Make the code more like PEP8

Also add torch and torchaudio back to the requirements, as silero needs them. Silero's requirements.txt should be everything needed to run the tests

.dockerignore

@@ -1,6 +1,7 @@
 .env
 Dockerfile
 /characters
+/extensions
 /loras
 /models
 /presets

Dockerfile

@@ -30,7 +30,8 @@ RUN apt-get update && \
     rm -rf /var/lib/apt/lists/*
 
 RUN --mount=type=cache,target=/root/.cache/pip pip3 install virtualenv
-RUN mkdir /app
+
+COPY . /app/
 
 WORKDIR /app
 
@@ -40,29 +41,21 @@ RUN test -n "${WEBUI_VERSION}" && git reset --hard ${WEBUI_VERSION} || echo "Usi
 RUN virtualenv /app/venv
 RUN . /app/venv/bin/activate && \
     pip3 install --upgrade pip setuptools && \
-    pip3 install torch torchvision torchaudio
+    pip3 install torch torchvision torchaudio && \
+    pip3 install -r requirements.txt
 
 COPY --from=builder /build /app/repositories/GPTQ-for-LLaMa
 RUN . /app/venv/bin/activate && \
     pip3 install /app/repositories/GPTQ-for-LLaMa/*.whl
 
-COPY extensions/api/requirements.txt /app/extensions/api/requirements.txt
-COPY extensions/elevenlabs_tts/requirements.txt /app/extensions/elevenlabs_tts/requirements.txt
-COPY extensions/google_translate/requirements.txt /app/extensions/google_translate/requirements.txt
-COPY extensions/silero_tts/requirements.txt /app/extensions/silero_tts/requirements.txt
-COPY extensions/whisper_stt/requirements.txt /app/extensions/whisper_stt/requirements.txt
+ENV CLI_ARGS=""
+
 RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/api && pip3 install -r requirements.txt
 RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/elevenlabs_tts && pip3 install -r requirements.txt
 RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/google_translate && pip3 install -r requirements.txt
 RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/silero_tts && pip3 install -r requirements.txt
 RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/whisper_stt && pip3 install -r requirements.txt
 
-COPY requirements.txt /app/requirements.txt
-RUN . /app/venv/bin/activate && \
-    pip3 install -r requirements.txt
-
 RUN cp /app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda118.so /app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so
 
-COPY . /app/
-ENV CLI_ARGS=""
 CMD . /app/venv/bin/activate && python3 server.py ${CLI_ARGS}

README.md

@@ -119,7 +119,7 @@ As an alternative to the recommended WSL method, you can install the web UI nati
 
 ```
 cp .env.example .env
-docker compose up --build
+docker-compose up --build
 ```
 
 Make sure to edit `.env.example` and set the appropriate CUDA version for your GPU.
@@ -191,82 +191,82 @@ Optionally, you can use the following command-line flags:
 
 #### Basic settings
 
-| Flag                                       | Description |
-|--------------------------------------------|-------------|
-| `-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. |
-| `--model MODEL`                            | Name of the model to load by default. |
-| `--lora LORA`                              | Name of the LoRA to apply to the model by default. |
-| `--model-dir MODEL_DIR`                    | Path to directory with all the models. |
-| `--lora-dir LORA_DIR`                      | Path to directory with all the loras. |
-| `--no-stream`                              | Don't stream the text output in real time. |
-| `--settings SETTINGS_FILE`                 | Load the default interface settings from this json file. See `settings-template.json` for an example. If you create a file called `settings.json`, this file will be loaded by default without the need to use the `--settings` flag. |
-| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
-| `--verbose`                                | Print the prompts to the terminal. |
+| Flag             | Description |
+|------------------|-------------|
+| `-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.|
+| `--model MODEL`  | Name of the model to load by default. |
+| `--lora LORA`    | Name of the LoRA to apply to the model by default. |
+|  `--model-dir MODEL_DIR`                    | Path to directory with all the models |
+|  `--lora-dir LORA_DIR`                      | Path to directory with all the loras |
+| `--no-stream`    | Don't stream the text output in real time. |
+| `--settings SETTINGS_FILE` | Load the default interface settings from this json file. See `settings-template.json` for an example. If you create a file called `settings.json`, this file will be loaded by default without the need to use the `--settings` flag.|
+|  `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
+|  `--verbose`                                | Print the prompts to the terminal. |
 
 #### Accelerate/transformers
 
-| Flag                                        | Description |
-|---------------------------------------------|-------------|
-| `--cpu`                                     | Use the CPU to generate text. |
-| `--auto-devices`                            | Automatically split the model across the available GPU(s) and CPU. |
-|  `--gpu-memory GPU_MEMORY [GPU_MEMORY ...]` | Maxmimum GPU memory in GiB to be allocated per GPU. Example: `--gpu-memory 10` for a single GPU, `--gpu-memory 10 5` for two GPUs. You can also set values in MiB like `--gpu-memory 3500MiB`. |
-| `--cpu-memory CPU_MEMORY`                   | Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.|
-| `--disk`                                    | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
-| `--disk-cache-dir DISK_CACHE_DIR`           | Directory to save the disk cache to. Defaults to `cache/`. |
-| `--load-in-8bit`                            | Load the model with 8-bit precision.|
-| `--bf16`                                    | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
-| `--no-cache`                                | Set `use_cache` to False while generating text. This reduces the VRAM usage a bit with a performance cost. |
+| Flag             | Description |
+|------------------|-------------|
+| `--cpu`          | Use the CPU to generate text.|
+| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.|
+|  `--gpu-memory GPU_MEMORY [GPU_MEMORY ...]` |  Maxmimum GPU memory in GiB to be allocated per GPU. Example: `--gpu-memory 10` for a single GPU, `--gpu-memory 10 5` for two GPUs. You can also set values in MiB like `--gpu-memory 3500MiB`. |
+| `--cpu-memory CPU_MEMORY` | Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.|
+| `--disk`         | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
+| `--disk-cache-dir DISK_CACHE_DIR` | Directory to save the disk cache to. Defaults to `cache/`. |
+| `--load-in-8bit` | Load the model with 8-bit precision.|
+| `--bf16`         | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
+| `--no-cache`     | Set `use_cache` to False while generating text. This reduces the VRAM usage a bit with a performance cost. |
 
 #### llama.cpp
 
-| Flag        | Description |
-|-------------|-------------|
-| `--threads` | Number of threads to use in llama.cpp. |
+| Flag             | Description |
+|------------------|-------------|
+| `--threads`     | Number of threads to use in llama.cpp. |
 
 #### GPTQ
 
-| Flag                      | Description |
-|---------------------------|-------------|
-| `--wbits WBITS`           | GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
-| `--model_type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. |
-| `--groupsize GROUPSIZE`   | GPTQ: Group size. |
-| `--pre_layer PRE_LAYER`   | GPTQ: The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. |
+| Flag             | Description |
+|------------------|-------------|
+| `--wbits WBITS`            | GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
+| `--model_type MODEL_TYPE`  | GPTQ: Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. |
+| `--groupsize GROUPSIZE`    | GPTQ: Group size. |
+| `--pre_layer PRE_LAYER`    | GPTQ: The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. |
 
 #### FlexGen
 
 | Flag             | Description |
 |------------------|-------------|
-| `--flexgen`                       | Enable the use of FlexGen offloading. |
-| `--percent PERCENT [PERCENT ...]` | FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0). |
-| `--compress-weight`               | FlexGen: Whether to compress weight (default: False).|
-| `--pin-weight [PIN_WEIGHT]`       | FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%). |
+| `--flexgen`      | Enable the use of FlexGen offloading. |
+|  `--percent PERCENT [PERCENT ...]` |  FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0). |
+|  `--compress-weight` |  FlexGen: Whether to compress weight (default: False).|
+|  `--pin-weight [PIN_WEIGHT]` |       FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%). |
 
