lucataco/segmind-vega:1587d0a4 – Run with an API on Replicate

Version

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lucataco /segmind-vega:1587d0a4

Input

prompt

string

Shift + Return to add a new line

A cinematic shot of a racoon wearing an intricate italian robe, with a crownA cinematic shot of a racoon wearing an intricate italian robe, with a crown

Input prompt

Default: "A cinematic shot of a racoon wearing an intricate italian robe, with a crown"

negative_prompt

string

Shift + Return to add a new line

worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketchworst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch

Negative Input prompt

Default: "worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch"

image

file

Input image for img2img or inpaint mode

mask

file

Input mask for inpaint mode. Black areas will be preserved, white areas will be inpainted.

width

integer

Width of output image

Default: 768

height

integer

Height of output image

Default: 768

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

scheduler

string

scheduler

Default: "K_EULER"

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 40

guidance_scale

number

(minimum: 1, maximum: 50)

Scale for classifier-free guidance

Default: 9

prompt_strength

number

(minimum: 0, maximum: 1)

Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image

Default: 0.8

seed

integer

Random seed. Leave blank to randomize the seed

apply_watermark

boolean

Applies a watermark to enable determining if an image is generated in downstream applications. If you have other provisions for generating or deploying images safely, you can use this to disable watermarking.

Default: true

disable_safety_checker

boolean

Disable safety checker for generated images. This feature is only available through the API. See https://replicate.com/docs/how-does-replicate-work#safety

Default: false

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

npx create-replicate --model=lucataco/segmind-vega

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 lucataco/segmind-vega using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/segmind-vega:1587d0a4f8897740324dea9d66cd9cfa8caaa2bcec3b5f8fb1ce032c67e53fea",
  {
    input: {
      width: 768,
      height: 768,
      prompt: "A cinematic shot of a racoon wearing an intricate italian robe, with a crown",
      scheduler: "K_EULER",
      num_outputs: 1,
      guidance_scale: 9,
      apply_watermark: true,
      negative_prompt: "worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch",
      prompt_strength: 0.8,
      num_inference_steps: 40
    }
  }
);

// 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 lucataco/segmind-vega using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/segmind-vega:1587d0a4f8897740324dea9d66cd9cfa8caaa2bcec3b5f8fb1ce032c67e53fea",
    input={
        "width": 768,
        "height": 768,
        "prompt": "A cinematic shot of a racoon wearing an intricate italian robe, with a crown",
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "guidance_scale": 9,
        "apply_watermark": True,
        "negative_prompt": "worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch",
        "prompt_strength": 0.8,
        "num_inference_steps": 40
    }
)
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 lucataco/segmind-vega 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": "lucataco/segmind-vega:1587d0a4f8897740324dea9d66cd9cfa8caaa2bcec3b5f8fb1ce032c67e53fea",
    "input": {
      "width": 768,
      "height": 768,
      "prompt": "A cinematic shot of a racoon wearing an intricate italian robe, with a crown",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "guidance_scale": 9,
      "apply_watermark": true,
      "negative_prompt": "worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch",
      "prompt_strength": 0.8,
      "num_inference_steps": 40
    }
  }' \
  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.