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You're looking at a specific version of this model. Jump to the model overview.
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=ml6/julius
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";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run ml6/julius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"ml6/julius:6207316f2475ab8324352c99701df338a9bf732ecd21a9a5dc2aa2644036f05d",
{
input: {
top_k: 40,
prompt: "A logo for a Indian restaurant featuring the taj mahal",
softmax_temperature: 1
}
}
);
console.log(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 ml6/julius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"ml6/julius:6207316f2475ab8324352c99701df338a9bf732ecd21a9a5dc2aa2644036f05d",
input={
"top_k": 40,
"prompt": "A logo for a Indian restaurant featuring the taj mahal",
"softmax_temperature": 1
}
)
# The ml6/julius model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
# https://replicate.com/ml6/julius/api#output-schema
print(item)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 ml6/julius 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": "6207316f2475ab8324352c99701df338a9bf732ecd21a9a5dc2aa2644036f05d",
"input": {
"top_k": 40,
"prompt": "A logo for a Indian restaurant featuring the taj mahal",
"softmax_temperature": 1
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:
brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/ml6/julius@sha256:6207316f2475ab8324352c99701df338a9bf732ecd21a9a5dc2aa2644036f05d \
-i 'top_k=40' \
-i 'prompt="A logo for a Indian restaurant featuring the taj mahal"' \
-i 'softmax_temperature=1'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/ml6/julius@sha256:6207316f2475ab8324352c99701df338a9bf732ecd21a9a5dc2aa2644036f05dcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "top_k": 40, "prompt": "A logo for a Indian restaurant featuring the taj mahal", "softmax_temperature": 1 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
Each run costs approximately $0.093. Alternatively, try out our featured models for free.
Sign in with GitHub
By signing in, you agree to our
terms of service and privacy policy
Output
{
"completed_at": "2022-07-29T08:28:10.208658Z",
"created_at": "2022-07-29T08:26:03.358625Z",
"data_removed": false,
"error": null,
"id": "5dexbxnehzbufioi3oya2ya7oe",
"input": {
"top_k": "40",
"prompt": "A logo for a Indian restaurant featuring the taj mahal",
"softmax_temperature": "1"
},
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[00:31<00:00, 8.24it/s]\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE\nNOW RESIZING\nDONE",
"metrics": {
"predict_time": 126.673197,
"total_time": 126.850033
},
"output": [
[
"https://replicate.delivery/mgxm/63ee94fb-2e81-49b9-8bb5-195a9fad733c/number0.jpg",
"https://replicate.delivery/mgxm/c73f0927-f2a4-4578-bfb5-7e308ae5d7ab/number1.jpg",
"https://replicate.delivery/mgxm/615101ef-cb4e-47b9-9e20-7f383c89d8b5/number2.jpg",
"https://replicate.delivery/mgxm/58bb4e82-ce29-46bd-9c97-9d03e1346845/number3.jpg",
"https://replicate.delivery/mgxm/46505f09-6ab8-4157-a1f6-c4c167598d1b/number4.jpg",
"https://replicate.delivery/mgxm/8ed5cbbf-acf8-4c02-8968-12b424700638/number5.jpg",
"https://replicate.delivery/mgxm/564c37ea-bba8-437b-aefd-561b210faac9/number6.jpg",
"https://replicate.delivery/mgxm/8cb698e0-8043-420c-a1ff-bf300b82c0be/number7.jpg",
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],
"started_at": "2022-07-29T08:26:03.535461Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/5dexbxnehzbufioi3oya2ya7oe",
"cancel": "https://api.replicate.com/v1/predictions/5dexbxnehzbufioi3oya2ya7oe/cancel"
},
"version": "0df0d4f867a4d0311ab6a7ca694d39685e8d650247a5053f2f21afc573696b81"
}Generated in
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NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONE
NOW RESIZING
DONEThis example was created by a different version, ml6/julius:0df0d4f8.