 #### DeepSpeed
 
-| Flag                                  | Description |
-|---------------------------------------|-------------|
-| `--deepspeed`                         | Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. |
+| Flag             | Description |
+|------------------|-------------|
+| `--deepspeed`    | Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. |
 | `--nvme-offload-dir NVME_OFFLOAD_DIR` | DeepSpeed: Directory to use for ZeRO-3 NVME offloading. |
-| `--local_rank LOCAL_RANK`             | DeepSpeed: Optional argument for distributed setups. |
+| `--local_rank LOCAL_RANK` | DeepSpeed: Optional argument for distributed setups. |
 
 #### RWKV
 
-| Flag                            | Description |
-|---------------------------------|-------------|
-| `--rwkv-strategy RWKV_STRATEGY` | RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
-| `--rwkv-cuda-on`                | RWKV: Compile the CUDA kernel for better performance. |
+| Flag             | Description |
+|------------------|-------------|
+|  `--rwkv-strategy RWKV_STRATEGY` |    RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
+|  `--rwkv-cuda-on` |   RWKV: Compile the CUDA kernel for better performance. |
 
 #### Gradio
 
-| Flag                                  | Description |
-|---------------------------------------|-------------|
-| `--listen`                            | Make the web UI reachable from your local network. |
-| `--listen-port LISTEN_PORT`           | The listening port that the server will use. |
-| `--share`                             | Create a public URL. This is useful for running the web UI on Google Colab or similar. |
-| `--auto-launch`                       | Open the web UI in the default browser upon launch. |
-| `--gradio-auth-path GRADIO_AUTH_PATH` | Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3" |
+| Flag             | Description |
+|------------------|-------------|
+|  `--listen`                                 | Make the web UI reachable from your local network. |
+|  `--listen-port LISTEN_PORT`                | The listening port that the server will use. |
+|  `--share`                                  | Create a public URL. This is useful for running the web UI on Google Colab or similar. |
+|  `--auto-launch`                            | Open the web UI in the default browser upon launch. |
+|  `--gradio-auth-path GRADIO_AUTH_PATH`      | Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3" |
 
 Out of memory errors? [Check the low VRAM guide](https://github.com/oobabooga/text-generation-webui/wiki/Low-VRAM-guide).
 

css/chat.css

@@ -36,8 +36,3 @@ div.svelte-362y77>*, div.svelte-362y77>.form>* {
 .wrap.svelte-6roggh.svelte-6roggh {
   max-height: 92.5%;
 }
-
-/* This is for the microphone button in the whisper extension */
-.sm.svelte-1ipelgc {
-  width: 100%;
-}

extensions/elevenlabs_tts/script.py

@@ -2,11 +2,10 @@ import re
 from pathlib import Path
 
 import gradio as gr
+import modules.shared as shared
 from elevenlabslib import ElevenLabsUser
 from elevenlabslib.helpers import save_bytes_to_path
 
-import modules.shared as shared
-
 params = {
     'activate': True,
     'api_key': '12345',

extensions/gallery/script.py

@@ -87,10 +87,10 @@ def ui():
         update = gr.Button("Refresh")
         gr.HTML(value="<style>" + generate_css() + "</style>")
         gallery = gr.Dataset(components=[gr.HTML(visible=False)],
-                             label="",
-                             samples=generate_html(),
-                             elem_classes=["character-gallery"],
-                             samples_per_page=50
-                             )
+            label="",
+            samples=generate_html(),
+            elem_classes=["character-gallery"],
+            samples_per_page=50
+        )
     update.click(generate_html, [], gallery)
     gallery.select(select_character, None, gradio['character_menu'])

extensions/llama_prompts/script.py

@@ -1,7 +1,6 @@
 import gradio as gr
-import pandas as pd
-
 import modules.shared as shared
+import pandas as pd
 
 df = pd.read_csv("https://raw.githubusercontent.com/devbrones/llama-prompts/main/prompts/prompts.csv")
 

extensions/sd_api_pictures/README.MD

@@ -1,78 +0,0 @@
-## Description:
-TL;DR: Lets the bot answer you with a picture!  
-
-Stable Diffusion API pictures for TextGen, v.1.1.0  
-An extension to [oobabooga's textgen-webui](https://github.com/oobabooga/text-generation-webui) allowing you to receive pics generated by [Automatic1111's SD-WebUI API](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
-
-<details>
-<summary>Interface overview</summary>
-
-![Interface](https://raw.githubusercontent.com/Brawlence/texgen-webui-SD_api_pics/main/illust/Interface.jpg)
-
-</details>
-
-Load it in the `--chat` mode with `--extension sd_api_pictures` alongside `send_pictures` (it's not really required, but completes the picture, *pun intended*).  
-
-The image generation is triggered either:  
-- manually through the 'Force the picture response' button while in `Manual` or `Immersive/Interactive` modes OR  
-- automatically in `Immersive/Interactive` mode if the words `'send|main|message|me'` are followed by `'image|pic|picture|photo|snap|snapshot|selfie|meme'` in the user's prompt  
-- always on in Picturebook/Adventure mode (if not currently suppressed by 'Suppress the picture response')  
-
-## Prerequisites
-
-One needs an available instance of Automatic1111's webui running with an `--api` flag. Ain't tested with a notebook / cloud hosted one but should be possible.   
-To run it locally in parallel on the same machine, specify custom `--listen-port` for either Auto1111's or ooba's webUIs.  
-
-## Features:
-- API detection (press enter in the API box)  
-- VRAM management (model shuffling)  
-- Three different operation modes (manual, interactive, always-on)  
-- persistent settings via settings.json
-
-The model input is modified only in the interactive mode; other two are unaffected. The output pic description is presented differently for Picture-book / Adventure mode.  
-
-Connection check (insert the Auto1111's address and press Enter):  
-![API-check](https://raw.githubusercontent.com/Brawlence/texgen-webui-SD_api_pics/main/illust/API-check.gif) 
-
-### Persistents settings
-
-Create or modify the `settings.json` in the `text-generation-webui` root directory to override the defaults
-present in script.py, ex:
-
-```json
-{
-    "sd_api_pictures-manage_VRAM": 1,
-    "sd_api_pictures-save_img": 1,
-    "sd_api_pictures-prompt_prefix": "(Masterpiece:1.1), detailed, intricate, colorful, (solo:1.1)",
-    "sd_api_pictures-sampler_name": "DPM++ 2M Karras"
-}
-```
-
-will automatically set the `Manage VRAM` & `Keep original images` checkboxes and change the texts in `Prompt Prefix` and `Sampler name` on load.
-
----
-
-## Demonstrations:
-
-Those are examples of the version 1.0.0, but the core functionality is still the same
-
-<details>
-<summary>Conversation 1</summary>
-
-![EXA1](https://user-images.githubusercontent.com/42910943/224866564-939a3bcb-e7cf-4ac0-a33f-b3047b55054d.jpg)
-![EXA2](https://user-images.githubusercontent.com/42910943/224866566-38394054-1320-45cf-9515-afa76d9d7745.jpg)
-![EXA3](https://user-images.githubusercontent.com/42910943/224866568-10ea47b7-0bac-4269-9ec9-22c387a13b59.jpg)
-![EXA4](https://user-images.githubusercontent.com/42910943/224866569-326121ad-1ea1-4874-9f6b-4bca7930a263.jpg)
-
-
-</details>
-
-<details>
-<summary>Conversation 2</summary>
-
-![Hist1](https://user-images.githubusercontent.com/42910943/224865517-c6966b58-bc4d-4353-aab9-6eb97778d7bf.jpg)
-![Hist2](https://user-images.githubusercontent.com/42910943/224865527-b2fe7c2e-0da5-4c2e-b705-42e233b07084.jpg)
-![Hist3](https://user-images.githubusercontent.com/42910943/224865535-a38d94e7-8975-4a46-a655-1ae1de41f85d.jpg)
-
-</details>
-

extensions/sd_api_pictures/script.py

@@ -1,78 +1,34 @@
 import base64
 import io
 import re
-import time
-from datetime import date
 from pathlib import Path
 
 import gradio as gr
+import modules.chat as chat
 import modules.shared as shared
 import requests
 import torch
-from modules.models import reload_model, unload_model
 from PIL import Image
 
 torch._C._jit_set_profiling_mode(False)
 
