nightmareai/majesty-diffusion:5390ebe2 – Run with an API on Replicate

Version

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nightmareai /majesty-diffusion:5390ebe2

Input

clip_prompts

string

Shift + Return to add a new line

The portrait of a Majestic Princess, trending on artstation
The portrait of a Majestic Princess, trending on artstation

Prompts for CLIP guidance, multiple prompts allowed, one per line. Supports weights.

Default: "The portrait of a Majestic Princess, trending on artstation\n"

latent_prompt

string

Shift + Return to add a new line

The portrait of a Majestic Princess, trending on artstationThe portrait of a Majestic Princess, trending on artstation

Prompt for latent diffusion, single prompt, no weights.

Default: "The portrait of a Majestic Princess, trending on artstation"

height

integer

Output height

Default: 512

width

integer

Output width

Default: 512

init_image

file

Initial image (optional, used instead of noise if supplied)

init_mask

file

A mask same width and height as the initial image with the color white indicating where to inpaint

init_scale

integer

Controls how much the init image should influence the final result. Experiment with values around 1000

Default: 1000

init_brightness

number

Init image brightness

Default: 0

clip_scale

integer

CLIP guidance scale

Default: 16000

latent_scale

integer

The `latent_diffusion_guidance_scale` will determine how much the `latent_prompts` affect the image. Lower help with text interpretation, higher help with composition. Try values between 0-15. If you see too much text, lower it

Default: 12

aesthetic_loss_scale

integer

Aesthetic loss scale

Default: 400

starting_timestep

number

Starting timestep

Default: 0.9

model

string

Latent diffusion model (recommended to stick with finetuned - ongo and erlich may need to download, taking extra time)

Default: "finetuned"

latent_negatives

string

Shift + Return to add a new line

Negative prompts for Latent Diffusion

Default: ""

output_steps

integer

Steps between outputs, 0 to disable progressive output. Minor speed impact.

Default: 10

custom_settings

string

Shift + Return to add a new line

Custom settings cfg content, for custom or API usage. See Majesty docs.

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

npx create-replicate --model=nightmareai/majesty-diffusion

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";

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

Run nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "nightmareai/majesty-diffusion:5390ebe21a7f445c7fedaea47568f43c2652e7afdcde8dd45e9932f02ac18990",
  {
    input: {
      model: "finetuned",
      width: 512,
      height: 512,
      clip_scale: 16000,
      init_scale: 1000,
      clip_prompts: "The portrait of a Majestic Princess, trending on artstation\n",
      latent_scale: 12,
      output_steps: 10,
      latent_prompt: "The portrait of a Majestic Princess, trending on artstation",
      init_brightness: 0,
      latent_negatives: "",
      starting_timestep: 0.9,
      aesthetic_loss_scale: 400
    }
  }
);

// 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 nightmareai/majesty-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "nightmareai/majesty-diffusion:5390ebe21a7f445c7fedaea47568f43c2652e7afdcde8dd45e9932f02ac18990",
    input={
        "model": "finetuned",
        "width": 512,
        "height": 512,
        "clip_scale": 16000,
        "init_scale": 1000,
        "clip_prompts": "The portrait of a Majestic Princess, trending on artstation\n",
        "latent_scale": 12,
        "output_steps": 10,
        "latent_prompt": "The portrait of a Majestic Princess, trending on artstation",
        "init_brightness": 0,
        "latent_negatives": "",
        "starting_timestep": 0.9,
        "aesthetic_loss_scale": 400
    }
)

# The nightmareai/majesty-diffusion model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/nightmareai/majesty-diffusion/api#output-schema
    print(item)

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 nightmareai/majesty-diffusion 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": "5390ebe21a7f445c7fedaea47568f43c2652e7afdcde8dd45e9932f02ac18990",
    "input": {
      "model": "finetuned",
      "width": 512,
      "height": 512,
      "clip_scale": 16000,
      "init_scale": 1000,
      "clip_prompts": "The portrait of a Majestic Princess, trending on artstation\\n",
      "latent_scale": 12,
      "output_steps": 10,
      "latent_prompt": "The portrait of a Majestic Princess, trending on artstation",
      "init_brightness": 0,
      "latent_negatives": "",
      "starting_timestep": 0.9,
      "aesthetic_loss_scale": 400
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

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