Fix silero_tts

Useful for starting a new reply.

.dockerignore

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

Dockerfile

@@ -26,12 +26,11 @@ LABEL maintainer="Your Name <your.email@example.com>"
 LABEL description="Docker image for GPTQ-for-LLaMa and Text Generation WebUI"
 
 RUN apt-get update && \
-    apt-get install --no-install-recommends -y git python3 python3-pip && \
+    apt-get install --no-install-recommends -y git python3 python3-pip make g++ && \
     rm -rf /var/lib/apt/lists/*
 
 RUN --mount=type=cache,target=/root/.cache/pip pip3 install virtualenv
-
+RUN mkdir /app
-COPY . /app/
 
 WORKDIR /app
 
@@ -41,21 +40,29 @@ 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
 
-ENV CLI_ARGS=""
+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
 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

@@ -1,11 +1,9 @@
 # Text generation web UI
 
-A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, OPT, and GALACTICA.
+A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.
 
 Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) of text generation.
 
-[[Try it on Google Colab]](https://colab.research.google.com/github/oobabooga/AI-Notebooks/blob/main/Colab-TextGen-GPU.ipynb)
-
 |![Image1](https://github.com/oobabooga/screenshots/raw/main/qa.png) | ![Image2](https://github.com/oobabooga/screenshots/raw/main/cai3.png) |
 |:---:|:---:|
 |![Image3](https://github.com/oobabooga/screenshots/raw/main/gpt4chan.png) | ![Image4](https://github.com/oobabooga/screenshots/raw/main/galactica.png) |
@@ -15,7 +13,7 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
 * Dropdown menu for switching between models
 * Notebook mode that resembles OpenAI's playground
 * Chat mode for conversation and role playing
-* Instruct mode compatible with Alpaca and Open Assistant formats **\*NEW!\***
+* Instruct mode compatible with Alpaca, Vicuna, and Open Assistant formats **\*NEW!\***
 * Nice HTML output for GPT-4chan
 * Markdown output for [GALACTICA](https://github.com/paperswithcode/galai), including LaTeX rendering
 * [Custom chat characters](https://github.com/oobabooga/text-generation-webui/wiki/Custom-chat-characters)
@@ -34,7 +32,6 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
 * [LoRA (loading and training)](https://github.com/oobabooga/text-generation-webui/wiki/Using-LoRAs)
 * Softprompts
 * [Extensions](https://github.com/oobabooga/text-generation-webui/wiki/Extensions)
-* [Google Colab](https://github.com/oobabooga/text-generation-webui/wiki/Running-on-Colab)
 
 ## Installation
 
@@ -73,9 +70,15 @@ On Linux or WSL, it can be automatically installed with these two commands:
 curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh"
 bash Miniconda3.sh
 ```
-
 Source: https://educe-ubc.github.io/conda.html
 
+#### 0.1 (Ubuntu/WSL) Install build tools
+
+```
+sudo apt install build-essential
+```
+
+
 #### 1. Create a new conda environment
 
 ```
@@ -119,7 +122,7 @@ 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.
@@ -192,25 +195,25 @@ Optionally, you can use the following command-line flags:
 #### Basic settings
 
 | Flag                                       | Description |
-|------------------|-------------|
+|--------------------------------------------|-------------|
-| `-h`, `--help`   | show this help message and exit |
+| `-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.|
+| `--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 |
+| `--model-dir MODEL_DIR`                    | Path to directory with all the models. |
-|  `--lora-dir LORA_DIR`                      | Path to directory with all the loras |
+| `--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.|
+| `--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.|
+| `--cpu`                                     | Use the CPU to generate text. Warning: Training on CPU is extremely slow.|
-| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.|
+| `--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. |
@@ -218,17 +221,19 @@ Optionally, you can use the following command-line flags:
 | `--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. |
+| `--xformers`                                | Use xformer's memory efficient attention. This should increase your tokens/s. |
+| `--sdp-attention`                           | Use torch 2.0's sdp attention. |
 
 #### 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. |
@@ -246,7 +251,7 @@ Optionally, you can use the following command-line flags:
 #### DeepSpeed
 
 | 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. |
@@ -254,14 +259,14 @@ Optionally, you can use the following command-line flags:
 #### 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. |
 
 #### 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. |
@@ -286,6 +291,8 @@ Check the [wiki](https://github.com/oobabooga/text-generation-webui/wiki/System-
 
 Pull requests, suggestions, and issue reports are welcome. 
 
+You are also welcome to review open pull requests.
+
 Before reporting a bug, make sure that you have:
 
 1. Created a conda environment and installed the dependencies exactly as in the *Installation* section above.

api-example-stream.py

@@ -12,6 +12,11 @@ import string
 
 import websockets
 
+# Note, Gradio may pick a different fn value as the definition of the Gradio app changes.
+# You can always launch the web UI and inspect the websocket stream using your browser's dev tools
+# to determine what value Gradio expects here.
+GRADIO_FN = 29
+
 
 def random_hash():
     letters = string.ascii_lowercase + string.digits
@@ -36,6 +41,10 @@ async def run(context):
         'length_penalty': 1,
         'early_stopping': False,
         'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'custom_stopping_strings': [],
+        'ban_eos_token': False
     }
     payload = json.dumps([context, params])
     session = random_hash()
@@ -47,14 +56,14 @@ async def run(context):
                 case "send_hash":
                     await websocket.send(json.dumps({
                         "session_hash": session,
-                        "fn_index": 12
+                        "fn_index": GRADIO_FN
                     }))
                 case "estimation":
                     pass
                 case "send_data":
                     await websocket.send(json.dumps({
                         "session_hash": session,
-                        "fn_index": 12,
+                        "fn_index": GRADIO_FN,
                         "data": [
                             payload
                         ]

api-example.py

@@ -22,10 +22,10 @@ server = "127.0.0.1"
 params = {
     'max_new_tokens': 200,
     'do_sample': True,
-    'temperature': 0.5,
+    'temperature': 0.72,
-    'top_p': 0.9,
+    'top_p': 0.73,
     'typical_p': 1,
-    'repetition_penalty': 1.05,
+    'repetition_penalty': 1.1,
     'encoder_repetition_penalty': 1.0,
     'top_k': 0,
     'min_length': 0,
@@ -35,6 +35,10 @@ params = {
     'length_penalty': 1,
     'early_stopping': False,
     'seed': -1,
+    'add_bos_token': True,
+    'custom_stopping_strings': [],
+    'truncation_length': 2048,
+    'ban_eos_token': False,
 }
 
 # Input prompt

css/chat.css

@@ -36,3 +36,8 @@ 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%;
+}

css/html_cai_style.css

@@ -7,11 +7,13 @@
     padding-right: 20px;
     display: flex;
     flex-direction: column-reverse;
+    word-break: break-word;
+    overflow-wrap: anywhere;
 }
 
 .message {
     display: grid;
-    grid-template-columns: 60px 1fr;
+    grid-template-columns: 60px minmax(0, 1fr);
     padding-bottom: 25px;
     font-size: 15px;
     font-family: Helvetica, Arial, sans-serif;
@@ -73,6 +75,13 @@
     display: inline !important;
 }
 
+.message-body code {
+    overflow-x: auto;
+}
+.message-body :not(pre) > code {
+    white-space: normal !important;
+}
+
 .dark .message-body p em {
     color: rgb(138, 138, 138) !important;
 }

css/html_instruct_style.css

@@ -7,6 +7,8 @@
     padding-right: 20px;
     display: flex;
     flex-direction: column-reverse;
+    word-break: break-word;
+    overflow-wrap: anywhere;
 }
 
 .message {
@@ -25,9 +27,7 @@
 .message-body {}
 
 .message-body p {
-    margin-bottom: 0 !important;
     font-size: 15px !important;
-    line-height: 1.428571429 !important;
 }
 
 .message-body li {
@@ -39,6 +39,13 @@
     display: inline !important;
 }
 
+.message-body code {
+    overflow-x: auto;
+}
+.message-body :not(pre) > code {
+    white-space: normal !important;
+}
+
 .dark .message-body p em {
     color: rgb(138, 138, 138) !important;
 }
@@ -51,15 +58,16 @@
   padding: 15px;
   border-radius: 20px;
   background-color: #0000000f;
-  margin-bottom: 17.5px;
+  margin-top: 9px !important;
+  margin-bottom: 18px !important;
 }
 
 .gradio-container .chat .user-message {
   padding: 15px;
   border-radius: 20px;
-  margin-bottom: 17.5px !important;
+  margin-bottom: 9px !important;
 }
 
 .dark .chat .assistant-message {
-  background-color: #ffffff21;
+  background-color: #374151;
 }

css/main.css

@@ -67,3 +67,13 @@ span.math.inline {
 div.svelte-15lo0d8 > *, div.svelte-15lo0d8 > .form > * {
   flex-wrap: nowrap;
 }
+
+.header_bar {
+  background-color: #f7f7f7;
+  margin-bottom: 40px;
+}
+
+.dark .header_bar {
+  border: none !important;
+  background-color: #8080802b;
+}

css/main.js

@@ -1,4 +1,4 @@
-document.getElementById("main").parentNode.childNodes[0].style = "border: none; background-color: #8080802b; margin-bottom: 40px";
+document.getElementById("main").parentNode.childNodes[0].classList.add("header_bar");
 document.getElementById("main").parentNode.style = "padding: 0; margin: 0";
 document.getElementById("main").parentNode.parentNode.parentNode.style = "padding: 0";
 

docker-compose.yml

@@ -6,7 +6,6 @@ services:
       args:
         # specify which cuda version your card supports: https://developer.nvidia.com/cuda-gpus
         TORCH_CUDA_ARCH_LIST: ${TORCH_CUDA_ARCH_LIST}
-        GPTQ_VERSION: ${GPTQ_VERSION}
         WEBUI_VERSION: ${WEBUI_VERSION}
     env_file: .env
     ports:

download-model.py

@@ -19,50 +19,6 @@ import requests
 import tqdm
 from tqdm.contrib.concurrent import thread_map
 
-parser = argparse.ArgumentParser()
-parser.add_argument('MODEL', type=str, default=None, nargs='?')
-parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.')
-parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.')
-parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).')
-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.')
-args = parser.parse_args()
-
-
-def get_file(url, output_folder):
-    filename = Path(url.rsplit('/', 1)[1])
-    output_path = output_folder / filename
-    if output_path.exists() and not args.clean:
-        # Check if the file has already been downloaded completely
-        r = requests.get(url, stream=True)
-        total_size = int(r.headers.get('content-length', 0))
-        if output_path.stat().st_size >= total_size:
-            return
-        # Otherwise, resume the download from where it left off
-        headers = {'Range': f'bytes={output_path.stat().st_size}-'}
-        mode = 'ab'
-    else:
-        headers = {}
-        mode = 'wb'
-
-    r = requests.get(url, stream=True, headers=headers)
-    with open(output_path, mode) as f:
-        total_size = int(r.headers.get('content-length', 0))
-        block_size = 1024
-        with tqdm.tqdm(total=total_size, unit='iB', unit_scale=True, bar_format='{l_bar}{bar}| {n_fmt:6}/{total_fmt:6} {rate_fmt:6}') as t:
-            for data in r.iter_content(block_size):
-                t.update(len(data))
-                f.write(data)
-
-
-def sanitize_branch_name(branch_name):
-    pattern = re.compile(r"^[a-zA-Z0-9._-]+$")
-    if pattern.match(branch_name):
-        return branch_name
-    else:
-        raise ValueError("Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.")
-
 
 def select_model_from_default_options():
     models = {
@@ -110,7 +66,20 @@ EleutherAI/pythia-1.4b-deduped
     return model, branch
 