 # parameters which can be customized in settings.json of webui
 params = {
+    'enable_SD_api': False,
     'address': 'http://127.0.0.1:7860',
-    'mode': 0,  # modes of operation: 0 (Manual only), 1 (Immersive/Interactive - looks for words to trigger), 2 (Picturebook Adventure - Always on)
-    'manage_VRAM': False,
     'save_img': False,
-    'SD_model': 'NeverEndingDream',  # not used right now
-    'prompt_prefix': '(Masterpiece:1.1), detailed, intricate, colorful',
+    'SD_model': 'NeverEndingDream',  # not really used right now
+    'prompt_prefix': '(Masterpiece:1.1), (solo:1.3), detailed, intricate, colorful',
     'negative_prompt': '(worst quality, low quality:1.3)',
-    'width': 512,
-    'height': 512,
-    'restore_faces': False,
-    'seed': -1,
-    'sampler_name': 'DDIM',
-    'steps': 32,
-    'cfg_scale': 7
+    'side_length': 512,
+    'restore_faces': False
 }
 
-
-def give_VRAM_priority(actor):
-    global shared, params
-
-    if actor == 'SD':
-        unload_model()
-        print("Requesting Auto1111 to re-load last checkpoint used...")
-        response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='')
-        response.raise_for_status()
-
-    elif actor == 'LLM':
-        print("Requesting Auto1111 to vacate VRAM...")
-        response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='')
-        response.raise_for_status()
-        reload_model()
-
-    elif actor == 'set':
-        print("VRAM mangement activated -- requesting Auto1111 to vacate VRAM...")
-        response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='')
-        response.raise_for_status()
-
-    elif actor == 'reset':
-        print("VRAM mangement deactivated -- requesting Auto1111 to reload checkpoint")
-        response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='')
-        response.raise_for_status()
-
-    else:
-        raise RuntimeError(f'Managing VRAM: "{actor}" is not a known state!')
-
-    response.raise_for_status()
-    del response
-
-
-if params['manage_VRAM']:
-    give_VRAM_priority('set')
-
-samplers = ['DDIM', 'DPM++ 2M Karras']  # TODO: get the availible samplers with http://{address}}/sdapi/v1/samplers
 SD_models = ['NeverEndingDream']  # TODO: get with http://{address}}/sdapi/v1/sd-models and allow user to select
 
 streaming_state = shared.args.no_stream  # remember if chat streaming was enabled
 picture_response = False  # specifies if the next model response should appear as a picture
+pic_id = 0
 
 
 def remove_surrounded_chars(string):
@@ -80,13 +36,7 @@ def remove_surrounded_chars(string):
     # 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
     return re.sub('\*[^\*]*?(\*|$)', '', string)
 
-
-def triggers_are_in(string):
-    string = remove_surrounded_chars(string)
-    # regex searches for send|main|message|me (at the end of the word) followed by
-    # a whole word of image|pic|picture|photo|snap|snapshot|selfie|meme(s),
-    # (?aims) are regex parser flags
-    return bool(re.search('(?aims)(send|mail|message|me)\\b.+?\\b(image|pic(ture)?|photo|snap(shot)?|selfie|meme)s?\\b', string))
+# I don't even need input_hijack for this as visible text will be commited to history as the unmodified string
 
 
 def input_modifier(string):
@@ -94,80 +44,75 @@ def input_modifier(string):
     This function is applied to your text inputs before
     they are fed into the model.
     """
-
-    global params
-
-    if not params['mode'] == 1:  # if not in immersive/interactive mode, do nothing
+    global params, picture_response
+    if not params['enable_SD_api']:
         return string
 
-    if triggers_are_in(string):  # if we're in it, check for trigger words
-        toggle_generation(True)
-        string = string.lower()
-        if "of" in string:
-            subject = string.split('of', 1)[1]  # subdivide the string once by the first 'of' instance and get what's coming after it
-            string = "Please provide a detailed and vivid description of " + subject
-        else:
-            string = "Please provide a detailed description of your appearance, your surroundings and what you are doing right now"
+    commands = ['send', 'mail', 'me']
+    mediums = ['image', 'pic', 'picture', 'photo']
+    subjects = ['yourself', 'own']
+    lowstr = string.lower()
+
+    # TODO: refactor out to separate handler and also replace detection with a regexp
+    if any(command in lowstr for command in commands) and any(case in lowstr for case in mediums):  # trigger the generation if a command signature and a medium signature is found
+        picture_response = True
+        shared.args.no_stream = True                                                               # Disable streaming cause otherwise the SD-generated picture would return as a dud
+        shared.processing_message = "*Is sending a picture...*"
+        string = "Please provide a detailed description of your surroundings, how you look and the situation you're in and what you are doing right now"
+        if any(target in lowstr for target in subjects):                                           # the focus of the image should be on the sending character
+            string = "Please provide a detailed and vivid description of how you look and what you are wearing"
 
     return string
 
 # Get and save the Stable Diffusion-generated picture
+
+
 def get_SD_pictures(description):
 
-    global params
-
-    if params['manage_VRAM']:
-        give_VRAM_priority('SD')
+    global params, pic_id
 
     payload = {
         "prompt": params['prompt_prefix'] + description,
-        "seed": params['seed'],
-        "sampler_name": params['sampler_name'],
-        "steps": params['steps'],
-        "cfg_scale": params['cfg_scale'],
-        "width": params['width'],
-        "height": params['height'],
+        "seed": -1,
+        "sampler_name": "DPM++ 2M Karras",
+        "steps": 32,
+        "cfg_scale": 7,
+        "width": params['side_length'],
+        "height": params['side_length'],
         "restore_faces": params['restore_faces'],
         "negative_prompt": params['negative_prompt']
     }
 
-    print(f'Prompting the image generator via the API on {params["address"]}...')
     response = requests.post(url=f'{params["address"]}/sdapi/v1/txt2img', json=payload)
-    response.raise_for_status()
     r = response.json()
 
     visible_result = ""
     for img_str in r['images']:
         image = Image.open(io.BytesIO(base64.b64decode(img_str.split(",", 1)[0])))
         if params['save_img']:
-            variadic = f'{date.today().strftime("%Y_%m_%d")}/{shared.character}_{int(time.time())}'
-            output_file = Path(f'extensions/sd_api_pictures/outputs/{variadic}.png')
-            output_file.parent.mkdir(parents=True, exist_ok=True)
+            output_file = Path(f'extensions/sd_api_pictures/outputs/{pic_id:06d}.png')
             image.save(output_file.as_posix())
-            visible_result = visible_result + f'<img src="/file/extensions/sd_api_pictures/outputs/{variadic}.png" alt="{description}" style="max-width: unset; max-height: unset;">\n'
-        else:
-            # lower the resolution of received images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history
-            image.thumbnail((300, 300))
-            buffered = io.BytesIO()
-            image.save(buffered, format="JPEG")
-            buffered.seek(0)
-            image_bytes = buffered.getvalue()
-            img_str = "data:image/jpeg;base64," + base64.b64encode(image_bytes).decode()
-            visible_result = visible_result + f'<img src="{img_str}" alt="{description}">\n'
-
-    if params['manage_VRAM']:
-        give_VRAM_priority('LLM')
+            pic_id += 1
+        # lower the resolution of received images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history
+        image.thumbnail((300, 300))
+        buffered = io.BytesIO()
+        image.save(buffered, format="JPEG")
+        buffered.seek(0)
+        image_bytes = buffered.getvalue()
+        img_str = "data:image/jpeg;base64," + base64.b64encode(image_bytes).decode()
+        visible_result = visible_result + f'<img src="{img_str}" alt="{description}">\n'
 
     return visible_result
 
 # 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):
     """
     This function is applied to the model outputs.
     """
-
-    global picture_response, params
+    global pic_id, picture_response, streaming_state
 
     if not picture_response:
         return string
@@ -180,18 +125,17 @@ def output_modifier(string):
 
     if string == '':
         string = 'no viable description in reply, try regenerating'
-        return string
 
