Upload app.py

app.py

@@ -7,8 +7,9 @@ import torch
 import logging
 import yt_dlp
 import json
+import copy
 import gradio as gr
-import spaces
+import urllib.parse
 import assets.themes.loadThemes as loadThemes
 from audio_separator.separator import Separator
 from assets.i18n.i18n import I18nAuto
@@ -20,16 +21,22 @@ i18n = I18nAuto()
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 config_file = os.path.join(now_dir, "assets", "config.json")
+models_file = os.path.join(now_dir, "assets", "models.json")
+default_settings_file = os.path.join(now_dir, "assets", "default_settings.json")
+custom_settings_file = os.path.join(now_dir, "assets", "custom_settings.json")
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 use_autocast = device == "cuda"
 
 if os.path.isdir("env"):
     if platform.system() == "Windows":
+        python_location = ".\\env\\python.exe"
         separator_location = ".\\env\\Scripts\\audio-separator.exe"
     elif platform.system() == "Linux":
+        python_location = "env/bin/python"
         separator_location = "env/bin/audio-separator"
 else:
+    python_location = None
     separator_location = "audio-separator"
 
 #=========================#
@@ -47,12 +54,16 @@ roformer_models = {
     'MelBand Roformer | Denoise-Debleed by Gabox' : 'mel_band_roformer_denoise_debleed_gabox.ckpt',
     'Mel-Roformer-Karaoke-Aufr33-Viperx': 'mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt',
     'MelBand Roformer | Karaoke by Gabox' : 'mel_band_roformer_karaoke_gabox.ckpt',
+    'MelBand Roformer | Karaoke by becruily' : 'mel_band_roformer_karaoke_becruily.ckpt',
     'MelBand Roformer | Vocals by Kimberley Jensen' : 'vocals_mel_band_roformer.ckpt',
     'MelBand Roformer Kim | FT by unwa' : 'mel_band_roformer_kim_ft_unwa.ckpt',
     'MelBand Roformer Kim | FT 2 by unwa' : 'mel_band_roformer_kim_ft2_unwa.ckpt',
     'MelBand Roformer Kim | FT 2 Bleedless by unwa' : 'mel_band_roformer_kim_ft2_bleedless_unwa.ckpt',
+    'MelBand Roformer Kim | FT 3 by unwa' : 'mel_band_roformer_kim_ft3_unwa.ckpt',
     'MelBand Roformer Kim | Inst V1 by Unwa' : 'melband_roformer_inst_v1.ckpt',
+    'MelBand Roformer Kim | Inst V1 Plus by Unwa' : 'melband_roformer_inst_v1_plus.ckpt',
     'MelBand Roformer Kim | Inst V1 (E) by Unwa' : 'melband_roformer_inst_v1e.ckpt',
+    'MelBand Roformer Kim | Inst V1 (E) Plus by Unwa' : 'melband_roformer_inst_v1e_plus.ckpt',
     'MelBand Roformer Kim | Inst V2 by Unwa' : 'melband_roformer_inst_v2.ckpt',
     'MelBand Roformer Kim | InstVoc Duality V1 by Unwa' : 'melband_roformer_instvoc_duality_v1.ckpt',
     'MelBand Roformer Kim | InstVoc Duality V2 by Unwa' : 'melband_roformer_instvox_duality_v2.ckpt',
@@ -70,6 +81,7 @@ roformer_models = {
     'MelBand Roformer | Instrumental 3 by Gabox' : 'mel_band_roformer_instrumental_3_gabox.ckpt',
     'MelBand Roformer | Instrumental Bleedless V1 by Gabox' : 'mel_band_roformer_instrumental_bleedless_v1_gabox.ckpt',
     'MelBand Roformer | Instrumental Bleedless V2 by Gabox' : 'mel_band_roformer_instrumental_bleedless_v2_gabox.ckpt',
+    'MelBand Roformer | Instrumental Bleedless V3 by Gabox' : 'mel_band_roformer_instrumental_bleedless_v3_gabox.ckpt',
     'MelBand Roformer | Instrumental Fullness V1 by Gabox' : 'mel_band_roformer_instrumental_fullness_v1_gabox.ckpt',
     'MelBand Roformer | Instrumental Fullness V2 by Gabox' : 'mel_band_roformer_instrumental_fullness_v2_gabox.ckpt',
     'MelBand Roformer | Instrumental Fullness V3 by Gabox' : 'mel_band_roformer_instrumental_fullness_v3_gabox.ckpt',
@@ -79,6 +91,10 @@ roformer_models = {
     'MelBand Roformer | INSTV6 by Gabox' : 'mel_band_roformer_instrumental_instv6_gabox.ckpt',
     'MelBand Roformer | INSTV6N by Gabox' : 'mel_band_roformer_instrumental_instv6n_gabox.ckpt',
     'MelBand Roformer | INSTV7 by Gabox' : 'mel_band_roformer_instrumental_instv7_gabox.ckpt',
+    'MelBand Roformer | INSTV7N by Gabox' : 'mel_band_roformer_instrumental_instv7n_gabox.ckpt',
+    'MelBand Roformer | INSTV8 by Gabox' : 'mel_band_roformer_instrumental_instv8_gabox.ckpt',
+    'MelBand Roformer | INSTV8N by Gabox' : 'mel_band_roformer_instrumental_instv8n_gabox.ckpt',
+    'MelBand Roformer | FVX by Gabox' : 'mel_band_roformer_instrumental_fvx_gabox.ckpt',
     'MelBand Roformer | De-Reverb by anvuew' : 'dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt',
     'MelBand Roformer | De-Reverb Less Aggressive by anvuew' : 'dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt',
     'MelBand Roformer | De-Reverb Mono by anvuew' : 'dereverb_mel_band_roformer_mono_anvuew.ckpt',
@@ -95,6 +111,7 @@ roformer_models = {
     'MelBand Roformer Kim | Big Beta 4 FT by unwa' : 'melband_roformer_big_beta4.ckpt',
     'MelBand Roformer Kim | Big Beta 5e FT by unwa' : 'melband_roformer_big_beta5e.ckpt',
     'MelBand Roformer | Big Beta 6 by unwa' : 'melband_roformer_big_beta6.ckpt',
+    'MelBand Roformer | Big Beta 6X by unwa' : 'melband_roformer_big_beta6x.ckpt',
     'BS Roformer | Chorus Male-Female by Sucial' : 'model_chorus_bs_roformer_ep_267_sdr_24.1275.ckpt',
     'BS Roformer | Male-Female by aufr33' : 'bs_roformer_male_female_by_aufr33_sdr_7.2889.ckpt',
     'MelBand Roformer | Aspiration by Sucial' : 'aspiration_mel_band_roformer_sdr_18.9845.ckpt',
@@ -266,30 +283,246 @@ def download_audio(url, output_dir="ytdl"):
 