 
-def get_download_links_from_huggingface(model, branch):
+def sanitize_model_and_branch_names(model, branch):
+    if model[-1] == '/':
+        model = model[:-1]
+    if branch is None:
+        branch = "main"
+    else:
+        pattern = re.compile(r"^[a-zA-Z0-9._-]+$")
+        if not pattern.match(branch):
+            raise ValueError("Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.")
+
+    return model, branch
+
+
+def get_download_links_from_huggingface(model, branch, text_only=False):
     base = "https://huggingface.co"
     page = f"/api/models/{model}/tree/{branch}?cursor="
     cursor = b""
@@ -142,14 +111,14 @@ def get_download_links_from_huggingface(model, branch):
             is_tokenizer = re.match("tokenizer.*\.model", fname)
             is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer
 
-            if any((is_pytorch, is_safetensors, is_pt, is_tokenizer, is_text)):
+            if any((is_pytorch, is_safetensors, is_pt, is_ggml, is_tokenizer, is_text)):
                 if 'lfs' in dict[i]:
                     sha256.append([fname, dict[i]['lfs']['oid']])
                 if is_text:
                     links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
                     classifications.append('text')
                     continue
-                if not args.text_only:
+                if not text_only:
                     links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
                     if is_safetensors:
                         has_safetensors = True
@@ -177,41 +146,67 @@ def get_download_links_from_huggingface(model, branch):
     return links, sha256, is_lora
 
 
-def download_files(file_list, output_folder, num_threads=8):
+def get_output_folder(model, branch, is_lora, base_folder=None):
-    thread_map(lambda url: get_file(url, output_folder), file_list, max_workers=num_threads, disable=True)
+    if base_folder is None:
-
-
-if __name__ == '__main__':
-    model = args.MODEL
-    branch = args.branch
-    if model is None:
-        model, branch = select_model_from_default_options()
-    else:
-        if model[-1] == '/':
-            model = model[:-1]
-            branch = args.branch
-        if branch is None:
-            branch = "main"
-        else:
-            try:
-                branch = sanitize_branch_name(branch)
-            except ValueError as err_branch:
-                print(f"Error: {err_branch}")
-                sys.exit()
-
-    links, sha256, is_lora = get_download_links_from_huggingface(model, branch)
-
-    if args.output is not None:
-        base_folder = args.output
-    else:
         base_folder = 'models' if not is_lora else 'loras'
 
     output_folder = f"{'_'.join(model.split('/')[-2:])}"
     if branch != 'main':
         output_folder += f'_{branch}'
     output_folder = Path(base_folder) / output_folder
+    return output_folder
 
-    if args.check:
+
+def get_single_file(url, output_folder, start_from_scratch=False):
+    filename = Path(url.rsplit('/', 1)[1])
+    output_path = output_folder / filename
+    if output_path.exists() and not start_from_scratch:
+        # Check if the file has already been downloaded completely
+        r = requests.get(url, stream=True)
+        total_size = int(r.headers.get('content-length', 0))
+        if output_path.stat().st_size >= total_size:
+            return
+        # Otherwise, resume the download from where it left off
+        headers = {'Range': f'bytes={output_path.stat().st_size}-'}
+        mode = 'ab'
+    else:
+        headers = {}
+        mode = 'wb'
+
+    r = requests.get(url, stream=True, headers=headers)
+    with open(output_path, mode) as f:
+        total_size = int(r.headers.get('content-length', 0))
+        block_size = 1024
+        with tqdm.tqdm(total=total_size, unit='iB', unit_scale=True, bar_format='{l_bar}{bar}| {n_fmt:6}/{total_fmt:6} {rate_fmt:6}') as t:
+            for data in r.iter_content(block_size):
+                t.update(len(data))
+                f.write(data)
+
+
+def start_download_threads(file_list, output_folder, start_from_scratch=False, threads=1):
+    thread_map(lambda url: get_single_file(url, output_folder, start_from_scratch=start_from_scratch), file_list, max_workers=threads, disable=True)
+
+
+def download_model_files(model, branch, links, sha256, output_folder, start_from_scratch=False, threads=1):
+    # Creating the folder and writing the metadata
+    if not output_folder.exists():
+        output_folder.mkdir()
+    with open(output_folder / 'huggingface-metadata.txt', 'w') as f:
+        f.write(f'url: https://huggingface.co/{model}\n')
+        f.write(f'branch: {branch}\n')
+        f.write(f'download date: {str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))}\n')
+        sha256_str = ''
+        for i in range(len(sha256)):
+            sha256_str += f'    {sha256[i][1]} {sha256[i][0]}\n'
+        if sha256_str != '':
+            f.write(f'sha256sum:\n{sha256_str}')
+
+    # Downloading the files
+    print(f"Downloading the model to {output_folder}")
+    start_download_threads(links, output_folder, start_from_scratch=start_from_scratch, threads=threads)
+
+
+def check_model_files(model, branch, links, sha256, output_folder):
     # Validate the checksums
     validated = True
     for i in range(len(sha256)):
@@ -236,21 +231,40 @@ if __name__ == '__main__':
     else:
         print('[-] Invalid checksums. Rerun download-model.py with the --clean flag.')
 
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('MODEL', type=str, default=None, nargs='?')
+    parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.')
+    parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.')
+    parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).')
+    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.')
+    args = parser.parse_args()
+
+    branch = args.branch
+    model = args.MODEL
+    if model is None:
+        model, branch = select_model_from_default_options()
+
+    # Cleaning up the model/branch names
+    try:
+        model, branch = sanitize_model_and_branch_names(model, branch)
+    except ValueError as err_branch:
+        print(f"Error: {err_branch}")
+        sys.exit()
+
+    # Getting the download links from Hugging Face
+    links, sha256, is_lora = get_download_links_from_huggingface(model, branch, text_only=args.text_only)
+
+    # Getting the output folder
+    output_folder = get_output_folder(model, branch, is_lora, base_folder=args.output)
+
+    if args.check:
+        # Check previously downloaded files
+        check_model_files(model, branch, links, sha256, output_folder)
     else:
-
+        # Download files
-        # Creating the folder and writing the metadata
+        download_model_files(model, branch, links, sha256, output_folder, threads=args.threads)
-        if not output_folder.exists():
-            output_folder.mkdir()
-        with open(output_folder / 'huggingface-metadata.txt', 'w') as f:
-            f.write(f'url: https://huggingface.co/{model}\n')
-            f.write(f'branch: {branch}\n')
-            f.write(f'download date: {str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))}\n')
-            sha256_str = ''
-            for i in range(len(sha256)):
-                sha256_str += f'    {sha256[i][1]} {sha256[i][0]}\n'
-            if sha256_str != '':
-                f.write(f'sha256sum:\n{sha256_str}')
-
-        # Downloading the files
-        print(f"Downloading the model to {output_folder}")
-        download_files(links, output_folder, args.threads)

extensions/api/script.py

@@ -57,12 +57,15 @@ class Handler(BaseHTTPRequestHandler):
                 'length_penalty': float(body.get('length_penalty', 1)),
                 'early_stopping': bool(body.get('early_stopping', False)),
                 'seed': int(body.get('seed', -1)),
+                'add_bos_token': int(body.get('add_bos_token', True)),
+                'custom_stopping_strings': body.get('custom_stopping_strings', []),
+                'truncation_length': int(body.get('truncation_length', 2048)),
+                'ban_eos_token': bool(body.get('ban_eos_token', False)),
             }
 
             generator = generate_reply(
                 prompt,
                 generate_params,
-                stopping_strings=body.get('stopping_strings', []),
             )
 
             answer = ''
@@ -78,6 +81,19 @@ class Handler(BaseHTTPRequestHandler):
                 }]
             })
             self.wfile.write(response.encode('utf-8'))
+        elif self.path == '/api/v1/token-count':
+            # Not compatible with KoboldAI api
+            self.send_response(200)
+            self.send_header('Content-Type', 'application/json')
+            self.end_headers()
+
+            tokens = encode(body['prompt'])[0]
+            response = json.dumps({
+                'results': [{
+                    'tokens': len(tokens)
+                }]
+            })
+            self.wfile.write(response.encode('utf-8'))
         else:
             self.send_error(404)
 

extensions/character_bias/script.py

@@ -1,8 +1,23 @@
 import gradio as gr
+import os
+
+# get the current directory of the script
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+# check if the bias_options.txt file exists, if not, create it
+bias_file = os.path.join(current_dir, "bias_options.txt")
+if not os.path.isfile(bias_file):
+    with open(bias_file, "w") as f:
+        f.write("*I am so happy*\n*I am so sad*\n*I am so excited*\n*I am so bored*\n*I am so angry*")
+
+# read bias options from the text file
+with open(bias_file, "r") as f:
+    bias_options = [line.strip() for line in f.readlines()]
 
 params = {
     "activate": True,
     "bias string": " *I am so happy*",
+    "use custom string": False,
 }
 
 
@@ -11,7 +26,6 @@ def input_modifier(string):
     This function is applied to your text inputs before
     they are fed into the model.
     """
-
     return string
 
 
@@ -19,7 +33,6 @@ def output_modifier(string):
     """
     This function is applied to the model outputs.
     """
-
     return string
 
 
@@ -29,8 +42,10 @@ def bot_prefix_modifier(string):
     the prefix text for the Bot and can be used to bias its
     behavior.
     """
-
     if params['activate']:
+        if params['use custom string']:
+            return f'{string} {params["custom string"].strip()} '
+        else:
             return f'{string} {params["bias string"].strip()} '
     else:
         return string
@@ -39,8 +54,29 @@ def bot_prefix_modifier(string):
 def ui():
     # Gradio elements
     activate = gr.Checkbox(value=params['activate'], label='Activate character bias')
-    string = gr.Textbox(value=params["bias string"], label='Character bias')
+    dropdown_string = gr.Dropdown(choices=bias_options, value=params["bias string"], label='Character bias', info='To edit the options in this dropdown edit the "bias_options.txt" file')
+    use_custom_string = gr.Checkbox(value=False, label='Use custom bias textbox instead of dropdown')
+    custom_string = gr.Textbox(value="", placeholder="Enter custom bias string", label="Custom Character Bias", info='To use this textbox activate the checkbox above')
 
     # Event functions to update the parameters in the backend
-    string.change(lambda x: params.update({"bias string": x}), string, None)
+    def update_bias_string(x):
+        if x:
+            params.update({"bias string": x})
+        else:
+            params.update({"bias string": dropdown_string.get()})
+        return x
+
+    def update_custom_string(x):
+        params.update({"custom string": x})
+
+    dropdown_string.change(update_bias_string, dropdown_string, None)
+    custom_string.change(update_custom_string, custom_string, None)
     activate.change(lambda x: params.update({"activate": x}), activate, None)
+    use_custom_string.change(lambda x: params.update({"use custom string": x}), use_custom_string, None)
+
+    # Group elements together depending on the selected option
+    def bias_string_group():
+        if use_custom_string.value:
+            return gr.Group([use_custom_string, custom_string])
+        else:
+            return dropdown_string

extensions/elevenlabs_tts/script.py

@@ -2,10 +2,11 @@ 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/llama_prompts/script.py

@@ -1,7 +1,8 @@
 import gradio as gr
-import modules.shared as shared
 import pandas as pd
 
+import modules.shared as shared
+
 df = pd.read_csv("https://raw.githubusercontent.com/devbrones/llama-prompts/main/prompts/prompts.csv")
 
 

extensions/sd_api_pictures/README.MD

@@ -0,0 +1,78 @@
+## 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

@@ -1,34 +1,78 @@
 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 really used right now
+    'SD_model': 'NeverEndingDream',  # not used right now
-    'prompt_prefix': '(Masterpiece:1.1), (solo:1.3), detailed, intricate, colorful',
+    'prompt_prefix': '(Masterpiece:1.1), detailed, intricate, colorful',
     'negative_prompt': '(worst quality, low quality:1.3)',
-    'side_length': 512,
+    'width': 512,
-    'restore_faces': False
+    'height': 512,
+    'restore_faces': False,
+    'seed': -1,
+    'sampler_name': 'DDIM',
+    'steps': 32,
+    'cfg_scale': 7
 }
 
+
+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):
@@ -36,7 +80,13 @@ 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)
 
