Merge branch 'main' into Honkware-main

README.md

@@ -193,6 +193,7 @@ Optionally, you can use the following command-line flags:
 | `-h`, `--help`                             | Show this help message and exit. |
 | `--notebook`                               | Launch the web UI in notebook mode, where the output is written to the same text box as the input. |
 | `--chat`                                   | Launch the web UI in chat mode. |
+| `--multi-user`                             | Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental. |
 | `--character CHARACTER`                    | The name of the character to load in chat mode by default. |
 | `--model MODEL`                            | Name of the model to load by default. |
 | `--lora LORA [LORA ...]`                   | The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. |
@@ -268,6 +269,7 @@ Optionally, you can use the following command-line flags:
 |`--gpu-split`     | Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. `20,7,7` |
 |`--max_seq_len MAX_SEQ_LEN`           | Maximum sequence length. |
 |`--compress_pos_emb COMPRESS_POS_EMB` | Positional embeddings compression factor. Should typically be set to max_seq_len / 2048. |
+|`--alpha_value ALPHA_VALUE`           | Positional embeddings alpha factor for NTK RoPE scaling. Same as above. Use either this or compress_pos_emb, not both. `
 
 #### GPTQ-for-LLaMa
 

api-examples/api-example-chat-stream.py

@@ -44,6 +44,7 @@ async def run(user_input, history):
         'tfs': 1,
         'top_a': 0,
         'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
         'top_k': 40,
         'min_length': 0,
         'no_repeat_ngram_size': 0,

api-examples/api-example-chat.py

@@ -38,6 +38,7 @@ def run(user_input, history):
         'tfs': 1,
         'top_a': 0,
         'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
         'top_k': 40,
         'min_length': 0,
         'no_repeat_ngram_size': 0,

api-examples/api-example-stream.py

@@ -33,6 +33,7 @@ async def run(context):
         'tfs': 1,
         'top_a': 0,
         'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
         'top_k': 40,
         'min_length': 0,
         'no_repeat_ngram_size': 0,

api-examples/api-example.py

@@ -25,6 +25,7 @@ def run(prompt):
         'tfs': 1,
         'top_a': 0,
         'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
         'top_k': 40,
         'min_length': 0,
         'no_repeat_ngram_size': 0,

css/main.css

@@ -6,6 +6,12 @@
     padding-top: 2.5rem
 }
 
+.small-button {
+    max-width: 171px;
+    height: 39.594px;
+    align-self: end;
+}
+
 .refresh-button {
     max-width: 4.4em;
     min-width: 2.2em !important;
@@ -50,7 +56,7 @@ ol li p, ul li p {
     display: inline-block;
 }
 
-#main, #parameters, #chat-settings, #interface-mode, #lora, #training-tab, #model-tab {
+#main, #parameters, #chat-settings, #lora, #training-tab, #model-tab, #session-tab {
     border: 0;
 }
 
@@ -121,10 +127,6 @@ button {
     font-size: 14px !important;
 }
 
-.small-button {
-    max-width: 171px;
-}
-
 .file-saver {
   position: fixed !important;
   top: 50%;
@@ -149,3 +151,7 @@ button {
 .checkboxgroup-table div {
     display: grid !important;
 }
+
+.markdown ul ol {
+    font-size: 100% !important;
+}

docs/Extensions.md

@@ -38,11 +38,11 @@ script.py may define the special functions and variables below.
 | `def ui()` | Creates custom gradio elements when the UI is launched. | 
 | `def custom_css()` | Returns custom CSS as a string. It is applied whenever the web UI is loaded. |
 | `def custom_js()` | Same as above but for javascript. |
-| `def input_modifier(string)`  | Modifies the input string before it enters the model. In chat mode, it is applied to the user message. Otherwise, it is applied to the entire prompt. |
+| `def input_modifier(string, state)`  | Modifies the input string before it enters the model. In chat mode, it is applied to the user message. Otherwise, it is applied to the entire prompt. |
-| `def output_modifier(string)`  | Modifies the output string before it is presented in the UI. In chat mode, it is applied to the bot's reply. Otherwise, it is applied to the entire output. |
+| `def output_modifier(string, state)`  | Modifies the output string before it is presented in the UI. In chat mode, it is applied to the bot's reply. Otherwise, it is applied to the entire output. |
+| `def bot_prefix_modifier(string, state)`  | Applied in chat mode to the prefix for the bot's reply. |
 | `def state_modifier(state)`  | Modifies the dictionary containing the UI input parameters before it is used by the text generation functions. |
 | `def history_modifier(history)`  | Modifies the chat history before the text generation in chat mode begins. |
-| `def bot_prefix_modifier(string)`  | Applied in chat mode to the prefix for the bot's reply. |
 | `def custom_generate_reply(...)` | Overrides the main text generation function. |
 | `def custom_generate_chat_prompt(...)` | Overrides the prompt generator in chat mode. |
 | `def tokenizer_modifier(state, prompt, input_ids, input_embeds)` | Modifies the `input_ids`/`input_embeds` fed to the model. Should return `prompt`, `input_ids`, `input_embeds`. See the `multimodal` extension for an example. |

docs/Generation-parameters.md

@@ -23,6 +23,7 @@ ExLlama only uses the following parameters:
 * top_p
 * top_k
 * repetition_penalty
+* repetition_penalty_range
 * typical_p
 
 ### RWKV

download-model.py

@@ -18,12 +18,16 @@ from pathlib import Path
 
 import requests
 import tqdm
+from requests.adapters import HTTPAdapter
 from tqdm.contrib.concurrent import thread_map
 
 
 class ModelDownloader:
-    def __init__(self):
+    def __init__(self, max_retries):
         self.s = requests.Session()
+        if max_retries:
+            self.s.mount('https://cdn-lfs.huggingface.co', HTTPAdapter(max_retries=max_retries))
+            self.s.mount('https://huggingface.co', HTTPAdapter(max_retries=max_retries))
         if os.getenv('HF_USER') is not None and os.getenv('HF_PASS') is not None:
             self.s.auth = (os.getenv('HF_USER'), os.getenv('HF_PASS'))
 
@@ -212,6 +216,7 @@ if __name__ == '__main__':
     parser.add_argument('--output', type=str, default=None, help='The folder where the model should be saved.')
     parser.add_argument('--clean', action='store_true', help='Does not resume the previous download.')
     parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.')
+    parser.add_argument('--max-retries', type=int, default=5, help='Max retries count when get error in download time.')
     args = parser.parse_args()
 
     branch = args.branch
@@ -221,7 +226,7 @@ if __name__ == '__main__':
         print("Error: Please specify the model you'd like to download (e.g. 'python download-model.py facebook/opt-1.3b').")
         sys.exit()
 
-    downloader = ModelDownloader()
+    downloader = ModelDownloader(max_retries=args.max_retries)
     # Cleaning up the model/branch names
     try:
         model, branch = downloader.sanitize_model_and_branch_names(model, branch)

extensions/api/blocking_api.py

@@ -72,7 +72,6 @@ class Handler(BaseHTTPRequestHandler):
             self.end_headers()
 
             user_input = body['user_input']
-            history = body['history']
             regenerate = body.get('regenerate', False)
             _continue = body.get('_continue', False)
 
@@ -80,9 +79,9 @@ class Handler(BaseHTTPRequestHandler):
             generate_params['stream'] = False
 
             generator = generate_chat_reply(
-                user_input, history, generate_params, regenerate=regenerate, _continue=_continue, loading_message=False)
+                user_input, generate_params, regenerate=regenerate, _continue=_continue, loading_message=False)
 
-            answer = history
+            answer = generate_params['history']
             for a in generator:
                 answer = a
 

extensions/api/streaming_api.py

@@ -55,14 +55,13 @@ async def _handle_connection(websocket, path):
             body = json.loads(message)
 
             user_input = body['user_input']
-            history = body['history']
             generate_params = build_parameters(body, chat=True)
             generate_params['stream'] = True
             regenerate = body.get('regenerate', False)
             _continue = body.get('_continue', False)
 
             generator = generate_chat_reply(
-                user_input, history, generate_params, regenerate=regenerate, _continue=_continue, loading_message=False)
+                user_input, generate_params, regenerate=regenerate, _continue=_continue, loading_message=False)
 
             message_num = 0
             for a in generator:

extensions/api/util.py

@@ -21,6 +21,7 @@ def build_parameters(body, chat=False):
         'tfs': float(body.get('tfs', 1)),
         'top_a': float(body.get('top_a', 0)),
         'repetition_penalty': float(body.get('repetition_penalty', body.get('rep_pen', 1.1))),
+        'repetition_penalty_range': int(body.get('repetition_penalty_range', 0)),
         'encoder_repetition_penalty': float(body.get('encoder_repetition_penalty', 1.0)),
         'top_k': int(body.get('top_k', 0)),
         'min_length': int(body.get('min_length', 0)),
@@ -64,6 +65,7 @@ def build_parameters(body, chat=False):
             'context_instruct': context_instruct,
             'turn_template': turn_template,
             'chat-instruct_command': str(body.get('chat-instruct_command', shared.settings['chat-instruct_command'])),
+            'history': body.get('history', {'internal': [], 'visible': []})
         })
 
     return generate_params

extensions/elevenlabs_tts/script.py

@@ -3,7 +3,9 @@ from pathlib import Path
 
 import elevenlabs
 import gradio as gr
+
 from modules import chat, shared
+from modules.utils import gradio
 
 params = {
     'activate': True,
@@ -35,24 +37,24 @@ def refresh_voices_dd():
     return gr.Dropdown.update(value=all_voices[0], choices=all_voices)
 
 
-def remove_tts_from_history():
+def remove_tts_from_history(history):
-    for i, entry in enumerate(shared.history['internal']):
+    for i, entry in enumerate(history['internal']):
-        shared.history['visible'][i] = [shared.history['visible'][i][0], entry[1]]
+        history['visible'][i] = [history['visible'][i][0], entry[1]]
+
+    return history
 
 
-def toggle_text_in_history():
+def toggle_text_in_history(history):
-    for i, entry in enumerate(shared.history['visible']):
+    for i, entry in enumerate(history['visible']):
         visible_reply = entry[1]
         if visible_reply.startswith('<audio'):
             if params['show_text']:
-                reply = shared.history['internal'][i][1]
+                reply = history['internal'][i][1]
-                shared.history['visible'][i] = [
+                history['visible'][i] = [history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>\n\n{reply}"]
-                    shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>\n\n{reply}"
-                ]
             else:
-                shared.history['visible'][i] = [
+                history['visible'][i] = [history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>"]
-                    shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>"
+
-                ]
+    return history
 
 
 def remove_surrounded_chars(string):
@@ -150,25 +152,24 @@ def ui():
         convert_cancel = gr.Button('Cancel', visible=False)
         convert_confirm = gr.Button('Confirm (cannot be undone)', variant="stop", visible=False)
 
+    if shared.is_chat():
         # Convert history with confirmation
         convert_arr = [convert_confirm, convert, convert_cancel]
         convert.click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr)
         convert_confirm.click(
             lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr).then(
-        remove_tts_from_history, None, None).then(
+            remove_tts_from_history, gradio('history'), gradio('history')).then(
-        chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
+            chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
-        chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+            chat.redraw_html, shared.reload_inputs, gradio('display'))
 
         convert_cancel.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
 
         # Toggle message text in history
         show_text.change(
             lambda x: params.update({"show_text": x}), show_text, None).then(
-        toggle_text_in_history, None, None).then(
+            toggle_text_in_history, gradio('history'), gradio('history')).then(
-        chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
+            chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
-        chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+            chat.redraw_html, shared.reload_inputs, gradio('display'))
-
-    convert_cancel.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
 
     # Event functions to update the parameters in the backend
     activate.change(lambda x: params.update({'activate': x}), activate, None)

extensions/openai/script.py

@@ -29,6 +29,7 @@ default_req_params = {
     'top_p': 1.0,
     'top_k': 1,
     'repetition_penalty': 1.18,
+    'repetition_penalty_range': 0,
     'encoder_repetition_penalty': 1.0,
     'suffix': None,
     'stream': False,

extensions/sd_api_pictures/script.py

@@ -10,7 +10,7 @@ import requests
 import torch
 from PIL import Image
 
-import modules.shared as shared
+from modules import shared
 from modules.models import reload_model, unload_model
 from modules.ui import create_refresh_button
 
@@ -126,7 +126,7 @@ def input_modifier(string):
     return string
 
 # Get and save the Stable Diffusion-generated picture
-def get_SD_pictures(description):
+def get_SD_pictures(description, character):
 
     global params
 
@@ -160,7 +160,7 @@ def get_SD_pictures(description):
         if params['save_img']:
             img_data = base64.b64decode(img_str)
 
-            variadic = f'{date.today().strftime("%Y_%m_%d")}/{shared.character}_{int(time.time())}'
+            variadic = f'{date.today().strftime("%Y_%m_%d")}/{character}_{int(time.time())}'
             output_file = Path(f'extensions/sd_api_pictures/outputs/{variadic}.png')
             output_file.parent.mkdir(parents=True, exist_ok=True)
 
