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CLI

CogKit provides a powerful command-line interface (CLI) that allows you to perform various tasks without writing Python code. This guide covers the available commands and their usage.

Overview​

The main CLI command is cogkit, which provides several subcommands:

cogkit [OPTIONS] COMMAND [ARGS]...

Available commands:

  • inference: Generate images or videos using AI models
  • launch: Launch a API server

Global options:

  • -v, --verbose: Increase verbosity (can be used multiple times)

Inference Command​

The inference command allows you to generate images or videos:

# Generate an image from text
cogkit inference "a beautiful sunset over mountains" "THUDM/CogView4-6B"

# Generate a video from text
cogkit inference "a cat playing with a ball" "THUDM/CogVideoX1.5-5B"

# Controlling image generation from text
cogkit inference "a dog" "THUDM/CogView4-6B-Control"
tip

See cogkit inference --help for more information.

Launch Command​

Before starting the server, make sure to configure the model paths that you want to serve. This step is necessary to specify which models will be available through the API server.

To configure the model paths:

  1. Create a .env file in your working directory

  2. Refer to the environment template and add needed environment variables to specify model paths. For example, to serve CogView4-6B as a service, you must specify COGVIEW4_PATH in your .env file:

    # /your/workdir/.env

    COGVIEW4_PATH="THUDM/CogView4-6B"  # or local path
    # other variables...

Then starts a API server, for example:

cogkit launch
tip

See cogkit launch --help for more information.

Client Interfaces​

The server API is OpenAI-compatible, which means you can use it with any OpenAI client library. Here's an example using the OpenAI Python client:

import base64

from io import BytesIO
from PIL import Image

from openai import OpenAI

client = OpenAI(
    api_key="foo",
    base_url="http://localhost:8000/v1"  # Your server URL
)

# Generate an image from cogview-4
response = client.images.generate(
    model="cogview-4",
    prompt="a beautiful sunset over mountains",
    n=1,
    size="1024x1024",
)
image_b64 = response.data[0].b64_json

# Decode the base64 string
image_data = base64.b64decode(image_b64)

# Create an image from the decoded data
image = Image.open(BytesIO(image_data))

# Save the image
image.save("output.png")