-# I don't even need input_hijack for this as visible text will be commited to history as the unmodified 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))
 
 
 def input_modifier(string):
@@ -44,55 +94,58 @@ def input_modifier(string):
     This function is applied to your text inputs before
     they are fed into the model.
     """
-    global params, picture_response
+
-    if not params['enable_SD_api']:
+    global params
+
+    if not params['mode'] == 1:  # if not in immersive/interactive mode, do nothing
         return string
 
-    commands = ['send', 'mail', 'me']
+    if triggers_are_in(string):  # if we're in it, check for trigger words
-    mediums = ['image', 'pic', 'picture', 'photo']
+        toggle_generation(True)
-    subjects = ['yourself', 'own']
+        string = string.lower()
-    lowstr = 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
-    # TODO: refactor out to separate handler and also replace detection with a regexp
+            string = "Please provide a detailed and vivid description of " + subject
-    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
+        else:
-        picture_response = True
+            string = "Please provide a detailed description of your appearance, your surroundings and what you are doing right now"
-        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, pic_id
+    global params
+
+    if params['manage_VRAM']:
+        give_VRAM_priority('SD')
 
     payload = {
         "prompt": params['prompt_prefix'] + description,
-        "seed": -1,
+        "seed": params['seed'],
-        "sampler_name": "DPM++ 2M Karras",
+        "sampler_name": params['sampler_name'],
-        "steps": 32,
+        "steps": params['steps'],
-        "cfg_scale": 7,
+        "cfg_scale": params['cfg_scale'],
-        "width": params['side_length'],
+        "width": params['width'],
-        "height": params['side_length'],
+        "height": params['height'],
         "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']:
-            output_file = Path(f'extensions/sd_api_pictures/outputs/{pic_id:06d}.png')
+            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)
             image.save(output_file.as_posix())
-            pic_id += 1
+            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()
@@ -102,17 +155,19 @@ def get_SD_pictures(description):
             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')
+
     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 pic_id, picture_response, streaming_state
+
+    global picture_response, params
 
     if not picture_response:
         return string
@@ -125,17 +180,18 @@ def output_modifier(string):
 
     if string == '':
         string = 'no viable description in reply, try regenerating'
+        return 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 = ""
-    text = f'*Description: "{string}"*'
+    if (params['mode'] < 2):
+        toggle_generation(False)
+        text = f'*Sends a picture which portrays: “{string}”*'
+    else:
+        text = string
 
-    image = get_SD_pictures(string)
+    string = get_SD_pictures(string) + "\n" + text
 
-    picture_response = False
+    return string
-
-    shared.processing_message = "*Is typing...*"
-    shared.args.no_stream = streaming_state
-    return image + "\n" + text
 
 
 def bot_prefix_modifier(string):
@@ -148,42 +204,91 @@ def bot_prefix_modifier(string):
     return string
 
 
-def force_pic():
+def toggle_generation(*args):
-    global picture_response
+    global picture_response, shared, streaming_state
-    picture_response = True
+
+    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 ui():
 
     # Gradio elements
-    with gr.Accordion("Stable Diffusion api integration", open=True):
+    # gr.Markdown('### Stable Diffusion API Pictures') # Currently the name of extension is shown as the title
+    with gr.Accordion("Parameters", open=True):
         with gr.Row():
-            with gr.Column():
+            address = gr.Textbox(placeholder=params['address'], value=params['address'], label='Auto1111\'s WebUI address')
-                enable = gr.Checkbox(value=params['enable_SD_api'], label='Activate SD Api integration')
+            mode = gr.Dropdown(["Manual", "Immersive/Interactive", "Picturebook/Adventure"], value="Manual", label="Mode of operation", type="index")
-                save_img = gr.Checkbox(value=params['save_img'], label='Keep original received images in the outputs subdir')
+            with gr.Column(scale=1, min_width=300):
-            with gr.Column():
+                manage_VRAM = gr.Checkbox(value=params['manage_VRAM'], label='Manage VRAM')
-                address = gr.Textbox(placeholder=params['address'], value=params['address'], label='Stable Diffusion host address')
+                save_img = gr.Checkbox(value=params['save_img'], label='Keep original images and use them in chat')
 
-        with gr.Row():
+            force_pic = gr.Button("Force the picture response")
-            force_btn = gr.Button("Force the next response to be a picture")
+            suppr_pic = gr.Button("Suppress the picture response")
-            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')
-                dimensions = gr.Slider(256, 702, value=params['side_length'], step=64, label='Image dimensions')
+                    sampler_name = gr.Textbox(placeholder=params['sampler_name'], value=params['sampler_name'], label='Sampler')
-                # model = gr.Dropdown(value=SD_models[0], choices=SD_models, label='Model')
+                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'])
 
     # Event functions to update the parameters in the backend
-    enable.change(lambda x: params.update({"enable_SD_api": x}), enable, None)
+    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)
     save_img.change(lambda x: params.update({"save_img": x}), save_img, None)
-    address.change(lambda x: params.update({"address": x}), address, None)
+
+    address.submit(fn=SD_api_address_update, inputs=address, outputs=address)
     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)
-    dimensions.change(lambda x: params.update({"side_length": x}), dimensions, None)
+    width.change(lambda x: params.update({"width": x}), width, None)
-    # model.change(lambda x: params.update({"SD_model": x}), model, None)
+    height.change(lambda x: params.update({"height": x}), height, None)
 
-    force_btn.click(force_pic)
+    sampler_name.change(lambda x: params.update({"sampler_name": x}), sampler_name, None)
-    generate_now_btn.click(force_pic)
+    steps.change(lambda x: params.update({"steps": x}), steps, None)
-    generate_now_btn.click(chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)
+    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)

extensions/send_pictures/script.py

@@ -25,7 +25,7 @@ def caption_image(raw_image):
 
 
 def generate_chat_picture(picture, name1, name2):
-    text = f'*{name1} sends {name2} a picture that contains the following: "{caption_image(picture)}"*'
+    text = f'*{name1} sends {name2} a picture that contains the following: “{caption_image(picture)}”*'
     # lower the resolution of sent images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history
     picture.thumbnail((300, 300))
     buffer = BytesIO()

extensions/silero_tts/requirements.txt

@@ -1,6 +1,5 @@
 ipython
+num2words
 omegaconf
 pydub
 PyYAML
-torch
-torchaudio

extensions/silero_tts/script.py

@@ -1,14 +1,16 @@
-import re
 import time
 from pathlib import Path
 
 import gradio as gr
-import modules.chat as chat
-import modules.shared as shared
 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)
 
+
 params = {
     'activate': True,
     'speaker': 'en_56',
@@ -20,6 +22,7 @@ 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()
@@ -37,26 +40,31 @@ table = str.maketrans({
     '"': """,
 })
 
+
 def xmlesc(txt):
     return txt.translate(table)
 
+
 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.to(params['device'])
     return model
-model = load_model()
 
-def remove_surrounded_chars(string):
-    # this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
-    # 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
-    return re.sub('\*[^\*]*?(\*|$)','',string)
 
-def remove_tts_from_history(name1, name2):
+def remove_tts_from_history(name1, name2, mode):
     for i, entry in enumerate(shared.history['internal']):
         shared.history['visible'][i] = [shared.history['visible'][i][0], entry[1]]
-    return chat.generate_chat_output(shared.history['visible'], name1, name2, shared.character)
+    return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
 
-def toggle_text_in_history(name1, name2):
+
+def toggle_text_in_history(name1, name2, mode):
     for i, entry in enumerate(shared.history['visible']):
         visible_reply = entry[1]
         if visible_reply.startswith('<audio'):
@@ -65,7 +73,8 @@ def toggle_text_in_history(name1, name2):
                 shared.history['visible'][i] = [shared.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>"]
-    return chat.generate_chat_output(shared.history['visible'], name1, name2, shared.character)
+    return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
+
 
 def input_modifier(string):
     """
@@ -75,12 +84,13 @@ def input_modifier(string):
 
     # Remove autoplay from the last reply
     if shared.is_chat() and len(shared.history['internal']) > 0:
-        shared.history['visible'][-1] = [shared.history['visible'][-1][0], shared.history['visible'][-1][1].replace('controls autoplay>','controls>')]
+        shared.history['visible'][-1] = [shared.history['visible'][-1][0], shared.history['visible'][-1][1].replace('controls autoplay>', 'controls>')]
 
     shared.processing_message = "*Is recording a voice message...*"
     shared.args.no_stream = True  # Disable streaming cause otherwise the audio output will stutter and begin anew every time the message is being updated
     return string
 
+
 def output_modifier(string):
     """
     This function is applied to the model outputs.
@@ -94,15 +104,11 @@ def output_modifier(string):
             current_params = params.copy()
             break
 
-    if params['activate'] == False:
+    if not params['activate']:
         return string
 
     original_string = string
-    string = remove_surrounded_chars(string)
+    string = tts_preprocessor.preprocess(string)
-    string = string.replace('"', '')
-    string = string.replace('“', '')
-    string = string.replace('\n', ' ')
-    string = string.strip()
 
     if string == '':
         string = '*Empty reply, try regenerating*'
@@ -121,6 +127,7 @@ def output_modifier(string):
     shared.args.no_stream = streaming_state  # restore the streaming option to the previous value
     return string
 
+
 def bot_prefix_modifier(string):
     """
     This function is only applied in chat mode. It modifies
@@ -130,17 +137,25 @@ def bot_prefix_modifier(string):
 
     return string
 
+
+def setup():
+    global model
+    model = load_model()
+
+
 def ui():
     # Gradio elements
     with gr.Accordion("Silero TTS"):
         with gr.Row():
             activate = gr.Checkbox(value=params['activate'], label='Activate TTS')
             autoplay = gr.Checkbox(value=params['autoplay'], label='Play TTS automatically')
+
         show_text = gr.Checkbox(value=params['show_text'], label='Show message text under audio player')
         voice = gr.Dropdown(value=params['speaker'], choices=voices_by_gender, label='TTS voice')
         with gr.Row():
             v_pitch = gr.Dropdown(value=params['voice_pitch'], choices=voice_pitches, label='Voice pitch')
             v_speed = gr.Dropdown(value=params['voice_speed'], choices=voice_speeds, label='Voice speed')
+
         with gr.Row():
             convert = gr.Button('Permanently replace audios with the message texts')
             convert_cancel = gr.Button('Cancel', visible=False)
@@ -148,16 +163,16 @@ def ui():
 
     # 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.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)
+    convert_confirm.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
-    convert_confirm.click(remove_tts_from_history, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'])
+    convert_confirm.click(remove_tts_from_history, [shared.gradio[k] for k in ['name1', 'name2', 'mode']], shared.gradio['display'])
-    convert_confirm.click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
+    convert_confirm.click(lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
-    convert_cancel.click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
+    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)
-    show_text.change(toggle_text_in_history, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'])
+    show_text.change(toggle_text_in_history, [shared.gradio[k] for k in ['name1', 'name2', 'mode']], shared.gradio['display'])
-    show_text.change(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
+    show_text.change(lambda: chat.save_history(timestamp=False), [], [], show_progress=False)
 