@@ -186,7 +186,7 @@ def get_SD_pictures(description):
 
 # TODO: how do I make the UI history ignore the resulting pictures (I don't want HTML to appear in history)
 # and replace it with 'text' for the purposes of logging?
-def output_modifier(string):
+def output_modifier(string, state):
     """
     This function is applied to the model outputs.
     """
@@ -213,7 +213,7 @@ def output_modifier(string):
     else:
         text = string
 
-    string = get_SD_pictures(string) + "\n" + text
+    string = get_SD_pictures(string, state['character_menu']) + "\n" + text
 
     return string
 

extensions/silero_tts/script.py

@@ -3,9 +3,10 @@ from pathlib import Path
 
 import gradio as gr
 import torch
-from modules import chat, shared
 
 from extensions.silero_tts import tts_preprocessor
+from modules import chat, shared
+from modules.utils import gradio
 
 torch._C._jit_set_profiling_mode(False)
 
@@ -56,20 +57,24 @@ def load_model():
     return model
 
 
-def remove_tts_from_history():
+def remove_tts_from_history(history):
-    for i, entry in enumerate(shared.history['internal']):
+    for i, entry in enumerate(history['internal']):
-        shared.history['visible'][i] = [shared.history['visible'][i][0], entry[1]]
+        history['visible'][i] = [history['visible'][i][0], entry[1]]
+
+    return history
 
 
-def toggle_text_in_history():
+def toggle_text_in_history(history):
-    for i, entry in enumerate(shared.history['visible']):
+    for i, entry in enumerate(history['visible']):
         visible_reply = entry[1]
         if visible_reply.startswith('<audio'):
             if params['show_text']:
-                reply = shared.history['internal'][i][1]
+                reply = history['internal'][i][1]
-                shared.history['visible'][i] = [shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>\n\n{reply}"]
+                history['visible'][i] = [history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>\n\n{reply}"]
             else:
-                shared.history['visible'][i] = [shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>"]
+                history['visible'][i] = [history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>"]
+
+    return history
 
 
 def state_modifier(state):
@@ -80,7 +85,7 @@ def state_modifier(state):
     return state
 
 
-def input_modifier(string):
+def input_modifier(string, state):
     if not params['activate']:
         return string
 
@@ -99,7 +104,7 @@ def history_modifier(history):
     return history
 
 
-def output_modifier(string):
+def output_modifier(string, state):
     global model, current_params, streaming_state
     for i in params:
         if params[i] != current_params[i]:
@@ -116,7 +121,7 @@ def output_modifier(string):
     if string == '':
         string = '*Empty reply, try regenerating*'
     else:
-        output_file = Path(f'extensions/silero_tts/outputs/{shared.character}_{int(time.time())}.wav')
+        output_file = Path(f'extensions/silero_tts/outputs/{state["character_menu"]}_{int(time.time())}.wav')
         prosody = '<prosody rate="{}" pitch="{}">'.format(params['voice_speed'], params['voice_pitch'])
         silero_input = f'<speak>{prosody}{xmlesc(string)}</prosody></speak>'
         model.save_wav(ssml_text=silero_input, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file))
@@ -155,23 +160,24 @@ def ui():
 
         gr.Markdown('[Click here for Silero audio samples](https://oobabooga.github.io/silero-samples/index.html)')
 
+    if shared.is_chat():
         # Convert history with confirmation
         convert_arr = [convert_confirm, convert, convert_cancel]
         convert.click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr)
         convert_confirm.click(
             lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr).then(
-        remove_tts_from_history, None, None).then(
+            remove_tts_from_history, gradio('history'), gradio('history')).then(
-        chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
+            chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
-        chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+            chat.redraw_html, shared.reload_inputs, gradio('display'))
 
         convert_cancel.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
 
         # Toggle message text in history
         show_text.change(
             lambda x: params.update({"show_text": x}), show_text, None).then(
-        toggle_text_in_history, None, None).then(
+            toggle_text_in_history, gradio('history'), gradio('history')).then(
-        chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
+            chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
-        chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+            chat.redraw_html, shared.reload_inputs, gradio('display'))
 
     # Event functions to update the parameters in the backend
     activate.change(lambda x: params.update({"activate": x}), activate, None)

extensions/superbooga/script.py

@@ -96,6 +96,8 @@ def apply_settings(chunk_count, chunk_count_initial, time_weight):
 def custom_generate_chat_prompt(user_input, state, **kwargs):
     global chat_collector
 
+    history = state['history']
+
     if state['mode'] == 'instruct':
         results = collector.get_sorted(user_input, n_results=params['chunk_count'])
         additional_context = '\nYour reply should be based on the context below:\n\n' + '\n'.join(results)
@@ -104,29 +106,29 @@ def custom_generate_chat_prompt(user_input, state, **kwargs):
 
         def make_single_exchange(id_):
             output = ''
-            output += f"{state['name1']}: {shared.history['internal'][id_][0]}\n"
+            output += f"{state['name1']}: {history['internal'][id_][0]}\n"
-            output += f"{state['name2']}: {shared.history['internal'][id_][1]}\n"
+            output += f"{state['name2']}: {history['internal'][id_][1]}\n"
             return output
 
-        if len(shared.history['internal']) > params['chunk_count'] and user_input != '':
+        if len(history['internal']) > params['chunk_count'] and user_input != '':
             chunks = []
-            hist_size = len(shared.history['internal'])
+            hist_size = len(history['internal'])
             for i in range(hist_size-1):
                 chunks.append(make_single_exchange(i))
 
             add_chunks_to_collector(chunks, chat_collector)
-            query = '\n'.join(shared.history['internal'][-1] + [user_input])
+            query = '\n'.join(history['internal'][-1] + [user_input])
             try:
                 best_ids = chat_collector.get_ids_sorted(query, n_results=params['chunk_count'], n_initial=params['chunk_count_initial'], time_weight=params['time_weight'])
                 additional_context = '\n'
                 for id_ in best_ids:
-                    if shared.history['internal'][id_][0] != '<|BEGIN-VISIBLE-CHAT|>':
+                    if history['internal'][id_][0] != '<|BEGIN-VISIBLE-CHAT|>':
                         additional_context += make_single_exchange(id_)
 
                 logger.warning(f'Adding the following new context:\n{additional_context}')
                 state['context'] = state['context'].strip() + '\n' + additional_context
                 kwargs['history'] = {
-                    'internal': [shared.history['internal'][i] for i in range(hist_size) if i not in best_ids],
+                    'internal': [history['internal'][i] for i in range(hist_size) if i not in best_ids],
                     'visible': ''
                 }
             except RuntimeError:

modules/LoRA.py

@@ -106,6 +106,8 @@ def add_lora_transformers(lora_names):
 
     # If any LoRA needs to be removed, start over
     if len(removed_set) > 0:
+        # shared.model may no longer be PeftModel
+        if hasattr(shared.model, 'disable_adapter'):  
             shared.model.disable_adapter()  
             shared.model = shared.model.base_model.model
 

modules/chat.py

@@ -1,12 +1,11 @@
 import base64
 import copy
 import functools
-import io
 import json
 import re
-from datetime import datetime
 from pathlib import Path
 
+import gradio as gr
 import yaml
 from PIL import Image
 
@@ -19,7 +18,12 @@ from modules.text_generation import (
     get_encoded_length,
     get_max_prompt_length
 )
-from modules.utils import delete_file, replace_all, save_file
+from modules.utils import (
+    delete_file,
+    get_available_characters,
+    replace_all,
+    save_file
+)
 
 
 def get_turn_substrings(state, instruct=False):
@@ -53,7 +57,7 @@ def generate_chat_prompt(user_input, state, **kwargs):
     impersonate = kwargs.get('impersonate', False)
     _continue = kwargs.get('_continue', False)
     also_return_rows = kwargs.get('also_return_rows', False)
-    history = kwargs.get('history', shared.history)['internal']
+    history = kwargs.get('history', state['history'])['internal']
     is_instruct = state['mode'] == 'instruct'
 
     # Find the maximum prompt size
@@ -75,10 +79,10 @@ def generate_chat_prompt(user_input, state, **kwargs):
         if impersonate:
             wrapper += substrings['user_turn_stripped'].rstrip(' ')
         elif _continue:
-            wrapper += apply_extensions("bot_prefix", substrings['bot_turn_stripped'])
+            wrapper += apply_extensions('bot_prefix', substrings['bot_turn_stripped'], state)
             wrapper += history[-1][1]
         else:
-            wrapper += apply_extensions("bot_prefix", substrings['bot_turn_stripped'].rstrip(' '))
+            wrapper += apply_extensions('bot_prefix', substrings['bot_turn_stripped'].rstrip(' '), state)
     else:
         wrapper = '<|prompt|>'
 
@@ -112,7 +116,7 @@ def generate_chat_prompt(user_input, state, **kwargs):
 
         # Add the character prefix
         if state['mode'] != 'chat-instruct':
-            rows.append(apply_extensions("bot_prefix", substrings['bot_turn_stripped'].rstrip(' ')))
+            rows.append(apply_extensions('bot_prefix', substrings['bot_turn_stripped'].rstrip(' '), state))
 
     while len(rows) > min_rows and get_encoded_length(wrapper.replace('<|prompt|>', ''.join(rows))) >= max_length:
         rows.pop(1)
@@ -152,7 +156,8 @@ def get_stopping_strings(state):
     return stopping_strings
 
 
-def chatbot_wrapper(text, history, state, regenerate=False, _continue=False, loading_message=True):
+def chatbot_wrapper(text, state, regenerate=False, _continue=False, loading_message=True):
+    history = state['history']
     output = copy.deepcopy(history)
     output = apply_extensions('history', output)
     state = apply_extensions('state', state)
@@ -173,11 +178,11 @@ def chatbot_wrapper(text, history, state, regenerate=False, _continue=False, loa
         if visible_text is None:
             visible_text = text
 
+        text = apply_extensions('input', text, state)
+
         # *Is typing...*
         if loading_message:
             yield {'visible': output['visible'] + [[visible_text, shared.processing_message]], 'internal': output['internal']}
-
-        text = apply_extensions('input', text)
     else:
         text, visible_text = output['internal'][-1][0], output['visible'][-1][0]
         if regenerate:
@@ -214,7 +219,7 @@ def chatbot_wrapper(text, history, state, regenerate=False, _continue=False, loa
             # We need this global variable to handle the Stop event,
             # otherwise gradio gets confused
             if shared.stop_everything:
-                output['visible'][-1][1] = apply_extensions("output", output['visible'][-1][1])
+                output['visible'][-1][1] = apply_extensions('output', output['visible'][-1][1], state)
                 yield output
                 return
 
@@ -240,7 +245,7 @@ def chatbot_wrapper(text, history, state, regenerate=False, _continue=False, loa
         else:
             cumulative_reply = reply
 
-    output['visible'][-1][1] = apply_extensions("output", output['visible'][-1][1])
+    output['visible'][-1][1] = apply_extensions('output', output['visible'][-1][1], state)
     yield output
 
 
@@ -273,14 +278,15 @@ def impersonate_wrapper(text, start_with, state):
     yield cumulative_reply.lstrip(' ')
 
 
-def generate_chat_reply(text, history, state, regenerate=False, _continue=False, loading_message=True):
+def generate_chat_reply(text, state, regenerate=False, _continue=False, loading_message=True):
+    history = state['history']
     if regenerate or _continue:
         text = ''
         if (len(history['visible']) == 1 and not history['visible'][0][0]) or len(history['internal']) == 0:
             yield history
             return
 
-    for history in chatbot_wrapper(text, history, state, regenerate=regenerate, _continue=_continue, loading_message=loading_message):
+    for history in chatbot_wrapper(text, state, regenerate=regenerate, _continue=_continue, loading_message=loading_message):
         yield history
 
 
@@ -288,151 +294,127 @@ def generate_chat_reply(text, history, state, regenerate=False, _continue=False,
 def generate_chat_reply_wrapper(text, start_with, state, regenerate=False, _continue=False):
     if start_with != '' and not _continue:
         if regenerate:
-            text = remove_last_message()
+            text, state['history'] = remove_last_message(state['history'])
             regenerate = False
 
         _continue = True
-        send_dummy_message(text)
+        send_dummy_message(text, state)
-        send_dummy_reply(start_with)
+        send_dummy_reply(start_with, state)
 
-    for i, history in enumerate(generate_chat_reply(text, shared.history, state, regenerate, _continue, loading_message=True)):
+    for i, history in enumerate(generate_chat_reply(text, state, regenerate, _continue, loading_message=True)):
-        if i != 0:
+        yield chat_html_wrapper(history, state['name1'], state['name2'], state['mode'], state['chat_style']), history
-            shared.history = copy.deepcopy(history)
-
-        yield chat_html_wrapper(history['visible'], state['name1'], state['name2'], state['mode'], state['chat_style'])
 