 def leaderboard(list_filter):
     try:
+        if python_location:
+            command = [python_location, separator_location, "-l", f"--list_filter={list_filter}"]
+        else:
+            command = [separator_location, "-l", f"--list_filter={list_filter}"]
+
         result = subprocess.run(
-            [separator_location, "-l", f"--list_filter={list_filter}"],
+            command,
             capture_output=True,
             text=True,
         )
         if result.returncode != 0:
             return f"Error: {result.stderr}"
-        
+
         return "<table border='1'>" + "".join(
-            f"<tr style='{'font-weight: bold; font-size: 1.2em;' if i == 0 else ''}'>" + 
-            "".join(f"<td>{cell}</td>" for cell in re.split(r"\s{2,}", line.strip())) + 
-            "</tr>" 
+            f"<tr style='{'font-weight: bold; font-size: 1.2em;' if i == 0 else ''}'>" +
+            "".join(f"<td>{cell}</td>" for cell in re.split(r"\s{2,}", line.strip())) +
+            "</tr>"
             for i, line in enumerate(re.findall(r"^(?!-+)(.+)$", result.stdout.strip(), re.MULTILINE))
         ) + "</table>"
-    
+
     except Exception as e:
         return f"Error: {e}"
+    
+def get_language_settings():
+    with open(config_file, "r", encoding="utf8") as file:
+        config = json.load(file)
+
+    if config["lang"]["override"] == False:
+        return "Language automatically detected by system"
+    else:
+        return config["lang"]["selected_lang"]
+    
+def save_lang_settings(selected_language):
+    with open(config_file, "r", encoding="utf8") as file:
+        config = json.load(file)
+
+    if selected_language == "Language automatically detected by system":
+        config["lang"]["override"] = False
+    else:
+        config["lang"]["override"] = True
+        config["lang"]["selected_lang"] = selected_language
+
+    gr.Info(i18n("Language have been saved. Restart UVR5 UI to apply the changes"))
+
+    with open(config_file, "w", encoding="utf8") as file:
+        json.dump(config, file, indent=2)
+
+def alternative_model_downloader(method, key, output_dir="models", progress=gr.Progress()):
+    logs.clear()
+
+    with open(models_file, 'r', encoding='utf-8') as file:
+        model_data = json.load(file)
+    
+    if key not in model_data:
+        return f"Model '{key}' cannot be found."
+    
+    total_files = len(model_data[key])
+    progress(0, desc="Starting downloads...")
+
+    for i, url in enumerate(model_data[key]):
+        filename = os.path.basename(urllib.parse.urlparse(url).path)
+        full_name = os.path.join(output_dir, filename)
+
+        if os.path.exists(full_name):
+            logs.append(f"{filename} already exists.")
+            continue
+
+        progress((i + 0.1) / total_files, desc=f"Starting download of {filename} ({i+1}/{total_files})")
+
+        if method == 'wget':
+            cmd = ['wget', '--progress=bar:force', '-O', full_name, url]
+        elif method == 'curl':
+            cmd = ['curl', '-L', '-#', '-o', full_name, url]
+
+        try:
+            process = subprocess.Popen(
+                cmd, 
+                stdout=subprocess.PIPE, 
+                stderr=subprocess.PIPE,
+                universal_newlines=True,
+                bufsize=1
+            )
+            
+            for line in process.stderr:
+                if method == 'wget' and '%' in line:
+                    try:
+                        percent = int(line.strip().split('%')[0].split()[-1])
+                        file_progress = percent / 100.0
+                        total_progress = (i + file_progress) / total_files
+                        progress(total_progress, desc=f"File {i+1}/{total_files}: {filename} ({percent}%)")
+                    except (ValueError, IndexError):
+                        pass
+                elif method == 'curl' and '##' in line:
+                    try:
+                        hash_count = line.count('#')
+                        file_progress = min(hash_count / 50.0, 1.0)
+                        total_progress = (i + file_progress) / total_files
+                        percent = int(file_progress * 100)
+                        progress(total_progress, desc=f"File {i+1}/{total_files}: {filename} ({percent}%)")
+                    except Exception:
+                        pass
+            
+            process.wait()
+            if process.returncode != 0:
+                logs.append(f"Error downloading {filename}")
+            else:
+                logs.append(f"{filename} downloaded successfully!")
+                progress((i + 1) / total_files, desc=f"File {i+1}/{total_files} completed")
+        
+        except Exception as e:
+            logs.append(f"Error running download command: {str(e)}")
+    
+    progress(1.0, desc="Download process completed")
+    return "\n".join(logs)
+
+def read_main_config():
+    try:
+        with open(config_file, "r", encoding="utf8") as f:
+            return json.load(f)
+    except Exception as e:
+        print(f"Error reading main config file '{config_file}': {e}")
+        gr.Warning(i18n("Error reading main config file"))
+    
+def write_main_config(data):
+    try:
+        with open(config_file, "w", encoding="utf8") as f:
+            json.dump(data, f, indent=2)
+    except Exception as e:
+        print(f"Error writing to main config file '{config_file}': {e}")
+        gr.Warning(i18n("Error writing to main config file"))
+
+def load_settings_from_file(filepath):
+    try:
+        with open(filepath, 'r', encoding='utf-8') as f:
+            return json.load(f)
+    except Exception as e:
+        print(f"Error reading settings file '{filepath}': {e}")
+        gr.Warning(i18n("Error reading settings file"))
+        return None
+    
+def get_initial_settings():
+    main_config = read_main_config()
+    load_custom = main_config.get('load_custom_settings', False)
+
+    settings_to_load = {}
+    default_settings = load_settings_from_file(default_settings_file)
+
+    if load_custom:
+        print("Attempting to load custom settings...")
+        custom_settings = load_settings_from_file(custom_settings_file)
+        if custom_settings:
+            settings_to_load = copy.deepcopy(default_settings)
+            for section, params in custom_settings.items():
+                if section in settings_to_load:
+                    for key, value in params.items():
+                        settings_to_load[section][key] = value
+                else:
+                    settings_to_load[section] = params
+            print("Custom settings loaded successfully.")
+        else:
+            print("Custom settings file not found or invalid. Falling back to default settings.")
+            settings_to_load = default_settings
+    else:
+        print("Loading default settings...")
+        settings_to_load = default_settings
+
+    return settings_to_load
+
+initial_settings = get_initial_settings()
+
+def get_all_components(components_dict):
+    all_comps = []
+    for section in components_dict.values():
+        all_comps.extend(section.values())
+    return all_comps
+
+def save_current_settings(*values):
+    global components
+    try:
+        current_config_data = {}
+        value_index = 0
+        for section_name, section_comps in components.items():
+            current_config_data[section_name] = {}
+            for comp_name in section_comps.keys():
+                current_config_data[section_name][comp_name] = values[value_index]
+                value_index += 1
+
+        with open(custom_settings_file, 'w', encoding='utf-8') as f:
+            json.dump(current_config_data, f, indent=4)
+
+        main_config = read_main_config()
+        main_config['load_custom_settings'] = True
+        write_main_config(main_config)
+        gr.Info(i18n("Current settings saved successfully! They will be loaded next time"))
+    except Exception as e:
+        print(f"Error saving settings: {e}")
+        gr.Warning(i18n("Error saving settings"))
+
+def reset_settings_to_default():
+    global components, default_settings_file
+    updates = []
+    all_comps_flat = get_all_components(components)
+    try:
+        default_settings = load_settings_from_file(default_settings_file)
+        for section_name, section_comps in components.items():
+            for comp_name, comp_instance in section_comps.items():
+                default_value = default_settings.get(section_name, {}).get(comp_name, None)
+
+                if isinstance(comp_instance, gr.Dropdown) and hasattr(comp_instance, 'choices') and default_value is not None:
+                    if default_value not in comp_instance.choices:
+                        print(f"Warning: Default value '{default_value}' for '{comp_name}' not in choices {comp_instance.choices}. Setting to None.")
+                        default_value = None
+
+                updates.append(gr.update(value=default_value))
+
+        main_config = read_main_config()
+        main_config['load_custom_settings'] = False
+        write_main_config(main_config)
+
+        gr.Info(i18n("Settings reset to default. Default settings will be loaded next time"))
+        return updates
+
+    except Exception as e:
+        print(f"Error resetting settings: {e}")
+        gr.Warning(i18n("Error resetting settings"))
+        return [gr.update() for _ in all_comps_flat]
+
+components = {
+    "Roformer": {}, "MDX23C": {}, "MDX-NET": {}, "VR Arch": {}, "Demucs": {}
+}
 
