kuprel/min-dalle:dd9bb5e3 – Run with an API on Replicate

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kuprel /min-dalle:dd9bb5e3

Playground API

Input

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

npx create-replicate --model=kuprel/min-dalle

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

const output = await replicate.run(
  "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7",
  {
    input: {
      text: "a watercolour painting of a goose and a bunny having afternoon tea",
      grid_size: 4,
      intermediate_outputs: true,
      log2_supercondition_factor: 4
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

output = replicate.run(
    "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7",
    input={
        "text": "a watercolour painting of a goose and a bunny having afternoon tea",
        "grid_size": 4,
        "intermediate_outputs": True,
        "log2_supercondition_factor": 4
    }
)

# The kuprel/min-dalle 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/kuprel/min-dalle/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 kuprel/min-dalle 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": "kuprel/min-dalle:dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7",
    "input": {
      "text": "a watercolour painting of a goose and a bunny having afternoon tea",
      "grid_size": 4,
      "intermediate_outputs": true,
      "log2_supercondition_factor": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2022-07-06T18:41:21.434053Z",
  "created_at": "2022-07-06T18:41:05.869054Z",
  "data_removed": false,
  "error": null,
  "id": "2g7mfi4ezfey7bmy3xc64ychpu",
  "input": {
    "text": "a watercolour painting of a goose and a bunny having afternoon tea",
    "grid_size": "4",
    "intermediate_outputs": true,
    "log2_supercondition_factor": 4
  },
  "logs": "tokenizing text\n['Ġa']\n['Ġwatercolour']\n['Ġpainting']\n['Ġof']\n['Ġa']\n['Ġgoose']\n['Ġand']\n['Ġa']\n['Ġbunny']\n['Ġhaving']\n['Ġafternoon']\n['Ġtea']\ntext tokens [0, 58, 18581, 1545, 111, 58, 14215, 128, 58, 8684, 8345, 12598, 2705, 2]\nencoding text tokens\nsampling row 1 of 16\nsampling row 2 of 16\ndetokenizing image\nsampling row 3 of 16\nsampling row 4 of 16\ndetokenizing image\nsampling row 5 of 16\nsampling row 6 of 16\ndetokenizing image\nsampling row 7 of 16\nsampling row 8 of 16\ndetokenizing image\nsampling row 9 of 16\nsampling row 10 of 16\ndetokenizing image\nsampling row 11 of 16\nsampling row 12 of 16\ndetokenizing image\nsampling row 13 of 16\nsampling row 14 of 16\ndetokenizing image\nsampling row 15 of 16\nsampling row 16 of 16\ndetokenizing image",
  "metrics": {
    "predict_time": 15.38574,
    "total_time": 15.564999
  },
  "output": [
    "https://replicate.delivery/mgxm/edd5c2f5-7e0f-41d1-bf1f-b92a4212bcfc/min-dalle-iter-1.jpg",
    "https://replicate.delivery/mgxm/747836bd-e773-4ef5-a903-bd1aa4938639/min-dalle-iter-2.jpg",
    "https://replicate.delivery/mgxm/ca863d2a-f4ec-4516-9777-d021f2403f56/min-dalle-iter-3.jpg",
    "https://replicate.delivery/mgxm/7034c1a9-ccf1-4d12-bbf3-eb2d54924e1a/min-dalle-iter-4.jpg",
    "https://replicate.delivery/mgxm/879e3e96-11b2-4d7f-8883-4ba80cfccba5/min-dalle-iter-5.jpg",
    "https://replicate.delivery/mgxm/fd391f90-87a5-48cd-9a67-2c0d56dec89d/min-dalle-iter-6.jpg",
    "https://replicate.delivery/mgxm/7adade5c-ba14-4460-801c-af35df62871f/min-dalle-iter-7.jpg",
    "https://replicate.delivery/mgxm/07ce46f4-f554-47b3-bda8-47326fedf8c6/min-dalle-iter-8.jpg"
  ],
  "started_at": "2022-07-06T18:41:06.048313Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/2g7mfi4ezfey7bmy3xc64ychpu",
    "cancel": "https://api.replicate.com/v1/predictions/2g7mfi4ezfey7bmy3xc64ychpu/cancel"
  },
  "version": "dd9bb5e3ec9c8ffe775546fc6cc39587d7e11a1c789582fd4c54e221a66158c7"
}

Generated in

15.4 seconds

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