 
-def remove_last_message():
+def remove_last_message(history):
-    if len(shared.history['visible']) > 0 and shared.history['internal'][-1][0] != '<|BEGIN-VISIBLE-CHAT|>':
+    if len(history['visible']) > 0 and history['internal'][-1][0] != '<|BEGIN-VISIBLE-CHAT|>':
-        last = shared.history['visible'].pop()
+        last = history['visible'].pop()
-        shared.history['internal'].pop()
+        history['internal'].pop()
     else:
         last = ['', '']
 
-    return last[0]
+    return last[0], history
 
 
-def send_last_reply_to_input():
+def send_last_reply_to_input(history):
-    if len(shared.history['internal']) > 0:
+    if len(history['internal']) > 0:
-        return shared.history['internal'][-1][1]
+        return history['internal'][-1][1]
     else:
         return ''
 
 
-def replace_last_reply(text):
+def replace_last_reply(text, state):
-    if len(shared.history['visible']) > 0:
+    history = state['history']
-        shared.history['visible'][-1][1] = text
+    if len(history['visible']) > 0:
-        shared.history['internal'][-1][1] = apply_extensions("input", text)
+        history['visible'][-1][1] = text
+        history['internal'][-1][1] = apply_extensions('input', text, state)
+
+    return history
 
 
-def send_dummy_message(text):
+def send_dummy_message(text, state):
-    shared.history['visible'].append([text, ''])
+    history = state['history']
-    shared.history['internal'].append([apply_extensions("input", text), ''])
+    history['visible'].append([text, ''])
+    history['internal'].append([apply_extensions('input', text, state), ''])
+    return history
 
 
-def send_dummy_reply(text):
+def send_dummy_reply(text, state):
-    if len(shared.history['visible']) > 0 and not shared.history['visible'][-1][1] == '':
+    history = state['history']
-        shared.history['visible'].append(['', ''])
+    if len(history['visible']) > 0 and not history['visible'][-1][1] == '':
-        shared.history['internal'].append(['', ''])
+        history['visible'].append(['', ''])
+        history['internal'].append(['', ''])
 
-    shared.history['visible'][-1][1] = text
+    history['visible'][-1][1] = text
-    shared.history['internal'][-1][1] = apply_extensions("input", text)
+    history['internal'][-1][1] = apply_extensions('input', text, state)
+    return history
 
 
-def clear_chat_log(greeting, mode):
+def clear_chat_log(state):
-    shared.history['visible'] = []
+    greeting = state['greeting']
-    shared.history['internal'] = []
+    mode = state['mode']
+    history = state['history']
 
+    history['visible'] = []
+    history['internal'] = []
     if mode != 'instruct':
         if greeting != '':
-            shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]]
+            history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]]
-            shared.history['visible'] += [['', apply_extensions("output", greeting)]]
+            history['visible'] += [['', apply_extensions('output', greeting, state)]]
-
-        save_history(mode)
-
-
-def redraw_html(name1, name2, mode, style, reset_cache=False):
-    return chat_html_wrapper(shared.history['visible'], name1, name2, mode, style, reset_cache=reset_cache)
-
-
-def tokenize_dialogue(dialogue, name1, name2):
-    history = []
-    messages = []
-    dialogue = re.sub('<START>', '', dialogue)
-    dialogue = re.sub('<start>', '', dialogue)
-    dialogue = re.sub('(\n|^)[Aa]non:', '\\1You:', dialogue)
-    dialogue = re.sub('(\n|^)\[CHARACTER\]:', f'\\g<1>{name2}:', dialogue)
-    idx = [m.start() for m in re.finditer(f"(^|\n)({re.escape(name1)}|{re.escape(name2)}):", dialogue)]
-    if len(idx) == 0:
-        return history
-
-    for i in range(len(idx) - 1):
-        messages.append(dialogue[idx[i]:idx[i + 1]].strip())
-
-    messages.append(dialogue[idx[-1]:].strip())
-    entry = ['', '']
-    for i in messages:
-        if i.startswith(f'{name1}:'):
-            entry[0] = i[len(f'{name1}:'):].strip()
-        elif i.startswith(f'{name2}:'):
-            entry[1] = i[len(f'{name2}:'):].strip()
-            if not (len(entry[0]) == 0 and len(entry[1]) == 0):
-                history.append(entry)
-
-            entry = ['', '']
-
-    print("\033[1;32;1m\nDialogue tokenized to:\033[0;37;0m\n", end='')
-    for row in history:
-        for column in row:
-            print("\n")
-            for line in column.strip().split('\n'):
-                print("|  " + line + "\n")
-
-            print("|\n")
-        print("------------------------------")
 
     return history
 
 
-def save_history(mode, timestamp=False, user_request=False):
+def redraw_html(history, name1, name2, mode, style, reset_cache=False):
-    # Instruct mode histories should not be saved as if
+    return chat_html_wrapper(history, name1, name2, mode, style, reset_cache=reset_cache)
-    # Alpaca or Vicuna were characters
-    if mode == 'instruct':
-        if not timestamp:
-            return
-
-        fname = f"Instruct_{datetime.now().strftime('%Y%m%d-%H%M%S')}.json"
-    else:
-        if shared.character == 'None' and not user_request:
-            return
-
-        if timestamp:
-            fname = f"{shared.character}_{datetime.now().strftime('%Y%m%d-%H%M%S')}.json"
-        else:
-            fname = f"{shared.character}_persistent.json"
-
-    if not Path('logs').exists():
-        Path('logs').mkdir()
-
-    with open(Path(f'logs/{fname}'), 'w', encoding='utf-8') as f:
-        f.write(json.dumps({'data': shared.history['internal'], 'data_visible': shared.history['visible']}, indent=2))
-
-    return Path(f'logs/{fname}')
 
 
-def load_history(file, name1, name2):
+def save_history(history, path=None):
-    file = file.decode('utf-8')
+    p = path or Path('logs/exported_history.json')
+    with open(p, 'w', encoding='utf-8') as f:
+        f.write(json.dumps(history, indent=4))
+
+    return p
+
+
+def load_history(file, history):
     try:
+        file = file.decode('utf-8')
         j = json.loads(file)
-        if 'data' in j:
+        if 'internal' in j and 'visible' in j:
-            shared.history['internal'] = j['data']
+            return j
-            if 'data_visible' in j:
-                shared.history['visible'] = j['data_visible']
         else:
-                shared.history['visible'] = copy.deepcopy(shared.history['internal'])
+            return history
     except:
-        shared.history['internal'] = tokenize_dialogue(file, name1, name2)
+        return history
-        shared.history['visible'] = copy.deepcopy(shared.history['internal'])
+
+
+def save_persistent_history(history, character, mode):
+    if mode in ['chat', 'chat-instruct'] and character not in  ['', 'None', None] and not shared.args.multi_user:
+        save_history(history, path=Path(f'logs/{character}_persistent.json'))
+
+
+def load_persistent_history(state):
+    if state['mode'] == 'instruct':
+        return state['history']
+
+    character = state['character_menu']
+    greeting = state['greeting']
+    p = Path(f'logs/{character}_persistent.json')
+    if not shared.args.multi_user and character not in ['None', '', None] and p.exists():
+        f = json.loads(open(p, 'rb').read())
+        if 'internal' in f and 'visible' in f:
+            history = f
+        else:
+            history = {'internal': [], 'visible': []}
+            history['internal'] = f['data']
+            history['visible'] = f['data_visible']
+    else:
+        history = {'internal': [], 'visible': []}
+        if greeting != "":
+            history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]]
+            history['visible'] += [['', apply_extensions('output', greeting, state)]]
+
+    return history
 
 
 def replace_character_names(text, name1, name2):
@@ -467,7 +449,6 @@ def generate_pfp_cache(character):
 
 
 def load_character(character, name1, name2, instruct=False):
-    shared.character = character
     context = greeting = turn_template = ""
     greeting_field = 'greeting'
     picture = None
@@ -476,7 +457,7 @@ def load_character(character, name1, name2, instruct=False):
     if Path("cache/pfp_character.png").exists():
         Path("cache/pfp_character.png").unlink()
 
-    if character != 'None':
+    if character not in ['None', '', None]:
         folder = 'characters' if not instruct else 'characters/instruction-following'
         picture = generate_pfp_cache(character)
         for extension in ["yml", "yaml", "json"]:
@@ -526,20 +507,6 @@ def load_character(character, name1, name2, instruct=False):
         greeting = shared.settings['greeting']
         turn_template = shared.settings['turn_template']
 
-    if not instruct:
-        shared.history['internal'] = []
-        shared.history['visible'] = []
-        if shared.character != 'None' and Path(f'logs/{shared.character}_persistent.json').exists():
-            load_history(open(Path(f'logs/{shared.character}_persistent.json'), 'rb').read(), name1, name2)
-        else:
-            # Insert greeting if it exists
-            if greeting != "":
-                shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]]
-                shared.history['visible'] += [['', apply_extensions("output", greeting)]]
-
-            # Create .json log files since they don't already exist
-            save_history('instruct' if instruct else 'chat')
-
     return name1, name2, picture, greeting, context, turn_template.replace("\n", r"\n")
 
 
@@ -567,16 +534,22 @@ def upload_character(json_file, img, tavern=False):
         img.save(Path(f'characters/{outfile_name}.png'))
 
     logger.info(f'New character saved to "characters/{outfile_name}.json".')
-    return outfile_name
+    return gr.update(value=outfile_name, choices=get_available_characters())
 
 
-def upload_tavern_character(img, name1, name2):
+def upload_tavern_character(img, _json):
-    _img = Image.open(io.BytesIO(img))
-    _img.getexif()
-    decoded_string = base64.b64decode(_img.info['chara'])
-    _json = json.loads(decoded_string)
     _json = {"char_name": _json['name'], "char_persona": _json['description'], "char_greeting": _json["first_mes"], "example_dialogue": _json['mes_example'], "world_scenario": _json['scenario']}
-    return upload_character(json.dumps(_json), _img, tavern=True)
+    return upload_character(json.dumps(_json), img, tavern=True)
+
+
+def check_tavern_character(img):
+    if "chara" not in img.info:
+        return "Not a TavernAI card", None, None, gr.update(interactive=False)
+    decoded_string = base64.b64decode(img.info['chara'])
+    _json = json.loads(decoded_string)
+    if "data" in _json:
+        _json = _json["data"]
+    return _json['name'], _json['description'], _json, gr.update(interactive=True)
 
 
 def upload_your_profile_picture(img):

modules/exllama.py

@@ -1,6 +1,8 @@
 import sys
 from pathlib import Path
 
+from torch import version as torch_version
+
 from modules import shared
 from modules.logging_colors import logger
 
@@ -52,6 +54,16 @@ class ExllamaModel:
             config.set_auto_map(shared.args.gpu_split)
             config.gpu_peer_fix = True
 
+        if shared.args.alpha_value:
+            config.alpha_value = shared.args.alpha_value
+            config.calculate_rotary_embedding_base()
+
+        if torch_version.hip:
+            config.rmsnorm_no_half2 = True
+            config.rope_no_half2 = True
+            config.matmul_no_half2 = True
+            config.silu_no_half2 = True
+
         model = ExLlama(config)
         tokenizer = ExLlamaTokenizer(str(tokenizer_model_path))
         cache = ExLlamaCache(model)
@@ -71,6 +83,7 @@ class ExllamaModel:
         self.generator.settings.top_k = state['top_k']
         self.generator.settings.typical = state['typical_p']
         self.generator.settings.token_repetition_penalty_max = state['repetition_penalty']
+        self.generator.settings.token_repetition_penalty_sustain = -1 if state['repetition_penalty_range'] <= 0 else state['repetition_penalty_range']
         if state['ban_eos_token']:
             self.generator.disallow_tokens([self.tokenizer.eos_token_id])
         else:

modules/exllama_hf.py

@@ -98,6 +98,16 @@ class ExllamaHF(PreTrainedModel):
             config.set_auto_map(shared.args.gpu_split)
             config.gpu_peer_fix = True
 
+        if shared.args.alpha_value:
+            config.alpha_value = shared.args.alpha_value
+            config.calculate_rotary_embedding_base()
+
+        if torch.version.hip:
+            config.rmsnorm_no_half2 = True
+            config.rope_no_half2 = True
+            config.matmul_no_half2 = True
+            config.silu_no_half2 = True
+
         # This slowes down a bit but align better with autogptq generation.
         # TODO: Should give user choice to tune the exllama config
         # config.fused_attn = False

modules/extensions.py

@@ -6,6 +6,8 @@ import gradio as gr
 import extensions
 import modules.shared as shared
 from modules.logging_colors import logger
+from inspect import signature
+
 
 state = {}
 available_extensions = []
@@ -52,10 +54,14 @@ def iterator():
 
 
 # Extension functions that map string -> string
-def _apply_string_extensions(function_name, text):
+def _apply_string_extensions(function_name, text, state):
     for extension, _ in iterator():
         if hasattr(extension, function_name):
-            text = getattr(extension, function_name)(text)
+            func = getattr(extension, function_name)
+            if len(signature(func).parameters) == 2:
+                text = func(text, state)
+            else:
+                text = func(text)
 
     return text
 

modules/github.py

@@ -14,16 +14,20 @@ def clone_or_pull_repository(github_url):
 
