andreasjansson/monkey-island-rdm – Run with an API on Replicate

Input

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

npx create-replicate --model=andreasjansson/monkey-island-rdm

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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "andreasjansson/monkey-island-rdm:f907c5ef70ba1efac7c6462f595ce5e68f67159596158928cec7886d91c739ba",
  {
    input: {
      seed: -1,
      scale: 5,
      steps: 100,
      prompt: "A pirate wedding",
      num_database_results: 10
    }
  }
);

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

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

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 andreasjansson/monkey-island-rdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "andreasjansson/monkey-island-rdm:f907c5ef70ba1efac7c6462f595ce5e68f67159596158928cec7886d91c739ba",
    input={
        "seed": -1,
        "scale": 5,
        "steps": 100,
        "prompt": "A pirate wedding",
        "num_database_results": 10
    }
)
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 andreasjansson/monkey-island-rdm 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": "andreasjansson/monkey-island-rdm:f907c5ef70ba1efac7c6462f595ce5e68f67159596158928cec7886d91c739ba",
    "input": {
      "seed": -1,
      "scale": 5,
      "steps": 100,
      "prompt": "A pirate wedding",
      "num_database_results": 10
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2022-08-19T17:18:09.719232Z",
  "created_at": "2022-08-19T17:18:00.536839Z",
  "data_removed": false,
  "error": null,
  "id": "xmdvmnfjgfgmtnasjjlzzhuvmu",
  "input": {
    "seed": -1,
    "scale": 5,
    "steps": 100,
    "prompt": "A pirate wedding",
    "num_database_results": "10"
  },
  "logs": "Using random seed 3030840391\nSeed: -1\nCLIP Text Embed: torch.Size([1, 1, 768])\nData shape for PLMS sampling is (1, 16, 48, 48)\nRunning PLMS Sampling with 100 timesteps\n\nPLMS Sampler:   0%|          | 0/100 [00:00<?, ?it/s]\nPLMS Sampler:   1%|          | 1/100 [00:00<00:16,  6.07it/s]\nPLMS Sampler:   3%|▎         | 3/100 [00:00<00:09, 10.21it/s]\nPLMS Sampler:   5%|▌         | 5/100 [00:00<00:08, 11.59it/s]\nPLMS Sampler:   7%|▋         | 7/100 [00:00<00:07, 12.42it/s]\nPLMS Sampler:   9%|▉         | 9/100 [00:00<00:07, 12.90it/s]\nPLMS Sampler:  11%|█         | 11/100 [00:00<00:06, 13.18it/s]\nPLMS Sampler:  13%|█▎        | 13/100 [00:01<00:06, 13.36it/s]\nPLMS Sampler:  15%|█▌        | 15/100 [00:01<00:06, 13.40it/s]\nPLMS Sampler:  17%|█▋        | 17/100 [00:01<00:06, 13.46it/s]\nPLMS Sampler:  19%|█▉        | 19/100 [00:01<00:06, 13.38it/s]\nPLMS Sampler:  21%|██        | 21/100 [00:01<00:05, 13.39it/s]\nPLMS Sampler:  23%|██▎       | 23/100 [00:01<00:05, 13.18it/s]\nPLMS Sampler:  25%|██▌       | 25/100 [00:01<00:05, 12.75it/s]\nPLMS Sampler:  27%|██▋       | 27/100 [00:02<00:05, 12.85it/s]\nPLMS Sampler:  29%|██▉       | 29/100 [00:02<00:05, 12.84it/s]\nPLMS Sampler:  31%|███       | 31/100 [00:02<00:05, 12.87it/s]\nPLMS Sampler:  33%|███▎      | 33/100 [00:02<00:05, 12.76it/s]\nPLMS Sampler:  35%|███▌      | 35/100 [00:02<00:05, 13.00it/s]\nPLMS Sampler:  37%|███▋      | 37/100 [00:02<00:04, 13.12it/s]\nPLMS Sampler:  39%|███▉      | 39/100 [00:03<00:04, 13.10it/s]\nPLMS Sampler:  41%|████      | 41/100 [00:03<00:04, 13.22it/s]\nPLMS Sampler:  43%|████▎     | 43/100 [00:03<00:04, 13.34it/s]\nPLMS Sampler:  45%|████▌     | 45/100 [00:03<00:04, 13.15it/s]\nPLMS Sampler:  47%|████▋     | 47/100 [00:03<00:04, 13.21it/s]\nPLMS Sampler:  49%|████▉     | 49/100 [00:03<00:03, 13.26it/s]\nPLMS Sampler:  51%|█████     | 51/100 [00:03<00:03, 13.32it/s]\nPLMS Sampler:  53%|█████▎    | 53/100 [00:04<00:03, 13.39it/s]\nPLMS Sampler:  55%|█████▌    | 55/100 [00:04<00:03, 13.42it/s]\nPLMS Sampler:  57%|█████▋    | 57/100 [00:04<00:03, 13.36it/s]\nPLMS Sampler:  59%|█████▉    | 59/100 [00:04<00:03, 13.38it/s]\nPLMS Sampler:  61%|██████    | 61/100 [00:04<00:02, 13.42it/s]\nPLMS Sampler:  63%|██████▎   | 63/100 [00:04<00:02, 13.44it/s]\nPLMS Sampler:  65%|██████▌   | 65/100 [00:04<00:02, 13.42it/s]\nPLMS Sampler:  67%|██████▋   | 67/100 [00:05<00:02, 13.40it/s]\nPLMS Sampler:  69%|██████▉   | 69/100 [00:05<00:02, 13.36it/s]\nPLMS Sampler:  71%|███████   | 71/100 [00:05<00:02, 13.25it/s]\nPLMS Sampler:  73%|███████▎  | 73/100 [00:05<00:02, 12.99it/s]\nPLMS Sampler:  75%|███████▌  | 75/100 [00:05<00:01, 13.19it/s]\nPLMS Sampler:  77%|███████▋  | 77/100 [00:05<00:01, 13.30it/s]\nPLMS Sampler:  79%|███████▉  | 79/100 [00:06<00:01, 13.39it/s]\nPLMS Sampler:  81%|████████  | 81/100 [00:06<00:01, 13.48it/s]\nPLMS Sampler:  83%|████████▎ | 83/100 [00:06<00:01, 13.56it/s]\nPLMS Sampler:  85%|████████▌ | 85/100 [00:06<00:01, 13.49it/s]\nPLMS Sampler:  87%|████████▋ | 87/100 [00:06<00:00, 13.48it/s]\nPLMS Sampler:  89%|████████▉ | 89/100 [00:06<00:00, 13.43it/s]\nPLMS Sampler:  91%|█████████ | 91/100 [00:06<00:00, 13.38it/s]\nPLMS Sampler:  93%|█████████▎| 93/100 [00:07<00:00, 13.19it/s]\nPLMS Sampler:  95%|█████████▌| 95/100 [00:07<00:00, 13.07it/s]\nPLMS Sampler:  97%|█████████▋| 97/100 [00:07<00:00, 13.10it/s]\nPLMS Sampler:  99%|█████████▉| 99/100 [00:07<00:00, 13.08it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.12it/s]",
  "metrics": {
    "predict_time": 8.974872,
    "total_time": 9.182393
  },
  "output": "https://replicate.delivery/mgxm/a006fb86-eba8-47d4-8c15-297064af68f0/out.png",
  "started_at": "2022-08-19T17:18:00.744360Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/xmdvmnfjgfgmtnasjjlzzhuvmu",
    "cancel": "https://api.replicate.com/v1/predictions/xmdvmnfjgfgmtnasjjlzzhuvmu/cancel"
  },
  "version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63"
}

Generated in

9.0 seconds

Tweak it Report

Using random seed 3030840391
Seed: -1
CLIP Text Embed: torch.Size([1, 1, 768])
Data shape for PLMS sampling is (1, 16, 48, 48)
Running PLMS Sampling with 100 timesteps

PLMS Sampler:   0%|          | 0/100 [00:00<?, ?it/s]
PLMS Sampler:   1%|          | 1/100 [00:00<00:16,  6.07it/s]
PLMS Sampler:   3%|▎         | 3/100 [00:00<00:09, 10.21it/s]
PLMS Sampler:   5%|▌         | 5/100 [00:00<00:08, 11.59it/s]
PLMS Sampler:   7%|▋         | 7/100 [00:00<00:07, 12.42it/s]
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PLMS Sampler: 100%|██████████| 100/100 [00:07<00:00, 13.12it/s]

This output was created using a different version of the model, andreasjansson/monkey-island-rdm:2c737891.

Run time and cost

This model costs approximately $0.031 to run on Replicate, or 32 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 A100 (80GB) GPU hardware. Predictions typically complete within 23 seconds. The predict time for this model varies significantly based on the inputs.