usamaehsan/controlnet-lineart-brightness-tile-inpainting:22f3c902

Version

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usamaehsan /controlnet-lineart-brightness-tile-inpainting:22f3c902

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

lineart_image

file

Preview

Control image for canny controlnet

lineart_conditioning_scale

number

Conditioning scale for canny controlnet

Default: 1

tile_image

file

Control image for tile controlnet

tile_conditioning_scale

number

Conditioning scale for tile controlnet

Default: 1

brightness_image

file

Control image for brightness controlnet

brightness_conditioning_scale

number

Conditioning scale for brightness controlnet

Default: 1

inpainting_image

file

Control image for inpainting controlnet

mask_image

file

mask image for inpainting controlnet

inpainting_conditioning_scale

number

Conditioning scale for brightness controlnet

Default: 1

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"

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

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

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

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

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"

low_res_fix

boolean

Using controlnet tile- after image generation

Default: false

low_res_fix_resolution

integer

controlnet tile resolution- after image generation

Default: 768

low_res_fix_steps

integer

controlnet tile resolution- after image generation

Default: 10

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

npx create-replicate --model=usamaehsan/controlnet-lineart-brightness-tile-inpainting

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 usamaehsan/controlnet-lineart-brightness-tile-inpainting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "usamaehsan/controlnet-lineart-brightness-tile-inpainting:22f3c902303d2ebefec06a4a53d96af6a41cf46ef30de46c5ad9cd8f70b30a84",
  {
    input: {
      eta: 0,
      prompt: "underwater kingdom",
      max_width: 412,
      scheduler: "DDIM",
      guess_mode: false,
      max_height: 412,
      low_res_fix: true,
      num_outputs: 1,
      lineart_image: "https://replicate.delivery/pbxt/JtxDFtHe0rVC4pvGUVGbnR8BOHLaFhGLA4YAScgno1FomeD9/ceb71f061de43744a245456771d6f95d.jpg",
      guidance_scale: 7,
      negative_prompt: "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      low_res_fix_steps: 10,
      sorted_controlnets: "tile, inpainting, lineart",
      num_inference_steps: 20,
      disable_safety_check: false,
      low_res_fix_resolution: 768,
      tile_conditioning_scale: 1,
      lineart_conditioning_scale: 1,
      brightness_conditioning_scale: 1,
      inpainting_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 usamaehsan/controlnet-lineart-brightness-tile-inpainting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "usamaehsan/controlnet-lineart-brightness-tile-inpainting:22f3c902303d2ebefec06a4a53d96af6a41cf46ef30de46c5ad9cd8f70b30a84",
    input={
        "eta": 0,
        "prompt": "underwater kingdom",
        "max_width": 412,
        "scheduler": "DDIM",
        "guess_mode": False,
        "max_height": 412,
        "low_res_fix": True,
        "num_outputs": 1,
        "lineart_image": "https://replicate.delivery/pbxt/JtxDFtHe0rVC4pvGUVGbnR8BOHLaFhGLA4YAScgno1FomeD9/ceb71f061de43744a245456771d6f95d.jpg",
        "guidance_scale": 7,
        "negative_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
        "low_res_fix_steps": 10,
        "sorted_controlnets": "tile, inpainting, lineart",
        "num_inference_steps": 20,
        "disable_safety_check": False,
        "low_res_fix_resolution": 768,
        "tile_conditioning_scale": 1,
        "lineart_conditioning_scale": 1,
        "brightness_conditioning_scale": 1,