     # Check if the repository is already cloned
     if os.path.exists(repo_path):
+        yield f"Updating {github_url}..."
         # Perform a 'git pull' to update the repository
         try:
             pull_output = subprocess.check_output(["git", "-C", repo_path, "pull"], stderr=subprocess.STDOUT)
+            yield "Done."
             return pull_output.decode()
         except subprocess.CalledProcessError as e:
             return str(e)
 
     # Clone the repository
     try:
+        yield f"Cloning {github_url}..."
         clone_output = subprocess.check_output(["git", "clone", github_url, repo_path], stderr=subprocess.STDOUT)
+        yield "Done."
         return clone_output.decode()
     except subprocess.CalledProcessError as e:
         return str(e)

modules/html_generator.py

@@ -129,7 +129,7 @@ def generate_4chan_html(f):
 def make_thumbnail(image):
     image = image.resize((350, round(image.size[1] / image.size[0] * 350)), Image.Resampling.LANCZOS)
     if image.size[1] > 470:
-        image = ImageOps.fit(image, (350, 470), Image.ANTIALIAS)
+        image = ImageOps.fit(image, (350, 470), Image.LANCZOS)
 
     return image
 
@@ -266,8 +266,8 @@ def generate_chat_html(history, name1, name2, reset_cache=False):
 
 def chat_html_wrapper(history, name1, name2, mode, style, reset_cache=False):
     if mode == 'instruct':
-        return generate_instruct_html(history)
+        return generate_instruct_html(history['visible'])
     elif style == 'wpp':
-        return generate_chat_html(history, name1, name2)
+        return generate_chat_html(history['visible'], name1, name2)
     else:
-        return generate_cai_chat_html(history, name1, name2, style, reset_cache)
+        return generate_cai_chat_html(history['visible'], name1, name2, style, reset_cache)

modules/loaders.py

@@ -57,12 +57,14 @@ loaders_and_params = {
         'gpu_split',
         'max_seq_len',
         'compress_pos_emb',
+        'alpha_value',
         'exllama_info',
     ],
     'ExLlama_HF' : [
         'gpu_split',
         'max_seq_len',
         'compress_pos_emb',
+        'alpha_value',
         'exllama_HF_info',
     ]
 }

modules/models.py

@@ -326,6 +326,7 @@ def clear_torch_cache():
 
 def unload_model():
     shared.model = shared.tokenizer = None
+    shared.lora_names = []
     clear_torch_cache()
 
 

modules/presets.py

@@ -15,6 +15,7 @@ def load_preset(name):
         'tfs': 1,
         'top_a': 0,
         'repetition_penalty': 1,
+        'repetition_penalty_range': 0,
         'encoder_repetition_penalty': 1,
         'top_k': 0,
         'num_beams': 1,
@@ -28,6 +29,7 @@ def load_preset(name):
         'mirostat_eta': 0.1,
     }
 
+    if name not in ['None', None, '']:
         with open(Path(f'presets/{name}.yaml'), 'r') as infile:
             preset = yaml.safe_load(infile)
 
@@ -46,9 +48,9 @@ def load_preset_memoized(name):
 def load_preset_for_ui(name, state):
     generate_params = load_preset(name)
     state.update(generate_params)
-    return state, *[generate_params[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']]
+    return state, *[generate_params[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']]
 
 
 def generate_preset_yaml(state):
-    data = {k: state[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']}
+    data = {k: state[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']}
     return yaml.dump(data, sort_keys=False)

modules/sampler_hijack.py

@@ -5,6 +5,7 @@ import transformers
 from transformers import LogitsWarper
 from transformers.generation.logits_process import (
     LogitNormalization,
+    LogitsProcessor,
     LogitsProcessorList,
     TemperatureLogitsWarper
 )
@@ -121,6 +122,29 @@ class MirostatLogitsWarper(LogitsWarper):
         return scores
 
 
+class RepetitionPenaltyLogitsProcessorWithRange(LogitsProcessor):
+    '''
+    Copied from the transformers library
+    '''
+    def __init__(self, penalty: float, _range: int):
+        if not isinstance(penalty, float) or not (penalty > 0):
+            raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
+
+        self.penalty = penalty
+        self._range = _range
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+
+        input_ids = input_ids[:, -self._range:]
+        score = torch.gather(scores, 1, input_ids)
+
+        # if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
+        score = torch.where(score < 0, score * self.penalty, score / self.penalty)
+
+        scores.scatter_(1, input_ids, score)
+        return scores
+
+
 def get_logits_warper_patch(self, generation_config):
     warpers = self._get_logits_warper_old(generation_config)
     warpers_to_add = LogitsProcessorList()
@@ -146,6 +170,19 @@ def get_logits_warper_patch(self, generation_config):
     return warpers
 
 
+def get_logits_processor_patch(self, **kwargs):
+    result = self._get_logits_processor_old(**kwargs)
+    repetition_penalty_range = kwargs['generation_config'].repetition_penalty_range
+    repetition_penalty = kwargs['generation_config'].repetition_penalty
+
+    if repetition_penalty_range > 0:
+        for i in range(len(result)):
+            if result[i].__class__.__name__ == 'RepetitionPenaltyLogitsProcessor':
+                result[i] = RepetitionPenaltyLogitsProcessorWithRange(repetition_penalty, repetition_penalty_range)
+
+    return result
+
+
 def generation_config_init_patch(self, **kwargs):
     self.__init___old(**kwargs)
     self.tfs = kwargs.pop("tfs", 1.0)
@@ -153,11 +190,15 @@ def generation_config_init_patch(self, **kwargs):
     self.mirostat_mode = kwargs.pop("mirostat_mode", 0)
     self.mirostat_eta = kwargs.pop("mirostat_eta", 0.1)
     self.mirostat_tau = kwargs.pop("mirostat_tau", 5)
+    self.repetition_penalty_range = kwargs.pop("repetition_penalty_range", 0)
 
 
 def hijack_samplers():
     transformers.GenerationMixin._get_logits_warper_old = transformers.GenerationMixin._get_logits_warper
     transformers.GenerationMixin._get_logits_warper = get_logits_warper_patch
 
+    transformers.GenerationMixin._get_logits_processor_old = transformers.GenerationMixin._get_logits_processor
+    transformers.GenerationMixin._get_logits_processor = get_logits_processor_patch
+
     transformers.GenerationConfig.__init___old = transformers.GenerationConfig.__init__
     transformers.GenerationConfig.__init__ = generation_config_init_patch

modules/shared.py

@@ -14,8 +14,6 @@ model_name = "None"
 lora_names = []
 
 # Chat variables
-history = {'internal': [], 'visible': []}
-character = 'None'
 stop_everything = False
 processing_message = '*Is typing...*'
 
@@ -83,6 +81,7 @@ parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpForma
 # Basic settings
 parser.add_argument('--notebook', action='store_true', help='Launch the web UI in notebook mode, where the output is written to the same text box as the input.')
 parser.add_argument('--chat', action='store_true', help='Launch the web UI in chat mode with a style similar to the Character.AI website.')
+parser.add_argument('--multi-user', action='store_true', help='Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental.')
 parser.add_argument('--character', type=str, help='The name of the character to load in chat mode by default.')
 parser.add_argument('--model', type=str, help='Name of the model to load by default.')
 parser.add_argument('--lora', type=str, nargs="+", help='The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.')
@@ -151,6 +150,7 @@ parser.add_argument('--desc_act', action='store_true', help='For models that don
 parser.add_argument('--gpu-split', type=str, help="Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. 20,7,7")
 parser.add_argument('--max_seq_len', type=int, default=2048, help="Maximum sequence length.")
 parser.add_argument('--compress_pos_emb', type=int, default=1, help="Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.")
+parser.add_argument('--alpha_value', type=int, default=1, help="Positional embeddings alpha factor for NTK RoPE scaling. Same as above. Use either this or compress_pos_emb, not both.")
 
 # FlexGen
 parser.add_argument('--flexgen', action='store_true', help='DEPRECATED')
@@ -204,6 +204,8 @@ if args.trust_remote_code:
     logger.warning("trust_remote_code is enabled. This is dangerous.")
 if args.share:
     logger.warning("The gradio \"share link\" feature uses a proprietary executable to create a reverse tunnel. Use it with care.")
+if args.multi_user:
+    logger.warning("The multi-user mode is highly experimental. DO NOT EXPOSE IT TO THE INTERNET.")
 
 
 def fix_loader_name(name):
@@ -246,6 +248,15 @@ def is_chat():
     return args.chat
 
 
+def get_mode():
+    if args.chat:
+        return 'chat'
+    elif args.notebook:
+        return 'notebook'
+    else:
+        return 'default'
+
+
 # Loading model-specific settings
 with Path(f'{args.model_dir}/config.yaml') as p:
     if p.exists():

modules/text_generation.py

@@ -190,7 +190,7 @@ def _generate_reply(question, state, stopping_strings=None, is_chat=False):
     original_question = question
     if not is_chat:
         state = apply_extensions('state', state)
-        question = apply_extensions('input', question)
+        question = apply_extensions('input', question, state)
 
     # Finding the stopping strings
     all_stop_strings = []
@@ -223,14 +223,14 @@ def _generate_reply(question, state, stopping_strings=None, is_chat=False):
             break
 
     if not is_chat:
-        reply = apply_extensions('output', reply)
+        reply = apply_extensions('output', reply, state)
 
     yield reply
 
 
 def generate_reply_HF(question, original_question, seed, state, stopping_strings=None, is_chat=False):
     generate_params = {}
-    for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'tfs', 'top_a', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta']:
+    for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'tfs', 'top_a', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta']:
         generate_params[k] = state[k]
 
     for k in ['epsilon_cutoff', 'eta_cutoff']:
@@ -262,7 +262,7 @@ def generate_reply_HF(question, original_question, seed, state, stopping_strings
     eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
     generate_params['eos_token_id'] = eos_token_ids
     generate_params['stopping_criteria'] = transformers.StoppingCriteriaList()
-    generate_params['stopping_criteria'].append(_StopEverythingStoppingCriteria());
+    generate_params['stopping_criteria'].append(_StopEverythingStoppingCriteria())
 
     t0 = time.time()
     try:

modules/training.py

@@ -240,6 +240,21 @@ def backup_adapter(input_folder):
     except Exception as e:
         print("An error occurred in backup_adapter:", str(e))
 
+def calc_trainable_parameters(model):
+    trainable_params = 0
+    all_param = 0
+    for _, param in model.named_parameters():
+        num_params = param.numel()
+        # if using DS Zero 3 and the weights are initialized empty
+        if num_params == 0 and hasattr(param, "ds_numel"):
+            num_params = param.ds_numel
+
+        all_param += num_params
+        if param.requires_grad:
+            trainable_params += num_params
+    
+    return trainable_params,all_param
+
 
 def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, overlap_len: int, newline_favor_len: int, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str, stop_at_loss: float):
 
@@ -268,7 +283,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
     else:
         model_id = "llama"
         if model_type == "PeftModelForCausalLM":
-            if len(shared.args.lora_names) > 0:
+            if len(shared.lora_names) > 0:
                 yield "You are trying to train a LoRA while you already have another LoRA loaded. This will work, but may have unexpected effects. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
                 logger.warning("Training LoRA over top of another LoRA. May have unexpected effects.")
             else:
@@ -431,6 +446,9 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
     if not always_override:
         backup_adapter(lora_file_path)
 
+    # == get model trainable params
+    model_trainable_params, model_all_params = calc_trainable_parameters(shared.model)
+
     try:
         logger.info("Creating LoRA model...")
         lora_model = get_peft_model(shared.model, config)
@@ -540,6 +558,12 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
     logger.info("Starting training...")
     yield "Starting..."
 
