Add --host and --port args to the invoke-train-ui command

README.md

@@ -2,8 +2,7 @@
 
 A library for training custom Stable Diffusion models (fine-tuning, LoRA training, textual inversion, etc.) that can be used in [InvokeAI](https://github.com/invoke-ai/InvokeAI).
 
-> [!WARNING]
-> `invoke-training` is still under active development, and breaking changes are likely. Full backwards compatibility will not be guranteed until v1.0.0.
+> [!WARNING] > `invoke-training` is still under active development, and breaking changes are likely. Full backwards compatibility will not be guaranteed until v1.0.0.
 > In the meantime, I recommend pinning to a specific commit hash.
 
 ## Documentation
@@ -13,14 +12,14 @@ https://invoke-ai.github.io/invoke-training/
 ## Training Modes
 
 - Stable Diffusion
-    - LoRA
-    - DreamBooth LoRA
-    - Textual Inversion
+  - LoRA
+  - DreamBooth LoRA
+  - Textual Inversion
 - Stable Diffusion XL
-    - LoRA
-    - DreamBooth LoRA
-    - Textual Inversion
-    - LoRA and Textual Inversion
+  - LoRA
+  - DreamBooth LoRA
+  - Textual Inversion
+  - LoRA and Textual Inversion
 
 More training modes coming soon!
 
@@ -43,27 +42,32 @@ pip install -e ".[test]" --extra-index-url https://download.pytorch.org/whl/cu12
 ### CLI
 
 Run training via the CLI with type-checked YAML configuration files for maximum control:
+
 ```bash
 invoke-train --cfg-file src/invoke_training/sample_configs/sdxl_textual_inversion_gnome_1x24gb.yaml
 ```
 
 ### GUI
 
 Run training via the GUI for a simpler starting point.
+
 ```bash
 invoke-train-ui
+
+# Or, you can optionally override the default host and port:
+invoke-train-ui --host 0.0.0.0 --port 1234
 ```
 
 ## Features
 
 Training progress can be monitored with [Tensorboard](https://www.tensorflow.org/tensorboard):
 ![Screenshot of the Tensorboard UI showing validation images.](docs/images/tensorboard_val_images_screenshot.png)
-*Validation images in the Tensorboard UI.*
+_Validation images in the Tensorboard UI._
 
 All trained models are compatible with InvokeAI:
 
 ![Screenshot of the InvokeAI UI with an example of a Yoda pokemon generated using a Pokemon LoRA model.](docs/images/invokeai_yoda_pokemon_lora.png)
-*Example image generated with the prompt "A cute yoda pokemon creature." and a trained Pokemon LoRA.*
+_Example image generated with the prompt "A cute yoda pokemon creature." and a trained Pokemon LoRA._
 
 ## Contributing
 

docs/get-started/quick-start.md

@@ -7,13 +7,19 @@ There is also a video introduction to `invoke-training`:
 <iframe width="560" height="315" src="https://www.youtube.com/embed/OZIz2vvtlM4?si=iR73F0IhlsolyYAl" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
 ## Quick Start - GUI
+
 ### 1. Installation
+
 Follow the [`invoke-training` installation instructions](./installation.md).
 
 ### 2. Launch the GUI
+
 ```bash
 # From the invoke-training directory:
 invoke-train-ui
+
+# Or, you can optionally override the default host and port:
+invoke-train-ui --host 0.0.0.0 --port 1234
 ```
 
 Access the GUI in your browser at the URL printed to the console.
@@ -33,17 +39,20 @@ Click on 'Generate Config' to generate a YAML configuration file. This YAML conf
 Click on the 'Start Training' and check your terminal for progress logs.
 
 ### 6. Monitor training
+
 Monitor the training process with Tensorboard by running `tensorboard --logdir output/` and visiting [localhost:6006](http://localhost:6006) in your browser. Here you can see generated validation images throughout the training process.
 
 ![Screenshot of the Tensorboard UI showing validation images.](../images/tensorboard_val_images_screenshot.png)
-*Validation images in the Tensorboard UI.*
+_Validation images in the Tensorboard UI._
 
 ### 7. Invokeai
+
 Select a checkpoint based on the quality of the generated images.
 
 If you haven't already, setup [InvokeAI](https://github.com/invoke-ai/InvokeAI) by following its documentation.
 
 Copy your selected LoRA checkpoint into your `${INVOKEAI_ROOT}/autoimport/lora` directory. For example:
+
 ```bash
 # Note: You will have to replace the timestamp in the checkpoint path.
 cp output/1691088769.5694647/checkpoint_epoch-00000002.safetensors ${INVOKEAI_ROOT}/autoimport/lora/pokemon_epoch-00000002.safetensors
@@ -52,12 +61,14 @@ cp output/1691088769.5694647/checkpoint_epoch-00000002.safetensors ${INVOKEAI_RO
 You can now use your trained Pokemon LoRA in the InvokeAI UI! 🎉
 
 ![Screenshot of the InvokeAI UI with an example of a Yoda pokemon generated using a Pokemon LoRA model.](../images/invokeai_yoda_pokemon_lora.png)
-*Example image generated with the prompt "A cute yoda pokemon creature." and Pokemon LoRA.*
-
+_Example image generated with the prompt "A cute yoda pokemon creature." and Pokemon LoRA._
 
 ## Quick Start - CLI
+
 ### 1. Installation
+
 Follow the [`invoke-training` installation instructions](./installation.md).
 
 ### 2. Training
+
 See the [Textual Inversion - SDXL](../tutorials/stable_diffusion/textual_inversion_sdxl.md) tutorial for instructions on how to train a model via the CLI.

src/invoke_training/scripts/invoke_train_ui.py

@@ -1,11 +1,26 @@
+import argparse
+
 import uvicorn
 
 from invoke_training.ui.app import build_app
 
 
 def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--host",
+        default="127.0.0.1",
+        help="The server host. Set `--host 0.0.0.0` to make the app available on your network.",
+    )
+    parser.add_argument("--port", default=8000, type=int, help="The server port.")
+    args = parser.parse_args()
+
     app = build_app()
-    uvicorn.run(app)
+    uvicorn.run(
+        app,
+        host=args.host,
+        port=args.port,
+    )
 
 
 if __name__ == "__main__":