     # Event functions to update the parameters in the backend
     activate.change(lambda x: params.update({"activate": x}), activate, None)

extensions/silero_tts/test_tts.py

@@ -0,0 +1,81 @@
+import time
+from pathlib import Path
+
+import torch
+import tts_preprocessor
+
+torch._C._jit_set_profiling_mode(False)
+
+
+params = {
+    'activate': True,
+    'speaker': 'en_49',
+    'language': 'en',
+    'model_id': 'v3_en',
+    'sample_rate': 48000,
+    'device': 'cpu',
+    'show_text': True,
+    'autoplay': True,
+    'voice_pitch': 'medium',
+    'voice_speed': 'medium',
+}
+
+current_params = params.copy()
+voices_by_gender = ['en_99', 'en_45', 'en_18', 'en_117', 'en_49', 'en_51', 'en_68', 'en_0', 'en_26', 'en_56', 'en_74', 'en_5', 'en_38', 'en_53', 'en_21', 'en_37', 'en_107', 'en_10', 'en_82', 'en_16', 'en_41', 'en_12', 'en_67', 'en_61', 'en_14', 'en_11', 'en_39', 'en_52', 'en_24', 'en_97', 'en_28', 'en_72', 'en_94', 'en_36', 'en_4', 'en_43', 'en_88', 'en_25', 'en_65', 'en_6', 'en_44', 'en_75', 'en_91', 'en_60', 'en_109', 'en_85', 'en_101', 'en_108', 'en_50', 'en_96', 'en_64', 'en_92', 'en_76', 'en_33', 'en_116', 'en_48', 'en_98', 'en_86', 'en_62', 'en_54', 'en_95', 'en_55', 'en_111', 'en_3', 'en_83', 'en_8', 'en_47', 'en_59', 'en_1', 'en_2', 'en_7', 'en_9', 'en_13', 'en_15', 'en_17', 'en_19', 'en_20', 'en_22', 'en_23', 'en_27', 'en_29', 'en_30', 'en_31', 'en_32', 'en_34', 'en_35', 'en_40', 'en_42', 'en_46', 'en_57', 'en_58', 'en_63', 'en_66', 'en_69', 'en_70', 'en_71', 'en_73', 'en_77', 'en_78', 'en_79', 'en_80', 'en_81', 'en_84', 'en_87', 'en_89', 'en_90', 'en_93', 'en_100', 'en_102', 'en_103', 'en_104', 'en_105', 'en_106', 'en_110', 'en_112', 'en_113', 'en_114', 'en_115']
+voice_pitches = ['x-low', 'low', 'medium', 'high', 'x-high']
+voice_speeds = ['x-slow', 'slow', 'medium', 'fast', 'x-fast']
+
+# Used for making text xml compatible, needed for voice pitch and speed control
+table = str.maketrans({
+    "<": "&lt;",
+    ">": "&gt;",
+    "&": "&amp;",
+    "'": "&apos;",
+    '"': "&quot;",
+})
+
+
+def xmlesc(txt):
+    return txt.translate(table)
+
+
+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()
+
+
+def output_modifier(string):
+    """
+    This function is applied to the model outputs.
+    """
+
+    global model, current_params
+
+    original_string = string
+    string = tts_preprocessor.preprocess(string)
+    processed_string = string
+
+    if string == '':
+        string = '*Empty reply, try regenerating*'
+    else:
+        output_file = Path(f'extensions/silero_tts/outputs/test_{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))
+
+        autoplay = 'autoplay' if params['autoplay'] else ''
+        string = f'<audio src="file/{output_file.as_posix()}" controls {autoplay}></audio>'
+
+        if params['show_text']:
+            string += f'\n\n{original_string}\n\nProcessed:\n{processed_string}'
+
+    print(string)
+
+
+if __name__ == '__main__':
+    import sys
+    output_modifier(sys.argv[1])

extensions/silero_tts/tts_preprocessor.py

@@ -0,0 +1,194 @@
+import re
+
+from num2words import num2words
+
+punctuation = r'[\s,.?!/)\'\]>]'
+alphabet_map = {
+    "A": " Ei ",
+    "B": " Bee ",
+    "C": " See ",
+    "D": " Dee ",
+    "E": " Eee ",
+    "F": " Eff ",
+    "G": " Jee ",
+    "H": " Eich ",
+    "I": " Eye ",
+    "J": " Jay ",
+    "K": " Kay ",
+    "L": " El ",
+    "M": " Emm ",
+    "N": " Enn ",
+    "O": " Ohh ",
+    "P": " Pee ",
+    "Q": " Queue ",
+    "R": " Are ",
+    "S": " Ess ",
+    "T": " Tee ",
+    "U": " You ",
+    "V": " Vee ",
+    "W": " Double You ",
+    "X": " Ex ",
+    "Y": " Why ",
+    "Z": " Zed "  # Zed is weird, as I (da3dsoul) am American, but most of the voice models sound British, so it matches
+}
+
+
+def preprocess(string):
+    # the order for some of these matter
+    # 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('\n', ' ')
+    string = convert_num_locale(string)
+    string = replace_negative(string)
+    string = replace_roman(string)
+    string = hyphen_range_to(string)
+    string = num_to_words(string)
+
+    # TODO Try to use a ML predictor to expand abbreviations. It's hard, dependent on context, and whether to actually
+    # try to say the abbreviation or spell it out as I've done below is not agreed upon
+
+    # 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
+    string = re.sub(rf'\s+({punctuation})', r'\1', string)
+    string = string.strip()
+    # compact whitespace
+    string = ' '.join(string.split())
+
+    return string
+
+
+def remove_surrounded_chars(string):
+    # this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
+    # 'as few symbols as possible (0 upwards) between an asterisk and the end of the 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)
+
+
+def replace_roman(string):
+    # find a string of roman numerals.
+    # Only 2 or more, to avoid capturing I and single character abbreviations, like names
+    pattern = re.compile(rf'\s[IVXLCDM]{{2,}}{punctuation}')
+    result = string
+    while True:
+        match = pattern.search(result)
+        if match is None:
+            break
+
+        start = match.start()
+        end = match.end()
+        result = result[0:start + 1] + str(roman_to_int(result[start + 1:end - 1])) + result[end - 1:len(result)]
+
+    return result
+
+
+def roman_to_int(s):
+    rom_val = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
+    int_val = 0
+    for i in range(len(s)):
+        if i > 0 and rom_val[s[i]] > rom_val[s[i - 1]]:
+            int_val += rom_val[s[i]] - 2 * rom_val[s[i - 1]]
+        else:
+            int_val += rom_val[s[i]]
+    return int_val
+
+
+def hyphen_range_to(text):
+    pattern = re.compile(r'(\d+)[-–](\d+)')
+    result = pattern.sub(lambda x: x.group(1) + ' to ' + x.group(2), text)
+    return result
+
+
+def num_to_words(text):
+    # 1000 or 10.23
+    pattern = re.compile(r'\d+\.\d+|\d+')
+    result = pattern.sub(lambda x: num2words(float(x.group())), text)
+    return result
+
+
+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}|$)')
+    result = string
+    while True:
+        match = pattern.search(result)
+        if match is None:
+            break
+
+        start = match.start()
+        end = match.end()
+        result = result[0:start] + replace_abbreviation(result[start:end]) + result[end:len(result)]
+
+    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
+    while True:
+        match = pattern.search(result)
+        if match is None:
+            break
+
+        start = match.start()
+        end = match.end()
+        result = result[0:start] + replace_abbreviation(result[start:end].upper()) + result[end:len(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]))
+
+
+if __name__ == "__main__":
+    import sys
+    __main__(sys.argv)

extensions/whisper_stt/script.py

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

modules/GPTQ_loader.py

@@ -100,10 +100,10 @@ def load_quantized(model_name):
     found_safetensors = list(path_to_model.glob("*.safetensors"))
     pt_path = None
 
-    if len(found_pts) == 1:
+    if len(found_pts) > 0:
-        pt_path = found_pts[0]
+        pt_path = found_pts[-1]
-    elif len(found_safetensors) == 1:
+    elif len(found_safetensors) > 0:
-        pt_path = found_safetensors[0]
+        pt_path = found_safetensors[-1]
     else:
         if path_to_model.name.lower().startswith('llama-7b'):
             pt_model = f'llama-7b-{shared.args.wbits}bit'
@@ -119,13 +119,14 @@ def load_quantized(model_name):
         # Try to find the .safetensors or .pt both in the model dir and in the subfolder
         for path in [Path(p + ext) for ext in ['.safetensors', '.pt'] for p in [f"{shared.args.model_dir}/{pt_model}", f"{path_to_model}/{pt_model}"]]:
             if path.exists():
-                print(f"Found {path}")
                 pt_path = path
                 break
 
     if not pt_path:
         print("Could not find the quantized model in .pt or .safetensors format, exiting...")
         exit()
+    else:
+        print(f"Found the following quantized model: {pt_path}")
 
     # qwopqwop200's offload
     if model_type == 'llama' and shared.args.pre_layer:

modules/LoRA.py

@@ -4,14 +4,7 @@ import torch
 from peft import PeftModel
 
 import modules.shared as shared
-from modules.models import load_model
+from modules.models import reload_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

@@ -12,53 +12,59 @@ 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 (fix_newlines, chat_html_wrapper,
+from modules.html_generator import (chat_html_wrapper, fix_newlines,
                                     make_thumbnail)
 from modules.text_generation import (encode, generate_reply,
                                      get_max_prompt_length)
 
 
-def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat_prompt_size, **kwargs):
+def generate_chat_prompt(user_input, state, **kwargs):
-    is_instruct = kwargs['is_instruct'] if 'is_instruct' in kwargs else False
-    end_of_turn = kwargs['end_of_turn'] if 'end_of_turn' in kwargs else ''
     impersonate = kwargs['impersonate'] if 'impersonate' in kwargs else False
+    _continue = kwargs['_continue'] if '_continue' in kwargs else False
     also_return_rows = kwargs['also_return_rows'] if 'also_return_rows' in kwargs else False
-    rows = [f"{context.strip()}\n"]
+    is_instruct = state['mode'] == 'instruct'
+    rows = [f"{state['context'].strip()}\n"]
 
     # Finding the maximum prompt size
+    chat_prompt_size = state['chat_prompt_size']
     if shared.soft_prompt:
         chat_prompt_size -= shared.soft_prompt_tensor.shape[1]
-    max_length = min(get_max_prompt_length(max_new_tokens), chat_prompt_size)
+    max_length = min(get_max_prompt_length(state), chat_prompt_size)
 
     if is_instruct:
-        prefix1 = f"{name1}\n"
+        prefix1 = f"{state['name1']}\n"
-        prefix2 = f"{name2}\n"
+        prefix2 = f"{state['name2']}\n"
     else:
-        prefix1 = f"{name1}: "
+        prefix1 = f"{state['name1']}: "
-        prefix2 = f"{name2}: "
+        prefix2 = f"{state['name2']}: "
 
     i = len(shared.history['internal']) - 1
-    while i >= 0 and len(encode(''.join(rows), max_new_tokens)[0]) < max_length:
+    while i >= 0 and len(encode(''.join(rows))[0]) < max_length:
-        rows.insert(1, f"{prefix2}{shared.history['internal'][i][1].strip()}{end_of_turn}\n")
+        if _continue and i == len(shared.history['internal']) - 1:
+            rows.insert(1, f"{prefix2}{shared.history['internal'][i][1]}")
+        else:
+            rows.insert(1, f"{prefix2}{shared.history['internal'][i][1].strip()}{state['end_of_turn']}\n")
         string = shared.history['internal'][i][0]
         if string not in ['', '<|BEGIN-VISIBLE-CHAT|>']:
-            rows.insert(1, f"{prefix1}{string.strip()}{end_of_turn}\n")
+            rows.insert(1, f"{prefix1}{string.strip()}{state['end_of_turn']}\n")
         i -= 1
 
     if impersonate:
         rows.append(f"{prefix1.strip() if not is_instruct else prefix1}")
         limit = 2
+    elif _continue:
+        limit = 3
     else:
         # Adding the user message
         user_input = fix_newlines(user_input)
         if len(user_input) > 0:
-            rows.append(f"{prefix1}{user_input}{end_of_turn}\n")
+            rows.append(f"{prefix1}{user_input}{state['end_of_turn']}\n")
 
         # Adding the Character prefix
         rows.append(apply_extensions(f"{prefix2.strip() if not is_instruct else prefix2}", "bot_prefix"))
         limit = 3
 
-    while len(rows) > limit and len(encode(''.join(rows), max_new_tokens)[0]) >= max_length:
+    while len(rows) > limit and len(encode(''.join(rows))[0]) >= max_length:
         rows.pop(1)
     prompt = ''.join(rows)
 
