visoar/product-photo:f100c24b – Run with an API on Replicate

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visoar /product-photo:f100c24b

Playground API

Input

image_path

*file

input image

product_size

string

Max product size

Default: "0.6 * width"

prompt

*string

Shift + Return to add a new line

Product name or prompt

negative_prompt

string

Shift + Return to add a new line

low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisementlow quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement

Anything you don't want in the photo

Default: "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement"

api_key

string

Shift + Return to add a new line

OpenAI api_key, enhance prompt with ChatGPT if provided

pixel

string

image total pixel

Default: "512 * 512"

scale

integer

(minimum: 0, maximum: 4)

Factor to scale image by (maximum: 4)

Default: 3

image_num

integer

(minimum: 0, maximum: 4)

Number of image to generate

Default: 1

guidance_scale

number

Guidance Scale

Default: 7.5

num_inference_steps

integer

Inference Steps

Default: 20

manual_seed

integer

Seed

Default: -1

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

npx create-replicate --model=visoar/product-photo

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

const output = await replicate.run(
  "visoar/product-photo:f100c24bfef70884b45018b98c939b7ca4cf658b3256d5b7f9dcd78d41fe13d5",
  {
    input: {
      pixel: "512 * 512",
      scale: 3,
      image_num: 1,
      manual_seed: -1,
      product_size: "0.6 * width",
      guidance_scale: 7.5,
      negative_prompt: "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement",
      num_inference_steps: 20
    }
  }
);
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 visoar/product-photo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "visoar/product-photo:f100c24bfef70884b45018b98c939b7ca4cf658b3256d5b7f9dcd78d41fe13d5",
    input={
        "pixel": "512 * 512",
        "scale": 3,
        "image_num": 1,
        "manual_seed": -1,
        "product_size": "0.6 * width",
        "guidance_scale": 7.5,
        "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement",
        "num_inference_steps": 20
    }
)
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 visoar/product-photo 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": "f100c24bfef70884b45018b98c939b7ca4cf658b3256d5b7f9dcd78d41fe13d5",
    "input": {
      "pixel": "512 * 512",
      "scale": 3,
      "image_num": 1,
      "manual_seed": -1,
      "product_size": "0.6 * width",
      "guidance_scale": 7.5,
      "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement",
      "num_inference_steps": 20
    }
  }' \
  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 visoar/product-photo using Cog (this will download the full model and run it in your local environment):

cog predict r8.im/visoar/product-photo@sha256:f100c24bfef70884b45018b98c939b7ca4cf658b3256d5b7f9dcd78d41fe13d5 \
  -i 'pixel="512 * 512"' \
  -i 'scale=3' \
  -i 'image_num=1' \
  -i 'manual_seed=-1' \
  -i 'product_size="0.6 * width"' \
  -i 'guidance_scale=7.5' \
  -i 'negative_prompt="low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement"' \
  -i 'num_inference_steps=20'

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

Pull and run visoar/product-photo 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/visoar/product-photo@sha256:f100c24bfef70884b45018b98c939b7ca4cf658b3256d5b7f9dcd78d41fe13d5
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "pixel": "512 * 512",
      "scale": 3,
      "image_num": 1,
      "manual_seed": -1,
      "product_size": "0.6 * width",
      "guidance_scale": 7.5,
      "negative_prompt": "low quality, out of frame, illustration, 3d, sepia, painting, cartoons, sketch, watermark, text, Logo, advertisement",
      "num_inference_steps": 20
    }
  }' \
  http://localhost:5000/predictions

Output

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