+    lora_trainable_param, lora_all_param = calc_trainable_parameters(lora_model)
+    
+    if lora_all_param>0:
+        print(f"Trainable params: {lora_trainable_param:,d} ({100 * lora_trainable_param / lora_all_param:.4f} %), All params: {lora_all_param:,d} (Model: {model_all_params:,d})")
+
+
     train_log.update({"base_model_name": shared.model_name})
     train_log.update({"base_model_class": shared.model.__class__.__name__})
     train_log.update({"base_loaded_in_4bit": getattr(lora_model, "is_loaded_in_4bit", False)})

modules/ui.py

@@ -1,3 +1,4 @@
+import json
 from pathlib import Path
 
 import gradio as gr
@@ -5,6 +6,7 @@ import torch
 
 from modules import shared
 
+
 with open(Path(__file__).resolve().parent / '../css/main.css', 'r') as f:
     css = f.read()
 with open(Path(__file__).resolve().parent / '../css/chat.css', 'r') as f:
@@ -14,7 +16,7 @@ with open(Path(__file__).resolve().parent / '../css/main.js', 'r') as f:
 with open(Path(__file__).resolve().parent / '../css/chat.js', 'r') as f:
     chat_js = f.read()
 
-refresh_symbol = '\U0001f504'  # 🔄
+refresh_symbol = '🔄'
 delete_symbol = '🗑️'
 save_symbol = '💾'
 
@@ -30,17 +32,105 @@ theme = gr.themes.Default(
 
 
 def list_model_elements():
-    elements = ['loader', 'cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'trust_remote_code', 'load_in_4bit', 'compute_dtype', 'quant_type', 'use_double_quant', 'wbits', 'groupsize', 'model_type', 'pre_layer', 'triton', 'desc_act', 'no_inject_fused_attention', 'no_inject_fused_mlp', 'no_use_cuda_fp16', 'threads', 'n_batch', 'no_mmap', 'mlock', 'n_gpu_layers', 'n_ctx', 'llama_cpp_seed', 'gpu_split', 'max_seq_len', 'compress_pos_emb']
+    elements = [
+        'loader',
+        'cpu_memory',
+        'auto_devices',
+        'disk',
+        'cpu',
+        'bf16',
+        'load_in_8bit',
+        'trust_remote_code',
+        'load_in_4bit',
+        'compute_dtype',
+        'quant_type',
+        'use_double_quant',
+        'wbits',
+        'groupsize',
+        'model_type',
+        'pre_layer',
+        'triton',
+        'desc_act',
+        'no_inject_fused_attention',
+        'no_inject_fused_mlp',
+        'no_use_cuda_fp16',
+        'threads',
+        'n_batch',
+        'no_mmap',
+        'mlock',
+        'n_gpu_layers',
+        'n_ctx',
+        'llama_cpp_seed',
+        'gpu_split',
+        'max_seq_len',
+        'compress_pos_emb',
+        'alpha_value'
+    ]
+
+
     for i in range(torch.cuda.device_count()):
         elements.append(f'gpu_memory_{i}')
 
     return elements
 
 
-def list_interface_input_elements(chat=False):
+def list_interface_input_elements():
-    elements = ['max_new_tokens', 'seed', 'temperature', 'top_p', 'top_k', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'do_sample', 'penalty_alpha', 'num_beams', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'add_bos_token', 'ban_eos_token', 'truncation_length', 'custom_stopping_strings', 'skip_special_tokens', 'preset_menu', 'stream', 'tfs', 'top_a']
+    elements = [
-    if chat:
+        'preset_menu',
-        elements += ['name1', 'name2', 'greeting', 'context', 'chat_generation_attempts', 'stop_at_newline', 'mode', 'instruction_template', 'character_menu', 'name1_instruct', 'name2_instruct', 'context_instruct', 'turn_template', 'chat_style', 'chat-instruct_command']
+        'max_new_tokens',
+        'seed',
+        'temperature',
+        'top_p',
+        'top_k',
+        'typical_p',
+        'epsilon_cutoff',
+        'eta_cutoff',
+        'repetition_penalty',
+        'repetition_penalty_range',
+        'encoder_repetition_penalty',
+        'no_repeat_ngram_size',
+        'min_length',
+        'do_sample',
+        'penalty_alpha',
+        'num_beams',
+        'length_penalty',
+        'early_stopping',
+        'mirostat_mode',
+        'mirostat_tau',
+        'mirostat_eta',
+        'add_bos_token',
+        'ban_eos_token',
+        'truncation_length',
+        'custom_stopping_strings',
+        'skip_special_tokens',
+        'stream',
+        'tfs',
+        'top_a',
+    ]
+
+    if shared.args.chat:
+        elements += [
+            'character_menu',
+            'history',
+            'name1',
+            'name2',
+            'greeting',
+            'context',
+            'chat_generation_attempts',
+            'stop_at_newline',
+            'mode',
+            'instruction_template',
+            'name1_instruct',
+            'name2_instruct',
+            'context_instruct',
+            'turn_template',
+            'chat_style',
+            'chat-instruct_command',
+        ]
+    else:
+        elements.append('textbox')
+        if not shared.args.notebook:
+            elements.append('output_textbox')
 
     elements += list_model_elements()
     return elements
@@ -48,10 +138,15 @@ def list_interface_input_elements(chat=False):
 
 def gather_interface_values(*args):
     output = {}
-    for i, element in enumerate(shared.input_elements):
+    for i, element in enumerate(list_interface_input_elements()):
         output[element] = args[i]
 
+    if not shared.args.multi_user:
         shared.persistent_interface_state = output
+        Path('logs').mkdir(exist_ok=True)
+        with open(Path(f'logs/session_{shared.get_mode()}_autosave.json'), 'w') as f:
+            f.write(json.dumps(output, indent=4))
+
     return output
 
 
@@ -59,11 +154,12 @@ def apply_interface_values(state, use_persistent=False):
     if use_persistent:
         state = shared.persistent_interface_state
 
-    elements = list_interface_input_elements(chat=shared.is_chat())
+    elements = list_interface_input_elements()
     if len(state) == 0:
         return [gr.update() for k in elements]  # Dummy, do nothing
     else:
-        return [state[k] if k in state else gr.update() for k in elements]
+        ans = [state[k] if k in state else gr.update() for k in elements]
+        return ans
 
 
 class ToolButton(gr.Button, gr.components.FormComponent):
@@ -92,6 +188,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
         inputs=[],
         outputs=[refresh_component]
     )
+
     return refresh_button
 
 

modules/utils.py

@@ -7,6 +7,14 @@ from modules import shared
 from modules.logging_colors import logger
 
 
+# Helper function to get multiple values from shared.gradio
+def gradio(*keys):
+    if len(keys) == 1 and type(keys[0]) is list:
+        keys = keys[0]
+
+    return [shared.gradio[k] for k in keys]
+
+
 def save_file(fname, contents):
     if fname == '':
         logger.error('File name is empty!')
@@ -111,3 +119,8 @@ def get_datasets(path: str, ext: str):
 
 def get_available_chat_styles():
     return sorted(set(('-'.join(k.stem.split('-')[1:]) for k in Path('css').glob('chat_style*.css'))), key=natural_keys)
+
+
+def get_available_sessions():
+    items = sorted(set(k.stem for k in Path('logs').glob(f'session_{shared.get_mode()}*')), key=natural_keys, reverse=True)
+    return [item for item in items if 'autosave' in item] + [item for item in items if 'autosave' not in item]

requirements.txt

@@ -23,5 +23,5 @@ llama-cpp-python==0.1.66; platform_system != "Windows"
 https://github.com/abetlen/llama-cpp-python/releases/download/v0.1.66/llama_cpp_python-0.1.66-cp310-cp310-win_amd64.whl; platform_system == "Windows"
 https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.2.2/auto_gptq-0.2.2+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
 https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.2.2/auto_gptq-0.2.2+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
-https://github.com/jllllll/exllama/releases/download/0.0.4/exllama-0.0.4+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
+https://github.com/jllllll/exllama/releases/download/0.0.5/exllama-0.0.5+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
-https://github.com/jllllll/exllama/releases/download/0.0.4/exllama-0.0.4+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
+https://github.com/jllllll/exllama/releases/download/0.0.5/exllama-0.0.5+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"

server.py

@@ -49,6 +49,7 @@ from modules.text_generation import (
     get_encoded_length,
     stop_everything_event
 )
+from modules.utils import gradio
 
 
 def load_model_wrapper(selected_model, loader, autoload=False):
@@ -225,6 +226,7 @@ def create_model_menus():
                         shared.gradio['gpu_split'] = gr.Textbox(label='gpu-split', info='Comma-separated list of VRAM (in GB) to use per GPU. Example: 20,7,7')
                         shared.gradio['max_seq_len'] = gr.Slider(label='max_seq_len', minimum=2048, maximum=16384, step=256, info='Maximum sequence length.', value=shared.args.max_seq_len)
                         shared.gradio['compress_pos_emb'] = gr.Slider(label='compress_pos_emb', minimum=1, maximum=8, step=1, info='Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.', value=shared.args.compress_pos_emb)
+                        shared.gradio['alpha_value'] = gr.Slider(label='alpha_value', minimum=1, maximum=8, step=1, info='Positional embeddings alpha factor for NTK RoPE scaling. Same as above. Use either this or compress_pos_emb, not both.', value=shared.args.alpha_value)
 
                     with gr.Column():
                         shared.gradio['triton'] = gr.Checkbox(label="triton", value=shared.args.triton)
@@ -257,40 +259,43 @@ def create_model_menus():
             with gr.Row():
                 shared.gradio['model_status'] = gr.Markdown('No model is loaded' if shared.model_name == 'None' else 'Ready')
 
-    shared.gradio['loader'].change(loaders.make_loader_params_visible, shared.gradio['loader'], [shared.gradio[k] for k in loaders.get_all_params()])
+    shared.gradio['loader'].change(loaders.make_loader_params_visible, gradio('loader'), gradio(loaders.get_all_params()))
 
     # In this event handler, the interface state is read and updated
     # with the model defaults (if any), and then the model is loaded
     # unless "autoload_model" is unchecked
     shared.gradio['model_menu'].change(
-        ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        apply_model_settings_to_state, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['interface_state']).then(
+        apply_model_settings_to_state, gradio('model_menu', 'interface_state'), gradio('interface_state')).then(
-        ui.apply_interface_values, shared.gradio['interface_state'], [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False).then(
+        ui.apply_interface_values, gradio('interface_state'), gradio(ui.list_interface_input_elements()), show_progress=False).then(
-        update_model_parameters, shared.gradio['interface_state'], None).then(
+        update_model_parameters, gradio('interface_state'), None).then(
-        load_model_wrapper, [shared.gradio[k] for k in ['model_menu', 'loader', 'autoload_model']], shared.gradio['model_status'], show_progress=False)
+        load_model_wrapper, gradio('model_menu', 'loader', 'autoload_model'), gradio('model_status'), show_progress=False)
 
     load.click(
-        ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        update_model_parameters, shared.gradio['interface_state'], None).then(
+        update_model_parameters, gradio('interface_state'), None).then(
-        partial(load_model_wrapper, autoload=True), [shared.gradio[k] for k in ['model_menu', 'loader']], shared.gradio['model_status'], show_progress=False)
+        partial(load_model_wrapper, autoload=True), gradio('model_menu', 'loader'), gradio('model_status'), show_progress=False).then(
+        lambda: shared.lora_names, None, gradio('lora_menu'))
 
     unload.click(
         unload_model, None, None).then(
-        lambda: "Model unloaded", None, shared.gradio['model_status'])
+        lambda: "Model unloaded", None, gradio('model_status')).then(
+        lambda: shared.lora_names, None, gradio('lora_menu'))
 
     reload.click(
         unload_model, None, None).then(
-        ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        update_model_parameters, shared.gradio['interface_state'], None).then(
+        update_model_parameters, gradio('interface_state'), None).then(
-        partial(load_model_wrapper, autoload=True), [shared.gradio[k] for k in ['model_menu', 'loader']], shared.gradio['model_status'], show_progress=False)
+        partial(load_model_wrapper, autoload=True), gradio('model_menu', 'loader'), gradio('model_status'), show_progress=False).then(
+        lambda: shared.lora_names, None, gradio('lora_menu'))
 
     save_settings.click(
-        ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        save_model_settings, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['model_status'], show_progress=False)
+        save_model_settings, gradio('model_menu', 'interface_state'), gradio('model_status'), show_progress=False)
 
