Upload 3 files

app.py

@@ -162,16 +162,34 @@ def process_string(input_string):
 
 ## BEGIN MOD
 from modutils import (
+    list_uniq,
     download_private_repo,
-    get_local_model_list,
     get_model_id_list,
-    escape_lora_basename,
-    list_uniq,
-    list_sub,
     get_tupled_embed_list,
-    update_lora_dict,
     get_lora_model_list,
     get_all_lora_tupled_list,
+    update_loras,
+    apply_lora_prompt,
+    set_prompt_loras,
+    get_my_lora,
+    upload_file_lora,
+    move_file_lora,
+    search_civitai_lora,
+    select_civitai_lora,
+    set_textual_inversion_prompt,
+    get_model_pipeline,
+    change_interface_mode,
+    get_t2i_model_info,
+    get_tupled_model_list,
+    save_gallery_images,
+    set_optimization,
+    set_sampler_settings,
+    set_quick_presets,
+    process_style_prompt,
+    optimization_list,
+    preset_styles,
+    preset_quality,
+    preset_sampler_setting,
 )
 from env import (
     hf_token,
@@ -237,35 +255,6 @@ embed_sdxl_list = get_model_list(directory_embeds_sdxl) + get_model_list(directo
 
 def get_embed_list(pipeline_name):
     return get_tupled_embed_list(embed_sdxl_list if pipeline_name == "StableDiffusionXLPipeline" else embed_list)
-
-def get_my_lora(link_url):
-    from pathlib import Path
-    before = get_local_model_list(directory_loras)
-    for url in [url.strip() for url in link_url.split(',')]:
-        if not Path(f"{directory_loras}/{url.split('/')[-1]}").exists():
-            download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
-    after = get_local_model_list(directory_loras)
-    new_files = list_sub(after, before)
-    for file in new_files:
-        path = Path(file)
-        if path.exists():
-            new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
-            path.resolve().rename(new_path.resolve())
-            update_lora_dict(str(new_path))
-    new_lora_model_list = get_lora_model_list()
-    new_lora_tupled_list = get_all_lora_tupled_list()
-    
-    return gr.update(
-        choices=new_lora_tupled_list, value=new_lora_model_list[-1]
-    ), gr.update(
-        choices=new_lora_tupled_list
-    ), gr.update(
-        choices=new_lora_tupled_list
-    ), gr.update(
-        choices=new_lora_tupled_list
-    ), gr.update(
-        choices=new_lora_tupled_list
-    )
 ## END MOD
 
 print('\033[33m🏁 Download and listing of valid models completed.\033[0m')
@@ -352,7 +341,6 @@ warnings.filterwarnings(action="ignore", category=FutureWarning, module="transfo
 from stablepy import logger
 logger.setLevel(logging.CRITICAL)
 
-
 from v2 import (
     V2UI,
     parse_upsampling_output,
@@ -376,29 +364,6 @@ from tagger import (
     translate_prompt,
     select_random_character,
 )
-from modutils import (
-    change_interface_mode,
-    get_t2i_model_info,
-    get_tupled_model_list,
-    save_gallery_images,
-    upload_file_lora,
-    move_file_lora,
-    set_lora_trigger,
-    set_lora_prompt,
-    apply_lora_prompt,
-    search_civitai_lora,
-    select_civitai_lora,
-    set_textual_inversion_prompt,
-    get_model_pipeline,
-    set_optimization,
-    set_sampler_settings,
-    process_style_prompt,
-    optimization_list,
-    preset_styles,
-    preset_quality,
-    preset_sampler_setting,
-    set_quick_presets,
-)
 def description_ui():
     gr.Markdown(
         """
@@ -584,9 +549,12 @@ class GuiSD:
         msg_lora = []
 
