ai-forever/kandinsky-2.2:ad9d7879 | Run with an API on Replicate

Version

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ai-forever /kandinsky-2.2:ad9d7879

Input

prompt

string

Shift + Return to add a new line

A moss covered astronaut with a black backgroundA moss covered astronaut with a black background

Input prompt

Default: "A moss covered astronaut with a black background"

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output

width

integer

Width of output image. Lower the setting if hits memory limits.

Default: 512

height

integer

Height of output image. Lower the setting if hits memory limits.

Default: 512

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 75

num_inference_steps_prior

integer

(minimum: 1, maximum: 500)

Number of denoising steps for priors

Default: 25

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

seed

integer

Random seed. Leave blank to randomize the seed

output_format

string

Output image format

Default: "webp"

Run this model in Node.js with one line of code:

npx create-replicate --model=ai-forever/kandinsky-2.2

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run ai-forever/kandinsky-2.2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ai-forever/kandinsky-2.2:ad9d7879fbffa2874e1d909d1d37d9bc682889cc65b31f7bb00d2362619f194a",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "A moss covered astronaut with a black background",
      num_outputs: 1,
      output_format: "webp",
      num_inference_steps: 75,
      num_inference_steps_prior: 25
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run ai-forever/kandinsky-2.2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ai-forever/kandinsky-2.2:ad9d7879fbffa2874e1d909d1d37d9bc682889cc65b31f7bb00d2362619f194a",
    input={
        "width": 512,
        "height": 512,
        "prompt": "A moss covered astronaut with a black background",
        "num_outputs": 1,
        "output_format": "webp",
        "num_inference_steps": 75,
        "num_inference_steps_prior": 25
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run ai-forever/kandinsky-2.2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "ai-forever/kandinsky-2.2:ad9d7879fbffa2874e1d909d1d37d9bc682889cc65b31f7bb00d2362619f194a",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "A moss covered astronaut with a black background",
      "num_outputs": 1,
      "output_format": "webp",
      "num_inference_steps": 75,
      "num_inference_steps_prior": 25
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/ai-forever/kandinsky-2.2@sha256:ad9d7879fbffa2874e1d909d1d37d9bc682889cc65b31f7bb00d2362619f194a \
  -i 'width=512' \
  -i 'height=512' \
  -i 'prompt="A moss covered astronaut with a black background"' \
  -i 'num_outputs=1' \
  -i 'output_format="webp"' \
  -i 'num_inference_steps=75' \
  -i 'num_inference_steps_prior=25'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/ai-forever/kandinsky-2.2@sha256:ad9d7879fbffa2874e1d909d1d37d9bc682889cc65b31f7bb00d2362619f194a
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "A moss covered astronaut with a black background",
      "num_outputs": 1,
      "output_format": "webp",
      "num_inference_steps": 75,
      "num_inference_steps_prior": 25
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

No output yet! Press "Submit" to start a prediction.