -36,9 +36,9 @@ from meta import DEFAULT_NEGATIVE_PROMPT, DEFAULT_FORMAT
 sdxl_pipe = load_model()
 
 models.load_model(
-    "KBlueLeaf/TITPOP-200M-dev",
+    "Amber-River/titpop",
     device="cuda",
-    subfolder="dan-cc-coyo_epoch2",
+    subfolder="500M-epoch3",
 )
 generate(max_new_tokens=4)
 DEFAULT_TAGS = """
@@ -184,6 +184,8 @@ if __name__ == "__main__":
             gr.Markdown(
                 """
 ## TITPOP Demo
+**The model for demo is 500M version with 4epoch training (25B token seen)**
+
 ### What is this
 TITPOP is a tool to extend, generate, refine the input prompt for T2I models.
 <br>It can work on both Danbooru tags and Natural Language. Which means you can use it on almost all the existed T2I models.
@@ -202,7 +204,7 @@ TITPOP is a tool to extend, generate, refine the input prompt for T2I models.
 ### Why inference code is private? When will it be open sourced?
 1. This model/tool is still under development, currently is early Alpha version.
 2. I'm doing some research and projects based on this.
-3. The model is released under CC-BY-NC-ND License currently. If you have interest, you can implement inference by yourself.
+3. The 200M model is released under CC-BY-NC-ND License currently. If you have interest, you can implement inference by yourself.
 4. Once the project/research are done, I will open source all these models/codes with Apache2 license.
 
 ### Notification
@@ -296,7 +298,7 @@ TITPOP is a tool to extend, generate, refine the input prompt for T2I models.
                 gen_img = gr.Button("Generate Image from Result", variant="primary", interactive=False)
                 with gr.Row():
                     with gr.Column():
-                        img1 = gr.Image(label="Original Propmt", interactive=False)
+                        img1 = gr.Image(label="Original Prompt", interactive=False)
                     with gr.Column():
                         img2 = gr.Image(label="Generated Prompt", interactive=False)
         def generate_wrapper(*args):