 ## BEGIN MOD
-        prompt, neg_prompt = insert_model_recom_prompt(prompt, neg_prompt, model_name)
         global lora_model_list
         lora_model_list = get_lora_model_list()
+        lora1, lora_scale1, lora2, lora_scale2, lora3, lora_scale3, lora4, lora_scale4, lora5, lora_scale5 = \
+                set_prompt_loras(prompt, syntax_weights, lora1, lora_scale1, lora2, lora_scale2, lora3,
+                                  lora_scale3, lora4, lora_scale4, lora5, lora_scale5)
+        prompt, neg_prompt = insert_model_recom_prompt(prompt, neg_prompt, model_name)
 ## END MOD
         
         if model_name in model_list:
@@ -1032,40 +1000,40 @@ with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", elem_id="main", css=CSS) as app:
                     hires_negative_prompt_gui = gr.Textbox(label="Hires Negative Prompt", placeholder="Main negative prompt will be use", lines=3)
 
                 with gr.Accordion("LoRA", open=False, visible=True) as menu_lora:
-                    lora1_gui = gr.Dropdown(label="Lora1", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
-                    lora_scale_1_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="Lora Scale 1")
+                    lora1_gui = gr.Dropdown(label="LoRA1", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
+                    lora_scale_1_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA Scale 1")
                     with gr.Row():
                         with gr.Group():
-                            lora1_trigger_gui = gr.Textbox(label="Lora1 prompts", info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
-                            lora1_copy_button = gr.Button(value="Copy example to prompt", visible=False)
+                            lora1_info_gui = gr.Textbox(label="LoRA1 prompts", info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
+                            lora1_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                         lora1_desc_gui = gr.Markdown(value="", visible=False)
-                    lora2_gui = gr.Dropdown(label="Lora2", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
-                    lora_scale_2_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="Lora Scale 2")
+                    lora2_gui = gr.Dropdown(label="LoRA2", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
+                    lora_scale_2_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA Scale 2")
                     with gr.Row():
                         with gr.Group():
-                            lora2_trigger_gui = gr.Textbox(label="Lora2 prompts", info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
-                            lora2_copy_button = gr.Button(value="Copy example to prompt", visible=False)
+                            lora2_info_gui = gr.Textbox(label="LoRA2 prompts", info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
+                            lora2_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                         lora2_desc_gui = gr.Markdown(value="", visible=False)
-                    lora3_gui = gr.Dropdown(label="Lora3", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
-                    lora_scale_3_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="Lora Scale 3")
+                    lora3_gui = gr.Dropdown(label="LoRA3", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
+                    lora_scale_3_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA Scale 3")
                     with gr.Row():
                         with gr.Group():
-                            lora3_trigger_gui = gr.Textbox(label="Lora3 prompts", info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
-                            lora3_copy_button = gr.Button(value="Copy example to prompt", visible=False)
+                            lora3_info_gui = gr.Textbox(label="LoRA3 prompts", info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
+                            lora3_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                         lora3_desc_gui = gr.Markdown(value="", visible=False)
-                    lora4_gui = gr.Dropdown(label="Lora4", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
-                    lora_scale_4_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="Lora Scale 4")
+                    lora4_gui = gr.Dropdown(label="LoRA4", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
+                    lora_scale_4_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA Scale 4")
                     with gr.Row():
                         with gr.Group():
-                            lora4_trigger_gui = gr.Textbox(label="Lora4 prompts", info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
-                            lora4_copy_button = gr.Button(value="Copy example to prompt", visible=False)
+                            lora4_info_gui = gr.Textbox(label="LoRA4 prompts", info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
+                            lora4_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                         lora4_desc_gui = gr.Markdown(value="", visible=False)
-                    lora5_gui = gr.Dropdown(label="Lora5", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
-                    lora_scale_5_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="Lora Scale 5")
+                    lora5_gui = gr.Dropdown(label="LoRA5", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
+                    lora_scale_5_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA Scale 5")
                     with gr.Row():
                         with gr.Group():
-                            lora5_trigger_gui = gr.Textbox(label="Lora5 prompts", info="Example of prompt", value="None", show_copy_button=True, interactive=False, visible=False)
-                            lora5_copy_button = gr.Button(value="Copy example to prompt", visible=False)
+                            lora5_info_gui = gr.Textbox(label="LoRA5 prompts", info="Example of prompt", value="None", show_copy_button=True, interactive=False, visible=False)
+                            lora5_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                         lora5_desc_gui = gr.Markdown(value="", visible=False)
                     with gr.Accordion("From URL", open=True, visible=True):
                         with gr.Row():
@@ -1590,31 +1558,26 @@ with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", elem_id="main", css=CSS) as app:
         sampler_selector_gui.change(set_sampler_settings, [sampler_selector_gui], [sampler_gui, steps_gui, cfg_gui, clip_skip_gui, img_width_gui, img_height_gui, optimization_gui], queue=False)
         optimization_gui.change(set_optimization, [optimization_gui, steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora5_gui, lora_scale_5_gui], [steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora5_gui, lora_scale_5_gui], queue=False)
 