-    text = ""
-    if (params['mode'] < 2):
-        toggle_generation(False)
-        text = f'*Sends a picture which portrays: “{string}”*'
-    else:
-        text = string
+    # I can't for the love of all that's holy get the name from shared.gradio['name1'], so for now it will be like this
+    text = f'*Description: "{string}"*'
 
-    string = get_SD_pictures(string) + "\n" + text
+    image = get_SD_pictures(string)
 
-    return string
+    picture_response = False
+
+    shared.processing_message = "*Is typing...*"
+    shared.args.no_stream = streaming_state
+    return image + "\n" + text
 
 
 def bot_prefix_modifier(string):
@@ -204,91 +148,42 @@ def bot_prefix_modifier(string):
     return string
 
 
-def toggle_generation(*args):
-    global picture_response, shared, streaming_state
-
-    if not args:
-        picture_response = not picture_response
-    else:
-        picture_response = args[0]
-
-    shared.args.no_stream = True if picture_response else streaming_state  # Disable streaming cause otherwise the SD-generated picture would return as a dud
-    shared.processing_message = "*Is sending a picture...*" if picture_response else "*Is typing...*"
-
-
-def filter_address(address):
-    address = address.strip()
-    # address = re.sub('http(s)?:\/\/|\/$','',address) # remove starting http:// OR https:// OR trailing slash
-    address = re.sub('\/$', '', address)  # remove trailing /s
-    if not address.startswith('http'):
-        address = 'http://' + address
-    return address
-
-
-def SD_api_address_update(address):
-
-    global params
-
-    msg = "✔️ SD API is found on:"
-    address = filter_address(address)
-    params.update({"address": address})
-    try:
-        response = requests.get(url=f'{params["address"]}/sdapi/v1/sd-models')
-        response.raise_for_status()
-        # r = response.json()
-    except:
-        msg = "❌ No SD API endpoint on:"
-
-    return gr.Textbox.update(label=msg)
+def force_pic():
+    global picture_response
+    picture_response = True
 
 
 def ui():
 
     # Gradio elements
-    # gr.Markdown('### Stable Diffusion API Pictures') # Currently the name of extension is shown as the title
-    with gr.Accordion("Parameters", open=True):
+    with gr.Accordion("Stable Diffusion api integration", open=True):
         with gr.Row():
-            address = gr.Textbox(placeholder=params['address'], value=params['address'], label='Auto1111\'s WebUI address')
-            mode = gr.Dropdown(["Manual", "Immersive/Interactive", "Picturebook/Adventure"], value="Manual", label="Mode of operation", type="index")
-            with gr.Column(scale=1, min_width=300):
-                manage_VRAM = gr.Checkbox(value=params['manage_VRAM'], label='Manage VRAM')
-                save_img = gr.Checkbox(value=params['save_img'], label='Keep original images and use them in chat')
+            with gr.Column():
+                enable = gr.Checkbox(value=params['enable_SD_api'], label='Activate SD Api integration')
+                save_img = gr.Checkbox(value=params['save_img'], label='Keep original received images in the outputs subdir')
+            with gr.Column():
+                address = gr.Textbox(placeholder=params['address'], value=params['address'], label='Stable Diffusion host address')
 
-            force_pic = gr.Button("Force the picture response")
-            suppr_pic = gr.Button("Suppress the picture response")
+        with gr.Row():
+            force_btn = gr.Button("Force the next response to be a picture")
+            generate_now_btn = gr.Button("Generate an image response to the input")
 
         with gr.Accordion("Generation parameters", open=False):
             prompt_prefix = gr.Textbox(placeholder=params['prompt_prefix'], value=params['prompt_prefix'], label='Prompt Prefix (best used to describe the look of the character)')
             with gr.Row():
-                with gr.Column():
-                    negative_prompt = gr.Textbox(placeholder=params['negative_prompt'], value=params['negative_prompt'], label='Negative Prompt')
-                    sampler_name = gr.Textbox(placeholder=params['sampler_name'], value=params['sampler_name'], label='Sampler')
-                with gr.Column():
-                    width = gr.Slider(256, 768, value=params['width'], step=64, label='Width')
-                    height = gr.Slider(256, 768, value=params['height'], step=64, label='Height')
-            with gr.Row():
-                steps = gr.Number(label="Steps:", value=params['steps'])
-                seed = gr.Number(label="Seed:", value=params['seed'])
-                cfg_scale = gr.Number(label="CFG Scale:", value=params['cfg_scale'])
+                negative_prompt = gr.Textbox(placeholder=params['negative_prompt'], value=params['negative_prompt'], label='Negative Prompt')
+                dimensions = gr.Slider(256, 702, value=params['side_length'], step=64, label='Image dimensions')
+                # model = gr.Dropdown(value=SD_models[0], choices=SD_models, label='Model')
 
     # Event functions to update the parameters in the backend
-    address.change(lambda x: params.update({"address": filter_address(x)}), address, None)
-    mode.select(lambda x: params.update({"mode": x}), mode, None)
-    mode.select(lambda x: toggle_generation(x > 1), inputs=mode, outputs=None)
-    manage_VRAM.change(lambda x: params.update({"manage_VRAM": x}), manage_VRAM, None)
-    manage_VRAM.change(lambda x: give_VRAM_priority('set' if x else 'reset'), inputs=manage_VRAM, outputs=None)
+    enable.change(lambda x: params.update({"enable_SD_api": x}), enable, None)
     save_img.change(lambda x: params.update({"save_img": x}), save_img, None)
-
-    address.submit(fn=SD_api_address_update, inputs=address, outputs=address)
+    address.change(lambda x: params.update({"address": x}), address, None)
     prompt_prefix.change(lambda x: params.update({"prompt_prefix": x}), prompt_prefix, None)
     negative_prompt.change(lambda x: params.update({"negative_prompt": x}), negative_prompt, None)
-    width.change(lambda x: params.update({"width": x}), width, None)
-    height.change(lambda x: params.update({"height": x}), height, None)
+    dimensions.change(lambda x: params.update({"side_length": x}), dimensions, None)
+    # model.change(lambda x: params.update({"SD_model": x}), model, None)
 
-    sampler_name.change(lambda x: params.update({"sampler_name": x}), sampler_name, None)
-    steps.change(lambda x: params.update({"steps": x}), steps, None)
-    seed.change(lambda x: params.update({"seed": x}), seed, None)
-    cfg_scale.change(lambda x: params.update({"cfg_scale": x}), cfg_scale, None)
-
-    force_pic.click(lambda x: toggle_generation(True), inputs=force_pic, outputs=None)
-    suppr_pic.click(lambda x: toggle_generation(False), inputs=suppr_pic, outputs=None)
+    force_btn.click(force_pic)
+    generate_now_btn.click(force_pic)
+    generate_now_btn.click(chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)

extensions/silero_tts/requirements.txt

@@ -3,3 +3,5 @@ num2words
 omegaconf
 pydub
 PyYAML
+torch
+torchaudio

extensions/silero_tts/script.py

@@ -3,11 +3,11 @@ from pathlib import Path
 
 import gradio as gr
 import torch
-
 from extensions.silero_tts import tts_preprocessor
 from modules import chat, shared
 from modules.html_generator import chat_html_wrapper
 
+
 torch._C._jit_set_profiling_mode(False)
 
