novelai 1.3.0

🎨 A lightweight async Python API for NovelAI image generation

NovelAI-API

A lightweight asynchronous Python wrapper for NovelAI image generation API.

Features

• Lightweight - Focuses on image generation only, providing a simple and easy-to-use interface.
• Concurrent - Supports both API and web backend, allowing to run two generating tasks simultaneously.
• Parameterized - Provides a Metadata class to easily set up generation parameters with type validation.
• Asynchronous - Utilizes asyncio to run generating tasks and return outputs efficiently.

[!IMPORTANT]

Unfortunately, NovelAI has depreciated their image generation function of the API endpoint starting from Mar 21, 2024. As a result, concurrent generation feature is no longer available.

Source

Table of Contents

• Features
• Table of Contents
• Installation
• Usage
  • Initialization
  • Image Generation
  • Image to Image
  • Inpainting
  • Vibe Transfer
  • Use in CLI
• References

Installation

[!NOTE]

This package requires Python 3.12 or higher.

Install/update with pip:

pip install -U novelai

Usage

Initialization

Import required packages and initialize a client with your NovelAI account credentials.

import asyncio
from novelai import NAIClient

# Replace argument values with your actual account credentials
username = "Your NovelAI username"
password = "Your NovelAI password"

async def main():
    client = NAIClient(username, password, proxy=None)
    await client.init(timeout=30)

asyncio.run(main())

Image Generation

After initializing successfully, you can generate images with the generate_image method. The method takes a Metadata object as the first argument, and an optional host argument to specify the backend to use.

By passing verbose=True, the method will print the estimated Anlas cost each time a generating request is going to be made.

The full parameter list of Metadata can be found in the class definition.

from novelai import Metadata, Resolution

async def main():
    metadata = Metadata(
        prompt="1girl",
        negative_prompt="bad anatomy",
        res_preset=Resolution.NORMAL_PORTRAIT,
        n_samples=1,
    )

    print(f"Estimated Anlas cost: {metadata.calculate_cost(is_opus=False)}")

    output = await client.generate_image(
        metadata, verbose=False, is_opus=False
    )

    for image in output:
        image.save(path="output images", verbose=True)

asyncio.run(main())

Image to Image

To perform img2img action, set action parameter in Metadata to Action.IMG2IMG, and image parameter to your base image. The base image needs to be converted into Base64-encoded format. This can be achieved using base64 module.

import base64
from novelai import Metadata, Action

async def main():
    with open("tests/images/portrait.jpg", "rb") as f:
        base_image = base64.b64encode(f.read()).decode("utf-8")

    metadata = Metadata(
        prompt="1girl",
        negative_prompt="bad anatomy",
        action=Action.IMG2IMG,
        width=832,
        height=1216,
        n_samples=1,
        image=base_image,
        strength=0.5,
        noise=0.1,
    )

    output = await client.generate_image(metadata, verbose=True)

    for image in output:
        image.save(path="output images", verbose=True)

asyncio.run(main())

Inpainting

To perform inpaint action, set action parameter in Metadata to Action.INPAINTING, and image parameter to your base image, and mask parameter to the black and white mask image, where white is the area to inpaint and black to keep as is. Both base image and mask need to be converted into Base64-encoded format. This can be achieved using base64 module.

import base64
from novelai import Metadata, Model, Action, Resolution

async def main():
    with open("tests/images/portrait.jpg", "rb") as f:
        base_image = base64.b64encode(f.read()).decode("utf-8")

    with open("tests/images/inpaint_left.jpg", "rb") as f:
        mask = base64.b64encode(f.read()).decode("utf-8")

    metadata = Metadata(
        prompt="1girl",
        negative_prompt="bad anatomy",
        model=Model.V3INP,
        action=Action.INPAINT,
        res_preset=Resolution.NORMAL_PORTRAIT,
        image=base_image,
        mask=mask,
    )

    output = await client.generate_image(metadata, verbose=True)

    for image in output:
        image.save(path="output images", verbose=True)

asyncio.run(main())

Vibe Transfer

Vibe transfer doesn't have its own action type. Instead, it is achieved by adding a reference_image_multiple parameter in Metadata. The reference image needs to be converted into Base64-encoded format. This can be achieved using base64 module.

import base64
from novelai import Metadata, Resolution

async def main():
    with open("tests/images/portrait.jpg", "rb") as f:
        base_image = base64.b64encode(f.read()).decode("utf-8")

    metadata = Metadata(
        prompt="1girl",
        negative_prompt="bad anatomy",
        res_preset=Resolution.NORMAL_PORTRAIT,
        reference_image_multiple=[base_image],
        reference_information_extracted_multiple=[1],
        reference_strength_multiple=[0.6],
    )

    output = await client.generate_image(metadata, verbose=True)

    for image in output:
        image.save(path="output images", verbose=True)

asyncio.run(main())

Use in CLI

Optionally, a module function is also provided to directly generate access token in CLI.

Once a access token is generated, it will be valid for 30 days. Token can be used as the authentication header to make requests to NovelAI.

# Replace argument values with your actual account credentials
python3 -m novelai login <username> <password>

References

NovelAI Backend

Aedial/novelai-api

NovelAI Unofficial Knowledgebase