@@ -68,16 +74,26 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
         return prompt
 
 
-def extract_message_from_reply(reply, name1, name2, stop_at_newline):
+def get_stopping_strings(state):
-    next_character_found = False
+    if state['mode'] == 'instruct':
+        stopping_strings = [f"\n{state['name1']}", f"\n{state['name2']}"]
+    else:
+        stopping_strings = [f"\n{state['name1']}:", f"\n{state['name2']}:"]
+    stopping_strings += state['custom_stopping_strings']
+    return stopping_strings
 
-    if stop_at_newline:
+
+def extract_message_from_reply(reply, state):
+    next_character_found = False
+    stopping_strings = get_stopping_strings(state)
+
+    if state['stop_at_newline']:
         lines = reply.split('\n')
         reply = lines[0].strip()
         if len(lines) > 1:
             next_character_found = True
     else:
-        for string in [f"\n{name1}:", f"\n{name2}:"]:
+        for string in stopping_strings:
             idx = reply.find(string)
             if idx != -1:
                 reply = reply[:idx]
@@ -86,7 +102,7 @@ def extract_message_from_reply(reply, name1, name2, stop_at_newline):
         # If something like "\nYo" is generated just before "\nYou:"
         # is completed, trim it
         if not next_character_found:
-            for string in [f"\n{name1}:", f"\n{name2}:"]:
+            for string in stopping_strings:
                 for j in range(len(string) - 1, 0, -1):
                     if reply[-j:] == string[:j]:
                         reply = reply[:-j]
@@ -99,20 +115,17 @@ def extract_message_from_reply(reply, name1, name2, stop_at_newline):
     return reply, next_character_found
 
 
-def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn, regenerate=False):
+def chatbot_wrapper(text, state, regenerate=False, _continue=False):
-    if mode == 'instruct':
-        stopping_strings = [f"\n{name1}", f"\n{name2}"]
-    else:
-        stopping_strings = [f"\n{name1}:", f"\n{name2}:"]
 
-    eos_token = '\n' if generate_state['stop_at_newline'] else None
+    # Defining some variables
-    name1_original = name1
+    cumulative_reply = ''
-    if 'pygmalion' in shared.model_name.lower():
+    last_reply = [shared.history['internal'][-1][1], shared.history['visible'][-1][1]] if _continue else None
-        name1 = "You"
+    just_started = True
+    visible_text = custom_generate_chat_prompt = None
+    eos_token = '\n' if state['stop_at_newline'] else None
+    stopping_strings = get_stopping_strings(state)
 
     # 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
@@ -122,29 +135,29 @@ def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_tu
 
     if visible_text is None:
         visible_text = text
+    if not _continue:
         text = apply_extensions(text, "input")
 
-    kwargs = {'end_of_turn': end_of_turn, 'is_instruct': mode == 'instruct'}
+    # Generating the prompt
+    kwargs = {'_continue': _continue}
     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)
+        prompt = generate_chat_prompt(text, state, **kwargs)
     else:
-        prompt = custom_generate_chat_prompt(text, generate_state['max_new_tokens'], name1, name2, context, generate_state['chat_prompt_size'], **kwargs)
+        prompt = custom_generate_chat_prompt(text, state, **kwargs)
 
     # Yield *Is typing...*
-    if not regenerate:
+    if not any((regenerate, _continue)):
         yield shared.history['visible'] + [[visible_text, shared.processing_message]]
 
     # Generate
-    cumulative_reply = ''
+    for i in range(state['chat_generation_attempts']):
-    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):
+        for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", state, eos_token=eos_token, stopping_strings=stopping_strings):
             reply = cumulative_reply + reply
 
             # Extracting the reply
-            reply, next_character_found = extract_message_from_reply(reply, name1, name2, generate_state['stop_at_newline'])
+            reply, next_character_found = extract_message_from_reply(reply, state)
-            visible_reply = re.sub("(<USER>|<user>|{{user}})", name1_original, reply)
+            visible_reply = re.sub("(<USER>|<user>|{{user}})", state['name1'], reply)
             visible_reply = apply_extensions(visible_reply, "output")
 
             # We need this global variable to handle the Stop event,
@@ -153,9 +166,15 @@ def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_tu
                 return shared.history['visible']
             if just_started:
                 just_started = False
+                if not _continue:
                     shared.history['internal'].append(['', ''])
                     shared.history['visible'].append(['', ''])
 
+            if _continue:
+                sep = list(map(lambda x: ' ' if len(x) > 0 and x[-1] != ' ' else '', last_reply))
+                shared.history['internal'][-1] = [text, f'{last_reply[0]}{sep[0]}{reply}']
+                shared.history['visible'][-1] = [visible_text, f'{last_reply[1]}{sep[1]}{visible_reply}']
+            else:
                 shared.history['internal'][-1] = [text, reply]
                 shared.history['visible'][-1] = [visible_text, visible_reply]
             if not shared.args.no_stream:
@@ -169,27 +188,22 @@ def chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_tu
     yield shared.history['visible']
 
 
-def impersonate_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
+def impersonate_wrapper(text, state):
-    if mode == 'instruct':
-        stopping_strings = [f"\n{name1}", f"\n{name2}"]
-    else:
-        stopping_strings = [f"\n{name1}:", f"\n{name2}:"]
 
-    eos_token = '\n' if generate_state['stop_at_newline'] else None
+    # Defining some variables
-    if 'pygmalion' in shared.model_name.lower():
+    cumulative_reply = ''
-        name1 = "You"
+    eos_token = '\n' if state['stop_at_newline'] else None
-
+    prompt = generate_chat_prompt(text, state, impersonate=True)
-    prompt = generate_chat_prompt(text, generate_state['max_new_tokens'], name1, name2, context, generate_state['chat_prompt_size'], impersonate=True, end_of_turn=end_of_turn)
+    stopping_strings = get_stopping_strings(state)
 
     # Yield *Is typing...*
     yield shared.processing_message
 
-    cumulative_reply = ''
+    for i in range(state['chat_generation_attempts']):
-    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):
+        for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", state, eos_token=eos_token, stopping_strings=stopping_strings):
             reply = cumulative_reply + reply
-            reply, next_character_found = extract_message_from_reply(reply, name1, name2, generate_state['stop_at_newline'])
+            reply, next_character_found = extract_message_from_reply(reply, state)
             yield reply
             if next_character_found:
                 break
@@ -200,22 +214,32 @@ def impersonate_wrapper(text, generate_state, name1, name2, context, mode, end_o
     yield reply
 
 
-def cai_chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
+def cai_chatbot_wrapper(text, state):
-    for history in chatbot_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
+    for history in chatbot_wrapper(text, state):
-        yield chat_html_wrapper(history, name1, name2, mode)
+        yield chat_html_wrapper(history, state['name1'], state['name2'], state['mode'])
 
 
-def regenerate_wrapper(text, generate_state, name1, name2, context, mode, end_of_turn):
+def regenerate_wrapper(text, state):
-    if (shared.character != 'None' and len(shared.history['visible']) == 1) or len(shared.history['internal']) == 0:
+    if (len(shared.history['visible']) == 1 and not shared.history['visible'][0][0]) or len(shared.history['internal']) == 0:
-        yield chat_html_wrapper(shared.history['visible'], name1, name2, mode)
+        yield chat_html_wrapper(shared.history['visible'], state['name1'], state['name2'], state['mode'])
     else:
         last_visible = shared.history['visible'].pop()
         last_internal = shared.history['internal'].pop()
         # Yield '*Is typing...*'
-        yield chat_html_wrapper(shared.history['visible'] + [[last_visible[0], shared.processing_message]], name1, name2, mode)
+        yield chat_html_wrapper(shared.history['visible'] + [[last_visible[0], shared.processing_message]], state['name1'], state['name2'], state['mode'])
-        for history in chatbot_wrapper(last_internal[0], generate_state, name1, name2, context, mode, end_of_turn, regenerate=True):
+        for history in chatbot_wrapper(last_internal[0], state, regenerate=True):
             shared.history['visible'][-1] = [last_visible[0], history[-1][1]]
-            yield chat_html_wrapper(shared.history['visible'], name1, name2, mode)
+            yield chat_html_wrapper(shared.history['visible'], state['name1'], state['name2'], state['mode'])
+
+
+def continue_wrapper(text, state):
+    if (len(shared.history['visible']) == 1 and not shared.history['visible'][0][0]) or len(shared.history['internal']) == 0:
+        yield chat_html_wrapper(shared.history['visible'], state['name1'], state['name2'], state['mode'])
+    else:
+        # Yield ' ...'
+        yield chat_html_wrapper(shared.history['visible'][:-1] + [[shared.history['visible'][-1][0], shared.history['visible'][-1][1] + ' ...']], state['name1'], state['name2'], state['mode'])
+        for history in chatbot_wrapper(shared.history['internal'][-1][0], state, _continue=True):
+            yield chat_html_wrapper(shared.history['visible'], state['name1'], state['name2'], state['mode'])
 
 
 def remove_last_message(name1, name2, mode):
@@ -243,6 +267,21 @@ def replace_last_reply(text, name1, name2, mode):
     return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
 
 
+def send_dummy_message(text, name1, name2, mode):
+    shared.history['visible'].append([text, ''])
+    shared.history['internal'].append([apply_extensions(text, "input"), ''])
+    return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
+
+
+def send_dummy_reply(text, name1, name2, mode):
+    if len(shared.history['visible']) > 0 and not shared.history['visible'][-1][1] == '':
+        shared.history['visible'].append(['', ''])
+        shared.history['internal'].append(['', ''])
+    shared.history['visible'][-1][1] = text
+    shared.history['internal'][-1][1] = apply_extensions(text, "input")
+    return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
+
+
 def clear_html():
     return chat_html_wrapper([], "", "")
 
@@ -255,6 +294,9 @@ def clear_chat_log(name1, name2, greeting, mode):
         shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]]
         shared.history['visible'] += [['', apply_extensions(greeting, "output")]]
 
+    # Save cleared logs
+    save_history(mode)
+
     return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
 
 
@@ -264,7 +306,7 @@ 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)
@@ -273,7 +315,6 @@ 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())
@@ -300,7 +341,14 @@ def tokenize_dialogue(dialogue, name1, name2, mode):
     return history
 
 
-def save_history(timestamp=True):
+def save_history(mode, timestamp=False):
+    # Instruct mode histories should not be saved as if
+    # 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 timestamp:
             fname = f"{shared.character}_{datetime.now().strftime('%Y%m%d-%H%M%S')}.json"
         else:
@@ -309,6 +357,7 @@ def save_history(timestamp=True):
         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}')
 
 
@@ -322,16 +371,6 @@ def load_history(file, name1, name2):
                 shared.history['visible'] = j['data_visible']
             else:
                 shared.history['visible'] = copy.deepcopy(shared.history['internal'])
-        # Compatibility with Pygmalion AI's official web UI
-        elif 'chat' in j:
-            shared.history['internal'] = [':'.join(x.split(':')[1:]).strip() for x in j['chat']]
-            if len(j['chat']) > 0 and j['chat'][0].startswith(f'{name2}:'):
-                shared.history['internal'] = [['<|BEGIN-VISIBLE-CHAT|>', shared.history['internal'][0]]] + [[shared.history['internal'][i], shared.history['internal'][i + 1]] for i in range(1, len(shared.history['internal']) - 1, 2)]
-                shared.history['visible'] = copy.deepcopy(shared.history['internal'])
-                shared.history['visible'][0][0] = ''
-            else:
-                shared.history['internal'] = [[shared.history['internal'][i], shared.history['internal'][i + 1]] for i in range(0, len(shared.history['internal']) - 1, 2)]
-                shared.history['visible'] = copy.deepcopy(shared.history['internal'])
     except:
         shared.history['internal'] = tokenize_dialogue(file, name1, name2)
         shared.history['visible'] = copy.deepcopy(shared.history['internal'])
@@ -367,8 +406,6 @@ def generate_pfp_cache(character):
 
 def load_character(character, name1, name2, mode):
     shared.character = character
-    shared.history['internal'] = []
-    shared.history['visible'] = []
     context = greeting = end_of_turn = ""
     greeting_field = 'greeting'
     picture = None
@@ -413,13 +450,22 @@ def load_character(character, name1, name2, mode):
         greeting = shared.settings['greeting']
         end_of_turn = shared.settings['end_of_turn']
 