 
@@ -22,7 +22,6 @@ params = {
     'autoplay': True,
     'voice_pitch': 'medium',
     'voice_speed': 'medium',
-    'local_cache_path': ''  # User can override the default cache path to something other via settings.json
 }
 
 current_params = params.copy()
@@ -46,16 +45,10 @@ def xmlesc(txt):
 
 
 def load_model():
-    torch_cache_path = torch.hub.get_dir() if params['local_cache_path'] == '' else params['local_cache_path']
-    model_path = torch_cache_path + "/snakers4_silero-models_master/src/silero/model/" + params['model_id'] + ".pt"
-    if Path(model_path).is_file():
-        print(f'\nUsing Silero TTS cached checkpoint found at {torch_cache_path}')
-        model, example_text = torch.hub.load(repo_or_dir=torch_cache_path + '/snakers4_silero-models_master/', model='silero_tts', language=params['language'], speaker=params['model_id'], source='local', path=model_path, force_reload=True)
-    else:
-        print(f'\nSilero TTS cache not found at {torch_cache_path}. Attempting to download...')
-        model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id'])
+    model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id'])
     model.to(params['device'])
     return model
+model = load_model()
 
 
 def remove_tts_from_history(name1, name2, mode):
@@ -104,7 +97,7 @@ def output_modifier(string):
             current_params = params.copy()
             break
 
-    if not params['activate']:
+    if params['activate'] == False:
         return string
 
     original_string = string
@@ -138,11 +131,6 @@ def bot_prefix_modifier(string):
     return string
 
 
-def setup():
-    global model
-    model = load_model()
-
-
 def ui():
     # Gradio elements
     with gr.Accordion("Silero TTS"):
@@ -161,6 +149,7 @@ def ui():
             convert_cancel = gr.Button('Cancel', visible=False)
             convert_confirm = gr.Button('Confirm (cannot be undone)', variant="stop", visible=False)
 
+
     # 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)

extensions/silero_tts/test_tts.py

@@ -2,8 +2,10 @@ import time
 from pathlib import Path
 
 import torch
+
 import tts_preprocessor
 
+
 torch._C._jit_set_profiling_mode(False)
 
 
@@ -43,8 +45,6 @@ def load_model():
     model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id'])
     model.to(params['device'])
     return model
-
-
 model = load_model()
 
 

extensions/silero_tts/tts_preprocessor.py

@@ -2,7 +2,8 @@ import re
 
 from num2words import num2words
 
-punctuation = r'[\s,.?!/)\'\]>]'
+# punctuation = r'[\s,.?!/)"\'\]>]“”'
+punctuation = r'[\s,.?!/)"\'\]>]'
 alphabet_map = {
     "A": " Ei ",
     "B": " Bee ",
@@ -38,8 +39,7 @@ def preprocess(string):
     # For example, you need to remove the commas in numbers before expanding them
     string = remove_surrounded_chars(string)
     string = string.replace('"', '')
-    string = string.replace('\u201D', '').replace('\u201C', '')  # right and left quote
-    string = string.replace('\u201F', '')  # italic looking quote
+    string = string.replace('“', '')
     string = string.replace('\n', ' ')
     string = convert_num_locale(string)
     string = replace_negative(string)
@@ -53,7 +53,6 @@ def preprocess(string):
     # For now, expand abbreviations to pronunciations
     # replace_abbreviations adds a lot of unnecessary whitespace to ensure separation
     string = replace_abbreviations(string)
-    string = replace_lowercase_abbreviations(string)
 
     # cleanup whitespaces
     # remove whitespace before punctuation
@@ -71,26 +70,6 @@ def remove_surrounded_chars(string):
     return re.sub(r'\*[^*]*?(\*|$)', '', string)
 
 
-def convert_num_locale(text):
-    # This detects locale and converts it to American without comma separators
-    pattern = re.compile(r'(?:\s|^)\d{1,3}(?:\.\d{3})+(,\d+)(?:\s|$)')
-    result = text
-    while True:
-        match = pattern.search(result)
-        if match is None:
-            break
-
-        start = match.start()
-        end = match.end()
-        result = result[0:start] + result[start:end].replace('.', '').replace(',', '.') + result[end:len(result)]
-
-    # removes comma separators from existing American numbers
-    pattern = re.compile(r'(\d),(\d)')
-    result = pattern.sub(r'\1\2', result)
-
-    return result
-
-
 def replace_negative(string):
     # handles situations like -5. -5 would become negative 5, which would then be expanded to negative five
     return re.sub(rf'(\s)(-)(\d+)({punctuation})', r'\1negative \3\4', string)
@@ -139,7 +118,7 @@ def num_to_words(text):
 
 def replace_abbreviations(string):
     # abbreviations 1 to 4 characters long. It will get things like A and I, but those are pronounced with their letter
-    pattern = re.compile(rf'(^|[\s(.\'\[<])([A-Z]{{1,4}})({punctuation}|$)')
+    pattern = re.compile(rf'(^|[\s("\'\[<])([A-Z]{{1,4}})({punctuation}|$)')
     result = string
     while True:
         match = pattern.search(result)
@@ -153,10 +132,26 @@ def replace_abbreviations(string):
     return result
 
 
-def replace_lowercase_abbreviations(string):
-    # abbreviations 1 to 4 characters long, separated by dots i.e. e.g.
-    pattern = re.compile(rf'(^|[\s(.\'\[<])(([a-z]\.){{1,4}})({punctuation}|$)')
-    result = string
+def replace_abbreviation(string):
+    result = ""
+    for char in string:
+        result = match_mapping(char, result)
+
+    return result
+
+
+def match_mapping(char, result):
+    for mapping in alphabet_map.keys():
+        if char == mapping:
+            return result + alphabet_map[char]
+
+    return result + char
+
+
+def convert_num_locale(text):
+    # This detects locale and converts it to American without comma separators
+    pattern = re.compile(r'(?:\s|^)\d{1,3}(?:\.\d{3})*(?:,\d+)?(?:\s|$)')
+    result = text
     while True:
         match = pattern.search(result)
         if match is None:
@@ -164,27 +159,15 @@ def replace_lowercase_abbreviations(string):
 
         start = match.start()
         end = match.end()
-        result = result[0:start] + replace_abbreviation(result[start:end].upper()) + result[end:len(result)]
+        result = result[0:start] + result[start:end].replace('.', '').replace(',', '.') + result[end:len(result)]
+
+    # removes comma separators from existing American numbers
+    pattern = re.compile(r'(\d),(\d)')
+    result = pattern.sub(r'\1\2', result)
 
     return result
 
 
-def replace_abbreviation(string):
-    result = ""
-    for char in string:
-        result += match_mapping(char)
-
-    return result
-
-
-def match_mapping(char):
-    for mapping in alphabet_map.keys():
-        if char == mapping:
-            return alphabet_map[char]
-
-    return char
-
-
 def __main__(args):
     print(preprocess(args[1]))
 

extensions/whisper_stt/script.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import speech_recognition as sr
-from modules import shared
 
 input_hijack = {
     'state': False,
@@ -8,7 +7,7 @@ input_hijack = {
 }
 
 
-def do_stt(audio):
+def do_stt(audio, text_state=""):
     transcription = ""
     r = sr.Recognizer()
 
@@ -22,23 +21,34 @@ def do_stt(audio):
     except sr.RequestError as e:
         print("Could not request results from Whisper", e)
 
-    return transcription
+    input_hijack.update({"state": True, "value": [transcription, transcription]})
+
+    text_state += transcription + " "
+    return text_state, text_state
 
