prunaai/hidream-l1-full:70e52dbc | Run with an API on Replicate

Version

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prunaai /hidream-l1-full:70e52dbc

Input

prompt

*string

Shift + Return to add a new line

a purple cheetah holding a sign that says "pip install pruna"a purple cheetah holding a sign that says "pip install pruna"

Prompt

model_type

string

Model type

Default: "full"

speed_mode

string

Speed optimization level

Default: "Lightly Juiced 🍊 (more consistent)"

resolution

string

Output resolution

Default: "1024 × 1024 (Square)"

seed

integer

Random seed (-1 for random)

Default: -1

output_format

string

Output format

Default: "webp"

output_quality

integer

(minimum: 1, maximum: 100)

Output quality (for jpg and webp)

Default: 100

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

npx create-replicate --model=prunaai/hidream-l1-full

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";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run prunaai/hidream-l1-full using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "prunaai/hidream-l1-full:70e52dbcff0149b38a2d1006427c5d35471e90010b1355220e40574fbef306fb",
  {
    input: {
      seed: 1,
      prompt: "a purple cheetah holding a sign that says \"pip install pruna\"",
      model_type: "full",
      resolution: "1024 × 1024 (Square)",
      speed_mode: "Juiced 🔥 (more speed)",
      output_format: "webp",
      output_quality: 80
    }
  }
);

// 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 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 prunaai/hidream-l1-full using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prunaai/hidream-l1-full:70e52dbcff0149b38a2d1006427c5d35471e90010b1355220e40574fbef306fb",
    input={
        "seed": 1,
        "prompt": "a purple cheetah holding a sign that says \"pip install pruna\"",
        "model_type": "full",
        "resolution": "1024 × 1024 (Square)",
        "speed_mode": "Juiced 🔥 (more speed)",
        "output_format": "webp",
        "output_quality": 80
    }
)
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 prunaai/hidream-l1-full 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": "prunaai/hidream-l1-full:70e52dbcff0149b38a2d1006427c5d35471e90010b1355220e40574fbef306fb",
    "input": {
      "seed": 1,
      "prompt": "a purple cheetah holding a sign that says \\"pip install pruna\\"",
      "model_type": "full",
      "resolution": "1024 × 1024 (Square)",
      "speed_mode": "Juiced 🔥 (more speed)",
      "output_format": "webp",
      "output_quality": 80
    }
  }' \
  https://api.replicate.com/v1/predictions

To 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 cog

If 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/prunaai/hidream-l1-full@sha256:70e52dbcff0149b38a2d1006427c5d35471e90010b1355220e40574fbef306fb \
  -i 'seed=1' \
  -i $'prompt="a purple cheetah holding a sign that says \\"pip install pruna\\""' \
  -i 'model_type="full"' \
  -i 'resolution="1024 × 1024 (Square)"' \
  -i 'speed_mode="Juiced 🔥 (more speed)"' \
  -i 'output_format="webp"' \
  -i 'output_quality=80'

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/prunaai/hidream-l1-full@sha256:70e52dbcff0149b38a2d1006427c5d35471e90010b1355220e40574fbef306fb
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 1,
      "prompt": "a purple cheetah holding a sign that says \\"pip install pruna\\"",
      "model_type": "full",
      "resolution": "1024 × 1024 (Square)",
      "speed_mode": "Juiced 🔥 (more speed)",
      "output_format": "webp",
      "output_quality": 80
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

Generated in

24.3 seconds

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