+    if mode != 'instruct':
+        shared.history['internal'] = []
+        shared.history['visible'] = []
+
         if Path(f'logs/{shared.character}_persistent.json').exists():
             load_history(open(Path(f'logs/{shared.character}_persistent.json'), 'rb').read(), name1, name2)
-    elif greeting != "":
+        else:
+            # Insert greeting if it exists
+            if greeting != "":
                 shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]]
                 shared.history['visible'] += [['', apply_extensions(greeting, "output")]]
 
-    return name1, name2, picture, greeting, context, end_of_turn, chat_html_wrapper(shared.history['visible'], name1, name2, mode, reset_cache=True)
+            # Create .json log files since they don't already exist
+            save_history(mode)
+
+    return name1, name2, picture, greeting, context, end_of_turn, chat_html_wrapper(shared.history['visible'], name1, name2, mode)
 
 
 def load_default_history(name1, name2):

modules/extensions.py

@@ -11,29 +11,31 @@ setup_called = set()
 
 
 def load_extensions():
-    global state
+    global state, setup_called
     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] == True:
+        if state[name][0]:
             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"):
@@ -57,14 +59,9 @@ 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/html_generator.py

@@ -164,10 +164,9 @@ def generate_instruct_html(history):
 def generate_cai_chat_html(history, name1, name2, reset_cache=False):
     output = f'<style>{cai_css}</style><div class="chat" id="chat">'
 
-    # The time.time() is to prevent the brower from caching the image
+    # We use ?name2 and ?time.time() to force the browser to reset caches
-    suffix = f"?{time.time()}" if reset_cache else f"?{name2}"
+    img_bot = f'<img src="file/cache/pfp_character.png?{name2}">' if Path("cache/pfp_character.png").exists() else ''
-    img_bot = f'<img src="file/cache/pfp_character.png{suffix}">' if Path("cache/pfp_character.png").exists() else ''
+    img_me = f'<img src="file/cache/pfp_me.png?{time.time() if reset_cache else ""}">' if Path("cache/pfp_me.png").exists() else ''
-    img_me = f'<img src="file/cache/pfp_me.png{suffix}">' if Path("cache/pfp_me.png").exists() else ''
 
     for i, _row in enumerate(history[::-1]):
         row = [convert_to_markdown(entry) for entry in _row]

modules/llama_attn_hijack.py

@@ -0,0 +1,176 @@
+import math
+import sys
+import torch
+import torch.nn as nn
+import transformers.models.llama.modeling_llama
+
+from typing import Optional
+from typing import Tuple
+
+import modules.shared as shared
+
+
+if shared.args.xformers:
+    try:
+        import xformers.ops
+    except Exception:
+        print("🔴 xformers not found! Please install it before trying to use it.", file=sys.stderr)
+
+
+def hijack_llama_attention():
+    if shared.args.xformers:
+        transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
+        print("Replaced attention with xformers_attention")
+    elif shared.args.sdp_attention:
+        transformers.models.llama.modeling_llama.LlamaAttention.forward = sdp_attention_forward
+        print("Replaced attention with sdp_attention")
+
+
+def xformers_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    bsz, q_len, _ = hidden_states.size()
+
+    query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[-2]
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+    # [bsz, nh, t, hd]
+
+    if past_key_value is not None:
+        # reuse k, v, self_attention
+        key_states = torch.cat([past_key_value[0], key_states], dim=2)
+        value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+
+    #We only apply xformers optimizations if we don't need to output the whole attention matrix
+    if not output_attentions:
+        dtype = query_states.dtype
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        
+        #This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros.
+        #We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros.
+        if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
+            # input and output should be of form (bsz, q_len, num_heads, head_dim)
+            attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=None)
+        else:
+            # input and output should be of form (bsz, q_len, num_heads, head_dim)
+            attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=xformers.ops.LowerTriangularMask())
+        attn_weights = None
+    else:
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2)
+
+    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+    attn_output = self.o_proj(attn_output)
+
+    return attn_output, attn_weights, past_key_value
+
+
+def sdp_attention_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    bsz, q_len, _ = hidden_states.size()
+
+    query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[-2]
+    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+    # [bsz, nh, t, hd]
+
+    if past_key_value is not None:
+        # reuse k, v, self_attention
+        key_states = torch.cat([past_key_value[0], key_states], dim=2)
+        value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+
+    #We only apply sdp attention if we don't need to output the whole attention matrix
+    if not output_attentions:
+        attn_output = torch.nn.functional.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask, is_causal=False)
+        attn_weights = None
+    else:
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+    attn_output = attn_output.transpose(1, 2)
+    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+    attn_output = self.o_proj(attn_output)
+
+    return attn_output, attn_weights, past_key_value

modules/models.py

@@ -1,3 +1,4 @@
+import gc
 import json
 import os
 import re
@@ -13,14 +14,14 @@ from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
                           BitsAndBytesConfig, LlamaTokenizer)
 
 import modules.shared as shared
+from modules import llama_attn_hijack
 
 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,
@@ -169,19 +170,46 @@ def load_model(model_name):
 
         model = AutoModelForCausalLM.from_pretrained(checkpoint, **params)
 
+    # Hijack attention with xformers
+    if any((shared.args.xformers, shared.args.sdp_attention)):
+        llama_attn_hijack.hijack_llama_attention()
+
     # Loading the tokenizer
     if any((k in shared.model_name.lower() for k in ['gpt4chan', 'gpt-4chan'])) and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists():
         tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/"))
     elif type(model) is transformers.LlamaForCausalLM:
         tokenizer = LlamaTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}/"), clean_up_tokenization_spaces=True)
+        # Leaving this here until the LLaMA tokenizer gets figured out.
+        # For some people this fixes things, for others it causes an error.
+        try:
+            tokenizer.eos_token_id = 2
+            tokenizer.bos_token_id = 1
+            tokenizer.pad_token_id = 0
+        except:
+            pass
     else:
         tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}/"))
-    tokenizer.truncation_side = 'left'
 
     print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
     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/shared.py

@@ -34,7 +34,13 @@ settings = {
     'context': 'This is a conversation with your Assistant. The Assistant is very helpful and is eager to chat with you and answer your questions.',
     'greeting': 'Hello there!',
     'end_of_turn': '',
+    'custom_stopping_strings': '',
     'stop_at_newline': False,
+    'add_bos_token': True,
+    'ban_eos_token': False,
+    'truncation_length': 2048,
+    'truncation_length_min': 0,
+    'truncation_length_max': 4096,
     'chat_prompt_size': 2048,
     'chat_prompt_size_min': 0,
     'chat_prompt_size_max': 2048,
@@ -44,7 +50,7 @@ settings = {
     'default_extensions': [],
     'chat_default_extensions': ["gallery"],
     'presets': {
-        'default': 'NovelAI-Sphinx Moth',
+        'default': 'Default',
         '.*(alpaca|llama)': "LLaMA-Precise",
         '.*pygmalion': 'NovelAI-Storywriter',
         '.*RWKV': 'Naive',
@@ -89,7 +95,7 @@ parser.add_argument('--extensions', type=str, nargs="+", help='The list of exten
 parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
 
 # Accelerate/transformers
-parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
+parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.')
 parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
 parser.add_argument('--gpu-memory', type=str, nargs="+", help='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.')
 parser.add_argument('--cpu-memory', type=str, help='Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.')
@@ -98,6 +104,8 @@ parser.add_argument('--disk-cache-dir', type=str, default="cache", help='Directo
 parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
 parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
 parser.add_argument('--no-cache', action='store_true', help='Set use_cache to False while generating text. This reduces the VRAM usage a bit at a performance cost.')
+parser.add_argument('--xformers', action='store_true', help="Use xformer's memory efficient attention. This should increase your tokens/s.")
+parser.add_argument('--sdp-attention', action='store_true', help="Use torch 2.0's sdp attention.")
 
 # llama.cpp
 parser.add_argument('--threads', type=int, default=0, help='Number of threads to use in llama.cpp.')

modules/text_generation.py

@@ -1,4 +1,4 @@
-import gc
+import random
 import re
 import time
 import traceback
@@ -12,28 +12,38 @@ 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 local_rank
+from modules.models import clear_torch_cache, local_rank
 
 
-def get_max_prompt_length(tokens):
+def get_max_prompt_length(state):
-    max_length = 2048 - tokens
+    max_length = state['truncation_length'] - state['max_new_tokens']
     if shared.soft_prompt:
         max_length -= shared.soft_prompt_tensor.shape[1]
     return max_length
 
 
-def encode(prompt, tokens_to_generate=0, add_special_tokens=True):
+def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None):
     if any((shared.is_RWKV, shared.is_llamacpp)):
         input_ids = shared.tokenizer.encode(str(prompt))
         input_ids = np.array(input_ids).reshape(1, len(input_ids))
         return input_ids
     else:
-        input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens)
+        input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', add_special_tokens=add_special_tokens)
 
+        # This is a hack for making replies more creative.
+        if not add_bos_token and input_ids[0][0] == shared.tokenizer.bos_token_id:
+            input_ids = input_ids[:, 1:]
+
+        # Llama adds this extra token when the first character is '\n', and this
+        # compromises the stopping criteria, so we just remove it
         if type(shared.tokenizer) is transformers.LlamaTokenizer and input_ids[0][0] == 29871:
             input_ids = input_ids[:, 1:]
 
-        if shared.args.cpu:
+    # Handling truncation
+    if truncation_length is not None:
+        input_ids = input_ids[:, -truncation_length:]
+
+    if any((shared.is_RWKV, shared.is_llamacpp, shared.args.cpu)):
         return input_ids
     elif shared.args.flexgen:
         return input_ids.numpy()
@@ -63,9 +73,8 @@ def generate_softprompt_input_tensors(input_ids):
     # filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens
     return inputs_embeds, filler_input_ids
 
+
 # Removes empty replies from gpt4chan outputs
-
-
 def fix_gpt4chan(s):
     for i in range(10):
         s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
@@ -73,9 +82,8 @@ def fix_gpt4chan(s):
         s = re.sub("--- [0-9]*\n\n\n---", "---", s)
     return s
 
+
 # Fix the LaTeX equations in galactica
-
-
 def fix_galactica(s):
     s = s.replace(r'\[', r'$')
     s = s.replace(r'\]', r'$')
@@ -101,48 +109,47 @@ 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:
+    seed = int(seed)
+    if seed == -1:
+        seed = random.randint(1, 2**31)
     torch.manual_seed(seed)
     if torch.cuda.is_available():
         torch.cuda.manual_seed_all(seed)
+    return seed
 
 
 def stop_everything_event():
     shared.stop_everything = True
 
 
-def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]):
+def generate_reply(question, state, eos_token=None, stopping_strings=[]):
     clear_torch_cache()
-    set_manual_seed(generate_state['seed'])
+    seed = set_manual_seed(state['seed'])
     shared.stop_everything = False
     generate_params = {}
     t0 = time.time()
 
     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")
 
     # 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)):
+
+        if shared.args.verbose:
+            print(f'\n\n{question}\n--------------------\n')
+
         for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
-            generate_params[k] = generate_state[k]
+            generate_params[k] = state[k]
-        generate_params["token_count"] = generate_state["max_new_tokens"]
+        generate_params['token_count'] = 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():
@@ -153,7 +160,7 @@ 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:
@@ -162,47 +169,53 @@ 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}, seed {seed})')
             return
 
-    input_ids = encode(question, generate_state['max_new_tokens'])
+    input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
     original_input_ids = input_ids
     output = input_ids[0]
 