 
-def auto_transcribe(audio, auto_submit):
+def update_hijack(val):
+    input_hijack.update({"state": True, "value": [val, val]})
+    return val
+
+
+def auto_transcribe(audio, audio_auto, text_state=""):
     if audio is None:
         return "", ""
-
-    transcription = do_stt(audio)
-    if auto_submit:
-        input_hijack.update({"state": True, "value": [transcription, transcription]})
-
-    return transcription, None
+    if audio_auto:
+        return do_stt(audio, text_state)
+    return "", ""
 
 
 def ui():
+    tr_state = gr.State(value="")
+    output_transcription = gr.Textbox(label="STT-Input",
+                                      placeholder="Speech Preview. Click \"Generate\" to send",
+                                      interactive=True)
+    output_transcription.change(fn=update_hijack, inputs=[output_transcription], outputs=[tr_state])
+    audio_auto = gr.Checkbox(label="Auto-Transcribe", value=True)
     with gr.Row():
         audio = gr.Audio(source="microphone")
-        auto_submit = gr.Checkbox(label='Submit the transcribed audio automatically', value=True)
-    audio.change(fn=auto_transcribe, inputs=[audio, auto_submit], outputs=[shared.gradio['textbox'], audio])
-    audio.change(None, auto_submit, None, _js="(check) => {if (check) { document.getElementById('Generate').click() }}")
+        audio.change(fn=auto_transcribe, inputs=[audio, audio_auto, tr_state], outputs=[output_transcription, tr_state])
+        transcribe_button = gr.Button(value="Transcribe")
+        transcribe_button.click(do_stt, inputs=[audio, tr_state], outputs=[output_transcription, tr_state])

modules/LoRA.py

@@ -4,7 +4,14 @@ import torch
 from peft import PeftModel
 
 import modules.shared as shared
-from modules.models import reload_model
+from modules.models import load_model
+from modules.text_generation import clear_torch_cache
+
+
+def reload_model():
+    shared.model = shared.tokenizer = None
+    clear_torch_cache()
+    shared.model, shared.tokenizer = load_model(shared.model_name)
 
 
 def add_lora_to_model(lora_name):

modules/chat.py

@@ -12,7 +12,7 @@ from PIL import Image
 import modules.extensions as extensions_module
 import modules.shared as shared
 from modules.extensions import apply_extensions
-from modules.html_generator import (chat_html_wrapper, fix_newlines,
+from modules.html_generator import (fix_newlines, chat_html_wrapper,
                                     make_thumbnail)
 from modules.text_generation import (encode, generate_reply,
                                      get_max_prompt_length)
@@ -105,16 +105,14 @@ def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_tu
     else:
         stopping_strings = [f"\n{name1}:", f"\n{name2}:"]
 
-    # Defining some variables
-    cumulative_reply = ''
-    just_started = True
-    name1_original = name1
-    visible_text = custom_generate_chat_prompt = None
     eos_token = '\n' if generate_state['stop_at_newline'] else None
+    name1_original = name1
     if 'pygmalion' in shared.model_name.lower():
         name1 = "You"
 
     # Check if any extension wants to hijack this function call
+    visible_text = None
+    custom_generate_chat_prompt = None
     for extension, _ in extensions_module.iterator():
         if hasattr(extension, 'input_hijack') and extension.input_hijack['state']:
             extension.input_hijack['state'] = False
@@ -126,7 +124,6 @@ def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_tu
         visible_text = text
     text = apply_extensions(text, "input")
 
-    # Generating the prompt
     kwargs = {'end_of_turn': end_of_turn, 'is_instruct': mode == 'instruct'}
     if custom_generate_chat_prompt is None:
         prompt = generate_chat_prompt(text, generate_state['max_new_tokens'], name1, name2, context, generate_state['chat_prompt_size'], **kwargs)
@@ -138,6 +135,8 @@ def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_tu
         yield shared.history['visible'] + [[visible_text, shared.processing_message]]
 
     # Generate
+    cumulative_reply = ''
+    just_started = True
     for i in range(generate_state['chat_generation_attempts']):
         reply = None
         for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", generate_state, eos_token=eos_token, stopping_strings=stopping_strings):
@@ -176,8 +175,6 @@ def impersonate_wrapper(text, generate_state, name1, name2, context, mode, end_o
     else:
         stopping_strings = [f"\n{name1}:", f"\n{name2}:"]
 
-    # Defining some variables
-    cumulative_reply = ''
     eos_token = '\n' if generate_state['stop_at_newline'] else None
     if 'pygmalion' in shared.model_name.lower():
         name1 = "You"
@@ -187,6 +184,7 @@ def impersonate_wrapper(text, generate_state, name1, name2, context, mode, end_o
     # Yield *Is typing...*
     yield shared.processing_message
 
+    cumulative_reply = ''
     for i in range(generate_state['chat_generation_attempts']):
         reply = None
         for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", generate_state, eos_token=eos_token, stopping_strings=stopping_strings):
@@ -208,7 +206,7 @@ def cai_chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_o
 
 
 def regenerate_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
-    if (len(shared.history['visible']) == 1 and not shared.history['visible'][0][0]) or len(shared.history['internal']) == 0:
+    if (shared.character != 'None' and len(shared.history['visible']) == 1) or len(shared.history['internal']) == 0:
         yield chat_html_wrapper(shared.history['visible'], name1, name2, mode)
     else:
         last_visible = shared.history['visible'].pop()
@@ -266,7 +264,7 @@ def redraw_html(name1, name2, mode):
 
 def tokenize_dialogue(dialogue, name1, name2, mode):
     history = []
-    messages = []
+
     dialogue = re.sub('<START>', '', dialogue)
     dialogue = re.sub('<start>', '', dialogue)
     dialogue = re.sub('(\n|^)[Aa]non:', '\\1You:', dialogue)
@@ -275,6 +273,7 @@ def tokenize_dialogue(dialogue, name1, name2, mode):
     if len(idx) == 0:
         return history
 
+    messages = []
     for i in range(len(idx) - 1):
         messages.append(dialogue[idx[i]:idx[i + 1]].strip())
     messages.append(dialogue[idx[-1]:].strip())

modules/extensions.py

@@ -11,31 +11,29 @@ setup_called = set()
 
 
 def load_extensions():
-    global state, setup_called
+    global state
     for i, name in enumerate(shared.args.extensions):
         if name in available_extensions:
             print(f'Loading the extension "{name}"... ', end='')
             try:
                 exec(f"import extensions.{name}.script")
-                extension = eval(f"extensions.{name}.script")
-                if extension not in setup_called and hasattr(extension, "setup"):
-                    setup_called.add(extension)
-                    extension.setup()
                 state[name] = [True, i]
                 print('Ok.')
             except:
                 print('Fail.')
                 traceback.print_exc()
 
-
 # This iterator returns the extensions in the order specified in the command-line
+
+
 def iterator():
     for name in sorted(state, key=lambda x: state[x][1]):
-        if state[name][0]:
+        if state[name][0] == True:
             yield eval(f"extensions.{name}.script"), name
 
-
 # Extension functions that map string -> string
+
+
 def apply_extensions(text, typ):
     for extension, _ in iterator():
         if typ == "input" and hasattr(extension, "input_modifier"):
@@ -59,9 +57,14 @@ def create_extensions_block():
                     extension.params[param] = shared.settings[_id]
 
     should_display_ui = False
+
+    # Running setup function
     for extension, name in iterator():
         if hasattr(extension, "ui"):
             should_display_ui = True
+        if extension not in setup_called and hasattr(extension, "setup"):
+            setup_called.add(extension)
+            extension.setup()
 
     # Creating the extension ui elements
     if should_display_ui:

modules/models.py

@@ -1,4 +1,3 @@
-import gc
 import json
 import os
 import re
@@ -17,10 +16,11 @@ import modules.shared as shared
 
 transformers.logging.set_verbosity_error()
 
+local_rank = None
+
 if shared.args.flexgen:
     from flexgen.flex_opt import CompressionConfig, ExecutionEnv, OptLM, Policy
 
-local_rank = None
 if shared.args.deepspeed:
     import deepspeed
     from transformers.deepspeed import (HfDeepSpeedConfig,
@@ -182,22 +182,6 @@ def load_model(model_name):
     return model, tokenizer
 