 @track_presence("Performing BS/Mel Roformer Separation")
 @spaces.GPU(duration=60)
 def roformer_separator(audio, model_key, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress(track_tqdm=True)):
-    base_name = os.path.splitext(os.path.basename(audio))[0]
     roformer_model = roformer_models[model_key]
+    model_path = os.path.join(models_dir, roformer_model)
     try:
+        if not os.path.exists(model_path):
+            gr.Info(f"This is the first time the {model_key} model is being used. The separation will take a little longer because the model needs to be downloaded.")
+        
         separator = Separator(
             log_level=logging.WARNING,
             model_file_dir=models_dir,
@@ -326,8 +559,11 @@ def roformer_separator(audio, model_key, out_format, segment_size, override_seg_
 @track_presence("Performing MDXC Separationn")
 @spaces.GPU(duration=60)
 def mdxc_separator(audio, model, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress(track_tqdm=True)):
-    base_name = os.path.splitext(os.path.basename(audio))[0]
+    model_path = os.path.join(models_dir, model)
     try:
+        if not os.path.exists(model_path):
+            gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")
+
         separator = Separator(
             log_level=logging.WARNING,
             model_file_dir=models_dir,
@@ -364,8 +600,11 @@ def mdxc_separator(audio, model, out_format, segment_size, override_seg_size, ov
 @track_presence("Performing MDX-NET Separation")
 @spaces.GPU(duration=60)
 def mdxnet_separator(audio, model, out_format, hop_length, segment_size, denoise, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress(track_tqdm=True)):
-    base_name = os.path.splitext(os.path.basename(audio))[0]
+    model_path = os.path.join(models_dir, model)
     try:
+        if not os.path.exists(model_path):
+            gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")
+
         separator = Separator(
             log_level=logging.WARNING,
             model_file_dir=models_dir,
@@ -403,8 +642,11 @@ def mdxnet_separator(audio, model, out_format, hop_length, segment_size, denoise
 @track_presence("Performing VR Arch Separation")
 @spaces.GPU(duration=60)
 def vrarch_separator(audio, model, out_format, window_size, aggression, tta, post_process, post_process_threshold, high_end_process, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress(track_tqdm=True)):
-    base_name = os.path.splitext(os.path.basename(audio))[0]
+    model_path = os.path.join(models_dir, model)
     try:
+        if not os.path.exists(model_path):
+            gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")
+
         separator = Separator(
             log_level=logging.WARNING,
             model_file_dir=models_dir,
@@ -444,8 +686,11 @@ def vrarch_separator(audio, model, out_format, window_size, aggression, tta, pos
 @track_presence("Performing Demucs Separation")
 @spaces.GPU(duration=60)
 def demucs_separator(audio, model, out_format, shifts, segment_size, segments_enabled, overlap, batch_size, norm_thresh, amp_thresh, progress=gr.Progress(track_tqdm=True)):
-    base_name = os.path.splitext(os.path.basename(audio))[0]
+    model_path = os.path.join(models_dir, model)
     try:
+        if not os.path.exists(model_path):
+            gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")
+
         separator = Separator(
             log_level=logging.WARNING,
             model_file_dir=models_dir,
@@ -487,10 +732,14 @@ def update_stems(model):
 
