stspanho/illusion – Run with an API on Replicate

stspanho / illusion

Creates an SD illusion from drawing + adds depth

Cold

Public
327 runs
L40S

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

concept art . digital artwork, illustrative, painterly, matte painting, highly detailedconcept art . digital artwork, illustrative, painterly, matte painting, highly detailed

The prompt to guide QR Code generation.

qr_code_content

string

Shift + Return to add a new line

The website/content your QR Code will point to.

negative_prompt

string

Shift + Return to add a new line

ugly, disfigured, low quality, blurry, nsfwugly, disfigured, low quality, blurry, nsfw

The negative prompt to guide image generation.

Default: "ugly, disfigured, low quality, blurry, nsfw"

num_inference_steps

integer

(minimum: 0, maximum: 100)

Number of diffusion steps

Default: 40

guidance_scale

number

(minimum: 0.1, maximum: 30)

Scale for classifier-free guidance

Default: 7.5

seed

integer

Seed

Default: -1

width

integer

Width out the output image

Default: 768

height

integer

Height out the output image

Default: 768

num_outputs

integer

(minimum: 1, maximum: 4)

Number of outputs

Default: 1

image

file

Input image. If none is provided, a QR code will be generated

controlnet_conditioning_scale

number

(minimum: 0, maximum: 4)

The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original unet.

Default: 1

border

integer

(minimum: 0, maximum: 4)

QR code border size

Default: 1

qrcode_background

string

Background color of raw QR code

Default: "gray"

model_type

string

An enumeration.

Default: "ZoeD_N"

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

npx create-replicate --model=stspanho/illusion

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 stspanho/illusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "stspanho/illusion:047c82671b5c148dd3911492baca1ae94d9666eacac12bfb74608453b3bc7c7a",
  {
    input: {
      seed: -1,
      image: "https://www.webxr.be/art/img/image664fbd5868a94.jpeg",
      width: 768,
      border: 4,
      height: 768,
      prompt: "concept art . digital artwork, illustrative, painterly, matte painting, highly detailed",
      model_type: "ZoeD_N",
      num_outputs: 1,
      guidance_scale: 7.5,
      negative_prompt: "ugly, disfigured, low quality, blurry, nsfw",
      qr_code_content: "",
      qrcode_background: "gray",
      num_inference_steps: 40,
      controlnet_conditioning_scale: 1
    }
  }
);