-    shared.gradio['lora_menu_apply'].click(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['model_status'], show_progress=False)
+    shared.gradio['lora_menu_apply'].click(load_lora_wrapper, gradio('lora_menu'), gradio('model_status'), show_progress=False)
-    shared.gradio['download_model_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=True)
+    shared.gradio['download_model_button'].click(download_model_wrapper, gradio('custom_model_menu'), gradio('model_status'), show_progress=True)
-    shared.gradio['autoload_model'].change(lambda x: gr.update(visible=not x), shared.gradio['autoload_model'], load)
+    shared.gradio['autoload_model'].change(lambda x: gr.update(visible=not x), gradio('autoload_model'), load)
 
 
 def create_chat_settings_menus():
@@ -327,23 +332,75 @@ def create_settings_menus(default_preset):
                 gr.Markdown('Main parameters')
                 with gr.Row():
                     with gr.Column():
-                        shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature', info='Primary factor to control randomness of outputs. 0 = deterministic (only the most likely token is used). Higher value = more randomness.')
+                        shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature')
-                        shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=generate_params['top_p'], step=0.01, label='top_p', info='If not set to 1, select tokens with probabilities adding up to less than this number. Higher value = higher range of possible random results.')
+                        shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=generate_params['top_p'], step=0.01, label='top_p')
-                        shared.gradio['top_k'] = gr.Slider(0, 200, value=generate_params['top_k'], step=1, label='top_k', info='Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.')
+                        shared.gradio['top_k'] = gr.Slider(0, 200, value=generate_params['top_k'], step=1, label='top_k')
-                        shared.gradio['typical_p'] = gr.Slider(0.0, 1.0, value=generate_params['typical_p'], step=0.01, label='typical_p', info='If not set to 1, select only tokens that are at least this much more likely to appear than random tokens, given the prior text.')
+                        shared.gradio['typical_p'] = gr.Slider(0.0, 1.0, value=generate_params['typical_p'], step=0.01, label='typical_p')
-                        shared.gradio['epsilon_cutoff'] = gr.Slider(0, 9, value=generate_params['epsilon_cutoff'], step=0.01, label='epsilon_cutoff', info='In units of 1e-4; a reasonable value is 3. This sets a probability floor below which tokens are excluded from being sampled. Should be used with top_p, top_k, and eta_cutoff set to 0.')
+                        shared.gradio['epsilon_cutoff'] = gr.Slider(0, 9, value=generate_params['epsilon_cutoff'], step=0.01, label='epsilon_cutoff')
-                        shared.gradio['eta_cutoff'] = gr.Slider(0, 20, value=generate_params['eta_cutoff'], step=0.01, label='eta_cutoff', info='In units of 1e-4; a reasonable value is 3. Should be used with top_p, top_k, and epsilon_cutoff set to 0.')
+                        shared.gradio['eta_cutoff'] = gr.Slider(0, 20, value=generate_params['eta_cutoff'], step=0.01, label='eta_cutoff')
 
                     with gr.Column():
-                        shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'], step=0.01, label='repetition_penalty', info='Exponential penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.')
+                        shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'], step=0.01, label='repetition_penalty')
-                        shared.gradio['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'], step=0.01, label='encoder_repetition_penalty', info='Also known as the "Hallucinations filter". Used to penalize tokens that are *not* in the prior text. Higher value = more likely to stay in context, lower value = more likely to diverge.')
+                        shared.gradio['repetition_penalty_range'] = gr.Slider(0, 4096, step=64, value=generate_params['repetition_penalty_range'], label='repetition_penalty_range')
-                        shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size', info='If not set to 0, specifies the length of token sets that are completely blocked from repeating at all. Higher values = blocks larger phrases, lower values = blocks words or letters from repeating. Only 0 or high values are a good idea in most cases.')
+                        shared.gradio['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'], step=0.01, label='encoder_repetition_penalty')
-                        shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'], label='min_length', info='Minimum generation length in tokens.')
+                        shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size')
+                        shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'], label='min_length')
                         shared.gradio['tfs'] = gr.Slider(0.0, 1.0, value=generate_params['tfs'], step=0.01, label='tfs')
                         shared.gradio['top_a'] = gr.Slider(0.0, 1.0, value=generate_params['top_a'], step=0.01, label='top_a')
                         shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
 
-            gr.Markdown('[Click here for more information.](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Generation-parameters.md)')
+            with gr.Accordion("Learn more", open=False):
+                gr.Markdown("""
+
+    Not all parameters are used by all loaders. See [this page](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Generation-parameters.md) for details.
+
+    For a technical description of the parameters, the [transformers documentation](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig) is a good reference.
+
+    The best presets, according to the [Preset Arena](https://github.com/oobabooga/oobabooga.github.io/blob/main/arena/results.md) experiment, are:
+
+    * Instruction following:
+        1) Divine Intellect
+        2) Big O
+        3) simple-1
+        4) Space Alien
+        5) StarChat
+        6) Titanic
+        7) tfs-with-top-a
+        8) Asterism
+        9) Contrastive Search
+
+    * Chat:
+        1) Midnight Enigma
+        2) Yara
+        3) Shortwave
+        4) Kobold-Godlike
+
+    ### Temperature
+    Primary factor to control randomness of outputs. 0 = deterministic (only the most likely token is used). Higher value = more randomness.
+    ### top_p
+    If not set to 1, select tokens with probabilities adding up to less than this number. Higher value = higher range of possible random results.
+    ### top_k
+    Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.
+    ### typical_p
+    If not set to 1, select only tokens that are at least this much more likely to appear than random tokens, given the prior text.
+    ### epsilon_cutoff
+    In units of 1e-4; a reasonable value is 3. This sets a probability floor below which tokens are excluded from being sampled. Should be used with top_p, top_k, and eta_cutoff set to 0.
+    ### eta_cutoff
+    In units of 1e-4; a reasonable value is 3. Should be used with top_p, top_k, and epsilon_cutoff set to 0.
+    ### repetition_penalty
+    Exponential penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.
+    ### repetition_penalty_range
+    The number of most recent tokens to consider for repetition penalty. 0 makes all tokens be used.
+    ### encoder_repetition_penalty
+    Also known as the "Hallucinations filter". Used to penalize tokens that are *not* in the prior text. Higher value = more likely to stay in context, lower value = more likely to diverge.
+    ### no_repeat_ngram_size
+    If not set to 0, specifies the length of token sets that are completely blocked from repeating at all. Higher values = blocks larger phrases, lower values = blocks words or letters from repeating. Only 0 or high values are a good idea in most cases.
+    ### min_length
+    Minimum generation length in tokens.
+    ### penalty_alpha
+    Contrastive Search is enabled by setting this to greater than zero and unchecking "do_sample". It should be used with a low value of top_k, for instance, top_k = 4.
+
+                """, elem_classes="markdown")
 
         with gr.Column():
             create_chat_settings_menus()
@@ -351,7 +408,7 @@ def create_settings_menus(default_preset):
                 with gr.Row():
                     with gr.Column():
                         gr.Markdown('Contrastive search')
-                        shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha', info='Contrastive Search is enabled by setting this to greater than zero and unchecking "do_sample". It should be used with a low value of top_k, for instance, top_k = 4.')
+                        shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha')
 
                         gr.Markdown('Beam search')
                         shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams')
@@ -376,7 +433,7 @@ def create_settings_menus(default_preset):
                         shared.gradio['skip_special_tokens'] = gr.Checkbox(value=shared.settings['skip_special_tokens'], label='Skip special tokens', info='Some specific models need this unset.')
                         shared.gradio['stream'] = gr.Checkbox(value=not shared.args.no_stream, label='Activate text streaming')
 
-    shared.gradio['preset_menu'].change(presets.load_preset_for_ui, [shared.gradio[k] for k in ['preset_menu', 'interface_state']], [shared.gradio[k] for k in ['interface_state', 'do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']])
+    shared.gradio['preset_menu'].change(presets.load_preset_for_ui, gradio('preset_menu', 'interface_state'), gradio('interface_state', 'do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a'))
 
 
 def create_file_saving_menus():
@@ -415,39 +472,80 @@ def create_file_saving_menus():
 
 def create_file_saving_event_handlers():
     shared.gradio['save_confirm'].click(
-        lambda x, y, z: utils.save_file(x + y, z), [shared.gradio[k] for k in ['save_root', 'save_filename', 'save_contents']], None).then(
+        lambda x, y, z: utils.save_file(x + y, z), gradio('save_root', 'save_filename', 'save_contents'), None).then(
-        lambda: gr.update(visible=False), None, shared.gradio['file_saver'])
+        lambda: gr.update(visible=False), None, gradio('file_saver'))
 
     shared.gradio['delete_confirm'].click(
-        lambda x, y: utils.delete_file(x + y), [shared.gradio[k] for k in ['delete_root', 'delete_filename']], None).then(
+        lambda x, y: utils.delete_file(x + y), gradio('delete_root', 'delete_filename'), None).then(
-        lambda: gr.update(visible=False), None, shared.gradio['file_deleter'])
+        lambda: gr.update(visible=False), None, gradio('file_deleter'))
 
-    shared.gradio['delete_cancel'].click(lambda: gr.update(visible=False), None, shared.gradio['file_deleter'])
+    shared.gradio['delete_cancel'].click(lambda: gr.update(visible=False), None, gradio('file_deleter'))
-    shared.gradio['save_cancel'].click(lambda: gr.update(visible=False), None, shared.gradio['file_saver'])
+    shared.gradio['save_cancel'].click(lambda: gr.update(visible=False), None, gradio('file_saver'))
     if shared.is_chat():
         shared.gradio['save_character_confirm'].click(
-            chat.save_character, [shared.gradio[k] for k in ['name2', 'greeting', 'context', 'character_picture', 'save_character_filename']], None).then(
+            chat.save_character, gradio('name2', 'greeting', 'context', 'character_picture', 'save_character_filename'), None).then(
-            lambda: gr.update(visible=False), None, shared.gradio['character_saver'])
+            lambda: gr.update(visible=False), None, gradio('character_saver'))
 
         shared.gradio['delete_character_confirm'].click(
-            chat.delete_character, shared.gradio['character_menu'], None).then(
+            chat.delete_character, gradio('character_menu'), None).then(
-            lambda: gr.update(visible=False), None, shared.gradio['character_deleter']).then(
+            lambda: gr.update(visible=False), None, gradio('character_deleter')).then(
-            lambda: gr.update(choices=utils.get_available_characters()), outputs=shared.gradio['character_menu'])
+            lambda: gr.update(choices=utils.get_available_characters()), None, gradio('character_menu'))
 
-        shared.gradio['save_character_cancel'].click(lambda: gr.update(visible=False), None, shared.gradio['character_saver'])
+        shared.gradio['save_character_cancel'].click(lambda: gr.update(visible=False), None, gradio('character_saver'))
-        shared.gradio['delete_character_cancel'].click(lambda: gr.update(visible=False), None, shared.gradio['character_deleter'])
+        shared.gradio['delete_character_cancel'].click(lambda: gr.update(visible=False), None, gradio('character_deleter'))
 
     shared.gradio['save_preset'].click(
-        ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        presets.generate_preset_yaml, shared.gradio['interface_state'], shared.gradio['save_contents']).then(
+        presets.generate_preset_yaml, gradio('interface_state'), gradio('save_contents')).then(
-        lambda: 'presets/', None, shared.gradio['save_root']).then(
+        lambda: 'presets/', None, gradio('save_root')).then(
-        lambda: 'My Preset.yaml', None, shared.gradio['save_filename']).then(
+        lambda: 'My Preset.yaml', None, gradio('save_filename')).then(
-        lambda: gr.update(visible=True), None, shared.gradio['file_saver'])
+        lambda: gr.update(visible=True), None, gradio('file_saver'))
 
     shared.gradio['delete_preset'].click(
-        lambda x: f'{x}.yaml', shared.gradio['preset_menu'], shared.gradio['delete_filename']).then(
+        lambda x: f'{x}.yaml', gradio('preset_menu'), gradio('delete_filename')).then(
-        lambda: 'presets/', None, shared.gradio['delete_root']).then(
+        lambda: 'presets/', None, gradio('delete_root')).then(
-        lambda: gr.update(visible=True), None, shared.gradio['file_deleter'])
+        lambda: gr.update(visible=True), None, gradio('file_deleter'))
+
+    if not shared.args.multi_user:
+
+        def load_session(session, state):
+            with open(Path(f'logs/{session}.json'), 'r') as f:
+                state.update(json.loads(f.read()))
+
+            if shared.is_chat():
+                chat.save_persistent_history(state['history'], state['character_menu'], state['mode'])
+
+            return state
+
+        if shared.is_chat():
+            shared.gradio['save_session'].click(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
+                lambda x: json.dumps(x, indent=4), gradio('interface_state'), gradio('save_contents')).then(
+                lambda: 'logs/', None, gradio('save_root')).then(
+                lambda x: f'session_{shared.get_mode()}_{x + "_" if x not in ["None", None, ""] else ""}{utils.current_time()}.json', gradio('character_menu'), gradio('save_filename')).then(
+                lambda: gr.update(visible=True), None, gradio('file_saver'))
+
+            shared.gradio['session_menu'].change(
+                load_session, gradio('session_menu', 'interface_state'), gradio('interface_state')).then(
+                ui.apply_interface_values, gradio('interface_state'), gradio(ui.list_interface_input_elements()), show_progress=False).then(
+                chat.redraw_html, shared.reload_inputs, gradio('display'))
+
+        else:
+            shared.gradio['save_session'].click(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
+                lambda x: json.dumps(x, indent=4), gradio('interface_state'), gradio('save_contents')).then(
+                lambda: 'logs/', None, gradio('save_root')).then(
+                lambda: f'session_{shared.get_mode()}_{utils.current_time()}.json', None, gradio('save_filename')).then(
+                lambda: gr.update(visible=True), None, gradio('file_saver'))
+
+            shared.gradio['session_menu'].change(
+                load_session, gradio('session_menu', 'interface_state'), gradio('interface_state')).then(
+                ui.apply_interface_values, gradio('interface_state'), gradio(ui.list_interface_input_elements()), show_progress=False)
+
+        shared.gradio['delete_session'].click(
+            lambda x: f'{x}.json', gradio('session_menu'), gradio('delete_filename')).then(
+            lambda: 'logs/', None, gradio('delete_root')).then(
+            lambda: gr.update(visible=True), None, gradio('file_deleter'))
 
 
 def set_interface_arguments(interface_mode, extensions, bool_active):
@@ -511,13 +609,17 @@ def create_interface():
 
