nicholascelestin/latent-diffusion:14a4a4fc | Run with an API on Replicate

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nicholascelestin /latent-diffusion:14a4a4fc

Playground API

Input

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

npx create-replicate --model=nicholascelestin/latent-diffusion

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

const output = await replicate.run(
  "nicholascelestin/latent-diffusion:14a4a4fc922abf673bd2c740071b241ea80282c039f5a16e89cf44039a2c5dbf",
  {
    input: {
      eta: 0,
      plms: true,
      steps: 50,
      width: 256,
      height: 256,
      batches: 1,
      batch_size: 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 nicholascelestin/latent-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "nicholascelestin/latent-diffusion:14a4a4fc922abf673bd2c740071b241ea80282c039f5a16e89cf44039a2c5dbf",
    input={
        "eta": 0,
        "plms": True,
        "steps": 50,
        "width": 256,
        "height": 256,
        "batches": 1,
        "batch_size": 4
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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 nicholascelestin/latent-diffusion 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": "nicholascelestin/latent-diffusion:14a4a4fc922abf673bd2c740071b241ea80282c039f5a16e89cf44039a2c5dbf",
    "input": {
      "eta": 0,
      "plms": true,
      "steps": 50,
      "width": 256,
      "height": 256,
      "batches": 1,
      "batch_size": 4
    }
  }' \
  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/nicholascelestin/latent-diffusion@sha256:14a4a4fc922abf673bd2c740071b241ea80282c039f5a16e89cf44039a2c5dbf \
  -i 'eta=0' \
  -i 'plms=true' \
  -i 'steps=50' \
  -i 'width=256' \
  -i 'height=256' \
  -i 'batches=1' \
  -i 'batch_size=4'

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/nicholascelestin/latent-diffusion@sha256:14a4a4fc922abf673bd2c740071b241ea80282c039f5a16e89cf44039a2c5dbf
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "eta": 0,
      "plms": true,
      "steps": 50,
      "width": 256,
      "height": 256,
      "batches": 1,
      "batch_size": 4
    }
  }' \
  http://localhost:5000/predictions

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

Output

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