+    if shared.args.verbose:
+        print(f'\n\n{decode(input_ids[0])}\n--------------------\n')
+
     cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
     eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
     if eos_token is not None:
         eos_token_ids.append(int(encode(eos_token)[0][-1]))
-    stopping_criteria_list = transformers.StoppingCriteriaList()
-    if type(stopping_strings) is list and len(stopping_strings) > 0:
-        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']
+    # Handling the stopping strings
+    stopping_criteria_list = transformers.StoppingCriteriaList()
+    for st in [stopping_strings, state['custom_stopping_strings']]:
+        if type(st) is list and len(st) > 0:
+            sentinel_token_ids = [encode(string, add_special_tokens=False) for string in st]
+            stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=sentinel_token_ids, starting_idx=len(input_ids[0])))
+            break
+
     if not shared.args.flexgen:
-        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"]:
+        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']:
-            generate_params[k] = generate_state[k]
+            generate_params[k] = state[k]
-        generate_params["eos_token_id"] = eos_token_ids
+        generate_params['eos_token_id'] = eos_token_ids
-        generate_params["stopping_criteria"] = stopping_criteria_list
+        generate_params['stopping_criteria'] = stopping_criteria_list
-        if shared.args.no_stream:
+        if state['ban_eos_token']:
-            generate_params["min_length"] = 0
+            generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id]
     else:
-        for k in ["do_sample", "temperature"]:
+        for k in ['max_new_tokens', 'do_sample', 'temperature']:
-            generate_params[k] = generate_state[k]
+            generate_params[k] = state[k]
-        generate_params["stop"] = generate_state["eos_token_ids"][-1]
+        generate_params['stop'] = 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_embeds': inputs_embeds})
-        generate_params.update({"inputs": filler_input_ids})
+        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.
@@ -217,7 +230,7 @@ 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)
 
@@ -244,7 +257,7 @@ 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
@@ -252,7 +265,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
 
         # Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
         else:
-            for i in range(generate_state['max_new_tokens'] // 8 + 1):
+            for i in range(state['max_new_tokens'] // 8 + 1):
                 clear_torch_cache()
                 with torch.no_grad():
                     output = shared.model.generate(**generate_params)[0]
@@ -262,7 +275,7 @@ 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
@@ -271,10 +284,10 @@ 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_embeds': inputs_embeds})
-                    generate_params.update({"inputs": filler_input_ids})
+                    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)
 
@@ -284,5 +297,5 @@ 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}, seed {seed})')
         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') as file:
+        with open(clean_path('training/datasets', f'{raw_text_file}.txt'), 'r', encoding='utf-8') 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
@@ -238,7 +238,7 @@ def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int
             warmup_steps=100,
             num_train_epochs=epochs,
             learning_rate=actual_lr,
-            fp16=True,
+            fp16=False if shared.args.cpu else True,
             logging_steps=20,
             evaluation_strategy="steps" if eval_data is not None else "no",
             save_strategy="steps",
@@ -248,7 +248,8 @@ def do_train(lora_name: str, micro_batch_size: int, batch_size: int, epochs: int
             save_total_limit=3,
             load_best_model_at_end=True if eval_data is not None else False,
             # TODO: Enable multi-device support
-            ddp_find_unused_parameters=None
+            ddp_find_unused_parameters=None,
+            no_cuda=shared.args.cpu
         ),
         data_collator=transformers.DataCollatorForLanguageModeling(shared.tokenizer, mlm=False),
         callbacks=list([Callbacks()])

presets/Default.txt

@@ -1,7 +1,6 @@
 do_sample=True
-top_p=0.5
+top_p=0.95
-top_k=40
+top_k=50
-temperature=0.7
+temperature=1
 repetition_penalty=1.2
 typical_p=1.0
-early_stopping=False

requirements.txt

@@ -1,10 +1,10 @@
 accelerate==0.18.0
-bitsandbytes==0.37.2
 datasets
 flexgen==0.1.7
 gradio==3.24.1
 markdown
 numpy
+Pillow>=9.5.0
 peft==0.2.0
 requests
 rwkv==0.7.3
@@ -13,3 +13,6 @@ sentencepiece
 pyyaml
 tqdm
 git+https://github.com/huggingface/transformers
+bitsandbytes==0.37.2; platform_system != "Windows"
+llama-cpp-python==0.1.30; platform_system != "Windows"
+https://github.com/abetlen/llama-cpp-python/releases/download/v0.1.30/llama_cpp_python-0.1.30-cp310-cp310-win_amd64.whl; platform_system == "Windows"

server.py

@@ -2,11 +2,14 @@ import os
 
 os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
 
+import importlib
 import io
 import json
+import os
 import re
 import sys
 import time
+import traceback
 import zipfile
 from datetime import datetime
 from pathlib import Path
@@ -15,12 +18,12 @@ import gradio as gr
 from PIL import Image
 
 import modules.extensions as extensions_module
-from modules import chat, shared, training, ui, api
+from modules import api, chat, shared, training, ui
 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
+from modules.models import load_model, load_soft_prompt, unload_model
-from modules.text_generation import (clear_torch_cache, generate_reply,
+from modules.text_generation import generate_reply, stop_everything_event
-                                     stop_everything_event)
+
 
 # Loading custom settings
 settings_file = None
@@ -79,11 +82,6 @@ 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
@@ -178,6 +176,34 @@ def create_prompt_menus():
     shared.gradio['save_prompt'].click(save_prompt, [shared.gradio['textbox']], [shared.gradio['status']], show_progress=False)
 
 
+def download_model_wrapper(repo_id):
+    try:
+        downloader = importlib.import_module("download-model")
+
+        model = repo_id
+        branch = "main"
+        check = False
+
+        yield ("Cleaning up the model/branch names")
+        model, branch = downloader.sanitize_model_and_branch_names(model, branch)
+
+        yield ("Getting the download links from Hugging Face")
+        links, sha256, is_lora = downloader.get_download_links_from_huggingface(model, branch, text_only=False)
+
+        yield ("Getting the output folder")
+        output_folder = downloader.get_output_folder(model, branch, is_lora)
+
+        if check:
+            yield ("Checking previously downloaded files")
+            downloader.check_model_files(model, branch, links, sha256, output_folder)
+        else:
+            yield (f"Downloading files to {output_folder}")
+            downloader.download_model_files(model, branch, links, sha256, output_folder, threads=1)
+            yield ("Done!")
+    except:
+        yield traceback.format_exc()
+
+
 def create_model_menus():
     with gr.Row():
         with gr.Column():
@@ -188,16 +214,26 @@ def create_model_menus():
             with gr.Row():
                 shared.gradio['lora_menu'] = gr.Dropdown(choices=available_loras, value=shared.lora_name, label='LoRA')
                 ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': get_available_loras()}, 'refresh-button')
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                with gr.Column():
+                    shared.gradio['custom_model_menu'] = gr.Textbox(label="Download custom model or LoRA",
+                                                                    info="Enter Hugging Face username/model path, e.g: facebook/galactica-125m")
+                with gr.Column():
+                    shared.gradio['download_button'] = gr.Button("Download")
+                    shared.gradio['download_status'] = gr.Markdown()
+        with gr.Column():
+            pass
 
     shared.gradio['model_menu'].change(load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_menu'], show_progress=True)
     shared.gradio['lora_menu'].change(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['lora_menu'], show_progress=True)
+    shared.gradio['download_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['download_status'], show_progress=False)
 
 
 def create_settings_menus(default_preset):
+
     generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', {}, return_dict=True)
-    for k in ['max_new_tokens', 'seed', 'stop_at_newline', 'chat_prompt_size', 'chat_generation_attempts']:
-        generate_params[k] = shared.settings[k]
-    shared.gradio['generate_state'] = gr.State(generate_params)
 
     with gr.Row():
         with gr.Column():
@@ -210,24 +246,24 @@ def create_settings_menus(default_preset):
     with gr.Row():
         with gr.Column():
             with gr.Box():
-                gr.Markdown('Custom generation parameters ([reference](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig))')
+                gr.Markdown('Custom generation parameters ([click here to view technical documentation](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig))')
                 with gr.Row():
                     with gr.Column():
-                        shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature')
+                        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['top_p'] = gr.Slider(0.0, 1.0, value=generate_params['top_p'], step=0.01, label='top_p')
+                        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_k'] = gr.Slider(0, 200, value=generate_params['top_k'], step=1, label='top_k')
+                        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['typical_p'] = gr.Slider(0.0, 1.0, value=generate_params['typical_p'], step=0.01, label='typical_p')
+                        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.')
                     with gr.Column():
-                        shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'], step=0.01, label='repetition_penalty')
+                        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['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'], step=0.01, label='encoder_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['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size')
+                        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['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'] if shared.args.no_stream else 0, label='min_length', interactive=shared.args.no_stream)
+                        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['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
         with gr.Column():
             with gr.Box():
                 gr.Markdown('Contrastive search')
                 shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha')
-            with gr.Box():
+
                 gr.Markdown('Beam search (uses a lot of VRAM)')
                 with gr.Row():
                     with gr.Column():
@@ -236,6 +272,13 @@ def create_settings_menus(default_preset):
                         shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty')
                 shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
 
+            with gr.Group():
+                with gr.Row():
+                    shared.gradio['add_bos_token'] = gr.Checkbox(value=shared.settings['add_bos_token'], label='Add the bos_token to the beginning of prompts', info='Disabling this can make the replies more creative.')
+                    shared.gradio['ban_eos_token'] = gr.Checkbox(value=shared.settings['ban_eos_token'], label='Ban the eos_token', info='This forces the model to never end the generation prematurely.')
+                shared.gradio['truncation_length'] = gr.Slider(value=shared.settings['truncation_length'], minimum=shared.settings['truncation_length_min'], maximum=shared.settings['truncation_length_max'], step=1, label='Truncate the prompt up to this length', info='The leftmost tokens are removed if the prompt exceeds this length. Most models require this to be at most 2048.')
+                shared.gradio['custom_stopping_strings'] = gr.Textbox(lines=1, value=shared.settings["custom_stopping_strings"] or None, label='Custom stopping strings', info='In addition to the defaults. Written between "" and separated by commas. For instance: "\\nYour Assistant:", "\\nThe assistant:"')
+
     with gr.Accordion('Soft prompt', open=False):
         with gr.Row():
             shared.gradio['softprompts_menu'] = gr.Dropdown(choices=available_softprompts, value='None', label='Soft prompt')
@@ -245,7 +288,7 @@ def create_settings_menus(default_preset):
         with gr.Row():
             shared.gradio['upload_softprompt'] = gr.File(type='binary', file_types=['.zip'])
 
-    shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio[k] for k in ['preset_menu', 'generate_state']], [shared.gradio[k] for k in ['generate_state', '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']])
+    shared.gradio['preset_menu'].change(load_preset_values, [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', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']])
     shared.gradio['softprompts_menu'].change(load_soft_prompt, shared.gradio['softprompts_menu'], shared.gradio['softprompts_menu'], show_progress=True)
     shared.gradio['upload_softprompt'].upload(upload_soft_prompt, shared.gradio['upload_softprompt'], shared.gradio['softprompts_menu'])
 
@@ -318,6 +361,21 @@ else:
 title = 'Text generation web UI'
 