 
-def clear_torch_cache():
-    gc.collect()
-    if not shared.args.cpu:
-        torch.cuda.empty_cache()
-
-
-def unload_model():
-    shared.model = shared.tokenizer = None
-    clear_torch_cache()
-
-
-def reload_model():
-    unload_model()
-    shared.model, shared.tokenizer = load_model(shared.model_name)
-
-
 def load_soft_prompt(name):
     if name == 'None':
         shared.soft_prompt = False

modules/text_generation.py

@@ -1,3 +1,4 @@
+import gc
 import re
 import time
 import traceback
@@ -11,7 +12,7 @@ from modules.callbacks import (Iteratorize, Stream,
                                _SentinelTokenStoppingCriteria)
 from modules.extensions import apply_extensions
 from modules.html_generator import generate_4chan_html, generate_basic_html
-from modules.models import clear_torch_cache, local_rank
+from modules.models import local_rank
 
 
 def get_max_prompt_length(tokens):
@@ -100,6 +101,12 @@ def formatted_outputs(reply, model_name):
         return reply
 
 
+def clear_torch_cache():
+    gc.collect()
+    if not shared.args.cpu:
+        torch.cuda.empty_cache()
+
+
 def set_manual_seed(seed):
     if seed != -1:
         torch.manual_seed(seed)
@@ -120,22 +127,22 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
 
     original_question = question
     if not shared.is_chat():
-        question = apply_extensions(question, 'input')
+        question = apply_extensions(question, "input")
     if shared.args.verbose:
-        print(f'\n\n{question}\n--------------------\n')
+        print(f"\n\n{question}\n--------------------\n")
 
     # These models are not part of Hugging Face, so we handle them
     # separately and terminate the function call earlier
     if any((shared.is_RWKV, shared.is_llamacpp)):
         for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
             generate_params[k] = generate_state[k]
-        generate_params['token_count'] = generate_state['max_new_tokens']
+        generate_params["token_count"] = generate_state["max_new_tokens"]
         try:
             if shared.args.no_stream:
                 reply = shared.model.generate(context=question, **generate_params)
                 output = original_question + reply
                 if not shared.is_chat():
-                    reply = original_question + apply_extensions(reply, 'output')
+                    reply = original_question + apply_extensions(reply, "output")
                 yield formatted_outputs(reply, shared.model_name)
             else:
                 if not shared.is_chat():
@@ -146,7 +153,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
                 for reply in shared.model.generate_with_streaming(context=question, **generate_params):
                     output = original_question + reply
                     if not shared.is_chat():
-                        reply = original_question + apply_extensions(reply, 'output')
+                        reply = original_question + apply_extensions(reply, "output")
                     yield formatted_outputs(reply, shared.model_name)
 
         except Exception:
@@ -155,7 +162,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
             t1 = time.time()
             original_tokens = len(encode(original_question)[0])
             new_tokens = len(encode(output)[0]) - original_tokens
-            print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})')
+            print(f"Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})")
             return
 
     input_ids = encode(question, generate_state['max_new_tokens'])
@@ -171,30 +178,31 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
         t = [encode(string, 0, add_special_tokens=False) for string in stopping_strings]
         stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
 
+    generate_params["max_new_tokens"] = generate_state['max_new_tokens']
     if not shared.args.flexgen:
-        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']:
+        for k in ["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"]:
             generate_params[k] = generate_state[k]
-        generate_params['eos_token_id'] = eos_token_ids
-        generate_params['stopping_criteria'] = stopping_criteria_list
+        generate_params["eos_token_id"] = eos_token_ids
+        generate_params["stopping_criteria"] = stopping_criteria_list
         if shared.args.no_stream:
-            generate_params['min_length'] = 0
+            generate_params["min_length"] = 0
     else:
-        for k in ['max_new_tokens', 'do_sample', 'temperature']:
+        for k in ["do_sample", "temperature"]:
             generate_params[k] = generate_state[k]
-        generate_params['stop'] = generate_state['eos_token_ids'][-1]
+        generate_params["stop"] = generate_state["eos_token_ids"][-1]
         if not shared.args.no_stream:
-            generate_params['max_new_tokens'] = 8
+            generate_params["max_new_tokens"] = 8
 
     if shared.args.no_cache:
-        generate_params.update({'use_cache': False})
+        generate_params.update({"use_cache": False})
     if shared.args.deepspeed:
-        generate_params.update({'synced_gpus': True})
+        generate_params.update({"synced_gpus": True})
     if shared.soft_prompt:
         inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
-        generate_params.update({'inputs_embeds': inputs_embeds})
-        generate_params.update({'inputs': filler_input_ids})
+        generate_params.update({"inputs_embeds": inputs_embeds})
+        generate_params.update({"inputs": filler_input_ids})
     else:
-        generate_params.update({'inputs': input_ids})
+        generate_params.update({"inputs": input_ids})
 
     try:
         # Generate the entire reply at once.
@@ -209,7 +217,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
             new_tokens = len(output) - len(input_ids[0])
             reply = decode(output[-new_tokens:])
             if not shared.is_chat():
-                reply = original_question + apply_extensions(reply, 'output')
+                reply = original_question + apply_extensions(reply, "output")
 
             yield formatted_outputs(reply, shared.model_name)
 
@@ -236,7 +244,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
                     new_tokens = len(output) - len(input_ids[0])
                     reply = decode(output[-new_tokens:])
                     if not shared.is_chat():
-                        reply = original_question + apply_extensions(reply, 'output')
+                        reply = original_question + apply_extensions(reply, "output")
 
                     if output[-1] in eos_token_ids:
                         break
@@ -254,7 +262,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
                 new_tokens = len(output) - len(original_input_ids[0])
                 reply = decode(output[-new_tokens:])
                 if not shared.is_chat():
-                    reply = original_question + apply_extensions(reply, 'output')
+                    reply = original_question + apply_extensions(reply, "output")
 
                 if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
                     break
@@ -263,10 +271,10 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
                 input_ids = np.reshape(output, (1, output.shape[0]))
                 if shared.soft_prompt:
                     inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
-                    generate_params.update({'inputs_embeds': inputs_embeds})
-                    generate_params.update({'inputs': filler_input_ids})
+                    generate_params.update({"inputs_embeds": inputs_embeds})
+                    generate_params.update({"inputs": filler_input_ids})
                 else:
-                    generate_params.update({'inputs': input_ids})
+                    generate_params.update({"inputs": input_ids})
 
             yield formatted_outputs(reply, shared.model_name)
 
@@ -276,5 +284,5 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
         t1 = time.time()
         original_tokens = len(original_input_ids[0])
         new_tokens = len(output) - original_tokens
-        print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})')
+        print(f"Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})")
         return

modules/training.py

@@ -152,7 +152,7 @@ def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int
     # == Prep the dataset, format, etc ==
     if raw_text_file not in ['None', '']:
         print("Loading raw text file dataset...")
-        with open(clean_path('training/datasets', f'{raw_text_file}.txt'), 'r', encoding='utf-8') as file:
+        with open(clean_path('training/datasets', f'{raw_text_file}.txt'), 'r') as file:
             raw_text = file.read()
         tokens = shared.tokenizer.encode(raw_text)
         del raw_text  # Note: could be a gig for a large dataset, so delete redundant data as we go to be safe on RAM

server.py

@@ -15,11 +15,12 @@ import gradio as gr
 from PIL import Image
 
 import modules.extensions as extensions_module
-from modules import api, chat, shared, training, ui
+from modules import chat, shared, training, ui, api
 from modules.html_generator import chat_html_wrapper
 from modules.LoRA import add_lora_to_model
-from modules.models import load_model, load_soft_prompt, unload_model
-from modules.text_generation import generate_reply, stop_everything_event
+from modules.models import load_model, load_soft_prompt
+from modules.text_generation import (clear_torch_cache, generate_reply,
+                                     stop_everything_event)
 
 # Loading custom settings
 settings_file = None
@@ -78,6 +79,11 @@ def get_available_loras():
     return ['None'] + sorted([item.name for item in list(Path(shared.args.lora_dir).glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower)
 
