Omost Deployment Instructions

Follow these steps to deploy Omost locally:

git clone https://github.com/lllyasviel/Omost.git
cd Omost
conda create -n omost python=3.10
conda activate omost
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt
python gradio_app.py

Usage

Once set up, users can interact with Omost through a conversational interface. Here’s an example of how it works:

Initialize the Canvas:

python
canvas = Canvas()

Set a Global Description:

Define the overall scene.

python
canvas.set_global_description(
    description='A fierce battle between warriors and a dragon.',
    detailed_descriptions=[
        'In this intense scene, a group of fierce warriors is engaged in an epic battle with a mighty dragon.',
        # Additional descriptions...
    ],
    tags='battle, warriors, dragon, fierce...',
    HTML_web_color_name='darkslategray',
)

Add Elements:

Add specific elements to the canvas, such as warriors and dragons, with detailed descriptions and attributes.

python
canvas.add_local_description(
    location='on the left',
    description='A group of brave warriors in shining armor, wielding swords and shields.',
    tags='warriors, armor, swords, shields',
    HTML_web_color_name='mediumseagreen',
)
canvas.add_local_description(
    location='on the right',
    description='A ferocious dragon with scales as hard as steel, breathing fire.',
    tags='dragon, scales, fire',
    HTML_web_color_name='darkred',
)

Generate the Scene:

After setting up all the elements and descriptions, generate the scene.

python
scene = canvas.generate_scene()

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