-        lora1_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_gui, lora1_gui, lora_scale_1_gui, lora2_gui, lora_scale_2_gui, lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui], [prompt_gui], queue=False)\
-        .success(set_lora_trigger, [lora1_gui], [lora1_trigger_gui, lora1_copy_button, lora1_desc_gui, lora1_gui], scroll_to_output=True, queue=False)
-        lora2_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_gui, lora1_gui, lora_scale_1_gui, lora2_gui, lora_scale_2_gui, lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui], [prompt_gui], queue=False)\
-        .success(set_lora_trigger, [lora2_gui], [lora2_trigger_gui, lora2_copy_button, lora2_desc_gui, lora2_gui], scroll_to_output=True, queue=False)
-        lora3_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_gui, lora1_gui, lora_scale_1_gui, lora2_gui, lora_scale_2_gui, lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui], [prompt_gui], queue=False)\
-        .success(set_lora_trigger, [lora3_gui], [lora3_trigger_gui, lora3_copy_button, lora3_desc_gui, lora3_gui], scroll_to_output=True, queue=False)
-        lora4_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_gui, lora1_gui, lora_scale_1_gui, lora2_gui, lora_scale_2_gui, lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui], [prompt_gui], queue=False)\
-        .success(set_lora_trigger, [lora4_gui], [lora4_trigger_gui, lora4_copy_button, lora4_desc_gui, lora4_gui], scroll_to_output=True, queue=False)
-        lora5_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_gui, lora1_gui, lora_scale_1_gui, lora2_gui, lora_scale_2_gui, lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui], [prompt_gui], queue=False)\
-        .success(set_lora_trigger, [lora5_gui], [lora5_trigger_gui, lora5_copy_button, lora5_desc_gui, lora5_gui], scroll_to_output=True, queue=False)
         gr.on(
-            triggers=[lora_scale_1_gui.change, lora_scale_2_gui.change, lora_scale_3_gui.change,
-                       lora_scale_4_gui.change, lora_scale_5_gui.change, prompt_syntax_gui.change],
-            fn=set_lora_prompt,
+            triggers=[lora1_gui.change, lora_scale_1_gui.change, lora2_gui.change, lora_scale_2_gui.change,
+                       lora3_gui.change, lora_scale_3_gui.change, lora4_gui.change, lora_scale_4_gui.change,
+                       lora5_gui.change, lora_scale_5_gui.change, prompt_syntax_gui.change],
+            fn=update_loras,
             inputs=[prompt_gui, prompt_syntax_gui, lora1_gui, lora_scale_1_gui, lora2_gui, lora_scale_2_gui,
                      lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui],
-            outputs=[prompt_gui],
-            trigger_mode="once",
+            outputs=[prompt_gui, lora1_gui, lora_scale_1_gui, lora1_info_gui, lora1_copy_gui, lora1_desc_gui,
+                    lora2_gui, lora_scale_2_gui, lora2_info_gui, lora2_copy_gui, lora2_desc_gui,
+                    lora3_gui, lora_scale_3_gui, lora3_info_gui, lora3_copy_gui, lora3_desc_gui, 
+                    lora4_gui, lora_scale_4_gui, lora4_info_gui, lora4_copy_gui, lora4_desc_gui,
+                    lora5_gui, lora_scale_5_gui, lora5_info_gui, lora5_copy_gui, lora5_desc_gui],
             queue=False,
+            trigger_mode="once",
         )
-        lora1_copy_button.click(apply_lora_prompt, [prompt_gui, lora1_trigger_gui], [prompt_gui], queue=False)
-        lora2_copy_button.click(apply_lora_prompt, [prompt_gui, lora2_trigger_gui], [prompt_gui], queue=False)
-        lora3_copy_button.click(apply_lora_prompt, [prompt_gui, lora3_trigger_gui], [prompt_gui], queue=False)
-        lora4_copy_button.click(apply_lora_prompt, [prompt_gui, lora4_trigger_gui], [prompt_gui], queue=False)
-        lora5_copy_button.click(apply_lora_prompt, [prompt_gui, lora5_trigger_gui], [prompt_gui], queue=False)
+        lora1_copy_gui.click(apply_lora_prompt, [prompt_gui, lora1_info_gui], [prompt_gui], queue=False)
+        lora2_copy_gui.click(apply_lora_prompt, [prompt_gui, lora2_info_gui], [prompt_gui], queue=False)
+        lora3_copy_gui.click(apply_lora_prompt, [prompt_gui, lora3_info_gui], [prompt_gui], queue=False)
+        lora4_copy_gui.click(apply_lora_prompt, [prompt_gui, lora4_info_gui], [prompt_gui], queue=False)
+        lora5_copy_gui.click(apply_lora_prompt, [prompt_gui, lora5_info_gui], [prompt_gui], queue=False)
         gr.on(
             triggers=[search_civitai_button_lora.click, search_civitai_query_lora.submit],
             fn=search_civitai_lora,