// 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 stspanho/illusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "stspanho/illusion:047c82671b5c148dd3911492baca1ae94d9666eacac12bfb74608453b3bc7c7a",
    input={
        "seed": -1,
        "image": "https://www.webxr.be/art/img/image664fbd5868a94.jpeg",
        "width": 768,
        "border": 4,
        "height": 768,
        "prompt": "concept art . digital artwork, illustrative, painterly, matte painting, highly detailed",
        "model_type": "ZoeD_N",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "negative_prompt": "ugly, disfigured, low quality, blurry, nsfw",
        "qr_code_content": "",
        "qrcode_background": "gray",
        "num_inference_steps": 40,
        "controlnet_conditioning_scale": 1
    }
)
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 stspanho/illusion 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": "stspanho/illusion:047c82671b5c148dd3911492baca1ae94d9666eacac12bfb74608453b3bc7c7a",
    "input": {
      "seed": -1,
      "image": "https://www.webxr.be/art/img/image664fbd5868a94.jpeg",
      "width": 768,
      "border": 4,
      "height": 768,
      "prompt": "concept art . digital artwork, illustrative, painterly, matte painting, highly detailed",
      "model_type": "ZoeD_N",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "negative_prompt": "ugly, disfigured, low quality, blurry, nsfw",
      "qr_code_content": "",
      "qrcode_background": "gray",
      "num_inference_steps": 40,
      "controlnet_conditioning_scale": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-05-23T22:04:19.096418Z",
  "created_at": "2024-05-23T22:04:08.922000Z",
  "data_removed": false,
  "error": null,
  "id": "1fvc0453b9rgp0cfmxxvzxzr4r",
  "input": {
    "image": "https://www.webxr.be/art/img/image664fbd5868a94.jpeg",
    "border": 4,
    "prompt": "concept art . digital artwork, illustrative, painterly, matte painting, highly detailed",
    "guidance_scale": 7.5,
    "negative_prompt": "ugly, disfigured, low quality, blurry, nsfw",
    "qr_code_content": "",
    "num_inference_steps": 40,
    "controlnet_conditioning_scale": 1
  },
  "logs": "Seed: 380864877\n  0%|          | 0/40 [00:00<?, ?it/s]\n  2%|▎         | 1/40 [00:00<00:11,  3.50it/s]\n  5%|▌         | 2/40 [00:00<00:07,  4.93it/s]\n  8%|▊         | 3/40 [00:00<00:06,  5.67it/s]\n 10%|█         | 4/40 [00:00<00:05,  6.09it/s]\n 12%|█▎        | 5/40 [00:00<00:05,  6.36it/s]\n 15%|█▌        | 6/40 [00:01<00:05,  6.53it/s]\n 18%|█▊        | 7/40 [00:01<00:04,  6.64it/s]\n 20%|██        | 8/40 [00:01<00:04,  6.71it/s]\n 22%|██▎       | 9/40 [00:01<00:04,  6.77it/s]\n 25%|██▌       | 10/40 [00:01<00:04,  6.81it/s]\n 28%|██▊       | 11/40 [00:01<00:04,  6.85it/s]\n 30%|███       | 12/40 [00:01<00:04,  6.86it/s]\n 32%|███▎      | 13/40 [00:02<00:03,  6.89it/s]\n 35%|███▌      | 14/40 [00:02<00:03,  6.91it/s]\n 38%|███▊      | 15/40 [00:02<00:03,  6.91it/s]\n 40%|████      | 16/40 [00:02<00:03,  6.91it/s]\n 42%|████▎     | 17/40 [00:02<00:03,  6.91it/s]\n 45%|████▌     | 18/40 [00:02<00:03,  6.91it/s]\n 48%|████▊     | 19/40 [00:02<00:03,  6.92it/s]\n 50%|█████     | 20/40 [00:03<00:02,  6.91it/s]\n 52%|█████▎    | 21/40 [00:03<00:02,  6.92it/s]\n 55%|█████▌    | 22/40 [00:03<00:02,  6.92it/s]\n 57%|█████▊    | 23/40 [00:03<00:02,  6.93it/s]\n 60%|██████    | 24/40 [00:03<00:02,  6.92it/s]\n 62%|██████▎   | 25/40 [00:03<00:02,  6.92it/s]\n 65%|██████▌   | 26/40 [00:03<00:02,  6.92it/s]\n 68%|██████▊   | 27/40 [00:04<00:01,  6.92it/s]\n 70%|███████   | 28/40 [00:04<00:01,  6.91it/s]\n 72%|███████▎  | 29/40 [00:04<00:01,  6.90it/s]\n 75%|███████▌  | 30/40 [00:04<00:01,  6.91it/s]\n 78%|███████▊  | 31/40 [00:04<00:01,  6.91it/s]\n 80%|████████  | 32/40 [00:04<00:01,  6.90it/s]\n 82%|████████▎ | 33/40 [00:04<00:01,  6.90it/s]\n 85%|████████▌ | 34/40 [00:05<00:00,  6.90it/s]\n 88%|████████▊ | 35/40 [00:05<00:00,  6.91it/s]\n 90%|█████████ | 36/40 [00:05<00:00,  6.91it/s]\n 92%|█████████▎| 37/40 [00:05<00:00,  6.91it/s]\n 95%|█████████▌| 38/40 [00:05<00:00,  6.91it/s]\n 98%|█████████▊| 39/40 [00:05<00:00,  6.91it/s]\n100%|██████████| 40/40 [00:05<00:00,  6.92it/s]\n100%|██████████| 40/40 [00:05<00:00,  6.75it/s]",
  "metrics": {
    "predict_time": 10.12624,
    "total_time": 10.174418
  },
  "output": [
    "https://replicate.delivery/pbxt/eqqsbrcyBoQxKqKAOyw5fFsen7UEXicIsu1Lywy1In2C7tulA/output-0.png",
    "https://replicate.delivery/pbxt/iX5aWzpVqQbeNafGzZFfp2OYrfZ87yzeMlineRhGnhLoYv1tE/output-depth-0.png"
  ],
  "started_at": "2024-05-23T22:04:08.970178Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/1fvc0453b9rgp0cfmxxvzxzr4r",
    "cancel": "https://api.replicate.com/v1/predictions/1fvc0453b9rgp0cfmxxvzxzr4r/cancel"
  },
  "version": "047c82671b5c148dd3911492baca1ae94d9666eacac12bfb74608453b3bc7c7a"
}

Generated in

10.1 seconds

ShareReport

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Examples

View more examples

Run time and cost

This model costs approximately $0.010 to run on Replicate, or 100 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 11 seconds.

Readme

This model doesn't have a readme.