usamaehsan/controlnet-x-ip-adapter-realistic-vision-v5:31cbf82d

Version

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usamaehsan /controlnet-x-ip-adapter-realistic-vision-v5:31cbf82d

Input

prompt

*string

Shift + Return to add a new line

Prompt - using compel, use +++ to increase words weight:: doc: https://github.com/damian0815/compel/tree/main/doc || https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#attention-weighting

negative_prompt

string

Shift + Return to add a new line

Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low qualityLongbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality

Negative prompt - using compel, use +++ to increase words weight//// negative-embeddings available ///// FastNegativeV2 , boring_e621_v4 , verybadimagenegative_v1 || to use them, write their keyword in negative prompt

Default: "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality"

num_inference_steps

integer

Steps to run denoising

Default: 20

guidance_scale

number

(minimum: 0.1, maximum: 30)

Scale for classifier-free guidance

Default: 7

seed

integer

Seed

disable_safety_check

boolean

Disable safety check. Use at your own risk!

Default: false

num_outputs

integer

(minimum: 1, maximum: 10)

Number of images to generate

Default: 1

max_width

integer

Max width/Resolution of image

Default: 512

max_height

integer

Max height/Resolution of image

Default: 512

scheduler

string

Choose a scheduler.

Default: "DDIM"

lineart_image

file

Control image for canny controlnet

lineart_conditioning_scale

number

Conditioning scale for canny controlnet

Default: 1

scribble_image

file

Control image for scribble controlnet

scribble_conditioning_scale

number

Conditioning scale for scribble controlnet

Default: 1

tile_image

file

Control image for tile controlnet

tile_conditioning_scale

number

Conditioning scale for tile controlnet

Default: 1

inpainting_image

file

Control image for inpainting controlnet

mask_image

file

mask image for inpainting controlnet

positive_auto_mask_text

string

Shift + Return to add a new line

// seperated list of objects for mask, AI will auto create mask of these objects, if mask text is given, mask image will not work - 'hairs // eyes // cloth'

negative_auto_mask_text

string

Shift + Return to add a new line

// seperated list of objects you dont want to mask - 'hairs // eyes // cloth'

inpainting_conditioning_scale

number

Conditioning scale for brightness controlnet

Default: 1

ip_adapter_ckpt

string

IP Adapter checkpoint

Default: "ip-adapter_sd15.bin"

ip_adapter_image

file

IP Adapter image

ip_adapter_weight

number

IP Adapter weight

Default: 1

Including eta and 12 more...

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

npx create-replicate --model=usamaehsan/controlnet-x-ip-adapter-realistic-vision-v5

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 usamaehsan/controlnet-x-ip-adapter-realistic-vision-v5 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "usamaehsan/controlnet-x-ip-adapter-realistic-vision-v5:31cbf82d3e6f368be33c12010c260de298982916a55a3c69c175475a022fbf79",
  {
    input: {
      eta: 0,
      max_width: 512,
      scheduler: "DDIM",
      guess_mode: false,
      int_kwargs: "",
      max_height: 512,
      num_outputs: 1,
      guidance_scale: 7,
      ip_adapter_ckpt: "ip-adapter_sd15.bin",
      negative_prompt: "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      img2img_strength: 0.5,
      ip_adapter_weight: 1,
      sorted_controlnets: "tile, inpainting, lineart",
      num_inference_steps: 20,
      disable_safety_check: false,
      film_grain_lora_weight: 0,
      tile_conditioning_scale: 1,
      add_more_detail_lora_scale: 0.5,
      detail_tweaker_lora_weight: 0,
      lineart_conditioning_scale: 1,
      scribble_conditioning_scale: 1,
      epi_noise_offset_lora_weight: 0,
      brightness_conditioning_scale: 1,
      inpainting_conditioning_scale: 1,
      color_temprature_slider_lora_weight: 0
    }
  }
);
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 usamaehsan/controlnet-x-ip-adapter-realistic-vision-v5 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "usamaehsan/controlnet-x-ip-adapter-realistic-vision-v5:31cbf82d3e6f368be33c12010c260de298982916a55a3c69c175475a022fbf79",
    input={
        "eta": 0,
        "max_width": 512,
        "scheduler": "DDIM",
        "guess_mode": False,
        "int_kwargs": "",
        "max_height": 512,
        "num_outputs": 1,
        "guidance_scale": 7,
        "ip_adapter_ckpt": "ip-adapter_sd15.bin",
        "negative_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
        "img2img_strength": 0.5,
        "ip_adapter_weight": 1,
        "sorted_controlnets": "tile, inpainting, lineart",
        "num_inference_steps": 20,
        "disable_safety_check": False,
        "film_grain_lora_weight": 0,
        "tile_conditioning_scale": 1,
        "add_more_detail_lora_scale": 0.5,
        "detail_tweaker_lora_weight": 0,
        "lineart_conditioning_scale": 1,
        "scribble_conditioning_scale": 1,
        "epi_noise_offset_lora_weight": 0,
        "brightness_conditioning_scale": 1,
        "inpainting_conditioning_scale": 1,
        "color_temprature_slider_lora_weight": 0
    }
)
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 usamaehsan/controlnet-x-ip-adapter-realistic-vision-v5 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": "31cbf82d3e6f368be33c12010c260de298982916a55a3c69c175475a022fbf79",
    "input": {
      "eta": 0,
      "max_width": 512,
      "scheduler": "DDIM",
      "guess_mode": false,
      "int_kwargs": "",
      "max_height": 512,
      "num_outputs": 1,
      "guidance_scale": 7,
      "ip_adapter_ckpt": "ip-adapter_sd15.bin",
      "negative_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      "img2img_strength": 0.5,
      "ip_adapter_weight": 1,
      "sorted_controlnets": "tile, inpainting, lineart",
      "num_inference_steps": 20,
      "disable_safety_check": false,
      "film_grain_lora_weight": 0,
      "tile_conditioning_scale": 1,
      "add_more_detail_lora_scale": 0.5,
      "detail_tweaker_lora_weight": 0,
      "lineart_conditioning_scale": 1,
      "scribble_conditioning_scale": 1,
      "epi_noise_offset_lora_weight": 0,
      "brightness_conditioning_scale": 1,
      "inpainting_conditioning_scale": 1,
      "color_temprature_slider_lora_weight": 0
    }
  }' \
  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.