modutils.py

@@ -296,37 +296,6 @@ def get_private_lora_model_lists():
 private_lora_model_list = get_private_lora_model_lists()
 
 
-def set_lora_prompt(prompt_gui, prompt_syntax_gui, lora1_gui, lora_scale_1_gui, lora2_gui, lora_scale_2_gui,\
-                     lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui):
-    import os
-    if not "Classic" in str(prompt_syntax_gui): return prompt_gui
-    loras = []
-    if lora1_gui and lora1_gui != "None":
-        basename = os.path.splitext(os.path.basename(lora1_gui))[0]
-        loras.append(f"<lora:{basename}:{lora_scale_1_gui:.2f}>")
-    if lora2_gui and lora2_gui != "None":
-        basename = os.path.splitext(os.path.basename(lora2_gui))[0]
-        loras.append(f"<lora:{basename}:{lora_scale_2_gui:.2f}>")
-    if lora3_gui and lora3_gui != "None":
-        basename = os.path.splitext(os.path.basename(lora3_gui))[0]
-        loras.append(f"<lora:{basename}:{lora_scale_3_gui:.2f}>")
-    if lora4_gui and lora4_gui != "None":
-        basename = os.path.splitext(os.path.basename(lora4_gui))[0]
-        loras.append(f"<lora:{basename}:{lora_scale_4_gui:.2f}>")
-    if lora5_gui and lora5_gui != "None":
-        basename = os.path.splitext(os.path.basename(lora5_gui))[0]
-        loras.append(f"<lora:{basename}:{lora_scale_5_gui:.2f}>")
-    tags = prompt_gui.split(",") if prompt_gui else []
-    prompts = []
-    for tag in tags:
-        tag = str(tag).strip()
-        if tag and not "<lora" in tag:
-            prompts.append(tag)
-    empty = [""]
-    prompt = ", ".join(prompts + loras + empty)
-    return gr.update(value=prompt)
-
-
 def get_civitai_info(path):
     global civitai_not_exists_list
     import requests
@@ -511,8 +480,9 @@ def get_valid_lora_wt(prompt: str, lora_path: str, lora_wt: float):
     return wt
 
 
-def set_prompt_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
+def set_prompt_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
     import re
+    if not "Classic" in str(prompt_syntax):  return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
     lora1 = get_valid_lora_name(lora1)
     lora2 = get_valid_lora_name(lora2)
     lora3 = get_valid_lora_name(lora3)
@@ -530,7 +500,6 @@ def set_prompt_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt,
     on4, label4, tag4, md4 = get_lora_info(lora4)
     on5, label5, tag5, md5 = get_lora_info(lora5)
     lora_paths = [lora1, lora2, lora3, lora4, lora5]
-
     prompts = prompt.split(",") if prompt else []
     for p in prompts:
         p = str(p).strip()
@@ -570,7 +539,6 @@ def set_prompt_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt,
                 lora_paths = [lora1, lora2, lora3, lora4, lora5]
                 lora5_wt = safe_float(wt)
                 on5 = True
-
     return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
 
