pandas9/big | Run with an API on Replicate

pandas9 / big

big sdxl checkpoint merge

Cold

Public
1.9K runs
L40S

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

Default: ""

negative_prompt

string

Shift + Return to add a new line

Things you do not want to see in your image

Default: ""

aspect_ratio

string

Aspect ratio for the generated image

Default: "1:1"

guidance_scale

number

(minimum: 0.1, maximum: 10)

Guidance for the generated image

Default: 7

num_inference_steps

number

(minimum: 1, maximum: 100)

Number of inference steps

Default: 50

denoise

number

(minimum: 0, maximum: 1)

Denoise for the generated image

Default: 1

high_num_inference_steps

number

(minimum: 1, maximum: 100)

Number of inference steps for the high salary

Default: 50

high_denoise

number

(minimum: 0, maximum: 1)

Denoise for the high salary

Default: 0.4

output_format

string

Format of the output images

Default: "webp"

output_quality

integer

(minimum: 0, maximum: 100)

Quality of the output images, from 0 to 100. 100 is best quality, 0 is lowest quality.

Default: 95

seed

integer

Set a seed for reproducibility. Random by default.

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

npx create-replicate --model=pandas9/big

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 pandas9/big using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pandas9/big:4e9eaf34ec0beea25f0e1afb3f6f3c1bcbe6df5ffc6d7453d3494717bf909fb4",
  {
    input: {
      prompt: "",
      denoise: 1,
      aspect_ratio: "1:1",
      high_denoise: 0.4,
      output_format: "webp",
      guidance_scale: 7,
      output_quality: 95,
      negative_prompt: "",
      num_inference_steps: 50,
      high_num_inference_steps: 50
    }
  }
);

// 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 pandas9/big using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pandas9/big:4e9eaf34ec0beea25f0e1afb3f6f3c1bcbe6df5ffc6d7453d3494717bf909fb4",
    input={
        "prompt": "",
        "denoise": 1,
        "aspect_ratio": "1:1",
        "high_denoise": 0.4,
        "output_format": "webp",
        "guidance_scale": 7,
        "output_quality": 95,
        "negative_prompt": "",
        "num_inference_steps": 50,
        "high_num_inference_steps": 50
    }
)

# 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 pandas9/big 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": "pandas9/big:4e9eaf34ec0beea25f0e1afb3f6f3c1bcbe6df5ffc6d7453d3494717bf909fb4",
    "input": {
      "prompt": "",
      "denoise": 1,
      "aspect_ratio": "1:1",
      "high_denoise": 0.4,
      "output_format": "webp",
      "guidance_scale": 7,
      "output_quality": 95,
      "negative_prompt": "",
      "num_inference_steps": 50,
      "high_num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

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

Run time and cost

This model costs approximately $0.027 to run on Replicate, or 37 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 29 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.