 @track_presence("Performing BS/Mel Roformer Batch Separation")
 @spaces.GPU(duration=60)
-def roformer_batch(path_input, path_output, model_key, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem):
+def roformer_batch(path_input, path_output, model_key, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress()):
     found_files.clear()
     logs.clear()
     roformer_model = roformer_models[model_key]
+    model_path = os.path.join(models_dir, roformer_model)
+
+    if not os.path.exists(model_path):
+        gr.Info(f"This is the first time the {model_key} model is being used. The separation will take a little longer because the model needs to be downloaded.")
 
     for audio_files in os.listdir(path_input):
         if audio_files.endswith(extensions):
@@ -499,14 +748,15 @@ def roformer_batch(path_input, path_output, model_key, out_format, segment_size,
 
     if total_files == 0:
         logs.append("No valid audio files.")
-        yield "\n".join(logs)
+        return "\n".join(logs)
     else:
         logs.append(f"{total_files} audio files found")
         found_files.sort()
+        progress(0, desc="Starting processing...")
 
-        for audio_files in found_files:
+        for i, audio_files in enumerate(found_files):
+            progress((i / total_files), desc=f"Processing file {i+1}/{total_files}")
             file_path = os.path.join(path_input, audio_files)
-            base_name = os.path.splitext(os.path.basename(file_path))[0]
             try:
                 separator = Separator(
                     log_level=logging.WARNING,
@@ -526,22 +776,26 @@ def roformer_batch(path_input, path_output, model_key, out_format, segment_size,
                 )
 
                 logs.append("Loading model...")
-                yield "\n".join(logs)
                 separator.load_model(model_filename=roformer_model)
 
                 logs.append(f"Separating file: {audio_files}")
-                yield "\n".join(logs)
                 separator.separate(file_path)
                 logs.append(f"File: {audio_files} separated!")
-                yield "\n".join(logs)
             except Exception as e:
-                raise RuntimeError(f"Roformer batch separation failed: {e}") from e
+                raise RuntimeError(f"BS/Mel Roformer batch separation failed: {e}") from e
+        
+        progress(1.0, desc="Processing complete")
+        return "\n".join(logs)
 
 @track_presence("Performing MDXC Batch Separation")
 @spaces.GPU(duration=60)
-def mdx23c_batch(path_input, path_output, model, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem):
+def mdx23c_batch(path_input, path_output, model, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress()):
     found_files.clear()
     logs.clear()
+    model_path = os.path.join(models_dir, model)
+
+    if not os.path.exists(model_path):
+        gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")
 
     for audio_files in os.listdir(path_input):
         if audio_files.endswith(extensions):
@@ -550,14 +804,15 @@ def mdx23c_batch(path_input, path_output, model, out_format, segment_size, overr
 
     if total_files == 0:
         logs.append("No valid audio files.")
-        yield "\n".join(logs)
+        return "\n".join(logs)
     else:
         logs.append(f"{total_files} audio files found")
         found_files.sort()
+        progress(0, desc="Starting processing...")
 
-        for audio_files in found_files:
+        for i, audio_files in enumerate(found_files):
+            progress((i / total_files), desc=f"Processing file {i+1}/{total_files}")
             file_path = os.path.join(path_input, audio_files)
-            base_name = os.path.splitext(os.path.basename(file_path))[0]
             try:
                 separator = Separator(
                     log_level=logging.WARNING,
@@ -577,22 +832,26 @@ def mdx23c_batch(path_input, path_output, model, out_format, segment_size, overr
                 )
 
                 logs.append("Loading model...")
-                yield "\n".join(logs)
                 separator.load_model(model_filename=model)
 
                 logs.append(f"Separating file: {audio_files}")
-                yield "\n".join(logs)
                 separator.separate(file_path)
                 logs.append(f"File: {audio_files} separated!")
-                yield "\n".join(logs)
             except Exception as e:
-                raise RuntimeError(f"Roformer batch separation failed: {e}") from e
+                raise RuntimeError(f"MDXC batch separation failed: {e}") from e
+        
+        progress(1.0, desc="Processing complete")
+        return "\n".join(logs)
 
 @track_presence("Performing MDX-NET Batch Separation")
 @spaces.GPU(duration=60)
-def mdxnet_batch(path_input, path_output, model, out_format, hop_length, segment_size, denoise, overlap, batch_size, norm_thresh, amp_thresh, single_stem):
+def mdxnet_batch(path_input, path_output, model, out_format, hop_length, segment_size, denoise, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress()):
     found_files.clear()
     logs.clear()
+    model_path = os.path.join(models_dir, model)
+
+    if not os.path.exists(model_path):
+        gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")
 
     for audio_files in os.listdir(path_input):
         if audio_files.endswith(extensions):
@@ -601,14 +860,15 @@ def mdxnet_batch(path_input, path_output, model, out_format, hop_length, segment
 
     if total_files == 0:
         logs.append("No valid audio files.")
-        yield "\n".join(logs)
+        return "\n".join(logs)
     else:
         logs.append(f"{total_files} audio files found")
         found_files.sort()
+        progress(0, desc="Starting processing...")
 