 
@@ -614,7 +582,7 @@ def normalize_prompt_list(tags: list[str]):
             prompts.append(tag)
     return prompts
 
-'''
+
 def apply_lora_prompt(prompt: str, lora_info: str):
     if lora_info == "None": return gr.update(value=prompt)
     tags = prompt.split(",") if prompt else []
@@ -627,9 +595,9 @@ def apply_lora_prompt(prompt: str, lora_info: str):
     empty = [""]
     prompt = ", ".join(list_uniq(prompts + lora_prompts) + empty)
     return gr.update(value=prompt)
-'''
 
-def update_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
+
+def update_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
     import re
     on1, label1, tag1, md1 = get_lora_info(lora1)
     on2, label2, tag2, md2 = get_lora_info(lora2)
@@ -638,59 +606,60 @@ def update_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora
     on5, label5, tag5, md5 = get_lora_info(lora5)
     lora_paths = [lora1, lora2, lora3, lora4, lora5]
 
-    prompts = prompt.split(",") if prompt else []
-    output_prompts = []
-    for p in prompts:
-        p = str(p).strip()
-        if "<lora" in p:
-            result = re.findall(r'<lora:(.+?):(.+?)>', p)
-            if not result: continue
-            key = result[0][0]
-            wt = result[0][1]
-            path = to_lora_path(key)
-            if not key in loras_dict.keys() or not path: continue
-            if path in lora_paths:
-                output_prompts.append(f"<lora:{to_lora_key(path)}:{safe_float(wt):.2f}>")
-            elif not on1:
-                lora1 = path
-                lora_paths = [lora1, lora2, lora3, lora4, lora5]
-                lora1_wt = safe_float(wt)
-                on1, label1, tag1, md1 = get_lora_info(lora1)
-                output_prompts.append(f"<lora:{to_lora_key(lora1)}:{lora1_wt:.2f}>")
-            elif not on2:
-                lora2 = path
-                lora_paths = [lora1, lora2, lora3, lora4, lora5]
-                lora2_wt = safe_float(wt)
-                on2, label2, tag2, md2 = get_lora_info(lora2)
-                output_prompts.append(f"<lora:{to_lora_key(lora2)}:{lora2_wt:.2f}>")
-            elif not on3:
-                lora3 = path
-                lora_paths = [lora1, lora2, lora3, lora4, lora5]
-                lora3_wt = safe_float(wt)
-                on3, label3, tag3, md3 = get_lora_info(lora3)
-                output_prompts.append(f"<lora:{to_lora_key(lora3)}:{lora3_wt:.2f}>")
-            elif not on4:
-                lora4 = path
-                lora_paths = [lora1, lora2, lora3, lora4, lora5]
-                lora4_wt = safe_float(wt)
-                on4, label4, tag4, md4 = get_lora_info(lora4)
-                output_prompts.append(f"<lora:{to_lora_key(lora4)}:{lora4_wt:.2f}>")
-            elif not on5:
-                lora5 = path
-                lora_paths = [lora1, lora2, lora3, lora4, lora5]
-                lora5_wt = safe_float(wt)
-                on5, label5, tag5, md5 = get_lora_info(lora5)
-                output_prompts.append(f"<lora:{to_lora_key(lora5)}:{lora5_wt:.2f}>")
-        elif p:
-            output_prompts.append(p)
-    lora_prompts = []
-    if on1: lora_prompts.append(f"<lora:{to_lora_key(lora1)}:{lora1_wt:.2f}>")
-    if on2: lora_prompts.append(f"<lora:{to_lora_key(lora2)}:{lora2_wt:.2f}>")
-    if on3: lora_prompts.append(f"<lora:{to_lora_key(lora3)}:{lora3_wt:.2f}>")
-    if on4: lora_prompts.append(f"<lora:{to_lora_key(lora4)}:{lora4_wt:.2f}>")
