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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: "Guybrush sword fighting a giant squid on a pirate ship",
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 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": "Guybrush sword fighting a giant squid on a pirate ship",
"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:2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63",
"input": {
"seed": -1,
"scale": 5,
"steps": 100,
"prompt": "Guybrush sword fighting a giant squid on a pirate ship",
"num_database_results": 10
}
}' \
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": "2022-08-19T16:14:47.906579Z",
"created_at": "2022-08-19T16:14:38.294953Z",
"data_removed": false,
"error": null,
"id": "6zl3evu2u5dotcmiim53yx7qvq",
"input": {
"seed": -1,
"scale": 5,
"steps": 100,
"prompt": "Guybrush sword fighting a giant squid on a pirate ship",
"num_database_results": 10
},
"logs": "Using random seed 2713816767\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<00:17, 5.65it/s]\nPLMS Sampler: 3%|▎ | 3/100 [00:00<00:09, 9.84it/s]\nPLMS Sampler: 5%|▌ | 5/100 [00:00<00:08, 11.11it/s]\nPLMS Sampler: 7%|▋ | 7/100 [00:00<00:07, 11.67it/s]\nPLMS Sampler: 9%|▉ | 9/100 [00:00<00:07, 11.89it/s]\nPLMS Sampler: 11%|█ | 11/100 [00:00<00:07, 12.42it/s]\nPLMS Sampler: 13%|█▎ | 13/100 [00:01<00:06, 12.69it/s]\nPLMS Sampler: 15%|█▌ | 15/100 [00:01<00:06, 12.34it/s]\nPLMS Sampler: 17%|█▋ | 17/100 [00:01<00:06, 12.11it/s]\nPLMS Sampler: 19%|█▉ | 19/100 [00:01<00:06, 12.10it/s]\nPLMS Sampler: 21%|██ | 21/100 [00:01<00:06, 12.20it/s]\nPLMS Sampler: 23%|██▎ | 23/100 [00:01<00:06, 12.17it/s]\nPLMS Sampler: 25%|██▌ | 25/100 [00:02<00:06, 12.25it/s]\nPLMS Sampler: 27%|██▋ | 27/100 [00:02<00:06, 12.11it/s]\nPLMS Sampler: 29%|██▉ | 29/100 [00:02<00:05, 12.15it/s]\nPLMS Sampler: 31%|███ | 31/100 [00:02<00:05, 12.11it/s]\nPLMS Sampler: 33%|███▎ | 33/100 [00:02<00:05, 12.20it/s]\nPLMS Sampler: 35%|███▌ | 35/100 [00:02<00:05, 12.34it/s]\nPLMS Sampler: 37%|███▋ | 37/100 [00:03<00:05, 12.48it/s]\nPLMS Sampler: 39%|███▉ | 39/100 [00:03<00:04, 12.69it/s]\nPLMS Sampler: 41%|████ | 41/100 [00:03<00:04, 12.75it/s]\nPLMS Sampler: 43%|████▎ | 43/100 [00:03<00:04, 12.73it/s]\nPLMS Sampler: 45%|████▌ | 45/100 [00:03<00:04, 12.03it/s]\nPLMS Sampler: 47%|████▋ | 47/100 [00:03<00:04, 12.38it/s]\nPLMS Sampler: 49%|████▉ | 49/100 [00:04<00:04, 12.48it/s]\nPLMS Sampler: 51%|█████ | 51/100 [00:04<00:04, 12.18it/s]\nPLMS Sampler: 53%|█████▎ | 53/100 [00:04<00:03, 12.32it/s]\nPLMS Sampler: 55%|█████▌ | 55/100 [00:04<00:03, 12.35it/s]\nPLMS Sampler: 57%|█████▋ | 57/100 [00:04<00:03, 12.32it/s]\nPLMS Sampler: 59%|█████▉ | 59/100 [00:04<00:03, 12.45it/s]\nPLMS Sampler: 61%|██████ | 61/100 [00:05<00:03, 12.57it/s]\nPLMS Sampler: 63%|██████▎ | 63/100 [00:05<00:02, 12.88it/s]\nPLMS Sampler: 65%|██████▌ | 65/100 [00:05<00:02, 12.54it/s]\nPLMS Sampler: 67%|██████▋ | 67/100 [00:05<00:02, 12.48it/s]\nPLMS Sampler: 69%|██████▉ | 69/100 [00:05<00:02, 12.68it/s]\nPLMS Sampler: 71%|███████ | 71/100 [00:05<00:02, 12.79it/s]\nPLMS Sampler: 73%|███████▎ | 73/100 [00:05<00:02, 12.91it/s]\nPLMS Sampler: 75%|███████▌ | 75/100 [00:06<00:01, 12.81it/s]\nPLMS Sampler: 77%|███████▋ | 77/100 [00:06<00:01, 12.83it/s]\nPLMS Sampler: 79%|███████▉ | 79/100 [00:06<00:01, 13.01it/s]\nPLMS Sampler: 81%|████████ | 81/100 [00:06<00:01, 12.67it/s]\nPLMS Sampler: 83%|████████▎ | 83/100 [00:06<00:01, 12.58it/s]\nPLMS Sampler: 85%|████████▌ | 85/100 [00:06<00:01, 12.15it/s]\nPLMS Sampler: 87%|████████▋ | 87/100 [00:07<00:01, 11.71it/s]\nPLMS Sampler: 89%|████████▉ | 89/100 [00:07<00:00, 11.91it/s]\nPLMS Sampler: 91%|█████████ | 91/100 [00:07<00:00, 12.07it/s]\nPLMS Sampler: 93%|█████████▎| 93/100 [00:07<00:00, 12.08it/s]\nPLMS Sampler: 95%|█████████▌| 95/100 [00:07<00:00, 12.25it/s]\nPLMS Sampler: 97%|█████████▋| 97/100 [00:07<00:00, 12.18it/s]\nPLMS Sampler: 99%|█████████▉| 99/100 [00:08<00:00, 12.08it/s]\nPLMS Sampler: 100%|██████████| 100/100 [00:08<00:00, 12.26it/s]",
"metrics": {
"predict_time": 9.47142,
"total_time": 9.611626
},
"output": "https://replicate.delivery/mgxm/99b7c910-d94b-4235-9044-f135c003f508/out.png",
"started_at": "2022-08-19T16:14:38.435159Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/6zl3evu2u5dotcmiim53yx7qvq",
"cancel": "https://api.replicate.com/v1/predictions/6zl3evu2u5dotcmiim53yx7qvq/cancel"
},
"version": "2c737891c668aa2a759ea6db63b35d3a4f569a803dbad2446c872475c4fb6c63"
}Generated in
Using random seed 2713816767
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<00:17, 5.65it/s]
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