jerray/meinamix-v11-controlnet:8f7a8d90 | Run with an API on Replicate

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jerray /meinamix-v11-controlnet:8f7a8d90

Playground API

Input

image

*file

Input image

control_image

file

Control image

prompt

string

Shift + Return to add a new line

a photo of an astronaut riding a horse on marsa photo of an astronaut riding a horse on mars

Input prompt

Default: "a photo of an astronaut riding a horse on mars"

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

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: 30

strength

number

(minimum: 0, maximum: 1)

Strength of diffusion. Higher values will result in more diffusion.

Default: 0.8

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 7.5

scheduler

string

Choose a scheduler.

Default: "DPMSolverMultistep"

canny_low_threshold

integer

(minimum: 1, maximum: 255)

Canny line detection low threshold

Default: 100

canny_high_threshold

integer

(minimum: 1, maximum: 255)

Canny line detection high threshold

Default: 200

controlnet_conditioning_scale

number

(minimum: 0, maximum: 2)

Control Weight

Default: 1

control_guidance_start

number

(minimum: 0, maximum: 1)

The percentage of total steps at which the controlnet starts applying

Default: 0

control_guidance_end

number

(minimum: 0, maximum: 1)

The percentage of total steps at which the controlnet stops applying

Default: 1

resize_mode

string

fill - The image is resized to fill the given dimension. cover - The image keeps its aspect ratio and fills the given dimension. The image will be clipped to fit. crop - The image keeps its aspect ratio and scales to the target size.

Default: "fill"

seed

integer

Random seed. Leave blank to randomize the seed

lora_model

string

An enumeration.

cross_attention_scale

number

(minimum: 0, maximum: 1)

A scale value of 0 is the same as not using your LoRA weights and you’re only using the base model weights, and a scale value of 1 means you’re only using the fully finetuned LoRA weights.

Default: 0.8

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

npx create-replicate --model=jerray/meinamix-v11-controlnet

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 jerray/meinamix-v11-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "jerray/meinamix-v11-controlnet:8f7a8d90071bf588089ad87d163134ede923f15b1209e8886693742be8d9921c",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "a photo of an astronaut riding a horse on mars",
      strength: 0.8,
      scheduler: "DPMSolverMultistep",
      lora_model: "adventurers_v1",
      num_outputs: 1,
      resize_mode: "fill",
      guidance_scale: 7.5,
      canny_low_threshold: 100,
      num_inference_steps: 30,
      canny_high_threshold: 200,
      control_guidance_end: 1,
      cross_attention_scale: 0.8,
      control_guidance_start: 0,
      controlnet_conditioning_scale: 1
    }
  }
);

// 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 jerray/meinamix-v11-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "jerray/meinamix-v11-controlnet:8f7a8d90071bf588089ad87d163134ede923f15b1209e8886693742be8d9921c",
    input={
        "width": 512,
        "height": 512,
        "prompt": "a photo of an astronaut riding a horse on mars",
        "strength": 0.8,
        "scheduler": "DPMSolverMultistep",
        "lora_model": "adventurers_v1",
        "num_outputs": 1,
        "resize_mode": "fill",
        "guidance_scale": 7.5,
        "canny_low_threshold": 100,
        "num_inference_steps": 30,
        "canny_high_threshold": 200,
        "control_guidance_end": 1,
        "cross_attention_scale": 0.8,
        "control_guidance_start": 0,
        "controlnet_conditioning_scale": 1
    }
)
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 jerray/meinamix-v11-controlnet 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": "jerray/meinamix-v11-controlnet:8f7a8d90071bf588089ad87d163134ede923f15b1209e8886693742be8d9921c",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "a photo of an astronaut riding a horse on mars",
      "strength": 0.8,
      "scheduler": "DPMSolverMultistep",
      "lora_model": "adventurers_v1",
      "num_outputs": 1,
      "resize_mode": "fill",
      "guidance_scale": 7.5,
      "canny_low_threshold": 100,
      "num_inference_steps": 30,
      "canny_high_threshold": 200,
      "control_guidance_end": 1,
      "cross_attention_scale": 0.8,
      "control_guidance_start": 0,
      "controlnet_conditioning_scale": 1
    }
  }' \
  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.