-    if on5: lora_prompts.append(f"<lora:{to_lora_key(lora5)}:{lora5_wt:.2f}>")
-    output_prompt = ", ".join(list_uniq(output_prompts + lora_prompts + [""]))
-
+    output_prompt = prompt
+    if "Classic" in str(prompt_syntax):
+        prompts = prompt.split(",") if prompt else []
+        output_prompts = []
+        for p in prompts:
+            p = str(p).strip()
+            if "<lora" in p:
+                result = re.findall(r'<lora:(.+?):(.+?)>', p)
+                if not result: continue
+                key = result[0][0]
+                wt = result[0][1]
+                path = to_lora_path(key)
+                if not key in loras_dict.keys() or not path: continue
+                if path in lora_paths:
+                    output_prompts.append(f"<lora:{to_lora_key(path)}:{safe_float(wt):.2f}>")
+                elif not on1:
+                    lora1 = path
+                    lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                    lora1_wt = safe_float(wt)
+                    on1, label1, tag1, md1 = get_lora_info(lora1)
+                    output_prompts.append(f"<lora:{to_lora_key(lora1)}:{lora1_wt:.2f}>")
+                elif not on2:
+                    lora2 = path
+                    lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                    lora2_wt = safe_float(wt)
+                    on2, label2, tag2, md2 = get_lora_info(lora2)
+                    output_prompts.append(f"<lora:{to_lora_key(lora2)}:{lora2_wt:.2f}>")
+                elif not on3:
+                    lora3 = path
+                    lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                    lora3_wt = safe_float(wt)
+                    on3, label3, tag3, md3 = get_lora_info(lora3)
+                    output_prompts.append(f"<lora:{to_lora_key(lora3)}:{lora3_wt:.2f}>")
+                elif not on4:
+                    lora4 = path
+                    lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                    lora4_wt = safe_float(wt)
+                    on4, label4, tag4, md4 = get_lora_info(lora4)
+                    output_prompts.append(f"<lora:{to_lora_key(lora4)}:{lora4_wt:.2f}>")
+                elif not on5:
+                    lora5 = path
+                    lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                    lora5_wt = safe_float(wt)
+                    on5, label5, tag5, md5 = get_lora_info(lora5)
+                    output_prompts.append(f"<lora:{to_lora_key(lora5)}:{lora5_wt:.2f}>")
+            elif p:
+                output_prompts.append(p)
+        lora_prompts = []
+        if on1: lora_prompts.append(f"<lora:{to_lora_key(lora1)}:{lora1_wt:.2f}>")
+        if on2: lora_prompts.append(f"<lora:{to_lora_key(lora2)}:{lora2_wt:.2f}>")
+        if on3: lora_prompts.append(f"<lora:{to_lora_key(lora3)}:{lora3_wt:.2f}>")
+        if on4: lora_prompts.append(f"<lora:{to_lora_key(lora4)}:{lora4_wt:.2f}>")
+        if on5: lora_prompts.append(f"<lora:{to_lora_key(lora5)}:{lora5_wt:.2f}>")
+        output_prompt = ", ".join(list_uniq(output_prompts + lora_prompts + [""]))
     choices = get_all_lora_tupled_list()
 
     return gr.update(value=output_prompt), gr.update(value=lora1, choices=choices), gr.update(value=lora1_wt),\
@@ -705,61 +674,34 @@ def update_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora
      gr.update(value=tag5, label=label5, visible=on5), gr.update(visible=on5), gr.update(value=md5, visible=on5)
 
