andreasjansson/plasma:cc8fda9d – Run with an API on Replicate

Version

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andreasjansson /plasma:cc8fda9d

Playground API

Input

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

npx create-replicate --model=andreasjansson/plasma

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

const output = await replicate.run(
  "andreasjansson/plasma:cc8fda9d792ea48be0bd70b66966614acdee2e2b7612fbb820ed1908b410a952",
  {
    input: {}
  }
);

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

output = replicate.run("andreasjansson/plasma:cc8fda9d792ea48be0bd70b66966614acdee2e2b7612fbb820ed1908b410a952")

# The andreasjansson/plasma 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/andreasjansson/plasma/api#output-schema
    print(item, end="")

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/plasma 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/plasma:cc8fda9d792ea48be0bd70b66966614acdee2e2b7612fbb820ed1908b410a952",
    "input": {}
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Speed

Resolution

Depth

vec2 effect(vec2 p, float i, float time) {
  return vec2(sin(length(p.x) * length(p.y) * i + time), cos(length(p.x) * length(p.y) * i - time));
}

{
  "completed_at": "2023-12-07T12:13:29.745194Z",
  "created_at": "2023-12-07T12:13:25.057641Z",
  "data_removed": false,
  "error": null,
  "id": "jji3fr3bucnr3mq7dsad6iwldi",
  "input": {},
  "logs": "from_string grammar:\nroot ::= [v] [e] [c] [2] [ ] [e] [f] [f] [e] [c] [t] [(] [v] [e] [c] [2] [ ] [p] [,] [ ] [f] [l] [o] [a] [t] [ ] [i] [,] [ ] [f] [l] [o] [a] [t] [ ] [t] [i] [m] [e] [)] [ ] [{] [<U+000A>] [ ] [ ] return-statement [<U+000A>] [}] \nreturn-statement ::= [r] [e] [t] [u] [r] [n] [ ] vec2-expression [;]\nvec2-expression ::= single-vec2-expression | single-vec2-expression operation vec2-expression\nsingle-vec2-expression ::= [v] [e] [c] [2] [(] scalar-expression [,] [ ] scalar-expression [)]\noperation ::= [ ] [+] [ ] | [ ] [-] [ ] | [ ] [*] [ ] | [ ] [/] [ ]\nscalar-expression ::= single-scalar-expression | single-scalar-expression operation scalar-expression\nsingle-scalar-expression ::= float-literal | variable | math-func [(] math-arg [)]\nfloat-literal ::= float-literal_11 [.] float-literal_12\nvariable ::= [p] [.] [x] | [p] [.] [y] | [i] | [t] [i] [m] [e]\nmath-func ::= [s] [i] [n] | [c] [o] [s] | [t] [a] [n] | [l] [e] [n] [g] [t] [h] | [s] [q] [r] [t] | [e] [x] [p]\nmath-arg ::= [p] [.] [x] | [p] [.] [y] | scalar-expression \nfloat-literal_11 ::= [0-9] float-literal_11 | [0-9]\nfloat-literal_12 ::= [0-9] float-literal_12 | [0-9]\nLlama.generate: prefix-match hit\nllama_print_timings:        load time =   314.60 ms\nllama_print_timings:      sample time =  3573.61 ms /    64 runs   (   55.84 ms per token,    17.91 tokens per second)\nllama_print_timings: prompt eval time =     0.00 ms /     1 tokens (    0.00 ms per token,      inf tokens per second)\nllama_print_timings:        eval time =   938.93 ms /    64 runs   (   14.67 ms per token,    68.16 tokens per second)\nllama_print_timings:       total time =  4668.46 ms",
  "metrics": {
    "predict_time": 4.680982,
    "total_time": 4.687553
  },
  "output": [
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  ],
  "started_at": "2023-12-07T12:13:25.064212Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://streaming-api.svc.us.c.replicate.net/v1/predictions/jji3fr3bucnr3mq7dsad6iwldi",
    "get": "https://api.replicate.com/v1/predictions/jji3fr3bucnr3mq7dsad6iwldi",
    "cancel": "https://api.replicate.com/v1/predictions/jji3fr3bucnr3mq7dsad6iwldi/cancel"
  },
  "version": "cc8fda9d792ea48be0bd70b66966614acdee2e2b7612fbb820ed1908b410a952"
}

Generated in

4.7 seconds

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