 
+def unload_model():
+    shared.model = shared.tokenizer = None
+    clear_torch_cache()
+
+
 def load_model_wrapper(selected_model):
     if selected_model != shared.model_name:
         shared.model_name = selected_model
@@ -324,7 +330,7 @@ def create_interface():
                 shared.gradio['display'] = gr.HTML(value=chat_html_wrapper(shared.history['visible'], shared.settings['name1'], shared.settings['name2'], 'cai-chat'))
                 shared.gradio['textbox'] = gr.Textbox(label='Input')
                 with gr.Row():
-                    shared.gradio['Generate'] = gr.Button('Generate', elem_id='Generate')
+                    shared.gradio['Generate'] = gr.Button('Generate')
                     shared.gradio['Stop'] = gr.Button('Stop', elem_id="stop")
                 with gr.Row():
                     shared.gradio['Impersonate'] = gr.Button('Impersonate')
@@ -394,71 +400,55 @@ def create_interface():
                 create_settings_menus(default_preset)
 
             shared.input_params = [shared.gradio[k] for k in ['Chat input', 'generate_state', 'name1', 'name2', 'context', 'Chat mode', 'end_of_turn']]
-            clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']]
-            reload_inputs = [shared.gradio[k] for k in ['name1', 'name2', 'Chat mode']]
 
             def set_chat_input(textbox):
                 return textbox, ""
 
-            gen_events.append(shared.gradio['Generate'].click(
-                set_chat_input, shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then(
-                chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
-                lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
-                lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
-            )
-
-            gen_events.append(shared.gradio['textbox'].submit(
-                set_chat_input, shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then(
-                chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
-                lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
-                lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
-            )
-
-            gen_events.append(shared.gradio['Regenerate'].click(
-                chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
-                lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
-                lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
-            )
-
-            shared.gradio['Replace last reply'].click(
-                chat.replace_last_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'Chat mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then(
-                lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
-                lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
-
-            shared.gradio['Clear history-confirm'].click(
-                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 ['name1', 'name2', 'greeting', 'Chat mode']], shared.gradio['display']).then(
-                lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
-
-            shared.gradio['Stop'].click(
-                stop_everything_event, [], [], queue=False, cancels=gen_events if shared.args.no_stream else None).then(
-                chat.redraw_html, reload_inputs, [shared.gradio['display']])
-
-            shared.gradio['Chat mode'].change(
-                lambda x: gr.update(visible=x == 'instruct'), shared.gradio['Chat mode'], shared.gradio['Instruction templates']).then(
-                chat.redraw_html, reload_inputs, [shared.gradio['display']])
-
-            shared.gradio['Instruction templates'].change(
-                lambda character, name1, name2, mode: chat.load_character(character, name1, name2, mode), [shared.gradio[k] for k in ['Instruction templates', 'name1', 'name2', 'Chat mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'end_of_turn', 'display']]).then(
-                chat.redraw_html, reload_inputs, [shared.gradio['display']])
-
-            shared.gradio['upload_chat_history'].upload(
-                chat.load_history, [shared.gradio[k] for k in ['upload_chat_history', 'name1', 'name2']], []).then(
-                chat.redraw_html, reload_inputs, [shared.gradio['display']])
-
+            gen_events.append(shared.gradio['Generate'].click(set_chat_input, shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False))
+            gen_events.append(shared.gradio['Generate'].click(chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
+            gen_events.append(shared.gradio['textbox'].submit(set_chat_input, shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False))
+            gen_events.append(shared.gradio['textbox'].submit(chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
+            gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
             gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream))
+            shared.gradio['Stop'].click(stop_everything_event, [], [], queue=False, cancels=gen_events if shared.args.no_stream else None)
+
             shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, [], shared.gradio['textbox'], show_progress=shared.args.no_stream)
+            shared.gradio['Replace last reply'].click(chat.replace_last_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'Chat mode']], shared.gradio['display'], show_progress=shared.args.no_stream)
+
+            # Clear history with confirmation
+            clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']]
             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-confirm'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
+            shared.gradio['Clear history-confirm'].click(chat.clear_chat_log, [shared.gradio[k] for k in ['name1', 'name2', 'greeting', 'Chat mode']], shared.gradio['display'])
             shared.gradio['Clear history-cancel'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
+            shared.gradio['Chat mode'].change(lambda x: gr.update(visible=x == 'instruct'), shared.gradio['Chat mode'], shared.gradio['Instruction templates'])
+
             shared.gradio['Remove last'].click(chat.remove_last_message, [shared.gradio[k] for k in ['name1', 'name2', 'Chat mode']], [shared.gradio['display'], shared.gradio['textbox']], show_progress=False)
             shared.gradio['download_button'].click(chat.save_history, inputs=[], outputs=[shared.gradio['download']])
             shared.gradio['Upload character'].click(chat.upload_character, [shared.gradio['upload_json'], shared.gradio['upload_img_bot']], [shared.gradio['character_menu']])
+
+            # Clearing stuff and saving the history
+            for i in ['Generate', 'Regenerate', 'Replace last reply']:
+                shared.gradio[i].click(lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False)
+                shared.gradio[i].click(lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
+            shared.gradio['Clear history-confirm'].click(lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
+            shared.gradio['textbox'].submit(lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False)
+            shared.gradio['textbox'].submit(lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
+
             shared.gradio['character_menu'].change(chat.load_character, [shared.gradio[k] for k in ['character_menu', 'name1', 'name2', 'Chat mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'end_of_turn', 'display']])
+            shared.gradio['Instruction templates'].change(lambda character, name1, name2, mode: chat.load_character(character, name1, name2, mode), [shared.gradio[k] for k in ['Instruction templates', 'name1', 'name2', 'Chat mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'end_of_turn', 'display']])
+            shared.gradio['upload_chat_history'].upload(chat.load_history, [shared.gradio[k] for k in ['upload_chat_history', 'name1', 'name2']], [])
             shared.gradio['upload_img_tavern'].upload(chat.upload_tavern_character, [shared.gradio['upload_img_tavern'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['character_menu']])
             shared.gradio['your_picture'].change(chat.upload_your_profile_picture, [shared.gradio[k] for k in ['your_picture', 'name1', 'name2', 'Chat mode']], shared.gradio['display'])
 
+            reload_inputs = [shared.gradio[k] for k in ['name1', 'name2', 'Chat mode']]
+            shared.gradio['upload_chat_history'].upload(chat.redraw_html, reload_inputs, [shared.gradio['display']])
+            shared.gradio['Stop'].click(chat.redraw_html, reload_inputs, [shared.gradio['display']])
+            shared.gradio['Instruction templates'].change(chat.redraw_html, reload_inputs, [shared.gradio['display']])
+            shared.gradio['Chat mode'].change(chat.redraw_html, reload_inputs, [shared.gradio['display']])
+
             shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js+ui.chat_js}}}")
-            shared.gradio['interface'].load(chat.load_default_history, [shared.gradio[k] for k in ['name1', 'name2']], None)
+            shared.gradio['interface'].load(lambda: chat.load_default_history(shared.settings['name1'], shared.settings['name2']), None, None)
             shared.gradio['interface'].load(chat.redraw_html, reload_inputs, [shared.gradio['display']], show_progress=True)
 
         elif shared.args.notebook:
@@ -551,12 +541,10 @@ def create_interface():
             shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode")
             shared.gradio['extensions_menu'] = gr.CheckboxGroup(choices=get_available_extensions(), value=shared.args.extensions, label="Available extensions")
             shared.gradio['bool_menu'] = gr.CheckboxGroup(choices=bool_list, value=bool_active, label="Boolean command-line flags")
-            shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface")
+            shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface", type="primary")
 
-            # 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(
-                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['reset_interface'].click(set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'bool_menu']], None)
+            shared.gradio['reset_interface'].click(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 []}')
 
         if shared.args.extensions is not None:
             extensions_module.create_extensions_block()