cjwbw/wavyfusion:3a38e179 – Run with an API on Replicate

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cjwbw /wavyfusion:3a38e179

Playground API

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: "blonde woman wa-vy style"

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output

width

integer

Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

height

integer

Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

prompt_strength

number

Prompt strength when using init image. 1.0 corresponds to full destruction of information in init image

Default: 0.8

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 7.5

scheduler

string

Choose a scheduler

Default: "K_EULER"

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/wavyfusion

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

const output = await replicate.run(
  "cjwbw/wavyfusion:3a38e1795ef77e3be3d6eb77fbafaeec79e67d13ee0025b36a93bb17e540efc9",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "blonde woman wa-vy style",
      scheduler: "K_EULER",
      num_outputs: 1,
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);
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 cjwbw/wavyfusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/wavyfusion:3a38e1795ef77e3be3d6eb77fbafaeec79e67d13ee0025b36a93bb17e540efc9",
    input={
        "width": 512,
        "height": 512,
        "prompt": "blonde woman wa-vy style",
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
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/wavyfusion 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": "3a38e1795ef77e3be3d6eb77fbafaeec79e67d13ee0025b36a93bb17e540efc9",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "blonde woman wa-vy style",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Install Cog

brew install cog

If you don’t have Homebrew, there are other installation options available.

Pull and run cjwbw/wavyfusion using Cog (this will download the full model and run it in your local environment):

cog predict r8.im/chenxwh/wavyfusion@sha256:3a38e1795ef77e3be3d6eb77fbafaeec79e67d13ee0025b36a93bb17e540efc9 \
  -i 'width=512' \
  -i 'height=512' \
  -i 'prompt="blonde woman wa-vy style"' \
  -i 'scheduler="K_EULER"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7.5' \
  -i 'prompt_strength=0.8' \
  -i 'num_inference_steps=50'

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

Pull and run cjwbw/wavyfusion using Docker (this will download the full model and run it in your local environment):

docker run -d -p 5000:5000 --gpus=all r8.im/chenxwh/wavyfusion@sha256:3a38e1795ef77e3be3d6eb77fbafaeec79e67d13ee0025b36a93bb17e540efc9
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "blonde woman wa-vy style",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  http://localhost:5000/predictions

Output

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