fermatresearch/high-resolution-controlnet-tile:13c637d3

Version

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fermatresearch /high-resolution-controlnet-tile:13c637d3

Playground API

Input

prompt

string

Shift + Return to add a new line

Prompt for the model

image

file

Control image for scribble controlnet

resolution

integer

Image resolution

Default: 2560

resemblance

number

(minimum: 0, maximum: 1)

Conditioning scale for controlnet

Default: 0.85

creativity

number

(minimum: 0.1, maximum: 1)

Denoising strength. 1 means total destruction of the original image

Default: 0.35

hdr

number

(minimum: 0, maximum: 1)

HDR improvement over the original image

Default: 0

scheduler

string

Choose a scheduler.

Default: "DDIM"

steps

integer

Steps

Default: 8

guidance_scale

number

(minimum: 0, maximum: 30)

Scale for classifier-free guidance

Default: 0

seed

integer

Seed

negative_prompt

string

Shift + Return to add a new line

Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutantTeeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant

Negative prompt

Default: "teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant"

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

tile_size

integer

Size of partitions of the image. A 1/4 of the final resolution is recommended for optimal.

Default: 768

lora_sharpness_strength

number

(minimum: -3, maximum: 10)

Strength of the image's sharpness. For it to be noticeable, it is recommended to use values between 2 and 5.

Default: 1.25

lora_details_strength

number

(minimum: -3, maximum: 3)

Strength of the image's details

Default: 1

format

string

Format of the output.

Default: "jpg"

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

npx create-replicate --model=fermatresearch/high-resolution-controlnet-tile

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 fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76",
  {
    input: {
      hdr: 0,
      image: "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
      steps: 8,
      format: "jpg",
      prompt: "a woman wearing a colorful suit",
      scheduler: "DDIM",
      tile_size: 512,
      creativity: 0.4,
      guess_mode: false,
      resolution: 2048,
      resemblance: 0.85,
      guidance_scale: 0,
      negative_prompt: "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
      lora_details_strength: 0.75,
      lora_sharpness_strength: 1
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", 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 fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76",
    input={
        "hdr": 0,
        "image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
        "steps": 8,
        "format": "jpg",
        "prompt": "a woman wearing a colorful suit",
        "scheduler": "DDIM",
        "tile_size": 512,
        "creativity": 0.4,
        "guess_mode": False,
        "resolution": 2048,
        "resemblance": 0.85,
        "guidance_scale": 0,
        "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
        "lora_details_strength": 0.75,
        "lora_sharpness_strength": 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 fermatresearch/high-resolution-controlnet-tile 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": "13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76",
    "input": {
      "hdr": 0,
      "image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
      "steps": 8,
      "format": "jpg",
      "prompt": "a woman wearing a colorful suit",
      "scheduler": "DDIM",
      "tile_size": 512,
      "creativity": 0.4,
      "guess_mode": false,
      "resolution": 2048,
      "resemblance": 0.85,
      "guidance_scale": 0,
      "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
      "lora_details_strength": 0.75,
      "lora_sharpness_strength": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

We were unable to load these images. Please make sure the URLs are valid.

{
  "input": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
  "outut": "https://replicate.delivery/pbxt/uVYXPohfE3U5WC67kSr4TahCjwuu1hJJZz3f633PfMwQtDYmA/output.jpg"
}

{
  "completed_at": "2024-07-25T15:13:45.687896Z",
  "created_at": "2024-07-25T15:12:37.239000Z",
  "data_removed": false,
  "error": null,
  "id": "4nt04qw3exrgm0cgx9stm0nek8",
  "input": {
    "hdr": 0,
    "image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
    "steps": 8,
    "format": "jpg",
    "prompt": "a woman wearing a colorful suit",
    "scheduler": "DDIM",
    "tile_size": 512,
    "creativity": 0.4,
    "guess_mode": false,
    "resolution": 2048,
    "resemblance": 0.85,
    "guidance_scale": 0,
    "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
    "lora_details_strength": 0.75,
    "lora_sharpness_strength": 1
  },
  "logs": "Using seed: 33864\nThe config attributes {'solver_order': 2, 'algorithm_type': 'deis', 'solver_type': 'logrho', 'lower_order_final': True, 'use_karras_sigmas': False} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  6.35it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.64it/s]\n100%|██████████| 3/3 [00:00<00:00,  9.96it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.80it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.21it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.66it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  3.28it/s]\n 67%|██████▋   | 2/3 [00:00<00:00,  4.88it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.80it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.23it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.72it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.08it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.54it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.72it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.08it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.54it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  3.28it/s]\n 67%|██████▋   | 2/3 [00:00<00:00,  4.87it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.78it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.21it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.77it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.16it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.61it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  6.38it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.61it/s]\n100%|██████████| 3/3 [00:00<00:00,  9.94it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.78it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.15it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.61it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  6.34it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.58it/s]\n100%|██████████| 3/3 [00:00<00:00,  9.91it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.71it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.04it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.51it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  3.28it/s]\n 67%|██████▋   | 2/3 [00:00<00:00,  4.86it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.76it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.20it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  3.27it/s]\n 67%|██████▋   | 2/3 [00:00<00:00,  4.86it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.75it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.19it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.70it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.04it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.50it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  6.35it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.59it/s]\n100%|██████████| 3/3 [00:00<00:00,  9.92it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.77it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.14it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.60it/s]\n  0%|          | 0/2 [00:00<?, ?it/s]\n 50%|█████     | 1/2 [00:01<00:01,  1.95s/it]\n100%|██████████| 2/2 [00:02<00:00,  1.39s/it]\n100%|██████████| 2/2 [00:02<00:00,  1.47s/it]",
  "metrics": {
    "predict_time": 34.368119126,
    "total_time": 68.448896
  },
  "output": "https://replicate.delivery/pbxt/uVYXPohfE3U5WC67kSr4TahCjwuu1hJJZz3f633PfMwQtDYmA/output.jpg",
  "started_at": "2024-07-25T15:13:11.319777Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/4nt04qw3exrgm0cgx9stm0nek8",
    "cancel": "https://api.replicate.com/v1/predictions/4nt04qw3exrgm0cgx9stm0nek8/cancel"
  },
  "version": "13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76"
}

Generated in

34.4 seconds

Tweak it ShareReport

Using seed: 33864
The config attributes {'solver_order': 2, 'algorithm_type': 'deis', 'solver_type': 'logrho', 'lower_order_final': True, 'use_karras_sigmas': False} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.
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