        "inpainting_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 usamaehsan/controlnet-lineart-brightness-tile-inpainting 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-lineart-brightness-tile-inpainting:22f3c902303d2ebefec06a4a53d96af6a41cf46ef30de46c5ad9cd8f70b30a84",
    "input": {
      "eta": 0,
      "prompt": "underwater kingdom",
      "max_width": 412,
      "scheduler": "DDIM",
      "guess_mode": false,
      "max_height": 412,
      "low_res_fix": true,
      "num_outputs": 1,
      "lineart_image": "https://replicate.delivery/pbxt/JtxDFtHe0rVC4pvGUVGbnR8BOHLaFhGLA4YAScgno1FomeD9/ceb71f061de43744a245456771d6f95d.jpg",
      "guidance_scale": 7,
      "negative_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      "low_res_fix_steps": 10,
      "sorted_controlnets": "tile, inpainting, lineart",
      "num_inference_steps": 20,
      "disable_safety_check": false,
      "low_res_fix_resolution": 768,
      "tile_conditioning_scale": 1,
      "lineart_conditioning_scale": 1,
      "brightness_conditioning_scale": 1,
      "inpainting_conditioning_scale": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-11-19T17:41:52.009888Z",
  "created_at": "2023-11-19T17:41:41.861875Z",
  "data_removed": false,
  "error": null,
  "id": "2yehosdb2qrbky2rsp7co2wvjq",
  "input": {
    "eta": 0,
    "prompt": "underwater kingdom",
    "max_width": 412,
    "scheduler": "DDIM",
    "guess_mode": false,
    "max_height": 412,
    "low_res_fix": true,
    "num_outputs": 1,
    "lineart_image": "https://replicate.delivery/pbxt/JtxDFtHe0rVC4pvGUVGbnR8BOHLaFhGLA4YAScgno1FomeD9/ceb71f061de43744a245456771d6f95d.jpg",
    "guidance_scale": 7,
    "negative_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
    "low_res_fix_steps": 10,
    "sorted_controlnets": "tile, inpainting, lineart",
    "num_inference_steps": 20,
    "disable_safety_check": false,
    "low_res_fix_resolution": 768,
    "tile_conditioning_scale": 1,
    "lineart_conditioning_scale": 1,
    "brightness_conditioning_scale": 1,
    "inpainting_conditioning_scale": 1
  },
  "logs": "lineart\nUsing seed: 29179\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:04,  4.69it/s]\n 20%|██        | 4/20 [00:00<00:01, 13.02it/s]\n 35%|███▌      | 7/20 [00:00<00:00, 16.80it/s]\n 50%|█████     | 10/20 [00:00<00:00, 18.66it/s]\n 65%|██████▌   | 13/20 [00:00<00:00, 19.80it/s]\n 80%|████████  | 16/20 [00:00<00:00, 20.65it/s]\n 95%|█████████▌| 19/20 [00:01<00:00, 21.24it/s]\n100%|██████████| 20/20 [00:01<00:00, 18.73it/s]\nsize:  (352, 408)\nRunning low res fix...\ncondition image resize took 0.014973163604736328 seconds\n  0%|          | 0/10 [00:00<?, ?it/s]\n 10%|█         | 1/10 [00:00<00:03,  2.63it/s]\n 20%|██        | 2/10 [00:00<00:02,  3.98it/s]\n 30%|███       | 3/10 [00:00<00:01,  4.75it/s]\n 40%|████      | 4/10 [00:00<00:01,  5.22it/s]\n 50%|█████     | 5/10 [00:01<00:00,  5.54it/s]\n 60%|██████    | 6/10 [00:01<00:00,  5.74it/s]\n 70%|███████   | 7/10 [00:01<00:00,  5.88it/s]\n 80%|████████  | 8/10 [00:01<00:00,  5.97it/s]\n 90%|█████████ | 9/10 [00:01<00:00,  6.03it/s]\n100%|██████████| 10/10 [00:01<00:00,  6.07it/s]\n100%|██████████| 10/10 [00:01<00:00,  5.44it/s]\nlow-res-fix took 2.102013349533081seconds",
  "metrics": {
    "predict_time": 8.40441,
    "total_time": 10.148013
  },
  "output": [
    "https://replicate.delivery/pbxt/3MyrCc47OoL5Fp92B27f3KUqxgueXySQUkfjrGgND5L9WfnHB/seed-29179.png",
    "https://replicate.delivery/pbxt/9x52xSzNWV4kKZejt8W78yU5GF82nu78fgfeWiRKwF6fb9PPC/low-res-40977.png"
  ],
  "started_at": "2023-11-19T17:41:43.605478Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/2yehosdb2qrbky2rsp7co2wvjq",
    "cancel": "https://api.replicate.com/v1/predictions/2yehosdb2qrbky2rsp7co2wvjq/cancel"
  },
  "version": "22f3c902303d2ebefec06a4a53d96af6a41cf46ef30de46c5ad9cd8f70b30a84"
}

Generated in

8.4 seconds

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