Update

Removed Llama3 loader as it is slow, takes a lot of RAM\VRAM and no one uses. Added [gokaygokay/Lamini-Prompt-Enchance-Long](https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long) instead.

app.py

@@ -10,7 +10,7 @@ from apscheduler.schedulers.background import BackgroundScheduler
 import json
 from modules.classifyTags import classify_tags,process_tags
 from modules.florence2 import process_image,single_task_list,update_task_dropdown
-from modules.llama_loader import llama_list,llama3reorganize
+from modules.reorganizer_model import reorganizer_list,reorganizer_class
 from modules.tag_enhancer import prompt_enhancer
 os.environ['PYTORCH_ENABLE_MPS_FALLBACK']='1'
 
@@ -147,7 +147,7 @@ class Predictor:
         character_thresh,
         character_mcut_enabled,
         characters_merge_enabled,
-        llama3_reorganize_model_repo,
+        reorganizer_model_repo,
         additional_tags_prepend,
         additional_tags_append,
         tag_results,
@@ -160,7 +160,7 @@ class Predictor:
             print(f"Predict load model: {model_repo}, gallery length: {gallery_len}")
 
             timer = Timer()  # Create a timer
-            progressRatio = 0.5 if llama3_reorganize_model_repo else 1
+            progressRatio = 0.5 if reorganizer_model_repo else 1
             progressTotal = gallery_len + 1
             current_progress = 0
 
@@ -181,12 +181,12 @@ class Predictor:
             character_res = None
             general_res = None
 
-            if llama3_reorganize_model_repo:
-                print(f"Llama3 reorganize load model {llama3_reorganize_model_repo}")
-                llama3_reorganize = llama3reorganize(llama3_reorganize_model_repo, loadModel=True)
+            if reorganizer_model_repo:
+                print(f"Reorganizer load model {reorganizer_model_repo}")
+                reorganizer = reorganizer_class(reorganizer_model_repo, loadModel=True)
                 current_progress += progressRatio/progressTotal;
-                progress(current_progress, desc="Initialize llama3 model finished")
-                timer.checkpoint(f"Initialize llama3 model")
+                progress(current_progress, desc="Initialize reoganizer model finished")
+                timer.checkpoint(f"Initialize reoganizer model")
                 
             timer.report()
 
@@ -290,17 +290,17 @@ class Predictor:
                     progress(current_progress, desc=f"image{idx:02d}, predict finished")
                     timer.checkpoint(f"image{idx:02d}, predict finished")
                     
-                    if llama3_reorganize_model_repo:
-                        print(f"Starting reorganize with llama3...")
-                        reorganize_strings = llama_loader.llama3_reorganize.reorganize(sorted_general_strings)
+                    if reorganizer_model_repo:
+                        print(f"Starting reorganizer...")
+                        reorganize_strings = reorganizer.reorganize(sorted_general_strings)
                         reorganize_strings = re.sub(r" *Title: *", "", reorganize_strings)
                         reorganize_strings = re.sub(r"\n+", ",", reorganize_strings)
                         reorganize_strings = re.sub(r",,+", ",", reorganize_strings)
-                        sorted_general_strings += "," + reorganize_strings
+                        sorted_general_strings += ",\n\n" + reorganize_strings
 
                         current_progress += progressRatio/progressTotal;
-                        progress(current_progress, desc=f"image{idx:02d}, llama3 reorganize finished")
-                        timer.checkpoint(f"image{idx:02d}, llama3 reorganize finished")
+                        progress(current_progress, desc=f"image{idx:02d}, reorganizer finished")
+                        timer.checkpoint(f"image{idx:02d}, reorganizer finished")
 
                     txt_file = self.create_file(sorted_general_strings, output_dir, image_name + ".txt")
                     txt_infos.append({"path":txt_file, "name": image_name + ".txt"})
@@ -331,9 +331,9 @@ class Predictor:
                         taggers_zip.write(info["path"], arcname=info["name"])
                 download.append(downloadZipPath)
             # End zip creation logic
-            if llama3_reorganize_model_repo:
-                llama_loader.llama3_reorganize.release_vram()
-                del llama3_reorganize
+            if reorganizer_model_repo:
+                reorganizer.release_vram()
+                del reorganizer
                  
             progress(1, desc=f"Predict completed")
             timer.report_all()  # Print all recorded times
@@ -460,11 +460,11 @@ with gr.Blocks(title=TITLE, css=css, theme="Werli/Multi-Tagger", fill_width=True
                         scale=1,
                     )
                 with gr.Row():
-                    llama3_reorganize_model_repo = gr.Dropdown(
-                        [None] + llama_list,
+                    reorganizer_model_repo = gr.Dropdown(
+                        [None] + reorganizer_list,
                         value=None,
-                        label="Llama3 Model",
-                        info="Use the Llama3 model to reorganize the article (Note: very slow)",
+                        label="Reorganizer Model",
+                        info="Use a model to create a description for you",
                     )
                 with gr.Row():
                     additional_tags_prepend = gr.Text(label="Prepend Additional tags (comma split)")
@@ -479,7 +479,7 @@ with gr.Blocks(title=TITLE, css=css, theme="Werli/Multi-Tagger", fill_width=True
                             character_thresh,
                             character_mcut_enabled,
                             characters_merge_enabled,
-                            llama3_reorganize_model_repo,
+                            reorganizer_model_repo,
                             additional_tags_prepend,
                             additional_tags_append,
                         ],
@@ -534,7 +534,7 @@ with gr.Blocks(title=TITLE, css=css, theme="Werli/Multi-Tagger", fill_width=True
                 character_thresh,
                 character_mcut_enabled,
                 characters_merge_enabled,
-                llama3_reorganize_model_repo,
+                reorganizer_model_repo,
                 additional_tags_prepend,
                 additional_tags_append,
                 tag_results,