 
+def list_interface_input_elements(chat=False):
+    elements = ['max_new_tokens', 'seed', 'temperature', 'top_p', 'top_k', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'do_sample', 'penalty_alpha', 'num_beams', 'length_penalty', 'early_stopping', 'add_bos_token', 'ban_eos_token', 'truncation_length', 'custom_stopping_strings']
+    if chat:
+        elements += ['name1', 'name2', 'greeting', 'context', 'end_of_turn', 'chat_prompt_size', 'chat_generation_attempts', 'stop_at_newline', 'mode']
+    return elements
+
+
+def gather_interface_values(*args):
+    output = {}
+    for i, element in enumerate(shared.input_elements):
+        output[element] = args[i]
+    output['custom_stopping_strings'] = eval(f"[{output['custom_stopping_strings']}]")
+    return output
+
+
 def create_interface():
     gen_events = []
     if shared.args.extensions is not None and len(shared.args.extensions) > 0:
@@ -325,27 +383,34 @@ def create_interface():
 
     with gr.Blocks(css=ui.css if not shared.is_chat() else ui.css + ui.chat_css, analytics_enabled=False, title=title) as shared.gradio['interface']:
         if shared.is_chat():
+
+            shared.input_elements = list_interface_input_elements(chat=True)
+            shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
             shared.gradio['Chat input'] = gr.State()
+
             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'], 'cai-chat'))
                 shared.gradio['textbox'] = gr.Textbox(label='Input')
                 with gr.Row():
-                    shared.gradio['Generate'] = gr.Button('Generate')
+                    shared.gradio['Generate'] = gr.Button('Generate', elem_id='Generate')
                     shared.gradio['Stop'] = gr.Button('Stop', elem_id="stop")
                 with gr.Row():
-                    shared.gradio['Impersonate'] = gr.Button('Impersonate')
                     shared.gradio['Regenerate'] = gr.Button('Regenerate')
+                    shared.gradio['Continue'] = gr.Button('Continue')
+                    shared.gradio['Impersonate'] = gr.Button('Impersonate')
                 with gr.Row():
-                    shared.gradio['Copy last reply'] = gr.Button('Copy last reply')
+                    shared.gradio['Send dummy message'] = gr.Button('Send dummy message')
+                    shared.gradio['Send dummy reply'] = gr.Button('Send dummy reply')
                     shared.gradio['Replace last reply'] = gr.Button('Replace last reply')
-                    shared.gradio['Remove last'] = gr.Button('Remove last')
+                    shared.gradio['Copy last reply'] = gr.Button('Copy last reply')
-
+                with gr.Row():
                     shared.gradio['Clear history'] = gr.Button('Clear history')
                     shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant="stop", visible=False)
                     shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False)
+                    shared.gradio['Remove last'] = gr.Button('Remove last')
 
-                shared.gradio["Chat mode"] = gr.Radio(choices=["cai-chat", "chat", "instruct"], value="cai-chat", label="Mode")
+                shared.gradio["mode"] = gr.Radio(choices=["cai-chat", "chat", "instruct"], value="cai-chat", label="Mode")
-                shared.gradio["Instruction templates"] = gr.Dropdown(choices=get_available_instruction_templates(), label="Instruction template", value="None", visible=False)
+                shared.gradio["Instruction templates"] = gr.Dropdown(choices=get_available_instruction_templates(), label="Instruction template", value="None", visible=False, info="Change this according to the model/LoRA that you are using.")
 
             with gr.Tab("Character", elem_id="chat-settings"):
                 with gr.Row():
@@ -392,66 +457,102 @@ def create_interface():
                     with gr.Row():
                         with gr.Column():
                             shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
-                            shared.gradio['chat_prompt_size_slider'] = gr.Slider(minimum=shared.settings['chat_prompt_size_min'], maximum=shared.settings['chat_prompt_size_max'], step=1, label='Maximum prompt size in tokens', value=shared.settings['chat_prompt_size'])
+                            shared.gradio['chat_prompt_size'] = gr.Slider(minimum=shared.settings['chat_prompt_size_min'], maximum=shared.settings['chat_prompt_size_max'], step=1, label='Maximum prompt size in tokens', value=shared.settings['chat_prompt_size'])
                         with gr.Column():
                             shared.gradio['chat_generation_attempts'] = gr.Slider(minimum=shared.settings['chat_generation_attempts_min'], maximum=shared.settings['chat_generation_attempts_max'], value=shared.settings['chat_generation_attempts'], step=1, label='Generation attempts (for longer replies)')
-                            shared.gradio['stop_at_newline'] = gr.Checkbox(value=shared.settings['stop_at_newline'], label='Stop generating at new line character?')
+                            shared.gradio['stop_at_newline'] = gr.Checkbox(value=shared.settings['stop_at_newline'], label='Stop generating at new line character')
 
                 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']]
+            shared.input_params = [shared.gradio[k] for k in ['Chat input', 'interface_state']]
-
-            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))
-            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']]
+            reload_inputs = [shared.gradio[k] for k in ['name1', 'name2', 'mode']]
+
+            gen_events.append(shared.gradio['Generate'].click(
+                gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                lambda x: (x, ''), 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(
+                chat.save_history, shared.gradio['mode'], None, show_progress=False)
+            )
+
+            gen_events.append(shared.gradio['textbox'].submit(
+                gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                lambda x: (x, ''), 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(
+                chat.save_history, shared.gradio['mode'], None, show_progress=False)
+            )
+
+            gen_events.append(shared.gradio['Regenerate'].click(
+                gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
+                chat.save_history, shared.gradio['mode'], None, show_progress=False)
+            )
+
+            gen_events.append(shared.gradio['Continue'].click(
+                gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                chat.continue_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
+                chat.save_history, shared.gradio['mode'], None, show_progress=False)
+            )
+
+            gen_events.append(shared.gradio['Impersonate'].click(
+                gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                chat.impersonate_wrapper, shared.input_params, 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', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then(
+                lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
+                chat.save_history, shared.gradio['mode'], None, show_progress=False)
+
+            shared.gradio['Send dummy message'].click(
+                chat.send_dummy_message, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then(
+                lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
+                chat.save_history, shared.gradio['mode'], None, show_progress=False)
+
+            shared.gradio['Send dummy reply'].click(
+                chat.send_dummy_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then(
+                lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
+                chat.save_history, shared.gradio['mode'], None, 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', 'mode']], shared.gradio['display']).then(
+                chat.save_history, shared.gradio['mode'], None, show_progress=False)
+
+            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, reload_inputs, shared.gradio['display'])
+
+            shared.gradio['mode'].change(
+                lambda x: gr.update(visible=x == 'instruct'), shared.gradio['mode'], shared.gradio['Instruction templates']).then(
+                lambda x: gr.update(interactive=x != 'instruct'), shared.gradio['mode'], shared.gradio['character_menu']).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', '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']], None).then(
+                chat.redraw_html, reload_inputs, shared.gradio['display'])
+
+            shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, None, shared.gradio['textbox'], show_progress=shared.args.no_stream)
             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', 'mode']], [shared.gradio['display'], shared.gradio['textbox']], show_progress=False)
-
+            shared.gradio['download_button'].click(lambda x: chat.save_history(x, timestamp=True), shared.gradio['mode'], shared.gradio['download'])
-            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']])
-
+            shared.gradio['character_menu'].change(chat.load_character, [shared.gradio[k] for k in ['character_menu', 'name1', 'name2', 'mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'end_of_turn', 'display']])
-            # 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'])
+            shared.gradio['your_picture'].change(chat.upload_your_profile_picture, [shared.gradio[k] for k in ['your_picture', 'name1', 'name2', '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(lambda: chat.load_default_history(shared.settings['name1'], shared.settings['name2']), None, None)
+            shared.gradio['interface'].load(chat.load_default_history, [shared.gradio[k] for k in ['name1', 'name2']], None)
-            shared.gradio['interface'].load(chat.redraw_html, reload_inputs, [shared.gradio['display']], show_progress=True)
+            shared.gradio['interface'].load(chat.redraw_html, reload_inputs, shared.gradio['display'], show_progress=True)
 
         elif shared.args.notebook:
+            shared.input_elements = list_interface_input_elements(chat=False)
+            shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
             with gr.Tab("Text generation", elem_id="main"):
                 with gr.Row():
                     with gr.Column(scale=4):
@@ -479,14 +580,27 @@ def create_interface():
             with gr.Tab("Parameters", elem_id="parameters"):
                 create_settings_menus(default_preset)
 
-            shared.input_params = [shared.gradio[k] for k in ['textbox', 'generate_state']]
+            shared.input_params = [shared.gradio[k] for k in ['textbox', 'interface_state']]
             output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']]
-            gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
+
-            gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
+            gen_events.append(shared.gradio['Generate'].click(
-            shared.gradio['Stop'].click(stop_everything_event, [], [], queue=False, cancels=gen_events if shared.args.no_stream else None)
+                gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)#.then(
+                #None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}")
+            )
+
+            gen_events.append(shared.gradio['textbox'].submit(
+                gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)#.then(
+                #None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; 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['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}")
 
         else:
+            shared.input_elements = list_interface_input_elements(chat=False)
+            shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
             with gr.Tab("Text generation", elem_id="main"):
                 with gr.Row():
                     with gr.Column():
@@ -512,12 +626,28 @@ def create_interface():
             with gr.Tab("Parameters", elem_id="parameters"):
                 create_settings_menus(default_preset)
 
-            shared.input_params = [shared.gradio[k] for k in ['textbox', 'generate_state']]
+            shared.input_params = [shared.gradio[k] for k in ['textbox', 'interface_state']]
             output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']]
-            gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
+
-            gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
+            gen_events.append(shared.gradio['Generate'].click(
-            gen_events.append(shared.gradio['Continue'].click(generate_reply, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=shared.args.no_stream))
+                gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
-            shared.gradio['Stop'].click(stop_everything_event, [], [], queue=False, cancels=gen_events if shared.args.no_stream else None)
+                generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)#.then(
+                #None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[1]; element.scrollTop = element.scrollHeight}")
+            )
+
+            gen_events.append(shared.gradio['textbox'].submit(
+                gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)#.then(
+                #None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[1]; element.scrollTop = element.scrollHeight}")
+            )
+
+            gen_events.append(shared.gradio['Continue'].click(
+                gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
+                generate_reply, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=shared.args.no_stream)#.then(
+                #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['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}")
 
         with gr.Tab("Model", elem_id="model-tab"):
@@ -541,26 +671,16 @@ 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", type="primary")
+            shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface")
 
-            shared.gradio['reset_interface'].click(set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'bool_menu']], None)
+            # Reset interface event
-            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 []}')
+            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 []}')
 
         if shared.args.extensions is not None:
             extensions_module.create_extensions_block()
 
-        def change_dict_value(d, key, value):
-            d[key] = value
-            return d
-
-        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', 'max_new_tokens', 'seed', 'stop_at_newline', 'chat_prompt_size_slider', 'chat_generation_attempts']:
-            if k not in shared.gradio:
-                continue
-            if type(shared.gradio[k]) in [gr.Checkbox, gr.Number]:
-                shared.gradio[k].change(lambda state, value, copy=k: change_dict_value(state, copy, value), inputs=[shared.gradio['generate_state'], shared.gradio[k]], outputs=shared.gradio['generate_state'])
-            else:
-                shared.gradio[k].release(lambda state, value, copy=k: change_dict_value(state, copy, value), inputs=[shared.gradio['generate_state'], shared.gradio[k]], outputs=shared.gradio['generate_state'])
-
         if not shared.is_chat():
             api.create_apis()
 

settings-template.json

@@ -7,7 +7,14 @@
     "name2": "Assistant",
     "context": "This is a conversation with your Assistant. The Assistant is very helpful and is eager to chat with you and answer your questions.",
     "greeting": "Hello there!",
+    "end_of_turn": "",
+    "custom_stopping_strings": "",
     "stop_at_newline": false,
+    "add_bos_token": true,
+    "ban_eos_token": true,
+    "truncation_length": 2048,
+    "truncation_length_min": 0,
+    "truncation_length_max": 4096,
     "chat_prompt_size": 2048,
     "chat_prompt_size_min": 0,
     "chat_prompt_size_max": 2048,
@@ -19,7 +26,8 @@
         "gallery"
     ],
     "presets": {
-        "default": "NovelAI-Sphinx Moth",
+        "default": "Default",
+        ".*(alpaca|llama)": "LLaMA-Precise",
         ".*pygmalion": "NovelAI-Storywriter",
         ".*RWKV": "Naive"
     },