usamaehsan/controlnet-x-realistic-vision – Run with an API on Replicate

usamaehsan / controlnet-x-realistic-vision

Cold

Public
73 runs
L40S

Iterate in playground

Run with an API

Playground API Examples README Versions

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

eta

number

Controls the amount of noise that is added to the input data during the denoising diffusion process. Higher value -> more noise

Default: 0

guess_mode

boolean

In this mode, the ControlNet encoder will try best to recognize the content of the input image even if you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.

Default: false

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

depth_image

file

Control image for depth controlnet

depth_conditioning_scale

number

Conditioning scale for depth controlnet

Default: 1

canny_image

file

Control image for canny controlnet

canny_conditioning_scale

number

Conditioning scale for canny controlnet

Default: 1

mlsd_image

file

Control image for mlsd controlnet

mlsd_conditioning_scale

number

Conditioning scale for mlsd 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

comma seperated list of objects for mask, AI will auto create mask of these objects, if mask text is given, mask image will not work

negative_auto_mask_text

string

Shift + Return to add a new line

comma seperated list of objects you dont want to mask, AI will auto delete these objects from mask, only works if positive_auto_mask_text is given

inpainting_conditioning_scale

number

Conditioning scale for brightness controlnet

Default: 1

sorted_controlnets

string

Shift + Return to add a new line

Comma seperated string of controlnet names, list of names: tile, inpainting, lineart,depth ,scribble , brightness /// example value: tile, inpainting, lineart

Default: "tile, inpainting, lineart"

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

img2img_image

file

Image2image image

img2img_strength

number

img2img strength, does not work when inpainting image is given, 0.1-same image, 0.99-complete destruction of image

Default: 0.5

add_more_detail_lora_scale

number

Scale/ weight of more_details lora, more scale = more details, disabled on 0

Default: 0.5

detail_tweaker_lora_weight

number

disabled on 0

Default: 0

film_grain_lora_weight

number

disabled on 0

Default: 0

epi_noise_offset_lora_weight

number

disabled on 0

Default: 0

color_temprature_slider_lora_weight

number

disabled on 0

Default: 0

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

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

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-realistic-vision 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-realistic-vision:42e6d0c900bf4181898b2b52aadcc3d024aeb8892e3d155a07d14eace602e156",
  {
    input: {
      eta: 0,
      max_width: 512,
      scheduler: "DDIM",
      guess_mode: false,
      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,
      mlsd_conditioning_scale: 1,
      canny_conditioning_scale: 1,
      depth_conditioning_scale: 1,
      add_more_detail_lora_scale: 0.5,
      detail_tweaker_lora_weight: 0,
      lineart_conditioning_scale: 1,
      epi_noise_offset_lora_weight: 0,
      inpainting_conditioning_scale: 1,
      color_temprature_slider_lora_weight: 0
    }
  }
);

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

output = replicate.run(
    "usamaehsan/controlnet-x-realistic-vision:42e6d0c900bf4181898b2b52aadcc3d024aeb8892e3d155a07d14eace602e156",
    input={
        "eta": 0,
        "max_width": 512,
        "scheduler": "DDIM",
        "guess_mode": False,
        "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,
        "mlsd_conditioning_scale": 1,
        "canny_conditioning_scale": 1,
        "depth_conditioning_scale": 1,
        "add_more_detail_lora_scale": 0.5,
        "detail_tweaker_lora_weight": 0,
        "lineart_conditioning_scale": 1,
        "epi_noise_offset_lora_weight": 0,
        "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-realistic-vision 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": "usamaehsan/controlnet-x-realistic-vision:42e6d0c900bf4181898b2b52aadcc3d024aeb8892e3d155a07d14eace602e156",
    "input": {
      "eta": 0,
      "max_width": 512,
      "scheduler": "DDIM",
      "guess_mode": false,
      "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,
      "mlsd_conditioning_scale": 1,
      "canny_conditioning_scale": 1,
      "depth_conditioning_scale": 1,
      "add_more_detail_lora_scale": 0.5,
      "detail_tweaker_lora_weight": 0,
      "lineart_conditioning_scale": 1,
      "epi_noise_offset_lora_weight": 0,
      "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.

Run time and cost

This model costs approximately $0.011 to run on Replicate, or 90 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 11 seconds.

Readme

This model doesn't have a readme.