 
-def set_lora_trigger(lora_gui: str):
-    if not lora_gui or lora_gui == "None": return gr.update(value="", visible=False), gr.update(visible=False),\
-          gr.update(value="", visible=False), gr.update(value="")
-    path = Path(lora_gui)
-    new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
-    if not new_path.stem in loras_dict.keys() and not str(path) in set(get_lora_model_list()):
-        return gr.update(value="", visible=False), gr.update(visible=False),\
-              gr.update(value="", visible=False), gr.update(value="")
-    if not new_path.exists():
-        download_private_file_from_somewhere(str(path), True)
-    basename = new_path.stem
-    tag = ""
-    label = f'Trigger: {basename}  /  Prompt:'
-    value = "None"
-    md = "None"
-    flag = False
-    items = loras_dict.get(basename, None)
-    if items == None:
-        items = get_civitai_info(str(new_path))
-        if items != None:
-            loras_dict[basename] = items
-            flag = True
-    if items and items[2] != "":
-        tag = items[0]
-        label = f'Trigger: {basename}  /  Prompt:'
-        if items[1] == "Pony":
-            label = f'Trigger: {basename}  /  Prompt (for Pony🐴):'
-        if items[4]:
-            md = f'<img src="{items[4]}" alt="thumbnail" width="150" height="240"><br>[LoRA Model URL]({items[3]})'
-        elif items[3]:
-            md = f'[LoRA Model URL]({items[3]})'
-    if tag and flag:
-        new_lora_model_list = get_lora_model_list()
-        return gr.update(value=tag, label=label, visible=True), gr.update(visible=True),\
-              gr.update(value=md, visible=True), gr.update(value=str(new_path), choices=get_lora_tupled_list(new_lora_model_list))
-    elif tag:
-        return gr.update(value=tag, label=label, visible=True), gr.update(visible=True),\
-              gr.update(value=md, visible=True), gr.update(value=str(new_path))
-    else:
-        return gr.update(value=value, label=label, visible=True), gr.update(visible=True),\
-              gr.update(value=md, visible=True), gr.update(visible=True)
-
-
-def apply_lora_prompt(prompt_gui: str, lora_trigger_gui: str):
-    if lora_trigger_gui == "None": return gr.update(value=prompt_gui)
-    tags = prompt_gui.split(",") if prompt_gui else []
-    prompts = normalize_prompt_list(tags)
-
-    lora_tag = lora_trigger_gui.replace("/",",")
-    lora_tags = lora_tag.split(",") if str(lora_trigger_gui) != "None" else []
-    lora_prompts = normalize_prompt_list(lora_tags)
- 
-    empty = [""]
-    prompt = ", ".join(list_uniq(prompts + lora_prompts) + empty)
-    return gr.update(value=prompt)
+def get_my_lora(link_url):
+    from pathlib import Path
+    before = get_local_model_list(directory_loras)
+    for url in [url.strip() for url in link_url.split(',')]:
+        if not Path(f"{directory_loras}/{url.split('/')[-1]}").exists():
+            download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
+    after = get_local_model_list(directory_loras)
+    new_files = list_sub(after, before)
+    for file in new_files:
+        path = Path(file)
+        if path.exists():
+            new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
+            path.resolve().rename(new_path.resolve())
+            update_lora_dict(str(new_path))
+    new_lora_model_list = get_lora_model_list()
+    new_lora_tupled_list = get_all_lora_tupled_list()
+    
+    return gr.update(
+        choices=new_lora_tupled_list, value=new_lora_model_list[-1]
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    )
 
 
 def upload_file_lora(files, progress=gr.Progress(track_tqdm=True)):
@@ -793,16 +735,57 @@ def move_file_lora(filepaths):
     )
 
