cjwbw/wuerstchen:91ce28e2 – Run with an API on Replicate

Version

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cjwbw /wuerstchen:91ce28e2

Input

prompt

string

Shift + Return to add a new line

Anthropomorphic cat dressed as a firefighterAnthropomorphic cat dressed as a firefighter

Input prompt

Default: "Anthropomorphic cat dressed as a firefighter"

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output

width

integer

Width of output image.

Default: 1536

height

integer

Height of output image.

Default: 1024

num_images_per_prompt

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

prior_num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of prior denoising steps.

Default: 60

prior_guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance in prior.

Default: 4

decoder_num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of prior denoising steps.

Default: 12

decoder_guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance in decoder.

seed

integer

Random seed. Leave blank to randomize the seed

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

npx create-replicate --model=cjwbw/wuerstchen

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

const output = await replicate.run(
  "cjwbw/wuerstchen:91ce28e22bba88fd991c7c76ab7e09c9d3dbd1aa94209430d41af682ede102f7",
  {
    input: {
      width: 1536,
      height: 1024,
      prompt: "Anthropomorphic cat dressed as a firefighter",
      prior_guidance_scale: 4,
      num_images_per_prompt: 1,
      prior_num_inference_steps: 60,
      decoder_num_inference_steps: 12
    }
  }
);

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

output = replicate.run(
    "cjwbw/wuerstchen:91ce28e22bba88fd991c7c76ab7e09c9d3dbd1aa94209430d41af682ede102f7",
    input={
        "width": 1536,
        "height": 1024,
        "prompt": "Anthropomorphic cat dressed as a firefighter",
        "prior_guidance_scale": 4,
        "num_images_per_prompt": 1,
        "prior_num_inference_steps": 60,
        "decoder_num_inference_steps": 12
    }
)
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 cjwbw/wuerstchen 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": "91ce28e22bba88fd991c7c76ab7e09c9d3dbd1aa94209430d41af682ede102f7",
    "input": {
      "width": 1536,
      "height": 1024,
      "prompt": "Anthropomorphic cat dressed as a firefighter",
      "prior_guidance_scale": 4,
      "num_images_per_prompt": 1,
      "prior_num_inference_steps": 60,
      "decoder_num_inference_steps": 12
    }
  }' \
  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/cjwbw/wuerstchen@sha256:91ce28e22bba88fd991c7c76ab7e09c9d3dbd1aa94209430d41af682ede102f7 \
  -i 'width=1536' \
  -i 'height=1024' \
  -i 'prompt="Anthropomorphic cat dressed as a firefighter"' \
  -i 'prior_guidance_scale=4' \
  -i 'num_images_per_prompt=1' \
  -i 'prior_num_inference_steps=60' \
  -i 'decoder_num_inference_steps=12'

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/cjwbw/wuerstchen@sha256:91ce28e22bba88fd991c7c76ab7e09c9d3dbd1aa94209430d41af682ede102f7
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1536,
      "height": 1024,
      "prompt": "Anthropomorphic cat dressed as a firefighter",
      "prior_guidance_scale": 4,
      "num_images_per_prompt": 1,
      "prior_num_inference_steps": 60,
      "decoder_num_inference_steps": 12
    }
  }' \
  http://localhost:5000/predictions

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

Output

No output yet! Press "Submit" to start a prediction.