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andreasjansson /monkey-island-rdm:2c737891
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 replicateSet 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 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:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63",
{
input: {
seed: -1,
scale: 5,
steps: 100,
prompt: "Pirates are ballroom dancing",
num_database_results: 5
}
}
);
// 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 replicateSet 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 replicateRun 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:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63",
input={
"seed": -1,
"scale": 5,
"steps": 100,
"prompt": "Pirates are ballroom dancing",
"num_database_results": 5
}
)
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:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63",
"input": {
"seed": -1,
"scale": 5,
"steps": 100,
"prompt": "Pirates are ballroom dancing",
"num_database_results": 5
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
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Output
{
"completed_at": "2022-08-19T16:37:07.950150Z",
"created_at": "2022-08-19T16:36:58.257868Z",
"data_removed": false,
"error": null,
"id": "tockkav6fzeb5lfonggibwofjq",
"input": {
"seed": -1,
"scale": 5,
"steps": 100,
"prompt": "Pirates are ballroom dancing",
"num_database_results": "5"
},
"logs": "Using random seed 2197080794\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<00:15, 6.32it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 10.16it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.32it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 11.85it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 12.01it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 12.07it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:07, 12.17it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 12.28it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.44it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.27it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.21it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.03it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 12.43it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:05, 12.24it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.32it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.15it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.48it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.24it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.56it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.39it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.14it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.13it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 11.92it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.10it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.40it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:04<00:03, 12.38it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.16it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.14it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.51it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.24it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 12.45it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.37it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 12.49it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 12.86it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 13.17it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 13.16it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 13.03it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:01, 12.56it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 12.31it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 12.03it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 11.79it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 11.75it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 11.84it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 11.62it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 11.62it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 11.59it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 11.96it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 11.88it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 11.88it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 12.07it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 12.16it/s]",
"metrics": {
"predict_time": 9.513308,
"total_time": 9.692282
},
"output": "https://replicate.delivery/mgxm/3f8d79c5-4ec0-4ed1-ae48-0ea68a015065/out.png",
"started_at": "2022-08-19T16:36:58.436842Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/tockkav6fzeb5lfonggibwofjq",
"cancel": "https://api.replicate.com/v1/predictions/tockkav6fzeb5lfonggibwofjq/cancel"
},
"version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63"
}Generated in
Using random seed 2197080794
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<00:15, 6.32it/s]
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