 
-def search_lora_on_civitai(query: str, allow_model: list[str]):
+def get_civitai_info(path):
+    global civitai_not_exists_list
+    global loras_url_to_path_dict
     import requests
+    from requests.adapters import HTTPAdapter
     from urllib3.util import Retry
+    default = ["", "", "", "", ""]
+    if path in set(civitai_not_exists_list): return default
+    if not Path(path).exists(): return None
+    user_agent = get_user_agent()
+    headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
+    base_url = 'https://civitai.com/api/v1/model-versions/by-hash/'
+    params = {}
+    session = requests.Session()
+    retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
+    session.mount("https://", HTTPAdapter(max_retries=retries))
+    import hashlib
+    with open(path, 'rb') as file:
+        file_data = file.read()
+    hash_sha256 = hashlib.sha256(file_data).hexdigest()
+    url = base_url + hash_sha256
+    try:
+        r = session.get(url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
+    except Exception as e:
+        return default
+    else:
+        if not r.ok: return None
+        json = r.json()
+        if not 'baseModel' in json:
+            civitai_not_exists_list.append(path)
+            return default
+        items = []
+        items.append(" / ".join(json['trainedWords']))                  # The words (prompts) used to trigger the model
+        items.append(json['baseModel'])                                 # Base model (SDXL1.0, Pony, ...)
+        items.append(json['model']['name'])                             # The name of the model version
+        items.append(f"https://civitai.com/models/{json['modelId']}")   # The repo url for the model
+        items.append(json['images'][0]['url'])                          # The url for a sample image
+        loras_url_to_path_dict[path] = json['downloadUrl']              # The download url to get the model file for this specific version
+        return items
+
+
+def search_lora_on_civitai(query: str, allow_model: list[str] = ["Pony", "SDXL 1.0"], limit: int = 100):
+    import requests
     from requests.adapters import HTTPAdapter
+    from urllib3.util import Retry
     if not query: return None
     user_agent = get_user_agent()
     headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
     base_url = 'https://civitai.com/api/v1/models'
     params = {'query': query, 'types': ['LORA'], 'sort': 'Highest Rated', 'period': 'AllTime',
-              'nsfw': 'true', 'supportsGeneration ': 'true'}
+              'nsfw': 'true', 'supportsGeneration ': 'true', 'limit': limit}
     session = requests.Session()
     retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
     session.mount("https://", HTTPAdapter(max_retries=retries))
@@ -810,26 +793,24 @@ def search_lora_on_civitai(query: str, allow_model: list[str]):
         r = session.get(base_url, params=params, headers=headers, stream=True, timeout=(3.0, 30))
     except Exception as e:
         return None
-    if not r.ok: return None
-    json = r.json()
-    if not 'items' in json: return None
-    items = []
-    for j in json['items']:
-        for model in j['modelVersions']:
-            item = {}
-            if not model['baseModel'] in set(allow_model): continue
-            item['name'] = j['name']
-            item['creator'] = j['creator']['username']
-            item['tags'] = j['tags']
-            item['model_name'] = model['name']
-            item['base_model'] = model['baseModel']
-            item['dl_url'] = model['downloadUrl']
-            item['md'] = f'<img src="{model["images"][0]["url"]}" alt="thumbnail" width="150" height="240"><br>[LoRA Model URL](https://civitai.com/models/{j["id"]})'
-            items.append(item)
-    return items
-
-
-civitai_lora_last_results = {}
+    else:
+        if not r.ok: return None
+        json = r.json()
+        if not 'items' in json: return None
+        items = []
+        for j in json['items']:
+            for model in j['modelVersions']:
+                item = {}
+                if not model['baseModel'] in set(allow_model): continue
+                item['name'] = j['name']
+                item['creator'] = j['creator']['username']
+                item['tags'] = j['tags']
+                item['model_name'] = model['name']
+                item['base_model'] = model['baseModel']
+                item['dl_url'] = model['downloadUrl']
+                item['md'] = f'<img src="{model["images"][0]["url"]}" alt="thumbnail" width="150" height="240"><br>[LoRA Model URL](https://civitai.com/models/{j["id"]})'
+                items.append(item)
+        return items
 
 
 def search_civitai_lora(query, base_model):
@@ -844,17 +825,19 @@ def search_civitai_lora(query, base_model):
         name = f"{item['name']} (for {base_model_name} / By: {item['creator']} / Tags: {', '.join(item['tags'])})"
         value = item['dl_url']
         choices.append((name, value))
-        civitai_lora_last_results[value] = item['md']
+        civitai_lora_last_results[value] = item
     if not choices: return gr.update(choices=[("", "")], value="", visible=False),\
           gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
-    md = civitai_lora_last_results.get(choices[0][1], "None")
+    result = civitai_lora_last_results.get(choices[0][1], "None")
+    md = result['md'] if result else ""
     return gr.update(choices=choices, value=choices[0][1], visible=True), gr.update(value=md, visible=True),\
           gr.update(visible=True), gr.update(visible=True)
 
 
 def select_civitai_lora(search_result):
     if not "http" in search_result: return gr.update(value=""), gr.update(value="None", visible=True)
-    md = civitai_lora_last_results.get(search_result, "None")
+    result = civitai_lora_last_results.get(search_result, "None")
+    md = result['md'] if result else ""
     return gr.update(value=search_result), gr.update(value=md, visible=True)