         # Create chat mode interface
         if shared.is_chat():
-            shared.input_elements = ui.list_interface_input_elements(chat=True)
+            shared.input_elements = ui.list_interface_input_elements()
-            shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
+
-            shared.gradio['Chat input'] = gr.State()
+            shared.gradio.update({
-            shared.gradio['dummy'] = gr.State()
+                'interface_state': gr.State({k: None for k in shared.input_elements}),
+                'Chat input': gr.State(),
+                'dummy': gr.State(),
+                'history': gr.State({'internal': [], 'visible': []}),
+            })
 
             with gr.Tab('Text generation', elem_id='main'):
-                shared.gradio['display'] = gr.HTML(value=chat_html_wrapper(shared.history['visible'], shared.settings['name1'], shared.settings['name2'], 'chat', 'cai-chat'))
+                shared.gradio['display'] = gr.HTML(value=chat_html_wrapper({'internal': [], 'visible': []}, shared.settings['name1'], shared.settings['name2'], 'chat', 'cai-chat'))
                 shared.gradio['textbox'] = gr.Textbox(label='Input')
                 with gr.Row():
                     shared.gradio['Stop'] = gr.Button('Stop', elem_id='stop')
@@ -553,7 +655,7 @@ def create_interface():
                     with gr.Row():
                         with gr.Column(scale=8):
                             with gr.Row():
-                                shared.gradio['character_menu'] = gr.Dropdown(choices=utils.get_available_characters(), label='Character', elem_id='character-menu', info='Used in chat and chat-instruct modes.', elem_classes='slim-dropdown')
+                                shared.gradio['character_menu'] = gr.Dropdown(value='None', choices=utils.get_available_characters(), label='Character', elem_id='character-menu', info='Used in chat and chat-instruct modes.', elem_classes='slim-dropdown')
                                 ui.create_refresh_button(shared.gradio['character_menu'], lambda: None, lambda: {'choices': utils.get_available_characters()}, 'refresh-button')
                                 shared.gradio['save_character'] = gr.Button('💾', elem_classes='refresh-button')
                                 shared.gradio['delete_character'] = gr.Button('🗑️', elem_classes='refresh-button')
@@ -597,18 +699,25 @@ def create_interface():
                             shared.gradio['upload_json'] = gr.File(type='binary', file_types=['.json'], label='JSON File')
                             shared.gradio['upload_img_bot'] = gr.Image(type='pil', label='Profile Picture (optional)')
 
-                        shared.gradio['Upload character'] = gr.Button(value='Submit', interactive=False)
+                        shared.gradio['Submit character'] = gr.Button(value='Submit', interactive=False)
 
                     with gr.Tab('TavernAI'):
-                        shared.gradio['upload_img_tavern'] = gr.File(type='binary', file_types=['image'], label='TavernAI PNG File')
+                        with gr.Row():
-                        shared.gradio['Upload tavern character'] = gr.Button(value='Submit', interactive=False)
+                            with gr.Column():
+                                shared.gradio['upload_img_tavern'] = gr.Image(type='pil', label='TavernAI PNG File', elem_id="upload_img_tavern")
+                                shared.gradio['tavern_json'] = gr.State()
+                            with gr.Column():
+                                shared.gradio['tavern_name'] = gr.Textbox(value='', lines=1, label='Name', interactive=False)
+                                shared.gradio['tavern_desc'] = gr.Textbox(value='', lines=4, max_lines=4, label='Description', interactive=False)
+
+                        shared.gradio['Submit tavern character'] = gr.Button(value='Submit', interactive=False)
 
             with gr.Tab("Parameters", elem_id="parameters"):
                 create_settings_menus(default_preset)
 
         # Create notebook mode interface
         elif shared.args.notebook:
-            shared.input_elements = ui.list_interface_input_elements(chat=False)
+            shared.input_elements = ui.list_interface_input_elements()
             shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
             shared.gradio['last_input'] = gr.State('')
             with gr.Tab("Text generation", elem_id="main"):
@@ -647,7 +756,7 @@ def create_interface():
 
         # Create default mode interface
         else:
-            shared.input_elements = ui.list_interface_input_elements(chat=False)
+            shared.input_elements = ui.list_interface_input_elements()
             shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
             shared.gradio['last_input'] = gr.State('')
             with gr.Tab("Text generation", elem_id="main"):
@@ -691,8 +800,8 @@ def create_interface():
         with gr.Tab("Training", elem_id="training-tab"):
             training.create_train_interface()
 
-        # Interface mode tab
+        # Session tab
-        with gr.Tab("Interface mode", elem_id="interface-mode"):
+        with gr.Tab("Session", elem_id="session-tab"):
             modes = ["default", "notebook", "chat"]
             current_mode = "default"
             for mode in modes[1:]:
@@ -705,9 +814,12 @@ def create_interface():
             bool_active = [k for k in bool_list if vars(shared.args)[k]]
 
             with gr.Row():
-                shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode")
+
-                shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface", elem_classes="small-button")
+                with gr.Column():
-                shared.gradio['toggle_dark_mode'] = gr.Button('Toggle dark/light mode', elem_classes="small-button")
+                    with gr.Row():
+                        shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode", elem_classes='slim-dropdown')
+                        shared.gradio['reset_interface'] = gr.Button("Apply and restart", elem_classes="small-button", variant="primary")
+                        shared.gradio['toggle_dark_mode'] = gr.Button('Toggle 💡', elem_classes="small-button")
 
                     with gr.Row():
                         with gr.Column():
@@ -716,212 +828,237 @@ def create_interface():
                         with gr.Column():
                             shared.gradio['bool_menu'] = gr.CheckboxGroup(choices=bool_list, value=bool_active, label="Boolean command-line flags", elem_classes='checkboxgroup-table')
 
+                with gr.Column():
+                    if not shared.args.multi_user:
                         with gr.Row():
-                extension_name = gr.Textbox(lines=1, label='Install or update an extension', info='Enter the GitHub URL below. For a list of extensions, see: https://github.com/oobabooga/text-generation-webui-extensions ⚠️  WARNING ⚠️ : extensions can execute arbitrary code. Make sure to inspect their source code before activating them.')
+                            shared.gradio['session_menu'] = gr.Dropdown(choices=utils.get_available_sessions(), value='None', label='Session', elem_classes='slim-dropdown', info='When saving a session, make sure to keep the initial part of the filename (session_chat, session_notebook, or session_default), otherwise it will not appear on this list afterwards.')
-                extension_install = gr.Button('Install or update', elem_classes="small-button")
+                            ui.create_refresh_button(shared.gradio['session_menu'], lambda: None, lambda: {'choices': utils.get_available_sessions()}, ['refresh-button'])
+                            shared.gradio['save_session'] = gr.Button('💾', elem_classes=['refresh-button'])
+                            shared.gradio['delete_session'] = gr.Button('🗑️', elem_classes=['refresh-button'])
 
+                    extension_name = gr.Textbox(lines=1, label='Install or update an extension', info='Enter the GitHub URL below and press Enter. For a list of extensions, see: https://github.com/oobabooga/text-generation-webui-extensions ⚠️  WARNING ⚠️ : extensions can execute arbitrary code. Make sure to inspect their source code before activating them.')
                     extension_status = gr.Markdown()
 
-            extension_install.click(
+            extension_name.submit(
                 clone_or_pull_repository, extension_name, extension_status, show_progress=False).then(
-                lambda: gr.update(choices=utils.get_available_extensions(), value=shared.args.extensions), outputs=shared.gradio['extensions_menu'])
+                lambda: gr.update(choices=utils.get_available_extensions(), value=shared.args.extensions), None, gradio('extensions_menu'))
 
             # Reset interface event
             shared.gradio['reset_interface'].click(
-                set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'bool_menu']], None).then(
+                set_interface_arguments, gradio('interface_modes_menu', 'extensions_menu', 'bool_menu'), None).then(
                 lambda: None, None, None, _js='() => {document.body.innerHTML=\'<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>\'; setTimeout(function(){location.reload()},2500); return []}')
 
             shared.gradio['toggle_dark_mode'].click(lambda: None, None, None, _js='() => {document.getElementsByTagName("body")[0].classList.toggle("dark")}')
 
         # chat mode event handlers
         if shared.is_chat():
-            shared.input_params = [shared.gradio[k] for k in ['Chat input', 'start_with', 'interface_state']]
+            shared.input_params = gradio('Chat input', 'start_with', 'interface_state')
-            clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']]
+            clear_arr = gradio('Clear history-confirm', 'Clear history', 'Clear history-cancel')
-            shared.reload_inputs = [shared.gradio[k] for k in ['name1', 'name2', 'mode', 'chat_style']]
+            shared.reload_inputs = gradio('history', 'name1', 'name2', 'mode', 'chat_style')
 
             gen_events.append(shared.gradio['Generate'].click(
-                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-                lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then(
+                lambda x: (x, ''), gradio('textbox'), gradio('Chat input', 'textbox'), show_progress=False).then(
-                chat.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then(
+                chat.generate_chat_reply_wrapper, shared.input_params, gradio('display', 'history'), show_progress=False).then(
-                chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
+                chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
                 lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
             )
 
             gen_events.append(shared.gradio['textbox'].submit(
-                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-                lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then(
+                lambda x: (x, ''), gradio('textbox'), gradio('Chat input', 'textbox'), show_progress=False).then(
-                chat.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then(
+                chat.generate_chat_reply_wrapper, shared.input_params, gradio('display', 'history'), show_progress=False).then(
-                chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
+                chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
                 lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
             )
 
             gen_events.append(shared.gradio['Regenerate'].click(
-                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-                partial(chat.generate_chat_reply_wrapper, regenerate=True), shared.input_params, shared.gradio['display'], show_progress=False).then(
+                partial(chat.generate_chat_reply_wrapper, regenerate=True), shared.input_params, gradio('display', 'history'), show_progress=False).then(
-                chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
+                chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
                 lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
             )
 
             gen_events.append(shared.gradio['Continue'].click(
-                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-                partial(chat.generate_chat_reply_wrapper, _continue=True), shared.input_params, shared.gradio['display'], show_progress=False).then(
+                partial(chat.generate_chat_reply_wrapper, _continue=True), shared.input_params, gradio('display', 'history'), show_progress=False).then(
-                chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
+                chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None).then(
                 lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
             )
 
             gen_events.append(shared.gradio['Impersonate'].click(
-                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-                lambda x: x, shared.gradio['textbox'], shared.gradio['Chat input'], show_progress=False).then(
+                lambda x: x, gradio('textbox'), gradio('Chat input'), show_progress=False).then(
-                chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=False).then(
+                chat.impersonate_wrapper, shared.input_params, gradio('textbox'), show_progress=False).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
                 lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
             )
 
             shared.gradio['Replace last reply'].click(
-                chat.replace_last_reply, shared.gradio['textbox'], None).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-                lambda: '', None, shared.gradio['textbox'], show_progress=False).then(
+                chat.replace_last_reply, gradio('textbox', 'interface_state'), gradio('history')).then(
-                chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
+                lambda: '', None, gradio('textbox'), show_progress=False).then(
-                chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+                chat.redraw_html, shared.reload_inputs, gradio('display')).then(
+                chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None)
 
             shared.gradio['Send dummy message'].click(
-                chat.send_dummy_message, shared.gradio['textbox'], None).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-                lambda: '', None, shared.gradio['textbox'], show_progress=False).then(
+                chat.send_dummy_message, gradio('textbox', 'interface_state'), gradio('history')).then(
-                chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
+                lambda: '', None, gradio('textbox'), show_progress=False).then(
-                chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+                chat.redraw_html, shared.reload_inputs, gradio('display')).then(
+                chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None)
 
             shared.gradio['Send dummy reply'].click(
-                chat.send_dummy_reply, shared.gradio['textbox'], None).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-                lambda: '', None, shared.gradio['textbox'], show_progress=False).then(
+                chat.send_dummy_reply, gradio('textbox', 'interface_state'), gradio('history')).then(
-                chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
+                lambda: '', None, gradio('textbox'), show_progress=False).then(
-                chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+                chat.redraw_html, shared.reload_inputs, gradio('display')).then(
+                chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None)
 
+            shared.gradio['Clear history'].click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr)
+            shared.gradio['Clear history-cancel'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
             shared.gradio['Clear history-confirm'].click(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
                 lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr).then(
-                chat.clear_chat_log, [shared.gradio[k] for k in ['greeting', 'mode']], None).then(
+                chat.clear_chat_log, gradio('interface_state'), gradio('history')).then(
-                chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
+                chat.redraw_html, shared.reload_inputs, gradio('display')).then(
-                chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+                chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None)
+
+            shared.gradio['Remove last'].click(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
+                chat.remove_last_message, gradio('history'), gradio('textbox', 'history'), show_progress=False).then(
+                chat.redraw_html, shared.reload_inputs, gradio('display')).then(
+                chat.save_persistent_history, gradio('history', 'character_menu', 'mode'), None)
+
+            shared.gradio['character_menu'].change(
+                partial(chat.load_character, instruct=False), gradio('character_menu', 'name1', 'name2'), gradio('name1', 'name2', 'character_picture', 'greeting', 'context', 'dummy')).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
+                chat.load_persistent_history, gradio('interface_state'), gradio('history')).then(
+                chat.redraw_html, shared.reload_inputs, gradio('display'))
 
             shared.gradio['Stop'].click(
                 stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None).then(
-                chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+                chat.redraw_html, shared.reload_inputs, gradio('display'))
 
             shared.gradio['mode'].change(
-                lambda x: gr.update(visible=x != 'instruct'), shared.gradio['mode'], shared.gradio['chat_style'], show_progress=False).then(
+                lambda x: gr.update(visible=x != 'instruct'), gradio('mode'), gradio('chat_style'), show_progress=False).then(
-                chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+                chat.redraw_html, shared.reload_inputs, gradio('display'))
 