-        for audio_files in found_files:
+        for i, audio_files in enumerate(found_files):
+            progress((i / total_files), desc=f"Processing file {i+1}/{total_files}")
             file_path = os.path.join(path_input, audio_files)
-            base_name = os.path.splitext(os.path.basename(file_path))[0]
             try:
                 separator = Separator(
                     log_level=logging.WARNING,
@@ -629,22 +889,26 @@ def mdxnet_batch(path_input, path_output, model, out_format, hop_length, segment
                 )
 
                 logs.append("Loading model...")
-                yield "\n".join(logs)
                 separator.load_model(model_filename=model)
 
                 logs.append(f"Separating file: {audio_files}")
-                yield "\n".join(logs)
                 separator.separate(file_path)
                 logs.append(f"File: {audio_files} separated!")
-                yield "\n".join(logs)
             except Exception as e:
-                raise RuntimeError(f"Roformer batch separation failed: {e}") from e
+                raise RuntimeError(f"MDX-NET batch separation failed: {e}") from e
+            
+        progress(1.0, desc="Processing complete")
+        return "\n".join(logs)
 
 @track_presence("Performing VR Arch Batch Separation")
 @spaces.GPU(duration=60)
-def vrarch_batch(path_input, path_output, model, out_format, window_size, aggression, tta, post_process, post_process_threshold, high_end_process, batch_size, norm_thresh, amp_thresh, single_stem):
+def vrarch_batch(path_input, path_output, model, out_format, window_size, aggression, tta, post_process, post_process_threshold, high_end_process, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress()):
     found_files.clear()
     logs.clear()
+    model_path = os.path.join(models_dir, model)
+
+    if not os.path.exists(model_path):
+        gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")
 
     for audio_files in os.listdir(path_input):
         if audio_files.endswith(extensions):
@@ -653,14 +917,15 @@ def vrarch_batch(path_input, path_output, model, out_format, window_size, aggres
 
     if total_files == 0:
         logs.append("No valid audio files.")
-        yield "\n".join(logs)
+        return "\n".join(logs)
     else:
         logs.append(f"{total_files} audio files found")
         found_files.sort()
+        progress(0, desc="Starting processing...")
 
-        for audio_files in found_files:
+        for i, audio_files in enumerate(found_files):
+            progress((i / total_files), desc=f"Processing file {i+1}/{total_files}")
             file_path = os.path.join(path_input, audio_files)
-            base_name = os.path.splitext(os.path.basename(file_path))[0]
             try:
                 separator = Separator(
                     log_level=logging.WARNING,
@@ -683,22 +948,26 @@ def vrarch_batch(path_input, path_output, model, out_format, window_size, aggres
                 )
 
                 logs.append("Loading model...")
-                yield "\n".join(logs)
                 separator.load_model(model_filename=model)
 
                 logs.append(f"Separating file: {audio_files}")
-                yield "\n".join(logs)
                 separator.separate(file_path)
                 logs.append(f"File: {audio_files} separated!")
-                yield "\n".join(logs)
             except Exception as e:
-                raise RuntimeError(f"Roformer batch separation failed: {e}") from e
+                raise RuntimeError(f"VR Arch batch separation failed: {e}") from e
+            
+        progress(1.0, desc="Processing complete")
+        return "\n".join(logs)
 
 @track_presence("Performing Demucs Batch Separation")
 @spaces.GPU(duration=60)
-def demucs_batch(path_input, path_output, model, out_format, shifts, segment_size, segments_enabled, overlap, batch_size, norm_thresh, amp_thresh):
+def demucs_batch(path_input, path_output, model, out_format, shifts, segment_size, segments_enabled, overlap, batch_size, norm_thresh, amp_thresh, progress=gr.Progress()):
     found_files.clear()
     logs.clear()
+    model_path = os.path.join(models_dir, model)
+
+    if not os.path.exists(model_path):
+        gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")
 
     for audio_files in os.listdir(path_input):
         if audio_files.endswith(extensions):
@@ -707,12 +976,14 @@ def demucs_batch(path_input, path_output, model, out_format, shifts, segment_siz
 
     if total_files == 0:
         logs.append("No valid audio files.")
-        yield "\n".join(logs)
+        return "\n".join(logs)
     else:
         logs.append(f"{total_files} audio files found")
         found_files.sort()
+        progress(0, desc="Starting processing...")
 
-        for audio_files in found_files:
+        for i, audio_files in enumerate(found_files):
+            progress((i / total_files), desc=f"Processing file {i+1}/{total_files}")
             file_path = os.path.join(path_input, audio_files)
             try:
                 separator = Separator(
@@ -733,34 +1004,35 @@ def demucs_batch(path_input, path_output, model, out_format, shifts, segment_siz
                 )
 
                 logs.append("Loading model...")
-                yield "\n".join(logs)
                 separator.load_model(model_filename=model)
 
                 logs.append(f"Separating file: {audio_files}")
-                yield "\n".join(logs)
                 separator.separate(file_path)
                 logs.append(f"File: {audio_files} separated!")
-                yield "\n".join(logs)
             except Exception as e:
-                raise RuntimeError(f"Roformer batch separation failed: {e}") from e
+                raise RuntimeError(f"Demucs batch separation failed: {e}") from e
+            
+        progress(1.0, desc="Processing complete")
+        return "\n".join(logs)
             
 with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 UVR5 UI 🎵") as app:
     gr.Markdown("<h1> 🎵 UVR5 UI 🎵 </h1>")
     gr.Markdown(i18n("If you liked this HF Space you can give me a ❤️"))
     gr.Markdown(i18n("Try UVR5 UI using Colab [here](https://colab.research.google.com/github/Eddycrack864/UVR5-UI/blob/main/UVR_UI.ipynb)"))
+    all_configurable_inputs = []
     with gr.Tabs():
         with gr.TabItem("BS/Mel Roformer"):
             with gr.Row():
                 roformer_model = gr.Dropdown(
                     label = i18n("Select the model"),
                     choices = list(roformer_models.keys()),
-                    value = lambda : None,
+                    value = initial_settings.get("Roformer", {}).get("model", None),
                     interactive = True
                 )
                 roformer_output_format = gr.Dropdown(
                     label = i18n("Select the output format"),
                     choices = output_format,
-                    value = lambda : None,
+                    value = initial_settings.get("Roformer", {}).get("output_format", None),
                     interactive = True
                 )
             with gr.Accordion(i18n("Advanced settings"), open = False):
@@ -772,13 +1044,13 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 32,
                             maximum = 4000,
                             step = 32,
-                            value = 256,
+                            value = initial_settings.get("Roformer", {}).get("segment_size", 256),
                             interactive = True
                         )
                         roformer_override_segment_size = gr.Checkbox(
                             label = i18n("Override segment size"),
                             info = i18n("Override model default segment size instead of using the model default value"),
-                            value = False,
+                            value = initial_settings.get("Roformer", {}).get("override_segment_size", False),
                             interactive = True
                         )
                     with gr.Row():
@@ -788,7 +1060,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 2,
                             maximum = 10,
                             step = 1,
-                            value = 8,
+                            value = initial_settings.get("Roformer", {}).get("overlap", 8),
                             interactive = True
                         )
                         roformer_batch_size = gr.Slider(
@@ -797,7 +1069,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 1,
                             maximum = 16,
                             step = 1,
-                            value = 1,
+                            value = initial_settings.get("Roformer", {}).get("batch_size", 1),
                             interactive = True
                         )
                     with gr.Row():
@@ -807,7 +1079,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 0.1,
                             maximum = 1,
                             step = 0.1,
-                            value = 0.9,
+                            value = initial_settings.get("Roformer", {}).get("normalization_threshold", 0.9),
                             interactive = True
                         )
                         roformer_amplification_threshold = gr.Slider(
@@ -816,15 +1088,30 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 0.1,
                             maximum = 1,
                             step = 0.1,
-                            value = 0.7,
+                            value = initial_settings.get("Roformer", {}).get("amplification_threshold", 0.7),
                             interactive = True
                         )
                     with gr.Row():
                         roformer_single_stem = gr.Textbox(
                             label = i18n("Output only single stem"),
                             placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
+                            value = initial_settings.get("Roformer", {}).get("single_stem", ""),
                             interactive = True
                         )
+
+                    components["Roformer"] = {
+                        "model": roformer_model,
+                        "output_format": roformer_output_format,
+                        "segment_size": roformer_segment_size,
+                        "override_segment_size": roformer_override_segment_size,
+                        "overlap": roformer_overlap,
+                        "batch_size": roformer_batch_size,
+                        "normalization_threshold": roformer_normalization_threshold,
+                        "amplification_threshold": roformer_amplification_threshold,
+                        "single_stem": roformer_single_stem
+                    }
+                    all_configurable_inputs.extend(components["Roformer"].values())
+
             with gr.Row():
                 roformer_audio = gr.Audio(
                     label = i18n("Input audio"),
@@ -893,13 +1180,13 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                 mdx23c_model = gr.Dropdown(
                     label = i18n("Select the model"),
                     choices = mdx23c_models,
-                    value = lambda : None,
+                    value = initial_settings.get("MDX23C", {}).get("model", None),
                     interactive = True
                 )
                 mdx23c_output_format = gr.Dropdown(
                     label = i18n("Select the output format"),
                     choices = output_format,
-                    value = lambda : None,
+                    value = initial_settings.get("MDX23C", {}).get("output_format", None),
                     interactive = True
                 )
             with gr.Accordion(i18n("Advanced settings"), open = False):
@@ -911,13 +1198,13 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             step = 32,
                             label = i18n("Segment size"),
                             info = i18n("Larger consumes more resources, but may give better results"),
-                            value = 256,
+                            value = initial_settings.get("MDX23C", {}).get("segment_size", 256),
                             interactive = True
                         )
                         mdx23c_override_segment_size = gr.Checkbox(
                             label = i18n("Override segment size"),
                             info = i18n("Override model default segment size instead of using the model default value"),
-                            value = False,
+                            value = initial_settings.get("MDX23C", {}).get("override_segment_size", False),
                             interactive = True
                         )
                     with gr.Row():
@@ -927,7 +1214,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             step = 1,
                             label = i18n("Overlap"),
                             info = i18n("Amount of overlap between prediction windows"),
-                            value = 8,
+                            value = initial_settings.get("MDX23C", {}).get("overlap", 8),
                             interactive = True
                         )
                         mdx23c_batch_size = gr.Slider(
@@ -936,7 +1223,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 1,
                             maximum = 16,
                             step = 1,
-                            value = 1,
+                            value = initial_settings.get("MDX23C", {}).get("batch_size", 1),
                             interactive = True
                         )
                     with gr.Row():
@@ -946,7 +1233,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 0.1,
                             maximum = 1,
                             step = 0.1,
-                            value = 0.9,
+                            value = initial_settings.get("MDX23C", {}).get("normalization_threshold", 0.9),
                             interactive = True
                         )
                         mdx23c_amplification_threshold = gr.Slider(
@@ -955,15 +1242,30 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 0.1,
                             maximum = 1,
                             step = 0.1,
-                            value = 0.7,
+                            value = initial_settings.get("MDX23C", {}).get("amplification_threshold", 0.7),
                             interactive = True
                         )
                     with gr.Row():
                         mdx23c_single_stem = gr.Textbox(
                             label = i18n("Output only single stem"),
                             placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
+                            value = initial_settings.get("MDX23C", {}).get("single_stem", ""),
                             interactive = True
                         )
+
+                    components["MDX23C"] = {
+                        "model": mdx23c_model,
+                        "output_format": mdx23c_output_format,
+                        "segment_size": mdx23c_segment_size,
+                        "override_segment_size": mdx23c_override_segment_size,
+                        "overlap": mdx23c_overlap,
+                        "batch_size": mdx23c_batch_size,
+                        "normalization_threshold": mdx23c_normalization_threshold,
+                        "amplification_threshold": mdx23c_amplification_threshold,
+                        "single_stem": mdx23c_single_stem
+                    }
+                    all_configurable_inputs.extend(components["MDX23C"].values())
+
             with gr.Row():
                 mdx23c_audio = gr.Audio(
                     label = i18n("Input audio"),
@@ -1032,13 +1334,13 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                 mdxnet_model = gr.Dropdown(
                     label = i18n("Select the model"),
                     choices = mdxnet_models,
-                    value = lambda : None,
+                    value = initial_settings.get("MDX-NET", {}).get("model", None),
                     interactive = True
                 )
                 mdxnet_output_format = gr.Dropdown(
                     label = i18n("Select the output format"),
                     choices = output_format,
-                    value = lambda : None,
+                    value = initial_settings.get("MDX-NET", {}).get("output_format", None),
                     interactive = True
                 )
             with gr.Accordion(i18n("Advanced settings"), open = False):
@@ -1050,7 +1352,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 32,
                             maximum = 2048,
                             step = 32,
-                            value = 1024,
+                            value = initial_settings.get("MDX-NET", {}).get("hop_length", 1024),
                             interactive = True
                         )
                         mdxnet_segment_size = gr.Slider(
@@ -1059,13 +1361,13 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             step = 32,
                             label = i18n("Segment size"),
                             info = i18n("Larger consumes more resources, but may give better results"),
-                            value = 256,
+                            value = initial_settings.get("MDX-NET", {}).get("segment_size", 256),
                             interactive = True
                         )
                         mdxnet_denoise = gr.Checkbox(
                             label = i18n("Denoise"),
                             info = i18n("Enable denoising during separation"),
-                            value = True,
+                            value = initial_settings.get("MDX-NET", {}).get("denoise", True),
                             interactive = True
                         )
                     with gr.Row():
@@ -1075,7 +1377,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 0.001,
                             maximum = 0.999,
                             step = 0.001,
-                            value = 0.25,
+                            value = initial_settings.get("MDX-NET", {}).get("overlap", 0.25),
                             interactive = True
                         )
                         mdxnet_batch_size = gr.Slider(
@@ -1084,7 +1386,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 1,
                             maximum = 16,
                             step = 1,
-                            value = 1,
+                            value = initial_settings.get("MDX-NET", {}).get("batch_size", 1),
                             interactive = True
                         )
                     with gr.Row():
@@ -1094,7 +1396,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 0.1,
                             maximum = 1,
                             step = 0.1,
-                            value = 0.9,
+                            value = initial_settings.get("MDX-NET", {}).get("normalization_threshold", 0.9),
                             interactive = True
                         )
                         mdxnet_amplification_threshold = gr.Slider(
@@ -1103,15 +1405,31 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 0.1,
                             maximum = 1,
                             step = 0.1,
-                            value = 0.7,
+                            value = initial_settings.get("MDX-NET", {}).get("amplification_threshold", 0.7),
                             interactive = True
                         )
                     with gr.Row():
                         mdxnet_single_stem = gr.Textbox(
                             label = i18n("Output only single stem"),
                             placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
+                            value = initial_settings.get("MDX-NET", {}).get("single_stem", ""),
                             interactive = True
                         )
+
+                    components["MDX-NET"] = {
+                        "model": mdxnet_model,
+                        "output_format": mdxnet_output_format,
+                        "hop_length": mdxnet_hop_length,
+                        "segment_size": mdxnet_segment_size,
+                        "denoise": mdxnet_denoise,
+                        "overlap": mdxnet_overlap,
+                        "batch_size": mdxnet_batch_size,
+                        "normalization_threshold": mdxnet_normalization_threshold,
+                        "amplification_threshold": mdxnet_amplification_threshold,
+                        "single_stem": mdxnet_single_stem
+                    }
+                    all_configurable_inputs.extend(components["MDX-NET"].values())
+
             with gr.Row():
                 mdxnet_audio = gr.Audio(
                     label = i18n("Input audio"),
@@ -1180,13 +1498,13 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                 vrarch_model = gr.Dropdown(
                     label = i18n("Select the model"),
                     choices = vrarch_models,
-                    value = lambda : None,
+                    value = initial_settings.get("VR Arch", {}).get("model", None),
                     interactive = True
                 )
                 vrarch_output_format = gr.Dropdown(
                     label = i18n("Select the output format"),
                     choices = output_format,
-                    value = lambda : None,
+                    value = initial_settings.get("VR Arch", {}).get("output_format", None),
                     interactive = True
                 )
             with gr.Accordion(i18n("Advanced settings"), open = False):
@@ -1198,7 +1516,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum=320,
                             maximum=1024,
                             step=32,
-                            value = 512,
+                            value = initial_settings.get("VR Arch", {}).get("window_size", 512),
                             interactive = True
                         )
                         vrarch_agression = gr.Slider(
@@ -1207,13 +1525,13 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             step = 1,
                             label = i18n("Agression"),
                             info = i18n("Intensity of primary stem extraction"),
-                            value = 5,
+                            value = initial_settings.get("VR Arch", {}).get("aggression", 5),
                             interactive = True
                         )
                         vrarch_tta = gr.Checkbox(
                             label = i18n("TTA"),
                             info = i18n("Enable Test-Time-Augmentation; slow but improves quality"),
-                            value = True,
+                            value = initial_settings.get("VR Arch", {}).get("tta", True),
                             visible = True,
                             interactive = True
                         )
@@ -1221,7 +1539,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                         vrarch_post_process = gr.Checkbox(
                             label = i18n("Post process"),
                             info = i18n("Identify leftover artifacts within vocal output; may improve separation for some songs"),
-                            value = False,
+                            value = initial_settings.get("VR Arch", {}).get("post_process", False),
                             visible = True,
                             interactive = True
                         )
@@ -1231,14 +1549,14 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 0.1,
                             maximum = 0.3,
                             step = 0.1,
-                            value = 0.2,
+                            value = initial_settings.get("VR Arch", {}).get("post_process_threshold", 0.2),
                             interactive = True
                         )
                     with gr.Row():
                         vrarch_high_end_process = gr.Checkbox(
                             label = i18n("High end process"),
                             info = i18n("Mirror the missing frequency range of the output"),
-                            value = False,
+                            value = initial_settings.get("VR Arch", {}).get("high_end_process", False),
                             visible = True,
                             interactive = True,
                         )
@@ -1248,7 +1566,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 1,
                             maximum = 16,
                             step = 1,
-                            value = 1,
+                            value = initial_settings.get("VR Arch", {}).get("batch_size", 1),
                             interactive = True
                         )
                     with gr.Row():
@@ -1258,7 +1576,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 0.1,
                             maximum = 1,
                             step = 0.1,
-                            value = 0.9,
+                            value = initial_settings.get("VR Arch", {}).get("normalization_threshold", 0.9),
                             interactive = True
                         )
                         vrarch_amplification_threshold = gr.Slider(
@@ -1267,15 +1585,33 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 0.1,
                             maximum = 1,
                             step = 0.1,
-                            value = 0.7,
+                            value = initial_settings.get("VR Arch", {}).get("amplification_threshold", 0.7),
                             interactive = True
                         )
                     with gr.Row():
                         vrarch_single_stem = gr.Textbox(
                             label = i18n("Output only single stem"),
                             placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
+                            value = initial_settings.get("VR Arch", {}).get("single_stem", ""),
                             interactive = True
                         )
+
+                    components["VR Arch"] = {
+                        "model": vrarch_model,
+                        "output_format": vrarch_output_format,
+                        "window_size": vrarch_window_size,
+                        "aggression": vrarch_agression,
+                        "tta": vrarch_tta,
+                        "post_process": vrarch_post_process,
+                        "post_process_threshold": vrarch_post_process_threshold,
+                        "high_end_process": vrarch_high_end_process,
+                        "batch_size": vrarch_batch_size,
+                        "normalization_threshold": vrarch_normalization_threshold,
+                        "amplification_threshold": vrarch_amplification_threshold,
+                        "single_stem": vrarch_single_stem
+                    }
+                    all_configurable_inputs.extend(components["VR Arch"].values())
+
             with gr.Row():
                 vrarch_audio = gr.Audio(
                     label = i18n("Input audio"),
@@ -1344,13 +1680,13 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                 demucs_model = gr.Dropdown(
                     label = i18n("Select the model"),
                     choices = demucs_models,
-                    value = lambda : None,
+                    value = initial_settings.get("Demucs", {}).get("model", None),
                     interactive = True
                 )
                 demucs_output_format = gr.Dropdown(
                     label = i18n("Select the output format"),
                     choices = output_format,
-                    value = lambda : None,
+                    value = initial_settings.get("Demucs", {}).get("output_format", None),
                     interactive = True
                 )
             with gr.Accordion(i18n("Advanced settings"), open = False):
@@ -1362,7 +1698,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 1,
                             maximum = 20,
                             step = 1,
-                            value = 2,
+                            value = initial_settings.get("Demucs", {}).get("shifts", 2),
                             interactive = True
                         )
                         demucs_segment_size = gr.Slider(
@@ -1371,13 +1707,13 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 1,
                             maximum = 100,
                             step = 1,
-                            value = 40,
+                            value = initial_settings.get("Demucs", {}).get("segment_size", 40),
                             interactive = True
                         )
                         demucs_segments_enabled = gr.Checkbox(
                             label = i18n("Segment-wise processing"),
                             info = i18n("Enable segment-wise processing"),
-                            value = True,
+                            value = initial_settings.get("Demucs", {}).get("segments_enabled", True),
                             interactive = True
                         )
                     with gr.Row():
@@ -1387,7 +1723,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum=0.001,
                             maximum=0.999,
                             step=0.001,
-                            value = 0.25,
+                            value = initial_settings.get("Demucs", {}).get("overlap", 0.25),
                             interactive = True
                         )
                         demucs_batch_size = gr.Slider(
@@ -1396,7 +1732,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 1,
                             maximum = 16,
                             step = 1,
-                            value = 1,
+                            value = initial_settings.get("Demucs", {}).get("batch_size", 1),
                             interactive = True
                         )
                     with gr.Row():
@@ -1406,7 +1742,7 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 0.1,
                             maximum = 1,
                             step = 0.1,
-                            value = 0.9,
+                            value = initial_settings.get("Demucs", {}).get("normalization_threshold", 0.9),
                             interactive = True
                         )
                         demucs_amplification_threshold = gr.Slider(
@@ -1415,9 +1751,23 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                             minimum = 0.1,
                             maximum = 1,
                             step = 0.1,
-                            value = 0.7,
+                            value = initial_settings.get("Demucs", {}).get("amplification_threshold", 0.7),
                             interactive = True
                         )
+
+                    components["Demucs"] = {
+                        "model": demucs_model,
+                        "output_format": demucs_output_format,
+                        "shifts": demucs_shifts,
+                        "segment_size": demucs_segment_size,
+                        "segments_enabled": demucs_segments_enabled,
+                        "overlap": demucs_overlap,
+                        "batch_size": demucs_batch_size,
+                        "normalization_threshold": demucs_normalization_threshold,
+                        "amplification_threshold": demucs_amplification_threshold
+                    }
+                    all_configurable_inputs.extend(components["Demucs"].values())
+
             with gr.Row():
                 demucs_audio = gr.Audio(
                     label = i18n("Input audio"),
@@ -1529,14 +1879,13 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                 info = i18n("Select the theme you want to use. (Requires restarting the App)"),
                 choices = loadThemes.get_list(),
                 value = loadThemes.read_json(),
-                visible = True
+                interactive = True
             )
-            dummy_output = gr.Textbox(visible = False)
 
             themes_select.change(
                 fn = loadThemes.select_theme,
                 inputs = themes_select,
-                outputs = [dummy_output]
+                outputs = []
             )
 
         with gr.TabItem(i18n("Credits")):
@@ -1548,13 +1897,14 @@ with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 U
                 * Thanks to [Mikus](https://github.com/cappuch) for the help with the code.
                 * Thanks to [Nick088](https://huggingface.co/Nick088) for the help to fix roformers.
                 * Thanks to [yt_dlp](https://github.com/yt-dlp/yt-dlp) devs.
-                * Separation by link source code and improvements by [Blane187](https://huggingface.co/Blane187).
+                * Separation by link source code and improvements by [NeoDev](https://github.com/TheNeodev).
                 * Thanks to [ArisDev](https://github.com/aris-py) for porting UVR5 UI to Kaggle and improvements.
                 * Thanks to [Bebra777228](https://github.com/Bebra777228)'s code for guiding me to improve my code.
                 * Thanks to Nick088, MrM0dZ, Ryouko-Yamanda65777, lucinamari, perariroswe, Enes, Léo and the_undead0 for helping translate UVR5 UI.
                 * Thanks to vadigr123 for creating the images for the Discord Rich Presence.
                 
-                You can donate to the original UVR5 project [here](https://www.buymeacoffee.com/uvr5)
+                You can donate to the original UVR5 project here:
+                [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/uvr5)
                 """
             )