-            shared.gradio['chat_style'].change(chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+            shared.gradio['chat_style'].change(chat.redraw_html, shared.reload_inputs, gradio('display'))
             shared.gradio['instruction_template'].change(
-                partial(chat.load_character, instruct=True), [shared.gradio[k] for k in ['instruction_template', 'name1_instruct', 'name2_instruct']], [shared.gradio[k] for k in ['name1_instruct', 'name2_instruct', 'dummy', 'dummy', 'context_instruct', 'turn_template']])
+                partial(chat.load_character, instruct=True), gradio('instruction_template', 'name1_instruct', 'name2_instruct'), gradio('name1_instruct', 'name2_instruct', 'dummy', 'dummy', 'context_instruct', 'turn_template'))
 
             shared.gradio['upload_chat_history'].upload(
-                chat.load_history, [shared.gradio[k] for k in ['upload_chat_history', 'name1', 'name2']], None).then(
+                chat.load_history, gradio('upload_chat_history', 'history'), gradio('history')).then(
-                chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+                chat.redraw_html, shared.reload_inputs, gradio('display'))
 
-            shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, None, shared.gradio['textbox'], show_progress=False)
+            shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, gradio('history'), gradio('textbox'), show_progress=False)
-            shared.gradio['Clear history'].click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr)
-            shared.gradio['Clear history-cancel'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
-            shared.gradio['Remove last'].click(
-                chat.remove_last_message, None, shared.gradio['textbox'], show_progress=False).then(
-                chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
-                chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
 
             # Save/delete a character
             shared.gradio['save_character'].click(
-                lambda x: x, shared.gradio['name2'], shared.gradio['save_character_filename']).then(
+                lambda x: x, gradio('name2'), gradio('save_character_filename')).then(
-                lambda: gr.update(visible=True), None, shared.gradio['character_saver'])
+                lambda: gr.update(visible=True), None, gradio('character_saver'))
 
-            shared.gradio['delete_character'].click(lambda: gr.update(visible=True), None, shared.gradio['character_deleter'])
+            shared.gradio['delete_character'].click(lambda: gr.update(visible=True), None, gradio('character_deleter'))
 
             shared.gradio['save_template'].click(
-                lambda: 'My Template.yaml', None, shared.gradio['save_filename']).then(
+                lambda: 'My Template.yaml', None, gradio('save_filename')).then(
-                lambda: 'characters/instruction-following/', None, shared.gradio['save_root']).then(
+                lambda: 'characters/instruction-following/', None, gradio('save_root')).then(
-                chat.generate_instruction_template_yaml, [shared.gradio[k] for k in ['name1_instruct', 'name2_instruct', 'context_instruct', 'turn_template']], shared.gradio['save_contents']).then(
+                chat.generate_instruction_template_yaml, gradio('name1_instruct', 'name2_instruct', 'context_instruct', 'turn_template'), gradio('save_contents')).then(
-                lambda: gr.update(visible=True), None, shared.gradio['file_saver'])
+                lambda: gr.update(visible=True), None, gradio('file_saver'))
 
             shared.gradio['delete_template'].click(
-                lambda x: f'{x}.yaml', shared.gradio['instruction_template'], shared.gradio['delete_filename']).then(
+                lambda x: f'{x}.yaml', gradio('instruction_template'), gradio('delete_filename')).then(
-                lambda: 'characters/instruction-following/', None, shared.gradio['delete_root']).then(
+                lambda: 'characters/instruction-following/', None, gradio('delete_root')).then(
-                lambda: gr.update(visible=True), None, shared.gradio['file_deleter'])
+                lambda: gr.update(visible=True), None, gradio('file_deleter'))
 
-            shared.gradio['download_button'].click(lambda x: chat.save_history(x, timestamp=True, user_request=True), shared.gradio['mode'], shared.gradio['download'])
+            shared.gradio['download_button'].click(chat.save_history, gradio('history'), gradio('download'))
-            shared.gradio['Upload character'].click(chat.upload_character, [shared.gradio['upload_json'], shared.gradio['upload_img_bot']], [shared.gradio['character_menu']])
+            shared.gradio['Submit character'].click(chat.upload_character, gradio('upload_json', 'upload_img_bot'), gradio('character_menu'))
-            shared.gradio['upload_json'].upload(lambda: gr.update(interactive=True), None, [shared.gradio['Upload character']])
+            shared.gradio['upload_json'].upload(lambda: gr.update(interactive=True), None, gradio('Submit character'))
-            shared.gradio['upload_json'].clear(lambda: gr.update(interactive=False), None, [shared.gradio['Upload character']])
+            shared.gradio['upload_json'].clear(lambda: gr.update(interactive=False), None, gradio('Submit character'))
 
-            shared.gradio['character_menu'].change(
+            shared.gradio['Submit tavern character'].click(chat.upload_tavern_character, gradio('upload_img_tavern', 'tavern_json'), gradio('character_menu'))
-                partial(chat.load_character, instruct=False), [shared.gradio[k] for k in ['character_menu', 'name1', 'name2']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'dummy']]).then(
+            shared.gradio['upload_img_tavern'].upload(chat.check_tavern_character, gradio('upload_img_tavern'), gradio('tavern_name', 'tavern_desc', 'tavern_json', 'Submit tavern character'), show_progress=False)
-                chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
+            shared.gradio['upload_img_tavern'].clear(lambda: (None, None, None, gr.update(interactive=False)), None, gradio('tavern_name', 'tavern_desc', 'tavern_json', 'Submit tavern character'), show_progress=False)
-
-            shared.gradio['Upload tavern character'].click(chat.upload_tavern_character, [shared.gradio['upload_img_tavern'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['character_menu']])
-            shared.gradio['upload_img_tavern'].upload(lambda: gr.update(interactive=True), None, [shared.gradio['Upload tavern character']])
-            shared.gradio['upload_img_tavern'].clear(lambda: gr.update(interactive=False), None, [shared.gradio['Upload tavern character']])
             shared.gradio['your_picture'].change(
-                chat.upload_your_profile_picture, shared.gradio['your_picture'], None).then(
+                chat.upload_your_profile_picture, gradio('your_picture'), None).then(
-                partial(chat.redraw_html, reset_cache=True), shared.reload_inputs, shared.gradio['display'])
+                partial(chat.redraw_html, reset_cache=True), shared.reload_inputs, gradio('display'))
 
         # notebook/default modes event handlers
         else:
-            shared.input_params = [shared.gradio[k] for k in ['textbox', 'interface_state']]
+            shared.input_params = gradio('textbox', 'interface_state')
             if shared.args.notebook:
-                output_params = [shared.gradio[k] for k in ['textbox', 'html']]
+                output_params = gradio('textbox', 'html')
             else:
-                output_params = [shared.gradio[k] for k in ['output_textbox', 'html']]
+                output_params = gradio('output_textbox', 'html')
 
             gen_events.append(shared.gradio['Generate'].click(
-                lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then(
+                lambda x: x, gradio('textbox'), gradio('last_input')).then(
-                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
                 generate_reply_wrapper, shared.input_params, output_params, show_progress=False).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
                 lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
                 # lambda: None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}")
             )
 
             gen_events.append(shared.gradio['textbox'].submit(
-                lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then(
+                lambda x: x, gradio('textbox'), gradio('last_input')).then(
-                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
                 generate_reply_wrapper, shared.input_params, output_params, show_progress=False).then(
+                ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
                 lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
                 # lambda: None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}")
             )
 
             if shared.args.notebook:
-                shared.gradio['Undo'].click(lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False)
+                shared.gradio['Undo'].click(lambda x: x, gradio('last_input'), gradio('textbox'), show_progress=False)
-                shared.gradio['markdown_render'].click(lambda x: x, shared.gradio['textbox'], shared.gradio['markdown'], queue=False)
+                shared.gradio['markdown_render'].click(lambda x: x, gradio('textbox'), gradio('markdown'), queue=False)
                 gen_events.append(shared.gradio['Regenerate'].click(
-                    lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False).then(
+                    lambda x: x, gradio('last_input'), gradio('textbox'), show_progress=False).then(
-                    ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                    ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
                     generate_reply_wrapper, shared.input_params, output_params, show_progress=False).then(
+                    ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
                     lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
                     # lambda: None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}")
                 )
             else:
-                shared.gradio['markdown_render'].click(lambda x: x, shared.gradio['output_textbox'], shared.gradio['markdown'], queue=False)
+                shared.gradio['markdown_render'].click(lambda x: x, gradio('output_textbox'), gradio('markdown'), queue=False)
                 gen_events.append(shared.gradio['Continue'].click(
-                    ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                    ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
                     generate_reply_wrapper, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=False).then(
+                    ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
                     lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}")
                     # lambda: None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[1]; element.scrollTop = element.scrollHeight}")
                 )
 
             shared.gradio['Stop'].click(stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None)
-            shared.gradio['prompt_menu'].change(load_prompt, shared.gradio['prompt_menu'], shared.gradio['textbox'], show_progress=False)
+            shared.gradio['prompt_menu'].change(load_prompt, gradio('prompt_menu'), gradio('textbox'), show_progress=False)
             shared.gradio['save_prompt'].click(
-                lambda x: x, shared.gradio['textbox'], shared.gradio['save_contents']).then(
+                lambda x: x, gradio('textbox'), gradio('save_contents')).then(
-                lambda: 'prompts/', None, shared.gradio['save_root']).then(
+                lambda: 'prompts/', None, gradio('save_root')).then(
-                lambda: utils.current_time() + '.txt', None, shared.gradio['save_filename']).then(
+                lambda: utils.current_time() + '.txt', None, gradio('save_filename')).then(
-                lambda: gr.update(visible=True), None, shared.gradio['file_saver'])
+                lambda: gr.update(visible=True), None, gradio('file_saver'))
 
             shared.gradio['delete_prompt'].click(
-                lambda: 'prompts/', None, shared.gradio['delete_root']).then(
+                lambda: 'prompts/', None, gradio('delete_root')).then(
-                lambda x: x + '.txt', shared.gradio['prompt_menu'], shared.gradio['delete_filename']).then(
+                lambda x: x + '.txt', gradio('prompt_menu'), gradio('delete_filename')).then(
-                lambda: gr.update(visible=True), None, shared.gradio['file_deleter'])
+                lambda: gr.update(visible=True), None, gradio('file_deleter'))
 
-            shared.gradio['count_tokens'].click(count_tokens, shared.gradio['textbox'], shared.gradio['status'], show_progress=False)
+            shared.gradio['count_tokens'].click(count_tokens, gradio('textbox'), gradio('status'), show_progress=False)
 
         create_file_saving_event_handlers()
+
         shared.gradio['interface'].load(lambda: None, None, None, _js=f"() => {{{js}}}")
+        shared.gradio['interface'].load(partial(ui.apply_interface_values, {}, use_persistent=True), None, gradio(ui.list_interface_input_elements()), show_progress=False)
         if shared.settings['dark_theme']:
             shared.gradio['interface'].load(lambda: None, None, None, _js="() => document.getElementsByTagName('body')[0].classList.add('dark')")
 
-        shared.gradio['interface'].load(partial(ui.apply_interface_values, {}, use_persistent=True), None, [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False)
+        if shared.is_chat():
+            shared.gradio['interface'].load(chat.redraw_html, shared.reload_inputs, gradio('display'))
 
         # Extensions tabs
         extensions_module.create_extensions_tabs()
@@ -1018,7 +1155,11 @@ if __name__ == "__main__":
         if shared.args.lora:
             add_lora_to_model(shared.args.lora)
 
-    # Force a character to be loaded
+    # Forcing some events to be triggered on page load
+    shared.persistent_interface_state.update({
+        'loader': shared.args.loader or 'Transformers',
+    })
+
     if shared.is_chat():
         shared.persistent_interface_state.update({
             'mode': shared.settings['mode'],
@@ -1026,11 +1167,11 @@ if __name__ == "__main__":
             'instruction_template': shared.settings['instruction_template']
         })
 
-    shared.persistent_interface_state.update({
+        if Path("cache/pfp_character.png").exists():
-        'loader': shared.args.loader or 'Transformers',
+            Path("cache/pfp_character.png").unlink()
-    })
 
     shared.generation_lock = Lock()
+
     # Launch the web UI
     create_interface()
     while True: